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Present Your Data Like a Pro

data presentation or analysis

Demystify the numbers. Your audience will thank you.

While a good presentation has data, data alone doesn’t guarantee a good presentation. It’s all about how that data is presented. The quickest way to confuse your audience is by sharing too many details at once. The only data points you should share are those that significantly support your point — and ideally, one point per chart. To avoid the debacle of sheepishly translating hard-to-see numbers and labels, rehearse your presentation with colleagues sitting as far away as the actual audience would. While you’ve been working with the same chart for weeks or months, your audience will be exposed to it for mere seconds. Give them the best chance of comprehending your data by using simple, clear, and complete language to identify X and Y axes, pie pieces, bars, and other diagrammatic elements. Try to avoid abbreviations that aren’t obvious, and don’t assume labeled components on one slide will be remembered on subsequent slides. Every valuable chart or pie graph has an “Aha!” zone — a number or range of data that reveals something crucial to your point. Make sure you visually highlight the “Aha!” zone, reinforcing the moment by explaining it to your audience.

With so many ways to spin and distort information these days, a presentation needs to do more than simply share great ideas — it needs to support those ideas with credible data. That’s true whether you’re an executive pitching new business clients, a vendor selling her services, or a CEO making a case for change.

“Knowing how to develop and deliver a data-driven presentation is now a crucial skill for many professionals, since we often have to tell our colleagues stories that are much more compelling when they’re backed by numbers,” says researcher and consultant Alexandra Samuel .

No problem, you may say. A bar graph here, and a pie chart there, and you’re off to the races, right?

Not so fast. Because while a good presentation includes data, data alone doesn’t guarantee a good presentation. It’s not the mere presence of data that gives the presenter power. It’s how that data is presented.

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The data-driven mindset.

Showcasing data may seem simple in the age of PowerPoint, Prezi, Canva, Visme, Haiku Deck, and other nonsensically named technological platforms. But raise your hand if you’ve ever been confused by a chart you saw at a conference or ever heard a presenter say, “You probably can’t see this diagram well but what it’s showing is…”? What could be a bigger chart fail than the chart itself being rendered useless?

How you present data can double — or decimate — its impact, so take note of these seven ways to ensure that your data is doing its job.

1) Make sure your data can be seen

This may sound obvious but sometimes you’re too close to your presentation — literally. What is readable on your laptop may be far less so when projected on a screen. Your audience won’t learn what it can’t see. To avoid the debacle of sheepishly translating hard-to-see numbers and labels, rehearse your presentation with colleagues sitting as far away as the actual audience would. Ask them, “Can you see this chart clearly?” If the answer is anything but a firm “yes,” redesign it to be easier on the eyes.

2) Focus most on the points your data illustrates

In comic book terms, you are Wonder Woman, and data is your magic lasso — a tool that strengthens your impact but has no value until you apply it purposefully. Don’t leave the burden of decoding your data to your audience. It’s your job to explain how the data supports your major points.

“Data slides aren’t really about the data. They’re about the meaning of the data,” explains presentation design expert Nancy Duarte. “It’s up to you to make that meaning clear before you click away. Otherwise, the audience won’t process — let alone buy — your argument.”

When you connect data to the essential points it supports, the transition should be explicit and sound like this:

“This data shows…”

“This chart illustrates…”

“These numbers prove…”

These transitions can be as important as the conclusions themselves, because you’re drawing the audience’s attention to those conclusions.

3) Share one — and only one — major point from each chart

The quickest way to confuse your audience is by sharing too many details at once. The only data points you should share are those that significantly support your point — and ideally, one point per chart. To keep your charts in check, ask yourself, “What’s the single most important learning I want my audience to extract from this data?” That’s the one learning you should convey. If you have several significant points to make, consider demonstrating each with a new visualization.

The mistake many presenters make is thinking they’re constitutionally required to share every bullet, idea, and data point on a slide. But if you’re sharing a pivotal trend that grew dramatically between 2014 and 2017, what happened in 2013 may be pointless. If 77% of respondents prefer one product and 21% prefer another, what the remaining 2% prefer may also be too insignificant to justify mentioning.

Data-presentation guru Scott Berinato says , “The impulse is to include everything you know, [but] busy charts communicate the idea that you’ve been just that — busy, as in: ‘Look at all the data I have and the work I’ve done.’”

4) Label chart components clearly

While you’ve been working with the same chart for weeks or months, your audience will be exposed to it for mere seconds. Give them the best chance of comprehending your data by using simple, clear, and complete language to identify X and Y axes, pie pieces, bars, and other diagrammatic elements. Try to avoid abbreviations that aren’t obvious, and don’t assume labeled components on one slide will be remembered on subsequent slides.

Some members of your audience are visual learners (like me!) who process what they see much better than what they hear, so your chart’s visual intuitiveness and clarity are crucial.

5) Visually highlight “Aha!” zones

Every valuable chart or pie graph has an “Aha!” zone — a number or range of data that reveals something crucial to your point.

Smart presenters explain the relevance of the “Aha!” zone orally, sharing the learning, trend, or story the data is telling.

Better presenters explain it out loud, but also write it on the slide as a bullet.

But the best presenters do all of the above AND visually highlight the “Aha!” zone itself with a circle or shading to reach the differentiated (aural, verbal, visual) learners in their audience, as well as to triple-reinforce the most important data takeaways.

6) Write a slide title that reinforces the data’s point

Even when data is presented effectively on a slide, the most valuable real estate is the page’s title because that’s the first item the audience will notice and process. But all too often, presenters use generic words and phrases like “Statistics” and “By the Numbers” that serve no functional purpose.

Even when the titles are specific, like “Millennial Preferences” or “Campaign Awareness,” they can still be elevated with more point-specific titles like “Millennials Prefer Mobile” or “Campaign Awareness is Increasing.”

7) Present to your audience, not to your data

Many presenters look at their slides while they share data as if the PowerPoint is their audience. But only your audience is your audience, and, as fellow human beings, they receive your points best when you look them in the eye. This doesn’t mean that you should never look at your data — just don’t have a conversation with it. Glance at your slides for reference, but make critical points directly to your audience.

When presented clearly and pointedly, data can elevate your point’s credibility and trustworthiness. Presenting data poorly not only squanders that opportunity but can damage your reputation as a presenter. Like Wonder Woman’s lasso, it’s a powerful tool to draw out compelling truths — wield it wisely.

data presentation or analysis

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10 Methods of Data Presentation with 5 Great Tips to Practice, Best in 2023

10 Methods of Data Presentation with 5 Great Tips to Practice, Best in 2023

Leah Nguyen • 24 May 2023 • 10 min read

Finding ways to present information effectively? You can end deathly boring and ineffective data presentation right now with our 10 methods of data presentation . Check out the examples from each technique!

Have you ever presented a data report to your boss/coworkers/teachers thinking it was super dope like you’re some cyber hacker living in the Matrix, but all they saw was a pile of static numbers that seemed pointless and didn’t make sense to them?

Understanding digits is rigid . Making people from non-analytical backgrounds understand those digits is even more challenging.

How can you clear up those confusing numbers in the types of presentation that have the flawless clarity of a diamond? So, let’s check out best way to present data. 💎

Table of Contents

#2 – Text

#3 – pie chart, #4 – bar chart, #5 – histogram, #6 – line graph, #7 – pictogram graph, #8 – radar chart, #9 – heat map, #10 – scatter plot.

Frequently Asked Questions

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What are Methods of Data Presentation?

The term ’data presentation’ relates to the way you present data in a way that makes even the most clueless person in the room understand. 

Some say it’s witchcraft (you’re manipulating the numbers in some ways), but we’ll just say it’s the power of turning dry, hard numbers or digits into a visual showcase that is easy for people to digest.

Presenting data correctly can help your audience understand complicated processes, identify trends, and instantly pinpoint whatever is going on without exhausting their brains.

Good data presentation helps…

Methods of Data Presentation and Examples

Imagine you have a delicious pepperoni, extra-cheese pizza. You can decide to cut it into the classic 8 triangle slices, the party style 12 square slices, or get creative and abstract on those slices. 

There are various ways for cutting a pizza and you get the same variety with how you present your data. In this section, we will bring you the 10 ways to slice a pizza – we mean to present your data – that will make your company’s most important asset as clear as day.

#1 – Tabular 

Tabular data is data presented in rows and columns. Excel or Google Sheets would qualify for the job. Nothing fancy.

a table displaying the changes in revenue between the year 2017 and 2018 in the East, West, North, and South region

This is an example of a tabular presentation of data on Google Sheets. Each row and column has an attribute (year, region, revenue, etc.), and you can do a custom format to see the change in revenue throughout the year.

When presenting data as text, all you do is write your findings down in paragraphs and bullet points, and that’s it. A piece of cake to you, a tough nut to crack for whoever has to go through all of the reading to get to the point.

(Source: CustomerThermometer )

All the above quotes present statistical information in textual form. Since not many people like going through a wall of texts, you’ll have to figure out another route when deciding to use this method, such as breaking the data down into short, clear statements, or even as catchy puns if you’ve got the time to think of them.

A pie chart (or a ‘donut chart’ if you stick a hole in the middle of it) is a circle divided into slices that show the relative sizes of data within a whole. . If you’re using it to show percentages, make sure all the slices add up to 100%.

Methods of data presentation

The pie chart is a familiar face at every party and is usually recognised by most people. However, one setback of using this method is our eyes sometimes can’t identify the differences in slices of a circle, and it’s nearly impossible to compare similar slices from two different pie charts, making them the villains in the eyes of data analysts.

a half-eaten pie chart

Bonus example: A literal ‘pie’ chart! 🥧

The bar chart is a chart that presents a bunch of items from the same category, usually in the form of rectangular bars that are placed at an equal distance from each other. Their heights or lengths depict the values they represent.

They can be as simple as this:

a simple bar chart example

Or more complex and detailed like this example of presentation of data. This one is a grouped bar chart that not only allows you to compare categories but also the groups within them as well.

an example of a grouped bar chart

Similar in appearance to the bar chart but the rectangular bars in histograms don’t often have the gap like their counterparts.

Instead of measuring categories like weather preferences or favourite films as a bar chart does, a histogram only measures things that can be put into numbers.

an example of a histogram chart showing the distribution of students' score for the IQ test

Teachers can use a histogram to see which score group most of the students fall into, like in this example above.

Line graphs are represented by a group of data points joined together by a straight line. There can be one or more lines to compare how several related things change over time. 

an example of the line graph showing the population of bears from 2017 to 2022

On a line chart’s horizontal axis, you usually have text labels, dates or years, while the vertical axis usually represents the quantity (e.g.: budget, temperature or percentage).

A pictogram graph uses pictures or icons relating to the main topic to visualise a small dataset. The fun combination of colours and illustrations makes it a frequent use at schools.

How to Create Pictographs and Icon Arrays in Visme-6 pictograph maker

Pictograms are a breath of fresh air if you want to stay away from the monotonous line chart or bar chart for a while. However, they can present a very limited amount of data and sometimes they are only there for displays and do not represent real statistics.

If presenting five or more variables in the form of a bar chart is too stuffy then you should try using a radar chart. 

Radar charts show data in terms of how they compare to each other starting from the same point. Some also call them ‘spider charts’ because each aspect combined looks like a spider web.

a radar chart showing the text scores between two students

Radar charts can be a great use for parents who’d like to compare their child’s grades with their peers to lower their self-esteem. You can see that each angular represents a subject with a score value ranging from 0 to 100. Each student’s score across 5 subjects is highlighted in a different colour.

a radar chart showing the power distribution of a Pokemon

If you think that this method of data presentation somehow feels familiar, then you’ve probably encountered one while playing Pokémon .

A heat map represents data density in colours. The bigger the number, the more colour intense that data will be represented.

a heatmap showing the electoral votes among the states between two candidates

Most U.S citizens would be familiar with this data presentation method in geography. For elections, many news outlets assign a specific colour code to a state, with blue representing one candidate and red representing the other. The shade of either blue or red in each state shows the strength of the overall vote in that state.

a heatmap showing which parts the visitors click on in a website

Another great thing you can use a heat map for is to map what visitors to your site click on. The more a particular section is clicked the ‘hotter’ the colour will turn, from blue to bright yellow to red.

If you present your data in dots instead of chunky bars, you’ll have a scatter plot. 

A scatter plot is a grid with several inputs showing the relationship between two variables. It’s good at collecting seemingly random data and revealing some telling trends.

a scatter plot example showing the relationship between beach visitors each day and the average daily temperature

For example, in this graph, each dot shows the average daily temperature versus the number of beach visitors across several days. You can see that the dots get higher as the temperature increases, so it’s likely that hotter weather leads to more visitors.

5 Data Presentation Mistakes to Avoid

#1 – assume your audience understands what the numbers represent.

You may know all the behind-the-scenes of your data since you’ve worked with them for weeks, but your audience doesn’t.

a sales data board from Looker

Showing without telling only invites more and more questions from your audience, as they have to constantly make sense of your data, wasting the time of both sides as a result.

Tell them what the data are about before hitting them with waves of numbers first. You can use interactive activities such as polls , word clouds and Q&A sections to assess their understanding of the data and address any confusion beforehand.

#2 – Use the wrong type of chart

Charts such as pie charts must have a total of 100% so if your numbers accumulate to 193% like this example below, you’re definitely doing it wrong.

a bad example of using a pie chart in the 2012 presidential run

Before making a chart, ask yourself: what do I want to accomplish with my data? Do you want to see the relationship between the data sets, show the up and down trends of your data, or see how segments of one thing make up a whole?

Remember, clarity always comes first. Some data visualisations may look cool, but if they don’t fit your data, steer clear of them. 

#3 – Make it 3D

The third dimension is cool, but full of risks.

data presentation or analysis

Can you see what’s behind those red bars? Because we can’t either. You may think that 3D charts add more depth to the design, but they can create false perceptions as our eyes see 3D objects closer and bigger than they appear, not to mention they cannot be seen from multiple angles.

#4 – Use different types of charts to compare contents in the same category

data presentation or analysis

This is like comparing a fish to a monkey. Your audience won’t be able to identify the differences and make an appropriate correlation between the two data sets. 

Next time, stick to one type of data presentation only. Avoid the temptation of trying various data visualisation methods in one go and make your data as accessible as possible.

#5 – Bombard the audience with too much information

The goal of data presentation is to make complex topics much easier to understand, and if you’re bringing too much information to the table, you’re missing the point.

a very complicated data presentation with too much information on the screen

The more information you give, the more time it will take for your audience to process it all. If you want to make your data understandable and give your audience a chance to remember it, keep the information within it to an absolute minimum.

What are the Best Methods of Data Presentation?

The answer is…

There is none 😄 Each type of presentation has its own strengths and weaknesses and the one you choose greatly depends on what you’re trying to do. 

For example:

example of how a bad pie chart represents the data in a complicated way

Got a question? We've got answers.

What is chart presentation?

When can i use charts for presentation, why should use charts for presentation, what are the 4 graphical methods of presenting data.

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Leah Nguyen

A former event organiser on the ultimate quest - to help presenters create the juiciest online experiences and leave all attendees on a high note.

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A Step-by-Step Guide to the Data Analysis Process

Like any scientific discipline, data analysis follows a rigorous step-by-step process. Each stage requires different skills and know-how. To get meaningful insights, though, it’s important to understand the process as a whole. An underlying framework is invaluable for producing results that stand up to scrutiny.

In this post, we’ll explore the main steps in the data analysis process. This will cover how to define your goal, collect data, and carry out an analysis. Where applicable, we’ll also use examples and highlight a few tools to make the journey easier. When you’re done, you’ll have a much better understanding of the basics. This will help you tweak the process to fit your own needs.

Here are the steps we’ll take you through:

On popular request, we’ve also developed a video based on this article. Scroll further along this article to watch that.

Ready? Let’s get started with step one.

1. Step one: Defining the question

The first step in any data analysis process is to define your objective. In data analytics jargon, this is sometimes called the ‘problem statement’.

Defining your objective means coming up with a hypothesis and figuring how to test it. Start by asking: What business problem am I trying to solve? While this might sound straightforward, it can be trickier than it seems. For instance, your organization’s senior management might pose an issue, such as: “Why are we losing customers?” It’s possible, though, that this doesn’t get to the core of the problem. A data analyst’s job is to understand the business and its goals in enough depth that they can frame the problem the right way.

Let’s say you work for a fictional company called TopNotch Learning. TopNotch creates custom training software for its clients. While it is excellent at securing new clients, it has much lower repeat business. As such, your question might not be, “Why are we losing customers?” but, “Which factors are negatively impacting the customer experience?” or better yet: “How can we boost customer retention while minimizing costs?”

Now you’ve defined a problem, you need to determine which sources of data will best help you solve it. This is where your business acumen comes in again. For instance, perhaps you’ve noticed that the sales process for new clients is very slick, but that the production team is inefficient. Knowing this, you could hypothesize that the sales process wins lots of new clients, but the subsequent customer experience is lacking. Could this be why customers don’t come back? Which sources of data will help you answer this question?

Tools to help define your objective

Defining your objective is mostly about soft skills, business knowledge, and lateral thinking. But you’ll also need to keep track of business metrics and key performance indicators (KPIs). Monthly reports can allow you to track problem points in the business. Some KPI dashboards come with a fee, like Databox and DashThis . However, you’ll also find open-source software like Grafana , Freeboard , and Dashbuilder . These are great for producing simple dashboards, both at the beginning and the end of the data analysis process.

2. Step two: Collecting the data

Once you’ve established your objective, you’ll need to create a strategy for collecting and aggregating the appropriate data. A key part of this is determining which data you need. This might be quantitative (numeric) data, e.g. sales figures, or qualitative (descriptive) data, such as customer reviews. All data fit into one of three categories: first-party, second-party, and third-party data. Let’s explore each one.

What is first-party data?

First-party data are data that you, or your company, have directly collected from customers. It might come in the form of transactional tracking data or information from your company’s customer relationship management (CRM) system. Whatever its source, first-party data is usually structured and organized in a clear, defined way. Other sources of first-party data might include customer satisfaction surveys, focus groups, interviews, or direct observation.

What is second-party data?

To enrich your analysis, you might want to secure a secondary data source. Second-party data is the first-party data of other organizations. This might be available directly from the company or through a private marketplace. The main benefit of second-party data is that they are usually structured, and although they will be less relevant than first-party data, they also tend to be quite reliable. Examples of second-party data include website, app or social media activity, like online purchase histories, or shipping data.

What is third-party data?

Third-party data is data that has been collected and aggregated from numerous sources by a third-party organization. Often (though not always) third-party data contains a vast amount of unstructured data points (big data). Many organizations collect big data to create industry reports or to conduct market research. The research and advisory firm Gartner is a good real-world example of an organization that collects big data and sells it on to other companies. Open data repositories and government portals are also sources of third-party data .

Tools to help you collect data

Once you’ve devised a data strategy (i.e. you’ve identified which data you need, and how best to go about collecting them) there are many tools you can use to help you. One thing you’ll need, regardless of industry or area of expertise, is a data management platform (DMP). A DMP is a piece of software that allows you to identify and aggregate data from numerous sources, before manipulating them, segmenting them, and so on. There are many DMPs available. Some well-known enterprise DMPs include Salesforce DMP , SAS , and the data integration platform, Xplenty . If you want to play around, you can also try some open-source platforms like Pimcore or D:Swarm .

Want to learn more about what data analytics is and the process a data analyst follows? We cover this topic (and more) in our free introductory short course for beginners. Check out tutorial one: An introduction to data analytics .

3. Step three: Cleaning the data

Once you’ve collected your data, the next step is to get it ready for analysis. This means cleaning, or ‘scrubbing’ it, and is crucial in making sure that you’re working with high-quality data . Key data cleaning tasks include:

A good data analyst will spend around 70-90% of their time cleaning their data. This might sound excessive. But focusing on the wrong data points (or analyzing erroneous data) will severely impact your results. It might even send you back to square one…so don’t rush it! You’ll find a step-by-step guide to data cleaning here . You may be interested in this introductory tutorial to data cleaning, hosted by Dr. Humera Noor Minhas.

Carrying out an exploratory analysis

Another thing many data analysts do (alongside cleaning data) is to carry out an exploratory analysis. This helps identify initial trends and characteristics, and can even refine your hypothesis. Let’s use our fictional learning company as an example again. Carrying out an exploratory analysis, perhaps you notice a correlation between how much TopNotch Learning’s clients pay and how quickly they move on to new suppliers. This might suggest that a low-quality customer experience (the assumption in your initial hypothesis) is actually less of an issue than cost. You might, therefore, take this into account.

Tools to help you clean your data

Cleaning datasets manually—especially large ones—can be daunting. Luckily, there are many tools available to streamline the process. Open-source tools, such as OpenRefine , are excellent for basic data cleaning, as well as high-level exploration. However, free tools offer limited functionality for very large datasets. Python libraries (e.g. Pandas) and some R packages are better suited for heavy data scrubbing. You will, of course, need to be familiar with the languages. Alternatively, enterprise tools are also available. For example, Data Ladder , which is one of the highest-rated data-matching tools in the industry. There are many more. Why not see which free data cleaning tools you can find to play around with?

4. Step four: Analyzing the data

Finally, you’ve cleaned your data. Now comes the fun bit—analyzing it! The type of data analysis you carry out largely depends on what your goal is. But there are many techniques available. Univariate or bivariate analysis, time-series analysis, and regression analysis are just a few you might have heard of. More important than the different types, though, is how you apply them. This depends on what insights you’re hoping to gain. Broadly speaking, all types of data analysis fit into one of the following four categories.

Descriptive analysis

Descriptive analysis identifies what has already happened . It is a common first step that companies carry out before proceeding with deeper explorations. As an example, let’s refer back to our fictional learning provider once more. TopNotch Learning might use descriptive analytics to analyze course completion rates for their customers. Or they might identify how many users access their products during a particular period. Perhaps they’ll use it to measure sales figures over the last five years. While the company might not draw firm conclusions from any of these insights, summarizing and describing the data will help them to determine how to proceed.

Learn more: What is descriptive analytics?

Diagnostic analysis

Diagnostic analytics focuses on understanding why something has happened . It is literally the diagnosis of a problem, just as a doctor uses a patient’s symptoms to diagnose a disease. Remember TopNotch Learning’s business problem? ‘Which factors are negatively impacting the customer experience?’ A diagnostic analysis would help answer this. For instance, it could help the company draw correlations between the issue (struggling to gain repeat business) and factors that might be causing it (e.g. project costs, speed of delivery, customer sector, etc.) Let’s imagine that, using diagnostic analytics, TopNotch realizes its clients in the retail sector are departing at a faster rate than other clients. This might suggest that they’re losing customers because they lack expertise in this sector. And that’s a useful insight!

Predictive analysis

Predictive analysis allows you to identify future trends based on historical data . In business, predictive analysis is commonly used to forecast future growth, for example. But it doesn’t stop there. Predictive analysis has grown increasingly sophisticated in recent years. The speedy evolution of machine learning allows organizations to make surprisingly accurate forecasts. Take the insurance industry. Insurance providers commonly use past data to predict which customer groups are more likely to get into accidents. As a result, they’ll hike up customer insurance premiums for those groups. Likewise, the retail industry often uses transaction data to predict where future trends lie, or to determine seasonal buying habits to inform their strategies. These are just a few simple examples, but the untapped potential of predictive analysis is pretty compelling.

Prescriptive analysis

Prescriptive analysis allows you to make recommendations for the future. This is the final step in the analytics part of the process. It’s also the most complex. This is because it incorporates aspects of all the other analyses we’ve described. A great example of prescriptive analytics is the algorithms that guide Google’s self-driving cars. Every second, these algorithms make countless decisions based on past and present data, ensuring a smooth, safe ride. Prescriptive analytics also helps companies decide on new products or areas of business to invest in.

Learn more:  What are the different types of data analysis?

5. Step five: Sharing your results

You’ve finished carrying out your analyses. You have your insights. The final step of the data analytics process is to share these insights with the wider world (or at least with your organization’s stakeholders!) This is more complex than simply sharing the raw results of your work—it involves interpreting the outcomes, and presenting them in a manner that’s digestible for all types of audiences. Since you’ll often present information to decision-makers, it’s very important that the insights you present are 100% clear and unambiguous. For this reason, data analysts commonly use reports, dashboards, and interactive visualizations to support their findings.

How you interpret and present results will often influence the direction of a business. Depending on what you share, your organization might decide to restructure, to launch a high-risk product, or even to close an entire division. That’s why it’s very important to provide all the evidence that you’ve gathered, and not to cherry-pick data. Ensuring that you cover everything in a clear, concise way will prove that your conclusions are scientifically sound and based on the facts. On the flip side, it’s important to highlight any gaps in the data or to flag any insights that might be open to interpretation. Honest communication is the most important part of the process. It will help the business, while also helping you to excel at your job!

Tools for interpreting and sharing your findings

There are tons of data visualization tools available, suited to different experience levels. Popular tools requiring little or no coding skills include Google Charts , Tableau , Datawrapper , and Infogram . If you’re familiar with Python and R, there are also many data visualization libraries and packages available. For instance, check out the Python libraries Plotly , Seaborn , and Matplotlib . Whichever data visualization tools you use, make sure you polish up your presentation skills, too. Remember: Visualization is great, but communication is key!

You can learn more about storytelling with data in this free, hands-on tutorial .  We show you how to craft a compelling narrative for a real dataset, resulting in a presentation to share with key stakeholders. This is an excellent insight into what it’s really like to work as a data analyst!

6. Step six: Embrace your failures

The last ‘step’ in the data analytics process is to embrace your failures. The path we’ve described above is more of an iterative process than a one-way street. Data analytics is inherently messy, and the process you follow will be different for every project. For instance, while cleaning data, you might spot patterns that spark a whole new set of questions. This could send you back to step one (to redefine your objective). Equally, an exploratory analysis might highlight a set of data points you’d never considered using before. Or maybe you find that the results of your core analyses are misleading or erroneous. This might be caused by mistakes in the data, or human error earlier in the process.

While these pitfalls can feel like failures, don’t be disheartened if they happen. Data analysis is inherently chaotic, and mistakes occur. What’s important is to hone your ability to spot and rectify errors. If data analytics was straightforward, it might be easier, but it certainly wouldn’t be as interesting. Use the steps we’ve outlined as a framework, stay open-minded, and be creative. If you lose your way, you can refer back to the process to keep yourself on track.

In this post, we’ve covered the main steps of the data analytics process. These core steps can be amended, re-ordered and re-used as you deem fit, but they underpin every data analyst’s work:

What next? From here, we strongly encourage you to explore the topic on your own. Get creative with the steps in the data analysis process, and see what tools you can find. As long as you stick to the core principles we’ve described, you can create a tailored technique that works for you.

To learn more, check out our free, 5-day data analytics short course . You might also be interested in the following:

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What is Data Analysis? Methods, Process and Types Explained

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Businesses today need every edge and advantage they can get. Thanks to obstacles like rapidly changing markets, economic uncertainty, shifting political landscapes, finicky consumer attitudes, and even global pandemics , businesses today are working with slimmer margins for error.

Companies that want to stay in business and thrive can improve their odds of success by making smart choices while answering the question: “What is data analysis?” And how does an individual or organization make these choices? They collect as much useful, actionable information as possible and then use it to make better-informed decisions!

This strategy is common sense, and it applies to personal life as well as business. No one makes important decisions without first finding out what’s at stake, the pros and cons, and the possible outcomes. Similarly, no company that wants to succeed should make decisions based on bad data. Organizations need information; they need data. This is where data analysis or data analytics enters the picture.

The job of understanding data is currently one of the growing industries in today's day and age, where data is considered as the 'new oil' in the market. Now, before getting into the details about the data analysis methods, let us first answer the question, what is data analysis?

Become an Expert in Data Analytics!

Become an Expert in Data Analytics!

What Is Data Analysis?

Although many groups, organizations, and experts have different ways of approaching data analysis, most of them can be distilled into a one-size-fits-all definition. Data analysis is the process of cleaning, changing, and processing raw data and extracting actionable, relevant information that helps businesses make informed decisions. The procedure helps reduce the risks inherent in decision-making by providing useful insights and statistics, often presented in charts, images, tables, and graphs.

A simple example of data analysis can be seen whenever we make a decision in our daily lives by evaluating what has happened in the past or what will happen if we make that decision. Basically, this is the process of analyzing the past or future and making a decision based on that analysis.

It’s not uncommon to hear the term “ big data ” brought up in discussions about data analysis. Data analysis plays a crucial role in processing big data into useful information. Neophyte data analysts who want to dig deeper by revisiting big data fundamentals should go back to the basic question, “ What is data ?”

Why is Data Analysis Important?

Here is a list of reasons why data analysis is crucial to doing business today.

What Is the Data Analysis Process?

Answering the question “what is data analysis” is only the first step. Now we will look at how it’s performed. The process of data analysis, or alternately, data analysis steps, involves gathering all the information, processing it, exploring the data, and using it to find patterns and other insights. The process of data analysis consists of:

Want to Become a Data Analyst? Learn From Experts!

Want to Become a Data Analyst? Learn From Experts!

What Is the Importance of Data Analysis in Research?

A huge part of a researcher’s job is to sift through data. That is literally the definition of “research.” However, today’s Information Age routinely produces a tidal wave of data, enough to overwhelm even the most dedicated researcher. From a birds eye view, data analysis:

1. plays a key role in distilling this information into a more accurate and relevant form, making it easier for researchers to do to their job.

2. provides researchers with a vast selection of different tools, such as descriptive statistics, inferential analysis, and quantitative analysis.

3. offers researchers better data and better ways to analyze and study said data.

What is Data Analysis: Types of Data Analysis

A half-dozen popular types of data analysis are available today, commonly employed in the worlds of technology and business. They are: 

Next, we will get into the depths to understand about the data analysis methods.

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Your Dream Career is Just Around The Corner!

Data Analysis Methods

Some professionals use the terms “data analysis methods” and “data analysis techniques” interchangeably. To further complicate matters, sometimes people throw in the previously discussed “data analysis types” into the fray as well! Our hope here is to establish a distinction between what kinds of data analysis exist, and the various ways it’s used.

Although there are many data analysis methods available, they all fall into one of two primary types: qualitative analysis and quantitative analysis .

We can further expand our discussion of data analysis by showing various techniques, broken down by different concepts and tools. 

Top Data Analysis Tools

So, here's a list of the top seven data analysis tools in terms of popularity, learning, and performance.

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Free Statistics Class from IIT Kanpur

How to Become a Data Analyst

Now that we have answered the question “what is data analysis”, if you want to pursue a career in data analytics , you should start by first researching what it takes to become a data analyst . You can even check out the PG Program in Data Analytics in partnership with Purdue University . This program provides a hands-on approach with case studies and industry-aligned projects to bring the relevant concepts live. You will get broad exposure to key technologies and skills currently used in data analytics.

According to Forbes, the data analytics profession is exploding . The United States Bureau of Labor Statistics forecasts impressively robust growth for data science jobs skills and predicts that the data science field will grow about 28 percent through 2026. So, if you want a career that pays handsomely and will always be in demand, then check out Simplilearn and get started on your new, brighter future!

1. What is the role of data analytics?

Data Analytics is the process of collecting, cleaning, sorting, and processing raw data to extract relevant and valuable information to help businesses. An in-depth understanding of data can improve customer experience, retention, targeting, reducing operational costs, and problem-solving methods.

2. What are the types of data analytics?

Diagnostic Analysis, Predictive Analysis, Prescriptive Analysis, Text Analysis, and Statistical Analysis are the most commonly used data analytics types. Statistical analysis can be further broken down into Descriptive Analytics and Inferential Analysis.

3. What are the analytical tools used in data analytics?

The top 10 data analytical tools are Sequentum Enterprise, Datapine, Looker, KNIME, Lexalytics, SAS Forecasting, RapidMiner, OpenRefine, Talend, and NodeXL. The tools aid different data analysis processes, from data gathering to data sorting and analysis. 

4. What is the career growth in data analytics?

Starting off as a Data Analysis, you can quickly move into Senior Analyst, then Analytics Manager, Director of Analytics, or even Chief Data Officer (CDO).

5. Why Is Data Analytics Important?

Data Analysis is essential as it helps businesses understand their customers better, improves sales, improves customer targeting, reduces costs, and allows for the creation of better problem-solving strategies. 

6. Who Is Using Data Analytics?

Data Analytics has now been adopted almost across every industry. Regardless of company size or industry popularity, data analytics plays a huge part in helping businesses understand their customer’s needs and then use it to better tweak their products or services. Data Analytics is prominently used across industries such as Healthcare, Travel, Hospitality, and even FMCG products.

Find our Post Graduate Program in Data Analytics Online Bootcamp in top cities:

About the author.

Karin Kelley

Karin has spent more than a decade writing about emerging enterprise and cloud technologies. A passionate and lifelong researcher, learner, and writer, Karin is also a big fan of the outdoors, music, literature, and environmental and social sustainability.

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How to do Data Presentation, analysis and Discussion

How to do Data Presentation


This comes up, usually, in Chapter Four of the research project. This is where the researcher presents the data collected from respondents though not in the raw form. In their raw forms, it is quite difficult to present and analyse data, which is why there is a need for the raw data to be organized and presented in more compact forms. Subjecting the data to tabulation, grouping or even graphic forms, so as to allow for easy handling and analysis, could do this.

In doing this, the chapter sets out on an introductory note often referred to as “Preamble” where the researcher provides useful background information on the respondents’ group (s), their characteristics with respect to their bio-data and the rate of returns of the data gathering instruments.

After this, he moves on to the main theme of his research by presenting necessary data in the form (s) considered most appropriate for the purpose of analysis. If, as an instance, the tabular mode of data presentation was used, the tables should be well titled; each followed by detailed explanation on the data presented. This pattern should be used for each of the tables presented. Also important under data analysis is the Discussion of Results segment.

This comes up, normally, after the entire presentation exercise had been concluded. It is the segment where the researcher gives a more detailed insight into the issues directly relating to the data presentation and analysis. The segment helps to articulate the issues emanating from the data analysis with respect to whatever implications they have on the subject of investigation.

If the study is concerned with hypotheses testing, it is in this segment that the implications of the outcomes of the tests as they relate to the subject of research would be explained. Here also, conclusions on the relationship of the outcomes of the present study with previous ones are drawn; with a view to establishing a link between the outcomes of the present study and those of previous studies as already established under the literature review.

Furthermore, the researcher dedicates a part of this segment to the interpretation of the outcomes of his findings, thereby giving more meaning and sense to the data analysis exercise.

Read also: How to write Research Methodology

The Use of Statistics in Data Analysis

Sulaiman {1997} defined the term statistics as “a branch of applied mathematics, which is employed in analysis of data to facilitate meaningful decision making.” It is also described as the theory and methods of analysis obtained from samples of observation in order to compare data from different empirical observations using hypothesized relationships in order to make meaningful decisions.

Even then, the methods of data analysis depend on the aims and objectives of the study and the nature of the data gathered. It becomes clear from the above, that statistical analysis could be useful for: –

(i) Reducing quantities of data to manageable and understandable

(ii) Aiding decision making

(iii) Summarizing samples from which they are calculated (iv) Aiding reliable references and decisions from the hypothesis

Statistics thus serves as a tool used in collecting organizing, analysing and interpreting data. Generally speaking, statistical methods are categorized into broad classes of Descriptive and Inferential Statistics. Descriptive Statistics are often used to summarise the data collected, while Inferential Statistics are used to determine the generalizability of findings arrived at, through the analysis of a sample, to the larger population.

Note that Descriptive Statistics can be used for both sample and population data but cannot be used to perform inferential tests on population data. This is because the results obtained from the descriptive analysis are definitive enough for the population of interest. The application of either Descriptive or Inferential statistics to a set of data largely depends on the levels or scales of measurement of underlying variables. In all, there are four (4) levels of measurement otherwise known as scale.

Nominal Scale

This is considered as the simplest and the least refined scale of measurement; one whose primary use is to provide a labelling function. A good example of this is the individual’s sex, which can be either male or female. There cannot be any other thing between these two. The Yes or No kinds of questions are also good examples of this. However, it lacks the property of order and magnitude.

Ordinal Scale

This kind of measurement also performs the labelling function apart from its ordering function. This is because it possesses the property of order and magnitude such that two things could be compared in terms of their relative magnitude. A good example of the Ordinal Scale relates to the degree of agreement with a statement such as Strongly Agree, Agree, Disagree, and Strongly Disagree. Using this scale to measure two units, one will be able to determine which is higher or lower and not just that they are not the same.

Interval Scale

This also has the property of order, magnitude and additivity since equal intervals on the scale represent that there is a difference in magnitude. The scale does not possess absolute zero because the zero is arbitrarily set. In addition to its ordering function, this scale can be used to determine the difference between two units. Measuring the temperature of a room in Celsius and Fahrenheit is a good example of this scale.

Ratio Scale

This scale is the highest level of measurement because it has an absolute zero. As a general rule, whatever statistical methods are applicable to variable measured in the nominal scale can be applied to those measured in ordinal and interval/ratio scales. Similarly, those statistical methods applicable to variables measured in ordinal scale can also be applied to those measured in interval/ratio.

There are, however, statistical methods that are applicable to variables measured in interval/ratio that could not be applied to variables measured in the nominal scale. Some examples of the ratio scale include weight, time and speed; thus possessing all the properties of the other scales.

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In data analysis, there are procedures and tools to be employed depending on the type of research as well as the nature of the data to be analysed. Regardless of the instruments/methods used in data collection, and whether the data is from a sample or population, the first step in data analysis is to describe the collected data. To do this, however, the data should be summarized either using a frequency table or chart. These two are veritable tools for presenting and communicating data in such writings as technical reports and journal articles.

The Frequency Table

There is no doubt that with the Frequency Table, the researcher can display the number of cases, which have each of the attributes of a given variable. It also serves to display both qualitative and quantitative data. When confronted with the number of attributes or categories of a variable that is too large, the Frequency Table adopts the grouped data approach by combining the attributes into classes.

E.g. with Age as a variable, the Frequency Table may present data

as: –

20-24 25-29 30-34 35-39 40-44

Just like the Frequency Tables, there are also Charts, which serve similar purposes. The two most commonly used Charts are the Pie and Bar Charts. That is, both could be used to present data summaries and also used to interpret and convey the message more quickly, concisely and clearly than frequency tables. Their great limitation however lies in the fact that they hardly cope in situations where the attributes of a variable to display are too many, especially when these are more than nine.

This is particularly so for Pie Charts which are quite useful in providing vivid picture of data but only in showing the distribution of variables with single responses. Thus, they are inappropriate tools for variables associated with multiple responses from the units of the study. Also, while they are most applicable for qualitative data, Pie Charts also serve to display quantitative data particularly those whose number of attributes or categories is not more than five.

As for the Bar Charts, they serve for qualitative data in particular, irrespective of the nature of the responses to the variables, either single or multiple. Since Bar Charts make it easier to compare the categories of a variable, they are more suitable for displaying data with more than five categories. They also serve to display quantitative data, particularly, the variable presented in a discrete fashion. However, Histogram remains the more appropriate tool for displaying continuous variables.

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Measures of Central Tendency

This is another approach to describing a set of data, considered useful in determining a typical attribute/value of a variable. The measure is also useful in comparing the performances of two or more groups or the performance of a group over two or more periods of time. The Mean, Mode and Median are the three most common Measures of Central Tendency.

The Mean is the arithmetic average of a set of data usually applicable to quantitative data. To obtain the Mean, sum up all the scores in a set of data to be divided by the number of scores. With the distribution of the variable that is skewed, however, the Median will better represent the distribution, as extreme values tend to increase or decrease the Mean.

The Median is considered as the middle value in a set of data when all the values are arranged in order of magnitude. In other words, the Median tends to show the grouping together of scores around a central point, dividing a set of data into two main parts. In short, the middle scores between the upper half and the lower half is the Median. Although the Median is most appropriate for Ordinal Data, it is also applicable to Ordinal, Interval and Ratio Data.

Meanwhile, the score, which has the largest frequency in a set of data, is referred to as the Mode. It refers to the most common attributes or value of a variable in which case it is possible for a set of data to have more than one Mode. Although most appropriate for Nominal Data, the Mode is also applicable to all types of data.

Measures of Variability

This is also known as the Measures of Dispersion in which a measure of variation or dispersion is calculated primarily to determine the homogeneity of a set of data. There are separate measures of variation for qualitative and quantitative data. For quantitative data, measures of variation include: –

(i) The Range

(ii) Standard Deviation

(iii) Variance or the Square of the Standard Deviation (iv) Coefficient of Variation

This refers to the difference between the highest and lowest attribute or value. Its primary objective is to give the researcher an idea of the data spread to determine the range for a grouped data, minus the highest limit from the lowest limit. Thus, the range is solely based on the two extreme values and fails to recognise how the data are actually distributed between these two values. Hence, the desirability of Standard Deviation to offset this inadequacy.

Standard Deviation

This is defined as the distance or the average deviation of all values from the Mean. The difference between each Score and the Mean is the Deviation Scores from the Mean. The bigger the Deviation, the more variable the set of Scores. The Standard Deviation is obtained by taking the square of the average of these deviations and divided by the number of Scores. Thus, it is an indication of the typical deviation of the values from the Mean. If the Standard Deviation is small, the group is considered homogeneous whereas a large Standard Deviation is an indication of a heterogeneous group.

This refers simply to the square of the Standard Deviation, obtained by subtracting each observation from the Mean (x), squaring the resulting difference (Xi -X) to eliminate negative signs of Deviation. They are added up to give the Sum of Squares (Xi-X) and finally dividing it by the number of observation ‘n’.

Coefficient of Variation

This is the Ratio of a distribution’s Standard Deviation expressed to its Mean, multiplied by 100, and is independent of the unit of measurement. Coefficient of Variation is employed when comparing the Variability of two sets of data particularly when they are expressed in different units of measurement.

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Statistical Hypothesis Testing

Unlike the general discussion on hypotheses as earlier on presented, the topic is being re-visited here (under data analysis), with particular reference to Inferential Statistics. By Inferential Statistics, we refer to drawing conclusions regarding the Population of the Study based on the information obtained from the Sample.

It means that this kind of Statistics will not be relevant in situations such as when one is working with Population Data and when one is not interested in making a general statement about the Population. At the centre of Inferential Statistics is the concept of Hypothesis Testing. This refers to the process whereby the research infers from a sample whether or not to accept a statement about the Population; where the statement itself is the Hypothesis.

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Hypotheses are stated either in the Null or Alternative forms for the researcher to validate; even though the Null Hypothesis remains the more commonly used of the two. As a matter of fact, it is always the Null Hypothesis that gets tested and it is mainly on the condition that it is rejected that one can accept the Alternative Hypotheses.

When testing Hypotheses, the maximum probability with which one may be willing to reject the Null Hypothesis is referred to as the Level of Significance. It is common practice to use an alpha level of 0.05 or 0.01; meaning that there are 5 or 1 of 100 chances of committing Type 1 Error. When the Reject Decision has been made at 0.05 level, it means that the outcome of the experiment is statistically significant at the 0.05% level.

The procedure, which enables one to decide whether to Reject or Accept Hypotheses or to determine whether observed Samples differ significantly from expected results is differently referred to as Test of Significance, Rules of Decision, or Test of Hypothesis. Thus, if against the assumption that a particular hypothesis is true, we find results observed in a random sample differ markedly from those under the hypothesis, we then conclude that the difference is Significant. On this basis, we can Reject the Null Hypothesis. Errors are sometimes made in Hypothesis testing and these have been categorized into: –

C a) Type 1 Error Cb) Type 11 Error

In a situation where we Reject the Null Hypothesis when, in fact, we should Accept it, it is said that we have committed a Type 1 Error of decision or judgement. On the other hand, if we Accept the Null Hypothesis when we should, indeed, reject it, we are said to have committed Type 11 Error. Such errors usually lead to wrong decisions.

To have a good Test of Hypothesis, there must a design to minimise these errors of decision. A sure way of doing this is to increase our sample size, since the larger the Sample Size, the less the possible errors. Some of the several kinds of Inferential Tests often employed in the analyses of data include: –

(b) Analysis of Variance Cc) Chi-Square

(d) Correlation and Regression Analyses

This is normally used to compare the Means of two groups of data; which means that the data being compared should be quantitative. These two groups of data may be for two independent samples or maybe for the same sample with the data collected at two different periods {i.e. paired samples}. If, based on the observed p-value, it is decided that the two groups are different, then, one should be able to state which group has the larger Mean.

Analysis of Variance

This Test, commonly referred to as ANOVA, is normally used to examine the effects of qualitative independent variables on a quantitative dependent variable. The One-way ANOVA is its simplest form and is used for comparing the Means for several groups. If, in the end, the Null Hypothesis is Accepted, it indicates that the Means for all the groups are the same.

On the other hand, a Rejected Null Hypothesis indicates that not all the Means are the same even as it does not mean that they are all different. To ascertain which pairs of means are different, it becomes necessary to conduct a multiple comparison test.

This kind of Test is often used to determine the existence of a relationship between two qualitative variables. Before applying the Test at all, a Contingency Table {Cross-tabulation} is usually formed to study the patterns of frequencies in the Table. If, at the end, the Null Hypothesis is Rejected, it means that there is a relationship between the two variables. It is after this that measures are used to determine the strength of the relationship

Correlation and Regression Analyses

These are used to study the existing relationships among quantitative variables; especially those between two quantitative variables. In particular, Correlation Analysis measures the strength of the relationship between the two variables, while Repression Analysis develops an equation that enables one to predict the value of the Dependent Variable for different values of the Independent Variable.

These two methods are commonly used either as Descriptive or Inferential procedures. As a Descriptive procedure, a Correlation Coefficient is calculated to determine the strength of the relationship between two variables. As an Inferential procedure, Correlation Analysis determines whether the observed correlation between the variables as determined from the sample can be generalized to the population.

The procedure requires that the p-value is calculated and used to Accept or Reject the Null Hypothesis. If the Null Hypothesis is accepted {i.e. there is no correlation between the two variables in the population}, there is no need to obtain a Regression Equation, as it cannot be used to predict the value of the dependent variable.

Sulaiman, S. N. {1997} Statistics & Analytical Methods for Researchers. Kaduna. NDA Computer Centre.

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Presentation of Data

Statistics deals with the collection, presentation and analysis of the data, as well as drawing meaningful conclusions from the given data. Generally, the data can be classified into two different types, namely primary data and secondary data. If the information is collected by the investigator with a definite objective in their mind, then the data obtained is called the primary data. If the information is gathered from a source, which already had the information stored, then the data obtained is called secondary data. Once the data is collected, the presentation of data plays a major role in concluding the result. Here, we will discuss how to present the data with many solved examples.

What is Meant by Presentation of Data?

As soon as the data collection is over, the investigator needs to find a way of presenting the data in a meaningful, efficient and easily understood way to identify the main features of the data at a glance using a suitable presentation method. Generally, the data in the statistics can be presented in three different forms, such as textual method, tabular method and graphical method.

Presentation of Data Examples

Now, let us discuss how to present the data in a meaningful way with the help of examples.

Consider the marks given below, which are obtained by 10 students in Mathematics:

36, 55, 73, 95, 42, 60, 78, 25, 62, 75.

Find the range for the given data.

Given Data: 36, 55, 73, 95, 42, 60, 78, 25, 62, 75.

The data given is called the raw data.

First, arrange the data in the ascending order : 25, 36, 42, 55, 60, 62, 73, 75, 78, 95.

Therefore, the lowest mark is 25 and the highest mark is 95.

We know that the range of the data is the difference between the highest and the lowest value in the dataset.

Therefore, Range = 95-25 = 70.

Note: Presentation of data in ascending or descending order can be time-consuming if we have a larger number of observations in an experiment.

Now, let us discuss how to present the data if we have a comparatively more number of observations in an experiment.

Consider the marks obtained by 30 students in Mathematics subject (out of 100 marks)

10, 20, 36, 92, 95, 40, 50, 56, 60, 70, 92, 88, 80, 70, 72, 70, 36, 40, 36, 40, 92, 40, 50, 50, 56, 60, 70, 60, 60, 88.

In this example, the number of observations is larger compared to example 1. So, the presentation of data in ascending or descending order is a bit time-consuming. Hence, we can go for the method called ungrouped frequency distribution table or simply frequency distribution table . In this method, we can arrange the data in tabular form in terms of frequency.

For example, 3 students scored 50 marks. Hence, the frequency of 50 marks is 3. Now, let us construct the frequency distribution table for the given data.

Therefore, the presentation of data is given as below:

The following example shows the presentation of data for the larger number of observations in an experiment.

Consider the marks obtained by 100 students in a Mathematics subject (out of 100 marks)

95, 67, 28, 32, 65, 65, 69, 33, 98, 96,76, 42, 32, 38, 42, 40, 40, 69, 95, 92, 75, 83, 76, 83, 85, 62, 37, 65, 63, 42, 89, 65, 73, 81, 49, 52, 64, 76, 83, 92, 93, 68, 52, 79, 81, 83, 59, 82, 75, 82, 86, 90, 44, 62, 31, 36, 38, 42, 39, 83, 87, 56, 58, 23, 35, 76, 83, 85, 30, 68, 69, 83, 86, 43, 45, 39, 83, 75, 66, 83, 92, 75, 89, 66, 91, 27, 88, 89, 93, 42, 53, 69, 90, 55, 66, 49, 52, 83, 34, 36.

Now, we have 100 observations to present the data. In this case, we have more data when compared to example 1 and example 2. So, these data can be arranged in the tabular form called the grouped frequency table. Hence, we group the given data like 20-29, 30-39, 40-49, ….,90-99 (As our data is from 23 to 98). The grouping of data is called the “class interval” or “classes”, and the size of the class is called “class-size” or “class-width”.

In this case, the class size is 10. In each class, we have a lower-class limit and an upper-class limit. For example, if the class interval is 30-39, the lower-class limit is 30, and the upper-class limit is 39. Therefore, the least number in the class interval is called the lower-class limit and the greatest limit in the class interval is called upper-class limit.

Hence, the presentation of data in the grouped frequency table is given below:

Hence, the presentation of data in this form simplifies the data and it helps to enable the observer to understand the main feature of data at a glance.

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Problem Solving with Excel

This course explores Excel as a tool for solving business problems. In this course you will learn the basic functions of excel through guided demonstration. Each week you will build on your excel skills and be provided an opportunity to practice what you’ve learned. Finally, you will have a chance to put your knowledge to work in a final project. Please note, the content in this course was developed using a Windows version of Excel 2013.

Data Visualization with Advanced Excel

In this course, you will get hands-on instruction of advanced Excel 2013 functions. You’ll learn to use PowerPivot to build databases and data models. We’ll show you how to perform different types of scenario and simulation analysis and you’ll have an opportunity to practice these skills by leveraging some of Excel's built in tools including, solver, data tables, scenario manager and goal seek. In the second half of the course, will cover how to visualize data, tell a story and explore data by reviewing core principles of data visualization and dashboarding. You’ll use Excel to build complex graphs and Power View reports and then start to combine them into dynamic dashboards.

Note: Learners will need PowerPivot to complete some of the exercises. Please use MS Excel 2013 version. If you have other MS Excel versions or a MAC you might not be able to complete all assignments. This course was created by PricewaterhouseCoopers LLP with an address at 300 Madison Avenue, New York, New York, 10017.

Effective Business Presentations with Powerpoint

This course is all about presenting the story of the data, using PowerPoint. You'll learn how to structure a presentation, to include insights and supporting data. You'll also learn some design principles for effective visuals and slides. You'll gain skills for client-facing communication - including public speaking, executive presence and compelling storytelling. Finally, you'll be given a client profile, a business problem, and a set of basic Excel charts, which you'll need to turn into a presentation - which you'll deliver with iterative peer feedback.


Alex Mannella


With offices in 157 countries and more than 208,000 people, PwC is among the leading professional services networks in the world. Our purpose is to build trust in society and solve important problems. We help organisations and individuals create the value they’re looking for, by delivering quality in assurance, tax and advisory services.

Frequently Asked Questions

What is the refund policy?

If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy .

Can I just enroll in a single course?

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Can I take the course for free?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid .

Is this course really 100% online? Do I need to attend any classes in person?

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

How long does it take to complete the Specialization?

Exactly how long it takes will vary, depending on your schedule. Most learners complete the Specialization in five to six months.

What background knowledge is necessary?

You don't need any background knowledge. We've designed this Specialization for learners who are new to the field of data and analytics.

Do I need to take the courses in a specific order?

We recommend you take them in the order they appear on Coursera. Each course builds on the knowledge you learned in the last one.

Will I earn university credit for completing the Specialization?

Coursera courses and certificates don't carry university credit, though some universities may choose to accept Specialization Certificates for credit. You should check with your institution to find out more.

What will I be able to do upon completing the Specialization?

You'll be able to use the data and analytics framework to develop a plan to solve a business problem. You'll be able to use Excel to analyze data using formulas and present a series of visualizations with a summary recommendation to solve the business problem. You'll also be able to take data and create a dynamic data dashboard in Excel that accepts inputs and refreshes with new data. Finally, you'll be able to develop and deliver a presentation using PowerPoint and the results of your data analysis - so you can share your point of view on how to solve the business problem.

How do I audit the Specialization?

If you'd like to audit the courses in this Specialization, you'll need to enroll in each course separately and then you will see the audit option.

What tools do I need for this Specialization?

In the "Data Visualization and Advance Excel" course learners will need PowerPivot to complete some of the exercises. Please use MS Excel 2013 version. If you have other MS Excel versions or a MAC you might not be able to complete all assignments.

More questions? Visit the Learner Help Center .

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10 Superb Data Presentation Examples To Learn From

The best way to learn how to present data effectively is to see data presentation examples from the professionals in the field.

We collected superb examples of graphical presentation and visualization of data in statistics, research, sales, marketing, business management, and other areas.

On this page:

How to present data effectively? Clever tips.

Download the above infographic in PDF

Your audience should be able to walk through the graphs and visualizations easily while enjoy and respond to the story.

[bctt tweet=”Your reports and graphical presentations should not just deliver statistics, numbers, and data. Instead, they must tell a story, illustrate a situation, provide proofs, win arguments, and even change minds.” username=””]

Before going to data presentation examples let’s see some essential tips to help you build powerful data presentations.

1. Keep it simple and clear

The presentation should be focused on your key message and you need to illustrate it very briefly.

Graphs and charts should communicate your core message, not distract from it. A complicated and overloaded chart can distract and confuse. Eliminate anything repetitive or decorative.

2. Pick up the right visuals for the job

A vast number of types of graphs and charts are available at your disposal – pie charts, line and bar graphs, scatter plot , Venn diagram , etc.

Choosing the right type of chart can be a tricky business. Practically, the choice depends on 2 major things: on the kind of analysis you want to present and on the data types you have.

Commonly, when we aim to facilitate a comparison, we use a bar chart or radar chart. When we want to show trends over time, we use a line chart or an area chart and etc.

3. Break the complex concepts into multiple graphics

It’s can be very hard for a public to understand a complicated graphical visualization. Don’t present it as a huge amount of visual data.

Instead, break the graphics into pieces and illustrate how each piece corresponds to the previous one.

4. Carefully choose the colors

Colors provoke different emotions and associations that affect the way your brand or story is perceived. Sometimes color choices can make or break your visuals.

It is no need to be a designer to make the right color selections. Some golden rules are to stick to 3 or 4 colors avoiding full-on rainbow look and to borrow ideas from relevant chart designs.

Another tip is to consider the brand attributes and your audience profile. You will see appropriate color use in the below data presentation examples.

5. Don’t leave a lot of room for words

The key point in graphical data presentation is to tell the story using visuals and images, not words. Give your audience visual facts, not text.

However, that doesn’t mean words have no importance.

A great advice here is to think that every letter is critical, and there’s no room for wasted and empty words. Also, don’t create generic titles and headlines, build them around the core message.

6. Use good templates and software tools

Building data presentation nowadays means using some kind of software programs and templates. There are many available options – from free graphing software solutions to advanced data visualization tools.

Choosing a good software gives you the power to create good and high-quality visualizations. Make sure you are using templates that provides characteristics like colors, fonts, and chart styles.

A small investment of time to research the software options prevents a large loss of productivity and efficiency at the end.

10 Superb data presentation examples 

Here we collected some of the best examples of data presentation made by one of the biggest names in the graphical data visualization software and information research.

These brands put a lot of money and efforts to investigate how professional graphs and charts should look.

1. Sales Stage History  Funnel Chart 

Data is beautiful and this sales stage funnel chart by Zoho Reports prove this. The above funnel chart represents the different stages in a sales process (Qualification, Need Analysis, Initial Offer, etc.) and shows the potential revenue for each stage for the last and this quarter.

The potential revenue for each sales stage is displayed by a different color and sized according to the amount. The chart is very colorful, eye-catching, and intriguing.

2. Facebook Ads Data Presentation Examples

These are other data presentation examples from Zoho Reports. The first one is a stacked bar chart that displays the impressions breakdown by months and types of Facebook campaigns.

Impressions are one of the vital KPI examples in digital marketing intelligence and business. The first graph is designed to help you compare and notice sharp differences at the Facebook campaigns that have the most influence on impression movements.

The second one is an area chart that shows the changes in the costs for the same Facebook campaigns over the months.

The 2 examples illustrate how multiple and complicated data can be presented clearly and simply in a visually appealing way.

3. Sales Opportunity Data Presentation

These two bar charts (stacked and horizontal bar charts) by Microsoft Power Bi are created to track sales opportunities and revenue by region and sales stage.

The stacked bar graph shows the revenue probability in percentage determined by the current sales stage (Lead, Quality, Solution…) over the months. The horizontal bar chart represents the size of the sales opportunity (Small, Medium, Large) according to regions (East, Central, West).

Both graphs are impressive ways for a sales manager to introduce the upcoming opportunity to C-level managers and stakeholders. The color combination is rich but easy to digest.

4. Power 100 Data Visualization 

Want to show hierarchical data? Treemaps can be perfect for the job. This is a stunning treemap example by that shows you who are the most influential industries. As you see the Government is on the top.

This treemap is a very compact and space-efficient visualization option for presenting hierarchies, that gives you a quick overview of the structure of the most powerful industries.

So beautiful way to compare the proportions between things via their area size.

When it comes to best research data presentation examples in statistics, Nielsen information company is an undoubted leader. The above professional looking line graph by Nielsen represent the slowing alcoholic grow of 4 alcohol categories (Beer, Wine, Spirits, CPG) for the period of 12 months.

The chart is an ideal example of a data visualization that incorporates all the necessary elements of an effective and engaging graph. It uses color to let you easily differentiate trends and allows you to get a global sense of the data. Additionally, it is incredibly simple to understand.

6. Digital Health Research Data Visualization Example

Digital health is a very hot topic nowadays and this stunning donut chart by IQVIA shows the proportion of different mobile health apps by therapy area (Mental Health, Diabetes, Kidney Disease, and etc.). 100% = 1749 unique apps.

This is a wonderful example of research data presentation that provides evidence of Digital Health’s accelerating innovation and app expansion.

Besides good-looking, this donut chart is very space-efficient because the blank space inside it is used to display information too.

7. Disease Research Data Visualization Examples

Presenting relationships among different variables is hard to understand and confusing -especially when there is a huge number of them. But using the appropriate visuals and colors, the IQVIA did a great job simplifying this data into a clear and digestible format.

The above stacked bar charts by IQVIA represents the distribution of oncology medicine spendings by years and product segments (Protected Brand Price, Protected Brand Volume, New Brands, etc.).

The chart allows you to clearly see the changes in spendings and where they occurred – a great example of telling a deeper story in a simple way.

8. Textual and Qualitative Data Presentation Example

When it comes to easy to understand and good looking textual and qualitative data visualization, pyramid graph has a top place. To know what is qualitative data see our post quantitative vs qualitative data .

9. Product Metrics Graph Example

If you are searching for excel data presentation examples, this stylish template from Smartsheet can give you good ideas for professional looking design.

The above stacked bar chart represents product revenue breakdown by months and product items. It reveals patterns and trends over the first half of the year that can be a good basis for data-driven decision-making .

10. Supply Chain Data Visualization Example 

This bar chart created by ClicData  is an excellent example of how trends over time can be effectively and professionally communicated through the use of well-presented visualization.

It shows the dynamics of pricing through the months based on units sold, units shipped, and current inventory. This type of graph pack a whole lot of information into a simple visual. In addition, the chart is connected to real data and is fully interactive.

The above data presentation examples aim to help you learn how to present data effectively and professionally.

About The Author

data presentation or analysis

Silvia Valcheva

Silvia Valcheva is a digital marketer with over a decade of experience creating content for the tech industry. She has a strong passion for writing about emerging software and technologies such as big data, AI (Artificial Intelligence), IoT (Internet of Things), process automation, etc.

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Press Release

Mumbai, india, may 9, 2023, gartner identifies the top 10 data and analytics trends for 2023, gartner analysts are discussing how organizations can leverage these trends at the gartner data & analytics summit, may 8-9, in mumbai, india.

Gartner, Inc. identified the top 10 data and analytics (D&A) trends for 2023 that can guide   D&A leaders to create new sources of value by anticipating change and transforming extreme uncertainty into new business opportunities.

“The need to deliver provable value to the organization at scale is driving these trends in D&A,” said Gareth Herschel , VP Analyst at Gartner. “ Chief data and analytics officers (CDAOs) and D&A leaders must engage with their organizations’ stakeholders to understand the best approach to drive D&A adoption. This means more and better analysis and insights, taking human psychology and values into account.”

Gartner analysts presented the top 10 D&A trends business and IT leaders must engage and incorporate into their D&A strategy (see Figure 1) at the Gartner Data & Analytics Summit , taking place in Mumbai through today.

Figure 1: Top 10 Trends in Data and Analytics for 2023

data presentation or analysis

Source: Gartner (May 2023)

Trend 1: Value Optimization

Most D&A leaders struggle to articulate the value they deliver for the organization in business terms. Value optimization from an organization’s data, analytics and artificial intelligence (AI) portfolio requires an integrated set of value-management competencies including value storytelling, value stream analysis, ranking and prioritizing investments, and measuring business outcomes to ensure expected value is realized.

“D&A leaders must optimize value by building value stories that establish clear links between D&A initiatives and the organization’s mission-critical priorities,” said Herschel.

Trend 2: Managing AI Risk

The growing use of AI has exposed companies to new risks such as ethical risks, poisoning of training data or fraud detection circumvention, which must be mitigated. Managing AI risks is not only about being compliant with regulations. Effective AI governance and responsible AI practices are also critical to building trust among stakeholders and catalyzing AI adoption and use.

Trend 3: Observability

Observability is a characteristic that allows the D&A system’s behavior to be understood and allows questions about their behavior to be answered.

“Observability enables organizations to reduce the time it takes to identify the root cause of performance-impacting problems and make timely, cost-effective business decisions using reliable and accurate data,” said Herschel. “D&A leaders need to evaluate data observability tools to understand the needs of the primary users and determine how the tools fit into the overall enterprise ecosystem.”

Trend 4: Data Sharing Is Essential

Data sharing includes sharing data both internally (between or among departments or across subsidiaries) and externally (between or among parties outside the ownership and control of your organization). Organizations can create “data as a product,” where D&A assets are prepared as a deliverable or shared product.

“Data sharing collaborations, including those external to an organization, increase data sharing value by adding reusable, previously created data assets,” said Kevin Gabbard , Senior Director, Analyst at Gartner. “Adopt a data fabric design to enable a single architecture for data sharing across heterogeneous internal and external data sources.”

Trend 5: D&A Sustainability

It is not enough for D&A leaders to provide analysis and insights for enterprise ESG (environmental, social, and governance) projects. D&A leaders must also try to optimize their own processes for sustainability improvement. The potential benefits are enormous. D&A and AI practitioners are becoming more aware of their growing energy footprint. As a result, a variety of practices are emerging, such as the use of renewable energy by (cloud) data centers, the use of more energy-efficient hardware, and the usage of small data and other machine learning (ML) techniques.

Trend 6: Practical Data Fabric

Data fabric is a data management design pattern leveraging all types of metadata to observe, analyze and recommend data management solutions. By assembling and enriching the semantics of the underlying data, and applying continuous analytics over metadata, data fabric generates alerts and recommendations that can be actioned by both humans and systems. It enables business users to consume data with confidence and facilitates less-skilled citizen developers to become more versatile in the integration and modeling process.

Trend 7: Emergent AI

ChatGPT and generative AI are the vanguard of the coming emergent AI trend. Emergent AI will change how most companies operate in terms of scalability, versatility and adaptability. The next wave of AI will enable organizations to apply AI in situations where it is not feasible today, making AI ever more pervasive and valuable.

Trend 8: Converged and Composable Ecosystems

Converged D&A ecosystems design and deploy the D&A platform to operate and function cohesively through seamless integrations, governance, and technical interoperability. An ecosystem's composability is delivered by architecting, assembling and deploying configurable applications and services.

With the right architecture D&A systems can be more modular, adaptable and flexible to scale dynamically and be more streamlined to meet the growing and changing business needs and enable evolution as the business and operating environment inevitably change.

Trend 9: Consumers Become Creators

The percentage of time users spend in predefined dashboards will be replaced by conversational, dynamic and embedded user experiences that address specific content consumers’ point-in-time needs.

Organizations can expand the adoption and impact of analytics by giving content consumers easy to use automated and embedded insights and conversational experiences they need to become content creators.

Trend 10: Humans Remain the Key Decision Makers

Not every decision can or should be automated. D&A groups are explicitly addressing decision support and the human role in automated and augmented decision making.

“Efforts to drive decision automation without considering the human role in decisions will result in a data-driven organization without conscience or consistent purpose,” said Herschel. “Organizations’ data literacy programs need to emphasize combining data and analytics with human decision-making.”

Gartner clients can read more in “Top Trends in Data and Analytics 2023.”

About the Gartner Data & Analytics Summit Gartner analysts are providing additional analysis on data and analytics trends at the Gartner Data & Analytics Summit 2023, taking place in  Mumbai . Upcoming Gartner Data & Analytics Summits include: May 22-24 in  London  and July 31-August 1 in  Sydney . Follow news and updates from the conferences on Twitter using  #GartnerDA .

About Gartner for Data & Analytics Leaders Gartner for Data & Analytics Leaders provides actionable, objective insight to CDAOs and data & analytics leaders to help them accelerate their D&A strategy and operating model to increase business value. Additional information is available at .

Follow news and updates from Gartner for D&A Leaders on  Twitter  and  LinkedIn  using #GartnerDA. Visit the  Gartner Newsroom  for more information and insights.

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Sonika Choubey Gartner [email protected]

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A Guide To The Methods, Benefits & Problems of The Interpretation of Data

Data interpretation blog post by datapine

Table of Contents

1) What Is Data Interpretation?

2) How To Interpret Data?

3) Why Data Interpretation Is Important?

4) Data Analysis & Interpretation Problems

5) Data Interpretation Techniques & Methods

6) The Use of Dashboards For Data Interpretation

Data analysis and interpretation have now taken center stage with the advent of the digital age… and the sheer amount of data can be frightening. In fact, a Digital Universe study found that the total data supply in 2012 was 2.8 trillion gigabytes! Based on that amount of data alone, it is clear the calling card of any successful enterprise in today’s global world will be the ability to analyze complex data, produce actionable insights and adapt to new market needs… all at the speed of thought.

Business dashboards are the digital age tools for big data. Capable of displaying key performance indicators (KPIs) for both quantitative and qualitative data analyses, they are ideal for making the fast-paced and data-driven market decisions that push today’s industry leaders to sustainable success. Through the art of streamlined visual communication, data dashboards permit businesses to engage in real-time and informed decision-making and are key instruments in data interpretation. First of all, let’s find a definition to understand what lies behind this practice.

What Is Data Interpretation?

Data interpretation refers to the process of using diverse analytical methods to review data and arrive at relevant conclusions. The interpretation of data helps researchers to categorize, manipulate, and summarize the information in order to answer critical questions.

The importance of data interpretation is evident and this is why it needs to be done properly. Data is very likely to arrive from multiple sources and has a tendency to enter the analysis process with haphazard ordering. Data analysis tends to be extremely subjective. That is to say, the nature and goal of interpretation will vary from business to business, likely correlating to the type of data being analyzed. While there are several types of processes that are implemented based on individual data nature, the two broadest and most common categories are “quantitative and qualitative analysis”.

Yet, before any serious data interpretation inquiry can begin, it should be understood that visual presentations of data findings are irrelevant unless a sound decision is made regarding scales of measurement. Before any serious data analysis can begin, the scale of measurement must be decided for the data as this will have a long-term impact on data interpretation ROI. The varying scales include:

  • Nominal Scale: non-numeric categories that cannot be ranked or compared quantitatively. Variables are exclusive and exhaustive.
  • Ordinal Scale: exclusive categories that are exclusive and exhaustive but with a logical order. Quality ratings and agreement ratings are examples of ordinal scales (i.e., good, very good, fair, etc., OR agree, strongly agree, disagree, etc.).
  • Interval: a measurement scale where data is grouped into categories with orderly and equal distances between the categories. There is always an arbitrary zero point.
  • Ratio: contains features of all three.

For a more in-depth review of scales of measurement, read our article on data analysis questions . Once scales of measurement have been selected, it is time to select which of the two broad interpretation processes will best suit your data needs. Let’s take a closer look at those specific methods and possible data interpretation problems.

How To Interpret Data?

Illustration of data interpretation on blackboard

When interpreting data, an analyst must try to discern the differences between correlation, causation, and coincidences, as well as many other biases – but he also has to consider all the factors involved that may have led to a result. There are various data interpretation methods one can use to achieve this.

The interpretation of data is designed to help people make sense of numerical data that has been collected, analyzed, and presented. Having a baseline method for interpreting data will provide your analyst teams with a structure and consistent foundation. Indeed, if several departments have different approaches to interpreting the same data while sharing the same goals, some mismatched objectives can result. Disparate methods will lead to duplicated efforts, inconsistent solutions, wasted energy, and inevitably – time and money. In this part, we will look at the two main methods of interpretation of data: qualitative and quantitative analysis.

Qualitative Data Interpretation

Qualitative data analysis can be summed up in one word – categorical. With this type of analysis, data is not described through numerical values or patterns, but through the use of descriptive context (i.e., text). Typically, narrative data is gathered by employing a wide variety of person-to-person techniques. These techniques include:

  • Observations: detailing behavioral patterns that occur within an observation group. These patterns could be the amount of time spent in an activity, the type of activity, and the method of communication employed.
  • Focus groups: Group people and ask them relevant questions to generate a collaborative discussion about a research topic.
  • Secondary Research: much like how patterns of behavior can be observed, various types of documentation resources can be coded and divided based on the type of material they contain.
  • Interviews: one of the best collection methods for narrative data. Inquiry responses can be grouped by theme, topic, or category. The interview approach allows for highly-focused data segmentation.

A key difference between qualitative and quantitative analysis is clearly noticeable in the interpretation stage. The first one is widely open to interpretation and must be “coded” so as to facilitate the grouping and labeling of data into identifiable themes. As person-to-person data collection techniques can often result in disputes pertaining to proper analysis, qualitative data analysis is often summarized through three basic principles: notice things, collect things, and think about things.

After qualitative data has been collected through transcripts, questionnaires, audio and video recordings, or the researcher’s notes, it is time to interpret it. For that purpose, there are some common methods used by researchers and analysts.

  • Content analysis : As its name suggests, this is a research method used to identify frequencies and recurring words, subjects and concepts in image, video, or audio content. It transforms qualitative information into quantitative data to help in the discovery of trends and conclusions that will later support important research or business decisions. This method is often used by marketers to understand brand sentiment from the mouths of customers themselves. Through that, they can extract valuable information to improve their products and services. It is recommended to use content analytics tools for this method as manually performing it is very time-consuming and can lead to human error or subjectivity issues. Having a clear goal in mind before diving into it is another great practice for avoiding getting lost in the fog.  
  • Thematic analysis: This method focuses on analyzing qualitative data such as interview transcripts, survey questions, and others, to identify common patterns and separate the data into different groups according to found similarities or themes. For example, imagine you want to analyze what customers think about your restaurant. For this purpose, you do a thematic analysis on 1000 reviews and find common themes such as “fresh food”, “cold food”, “small portions”, “friendly staff”, etc. With those recurring themes in hand, you can extract conclusions about what could be improved or enhanced based on your customer’s experiences. Since this technique is more exploratory, be open to changing your research questions or goals as you go. 
  • Narrative analysis: A bit more specific and complicated than the two previous methods, narrative analysis is used to analyze stories and discover the meaning behind them. These stories can be extracted from testimonials, case studies, and interviews as these formats give people more space to tell their experiences. Given that collecting this kind of data is harder and more time-consuming, sample sizes for narrative analysis are usually smaller, which makes it harder to reproduce its findings. However, it still proves to be a valuable technique in cases such as understanding customers' preferences and mindsets.  
  • Discourse analysis : This method is used to draw the meaning of any type of visual, written, or symbolic language in relation to a social, political, cultural, or historical context. It is used to understand how context can affect the way language is carried out and understood. For example, if you are doing research on power dynamics, using discourse analysis to analyze a conversation between a janitor and a CEO and draw conclusions about their responses based on the context and your research questions is a great use case for this technique. That said, like all methods in this section, discourse analytics is time-consuming as the data needs to be analyzed until no new insights emerge.  
  • Grounded theory analysis : The grounded theory approach aims at creating or discovering a new theory by carefully testing and evaluating the data available. Unlike all other qualitative approaches on this list, grounded theory analysis helps in extracting conclusions and hypotheses from the data, instead of going into the analysis with a defined hypothesis. This method is very popular amongst researchers, analysts, and marketers as the results are completely data-backed, providing a factual explanation of any scenario. It is often used when researching a completely new topic or with little knowledge as this space to start from the ground up. 

Quantitative Data Interpretation

If quantitative data interpretation could be summed up in one word (and it really can’t) that word would be “numerical.” There are few certainties when it comes to data analysis, but you can be sure that if the research you are engaging in has no numbers involved, it is not quantitative research as this analysis refers to a set of processes by which numerical data is analyzed. More often than not, it involves the use of statistical modeling such as standard deviation, mean and median. Let’s quickly review the most common statistical terms:

  • Mean: a mean represents a numerical average for a set of responses. When dealing with a data set (or multiple data sets), a mean will represent a central value of a specific set of numbers. It is the sum of the values divided by the number of values within the data set. Other terms that can be used to describe the concept are arithmetic mean, average and mathematical expectation.
  • Standard deviation: this is another statistical term commonly appearing in quantitative analysis. Standard deviation reveals the distribution of the responses around the mean. It describes the degree of consistency within the responses; together with the mean, it provides insight into data sets.
  • Frequency distribution: this is a measurement gauging the rate of a response appearance within a data set. When using a survey, for example, frequency distribution, it can determine the number of times a specific ordinal scale response appears (i.e., agree, strongly agree, disagree, etc.). Frequency distribution is extremely keen in determining the degree of consensus among data points.

Typically, quantitative data is measured by visually presenting correlation tests between two or more variables of significance. Different processes can be used together or separately, and comparisons can be made to ultimately arrive at a conclusion. Other signature interpretation processes of quantitative data include:

  • Regression analysis: Essentially, it uses historical data to understand the relationship between a dependent variable and one or more independent variables. Knowing which variables are related and how they developed in the past allows you to anticipate possible outcomes and make better decisions going forward. For example, if you want to predict your sales for next month you can use regression to understand what factors will affect them such as products on sale, and the launch of a new campaign, among many others. 
  • Cohort analysis: This method identifies groups of users who share common characteristics during a particular time period. In a business scenario, cohort analysis is commonly used to understand customer behaviors. For example, a cohort could be all users who have signed up for a free trial on a given day. An analysis would be carried out to see how these users behave, what actions they carry out, and how their behavior differs from other user groups.
  • Predictive analysis: As its name suggests, the predictive method aims to predict future developments by analyzing historical and current data. Powered by technologies such as artificial intelligence and machine learning, predictive analytics practices enable businesses to identify patterns or potential issues and plan informed strategies in advance.
  • Prescriptive analysis: Also powered by predictions, the prescriptive method uses techniques such as graph analysis, complex event processing, and neural networks, among others, to try to unravel the effect that future decisions will have in order to adjust them before they are actually made. This helps businesses to develop responsive, practical business strategies.
  • Conjoint analysis: Typically applied to survey analysis, the conjoint approach is used to analyze how individuals value different attributes of a product or service. This helps researchers and businesses to define pricing, product features, packaging, and many other attributes. A common use is menu-based conjoint analysis in which individuals are given a “menu” of options from which they can build their ideal concept or product. Through this analysts can understand which attributes they would pick above others and drive conclusions.
  • Cluster analysis: Last but not least, cluster is a method used to group objects into categories. Since there is no target variable when using cluster analysis, it is a useful method to find hidden trends and patterns in the data. In a business context clustering is used for audience segmentation to create targeted experiences, and in market research, it is often used to identify age groups, geographical information, and earnings, among others.

Now that we have seen how to interpret data, let's move on and ask ourselves some questions: what are some data interpretation benefits? Why do all industries engage in data research and analysis? These are basic questions, but they often don’t receive adequate attention.

Why Data Interpretation Is Important

illustrating quantitative data interpretation with charts & graphs

The purpose of collection and interpretation is to acquire useful and usable information and to make the most informed decisions possible. From businesses to newlyweds researching their first home, data collection and interpretation provides limitless benefits for a wide range of institutions and individuals.

Data analysis and interpretation, regardless of the method and qualitative/quantitative status, may include the following characteristics:

  • Data identification and explanation
  • Comparing and contrasting data
  • Identification of data outliers
  • Future predictions

Data analysis and interpretation, in the end, help improve processes and identify problems. It is difficult to grow and make dependable improvements without, at the very least, minimal data collection and interpretation. What is the keyword? Dependable. Vague ideas regarding performance enhancement exist within all institutions and industries. Yet, without proper research and analysis, an idea is likely to remain in a stagnant state forever (i.e., minimal growth). So… what are a few of the business benefits of digital age data analysis and interpretation? Let’s take a look!

1) Informed decision-making: A decision is only as good as the knowledge that formed it. Informed data decision-making has the potential to set industry leaders apart from the rest of the market pack. Studies have shown that companies in the top third of their industries are, on average, 5% more productive and 6% more profitable when implementing informed data decision-making processes. Most decisive actions will arise only after a problem has been identified or a goal defined. Data analysis should include identification, thesis development, and data collection followed by data communication.

If institutions only follow that simple order, one that we should all be familiar with from grade school science fairs, then they will be able to solve issues as they emerge in real-time. Informed decision-making has a tendency to be cyclical. This means there is really no end, and eventually, new questions and conditions arise within the process that needs to be studied further. The monitoring of data results will inevitably return the process to the start with new data and sights.

2) Anticipating needs with trends identification: data insights provide knowledge, and knowledge is power. The insights obtained from market and consumer data analyses have the ability to set trends for peers within similar market segments. A perfect example of how data analytics can impact trend prediction can be evidenced in the music identification application, Shazam . The application allows users to upload an audio clip of a song they like, but can’t seem to identify. Users make 15 million song identifications a day. With this data, Shazam has been instrumental in predicting future popular artists.

When industry trends are identified, they can then serve a greater industry purpose. For example, the insights from Shazam’s monitoring benefits not only Shazam in understanding how to meet consumer needs, but it grants music executives and record label companies an insight into the pop-culture scene of the day. Data gathering and interpretation processes can allow for industry-wide climate prediction and result in greater revenue streams across the market. For this reason, all institutions should follow the basic data cycle of collection, interpretation, decision-making, and monitoring.

3) Cost efficiency: Proper implementation of data analysis processes can provide businesses with profound cost advantages within their industries. A recent data study performed by Deloitte vividly demonstrates this in finding that data analysis ROI is driven by efficient cost reductions. Often, this benefit is overlooked because making money is typically viewed as “sexier” than saving money. Yet, sound data analyses have the ability to alert management to cost-reduction opportunities without any significant exertion of effort on the part of human capital.

A great example of the potential for cost efficiency through data analysis is Intel. Prior to 2012, Intel would conduct over 19,000 manufacturing function tests on their chips before they could be deemed acceptable for release. To cut costs and reduce test time, Intel implemented predictive data analyses. By using historic and current data, Intel now avoids testing each chip 19,000 times by focusing on specific and individual chip tests. After its implementation in 2012, Intel saved over $3 million in manufacturing costs . Cost reduction may not be as “sexy” as data profit, but as Intel proves, it is a benefit of data analysis that should not be neglected.

4) Clear foresight: companies that collect and analyze their data gain better knowledge about themselves, their processes, and their performance. They can identify performance challenges when they arise and take action to overcome them. Data interpretation through visual representations lets them process their findings faster and make better-informed decisions on the future of the company.

Common Data Analysis And Interpretation Problems

Man running away from common data interpretation problems

The oft-repeated mantra of those who fear data advancements in the digital age is “big data equals big trouble.” While that statement is not accurate, it is safe to say that certain data interpretation problems or “pitfalls” exist and can occur when analyzing data, especially at the speed of thought. Let’s identify some of the most common data misinterpretation risks and shed some light on how they can be avoided:

1) Correlation mistaken for causation: our first misinterpretation of data refers to the tendency of data analysts to mix the cause of a phenomenon with correlation. It is the assumption that because two actions occurred together, one caused the other. This is not accurate as actions can occur together absent a cause-and-effect relationship.

  • Digital age example: assuming that increased revenue is the result of increased social media followers… there might be a definitive correlation between the two, especially with today’s multi-channel purchasing experiences. But, that does not mean an increase in followers is the direct cause of increased revenue. There could be both a common cause and an indirect causality.
  • Remedy: attempt to eliminate the variable you believe to be causing the phenomenon.

2) Confirmation bias: our second problem is data interpretation bias. It occurs when you have a theory or hypothesis in mind but are intent on only discovering data patterns that provide support to it while rejecting those that do not.

  • Digital age example: your boss asks you to analyze the success of a recent multi-platform social media marketing campaign. While analyzing the potential data variables from the campaign (one that you ran and believe performed well), you see that the share rate for Facebook posts was great, while the share rate for Twitter Tweets was not. Using only Facebook posts to prove your hypothesis that the campaign was successful would be a perfect manifestation of confirmation bias.
  • Remedy: as this pitfall is often based on subjective desires, one remedy would be to analyze data with a team of objective individuals. If this is not possible, another solution is to resist the urge to make a conclusion before data exploration has been completed. Remember to always try to disprove a hypothesis, not prove it.

3) Irrelevant data: the third data misinterpretation pitfall is especially important in the digital age. As large data is no longer centrally stored, and as it continues to be analyzed at the speed of thought, it is inevitable that analysts will focus on data that is irrelevant to the problem they are trying to correct.

  • Digital age example: in attempting to gauge the success of an email lead generation campaign, you notice that the number of homepage views directly resulting from the campaign increased, but the number of monthly newsletter subscribers did not. Based on the number of homepage views, you decide the campaign was a success when really it generated zero leads.
  • Remedy: proactively and clearly frame any data analysis variables and KPIs prior to engaging in a data review. If the metric you are using to measure the success of a lead generation campaign is newsletter subscribers, there is no need to review the number of homepage visits. Be sure to focus on the data variable that answers your question or solves your problem and not on irrelevant data.

4) Truncating an Axes: When creating a graph to start interpreting the results of your analysis it is important to keep the axes truthful and avoid generating misleading visualizations. Starting the axes in a value that doesn’t portray the actual truth about the data can lead to false conclusions. 

  • Digital age example: In the image below we can see a graph from Fox News in which the Y-axes start at 34%, making it seem that the difference between 35% and 39.6% is way higher than it actually is. This could lead to a misinterpretation of the tax rate changes. 

Fox news graph truncating an axes

* Source : *

  • Remedy: Be careful with the way your data is visualized. Be respectful and realistic with axes to avoid misinterpretation of your data. See below how the Fox News chart looks when using the correct axes values. This chart was created with datapine's modern online data visualization tool.

Fox news graph with the correct axes values

5) (Small) sample size: Another common problem is the use of a small sample size. Logically, the bigger the sample size the most accurate and reliable the results. However, this also depends on the size of the effect of the study. For example, the sample size in a survey about the quality of education will not be the same as for one about people doing outdoor sports in a specific area. 

  • Digital age example: Imagine you ask 30 people a question and 29 answers “yes” resulting in 95% of the total. Now imagine you ask the same question to 1000 and 950 of them answer “yes”, which is again 95%. While these percentages might look the same, they certainly do not mean the same thing as a 30 people sample size is not a significant number to establish a truthful conclusion. 
  • Remedy: Researchers say that in order to determine the correct sample size to get truthful and meaningful results it is necessary to define a margin of error that will represent the maximum amount they want the results to deviate from the statistical mean. Paired with this, they need to define a confidence level that should be between 90 and 99%. With these two values in hand, researchers can calculate an accurate sample size for their studies.

6) Reliability, subjectivity, and generalizability : When performing qualitative analysis, researchers must consider practical and theoretical limitations when interpreting the data. In some cases, this type of research can be considered unreliable because of uncontrolled factors that might or might not affect the results. This is paired with the fact that the researcher has a primary role in the interpretation process, meaning he or she decides what is relevant and what is not, and as we know, interpretations can be very subjective.

Generalizability is also an issue that researchers face when dealing with qualitative analysis. As mentioned in the point about having a small sample size, it is difficult to draw conclusions that are 100% representative because the results might be biased or unrepresentative of a wider population. 

While these factors are mostly present in qualitative research, they can also affect the quantitative analysis. For example, when choosing which KPIs to portray and how to portray them, analysts can also be biased and represent them in a way that benefits their analysis.

  • Digital age example: Biased questions in a survey are a great example of reliability and subjectivity issues. Imagine you are sending a survey to your clients to see how satisfied they are with your customer service with this question: “how amazing was your experience with our customer service team?”. Here we can see that this question is clearly influencing the response of the individual by putting the word “amazing” on it. 
  • Remedy: A solution to avoid these issues is to keep your research honest and neutral. Keep the wording of the questions as objective as possible. For example: “on a scale of 1-10 how satisfied were you with our customer service team”. This is not leading the respondent to any specific answer, meaning the results of your survey will be reliable. 

Data Interpretation Techniques and Methods

Data interpretation methods and techniques by datapine

Data analysis and interpretation are critical to developing sound conclusions and making better-informed decisions. As we have seen with this article, there is an art and science to the interpretation of data. To help you with this purpose here we will list a few relevant techniques, methods, and tricks you can implement for a successful data management process. 

As mentioned at the beginning of this post, the first step to interpreting data in a successful way is to identify the type of analysis you will perform and apply the methods respectively. Clearly differentiate between qualitative (observe, document, and interview notice, collect and think about things) and quantitative analysis (you lead research with a lot of numerical data to be analyzed through various statistical methods). 

1) Ask the right data interpretation questions

The first data interpretation technique is to define a clear baseline for your work. This can be done by answering some critical questions that will serve as a useful guideline to start. Some of them include: what are the goals and objectives of my analysis? What type of data interpretation method will I use? Who will use this data in the future? And most importantly, what general question am I trying to answer?

Once all this information has been defined, you will be ready for the next step, collecting your data. 

2) Collect and assimilate your data

Now that a clear baseline has been established it is time to collect the information you will use. Always remember your methods for data collection will vary depending on what type of analysis method you use which can be qualitative or quantitative. Based on that, relying on professional online data analysis tools to facilitate the process is a great practice in this regard, as manually collecting and assessing raw data is not only very time-consuming and expensive but is also at risk of errors and subjectivity. 

Once your data is collected, you need to carefully assess it to understand if the quality is appropriate to be used during a study. This means, is the sample size big enough? Were the procedures used to collect the data implemented correctly? Is the date range from the data correct? If coming from an external source, is it a trusted and objective one? 

With all the needed information in hand, you are ready to start the interpretation process, but first, you need to visualize your data. 

3) Use the right data visualization type 

Data visualizations such as business graphs , charts, and tables are fundamental to successfully interpreting data. This is because the visualization of data via interactive charts and graphs makes the information more understandable and accessible. As you might be aware, there are different types of visualizations you can use but not all of them are suitable for any analysis purpose. Using the wrong graph can lead to misinterpretation of your data so it’s very important to carefully pick the right visual for it. Let’s look at some use cases of common data visualizations. 

  • Bar chart: One of the most used chart types, the bar chart uses rectangular bars to show the relationship between 2 or more variables. There are different types of bar charts for different interpretations including the horizontal bar chart, column bar chart, and stacked bar chart. 
  • Line chart: Most commonly used to show trends, acceleration or decelerations, and volatility, the line chart aims to show how data changes over a period of time for example sales over a year. A few tips to keep this chart ready for interpretation are to not use many variables that can overcrowd the graph and keep your axis scale close to the highest data point to avoid making the information hard to read. 
  • Pie chart: Although it doesn’t do a lot in terms of analysis due to its uncomplex nature, pie charts are widely used to show the proportional composition of a variable. Visually speaking, showing a percentage in a bar chart is way more complicated than showing it in a pie chart. However, this also depends on the number of variables you are comparing. If your pie chart would need to be divided into 10 portions then it is better to use a bar chart instead. 
  • Tables: While they are not a specific type of chart, tables are wildly used when interpreting data. Tables are especially useful when you want to portray data in its raw format. They give you the freedom to easily look up or compare individual values while also displaying grand totals. 

With the use of data visualizations becoming more and more critical for businesses’ analytical success, many tools have emerged to help users visualize their data in a cohesive and interactive way. One of the most popular ones is the use of BI dashboards . These visual tools provide a centralized view of various graphs and charts that paint a bigger picture of a topic. We will discuss the power of dashboards for an efficient data interpretation practice in the next portion of this post. If you want to learn more about different types of graphs and charts take a look at our complete guide on the topic. 

4) Start interpreting 

After the tedious preparation part, you are ready to start extracting conclusions from your data. As mentioned many times throughout the post, the way you decide to interpret the data will solely depend on the methods you initially decided to use. If you had initial research questions or hypotheses then you should look for ways to prove their validity. If you are going into the data with no defined hypothesis, then start looking for relationships and patterns that will allow you to extract valuable conclusions from the information. 

During the process of interpretation, stay curious and creative, dig into the data and determine if there are any other critical questions that should be asked. If any new questions arise, you need to assess if you have the necessary information to answer them. Being able to identify if you need to dedicate more time and resources to the research is a very important step. No matter if you are studying customer behaviors or a new cancer treatment, the findings from your analysis may dictate important decisions in the future, therefore, taking the time to really assess the information is key. For that purpose, data interpretation software proves to be very useful.

5) Keep your interpretation objective

As mentioned above, objectivity is one of the most important data interpretation skills but also one of the hardest. Being the person closest to the investigation, it is easy to become subjective when looking for answers in the data. A good way to stay objective is to show the information to other people related to the study, for example, research partners or even the people that will use your findings once they are done. This can help avoid confirmation bias and any reliability issues with your interpretation. 

Remember, using a visualization tool such as a modern dashboard will make the interpretation process way easier and more efficient as the data can be navigated and manipulated in an easy and organized way. And not just that, using a dashboard tool to present your findings to a specific audience will make the information easier to understand and the presentation way more engaging thanks to the visual nature of these tools. 

6) Mark your findings and draw conclusions

Findings are the observations you extracted from your data. They are the facts that will help you drive deeper conclusions about your research. For example, findings can be trends and patterns you found during your interpretation process. To put your findings into perspective you can compare them with other resources that used similar methods and use them as benchmarks.

Reflect on your own thinking and reasoning and be aware of the many pitfalls data analysis and interpretation carries. Correlation versus causation, subjective bias, false information, inaccurate data, etc. Once you are comfortable with your interpretation of the data you will be ready to develop conclusions, see if your initial question were answered, and suggest recommendations based on them.

Interpretation of Data: The Use of Dashboards Bridging The Gap

As we have seen, quantitative and qualitative methods are distinct types of data interpretation and analysis. Both offer a varying degree of return on investment (ROI) regarding data investigation, testing, and decision-making. Because of their differences, it is important to understand how dashboards can be implemented to bridge the quantitative and qualitative information gap. How are digital data dashboard solutions playing a key role in merging the data disconnect? Here are a few of the ways:

1) Connecting and blending data. With today’s pace of innovation, it is no longer feasible (nor desirable) to have bulk data centrally located. As businesses continue to globalize and borders continue to dissolve, it will become increasingly important for businesses to possess the capability to run diverse data analyses absent the limitations of location. Data dashboards decentralize data without compromising on the necessary speed of thought while blending both quantitative and qualitative data. Whether you want to measure customer trends or organizational performance, you now have the capability to do both without the need for a singular selection.

2) Mobile Data. Related to the notion of “connected and blended data” is that of mobile data. In today’s digital world, employees are spending less time at their desks and simultaneously increasing production. This is made possible by the fact that mobile solutions for analytical tools are no longer standalone. Today, mobile analysis applications seamlessly integrate with everyday business tools. In turn, both quantitative and qualitative data are now available on-demand where they’re needed, when they’re needed, and how they’re needed via interactive online dashboards .

3) Visualization. Data dashboards are merging the data gap between qualitative and quantitative data interpretation methods, through the science of visualization. Dashboard solutions come “out of the box” well-equipped to create easy-to-understand data demonstrations. Modern online data visualization tools provide a variety of color and filter patterns, encourage user interaction, and are engineered to help enhance future trend predictability. All of these visual characteristics make for an easy transition among data methods – you only need to find the right types of data visualization to tell your data story the best way possible.

To give you an idea of how a market research dashboard fulfills the need of bridging quantitative and qualitative analysis and helps in understanding how to interpret data in research thanks to visualization, have a look at the following one. It brings together both qualitative and quantitative data knowledgeably analyzed and visualizes it in a meaningful way that everyone can understand, thus empowering any viewer to interpret it:

market research example on customers' satisfaction with a brand

**click to enlarge**

To see more data analysis and interpretation examples, visit our library of business dashboards .

To Conclude…

As we reach the end of this insightful post about data interpretation and analysis we hope you have a clear understanding of the topic. We've covered the definition, and given some examples and methods to perform a successful interpretation process.

The importance of data interpretation is undeniable. Dashboards not only bridge the information gap between traditional data interpretation methods and technology, but they can help remedy and prevent the major pitfalls of the process. As a digital age solution, they combine the best of the past and the present to allow for informed decision-making with maximum data interpretation ROI.

To start visualizing your insights in a meaningful and actionable way, test our online reporting software for free with our 14-day trial !

Analytics , Announcements , Azure Data Explorer , Azure Data Factory , Azure OpenAI Service , Azure Synapse Analytics , Internet of Things

Introducing Microsoft Fabric: Data analytics for the era of AI

By Arun Ulagaratchagan Corporate Vice President, Azure Data

Posted on May 23, 2023 10 min read

Image of Microsoft Fiber graphic.

Today’s world is awash with data—ever-streaming from the devices we use, the applications we build, and the interactions we have. Organizations across every industry have harnessed this data to digitally transform and gain competitive advantages. And now, as we enter a new era defined by AI, this data is becoming even more important.  

Generative AI and language model services, such as Azure OpenAI Service, are enabling customers to use and create everyday AI experiences that are reinventing how employees spend their time. Powering organization-specific AI experiences requires a constant supply of clean data from a well-managed and highly integrated analytics system. But most organizations’ analytics systems are a labyrinth of specialized and disconnected services.  

And it’s no wonder given the massively fragmented data and AI technology market with hundreds of vendors and thousands of services. Customers must stitch together a complex set of disconnected services from multiple vendors themselves and incur the costs and burdens of making these services function together. 

Introducing Microsoft Fabric 

Today we are unveiling Microsoft Fabric —an end-to-end, unified analytics platform that brings together all the data and analytics tools that organizations need. Fabric integrates technologies like Azure Data Factory, Azure Synapse Analytics, and Power BI into a single unified product, empowering data and business professionals alike to unlock the potential of their data and lay the foundation for the era of AI. 

Watch a quick overview:  


What sets Microsoft Fabric apart? 

Fabric is an end-to-end analytics product that addresses every aspect of an organization’s analytics needs. But there are five areas that really set Fabric apart from the rest of the market:

1. Fabric is a complete analytics platform 

Every analytics project has multiple subsystems. Every subsystem needs a different array of capabilities, often requiring products from multiple vendors. Integrating these products can be a complex, fragile, and expensive endeavor.  

With Fabric, customers can use a single product with a unified experience and architecture that provides all the capabilities required for a developer to extract insights from data and present it to the business user. And by delivering the experience as software as a service (SaaS), everything is automatically integrated and optimized, and users can sign up within seconds and get real business value within minutes.  

Fabric empowers every team in the analytics process with the role-specific experiences they need, so data engineers, data warehousing professionals, data scientists, data analysts, and business users feel right at home.  

Screenshot of Microsoft Fabric features.

Fabric comes with seven core workloads: 

  • Data Factory (preview) provides more than 150 connectors to cloud and on-premises data sources, drag-and-drop experiences for data transformation, and the ability to orchestrate data pipelines.
  • Synapse Data Engineering (preview) enables great authoring experiences for Spark, instant start with live pools, and the ability to collaborate.
  • Synapse Data Science (preview) provides an end-to-end workflow for data scientists to build sophisticated AI models, collaborate easily, and train, deploy, and manage machine learning models. 
  • Synapse Data Warehousing (preview) provides a converged lake house and data warehouse experience with industry-leading SQL performance on open data formats.
  • Synapse Real-Time Analytics (preview) enables developers to work with data streaming in from the Internet of Things (IoT) devices, telemetry, logs, and more, and analyze massive volumes of semi-structured data with high performance and low latency.
  • Power BI in Fabric provides industry-leading visualization and AI-driven analytics that enable business analysts and business users to gain insights from data. The Power BI experience is also deeply integrated into Microsoft 365, providing relevant insights where business users already work.  
  • Data Activator (coming soon) provides real-time detection and monitoring of data and can trigger notifications and actions when it finds specified patterns in data—all in a no-code experience. 

You can try these experiences today by signing up for the Microsoft Fabric free trial . 

2. Fabric is lake-centric and open 

Today’s data lakes can be messy and complicated, making it hard for customers to create, integrate, manage, and operate data lakes. And once they are operational, multiple data products using different proprietary data formats on the same data lake can cause significant data duplication and concerns about vendor lock-in.  

OneLake—The OneDrive for data 

Fabric comes with a SaaS, multi-cloud data lake called OneLake that is built-in and automatically available to every Fabric tenant. All Fabric workloads are automatically wired into OneLake, just like all Microsoft 365 applications are wired into OneDrive. Data is organized in an intuitive data hub, and automatically indexed for discovery, sharing, governance, and compliance.  

OneLake serves developers, business analysts, and business users alike, helping eliminate pervasive and chaotic data silos created by different developers provisioning and configuring their own isolated storage accounts. Instead, OneLake provides a single, unified storage system for all developers, where discovery and sharing of data are easy with policy and security settings enforced centrally. At the API layer, OneLake is built on and fully compatible with Azure Data Lake Storage Gen2 (ADLSg2), instantly tapping into ADLSg2’s vast ecosystem of applications, tools, and developers.  

A key capability of OneLake is “Shortcuts.” OneLake allows easy sharing of data between users and applications without having to move and duplicate information unnecessarily. Shortcuts allow OneLake to virtualize data lake storage in ADLSg2, Amazon Simple Storage Service (Amazon S3), and Google Storage (coming soon), enabling developers to compose and analyze data across clouds. 

Open data formats across analytics offerings 

Fabric is deeply committed to open data formats across all its workloads and tiers. Fabric treats Delta on top of Parquet files as a native data format that is the default for all workloads. This deep commitment to a common open data format means that customers need to load the data into the lake only once and all the workloads can operate on the same data, without having to separately ingest it. It also means that OneLake supports structured data of any format and unstructured data, giving customers total flexibility.  

By adopting OneLake as our store and Delta and Parquet as the common format for all workloads, we offer customers a data stack that’s unified at the most fundamental level. Customers do not need to maintain different copies of data for databases, data lakes, data warehousing, business intelligence, or real-time analytics. Instead, a single copy of the data in OneLake can directly power all the workloads.  

Managing data security (table, column, and row levels) across different data engines can be a persistent nightmare for customers. Fabric will provide a universal security model that is managed in OneLake, and all engines enforce it uniformly as they process queries and jobs. This model is coming soon.  

3. Fabric is powered by AI  

We are infusing Fabric with Azure OpenAI Service at every layer to help customers unlock the full potential of their data, enabling developers to leverage the power of generative AI against their data and assisting business users to find insights in their data. With Copilot in Microsoft Fabric in every data experience, users can use conversational language to create dataflows and data pipelines, generate code and entire functions, build machine learning models, or visualize results. Customers can even create their own conversational language experiences that combine Azure OpenAI Service models and their data and publish them as plug-ins.   

Copilot in Microsoft Fabric builds on our existing commitments to data security and privacy in the enterprise. Copilot inherits an organization’s security, compliance, and privacy policies. Microsoft does not use organizations’ tenant data to train the base language models that power Copilot. 

Copilot in Microsoft Fabric will be coming soon. Stay tuned to the Microsoft Fabric blog for the latest updates and public release date for Copilot in Microsoft Fabric.  

4. Fabric empowers every business user 

Customers aspire to drive a data culture where everyone in their organization is making better decisions based on data. To help our customers foster this culture, Fabric deeply integrates with the Microsoft 365 applications people use every day.  

Power BI is a core part of Fabric and is already infused across Microsoft 365. Through Power BI’s deep integrations with popular applications such as Excel, Microsoft Teams, PowerPoint, and SharePoint, relevant data from OneLake is easily discoverable and accessible to users right from Microsoft 365—helping customers drive more value from their data

With Fabric, you can turn your Microsoft 365 apps into hubs for uncovering and applying insights. For example, users in Microsoft Excel can directly discover and analyze data in OneLake and generate a Power BI report with a click of a button. In Teams, users can infuse data into their everyday work with embedded channels, chat, and meeting experiences. Business users can bring data into their presentations by embedding live Power BI reports directly in Microsoft PowerPoint. Power BI is also natively integrated with SharePoint, enabling easy sharing and dissemination of insights. And with Microsoft Graph Data Connect (preview), Microsoft 365 data is natively integrated into OneLake so customers can unlock insights on their customer relationships, business processes, security and compliance, and people productivity.  

5. Fabric reduces costs through unified capacities 

Today’s analytics systems typically combine products from multiple vendors in a single project. This results in computing capacity provisioned in multiple systems like data integration, data engineering, data warehousing, and business intelligence. When one of the systems is idle, its capacity cannot be used by another system causing significant wastage.  

Purchasing and managing resources is massively simplified with Fabric. Customers can purchase a single pool of computing that powers all Fabric workloads. With this all-inclusive approach, customers can create solutions that leverage all workloads freely without any friction in their experience or commerce. The universal compute capacities significantly reduce costs, as any unused compute capacity in one workload can be utilized by any of the workloads. 

Explore how our customers are already using Microsoft Fabric  

Ferguson .

Ferguson is a leading distributor of plumbing, HVAC, and waterworks supplies, operating across North America. And by using Fabric to consolidate their analytics stack into a unified solution, they are hoping to reduce their delivery time and improve efficiency. 

“ Microsoft Fabric reduces the delivery time by removing the overhead of using multiple disparate services. By consolidating the necessary data provisioning, transformation, modeling, and analysis services into one UI, the time from raw data to business intelligence is significantly reduced. Fabric meaningfully impacts Ferguson’s data storage, engineering, and analytics groups since all these workloads can now be done in the same UI for faster delivery of insights .” —George Rasco, Principal Database Architect, Ferguson

See Fabric in action at Ferguson: 


T-Mobile, one of the largest providers of wireless communications services in the United States, is focused on driving disruption that creates innovation and better customer experiences in wireless and beyond. With Fabric, T-Mobile hopes they can take their platform and data-driven decision-making to the next level. 

“ T-Mobile loves our customers and providing them with new Un-Carrier benefits! We think that Fabric’s upcoming abilities will help us eliminate data silos, making it easier for us to unlock new insights into how we show our customers even more love. Querying across the lakehouse and warehouse from a single engine—that’s a game changer. Spark compute on-demand, rather than waiting for clusters to spin up, is a huge improvement for both standard data engineering and advanced analytics. It saves three minutes on every job, and when you’re running thousands of jobs an hour, that really adds up. And being able to easily share datasets across the company is going to eliminate so much data duplication. We’re really looking forward to these new features .” —Geoffrey Freeman, MTS, Data Solutions and Analytics, T-Mobile


Aon provides professional services and management consulting services to a vast global network of customers. With the help of Fabric, Aon hopes that they can consolidate more of their current technology stack and focus on adding more value to their clients. 

“ What’s most exciting to me about Fabric is simplifying our existing analytics stack. Currently, there are so many different PaaS services across the board that when it comes to modernization efforts for many developers, Fabric helps simplify that. We can now spend less time building infrastructure and more time adding value to our business .”    —Boby Azarbod, Data Services Lead, Aon

What happens to current Microsoft analytics solutions? 

Existing Microsoft products such as Azure Synapse Analytics, Azure Data Factory, and Azure Data Explorer will continue to provide a robust, enterprise-grade platform as a service (PaaS) solution for data analytics. Fabric represents an evolution of those offerings in the form of a simplified SaaS solution that can connect to existing PaaS offerings. Customers will be able to upgrade from their current products into Fabric at their own pace.  

Get started with Microsoft Fabric

Microsoft Fabric is currently in preview. Try out everything Fabric has to offer by signing up for the free trial—no credit card information is required. Everyone who signs up gets a fixed Fabric trial capacity, which may be used for any feature or capability from integrating data to creating machine learning models. Existing Power BI Premium customers can simply turn on Fabric through the Power BI admin portal. After July 1, 2023, Fabric will be enabled for all Power BI tenants. 

Microsoft Fabric graphic

Microsoft Fabric resources 

If you want to learn more about Microsoft Fabric, consider:  

  • Signing up for the Microsoft Fabric free trial .
  • Visiting the Microsoft Fabric website .
  • Data Factory experience in Fabric blog
  • Synapse Data Engineering experience in Fabric blog
  • Synapse Data Science experience in Fabric blog
  • Synapse Data Warehousing experience in Fabric blog
  • Synapse Real-Time Analytics experience in Fabric blog
  • Power BI announcement blog
  • Data Activator experience in Fabric blog
  • Administration and governance in Fabric blog
  • OneLake in Fabric blog
  • Fabric event streams blog
  • Microsoft 365 data integration in Fabric blog
  • Dataverse and Microsoft Fabric integration blog
  • Exploring the Fabric technical documentation .
  • Reading the free e-book on getting started with Fabric . 
  • Exploring Fabric through the Guided Tour .
  • Joining the Fabric community to post your questions, share your feedback, and learn from others. 

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Assessing the effects of generation using age-period-cohort analysis

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(Related posts:  5 things to keep in mind when you hear about Gen Z, Millennials, Boomers and other generations  and  How Pew Research Center will report on generations moving forward )

Opinions often differ by generation in the United States. For example, Gen Zers and Millennials are more likely than older generations to want the government to do more to solve problems , according to a January 2020 Pew Research Center survey. But will Gen Zers and Millennials always feel that way, or might their views on government become more conservative as they age? In other words, are their attitudes an enduring trait specific to their generation, or do they simply reflect a stage in life?

That question cannot be answered with a single survey. Instead, researchers need two things: 1) survey data collected over many years – ideally at least 50 years , or long enough for multiple generations to advance through the same life stages; and 2) a statistical tool called age-period-cohort (APC) analysis.

In this piece, we’ll demonstrate how to conduct age-period-cohort analysis to determine the effects of generation, using nearly 60 years of data from the U.S. Census Bureau’s Current Population Survey. Specifically, we’ll revisit two previous Center analyses that looked at generational differences in marriage rates and the likelihood of having moved residences in the past year to see how they hold up when we use APC analysis.

What is age-period-cohort analysis?

In a typical survey wave, respondents’ generation and age are perfectly correlated with each other. The two cannot be disentangled. Separating the influence of generation and life cycle requires us to have many years of data.

If we have data not just from 2020, but also from 2000, 1980 and earlier decades, we can compare the attitudes of different generations while they were passing through the same stages in the life cycle. For example, we can contrast a 25-year-old respondent in 2020 (who would be a Millennial) with a 25-year-old in 2000 (a Gen Xer) and a 25-year-old in 1980 (a Baby Boomer).

A dataset compiled over many years allows age and generation to be examined separately using age-period-cohort analysis. In this context, “age” denotes a person’s stage in the life cycle, “period” refers to when the data was collected, and “cohort” refers to a group of people who were born within the same time period. For this analysis, the cohorts we are interested in are generations – Generation Z, Millennials, Generation X and so on .

APC analysis seeks to parse the effects of age, period and cohort on a phenomenon. In a strictly mathematical sense, this is a problem because any one of those things can be exactly calculated from the other two (e.g., if someone was 50 years old in 2016, then we also know they were born circa 1966 because 2016 – 50 = 1966). If we only have data collected at one point in time, it’s not possible to identify conclusively whether apparent differences between generations are not just due to how old the respondents were when the data was collected. For example, we don’t know whether Millennials’ lower marriage rate in 2016 is a generational difference or simply a result of the fact that people ages 23 to 38 are less likely to be married, regardless of when they were born. Even if we have data from many years, any apparent trends could also be due to other factors that apply to all generations and age groups equally. We might mistakenly attribute a finding to age or cohort when it really should be attributed to period. This is called the “identification problem.” Approaches to APC analysis are all about getting around the identification problem in some way.

Using multilevel modeling for age-period-cohort analysis

A common approach for APC analysis is multilevel modeling. A multilevel model is a kind of regression model that can be fit to data that is structured in “groups,” which themselves can be units of analysis.

A classic example of multilevel modeling is in education research, where a researcher might have data on students from many schools. In this example, the two levels in the data are students and the schools they attend. Traits on both the student level (e.g., grade-point average, test scores) and the school level (e.g., funding, class size) could be important to understanding outcomes.

In APC analysis, things are a little different. In a dataset collected over many years, we can think of each respondent as belonging to two different but overlapping groups. The first is their generation, as determined by the year in which they were born. The second is the year in which the data was collected.

Fitting a multilevel model with groups for generation and year lets us isolate differences between cohorts (generations) and periods (years) while holding individual characteristics like age, sex and race constant. Placing period and cohort on a different level from age addresses the identification problem by allowing us to model all of these variables simultaneously.

Conducting age-period-cohort analysis with the Current Population Survey

One excellent resource that can support APC analysis is the Current Population Survey’s Annual Social and Economic Supplement (ASEC), conducted almost every year from 1962 to 2021. We’ll use data from the ASEC to address two questions:

  • Does the lower marriage rate among today’s young adults reflect a generational effect, or is it explained by other factors?
  • Does the relatively low rate of moving among today’s young adults reflect a generational effect, or is it explained by other factors?

In previous Center analyses, we held only age constant. This time, we want to separate generation not just from age, but also from period, race, gender and education.

Getting started

The data we’ll use in this analysis can be accessed through the Integrated Public Use Microdata Series (IPUMS) . After selecting the datasets and variables you need, download the data (as a dat.gz file) and the XML file that describes the data and put them in the same folder. Then, use the package ipumsr to read the data in as follows:

Next, process and clean the data. First, filter the data so it only includes adults (people ages 18 and older), and then create clean versions of the variables that will be in the model.

Here’s a look at the cleaning and filtering code we used. We coded anyone older than 80 as being 80 because the ASEC already coded them that way for some years. We applied that rule to every year in order to ensure consistency. We only created White, Black and Other categories for race because categories such as Hispanic and Asian weren’t measured until later. Finally, there were several years when the ASEC did not measure whether people moved residences in the past year, so we excluded those years from our analysis.

The full ASEC dataset has more than 6.7 million observations, with around 60,000 to 100,000 cases from each year. Without the computing power to fit a model to the entire dataset in any reasonable amount of time, one option is to sample a smaller number of cases per year, such as 2,000. To ensure that the sampled cases are still representative, sample them proportionally to their survey weight.

Model fitting

Now that the data has been processed, it’s ready for model fitting. This example uses the rstanarm package to fit the model using Bayesian inference.

Below, we fit a multilevel logistic regression model with marriage as the outcome variable; with age, number of adults in the household, sex, race and education as individual-level explanatory variables; and with period and generation as normally distributed random effects that shift the intercept depending on which groups an individual is in.

Regression models are largely made up of two components: the outcome variable and some explanatory variables. Characteristics that are measured on each individual in the data and that could be related to the outcome variable are potentially good explanatory variables. Every model also contains residual error, which captures anything that influences the outcome variable other than the explanatory variables; this can be thought of as encompassing unique qualities that make all individuals in the data different from one another. A multilevel regression model will also capture unique qualities that make each group in the data different from one another. The model may optionally include group-level explanatory variables as well.

The groups don’t need to be neatly nested within one another, allowing for flexibility in the kinds of situations to which multilevel modeling can apply. In our example, we model age as a continuous, individual-level predictor while modeling generation (cohort) and period as groups that each have different, discrete effects on the outcome variable. Separating age from period and cohort by placing them on different levels allows us to model all of them without running head-on into the identification problem. However, this is premised on a number of important assumptions that may not always hold up in practice.

By modeling the data like this, we are treating the relationship between period or generation and marriage rates as discrete, where each period or generation has its own distinct relationship. If there is a smooth trend over time, the model does not estimate the trend itself, instead looking at each period or generation in isolation. We are, however, modeling age as a continuous trend.

Separating generation from other factors

Our research questions above concern whether the differences by generation that show up in the data can still be attributed to generation after controlling for age, cohort and other explanatory variables.

In a report on Millennials and family life , Pew Research Center looked at ASEC’s data on people who were 23 to 38 years old in four specific years – 2019, 2003, 1987 and 1968. Each of these groups carried a generation label – Millennial, Gen X, Boomer and Silent – and the report noted that younger generations were less likely to be married than older ones .

Now that we have a model, we can reexamine this conclusion, decoupling generation from age and period. The model can return predicted probabilities of being married for any combination of variables passed to it, including combinations that didn’t previously exist in the data – and combinations that are, by definition, impossible. The model can, for example, predict how likely it is that a Millennial who was between ages 23 and 38 in 1968 would be married, even if no such person can exist. That’s useful not for what it represents in and of itself, but for what it can explain about the influence of generation on getting married.

In order to create predicted probabilities, the first requirement is to create a dataset on which the model will predict probabilities. This dataset should have every variable used in the model.

Here, we create a function that takes five inputs: the model, the original data, a generation category (cohorts), a range of years (periods) and a range of ages. This function will take everybody in the ASEC data for the given years and ages (regardless of whether they were in the subset used to fit the model) and set all their generations to be the same while keeping everything else unchanged, whether the resulting input makes sense in real life or not. This simulated ASEC data is then passed to the posterior_predict() function from rstanarm , which gives us a 2,000-by-n matrix, where n is the number of observations in the new data passed to it. We then compute the weighted mean of the predicted probabilities across the observations, giving us 2,000 weighted means. These 2,000 weighted means represent draws from a distribution estimating the predicted probability. We summarize this distribution by taking its median, as well as the 2.5th and 97.5th percentiles to create a 95% interval to express uncertainty.

We then run this function four times, once for each generation, on everyone in the ASEC data who was ages 23 to 38 during each of the four years we studied in the report. First, the function estimates the marriage rate among those people if, hypothetically, they were all Millennials, regardless of what year they appear in the ASEC data. Next, it estimates the marriage rate among those same people if, hypothetically, they were all Gen Xers. Then the function estimates the marriage rate if they were all Boomers or members of the Silent Generation, respectively.

In statistical terms, we are drawing from the “posterior predictive distribution.” This allows us to generate estimates for hypothetical scenarios in which we manipulate generation while holding age, period and all other individual characteristics constant. While this is not anything that could ever happen in the real world, it’s a convenient and interpretable way to visualize how changing one predictor would affect the outcome (marriage rate).

Determining the role of generation

To answer the first of our research questions, let’s plot our predicted probabilities of being married. If we took all these people at each of these points in time and magically imbued them with everything that is unique to being a Millennial, the model estimates that 38% of them would be married. In contrast, if you imbued everyone with the essence of the Silent Generation, that estimate would be 68%. The numbers themselves aren’t important, as they don’t describe an actual population. Instead, we’re mainly interested in how different the numbers are from one another. In this case, the intervals do not overlap at all between the generations and there is a clear downward trend in the marriage rate. This is what we would expect to see if the lower marriage rate among Millennials reflects generational change that is not explained by the life cycle (age) or by other variables in the model (gender, race, education).

A chart showing that Generation is an important factor in predicting the likelihood that young people will be married

What about our second question about generational differences in the likelihood of having moved residences in the past year? The Center previously reported that Millennials were less likely to move than prior generations of young adults. That analysis was based on a similar approach that considered people who were ages 25 to 35 in 2016, 2000, 1990, 1981 and 1963, and identified them as Millennials, Gen Xers, “Late Boomers,” “Early Boomers” and Silents.

Using the ASEC dataset described above, we reexamined this pattern using age-period-cohort analysis. We fit the same kind of multilevel model to this outcome and plotted the predicted probabilities in the same way. In this case, there are no clear differences or trend across the generations, with overlapping intervals for the estimated share who moved in the past year. This suggests that the apparent differences between the generations are better explained by other factors in the model, not generation.

A chart showing that Differences in moving rates are explained by factors other than generation

These twin analyses of marriage rates and moving rates illustrates several key points in generational research.

In some cases (e.g., moving), what looks like a generation effect is actually explained by other factors, such as race or education. In other cases (e.g., marriage), there is evidence of an enduring effect associated with one’s generation.

APC analysis requires an extended time series of data; a theory for why generation may matter; a careful statistical approach; and an understanding of the underlying assumptions being made.

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  • Open Access
  • Published: 17 May 2023

GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19

  • Erola Pairo-Castineira   ORCID: 1 , 2 , 3   na1 ,
  • Konrad Rawlik   ORCID: 1   na1 ,
  • Andrew D. Bretherick   ORCID: 1 , 2 , 4 ,
  • Ting Qi 5 , 6 ,
  • Yang Wu   ORCID: 7 ,
  • Isar Nassiri 8 ,
  • Glenn A. McConkey 9 ,
  • Marie Zechner 1 , 3 ,
  • Lucija Klaric   ORCID: 2 ,
  • Fiona Griffiths 1 , 3 ,
  • Wilna Oosthuyzen 1 , 3 ,
  • Athanasios Kousathanas 10 ,
  • Anne Richmond 2 ,
  • Jonathan Millar 1 , 3 , 11 ,
  • Clark D. Russell 1 ,
  • Tomas Malinauskas   ORCID: 8 ,
  • Ryan Thwaites   ORCID: 12 ,
  • Kirstie Morrice 13 ,
  • Sean Keating   ORCID: 11 ,
  • David Maslove 14 ,
  • Alistair Nichol   ORCID: 15 ,
  • Malcolm G. Semple   ORCID: 16 , 17 ,
  • Julian Knight 8 ,
  • Manu Shankar-Hari   ORCID: 11 , 18 ,
  • Charlotte Summers   ORCID: 19 ,
  • Charles Hinds   ORCID: 20 ,
  • Peter Horby 21 ,
  • Lowell Ling 22 ,
  • Danny McAuley   ORCID: 23 , 24 ,
  • Hugh Montgomery   ORCID: 25 ,
  • Peter J. M. Openshaw   ORCID: 12 , 26 ,
  • Colin Begg 27 ,
  • Timothy Walsh 11 ,
  • Albert Tenesa   ORCID: 2 , 3 , 28 ,
  • Carlos Flores 29 , 30 , 31 , 32 ,
  • José A. Riancho 33 , 34 , 35 ,
  • Augusto Rojas-Martinez 36 ,
  • Pablo Lapunzina 37 , 38 , 39 ,
  • GenOMICC Investigators ,
  • SCOURGE Consortium ,
  • ISARICC Investigators ,
  • The 23andMe COVID-19 Team ,
  • Jian Yang   ORCID: 5 , 6 ,
  • Chris P. Ponting   ORCID: 2 ,
  • James F. Wilson   ORCID: 2 , 28 ,
  • Veronique Vitart   ORCID: 2 ,
  • Malak Abedalthagafi 40 , 41 ,
  • Andre D. Luchessi 42 , 43 ,
  • Esteban J. Parra 43 ,
  • Raquel Cruz 37 , 44 ,
  • Angel Carracedo 37 , 44 , 45 , 46 ,
  • Angie Fawkes 13 ,
  • Lee Murphy   ORCID: 13 ,
  • Kathy Rowan   ORCID: 47 ,
  • Alexandre C. Pereira 48 ,
  • Andy Law   ORCID: 3 ,
  • Benjamin Fairfax   ORCID: 8 ,
  • Sara Clohisey Hendry   ORCID: 1 , 3 &
  • J. Kenneth Baillie   ORCID: 1 , 2 , 3 , 11  

Nature volume  617 ,  pages 764–768 ( 2023 ) Cite this article

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  • Genetics research
  • Genome-wide association studies
  • Viral infection

Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown 1 to be highly efficient for discovery of genetic associations 2 . Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group 3 . Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling ( JAK1 ), monocyte–macrophage activation and endothelial permeability ( PDE4A ), immunometabolism ( SLC2A5 and AK5 ), and host factors required for viral entry and replication ( TMPRSS2 and RAB2A ).

The design of the GenOMICC study and the rationale for focusing on critical illness has been previously described 1 , 2 . In brief, patients with confirmed COVID-19 requiring continuous cardiorespiratory monitoring or organ support (a generalizable definition for critical illness) were recruited in 2020–2022. We first performed ancestry-specific GWAS analyses according to the methods that we described previously 1 , 2 . Using the results of these GWAS analyses, previously reported results obtained using GenOMICC participants with whole-genome sequencing data 2 and data from GenOMICC Brazil, we performed trans-ancestry and -platform meta-analyses within the GenOMICC study for a critically ill COVID-19 phenotype and a hospitalized COVID-19 phenotype (Extended Data Fig. 1 ). The results of these GenOMICC-only meta-analyses are presented for both critically ill and hospitalized phenotypes (Table 1 and Extended Data Fig. 2 ). To put these results into the context of existing knowledge, we performed comprehensive meta-analyses, drawing on further GWAS results, including data shared by the SCOURGE consortium and published data from the COVID-19 Human Genetics Initiative (HGIv6, 2021) 4 . The characteristics of the contributing studies are summarized in Supplementary Tables 13 and 14 for the critically ill and hospitalized phenotypes, with further details on each study provided in the  Supplementary Information . We used a mathematical subtraction approach, as done in our previous work 2 , to remove signals of previous GenOMICC releases from HGIv6, yielding an independent dataset.

As no replication cohorts exist for these meta-analyses, we used the heterogeneity across studies to assess the reliability of individual findings (Supplementary Table 15 ). Owing to the unusually extreme phenotype in the GenOMICC study, some heterogeneity is expected for the strongest associations when compared with studies with more permissive inclusion criteria. Importantly, significant heterogeneity was not detected for any of the findings that we report here (Supplementary Table 15 ). Comparing effect estimates between studies using a regression approach that takes into account estimation errors ( Methods ), we detected systematic differences in effect sizes between studies (Extended Data Fig. 3 ). For example, effects for the HGI critical illness phenotype (which was designed to parallel the GenOMICC inclusion criteria) are smaller than those obtained using prospective recruitment in GenOMICC by a factor of 0.68. As the effect sizes in GenOMICC are consistently larger than other studies, and GenOMICC contributes a disproportionately large signal to meta-analyses of both critical and hospitalized phenotypes (Extended Data Fig. 4 ), between-study heterogeneity is likely to reflect the careful case ascertainment and extreme phenotype in GenOMICC compared with other studies.

We found 49 common genetic associations with critical COVID-19 meeting our criteria for genome-wide significance in the absence of heterogeneity (Extended Data Fig. 2 and Table 1 ). Findings from previous reports were consistently replicated (Extended Data Table 2 ). Conditional analysis revealed two additional lead variants (Table 1 ) and statistical fine-mapping provided credible sets of putative causal variants for a majority of lead variants (Supplementary Figs. 27 – 44 and Supplementary Table 5 ). Gene-level analyses found 196 significantly associated genes at a Bonferroni-corrected threshold (Supplementary Table 10 ). There were no genome-wide significant differences in the effects between sexes in a sex-stratified meta-analysis using a subset of cohorts (Supplementary Fig. 1 ).

Therapeutic implications

Our analysis is limited to common variants that are detectable on genotyping arrays and imputation panels. Although most lead variants are not directly causal, in some cases, they highlight molecular mechanisms that alter clinical outcomes in COVID-19, and may have direct therapeutic relevance. To investigate the disease mechanisms, we first quantified the effect of inferred gene expression on critical illness in three relevant tissue/cell types. Many of the genes that we have found to be implicated in critical COVID-19 (refs. 1 , 2 ) are highly expressed in the monocyte–macrophage system, which has poor coverage in existing expression quantitative trait loci (eQTL) datasets. For this reason, we constructed a new TWAS model in primary monocytes obtained from 176 individuals ( Methods ). We found significant associations after Bonferroni correction between critical COVID-19 and predicted gene expression in lung (33), blood (21), monocyte (37) and all-tissue (107) meta-analysis (Supplementary Table 2 and Supplementary Table 11 ). We extended these findings using generalized summary-level data Mendelian randomization (GSMR) for RNA expression (Fig. 2 , Extended Data Table 1 , Supplementary Figs. 11 – 18 and Supplementary Table 4 ).

In parallel, we assessed the effect of genetically determined variation in circulating protein levels on the critical illness phenotype using GSMR 5 . We identified 15 unique proteins linked to critical illness, as summarized in Extended Data Table 1 (Supplementary Table 3 ). Of the significant results, we found causal evidence implicating five new proteins in comparison to our previous GSMR analysis 2 : QSOX2, CREB3L4, myeloperoxidase (MPO), ADAMTS13 and mannose-binding lectin-2 (MBL2) (Supplementary Fig. 10 ). These include well-studied biomarkers and potential drug targets in sepsis—the innate immune pattern recognition receptor MBL2 and the neutrophil effector enzyme MPO. ADAMTS13 modulates von Willebrand-factor-mediated platelet thrombus formation and may have a role in the hypercoagulable state in critical COVID-19 (Extended Data Fig. 5 ).

Three genes containing non-synonymous protein-coding changes associated with severe disease were also found to have significant effects from differential gene expression: SLC22A31 (ref. 2 ) (Fig. 1 ), SFTPD 4 (Fig. 1 ) and TKY2 (ref. 1 ) (Extended Data Fig. 6 ). Further biological and clinical research will be required to dissect the genetic evidence at these loci. In the example of TYK2 , there is now a therapeutic test of the genetic predictions. Our previous report of association between higher expression and critical illness 1 led directly to the inclusion of a new drug, baricitinib, in a large clinical trial; the result demonstrated a clear therapeutic benefit 3 . This therapeutic signal is consistent across multiple trials, providing the first proof-of-concept for drug target identification using genetics in critical illness and infectious disease.

figure 1

a , Effect-size plot for the effect of multiple variants on SLC22A31 expression (eQTLgen, x axis) against increasing susceptibility to critical COVID-19 ( β xy  = 0.11; P xy  = 1.3 × 10 −9 ). The colour shows linkage disequilibrium (LD) with the missense variant rs117169628. b , Three cartoon views of an AlphaFold 22 model of putative solute carrier family 22 member 31 (SLC22A31; UniProtKB: A6NKX4 ). The side chains of Pro474 and interacting amino acids are shown as connected spheres. A putative channel for small-molecule transport across the cell membrane is indicated by a dashed circle. Pro474 is predicted to be located in the transmembrane helix and point towards a putative transport pathway of a small molecule. The risk variant, P474L (Ala at rs117169628) would be expected to introduce more flexibility to the transmembrane helix and might therefore affect the transport properties of SLC22A31. Pro474 is predicted to be in a tightly packed environment, and may therefore affect the folding of SLC22A31. c , Effect-size plot for effect of multiple variants on SFTPD expression (eQTLgen, x axis) against increasing susceptibility to critical COVID-19 ( β xy  = 0.16; P xy  = 9.7 × 10 −6 ). Colour shows linkage disequilibrium with the missense variant rs721917. d , Three cartoon views of an AlphaFold 22 model of pulmonary surfactant-associated protein D (SFTPD; UniProtKB: P35247 ). The side chain of the variant Met31 is shown as connected spheres. Met31 is predicted to be located in the secondary-structure-lacking region of SFTPD. In the diagram on the right, oxygen and nitrogen atoms are coloured red and blue respectively, and the sulfur atom is coloured yellow.

To assess the immediate therapeutic use of our results for repurposing of existing compounds, we considered the drug therapies under consideration by the UK COVID-19 Therapeutic Advisory Panel (UK-CTAP), a national independent review group supported by an expert due-diligence panel 6 . Consistent evidence from gene-level GWAS (Supplementary Table 6 and Supplementary Table 10 ) and post-GWAS analyses was identified for several licensed compounds (Supplementary Table 12 ). For example, we found an association in another gene encoding a protein that is inhibited by baricitinib and other JAK inhibitors—the intracellular signalling kinase, JAK1 , which is stimulated by numerous cytokines including type I interferons and IL-6. Mendelian randomization analysis of RNA expression revealed a significant positive association between the expression of the gene encoding a canonical inflammatory cytokine, tumour necrosis factor ( TNF ), and severe disease (Fig. 2 ). This suggests that inhibition of TNF signalling may be an effective therapy in severe COVID-19.

figure 2

a , b , The predicted effect of change in protein concentration ( a ) and gene expression ( b ) on the risk of critical COVID-19 is shown for proteins and genes significantly linked to critical COVID-19 by GSMR (false-discovery rate (FDR) < 0.01). The bars show 95% confidence intervals.

Our additional expression data in monocytes reveal a marked tissue-specific effect on expression of PDE4A . This phosphodiesterase regulates the production of multiple inflammatory cytokines by myeloid cells. In contrast to the negative correlations seen in the lungs and blood, we show that a genetic tendency for higher expression of PDE4A in monocytes is associated with critical COVID-19 (Supplementary Table 11 ). Inhibition of PDE4A by several existing drugs is under investigation in multiple inflammatory diseases 7 , reduces pulmonary endothelial permeability 8 and appears to be safe in small clinical trials in patients with COVID-19.

The postulated biological role of genes associated with critical COVID-19 in GWAS, TWAS and GSMR results is shown in Extended Data Fig. 5 , which highlights the preponderance of genes with expression or functions in the mononuclear phagocyte system. This includes SLC2A5 , encoding the GLUT5 fructose transporter, which is strongly inducible in primary macrophages in response to inflammatory stimulation 9 , and XCR1 , a dendritic cell receptor with a critical role in cytotoxic T cell-mediated antiviral immunity 10 . NPNT , a significant meta-TWAS association in the genome-wide significant region on chromosome 4 (chr4:105673359; Supplementary Table 11 ), encodes a pulmonary basement membrane protein that may have a protective role in acute lung injury 11 .

Host–pathogen interaction

Our results also demonstrate the capacity of host genetics to reveal core mechanisms of disease. Multiple genes implicated in viral entry are associated with severe disease. In addition to ACE2 , we detect a genome-wide significant association in TMPRSS2 , a key host protease that facilitates viral entry that we have previously studied as a candidate gene 12 . This effect may be viral-lineage specific 13 . A strong GWAS association is seen in RAB2A (Table 1 ), with TWAS evidence suggesting that more expression of this gene is associated with worse disease (Supplementary Table 11 ). RAB2A is highly ranked in our previous meta-analysis by information content 14 study of host genes implicated in SARS-CoV-2 interaction using in vitro and clinical data 15 , and is consistent with CRISPR screen data showing that RAB2A is required for viral replication 16 .

Although our focus on critical illness enhances discovery power (Extended Data Fig. 4 ), it has the disadvantage of combining genetic signals for multiple stages in disease progression, including viral exposure, infection and replication, and development of inflammatory lung disease. From these data alone we cannot identify when in disease progression the causal effect is mediated, although clinical evidence helps to make some predictions 17 (Extended Data Fig. 5 ). As most cases included were recruited before vaccinations and treatments became available (Extended Data Fig. 7 ), at present, our study does not have sufficient statistical power to dissect the genetic effects of treatments or vaccination. These effects may include the masking of true associations, or the detection of genetic effects mediated by vaccine or drug response, rather than COVID-19 susceptibility. However, the absence of divergent genetic effects between studies (Supplementary Figs. 2 – 5 ) or consistent changes in effect allele frequency among cases over time (Supplementary Figs. 45 – 48 ) suggests that treatment and vaccination have not substantially affected the association between the specific variants that we report and the risk of critical illness.

As we performed a meta-analysis of multiple studies that may have slightly different definitions of the phenotype, effect sizes differ between studies (Supplementary Figs. 2 – 5 ). This, together with ancestry-specific effects 1 , may explain the heterogeneity in strong GWAS signals, such as the LZTFL1 signal in Table 1 . Different studies also have sets of variants that are not completely overlapping, so P values between variants in high linkage disequilibrium are more different than expected. Although most of the studies contain individuals from multiple ancestries, a large majority of the individuals are of European ancestry. In future research, there is a scientific and moral imperative to include the full diversity of human populations.

Together, these results deepen our understanding of the pathogenesis of critical COVID-19 and highlight new biological mechanisms of disease, several of which have immediate potential for therapeutic targeting.

Hospitalization meta-analysis

The hospitalized phenotype includes patients who were hospitalized with a laboratory-confirmed SARS-Cov2 infection. In this analysis we included GenOMICC, GenOMICC Brazil, GenOMICC Saudi Arabia, ISARIC4C, HGIv6 B2 phenotype with subtraction of GenOMICC data, SCOURGE hospitalized versus population and mild cases, and 23andMe broad respiratory phenotype. A summary description of each analysis is given above, a table with the included studies can be found in Supplementary Table 14 and an extended description can be found in Supplementary Table 1 .

Critical illness meta-analysis

The critically ill COVID-19 group included patients who were hospitalized owing to symptoms associated with laboratory-confirmed SARS-CoV-2 infection and who required respiratory support or whose cause of death was associated with COVID-19. In the critical illness analysis, we included GenOMICC, patients with critical illness from ISARIC4C, HGIv6 phenotype A2 with subtraction of GenOMICC data, SCOURGE severity grades 3 and 4 versus population controls, and 23andMe respiratory support phenotype. A summary description of each analysis can be found above, a table with the included studies can be found in Supplementary Table 13 and an extended description can be found in Supplementary Table 1 .


All meta-analyses across studies were performed using a fixed-effect inverse-variance weighting method and control for population stratification in the METAL software 23 . Allele frequency was calculated as the average frequency across studies with the METAL option AVERAGEFREQ. P values for heterogeneity in effect sizes between studies were calculated using a Cochran’s Q -test implemented in METAL. For variants in the same position with different REF and ALT alleles across studies, the GenoMICC variant in the European population was selected and the rest were removed. Finally, variants with switched ALT and REF alleles between HGIv6 and GenOMICC were also removed on the basis of differences in allele frequency of the alternative allele. Variants were annotated to the closest genes using dbsnp v.b151 GRCh38p7 and bionrRt R package (v.2.46.3) 24 . As each single-nucleotide polymorphism (SNP) of the meta-analysis can be present in different subsets of cohorts, there may be large differences in P values in SNPs with a high level of linkage disequilibrium, which may have an effect on downstream analyses. For this reason, variants that were not present in one of the three biggest studies—GenOMICC European ancestry, HGIv6 or SCOURGE—were filtered out from post-GWAS analysis.

Conditional analysis

We performed a step-wise conditional analysis to find independent signals. As European-specific data are not available in some cohorts but European ancestry is largely predominant (87.2% of cases with critical illness), we performed the conditional analysis using a European reference panel and the meta-analysis results of the whole cohort. To perform the conditional analysis, we used the GCTA (v.1.9.3) --cojo-slct function 25 . The parameters for the function were P  = 5 × 10 −8 , a distance of 10,000 kb and a co-linear threshold of 0.9 (ref. 26 ), and the reference population for the conditional analysis was individuals of European ancestry with whole-genome sequence available in the GenOMICC study and whole genomes from the 100,000 Genomics England project 2 .

Credible set fine-mapping

We performed fine-mapping using the SuSiE model 27 to construct credible sets for the independent signals identified using conditional analysis. As for conditional analysis, we used a European reference panel and the meta-analysis results of the whole critical illness cohort. We performed analyses in 1 Mb windows centred on the lead variants identified through conditional analysis. In cases in which windows for multiple variants overlapped, they were joined into a single window. For each window, we fitted the SuSiE summary statistics model setting the expected number of independent signals to the number of identified though conditional analysis. Models for three windows did not converge in 500 iterations and have been excluded. As a reference, we used the publically available linkage disequilibrium information for non-Finish Europeans from the GNOMAD 2.1.1 release. Full data for all variants included in credible sets are included in Supplementary Table 5 .

Gene-level analysis

We performed an analysis summarizing the genetic associations at the gene level using the mBAT-combo method 28 . We used the COVID ‘all critical cohorts’ meta-analysis (GenOMICC, HGIv6 phenotype A2, SCOURGE and 23andMe) summary statistics. As this is a trans-ethnic meta-analysis, we used a mixed ancestry linkage disequilibrium reference panel, consisting of 3,202 1000 Genomes phase 3 samples. We considered a list of protein-coding genes with unique ensemble gene ID based on the release from GENCODE (v.40) for hg38, which can be found on the mBAT-combo website ( ). A gene region was taken to span 50 kb upstream to 50 kb downstream of the gene’s untranslated regions.

Sex-stratified meta-analysis

To test for differences in genetic effects, we performed sex-stratified GWAS of the COVID-19 critical illness phenotype in the European ancestry GenOMICC WGS and genotyped cohorts and SCOURGE. We then performed a meta-analysis for each sex following the same methods as for the main analysis. We tested for differences in effects between the meta-analyses of the two sexes following previously described methods 29 .

Mendelian randomization

GSMR 5 was performed. We used the COVID ‘all critical cohorts’ meta-analysis (GenOMICC, HGIv6 phenotype A2, SCOURGE and 23andMe) as the outcome, protein expression quantitative-trait loci (pQTLs) from ref. 30 and RNA expression quantitative-trait loci (eQTLs) from eQTLgen 31 (2019-12-23 data release) as exposures, and 10,000 individuals of European ancestry randomly sampled from the UK Biobank as the linkage disequilibrium reference cohort (50,000 for linkage disequilibrium to missense variant plots). GSMR was performed for all exposures for which we were able to identify two or more suitable SNPs. SNPs were chosen to meet the following criteria: (1) SNP to exposure association P  < 5 × 10 −8 ; (2) linkage disequilibrium clumping lead SNPs only (±1 Mb, r 2  < 0.05); (3) SNP not removed by HEIDI-outlier filtering (for the removal of SNPs with evidence of horizontal pleiotropy) at the default threshold value of 0.01. eQTLGen effect sizes and standard errors were estimated as described in supplementary note 2 of ref. 32 . We considered as significant those exposure–outcome pairs with FDR < 0.05.

TWAS analysis

To perform TWAS analysis in GTExv8 tissues 33 , we used the MetaXcan framework and the GTExv8 eQTL and sQTL MASHR-M models available for download online ( ) and the ‘all critical cohorts’ meta-analysis. We first calculated individual TWAS for whole blood and lungs using the S-PrediXcan function 34 , 35 . We next performed a metaTWAS including data from all tissues to increase the statistical power using s-MultiXcan 36 . We applied Bonferroni correction to the results to choose significant genes and introns for each analysis.

Monocyte gene expression

To detect eQTLs, untreated primary monocytes were prepared from 174 healthy individuals of Northern European (British) ancestry recruited through the Oxford Biobank. Poly(A) RNA was paired-end 100 bp sequenced in the Oxford Genome Centre using the Illumina HiSeq-4000 machines (median = 47,735,438 reads per sample). Reads were aligned to CRGh38/hg38 using HISAT2 with the default parameters. High mapping quality reads were selected on the basis of MAPQ score using bamtools. Duplicate reads were marked and removed using picard (v.1.105). Samtools was used to pass through the mapped reads and calculate statistics. Read count information was generated using HTSeq and normalized using DESeq2. Sample contamination and swaps were detected by comparing the imputed SNP-array genotypes with genotypes called from RNA-seq using verifyBamID. Genotyping was performed with Illumina HumanOmniExpress with coverage of 733,202 separate markers. Genotypes were pre-phased with SHAPEIT2, and missing genotypes were imputed with PBWT. Poly(A) RNA was paired-end sequenced at the Oxford Genome Centre using the Illumina HiSeq-4000 machines. vcftools (v0.1.12b) was applied on genetic variation data in the form of variant call format (VCF) files to filter out indels and SNPs with a minor allele frequency of less than 0.04.

TWAS analysis for monocyte data was performed using genotyping and monocyte RNA-sequencing data from 174 individuals. Using a region of 500 kb around each gene, we calculated gene expression models using the Fusion R package 37 . For each gene, three models were calculated adding as covariates the two first principal components calculated from the genotype: blup, elastic networks and lasso. The model with a better r 2 between predicted and measured expression in a fivefold cross-validation was chosen. Then SNP genetic heritability was calculated for the 500 kb region for each gene and those genes with a nominal significant SNP heritability estimate ( P  ≤ 0.01) were chosen for the TWAS analysis. Summary statistics for the ‘all critical cohorts’ meta-analysis and the best model for each gene were then used to perform the TWAS.


Significant genes in the TWAS and metaTWAS were selected for a colocalization analysis using the coloc R package. The lead SNPs and a region of 200 Mb around the gene were used to colocalize with significant genes in the TWAS with eQTL summary statistics data on the region from GTExv8 lung, GTExv8 whole blood, eQTLgen or monocyte eqtl. As in our previous analysis 2 , we first performed a sensitivity analysis of the posterior probability of colocalization (PPH4) on the prior probability of colocalization (P12), going from P12 = 10 −8 to P12 = 10 −4 , with the default threshold being P12 = 10 −5 . eQTL signal and GWAS signals were deemed to colocalize if these two criteria were met: (1) at P12 = 5 × 10 −5 the probability of colocalization PPH4 > 0.5; and (2) at P12 = 10 −5 the probability of independent signal (PPH3) was not the main hypothesis (PPH3 < 0.5). These criteria were chosen to allow eQTLs with weaker P values, owing to lack of power in GTEx v.8, to be colocalized with the signal when the main hypothesis using small priors was that there was not any signal in the eQTL data.

Effect comparison

We compared the estimates of effect sizes between the individual GWASs used in the meta-analysis, for all variants that were genome-wide significant in at least one of the individual GWASs. To this end, we regressed the effects obtained using critical illness and hospitalization in the SCOURGE and 23andMe cohorts, as well as the HGI meta-analyses on the effect estimates obtained using the GenOMICC cohort. To account for estimation errors present in both the dependent and independent variables of the regression we used orthogonal distance regression 38 .

Weight of studies

To calculate the weight of GenOMICC, we downloaded the leave-one-out data of HGIv7. As the meta-analysis is performed using a variance-weighted method, we can recover the variance for each SNP as \(v=\frac{1}{{{\rm{s.e.}}}^{2}}\) , for the meta-analysis of all of the cohorts and for each one of the leave-one-out analysis. The total weight is \({w}_{{\rm{tot}}}=\frac{1}{v}\) and the weight leaving out a specific study is \({w}_{{\rm{loo}}}=\frac{1}{{v}_{{\rm{loo}}}}\) . The weight of a cohort is then \({w}_{{\rm{tot}}}-{w}_{{\rm{loo}}}\) . We calculated the weight for each the significant SNPs in our analysis for each study and normalized it using the total weight. Finally, we calculated the mean and s.d. from the significant SNPs for each cohort.

Forest plots

To compare effects between cohorts, we first performed a trans-ancestry meta-analysis for GenOMICC and 23andMe using METAL 23 . Then, we used the metagen and forest functions of the meta R package to produce forest plots for critical illness and hospitalization separately.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

Downloadable summary data are available through the GenOMICC data site ( ). Summary statistics are available, but without the 23andMe summary statistics, except for the 10,000 most significant hits, for which full summary statistics are available. The full GWAS summary statistics for the 23andMe discovery dataset will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. For further information and to apply for access to the data, see the 23andMe website ( ). All individual-level genotype and whole-genome sequencing data (for both academic and commercial uses) can be accessed through the UKRI/HDR UK Outbreak Data Analysis Platform ( ). A restricted dataset for a subset of GenOMICC participants is also available through the Genomics England data service. Monocyte RNA-seq data are available under the title ‘Monocyte gene expression data’ within the Oxford University Research Archives ( ). Sequencing data will be made freely available to organizations and researchers to conduct research in accordance with the UK Policy Framework for Health and Social Care Research through a data access agreement. Sequencing data have been deposited at the European Genome–Phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGAS00001007111 .

Code availability

Code to calculate the imputation of P values on the basis of SNPs in linkage disequilibrium is available at GitHub ( ).

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We thank the patients and their loved ones who volunteered to contribute to this study at one of the most difficult times in their lives, and the research staff in every intensive care unit who recruited patients at personal risk during the most extreme conditions ever witnessed in most hospitals. GenOMICC was funded by Sepsis Research (the Fiona Elizabeth Agnew Trust), the Intensive Care Society, a Wellcome Trust Senior Research Fellowship (to J.K.B., 223164/Z/21/Z), the Department of Health and Social Care (DHSC), Illumina, LifeArc, the Medical Research Council, UKRI, a BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070 and BBS/E/D/30002275) and UKRI grants MC_PC_20004, MC_PC_19025, MC_PC_1905 and MRNO2995X/1. A.D.B. acknowledges funding from the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z), the Edinburgh Clinical Academic Track (ECAT) programme. This research is supported in part by the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant MC_PC_20029). Laboratory work was funded by a Wellcome Intermediate Clinical Fellowship to B.F. (201488/Z/16/Z). We acknowledge the staff at NHS Digital, Public Health England and the Intensive Care National Audit and Research Centre who provided clinical data on the participants; and the National Institute for Healthcare Research Clinical Research Network (NIHR CRN) and the Chief Scientist’s Office (Scotland), who facilitate recruitment into research studies in NHS hospitals, and to the global ISARIC and InFACT consortia. GenOMICC genotype controls were obtained using UK Biobank Resource under project 788 funded by Roslin Institute Strategic Programme Grants from the BBSRC (BBS/E/D/10002070 and BBS/E/D/30002275) and Health Data Research UK (HDR-9004 and HDR-9003). UK Biobank data were used in the GSMR analyses presented here under project 66982. The UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government, British Heart Foundation and Diabetes UK. The work of L.K. was supported by an RCUK Innovation Fellowship from the National Productivity Investment Fund (MR/R026408/1). J.Y. is supported by the Westlake Education Foundation. SCOURGE is funded by the Instituto de Salud Carlos III (COV20_00622 to A.C., PI20/00876 to C.F.), European Union (ERDF) ‘A way of making Europe’, Fundación Amancio Ortega, Banco de Santander (to A.C.), Cabildo Insular de Tenerife (CGIEU0000219140 ‘Apuestas científicas del ITER para colaborar en la lucha contra la COVID-19’ to C.F.) and Fundación Canaria Instituto de Investigación Sanitaria de Canarias (PIFIISC20/57 to C.F.). We also acknowledge the contribution of the Centro National de Genotipado (CEGEN) and Centro de Supercomputación de Galicia (CESGA) for funding this project by providing supercomputing infrastructures. A.D.L. is a recipient of fellowships from the National Council for Scientific and Technological Development (CNPq)-Brazil (309173/2019-1 and 201527/2020-0). We thank the members of the Banco Nacional de ADN and the [email protected] cohort group; and the research participants and employees of 23andMe for making this work possible. A full list of contributors who have provided data that were collated in the HGI project, including previous iterations, is available online ( ).

Author information

These authors contributed equally: Erola Pairo-Castineira, Konrad Rawlik

Authors and Affiliations

Baillie Gifford Pandemic Science Hub, Centre for Inflammation Research, The Queen’s Medical Research Institute, University of Edinburgh, Edinburgh, UK

Erola Pairo-Castineira, Konrad Rawlik, Andrew D. Bretherick, Marie Zechner, Fiona Griffiths, Wilna Oosthuyzen, Jonathan Millar, Clark D. Russell, Sara Clohisey Hendry & J. Kenneth Baillie

MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK

Erola Pairo-Castineira, Andrew D. Bretherick, Lucija Klaric, Anne Richmond, Albert Tenesa, Alison M. Meynert, Murray Wham, Chris P. Ponting, James F. Wilson, Veronique Vitart & J. Kenneth Baillie

Roslin Institute, University of Edinburgh, Edinburgh, UK

Erola Pairo-Castineira, Marie Zechner, Fiona Griffiths, Wilna Oosthuyzen, Jonathan Millar, Albert Tenesa, Sara Clohisey, Johnny Millar, Emma Aitkin, Ruth Armstrong, J. Kenneth Baillie, Ceilia Boz, Adam Brown, Primmy Chikowore, Judy Coyle, Louise Cullum, Nicky Day, Esther Duncan, Paul Finernan, Max Head Fourman, James Furniss, Bernadette Gallagher, Ailsa Golightly, Fiona Griffiths, Debbie Hamilton, Ross Hendry, Naomi Kearns, Dawn Law, Rachel Law, Sarah Law, Rebecca Lidstone-Scott, Hanning Mal, Ellie McMaster, Jen Meikle, Hellen Mybaya, Miranda Odam, Wilna Oosthuyzen, Nick Parkinson, Trevor Paterson, Andrew Stenhouse, Maaike Swets, Helen Szoor-McElhinney, Filip Taneski, Tony Wackett, Mairi Ward, Jane Weaver, Marie Zechner, Andrew Law, Andy Law, Sara Clohisey Hendry & J. Kenneth Baillie

Pain Service, NHS Tayside, Ninewells Hospital and Medical School, Dundee, UK

Andrew D. Bretherick

School of Life Sciences, Westlake University, Hangzhou, China

Ting Qi & Jian Yang

Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China

Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia

Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK

Isar Nassiri, Tomas Malinauskas, Julian Knight, David Stuart & Benjamin Fairfax

Faculty of Biological Sciences, University of Leeds, Leeds, UK

Glenn A. McConkey

Genomics England, London, UK

Athanasios Kousathanas

Intensive Care Unit, Royal Infirmary of Edinburgh, Edinburgh, UK

Jonathan Millar, Sean Keating, Manu Shankar-Hari, Timothy Walsh, J. Kenneth Baillie, Annemarie B. Docherty, Seán Keating & J. Kenneth Baillie

National Heart and Lung Institute, Imperial College London, London, UK

Ryan Thwaites, Peter J. M. Openshaw, Jake Dunning & Ryan S. Thwaites

Edinburgh Clinical Research Facility, Western General Hospital, University of Edinburgh, Edinburgh, UK

Kirstie Morrice, Richard Clark, Audrey Coutts, Lorna Donnelly, Tammy Gilchrist, Katarzyna Hafezi, Christen Lauder, Louise Macgillivray, Alan Maclean, Sarah McCafferty, Nicola Wrobel, Angie Fawkes & Lee Murphy

Department of Critical Care Medicine, Queen’s University and Kingston Health Sciences Centre, Kingston, Ontario, Canada

David Maslove

Clinical Research Centre at St Vincent’s University Hospital, University College Dublin, Dublin, Ireland

Alistair Nichol

NIHR Health Protection Research Unit for Emerging and Zoonotic Infections, Institute of Infection, Veterinary and Ecological Sciences University of Liverpool, Liverpool, UK

Malcolm G. Semple, Shona C. Moore, Lance Turtle, Tom Solomon, Lance C. W. Turtle & Hayley Hardwick

Respiratory Medicine, Alder Hey Children’s Hospital, Institute in The Park, University of Liverpool, Alder Hey Children’s Hospital, Liverpool, UK

Malcolm G. Semple

Centre for Inflammation Research, The Queen’s Medical Research Institute, University of Edinburgh, Edinburgh, UK

Manu Shankar-Hari, Manu Shankar-Hari, Debby Bogaert & Clark D. Russell

Department of Medicine, University of Cambridge, Cambridge, UK

Charlotte Summers

William Harvey Research Institute Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK

Charles Hinds

Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK

Peter Horby & Peter W. Horby

Department of Anaesthesia and Intensive Care, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China

Lowell Ling

Wellcome-Wolfson Institute for Experimental Medicine, Queen’s University Belfast, Belfast, UK

Danny McAuley

Department of Intensive Care Medicine, Royal Victoria Hospital, Belfast, UK

UCL Centre for Human Health and Performance, London, UK

Hugh Montgomery

Imperial College Healthcare NHS Trust, London, UK

Peter J. M. Openshaw

Royal Hospital for Children, Glasgow, UK

Colin Begg, Fiona Bowman, Barry Milligan & Liane McPherson

Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, Edinburgh, UK

Albert Tenesa & James F. Wilson

Genomics Division, Instituto Tecnológico y de Energías Renovables, Santa Cruz de Tenerife, Spain

Carlos Flores

Research Unit, Hospital Universitario N.S. de Candelaria, Santa Cruz de Tenerife, Spain

Carlos Flores, Almudena Corrales & Beatriz Guillen-Guio

Centre for Biomedical Network Research on Respiratory Diseases (CIBERES), Instituto de Salud Carlos III, Madrid, Spain

Carlos Flores, Almudena Corrales, Ignacio Mahillo, Patricia Muñoz García, Germán Peces-Barba & Jordi Solé-Violán

Department of Clinical Sciences, University Fernando Pessoa Canarias, Las Palmas de Gran Canaria, Spain

Carlos Flores & Carlos Rodriguez-Gallego

IDIVAL, Santander, Spain

José A. Riancho, Ana Arnaiz, María Carmen Fariñas, Ramón Fernández, Marta Fernández-Sampedro, Carmen García-Ibarbia, Silvia Martínez, J. Gonzalo Ocejo-Vinyals, Fernando Ortiz-Flores, Juan J. Ruiz-Cubillan & Miriam Vieitez-Santiago

Universidad de Cantabria, Santander, Spain

José A. Riancho, Ana Arnaiz, María Carmen Fariñas, Marta Fernández-Sampedro & Carmen García-Ibarbia

Hospital U M Valdecilla, Santander, Spain

José A. Riancho, Ana Arnaiz, María Carmen Fariñas, Marta Fernández-Sampedro, Carmen García-Ibarbia, Silvia Martínez, J. Gonzalo Ocejo-Vinyals, Fernando Ortiz-Flores, Juan J. Ruiz-Cubillan & Miriam Vieitez-Santiago

Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud and Hospital San Jose TecSalud, Monterrey, Mexico

Augusto Rojas-Martinez, Servando Cardona-Huerta, Oscar Cienfuegos-Jimenez, Jose L. Cortes-Sanchez, Emiliano Garza-Frias, Michel F. Martinez-Resendez & Rocio Ortiz-Lopez

Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain

Pablo Lapunzina, Berta Almoguera, Carmen Ayuso, Carlos Casasnovas, Luis Castano, Marta Corton, Raquel Cruz, Silvia Diz-de Almeida, Lidia Fernandez-Caballero, Ruth Fernández-Sánchez, Inés García, Carlos Garcia-Cerrada, Rosario Lopez-Rodriguez, Eduardo López Granados, Juan José Martínez, Andrea Martínez-Ramas, Laura Marzal, Pablo Minguez, Julian Nevado, Lorena Ondo, Francesc Pla-Junca, Laura Planas-Serra, Aurora Pujol, Montserrat Ruiz, Agatha Schlüter, Marta Sevilla Porras, Cristina Silván Fuentes, Jair Antonio Tenorio Castaño, Cristina Villaverde, Miguel López de Heredia, Ingrid Mendes, Rocío Moreno, Esther Sande, Pablo Lapunzina, Angel Carracedo, Raquel Cruz & Angel Carracedo

Instituto de Genética Médica y Molecular (INGEMM), Hospital Universitario La Paz-IDIPAZ, Madrid, Spain

Pablo Lapunzina, Natalia Gallego, Julian Nevado, Marta Sevilla Porras, Jair Antonio Tenorio Castaño & Pablo Lapunzina

ERN-ITHACA-European Reference Network, Paris, France

Pablo Lapunzina

Genomic Research Department, King Fahad Medical City, Riyadh, Saudi Arabia

Malak Abedalthagafi

Department of Pathology & Laboratory Medicine, Emory University Hospital, Atlanta, GA, USA

Department of Clinical Analysis and Toxicology, Federal University of Rio Grande do Norte, Natal, Brazil

Andre D. Luchessi

Department of Anthropology, University of Toronto at Mississauga, Mississauga, Ontario, Canada

Andre D. Luchessi & Esteban J. Parra

Centro Singular de Investigación en Medicina Molecular y Enfermedades Crónicas (CIMUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain

Raquel Cruz, Silvia Diz-de Almeida, Esther Sande, Angel Carracedo, Raquel Cruz & Angel Carracedo

Instituto de Investigación Sanitaria de Santiago (IDIS), Santiago de Compostela, Spain

Raquel Cruz, Manuela Gago-Domínguez, Emilio Rodríguez-Ruiz, Esther Sande, Angel Carracedo & Angel Carracedo

Fundación Pública Galega de Medicina Xenómica, Sistema Galego de Saúde (SERGAS) Santiago de Compostela, Santiago de Compostela, Spain

Manuela Gago-Domínguez, Angel Carracedo & Angel Carracedo

Intensive Care National Audit & Research Centre, London, UK

Kathy Rowan

Heart Institute, University of Sao Paulo, Butanta, Brazil

Alexandre C. Pereira

NIHR Clinical Research Network (CRN), North West London Core Team, Hammersmith Hospital, London, UK

Latha Aravindan, Sukamal Das, Sheena Murphy & Mihaela Das

Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK

Heather Biggs, Anita Furlong, Petra Tucker & Katherine Schon

Biostatistics Group, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China

Chenqing Zheng & Jiantao Chen

Department of Infectious Diseases, Leiden University Medical Center, Leiden, The Netherlands

Maaike Swets

Guys and St Thomas’ Hospital, London, UK

Jacqueline Pan, Neus Grau, Tim Owen Jones, Rosario Lim, Martina Marotti, Christopher Whitton, Aneta Bociek, Sara Campos, Gill Arbane, Manu Shankar-Hari, Marlies Ostermann, Mina Cha, Fabiola DAmato, Eirini Kosifidou, Shelley Lorah & Kyma Morera

James Cook University Hospital, Middlesbrough, UK

Laura Brady, Keith Hugill, Jeremy Henning, Stephen Bonner, Evie Headlam, Jessica Jones, Abigail List, Joanne Morley, Amy Welford, Bobette Kamangu, Anitha Ratnakumar & Abiola Shoremekun

Barts Health NHS Trust, London, UK

Zoe Alldis, Raine Astin-Chamberlain, Fatima Bibi, Jack Biddle, Sarah Blow, Matthew Bolton, Catherine Borra, Ruth Bowles, Maudrian Burton, Yasmin Choudhury, Amber Cox, Amy Easthope, Patrizia Ebano, Stavros Fotiadis, Jana Gurasashvili, Rosslyn Halls, Pippa Hartridge, Delordson Kallon, Jamila Kassam, Ivone Lancoma-Malcolm, Maninderpal Matharu, Peter May, Oliver Mitchelmore, Tabitha Newman, Mital Patel, Jane Pheby, Irene Pinzuti, Zoe Prime, Oleksandra Prysyazhna, Julian Shiel, Melanie Taylor, Carey Tierney, Olivier Zongo, Suzanne Wood, Anne Zak & David Collier

Royal Stoke University Hospital, Stoke-on-Trent, UK

Manuela Mundy, Christopher Thompson, Lisa Pritchard, Minnie Gellamucho, David Cartlidge, Nageswar Bandla, Lucy Bailey, Michelle Davies, Jane Delaney & Leanne Scott

North Middlesex University Hospital NHS Trust, London, UK

Marwa Abdelrazik, Frater Alasdair, David Carter, Munzir Elhassan, Arunkumar Ganesan, Samuel Jenkins, Zoe Lamond, Dharam Purohit, Kumar Rohit, Malik Saleem, Alanna Wall, Kugan Xavier, Dhanalaksmi Bakthavatsalam, Kirolos Gehad, Pakeerathan Gnanapragasam, Kapil Jain, Swati Jain, Abdul Malik, Naveen Pappachan, Jeronimo Moreno-Cuesta, Anne Haldeos, Rachel Vincent & Maryjane Oziegb

King’s College Hospital, London, UK

Anna Cavazza, Maeve Cockrell, Eleanor Corcoran, Maria Depante, Clare Finney, Ellen Jerome, Abigail Knighton, Monalisa Nayak, Evita Pappa, Rohit Saha, Sian Saha, Andrew Dodd, Kevin O’Reilly, Mark McPhail, Emma Clarey, Harriet Noble & John Smith

Charing Cross Hospital, St Mary’s Hospital and Hammersmith Hospital, London, UK

Phoebe Coghlan, Stephen Brett, Anthony Gordon, Maie Templeton, David Antcliffe, Dorota Banach, Sarah Darnell, Ziortza Fernandez, Eleanor Jepson, Amal Mohammed, Roceld Rojo, Sonia Sousa Arias, Anita Tamang Gurung & Jenny Wong

The Royal Liverpool University Hospital, Liverpool, UK

Jaime Fernandez-Roman, David O. Hamilton, Emily Johnson, Brian Johnston, Maria Lopez Martinez, Suleman Mulla, Alicia A. C. Waite, Karen Williams, Victoria Waugh, Ingeborg Welters, Jessica Emblem, Maria Norris & David Shaw

John Radcliffe Hospital, Oxford, UK

Archana Bashyal, Sally Beer, Paula Hutton, Stuart McKechnie, Neil Davidson, Soya Mathew, Grace Readion, Jung Ryu & Jean Wilson

Addenbrooke’s Hospital, Cambridge, UK

Shruti Agrawal, Kay Elston, Megan Jones, Eoghan Meaney, Petra Polgarova, Muhammad Elbehery, Charlotte Summers, Esther Daubney, Anthony Ng, Jocelyn Marshall, Nazima Pathan, Katerina Stroud & Deborah White

Nottingham University Hospital, Nottingham, UK

Angela Andrew, Saima Ashraf, Amy Clark, Martin Dent, Margaret Langley, Cecilia Peters, Lucy Ryan, Julia Sampson, Shuying Wei, Alice Baddeley, Megan Meredith, Lucy Morris, Alexandra Gibbons & Lisa McLoughlin

St George’s Hospital, London, UK

Carlos Castro Delgado, Victoria Clark, Deborah Dawson, Lijun Ding, Georgia Durrant, Obiageri Ezeobu, Abiola Harrison, William James Hurt, Rebecca Kanu, Ashley Kinch, Susannah Leaver, Ana Lisboa, Jisha Mathew, Kamal Patel, Romina Pepermans Saluzzio, John Rawlins, Tinashe Samakomva, Nirav Shah, Christine Sicat, Joana Texeira, Joana Gomes De Queiroz, Edna Fernandes Da Gloria, Elena Maccacari, Nikki Yun, Soumendu Manna, Sarah Farnell-Ward, Maria Maizcordoba, Maria Thanasi & Hawakin Haji Ali

BHRUT (Barking Havering)—Queens Hospital and King George Hospital, Ilford, UK

Janice Hastings, Lina Grauslyte, Musarat Hussain, Bobby Ruge, Sam King, Tatiana Pogreban, Lace Rosaroso, Helen Smith, Mandeep-Kaur Phull, Nikkita Adams, George Franke, Aparna George, Erika Salciute, Joanna Wong, Karen Dunne, Luke Flower, Emma Sharland & Sukhmani Sra

Royal Infirmary of Edinburgh, Edinburgh, UK

Gillian Andrew, Marie Callaghan, Lucy Barclay, Lucy Marshall, Kenneth Baillie, Maria Amamio, Sophie Birch, Kate Briton, Sarah Clark, Katerine Doverman, Dave Hope, Corrienne Mcculloch, Scott Simpson & Jo Singleton

Kingston Hospital, London, UK

Rita Fernandez, Meryem Allen, David Baptista, Rebecca Crowe, Jonathan Fox, Jacyntha Khera, Adam Loveridge, India McKenley, Eriko Morino, Andres Naranjo, Denise O’Connor, Richard Simms, Kathryn Sollesta, Andrew Swain, Harish Venkatesh, Rosie Herdman-Grant & Anna Joseph

Queen Alexandra Hospital, Portsmouth, UK

Angela Nown, Steve Rose, David Pogson, Helen Boxall, Lutece Brimfield, Helen Claridge, Zoe Daly, Shenu George & Andrew Gribbin

Royal Gwent Hospital, Newport, UK

Yusuf Cheema, Sean Cutler, Owen Richards, Anna Roynon-Reed, Shiney Cherian, Anne Emma Heron, Gemma Williams, Tamas Szakmany, Abby Waters, Kim Collins, Jill Dunhill, Ffion Jones, Rebecca Morris, Lucy Ship & Amy Cardwell

Royal Blackburn Teaching Hospital, Blackburn, UK

Syamlan Ali, Ravi Bhatterjee, Rachel Bolton, Srikanth Chukkambotla, Dabheoc Coleman, Jack Dalziel, Joseph Dykes, Christopher Fine, Bethan Gay, Wendy Goddard, Drew Goodchild, Rhiannan Harling, Muhammad Hijazi, Sarah Keith, Meherunnisa Khan, Roseanna Matt, Janet Ryan-Smith, Samuel Saad, Philippa Springle, Jacqueline Thomas, Nick Truman, Aayesha Kazi, Matthew Smith, Heather Collier, Chloe Davison, Stephen Duberley, Jeanette Hargreaves, Janice Hartley, Tahera Patel & Ellen Smith

Stepping Hill Hospital, Stockport, UK

Alissa Kent, Emma Goodwin, Ahmed Zaki, Clare Tibke, Susan Hopkins, Hywel Gerrard, Matthew Jackson, Sara Bennett, Liane Marsh & Rebecca Mills

Northumbria Healthcare NHS Foundation Trust, North Shields, UK

Jessica Bell, Helen Campbell, Angela Dawson, Steve Dodds, Stacey Duffy, Lisa Gallagher, Gemma McCafferty, Stacey Short, Tracy Smith, Kirsty Thomas, Claire Walker, Jessica Reynolds, Bryan Yates, Hayley McKie, Maria Panteli, Maria Thompson & Gail Waddell

Countess of Chester Hospital, Chester, UK

Sarah De Beger, Azmerelda Abraheem, Charlie Dunmore, Rumanah Girach, Rhianna Jones, Emily London, Imrun Nagra, Farah Nasir, Hannah Sainsbury, Clare Smedley, Stephen Brearey, Caroline Burchett, Kathryn Cawley, Maria Faulkner, Helen Jeffrey, Peter Bamford, Firdaus Shaikh, Lauren Slack & Angela Davies

Pinderfields General Hospital, Wakefield, UK

Hollie Brooke, Jose Cebrian Suarez, Ruth Charlesworth, Karen Hansson, John Norris, Alice Poole, Rajdeep Sandhu, Elizabeth Smithson, Muthu Thirumaran, Veronica Wagstaff, Sarah Buckley, Brendan Sloan, Alastair Rose, Amy Major & Alexandra Metcalfe

Ninewells Hospital, Dundee, UK

Christine Almaden-Boyle, Pauline Austin, Susan Chapman, Alexandre Eros, Louise Cabrelli, Stephen Cole, Clare Whyte & Matt Casey

Croydon University Hospital, Croydon, UK

Vasileios Bafitis, George Tsinaslanidis, Cassandra George, Reena Khade, Christopher Black & Sundar Raj Ashok

Morriston Hospital, Swansea, UK

Sean Farley, Elaine Brinkworth, Rachel Harford, Carl Murphy, Marie Williams, Luke Newey, Hannah Toghill, Sophie Lewis, Tabitha Rees, Ceri Battle, Mark Baker, Jenny Travers & Karen Chesters

Queen Elizabeth University Hospital, Glasgow, UK

Nicola Baxter, Andrew Arnott, Gordan McCreath, Christopher McParland, Laura Rooney, Malcolm Sim, Steven Henderson, Lynn Abel, Carol Dalton, Sophie Kennedy-Hay, Lynn O’Donohoe, Megan O’Hare, Izabela Orlikowska & Natasha Parker

Broomfield Hospital, Chelmsford, UK

Fiona McNeela, Amanda Lyle, Alistair Hughes, Jayachandran Radhakrishnan & Sian Gibson

Heartlands Hospital, Birmingham, UK

Hollie Bancroft, Mary Bellamy, Jacqueline Daglish, Salma Kadiri, Faye Moore, Joanne Rhodes, Mirriam Sangombe, Zhane Peterkin, James Scriven & Margaret Carmody

Royal Sussex County Hospital, Brighton, UK

Juliet Cottle, Emily Peasgood, Laura Ortiz-Ruiz de Gordoa, Claire Phillips & Denise Skinner

York Hospital, York, UK

Zoe Cinquina, Kate Howard, Rosie Joy, Samantha Roche, Isobel Birkinshaw, Joseph Carter, Jo Ingham, Nicola Marshall, Harriet Pearson & Zoe Scott

Queen Elizabeth Hospital, Birmingham, UK

Jo Dasgin, Jaspret Gill, Annette Nilsson, Amy Bamford, Diana Hull, James Scriven, Nafeesah Ahmadhaider, Michelle Bates & Christopher McGhee

Royal Glamorgan Hospital, Pontyclun, UK

Hannah Ellis, Gwenllian Sera Howe, Jayaprakash Singh, Natalie Stroud, Lisa Roche, Ceri Lynch, Bethan Deacon, Carla Pothecary, Justyna Smeaton & Kevin Agravante

Barnet Hospital, London, UK

Vinodh Krishnamurthy, Cynthia Diaba, Lincy John, Lai Lim & Rajeev Jha

Wythenshawe Hospital, Manchester, UK

Jasmine Egan, Timothy Felton, Susannah Glasgow, Grace Padden, Ozerah Choudhr, Joanne Bradley-Potts, Stuart Moss, Saejohn Lingeswaran, Peter Alexander, Craig Brandwood, Sofia Fiouni, Luke Ward, Schvearn Allen, Jane Shaw & Christopher Smith

Medway Maritime Hospital, Gillingham, UK

Oluronke Adanini, Rebecca Collins, Maines Msiska, Linda Ofori, Nikhil Bhatia & Hayley Dolan

Royal Berkshire NHS Foundation Trust, Reading, UK

Mark Brunton, Jess Caterson, Holly Coles, Liza Keating, Emma Tilney, Nicola Jacques, Matthew Frise, Jennifer Armistead, Shauna Bartley, Parminder Bhuie, Sabi Rai & Gabriela Tomkova

Whiston Hospital, Prescot, UK

Sandra Greer, Karen Shuker & Ascanio Tridente

The Royal Oldham Hospital, Manchester, UK

Emma Dobson, Jodie Hunt, Redmond Tully, Joy Dearden, Andrew Drummond, Prakash Kamath, Emily Bullock, Michelle Mulcahy, Shelia Munt, Grainne O’Connor, Jennifer Philbin, Chloe Rishton, Chloe Scott & Sarah Winnard

Chesterfield Royal Hospital Foundation Trust, Chesterfield, UK

Nurkamalia Hasni, Rachel Gascoyne, Joanne Hawes, Kelly Pritchard, Lesley Stevenson, Amanda Whileman, Sarah Beavis, Lauren Bishop, Cindy Cart, Katie Dale, Mary Kelly-Baxter, Adam Mendelski, Emma Moakes, Rheanna Smith, Jan Woodward & Stephanie Wright

Aberdeen Royal Infirmary, Aberdeen, UK

Angela Allan, Adriana Botello, Jade Liew, Jasmine Medhora, Erin Trumper, Felicity Savage, Teresa Scott, Marc Place & Callum Kaye

Royal Devon and Exeter Hospital, Exeter, UK

Sarah Benyon, Suzie Marriott, Linda Park, Helen Quinn, Daisy Skyes, Lily Zitter, Kizzy Baines, Elizabeth Gordon, Samantha Keenan & Andrew Pitt

Glasgow Royal Infirmary, Glasgow, UK

Katharine Duffy, Jane Ireland, Gary Semple, Lynne Turner, Susanne Cathcart, Dominic Rimmer, Alex Puxty, Kathryn Puxty, Andrew Hurst, Jennifer Miller, Susan Speirs & Lauren Walker

Blackpool Victoria Hospital, Blackpool, UK

Zena Bradshaw, Joanna Brown, Sarah Melling, Stephen Preston, Nicola Slawson, Scott Warden, Alanna Beasley, Emma Stoddard, Leonie Benham, Jason Cupitt, Melanie Caswell, Lisa Elawamy & Ashleigh Wignall

Southampton General Hospital, Southampton, UK

Belinda Roberts, Hannah Golding, Samantha Leggett, Michelle Male, Martyna Marani, Kirsty Prager, Toran Williams, Kim Golder, Oliver Jones, Rebecca Cusack, Clare Bolger, Rachel Burnish, Michael Carter, Susan Jackson, Karen Salmon & Jonathan Biss

Ashford and St Peter’s Hospital, Chertsey, UK

Maia Aquino, Maria Croft, Victoria Frost, Ian White & Keshnie Govender

Derriford Hospital, Plymouth, UK

Natasha Webb, Liana Stapleton, Colin Wells, Nikitas Nikitas, Ana Sanchez-Rodriguez, Kayleigh Spencer & Bethan Stowe

East Surrey Hospital, Redhill, UK

Yvonne Izzard, Michelle Poole, Sonja Monnery, Sallyanne Trotman, Valerie Beech, Edward Combes & Teishel Joefield

Poole Hospital, Poole, UK

Patrick Covernton, Sarah Savage, Elizabeth Woodward, Julie Camsooksai, Henrik Reschreiter, Charlotte Barclay, Yasmin DeAth, Judith Dube, Charlotte Humphrey, Sarah Jenkins, Emma Langridge, Rebecca Milne, Beverley Wadams & Megan Woolcock

Royal Alexandra Hospital, Paisley, UK

Michael Brett, Brian Digby, Lisa Gemmell, James Hornsby, Patrick MacGoey, Pauline O’Neil, Richard Price, Radha Sundaram, Lynn Abel, Natalie Rodden, Nicola Thomson, Kevin Rooney, Susan Currie, Natasha Parker, Lauren Walker & Philip Henderson

St James’s University Hospital and Leeds General Infirmary, Leeds, UK

Bethan Ogg, Simon Whiteley, Liz Wilby, Kate Long, Shailamma Matthew, Sheila Salada, Susan Trott, Sarah Watts, Zoe Friar & Abigail Speight

Bedford Hospital, Bedford, UK

Victoria Bastion, Humza Chandna, Brice Djeugam, Muhammad Haseeb, Harriet Kent, Gamu Lubimbi, Sophie Murdoch, Alastair Thomas, Beena David, Rachel Lorusso, Ana Vochin, Melchizedek Penacerrada & Retno Wulandari

Southport and Formby District General Hospital, Ormskirk, UK

Charlotte Heath, Srinivas Jakkula, Anna Morris, Ashar Ahmed, Arvind Nune, Claire Buttriss & Emma Whitaker

The Tunbridge Wells Hospital and Maidstone Hospital, Kent, UK

Miriam Davey, David Golden, Amy Acklery, Fabio Fernandes, Bec Seaman & Victoria Earl

Queen Elizabeth Hospital, Woolwich, London, UK

Amy Collins, Waqas Khaliq, Rachel Adam & Estefania Treus

North Manchester General Hospital, Manchester, UK

Sarah Holland, Jordan Alfonso, Bethan Blackledge, Michelle Bruce, Laura Jayne Durrans, Ayaa Eltayeb, Jade Harris, Samuel Hey, Martin Hruska, Thomas Lamb, Joanne Rothwell, Adele Fitzgerald, Gabriella Lindergard, Helen T-Michael, Tracey Duncan, Sharon Baxter-Dore, Lisa Cooper, Claire Fox, Jacinta Guerin, Tracey Hodgkiss & Karen Connolly

Royal Victoria Infirmary, Newcastle Upon Tyne, UK

Paul McAlinden, Victoria Bridgett, Maggie Fearby, A. Gulati, Helen Hanson, Sinead Kelly, Louise McCormack, Rachel Nixon, Philip Robinson, Victoria Slater, Elaine Stephenson, Andrea Webster, K. Webster, Carole Hays, Anne Hudson, Bijal Patel, Ian Clement, John Davis, Sarah Francis & Douglas Jerry

Hull Royal Infirmary, Hull, UK

Caroline Abernathy, Louise Foster, Andrew Gratrix, Llucia Cabral-Ortega, Matthew Hines, Victoria Martinson, Elizabeth Stones & Karen Winter

Manchester Royal Infirmary, Manchester, UK

Esther Barrow, Katharine Wylie, Deborah Baines, Katie Birchall, Laurel Kolakaluri, Richard Clark, Anila Sukumaran, Craig Brandwood, Melanie Barker, Deborah Paripoorani, Lara Smith & Charlotte Taylor

Royal Derby Hospital, Derby, UK

Charlotte Downes, Melanie Hayman, Katie Riches, Priya Daniel, Deepak Subramanian, Kathleen Holding, Mary Hilton, Carly McDonald & Georgina Richardson

Aintree University Hospital, Liverpool, UK

Georgia Halladay, Peter Harding, Amie Reddy, Ian Turner-Bone, Laura Wilding, Robert Parker, Michaela Lloyd, Leanne Smith & Charlie Kelly

Fairfield General Hospital, Bury, UK

Maria Lazo, Alan Neal, Olivia Walton, Julie Melville, Jay Naisbitt, Emily Bullock & Rosane Joseph

Norfolk and Norwich University Hospital (NNUH), Norwich, UK

Sara Callam, Lisa Hudig, Jocelyn Keshet-Price, Katie Stammers, Karen Convery, Georgina Randell & Deirdre Fottrell-Gould

Milton Keynes University Hospital, Milton Keynes, UK

Esther Mwaura, Sara-Beth Sutherland, Richard Stewart, Louise Mew & Lynn Wren

Good Hope Hospital, Birmingham, UK

Laura Thrasyvoulou, Heather Willis, James Scriven, Bridget Hopkins, Daniel Lenton & Abigail Roberts

Queen Elizabeth Hospital Gateshead, Gateshead, UK

Maria Bokhari, Rachael Lucas, Wendy McCormick, Jenny Ritzema, Vanessa Linnett, Amanda Sanderson & Helen Wild

Royal Bolton Hospital, Bolton, UK

Rebecca Flanagan, Robert Hull, Kat Rhead, Emma McKenna, Gareth Hughes, Jennifer Anderson, Kelly Jones, Scott Latham & Heather Riley

Tameside General Hospital, Ashton Under Lyne, UK

Martina Coulding, Martyn Clark, Jacqueline McCormick, Oliver Mercer, Darsh Potla, Hafiz Rehman, Heather Savill, Victoria Turner, Edward Jude & Susan Kilroy

Salford Royal Hospital, Manchester, UK

Elena Apetri, Cathrine Basikolo, Bethan Blackledge, Laura Catlow, Matthew Collis, Reece Doonan, Jade Harris, Alice Harvey, Karen Knowles, Stephanie Lee, Diane Lomas, Chloe Lyons, Liam McMorrow, Angiy Michael, Jessica Pendlebury, Jane Perez, Maria Poulaka, Nicola Proudfoot, Kathryn Slevin, Vicky Thomas, Danielle Walker, Paul Dark, Bethan Charles, Danielle McLaughlan, Melanie Slaughter, Dan Horner, Kathryn Cawley & Tracy Marsden

Great Ormond St Hospital and UCL Great Ormond St Institute of Child Health NIHR Biomedical Research Centre, London, UK

Joyann Andrews, Emily Beech, Olugbenga Akinkugbe, Alasdair Bamford, Holly Belfield, Gareth A. L. Jones, Tara McHugh, Hamza Meghari, Samiran Ray, Ana Luisa Tomas, Lauran O’Neill, Mark Peters, Michael Bell, Sarah Benkenstein, Catherine Chisholm, Charlene Davies, Klaudia Kupiec & Caroline Payne

Southmead Hospital, Bristol, UK

Joanna Halls, Hayley Blakemore, Elizabeth Goff, Kati Hayes, Kerry Smith, Deanna Stephens, Ruth Worner, Borislava Borislavova, Beverley Faulkner, Matt Thomas, Ruth Cookson, Emma Gendall, Georgina Larman, Rebecca Pope & Artur Smalira

William Harvey Hospital, Ashford, UK

Victoria Priestley, Tracey Cosier, Gemma Millen, James Rand, Natasha Schumacher, Roxana Sandhar, Heather Weston, Neil Richardson & Lucy Cooper

Arrowe Park Hospital, Wirral, UK

Cathy Jones, Ya-Wen Jessica Huang, Reni Jacob, Craig Denmade & Lewis McIntyre

Royal Hampshire County Hospital, Winchester, UK

Dawn Trodd, Jane Martin, Geoff Watson, Emily Bevan & Caroline Wreybrown

Bradford Royal Infirmary, Bradford, UK

Shereen Bano, Ruth Bellwood, Michael Bentley, Matt Bromley, Lucy Gurr, Camilla Ledgard, Janet McGowan, Kate Pye, Kirsten Sellick, Amelia Stacey, Deborah Warren, Brian Wilkinson, Louise Akeroyd, Huma Shafique, James Morgan, Susan Shorter, Rachel Swinger, Emily Waters & Tom Lawton

Glan Clwyd Hospital, Bodelwyddan, UK

Elizabeth Allan, Kate Darlington, Ffyon Davies, Llinos Davies, Jack Easton, Sumit Kumar, Richard Lean, Callum Mackay, Richard Pugh, Xinyi Qiu, Stephanie Rees, Jeremy Scanlon, Joanne Lewis, Daniel Menzies, Annette Bolger, Gwyneth Davies, Jennifer Davies, Esther Garrod, Helen Jones, Rachel Manley & Hannah Williams

Royal Bournemouth Hospital, Bournemouth, UK

Jordan Frankham, Sally Pitts, Nigel White, Debbie Branney & Heather Tiller

Bristol Royal Infirmary, Bristol, UK

Georgia Efford, Zoe Garland, Lisa Grimmer, Bethany Gumbrill, Rebekah Johnson, Katie Sweet, Jeremy Bewley, Christina Coleman, Katie Corcoran, Eva Maria Hernandez Morano, Rachel Shiel, Denise Webster, Josephine Bonnici, Eleanor Daniel & Abbie Dell

University Hospital North Durham, Darlington, UK

Melanie Kent, Ami Wilkinson, Ellen Brown, Andrea Kay, Suzanne Campbell, Amanda Cowton, Mark Birt, Vicki Greenaway, Kathryn Potts, Clare Hutton & Andrew Shepperson

Darlington Memorial Hospital, Darlington, UK

Basildon Hospital, Basildon, UK

Miranda Forsey, Alice Nicholson, Mark Vertue, Joanne Riches, Agilan Kaliappan & Anne Nicholson

University College Hospital, London, UK

Niall MacCallum, Eamon Raith, Georgia Bercades, Ingrid Hass, David Brealey, Gladys Martir, Anna Reyes, Deborah Smyth & Maria Zapatamartinez

Whittington Hospital, London, UK

Ana Alvaro, Champa Jetha, Louise Ma, Lauren Booker, Loreta Mostoles, Anezka Pratley, Abdelhakim Altabaibeh, Chetan Parmar & Kayleigh Gilbert

Western General Hospital, Edinburgh, UK

Susie Ferguson, Amy Shepherd, Sheila Morris, Jo Singleton, Rosie Baruah, Maria Amamio, Sophie Birch, Kate Briton, Sarah Clark, Katherine Doverman, Lucy Marshall & Scott Simpson

Ipswich Hospital, Ipswich, UK

Georgina Lloyd, Stephanie Bell, Vanessa Rivers & Bally Purewal

Hereford County Hospital, Hereford, UK

Kate Hammerton, Susan Anderson, Janine Birch, Emma Collins & Ryan Oleary

Sunderland Royal Hospital, Sunderland, UK

Sarah Cornell, Jordan Jarmain, Kimberley Rogerson, Fiona Wakinshaw, Lindsey Woods, Anthony Rostron, Zeynep Elcioglu & Alistair Roy

Queens Hospital Burton, Burton-On-Trent, UK

Gillian Bell, Holly Dickson, Louise Wilcox, Amro Katary & Katy English

Musgrove Park Hospital, Taunton, UK

Joanne Hutter, Corinne Pawley, Patricia Doble, Charmaine Shovelton, Marius Vaida, Rebecca Purnell & Ashly Thomas

The Royal Papworth Hospital, Cambridge, UK

Lenka Cagova, Adama Fofano, Helen Holcombe, Alice Michael Mitchell, Lucy Mwaura, Krithivasan P. Raman, Lucie Garnr, Sue Mepham, Kitty Paques, Alain Vuylsteke, Jennifer Mackie, Carmen Pearn & Julie Zamikula

University Hospital Lewisham, London, UK

Mark Birt, Estefania Treus Gude, Maggie Nyirenda, Lisa Capozzi, Rosie Reece-Anthony, Waqas Khaliq, Hazma Noor & Alfa Cresia Nilo

The Princess Alexandra Hospital, Harlow, UK

Michelle Grove, Amelia Daniel, Amy Easthope, Joanne Finn, Nikki White, Rajnish Saha, Bibi Badal & Karen Ixer

University Hospital of Wales, Cardiff, UK

Donna Duffin, Ben Player, Helen Hill, Jade Cole, Jenny Brooks, Michelle Davies, Rhys Davies, Lauren Hunt, Emma Thomas & Angharad Williams

West Middlesex Hospital, Isleworth, UK

Metod Oblak, Mini Thankachen, Jamie Irisari, Amrinder Sayan & Monica Popescu

Royal Albert Edward Infirmary, Wigan, UK

Cheryl Finch, Andrew Jamieson, Alison Quinn, Joshua Cooper, Sarah Liderth & Natalia Waddington

Stoke Mandeville Hospital, Aylesbury, UK

Iona Burn, Katarina Manso, Ruth Penn, Julie Tebbutt, Danielle Thornton, James Winchester, Geraldine Hambrook & Pradeep Shanmugasundaram

Royal Lancaster Infirmary, Lancaster, UK

Jayne Craig, Kerry Simpson, Andrew Higham & Louise Sibbett

Basingstoke and North Hampshire Hospital, Basingstoke, UK

Sheila Paine, Annabel Reed, Jo-Anna Conyngham, McDonald Mupudzi, Rachel Thomas, Mary Wright, Denise Griffin, Richard Partridge, Maria Alvarez Corral, Nycola Muchenje, Mildred Sitonik & Caroline Wrey Brown

Worthing Hospital, Worthing, UK

Aaron Butler, Linda Folkes, Heather Fox, Amy Gardner, David Helm, Gillian Hobden, Kirsten King, Jordi Margalef, Michael Margarson, Tim Martindale, Emma Meadows, Dana Raynard, Yvette Thirlwall, Yolanda Baird, Raquel Gomez, Darren Martin, Luke Hodgson, Clinton Corin, Erikka Sidall, Densie Szabo & Sharon Floyd

St Richard’s Hospital, Chichester, UK

The Alexandra Hospital, Redditch and Worcester Royal Hospital, Worcester, UK

Hannah Davies, Karen Austin, Olivia Kelsall, Hannah Wood, Peter Anderson, Katie Archer, Andrew Burtenshaw, Sarah Clayton, Naiara Cother, Nicholas Cowley, Caroline Davis, Stephen Digby, Alison Durie, Alison Harrison, Emma Low, Michael McAlindon, Alex McCurdy, Aled Morgan, Tobias Rankin, Jessica Thrush, Helen Tranter, Charlie Vigurs & Laura Wild

Royal Cornwall Hospital, Truro, UK

Thomas Cornell, Kate Ralph, Sarah Bean, Karen Burt, Michael Spivey, Carol Richards & Rachel Tedstone

Watford General Hospital, Watford, UK

Siobhain Carmody, Xiaobei Zhao, Valerie Page, Mark Louie Guanco, Elvira Hoxha & Camilla Zorloni

Macclesfield District General Hospital, Macclesfield, UK

Charlotte Dean, Emma Jones, Emma Carter, Joshua Dunn, Thomas Kong, Mervin Mahenthran, Chris Marsh, Maureen Holland, Natalie Keenan, Mohamed Mahmoud, Marc Lyons, Joanne Bradley-Potts, Helen Wassall & Meghan Young

Royal Surrey County Hospital, Guildford, UK

Paul Bradley, Dorota Burda, Sinead Donlon, Lesley Harden, Celia Harris, Irving Mayangao, Rugia Montaser, Sheila Mtuwa, Charles Piercy, Eleanor Smith, Sarah Stone, Jerik Verula, Helen Blackman, Cheryl Marriott, Natalia Michalak, Ben Creagh-Brown, Armorel Salberg, Naomi Boyer & Veronika Pristopan

Rotherham General Hospital, Rotherham, UK

Victoria Maynard, Rachel Walker, Anil Hormis, Dawn Collier, Cheryl Graham, Vicky Maynard, Jake McCormick & Jake Warrington

Craigavon Area Hospital, Portadown, UK

Denise Cosgrove, Denise McFarland, Judith Ratcliffe & Rob Charnock

King’s Mill Hospital, Nottingham, UK

Inez Wynter, Mandy Gill, Jill Kirk, Paul Paul, Valli Ratnam & Sarah Shelton

Dumfries and Galloway Royal Infirmary, Dumfries, UK

Catherine Jardine, Alasdair Hay & Dewi Williams

Prince Charles Hospital, Merthyr Tydfil, UK

Bethan Deacon, Latha Durga, Meg Hibbert, Gareth Kennard-Holden, Chrsitopher Woodford, Carla Pothecary, Lisa Roche, Dariusz Tetla, Kevin Agravante & Justyna Smeaton

Ysbyty Gwynedd, Bangor, UK

Alicia Price, Alice Thomas, Chris Thorpe, Ellen Knights & Donna Ward

Royal Preston Hospital, Preston, UK

Shondipon Laha, Mark Verlander & Alexandra Williams

The Great Western Hospital, Swindon, UK

Rachel Prout, Helen Langton, Malcolm Watters, Charlotte Hunt & Catherine Novis

Lincoln County Hospital, Lincoln, UK

Sarwat Arif, Amy Cunningham, Claire Hewitt, Julia Hindale, Karen Jackson-Lawrence, Sarah Shepardson, Maryanne Wills, Susie Butler, Silivia Tavares, Russell Barber, Annette Hilldrith & Kelly Hubbard

University Hospital of North Tees, Stockton on Tees, UK

Dawn Egginton, Michele Clark, Sarah Purvis, Simon Sinclair & Vicky Collins

Glangwili General Hospital, Camarthen, UK

Bethan Landeg, Craig Sell, Samantha Coetzee, Alistair Gales, Igor Otahal, Becky Icke, Meena Raj, Caroline Williams, Jill Williams & Lucy Hill

Southend University Hospital, Westcliff-on-Sea, UK

Abdul Kayani, Bridgett Masunda, Prisca Gondo & Nigara Atayeva

Lister Hospital, Stevenage, UK

Carina Cruz & Natalie Pattison

Diana Princess of Wales Hospital, Grimsby, UK

Caroline Burnett, Jonathan Hatton, Elaine Heeney, Maria Newton, Hassan Al-Moasseb, Teresa Behan, Jasmine Player, Rachael Stead, Atideb Mitra & Kirsty Nauyokas

West Suffolk Hospital, Bury St Edmunds, UK

Sally Humphreys, Helen Cockerill & Ruth Tampsett

Victoria Hospital, Kirkcaldy, UK

Evgeniya Postovalova, Tina Coventry, Amanda McGregor, Susan Fowler, Mike Macmahon, Patricia Cochrane & Sandra Pirie

Calderdale Royal Hospital, Halifax, UK

Sarah Hanley, Asifa Ali, Megan Brady, Sam Dale, Annalisa Dance, Lisa Gledhill, Jill Greig, Kathryn Hanson, Kelly Holdroyd, Marie Home, Tahira Ishaq, Diane Kelly, Lear Matapure, Deborah Melia, Samantha Mellor, Ekta Merwaha, Tonicha Nortcliffe, Lisa Shaw, Ryan Shaw, Tracy Wood, Lee-Ann Bayo, Miranda Usher, Alison Wilson, Ross Kitson, Jez Pinnell, Matthew Robinson & Kaitlin Boltwood

Huddersfield Royal Infirmary, Huddersfield, UK

Dorset County Hospital, Dorchester, UK

Jenny Birch, Laura Bough, Rebecca Tutton, Barbara Winter-Goodwin, Josie Goodsell, Kate Taylor, Patricia Williams, Sarah Williams, Ashleigh Cave & James Rees

Russell’s Hall Hospital, Dudley, UK

Janet Imeson-Wood, Jacqueline Smith, Vishal Amin, Komala Karthik, Rizwana Kausar, Elena Anastasescu, Karen Reid, Vikram Anumakonda & Ella Stoddart

Royal United Hospital, Bath, UK

Carrie Demetriou, Charlotte Eckbad, Lucy Howie, Sarah Mitchard, Lidia Ramos, Katie White, Sarah Hierons, Fiona Kelly, Alfredo Serrano-Ruiz & Gabrielle Evans

St Mary’s Hospital, Newport, UK

Liz Nicol, Joy Wilkins, Kim Hulacka, Gabor Debreceni, Alison Brown & Vikki Crickmore

George Eliot Hospital NHS Trust, Nuneaton, UK

Kay Hill & Thogulava Kannan

Yeovil Hospital, Yeovil, UK

Zenaida Dagutao, Kate Beesley, Alison Lewis, Jess Perry, Sherly Antony, Sarah Board, Clare Buckley, Lucy Pippard, Alfonso Tanate, Diane Wood, Agnieska Kubisz-Pudelko & Ayman Gouda

Forth Valley Royal Hospital, Falkirk, UK

Fiona Auld, Joanne Donnachie, Euan Murdoch, Lynn Prentice, Nikole Runciman, Dhaneesha Senaratne, Abigail Short, Laura Sweeney, Lesley Symon, Anne Todd, Patricia Turner, Erin McCann, Dario Salutous, Ian Edmond & Lesley Whitelaw

Frimley Park Hospital, Camberley, UK

Harish Venkatesh, Yvonne Bland, Istvan Kajtor, Lisa Kavanagh, Karen Singler & George Linfield-Brown

Chelsea & Westminster NHS Foundation Trust, London, UK

Luke Stephen Prockter Moore, Marcela Vizcaychipi, Laura Martins, Luke Moore, Rhian Bull & Jaime Carungcong

Queen Elizabeth the Queen Mother Hospital, Margate, UK

Louise Allen, Eva Beranova, Alicia Knight, Carly Price, Sorrell Tilbey, Sharon Turney, Tracy Hazelton, Gabriella Tutt, Mansi Arora, Salah Turki, Emily Sinfield, Joanne Deery & Hazel Ramos

Royal Brompton Hospital, London, UK

Daniele Cristiano, Natalie Dormand, Zohreh Farzad, Mahitha Gummadi, Sara Salmi, Geraldine Sloane, Mathew Varghese, Vicky Thwaites, Brijesh Patel, Liyanage Kamal & Anelise Catelan Zborowski

Darent Valley Hospital, Dartford, UK

Ryan Coe, Madeleine Anderson, Jane Beadle, Charlotte Coates, Katy Collins, Maria Crowley, Laura Johnson, Laura King, Remi Paramsothy, Janet Sargeant, Pedro Silva, Carmel Stuart, June Taylor, David Tyl, Phillipa Wakefield, Charlotte Kamundi, Olumide Olufuwa, Zakaulla Belagodu, Anca Gherman & Naomi Oakley

University Hospital Crosshouse, Kilmarnock, UK

John Allan, Tim Geary, Alistair Meikle, Peter O’Brien, Stephen Wood, Andrew Clark & Gordon Houston

University Hospital Wishaw, Wishaw, UK

Karen Black, Michelle Clarkson, Stuart D’Sylva, Alan Morrison, Kathryn Norman, Margaret Taylor, Suzanne Clements, Catriona Cohrane, Nora Gonzalez, Dominic Strachan, Claire Beith & Kirsten Moar

University College Dublin, St Vincent’s University Hospital, Dublin, Ireland

Lorna Murphy, Michelle Smythe, Alistair Nichol & Kathy Brickell

The Queen Elizabeth Hospital, King’s Lynn, UK

Inthakab Ali Mohamed Ali, Karen Beaumont, Mohamed Elsaadany, Kay Fernandes, Sameena Mohamed Ally, Harini Rangarajan, Varun Sarathy, Sivarupan Selvanayagam, Dave Vedage, Matthew White, Zoe Coton, Aricsa Joshy, Mark Blunt & Hollie Curgenven

Walsall Manor Hospital, Walsall, UK

Liam Botfield, Catherine Dexter, Aditya Kuravi, Joanne Butler, Robert Chadwick, Poonam Ranga, Lisa Richardson, Emma Virgilio, Maddiha Anwer, Atul Garg, Donna Botfield & Xana Marriott

Princess Royal Hospital, Brighton, UK

Keely Stewart, Dee Mullan, Claire Phillips, Jane Gaylard, Justyna Nowak & Denise Skinner

Barnsley Hospital, Barnsley, UK

Sian Jones, Rikki Crawley, Abigail Crew, Mishell Cunningham, Allison Daniels, Laura Harrison, Susan Hope, Nicola Lancaster, Jamie Matthews, Gemma Wray, Alice Nicholson, Ken Inweregbu, Sarah Cutts & Katharine Miller

Warrington General Hospital, Warrington, UK

Ailbhe Brady, Rebekah Chan, Shane McIvor, Helena Prady, Bijoy Mathew, Jeff Little & Tim Furniss

Royal Victoria Hospital, Belfast, UK

Chris Wright, Bernadette King, Christopher Wasson, Aisling O’Neill, Christine Turley, Peter McGuigan, Erin Collins, Stephanie Finn, Jackie Green, Julie McAuley, Abitha Nair, Charlotte Quinn, Suzanne Tauro, Kathryn Ward, Michael McGinlay & Kiran Reddy

Royal Hallamshire Hospital and Northern General Hospital, Sheffield, UK

Norfaizan Ahmad, Samantha Anderson, Joann Barker, Kris Bauchmuller, Kathryn Birchall, Sarah Bird, Kay Cawthron, Luke Chetam, Joby Cole, Ben Donne, David Foote, Amber Ford, Helena Hanratty, Kate Harrington, Lisa Hesseldon, Kay Housley, Yvonne Jackson, Claire Jarman, Faith Kibutu, Becky Lenagh, Irene Macharia, Shamiso Masuko, Leanne Milner, Helen Newell, Lorenza Nwafor, Simon Oxspring, Patrick Phillips, Ajay Raithatha, Sarah Rowland-Jones, Jacqui Smith, Roger Thompson, Helen Trower, Sara Walker, James Watson, Matthew Wiles, Alison Lye, Jayne Willson, Gary Mills, Sansha Harris & Eleanor Hartill

Harefield Hospital, London, UK

Anthony Barron, Ciara Collins, Sundeep Kaul, Claire Nolan, Oliver Polgar, Claire Prendergast, Paula Rogers, Rajvinder Shokkar, Meriel Woodruff, Kanta Mahay, Vicky Thwaites, Anna Reed, Hayley Meyrick, Heather Passmore & James Farwell

Cumberland Infirmary, Carlisle, UK

Alison Brown, Susan O’Connell, Jane Gregory, Luigi Barberis, Rosemary Harper, Tim Smith & Diane Armstrong

Eastbourne District General Hospital, Eastbourne, UK

Angie Bowey, Anne Cowley, Andrew Corner, Judith Highgate, Claire Rutherfurd, Jo-Anne Taylor, Sarah Goodwin & Claire Rutherford

Conquest Hospital, Saint Leonards-on-Sea, UK

Salisbury District Hospital, Salisbury, UK

Beena Eapen, Fiona Trim & Phil Donnison

Airedale General Hospital, Keighley, UK

Lisa Armstrong, Hayley Bates, Emma Dooks, Fiona Farquhar, Amy Kitching, Chantal McParland, Sophie Packham & Brigid Hairsine

Leicester Royal Infirmary, Leicester, UK

Premetie Andreou, Dawn Hales, Megha Mathews, Rekha Patel, Peter Barry, Neil Flint, Jessica Hailstone, Navneet Ghuman, Bethany Leonard & Rachel Lees

Peterborough City Hospital, Peterborough, UK

Deborah Butcher, Katy Leng, Nicola Butterworth-Cowin & Susie O’Sullivan

Hinchingbrooke Hospital, Huntingdon, UK

Colchester General Hospital, Colchester, UK

Alison Ghosh & Emma Williams

Princess Royal Hospital, Telford and Royal Shrewsbury Hospital, Shrewsbury, UK

Colene Adams, Anita Agasou, Tracie Arden, Mandy Beekes, Amy Bowes, Pauline Boyle, Heather Button, Mandy Carnahan, Anne Carter, Danielle Childs, Jane Gaylard, Fran Hurford, Yasmin Hussain, Ayesha Javaid, James Jones, Michael Leigh, Terry Martin, Helen Millward, Nichola Motherwell, Dee Mullan, Julie Newman, Rachel Rikunenko, Jo Stickley, Julie Summers, Louise Ting, Helen Tivenan, Denise Donaldson, Nigel Capps, Emily Cale, Sanal Jose, Wendy Osbourne, Susie Pajak, Jayne Rankin & Louise Tonks

University Hospital Monklands, Airdrie, UK

Tracy Baird, Margaret Harkins, Jim Ruddy & Joe West

Wrexham Maelor Hospital, Wrexham, UK

Joseph Duffield, Lewis Mallon, Oliver Smith, Sara Smuts, Andy Campbell, Cate Davies, Sarah Davies, Rachel Hughes, Lisa Jobes, Victoria Whitehead & Clare Watkins

New Cross Hospital, Wolverhampton, UK

Stella Metherell, Nichola Harris, Victoria Lake, Elizabeth Radford, Andy Smallwood, Shameer Gopal & Katherine Vassell

University Hospital Hairmyres, East Kilbride, UK

Dina Bell, Rosalind Boyle, Katie Douglas, Lynn Glass, Liz Lennon, Austin Rattray, Claire Beith & Emma Lee

Warwick Hospital, Warwick, UK

Danielle Jones, Penny Parsons, Ben Attwood, Paul Jefferson, Mohan Ranganathan, Inderjit Atwal, Bridget Campbell, Angela Day & Camilla Stagg

Sandwell General Hospital and City Hospital, Birmingham, UK

Emma Haynes, Cecilia Ahmed, Sarah Clamp, Julie Colley, Risna Haq, Anne Hayes, Sibet Joseph, Zahira Maqsood, Samia Hussain, Jonathan Hulme, Patience Domingos, Rita Kumar, Manjit Purewal & Becky Taylor

Royal Manchester Children’s Hospital, Manchester, UK

Lara Bunni, Monica Latif, Claire Jennings, Shilu Jose, Rebecca Marshall, Aleksandra Metryka & Gayathri Subramanian

Gloucestershire Royal Hospital, Gloucester, UK

Adam Burgoyne, Susan O’Connell, Amanda Tyler, Joanne Waldron, Paula Hilltout & Jayne Evitts

University Hospitals Coventry & Warwickshire NHS Trust, Coventry, UK

Geraldine Ward, Pamela Bremmer, Carl Hawkins, Sophie Jackman & Michal Ogorek

Torbay Hospital, Torquay, UK

Kylie Ashby, Lorraine Thornton, Pauline Mercer, Matthew Halkes & Adam Revill

Pilgrim Hospital, Lincoln, UK

Bryony Saint, Jo Fletcher, Kimberley Netherton, Manish Chablani, Amy Kirkby, Amanda Roper & Kinga Szymiczek

Prince Philip Hospital, Lianelli, UK

Isobel Sutherland, Linda O’Brien, Igor Otahal, Joanne Connell, Kim Davies, Tracy Lewis, Zohra Omar & Emma Perkins

Princess of Wales Hospital, Llantrisant, UK

Lisa Roche, Sonia Sathe & Ellie Davies

Northampton General Hospital NHS Trust, Northampton, UK

Alex Lyon, Isheunesu Mapfunde, Charlotte Willis, Rachael Hitchcock, Kathryn Hall & Christopher King

The Christie NHS Foundation Trust, Manchester, UK

Andrew Fagan, Roonak Nazari, Lucy Worsley, Suzanne Allibone, Vidya Kasipandian, Amit Patel, Parisa Cutting, Roman Genetu, Ainhi Mac, Anthony Murphy, Sinead Ward & Fatima Butt

James Paget University Hospital NHS Trust, Great Yarmouth, UK

Amanda Ayers, Wendy Harrison, Katherine Mackintosh & Julie North

Birmingham Children’s Hospital, Birmingham, UK

Lydia Ashton, Rehana Bi, Samantha Owen, Helen Winmill & Barney Scholefield

Withybush General Hospital, Haverfordwest, UK

Hannah Blowing, Erin Williams, Michaela Duskova, Michelle Edwards, Alun Rees, Helen Thomas, Rachel Hughes, Igor Otahal, Jolene Brooks, Janet Phipps & Suzanne Brooks

Northwick Park Hospital, London, UK

Catherine Dennis, Vicki Parris, Sinduya Srikaran, Anisha Sukha, Alistair McGregor & Gerlynn Tiongson

North Devon District Hospital, Barnstaple, UK

Katie Adams, Benedict Andrew, Adam Brayne, Sasha Carter, Louise Findlay, Emma Fisher, Peter Jackson, Duncan Kaye, Juliet Parkin, Victoria Tuckey, Jane Hunt, Nicholas Love, Lynne van Koutrick & Ashley Hanson

Scunthorpe General Hospital, Scunthorpe, UK

Kathy Dent, Elizabeth Horsley, Sandra Pearson, Sue Spencer, Dorothy Hutchinson, Jasmine Player, Dorota Potoczna, Muhammad Nauman Akhtar, Lisa-Jayne Cottam, Kirsty Nauyokas & Jack Sanders

Royal Free Hospital, London, UK

Sara Mingo Garcia, Glykeria Pakou, Cynthia Diaba, Helder Filipe, Lincy John, Amitaa Maharajh, Mark de Neef, Daniel Martin, Christine Eastgate & Poh Choo Teoh

Raigmore Hospital, Inverness, UK

Fiona Barrett, Clare Bradley, Avril Donaldson, Mairi Mascarenhas, Marianne O’Hara, Laura Okeefe, Noreen Clarke, Jonathan Whiteside, Rachael Campbell, Joanna Matheson, Deborah McDonald & Donna Patience

West Cumberland Hospital, Whitehaven, UK

Polly Rice, Tim Smith, Melanie Clapham, Rachel Mutch, Luigi Barberis, Rosemary Harper, Hannah Craig & Una Poultney

Furness General Hospital, Barrow-in-Furness, UK

Karen Burns & Andrew Higham

Liverpool Heart and Chest Hospital, Liverpool, UK

Sophie Twiss, Janet Barton, Linsha George, Clare Harrop, Sherly Mathew & David Justin Wright

Scarborough General Hospital, Scarborough, UK

Rachel Harrison, Jordan Toohie, Ben Chandler, Alison Turnbull, Janine Mallinson & Kerry Elliott

Bronglais General Hospital, Aberystwyth, UK

Rebecca Wolf-Roberts, Helen Tench, Igor Otahal, Maria Hobrok, Ronda Loosley, Heather McGuinness & Tanya Sims

Alder Hey Children’s Hospital, Liverpool, UK

Deborah Afolabi, Kathryn Sian Allison, Taya Anderson, Rachael Dore, Dawn Jones, Naomi Rogers, Paula Saunderson, Jennifer Whitbread, Laura O’Malley, Laura Rad & Daniel Hawcutt

Borders General Hospital, Melrose, UK

Jonathan Aldridge, Melanie Tolson & Sweyn Garrioch

Leighton Hospital, Crewe, UK

Joanne Tomlinson & Michael Grosdenier

Kent & Canterbury Hospital, Canterbury, UK

David Loader, Ritoo Kapoor & Gemma Hector

Harrogate and District NHS Foundation Trust, Harrogate, UK

Joslan Scherewode, Chunda Sri-Chandana, Lorraine Stephenson & Sarah Marsh

The Royal Marsden Hospital, London, UK

Arnold Dela Rosa, Shaman Jhanji, Thomas Bemand, Ryan Howle, Ravishankar Rao Baikady, Benjamin Thomas, Ethel Black & Kate Tatham

Ealing Hospital, Southall, UK

Sambasivarao Gurram, Ekaterina Watson, Vicki Parris, Sheena Quaid & Alistair McGregor

St John’s Hospital Livingston, Livingston, UK

Anne Saunderson, Rachel O’Brien, Sam Moultrie, Jen Service, Clare Cheyne, Miranda Odam & Alison Wiliams

Wexham Park Hospital, Slough, UK

Nicky Barnes, Peter Csabi, Joana Da Rocha & Louika Glynou

Sheffield Children’s Hospital, Sheffield, UK

Amy Huffenberger, Jade Bryant, Amy Pickard, Nicholas Roe, Arianna Bellini, Anton Mayer, Amy Burrow, Natalie Colley, Jayne Evans, Alex Howlett & Zeinab Khalifeh

Homerton University Hospital Foundation NHS Trust, London, UK

Jerldine Pryce, Claire Gorman, Amy Easthope, Rebecca Brady, Elizabeth Timlick, Pierre Antoine & Abhinhav Gupta

National Hospital for Neurology and Neurosurgery, London, UK

John Hardy, Henry Houlden, Eleanor Moncur, Arianna Tucci, Eamon Raith, Ambreen Tariq & David Brealey

The Royal Alexandra Children’s Hospital, Brighton, UK

Emma Tagliavini, Becky Ramsay, Katy Fidler, Kevin Donnelly & Rebecca Hollis

Golden Jubilee National Hospital, Clydebank, UK

Jocelyn Barr, Elizabeth Boyd, Val Irvine, Ben Shelley, Julie Buckley, Charlene Hamilton & Kathryn Valdeavella

Hospital Universitario Mostoles, Medicina Interna, Madrid, Spain

Javier Abellan, Carlos Garcia-Cerrada, Victor Moreno Cuerda, Belén Rodríguez Maya & Jorge Sánchez Redondo

Universidad Francisco de Vitoria, Madrid, Spain

Javier Abellan, Carlos Garcia-Cerrada, María Carmen García Torrejón & Victor Moreno Cuerda

Haemostasis and Thrombosis Unit, Hospital de la Santa Creu i Snt Pau, IIB Sant Pau, Barcelona, Spain

René Acosta-Isaac & Juan Carlos Souto

Unit of Infectious Diseases, Hospital Universitario 12 de Octubre, Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), Madrid, Spain

Jose María Aguado

Spanish Network for Research in Infectious Diseases (REIPI RD16/0016/0002), Instituto de Salud Carlos III, Madrid, Spain

School of Medicine, Universidad Complutense, Madrid, Spain

Jose María Aguado, Álvaro Andreu-Bernabeu, Celso Arango, Covadonga M. Diaz-Caneja, Javier González-Peñas, Patricia Muñoz García & Mara Parellada

Centro de Investigación Biomédica en Red de Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Madrid, Spain

Jose María Aguado, Óscar Brochado-Kith, Amanda Fernández-Rodríguez, Juan P. Horcajada, María A. Jimenez-Sousa & Salvador Resino

Hospital General Santa Bárbara de Soria, Soria, Spain

Carlos Aguilar & Fernando Sevil Puras

Pediatric Neurology Unit, Department of Pediatrics, Navarra Health Service Hospital, Pamplona, Spain

Sergio Aguilera-Albesa

Navarra Health Service, NavarraBioMed Research Group, Pamplona, Spain

Complejo Asistencial Universitario de León, León, Spain

Abdolah Ahmadi Sabbagh, Belén Ballina Martín, Silvia Fernández Ferrero, Yolanda Fernández Martínez, Marta Fuertes Núñez, Cristina Hernández Moro, Violeta Martínez Robles, Irene Padilla Conejo, Lisbeth A. Pichardo, José A. Rodriguez-Garcia, Filomeno Rondón García, Javier Sánchez Real, Julia Vidán Estévez & Lavinia Villalobos

Infectious Diseases Department, Hospital Universitario San Pedro, Logroño, Spain

Jorge Alba & José A. Oteo

Fundación Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, Hospital de Neurorehabilitació, Barcelona, Spain

Sergiu Albu

Universitat Autònoma de Barcelona (UAB), Barcelona, Spain

Sergiu Albu & Juan P. Horcajada

Fundació Institut d’Investigació en Ciències de la Salut Germans Trias i Pujol, Barcelona, Spain

Hospital General de Occidente, Guadalajara, Mexico

Karla A. M. Alcalá-Gallardo & Marco A. Cid-Lopez

Microbiology Unit, Hospital Universitario N.S. de Candelaria, Santa Cruz de Tenerife, Spain

Julia Alcoba-Florez

Servicio de Neumología, Hospital Universitario La Paz-IDIPAZ, Madrid, Spain

Sergio Alcolea Batres, Rodolfo Alvarez-Sala Walther, Carlos Carpio Segura, Luis Gómez Carrera, Daniel Laorden, Pablo Mariscal Aguilar & María Concepción Sánchez Prados

Camino Universitario Adelita de Char, Mired IPS, Barranquilla, Colombia

Holmes Rafael Algarin-Lara, Yady Álvarez-Benítez, Sylena Chiquillo-Gómez & María Eugenia Quevedo Chávez

Facultad de Ciencias de la Salud, Universidad Simón Bolívar, Barranquilla, Colombia

Holmes Rafael Algarin-Lara, Yady Álvarez-Benítez, Andrea Barranco-Díaz, Sylena Chiquillo-Gómez, Anderson Díaz-Pérez, Lácides Fuenmayor-Hernández, Humberto Mendoza Charris, Xenia Morelos-Arnedo, Junior Moreno-Escalante, Fredy Javier Pacheco-Miranda, María Eugenia Quevedo Chávez, Marena Rodríguez-Ferrer, Andrea Romero-Coronado & Zuleima Yáñez

Neumología, Hospital Universitario Virgen Macarena, Seville, Spain

Virginia Almadana & Agustín Valido

Departamento de Medicina, Universidad de Salamanca, Salamanca, Spain

Julia Almeida & Alberto Orfao

Centro de Investigación del Cáncer (IBMCC), Universidad de Salamanca, CSIC, Salamanca, Spain

Biomedical Research Institute of Salamanca (IBSAL), Salamanca, Spain

Centre for Biomedical Network Research on Cancer (CIBERONC), Instituto de Salud Carlos III, Madrid, Spain

Julia Almeida, Alberto Orfao & Federico Rojo

Department of Genetics & Genomics, Instituto de Investigación Sanitaria–Fundación Jiménez Díaz University Hospital—Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain

Berta Almoguera, Carmen Ayuso, Marta Corton, Lidia Fernandez-Caballero, Ruth Fernández-Sánchez, Inés García, Rosario Lopez-Rodriguez, Andrea Martínez-Ramas, Laura Marzal, Pablo Minguez, Lorena Ondo & Cristina Villaverde

Human Genotyping—CEGEN Unit, Spanish National Cancer Research Centre, Madrid, Spain

María R. Alonso, Nuria Alvarez, Anna González-Neira, Belen Herraez, Rocio Nuñez-Torres & Guillermo Pita

Servicio de Medicina Interna, Hospital Universitario de Salamanca-IBSAL, Salamanca, Spain

Felipe Álvarez-Navia, Cristina Carbonell, Guillermo Hernández-Pérez, Amparo López-Bernús, Miguel Marcos & José-Ángel Martín-Oterino

Universidad de Salamanca, Salamanca, Spain

Felipe Álvarez-Navia, Moncef Belhassen-Garcia, Cristina Carbonell, Amparo López-Bernús, Miguel Marcos, José-Ángel Martín-Oterino & Pedro-Luis Sánchez

Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón (IiSGM), Madrid, Spain

Álvaro Andreu-Bernabeu, Celso Arango, Covadonga M. Diaz-Caneja, Javier González-Peñas & Mara Parellada

Clinical Pharmacology Service, Hospital de la Santa Creu i Sant Pau, IIB Sant Pau, Barcelona, Spain

Maria Rosa Antonijoan & Maria Angeles Quijada

Biocruces Bizkai HRI, Barakaldo, Spain

Eunate Arana-Arri, Luis Castano, Ana B. de la Hoz, Aitor García-de-Vicuña, Natale Imaz-Ayo & Gustavo Perez-de-Nanclares

Cruces University Hospital, Osakidetza, Barakaldo, Spain

Eunate Arana-Arri

Hospital Infanta Elena, Madrid, Spain

Carlos Aranda, Maria Sanchez Carpintero, Carlos F. Castaño, Lidia Gagliardi, Mercedes García, Leticia García, María Herrera, Ángel Jiménez, María Rubio Olivera & Virginia Víctor

Instituto de Investigación Sanitaria–Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain

Carlos Aranda, Yolanda Cañadas, Maria Sanchez Carpintero, Carlos F. Castaño, Lidia Gagliardi, Mercedes García, Leticia García, María Herrera, Ángel Jiménez, María Rubio Olivera, Javier Ruiz-Hornillos & Virginia Víctor

Centre for Biomedical Network Research on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain

Celso Arango, Covadonga M. Diaz-Caneja, Javier González-Peñas & Mara Parellada

Fundación Hospital Infantil Universitario de San José, Bogotá, Colombia

Carolina Araque, César O. Enciso-Olivera & Hector D. Salamanca

Fundación Universitaria de Ciencias de la Salud, Bogotá, Colombia

Carolina Araque, Jessica G. Chaux, Walter G. Chaves-Santiago, César O. Enciso-Olivera, Mario Gómez-Duque, Luz D. Gutierrez-Castañeda, Luz Adriana Pinzón, Carlos S. Rivadeneira-Chamorro, Marilyn Johanna Rodriguez, Hector D. Salamanca, John J. Sprockel & Lilian Torres-Tobar

Programa de Pós-graduação em Ciências da Saúde, Universidade Federal do Rio Grande do Norte, Natal, Brazil

Nathalia K. Araujo, Marina S. Cruz, Raquel C. S. Dantas-Komatsu & Jeane F. P. Medeiros

Departamento de Medicina Clínica, Universidade Federal do Rio Grande do Norte, Natal, Brazil

Izabel M. T. Araujo, Gabriela V. da Silva & Alice M. Duarte

Departamento de Genética e Morfologia, Instituto de Ciências Biológicas, Universidade de Brasília, Brasilia, Brazil

Ana C. Arcanjo, Marcela C. Campos, Joana F. R. Nunes & Fabiola T. C. Silva

Colégio Marista de Brasilia, Brasilia, Brazil

Ana C. Arcanjo

Associação Brasileira de Educação e Cultura, Londrina, Brazil

Servicio de Medicina Interna, Hospital Universitario La Paz-IDIPAZ, Madrid, Spain

Francisco Arnalich Fernández, José Ramon Arribas Lopez & Ana Méndez-Echevarria

Fundació Docència I Recerca Mutua Terrassa, Barcelona, Spain

María J. Arranz

Spanish National Cancer Research Center, CNIO Biobank, Madrid, Spain

Maria-Jesus Artiga & Eva Ortega-Paino

Departamento Patologia y Laboratorios, Fundación Santa Fe de Bogota, Bogotá, Colombia

Yubelly Avello-Malaver, Ana María Baldion, Viviana Barrera-Penagos, Oscar Martinez-Nieto, Diana Roa-Agudelo, Paula A. Rodriguez-Urrego & David A. Suarez-Zamora

Hospital General de Occidente, Zapopan, Mexico

Raúl C. Baptista-Rosas

Centro Universitario de Tonalá, Universidad de Guadalajara, Tonalá, Mexico

Centro de Investigación Multidisciplinario en Salud, Universidad de Guadalajara, Guadalajara, Mexico

Raúl C. Baptista-Rosas, Luis D. Hernandez-Ortega & Arieh R. Mercado-Sesma

Instituto Murciano de Investigación Biosanitaria (IMIB-Arrixaca), Murcia, Spain

María Barreda-Sánchez, Enrique Bernal, Josefina Garcia-García, Elisa García-Vázquez, Encarna Guillen-Navarro, M. Teresa Herranz, Rubén Jara, Antonio Moreno-Docón & M. Elena Pérez-Tomás

Universidad Católica San Antonio de Murcia (UCAM), Murcia, Spain

María Barreda-Sánchez

Servicio de Medicina Interna-Unidad de Enfermedades Infecciosas, Hospital Universitario de Salamanca-IBSAL, Salamanca, Spain

Moncef Belhassen-Garcia

Department of Internal Medicine, Hospital Universitario de Fuenlabrada, Madrid, Spain

David Bernal-Bello

Laboratorio de Vigilancia Molecular Aplicada, Escola Tecnica de Saúde, Pará, Brazil

Joao F. Bezerra

Genetics Postgraduate Program, Federal University of Pernambuco, Recife, Brazil

Marcos A. C. Bezerra

Servicio de Alergia, Hospital Universitario Infanta Leonor, Madrid, Spain

Natalia Blanca-López & Laura Martin-Pedraza

Servicio de Medicina Intensiva, Hospital Universitario del Tajo, Toledo, Spain

Rafael Blancas & Óscar Martínez-González

Hospital Universitario Mutua Terrassa, Barcelona, Spain

Lucía Boix-Palop & Anna Sangil

Servicio de Farmacología, Hospital Universitario La Paz-IDIPAZ, Madrid, Spain

Alberto Borobia, Irene García-García & Alicia Marín Candon

Alcaldía de Barranquilla, Secretaría de Salud, Barranquilla, Colombia

Elsa Bravo, Humberto Mendoza Charris & Xenia Morelos-Arnedo

Xenética Cardiovascular, Instituto de Investigación Sanitaria de Santiago (IDIS), Santiago de Compostela, Spain

María Brion

Centre for Biomedical Network Research on Cardiovascular Diseases (CIBERCV), Instituto de Salud Carlos III, Madrid, Spain

María Brion, Ramón Brugada, Alexandra Pérez-Serra & Mel·lina Pinsach-Abuin

Unidad de Infección Viral e Inmunidad, Centro Nacional de Microbiología (CNM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain

Óscar Brochado-Kith, Francisco C. Ceballos, Amanda Fernández-Rodríguez, María A. Jimenez-Sousa, María Martín-Vicente, Salvador Resino & Ana Virseda-Berdices

Cardiovascular Genetics Center, Institut d’Investigació Biomèdica Girona (IDIBGI), Girona, Spain

Ramón Brugada, Alexandra Pérez-Serra & Mel·lina Pinsach-Abuin

Medical Science Department, School of Medicine, University of Girona, Girona, Spain

Ramón Brugada

Cardiology Service, Hospital Josep Trueta, Girona, Spain

Institute of Biomedicine of Seville (IBiS), Consejo Superior de Investigaciones Científicas (CSIC), University of Seville, Virgen del Rocio University Hospital, Seville, Spain

Matilde Bustos & María A. Rodriguez-Hernandez

Division of Infectious Diseases, Instituto de Investigación Sanitaria–Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain

Alfonso Cabello & Miguel Górgolas

Intensive Care Unit, Hospital Universitario Insular de Gran Canaria, Las Palmas de Gran Canaria, Spain

Juan J. Caceres-Agra

Hospital Universitario Mutua Terrassa, Terrassa, Spain

Esther Calbo, David Dalmau & Beatriz Dietl

Departemento de Medicina, Hospital Universitario Virgen del Rocío, Universidad de Sevilla, Seville, Spain

Enrique J. Calderón & Francisco J. Medrano

Centre for Biomedical Network Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain

Enrique J. Calderón, Juan De la Cruz Troca, Vicente Friaza, Iolanda Jordan, Esther Lopez-Garcia, Vicente Martín, Francisco J. Medrano, Antonio J. J. Molina & Fernando Rodriguez-Artalejo

Instituto de Biomedicina de Sevilla, Seville, Spain

Enrique J. Calderón, Carmen de la Horra, Vicente Friaza, Francisco J. Medrano & Rubén Morilla

Facultad de Ciencias, Universidad de los Andes, Bogotá, Colombia

Shirley Camacho, María A. Castillo, María Claudia Lattig & Lorena Salazar-García

Department of Nutrition, University of Fortaleza (UNIFOR), Fortaleza, Brazil

Antonio Augusto F. Carioca

Departamento de Química, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo, São Paulo, Brazil

Thássia M. T. Carratto & Vitor M. S. Moraes

Andalusian Public Health System Biobank, Granada, Spain

José Antonio Carrillo-Avila & Sonia Panadero-Fajardo

Programa de Pós-Graduação em Ciências Farmacêuticas, Universidade Federal do Rio Grande do Norte, Natal, Brazil

Maria C. C. Carvalho, Katiusse A. dos Santos & Karla S. C. Souza

Neuromuscular Unit, Neurology Department, Hospital Universitari de Bellvitge, L’Hospitalet de Llobregat, Barcelona, Spain

Carlos Casasnovas & Valentina Vélez-Santamaría

Bellvitge Biomedical Research Institute (IDIBELL), Neurometabolic Diseases Laboratory, L’Hospitalet de Llobregat, Barcelona, Spain

Carlos Casasnovas, Juan José Martínez, Laura Planas-Serra, Aurora Pujol, Agustí Rodriguez-Palmero, Montserrat Ruiz, Agatha Schlüter & Valentina Vélez-Santamaría

Osakidetza, Cruces University Hospital, Barakaldo, Spain

Luis Castano, Aitor García-de-Vicuña & Gustavo Perez-de-Nanclares

Centre for Biomedical Network Research on Diabetes and Metabolic Associated Diseases (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain

Luis Castano

University of Pais Vasco, UPV/EHU, Bizkaia, Spain

Oncology and Genetics Unit, Instituto de Investigacion Sanitaria Galicia Sur, Xerencia de Xestion Integrada de Vigo-Servizo Galego de Saúde, Vigo, Spain

Jose E. Castelao

Hospital Universitario La Paz, Hospital Carlos III, Madrid, Spain

Aranzazu Castellano Candalija, Carmen Fernéndez Capitán, Cristina Marcelo Calvo, Alberto Moreno Fernández & Giorgina Gabriela Salgueiro Origlia

Hospital de San José, Sociedad de Cirugía de Bogota, Bogotá, Colombia

Walter G. Chaves-Santiago, Mario Gómez-Duque, Luz Adriana Pinzón & John J. Sprockel

Hospital Universitario Río Hortega, Valladolid, Spain

Rosa Conde-Vicente & Manuel Gonzalez-Sagrado

Servicio de Medicina Intensiva, Complejo Hospitalario Universitario de A Coruña (CHUAC), Sistema Galego de Saúde (SERGAS), A Coruña, Spain

M. Lourdes Cordero-Lorenzana

Preventive Medicine Department, Valencia University, Valencia, Spain

Dolores Corella & Jose V. Sorlí

Centre for Biomedical Network Research on Physiopatology of Obesity and Nutrition (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain

Department of Microgravity and Translational Regenerative Medicine, Otto von Guericke University, Magdeburg, Germany

Jose L. Cortes-Sanchez

Maternidade Escola Janário Cicco, Natal, Brazil

Tatiana X. Costa

Centro Nacional de Genotipado (CEGEN), Universidade de Santiago de Compostela, Santiago de Compostela, Spain

Raquel Cruz, María Gómez García, Inés Quintela, José Javier Suárez-Rama & Angel Carracedo

Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón (IiSGM), Madrid, Spain

Luisa Cuesta

Programa de Pós Graduação em Ciências da Saúde, Faculdade de Medicina, Universidade de Brasília, Brasilia, Brazil

Gabriela C. R. Cunha & Vanessa S. Souza

Fundació Docència I Recerca Mutua Terrassa, Terrassa, Spain

David Dalmau & Alex Serra-Llovich

Unidad de Genética, Hospital Universitario Mostoles, Madrid, Spain

M. Teresa Darnaude & Aranzazu Diaz de Bustamante

Internal Medicine Department, Instituto de Investigación Sanitaria–Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain

Raimundo de Andrés

Universidade Federal do Rio Grande do Norte, Pós-graduação em Biotecnologia, Rede de Biotecnologia do Nordeste (Renorbio), Natal, Brazil

Jéssica N. G. de Araújo & Nayara S. Silva

Servicio de Medicina Interna, Hospital Universitario Severo Ochoa, Madrid, Spain

Carmen de Juan & Patricia Moreira-Escriche

Department of Preventive Medicine and Public Health, School of Medicine, Universidad Autónoma de Madrid, Madrid, Spain

Juan De la Cruz Troca, Esther Lopez-Garcia & Fernando Rodriguez-Artalejo

IdiPaz (Instituto de Investigación Sanitaria Hospital Universitario La Paz), Madrid, Spain

Instituto Aragonés de Ciencias de la Salud (IACS), Zaragoza, Spain

Alba De Martino-Rodríguez, Javier Gómez-Arrue, Beatriz González Álvarez, Delia Recalde & Carlos Tellería

Instituto Investigación Sanitaria Aragón (IIS-Aragon), Zaragoza, Spain

Programa de Pós Graduação em Nutrição, Universidade Federal do Rio Grande do Norte, Natal, Brazil

Julianna Lys de Sousa Alves Neri

Preventive Medicine Department, Instituto de Investigacion Sanitaria Galicia Sur, Xerencia de Xestion Integrada de Vigo-Servizo Galego de Saúde, Vigo, Spain

Victor del Campo-Pérez

Servicio de Medicina Interna, Hospital Universitario Virgen del Rocío, Seville, Spain

Juan Delgado-Cuesta

Departamento de Infectologia, Universidade Federal do Rio Grande do Norte, Natal, Brazil

Manoella do Monte Alves

Hospital de Doenças Infecciosas Giselda Trigueiro, Natal, Brazil

Unidad Diagnóstico Molecular, Fundación Rioja Salud, La Rioja, Spain

Elena Domínguez-Garrido

Hospital Universitario Quironsalud Madrid, Madrid, Spain

Jose Echave-Sustaeta & Pablo Guisado-Vasco

Servicio de Cardiología, Hospital Universitario de Salamanca-IBSAL, Salamanca, Spain

Rocío Eiros, Pedro-Luis Sánchez & Clara Sánchez-Pablo

Servicio de Medicina Interna, Hospital Universitario Puerta de Hierro, Majadahonda, Spain

Gabriela Escudero

Biocruces Bizkaia Health Research Institute, Galdakao University Hospital, Osakidetza, Bizkaia, Spain

Pedro Pablo España & Carmen Mar

Instituto Regional de Investigación en Salud-Universidad Nacional de Caaguazú, Caaguazú, Paraguay

Gladys Mercedes Estigarribia Sanabria

Núcleo de Pesquisas em Oncologia, Universidade Federal do Pará, Belém, Brazil

Marianne R. Fernandes & Ney P. C. Santos

Departamento de Ensino e Pesquisa, Hospital Ophir Loyola, Belém, Brazil

Marianne R. Fernandes

Fundación Asilo San Jose, Santander, Spain

Ramón Fernández

Unidad de Enfermedades Infecciosas, Servicio de Medicina Interna, Hospital Universitario Puerta de Hierro, Instituto de Investigación Sanitaria Puerta de Hierro—Segovia de Arana, Madrid, Spain

Ana Fernández-Cruz

Universidad Nacional de Asunción, Facultad de Politécnica, Paraguay

María J. Fernandez-Nestosa

Urgencias Hospitalarias, Complejo Hospitalario Universitario de A Coruña (CHUAC), Sistema Galego de Saúde (SERGAS), A Coruña, Spain

Uxía Fernández-Robelo

Grupo de Investigación en Interacciones Gen-Ambiente y Salud (GIIGAS), Instituto de Biomedicina (IBIOMED), Universidad de León, León, Spain

Tania Fernández-Villa

Pediatrics Department, Hospital Universitario Niño Jesús, Madrid, Spain

Patricia Flores-Pérez

Unitat de Malalties Infeccioses i Importades, Servei de Pediatría, Infectious and Imported Diseases, Pediatric Unit, Hospital Universitari Sant Joan de Deú, Barcelona, Spain

Victoria Fumadó

Microbiology Department, Instituto de Investigación Sanitaria–Fundación Jiménez Díaz University Hospita, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain

Ignacio Gadea & Celia Perales

Hospital de Niños Ricardo Gutierrez, Buenos Aires, Argentina

Cristina Galoppo, Angela Gentile, Florencia Guaragna, Perez Maria Jazmin, Ailen Lauriente, Pablo Neira, Virginia Olivar, German Ezequiel Rodriguez Novoa & Alejandro Teper

University of Salamanca, Biomedical Research Institute of Salamanca (IBSAL), Salamanca, Spain

Andrés C. García-Montero

Department of Immunology, IRYCIS, Hospital Universitario Ramón y Cajal, Madrid, Spain

Ana García-Soidán, Israel Nieto-Gañán & Roberto Pariente-Rodríguez

Servicio de Medicina Intensiva, Hospital Infanta Elena, Madrid, Spain

María Carmen García Torrejón

Servicio de Genética, Hospital Universitario de Getafe, Madrid, Spain

Belén Gil-Fournier & Soraya Ramiro León

Pneumology Department, Hospital General Universitario Gregorio Marañón (iiSGM), Madrid, Spain

Ángela Gómez Sacristán

Ministerio de Salud Ciudad de Buenos Aires, Buenos Aires, Argentina

Fernan Gonzalez Bernaldo de Quirós

Unidad de Apoyo a la Investigación, Hospital Clinico Universitario de Valladolid, Valladolid, Spain

Hugo Gonzalo Benito & Pedro Martinez-Paz

Departamento de Cirugía, Universidad de Valladolid, Valladolid, Spain

Oscar Gorgojo-Galindo & Eduardo Tamayo

Secretaria Municipal de Saude de Apodi, Natal, Brazil

Genilson P. Guegel

Sección Genética Médica, Servicio de Pediatría, Hospital Clínico Universitario Virgen de la Arrixaca, Servicio Murciano de Salud, Murcia, Spain

Encarna Guillen-Navarro

Departamento Cirugía, Pediatría, Obstetricia y Ginecología, Facultad de Medicina, Universidad de Murcia (UMU), Murcia, Spain

Grupo Clínico Vinculado, Centre for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain

Servicio de Análisis Clínicos e Inmunología, Hospital Universitario Virgen de las Nieves, Granada, Spain

Juan F. Gutiérrez-Bautista, Pilar Jiménez, Antonio Rodriguez-Nicolas & Francisco Ruiz-Cabello

Hospital Universitario Centro Dermatológico Federico Lleras Acosta, Bogotá, Colombia

Luz D. Gutierrez-Castañeda

Intermediate Respiratory Care Unit, Department of Pneumology, Instituto de Investigación Sanitaria–Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain

Sarah Heili-Frades

Clinica Comfamiliar Risaralda, Pereira, Colombia

Estefania Hernandez, Ricardo Martínez, Juan José Montoya, Manuel Pacheco & Gloria L. Porras-Hurtado

Centro Universitario de Tonalá, Universidad de Guadalajara, Guadalajara, Mexico

Luis D. Hernandez-Ortega & Arieh R. Mercado-Sesma

Unidad de Cuidados Intensivos, Hospital Clínico Universitario de Santiago (CHUS), Sistema Galego de Saúde (SERGAS), Santiago de Compostela, Spain

Rebeca Hernández-Vaquero, Pedro Rascado Sedes, Montserrat Robelo Pardo & Emilio Rodríguez-Ruiz

Plataforma de Farmacogenética, IIS La Fe, Valencia, Spain

María José Herrero

Departamento de Farmacología, Universidad de Valencia, Valencia, Spain

Data Analysis Department, Instituto de Investigación Sanitaria–Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain

Antonio Herrero-Gonzalez & Juan Carlos Taracido-Fernandez

Infectious Diseases Service, Hospital del Mar, Barcelona, Spain

Juan P. Horcajada

Institut Hospital del Mar d’Investigacions Mèdiques (IMIM), Barcelona, Spain

CEXS-Universitat Pompeu Fabra, Spanish Network for Research in Infectious Diseases (REIPI), Barcelona, Spain

Biocruces Bizkaia Health Research Institute, Basurto University Hospital, Osakidetza, Bizkaia, Spain

Maider Intxausti-Urrutibeaskoa & Cristina Sancho-Sainz

Infectious Diseases, Microbiota and Metabolism Unit, Center for Biomedical Research of La Rioja (CIBIR), Logroño, Spain

María Íñiguez, José A. Oteo, Patricia Pérez-Matute, Emma Recio-Fernández & Pablo Villoslada-Blanco

Sabin Medicina Diagnóstica, São Paulo, Brazil

Rafael H. Jacomo

Opthalmology Department, Instituto de Investigación Sanitaria–Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain

Ignacio Jiménez-Alfaro

Pediatric Critical Care Unit, Hospital Sant Joan de Deu, Barcelona, Spain

Iolanda Jordan

Paediatric Intensive Care Unit, Agrupación Hospitalaria Clínic-Sant Joan de Déu, Esplugues de Llobregat, Barcelona, Spain

Department of Immunology, Hospital Universitario 12 de Octubre, Madrid, Spain

Rocío Laguna-Goya, María Lasa-Lazaro, Esther Mancebo & Estela Paz-Artal

Transplant Immunology and Immunodeficiencies Group, Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), Madrid, Spain

SIGEN Alianza Universidad de los Andes, Fundación Santa Fe de Bogotá, Bogotá, Colombia

María Claudia Lattig & Oscar Martinez-Nieto

Medicina Intensiva, Hospital General de Segovia, Segovia, Spain

Anabel Liger Borja, María Lozano-Espinosa & Caridad Martín-López

Clinical Trials Unit, Instituto de Investigación Sanitaria–Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain

Lucía Llanos

IMDEA-Food Institute, CEI UAM+CSIC, Madrid, Spain

Esther Lopez-Garcia & Fernando Rodriguez-Artalejo

Servicio de Enfermedades Infecciosas, Hospital Universitario Virgen de las Nieves, Granada, Spain

Miguel A. López-Ruz

Instituto de Investigación Biosanitaria de Granada (ibs.GRANADA), Granada, Spain

Miguel A. López-Ruz & Francisco Ruiz-Cabello

Departamento de Medicina, Universidad de Granada, Granada, Spain

Servicio de Inmunología, Hospital Universitario La Paz-IDIPAZ, Madrid, Spain

Eduardo López Granados

Lymphocyte Pathophysiology in Immunodeficiencies Group, La Paz Institute for Health Research (IdiPAZ), Madrid, Spain

Intensive Care Unit, Hospital Universitario de Canarias, La Laguna, Spain

Leonardo Lorente

Dirección General de Salud Pública, Consejería de Sanidad, Junta de Castilla y León, Valladolid, Spain

José E. Lozano

Departamento de Analises Clinicas e Toxicologicas, Universidade Federal do Rio Grande do Norte, Natal, Brazil

Andre D. Luchessi & Vivian N. Silbiger

Epidemiology, Fundación Jiménez Díaz, Madrid, Spain

Ignacio Mahillo

Department of Medicine, Universidad Autónoma de Madrid, Madrid, Spain

Instituto de Biomedicina (IBIOMED), Universidad de León, León, Spain

Alba Marcos-Delgado, Vicente Martín & Antonio J. J. Molina

Intensive Care Unit, Hospital Universitario N. S. de Candelaria, Santa Cruz de Tenerife, Spain

María M. Martín

Preventive Medicine Department, Instituto de Investigación Sanitaria–Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain

María Dolores Martín

Departamento de Medicina, Universidad de Valladolid, Valladolid, Spain

Marta Martin-Fernandez

Servicio de Medicina Intensiva, Hospital Universitario Infanta Leonor, Madrid, Spain

Amalia Martinez

Servicio de Medicina Interna, Sanatorio Franchin, Buenos Aires, Argentina

Eleno Martínez-Aquino

Unidad de Genética y Genómica Islas Baleares, Hospital Universitario Son Espases, Islas Baleares, Spain

Iciar Martinez-Lopez

Unidad de Diagnóstico Molecular y Genética Clínica, Hospital Universitario Son Espases, Islas Baleares, Spain

Genomics of Complex Diseases Unit, Research Institute of Hospital de la Santa Creu i Sant Pau, IIB Sant Pau, Barcelona, Spain

Angel Martinez-Perez & José Manuel Soria

Faculdade de Medicina, Universidade de Brasília, Brasilia, Brazil

Juliana F. Mazzeu, Silviene F. Oliveira & Renata R. Sousa

Programa de Pós-Graduação em Ciências Médicas, Universidade de Brasília, Brasilia, Brazil

Juliana F. Mazzeu & Silviene F. Oliveira

Programa de Pós-Graduação em Ciências da Saúde, Universidade de Brasília, Brasilia, Brazil

Hospital das Forças Armadas, Brasília, Brazil

Kelliane A. Medeiros, Susana M. T. Pinho & Adriana P. Ribeiro

Exército Brasileiro, Cruzeiro, Brazil

Kelliane A. Medeiros & Adriana P. Ribeiro

Hospital El Bierzo, Gerencia de Asistencia Sanitaria del Bierzo (GASBI), Gerencia Regional de Salud (SACYL), Ponferrada, Spain

Xose M. Meijome

Grupo INVESTEN, Instituto de Salud Carlos III, Madrid, Spain

Unidad de Cuidados Intensivos, Complejo Universitario de A Coruña (CHUAC), Sistema Galego de Saúde (SERGAS), A Coruña, Spain

Natalia Mejuto-Montero & Xiana Taboada-Fraga

Unidad Cuidados Intensivos, Hospital El Bierzo, León, Spain

Eleuterio Merayo Macías

Familial Cancer Clinical Unit, Spanish National Cancer Research Centre, Madrid, Spain

Fátima Mercadillo & Miguel Urioste

Instituto de Investigación Sanitaria San Carlos (IdISSC), Hospital Clínico San Carlos (HCSC), Madrid, Spain

Elena Molina-Roldán

Departamento de Enfermería, Universidad de Sevilla, Seville, Spain

Rubén Morilla

Hospital General Universitario Gregorio Marañón (IiSGM), Madrid, Spain

Patricia Muñoz García

ERN-ITHACA-European Reference Network on Rare Congenital Malformations and Rare Intellectual Disability, Brussels, Belgium

Julian Nevado, Jair Antonio Tenorio Castaño & Pablo Lapunzina

Unidad de Genética y Genómica Islas Baleares, Unidad de Diagnóstico Molecular y Genética Clínica, Hospital Universitario Son Espases, Islas Baleares, Spain

Antònia Obrador-Hevia

Instituto de Investigación Sanitaria Islas Baleares (IdISBa), Islas Baleares, Spain

Programa de Pós-Graduação em Biologia Animal, Universidade de Brasília, Brasília, Brazil

Silviene F. Oliveira

Programa de Pós-Graduação Profissional em Ensino de Biologia, Universidade de Brasília, Brasília, Brazil

Anatomía Patológica, Instituto de Investigación Sanitaria San Carlos (IdISSC), Hospital Clínico San Carlos (HCSC), Madrid, Spain

Luis Ortega

Tecnológico de Monterrey, Monterrey, Mexico

Rocio Ortiz-Lopez

Centro de Investigación en Anomalías Congénitas y Enfermedades Raras (CIACER), Universidad Icesi, Cali, Colombia

Harry Pachajoa

Departamento de Genetica, Fundación Valle del Lili, Cali, Colombia

Department of Immunology, Ophthalmology and ENT, Universidad Complutense de Madrid, Madrid, Spain

Estela Paz-Artal

Department of Neumology, Instituto de Investigación Sanitaria–Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain

Germán Peces-Barba & Felipe Villar

Hospital Nuestra Señora de Sonsoles, Ávila, Spain

Miguel S. Pedromingo Kus & Ronald P. Torres Gutiérrez

Inditex, A Coruña, Spain

Patricia Perez

Intensive Care Department, Instituto de Investigación Sanitaria–Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain

César Pérez & Arnoldo Santos

Servicio de Microbiología Clínica, Hospital Universitario Príncipe de Asturias, Madrid, Spain

Felipe Pérez-García

Departamento de Biomedicina y Biotecnología, Facultad de Medicina y Ciencias de la Salud, Universidad de Alcalá de Henares, Madrid, Spain

GENYCA, Madrid, Spain

Teresa Perucho & Eva Ruiz-Casares

Marinha do Brasil, Brasil, Brazil

Susana M. T. Pinho

Universidade de Brasília, Brasilia, Brazil

Susana M. T. Pinho & Adriana P. Ribeiro

Neuromuscular Diseases Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain

Francesc Pla-Junca & Sonia Segovia

Unidad de Investigación Médica en Enfermedades Infecciosas y Parasitarias, Instituto Mexicano del Seguro Social (IMSS), Centro Médico Nacional Siglo XXI, Mexico City, Mexico

Ericka N. Pompa-Mera

Catalan Institution of Research and Advanced Studies (ICREA), Barcelona, Spain

Aurora Pujol

Drug Research Centre, Institut d’Investigació Biomèdica Sant Pau, IIB-Sant Pau, Barcelona, Spain

Maria Angeles Quijada

Departamento de Genetica, Clinica imbanaco, Cali, Colombia

Diana Ramirez-Montaño

Department of Immunology, Hospital Universitario de Gran Canaria Dr Negrín, Las Palmas de Gran Canaria, Spain

Carlos Rodriguez-Gallego

Pediatrics Department, University Hospital Germans Trias i Pujol, Badalona, Spain

Agustí Rodriguez-Palmero

Department of Pathology, Biobank, Instituto de Investigación Sanitaria–Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain

Federico Rojo & Sandra Zazo

Faculdade de Ciências da Saúde, Universidade de Brasília, Brasilia, Brazil

Lidia S. Rosa

Servicio de Medicina Interna, Hospital Universitario Virgen de las Nieves, Granada, Spain

Antonio Rosales-Castillo

Grupo de Ciencias Básicas en Salud (CBS), Fundación Universitaria de Ciencias de la Salud, Bogotá, Colombia

Cladelis Rubio

Sociedad de Cirugía de Bogotá, Hospital de San José, Bogotá, Colombia

Departamento Bioquímica, Biología Molecular e Inmunología III, Universidad de Granada, Granada, Spain

Francisco Ruiz-Cabello

Allergy Unit, Hospital Infanta Elena, Madrid, Spain

Javier Ruiz-Hornillos

Faculty of Medicine, Universidad Francisco de Vitoria, Madrid, Spain

Hospital Universitario Infanta Leonor, Madrid, Spain

Pablo Ryan & Jesús Troya

Complutense University of Madrid, Madrid, Spain

Gregorio Marañón Health Research Institute (IiSGM), Madrid, Spain

Reumathology Service, Instituto de Investigación Sanitaria–Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), Madrid, Spain

Olga Sánchez-Pernaute

Biobank, Puerta de Hierro-Segovia de Arana Health Research Institute, Madrid, Spain

Antonio J. Sánchez López

Universidad Rey Juan Carlos, Madrid, Spain

Jorge Sánchez Redondo

The John Walton Muscular Dystrophy Research Centre, Newcastle University and Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK

Sonia Segovia

Neuromuscular Unit, Neuropediatrics Department, Institut de Recerca Sant Joan de Déu, Hospital Sant Joan de Déu, Barcelona, Spain

Casa de Saúde São Lucas, Natal, Brazil

Miguel A. Sicolo

Hospital Rio Grande, Natal, Brazil

Intensive Care Unit, Hospital Universitario de Gran Canaria Dr Negrín, Las Palmas de Gran Canaria, Spain

Jordi Solé-Violán

Universidad Fernando Pessoa Canarias, Las Palmas de Gran Canaria, Spain

Servicio de Anestesiologia y Reanimación, Hospital Clinico Universitario de Valladolid, Valladolid, Spain

Eduardo Tamayo

Servicio de Hematologia y Hemoterapia, Hospital Clinico Universitario de Valladolid, Valladolid, Spain

Alvaro Tamayo-Velasco

Hospital Universitario Lauro Wanderley, João Pessoa, Brazil

Nathali A. C. Tavares & Romero H. T. Vasconcelos

Servicio de Medicina Interna, Hospital Universitario Infanta Leonor, Madrid, Spain

Juan Torres-Macho

University Hospital of Burgos, Burgos, Spain

Juan Valencia-Ramos

Universidad de Sevilla, Seville, Spain

Agustín Valido

Fundación Santa Fe de Bogota, Instituto de Servicios Medicos de Emergencia y Trauma, Bogotá, Colombia

Juan Pablo Vargas Gallo

Universidad de los Andes, Bogotá, Colombia

Quironprevención, A Coruña, Spain

Belén Varón

Junta de Castilla y León, Consejería de Sanidad, Valladolid, Spain

Gerencia Atención Primaria de Burgos, Burgos, Spain

Santiago Velasco-Quirce

Immunogenetics–Histocompatibility group, Servicio de Inmunología, Instituto de Investigación Sanitaria Puerta de Hierro, Segovia de Arana, Madrid, Spain

Carlos Vilches

Department of Infectious Diseases, Hospital del Mar, Barcelona, Spain

Judit Villar-Garcia

IMIM—Hospital del Mar Medical Research Institute, Institut Hospital del Mar d’Investigacions Mediques, Barcelona, Spain

Department of Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain

Consejería de Sanidad, Comunidad de Madrid, Madrid, Spain

Antonio Zapatero-Gaviria

Centro para el Desarrollo de la Investigación Científica, Asunción, Paraguay

Ruth Zarate

School of Informatics, University of Edinburgh, Edinburgh, UK

Beatrice Alex, Benjamin Bach & James Scott-Brown

Section of Biomolecular Medicine, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Sir Alexander Fleming Building, Imperial College London, London, UK

Petros Andrikopoulos, Kanta Chechi, Marc-Emmanuel Dumas, Julian Griffin, Sonia Liggi, Michael Olanipekun, Anthonia Osagie & Zoltan Takats

Section of Genomic and Environmental Medicine, Respiratory Division, National Heart and Lung Institute, London, UK

Petros Andrikopoulos, Marc-Emmanuel Dumas, Michael Olanipekun & Anthonia Osagie

Section of Molecular Virology, Imperial College London, London, UK

Wendy S. Barclay

Antimicrobial Resistance and Hospital Acquired Infection Department, Public Health England, London, UK

Meera Chand

Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, UK

Kanta Chechi

Department of Infectious Disease, Imperial College London, London, UK

Graham S. Cooke & Shiranee Sriskandan

MRC-University of Glasgow Centre for Virus Research, Glasgow, UK

Ana da Silva Filipe, Antonia Y. W. Ho, Massimo Palmarini, David L. Robertson, Janet T. Scott & Emma C. Thomson

The Florey Institute for Host-Pathogen Interactions, Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK

Thushan de Silva

Centre for Medical Informatics, The Usher Institute, University of Edinburgh, Edinburgh, UK

Annemarie B. Docherty, Ewen M. Harrison, Thomas M. Drake, Cameron J. Fairfield, Stephen R. Knight, Kenneth A. Mclean, Derek Murphy, Lisa Norman, Riinu Pius & Catherine A. Shaw

National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK

Gonçalo dos Santos Correia, Matthew Lewis, Lynn Maslen, Caroline Sands & Panteleimon Takis

Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK

European Genomic Institute for Diabetes, CNRS UMR 8199, INSERM UMR 1283, Institut Pasteur de Lille, Lille University Hospital, University of Lille, Lille, France

Marc-Emmanuel Dumas

McGill University and Genome Quebec Innovation Centre, Montréal, Quebec, Canada

National Infection Service, Public Health England, London, UK

Jake Dunning & Maria Zambon

Liverpool School of Tropical Medicine, Liverpool, UK

Tom Fletcher

Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK

Christopher A. Green

Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, UK

William Greenhalf

Institute for Global Health, University College London, London, UK

Rishi K. Gupta

Department of Infectious Diseases, Queen Elizabeth University Hospital, Glasgow, UK

Antonia Y. W. Ho

University of Liverpool, Liverpool, UK

Karl Holden

Virology Reference Department, National Infection Service, Public Health England, London, UK

Samreen Ijaz

Department of Pharmacology, University of Liverpool, Liverpool, UK

Nuffield Department of Medicine, Peter Medawar Building for Pathogen Research, University of Oxford, Oxford, UK

Paul Klenerman

Translational Gastroenterology Unit, Nuffield Department of Medicine, University of Oxford, Oxford, UK

Nottingham University Hospitals NHS Trust, Nottingham, UK

Wei Shen Lim

Nuffield Department of Medicine, John Radcliffe Hospital, Oxford, UK

Alexander J. Mentzer

Department of Microbiology/Infectious Diseases, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK

ISARIC Global Support Centre, Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK

Laura Merson, Louise Sigfrid & Gail Carson

Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK

Shona C. Moore, William A. Paxton & Georgios Pollakis

Division of Infection and Immunity, University College London, London, UK

Mahdad Noursadeghi

Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK

Carlo Palmieri

Clatterbridge Cancer Centre NHS Foundation Trust, Liverpool, UK

NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, Liverpool, UK

William A. Paxton & Georgios Pollakis

Centre for Clinical Infection and Diagnostics Research, Department of Infectious Diseases, School of Immunology and Microbial Sciences, King’s College London, London, UK

Nicholas Price

Department of Infectious Diseases, Guy’s and St Thomas’ NHS Foundation Trust, London, UK

Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK

Andrew Rambaut

Department of Pediatrics and Virology, St Mary’s Medical School Building, Imperial College London, London, UK

Vanessa Sancho-Shimizu

NHS Greater Glasgow & Clyde, Glasgow, UK

Janet T. Scott

Walton Centre NHS Foundation Trust, Liverpool, UK

Tom Solomon

MRC Centre for Molecular Bacteriology and Infection, Imperial College London, London, UK

Shiranee Sriskandan

Department of Child Life and Health, University of Edinburgh, Edinburgh, UK

Olivia V. Swann

National Phenome Centre, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK

Zoltan Takats

Blood Borne Virus Unit, Virus Reference Department, National Infection Service, Public Health England, London, UK

Richard S. Tedder

Transfusion Microbiology, National Health Service Blood and Transplant, London, UK

Department of Medicine, Imperial College London, London, UK

Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK

A. A. Roger Thompson

Tropical & Infectious Disease Unit, Royal Liverpool University Hospital, Liverpool, UK

Lance C. W. Turtle

Liverpool Clinical Trials Centre, University of Liverpool, Liverpool, UK

Marie Connor, Jo Dalton, Carrol Gamble, Michelle Girvan, Sophie Halpin, Janet Harrison, Clare Jackson, Laura Marsh, Stephanie Roberts, Egle Saviciute & Chloe Donohue

Public Health Scotland, Edinburgh, UK

Susan Knight, Eva Lahnsteiner & Sarah Tait

Centre for Health Informatics, Division of Informatics, Imaging and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK

Gary Leeming

EPCC, University of Edinburgh, Edinburgh, UK

Lucy Norris

ISARIC, Global Support Centre, COVID-19 Clinical Research Resources, Epidemic diseases Research Group, Oxford (ERGO), University of Oxford, Oxford, UK

James Lee & Daniel Plotkin

Institute of Infection, Veterinary and Ecological Sciences, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, UK

Cara Donegan & Rebecca G. Spencer

23andMe, Sunnyvale, CA, USA

Janie F. Shelton, Anjali J. Shastri, Chelsea Ye, Catherine H. Weldon, Teresa Filshtein-Sonmez, Daniella Coker, Antony Symons, Stella Aslibekyan & Adam Auton

Human genetics R&D, GSK Medicines Research Centre, Target Sciences R&D, Stevenage, UK

Jorge Esparza-Gordillo

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GenOMICC Investigators


Management, Laboratory and Data Team

Royal Stoke University Hospital, Staffordshire, UK

North Middlesex University Hospital NHS trust, London, UK

BHRUT (Barking Havering)—Queens Hospital and King George Hospital, Essex, UK

Kingston Hospital, Surrey, UK

Royal Berkshire NHS Foundation Trust, Berkshire, UK

Ashford and St Peter’s Hospital, Surrey, UK

Norfolk and Norwich University hospital (NNUH), Norwich, UK

Royal Hampshire County Hospital, Hampshire, UK

University Hospital North Durham, Darlington, UK and Darlington Memorial Hospital, Darlington, UK

Stoke Mandeville Hospital, Buckinghamshire, UK

Worthing Hospital, Worthing, UK and St Richard’s Hospital, Chichester, UK

Craigavon Area Hospital, County Armagh, Northern Ireland

Calderdale Royal Hospital, Halifax, UK and Huddersfield Royal Infirmary, Huddersfield, UK

Frimley Park Hospital, Surrey, UK

Royal Victoria Hospital, Belfast, NI

Eastbourne District General Hospital, East Sussex, UK and Conquest Hospital, East Sussex, UK

Peterborough City Hospital, Peterborough, UK and Hinchingbrooke Hospital, Huntingdon, UK

Wrexham Maelor Hospital, Wrexham, Wales

Withybush General Hospital, Pembrokeshire, Wales

Leighton Hospital, Cheshire, UK

Homerton University Hospital Foundation NHS Trust, London UK

SCOURGE Consortium

ISARICC Investigators

Data Analysis Team

Data Architecture Team

Data Analysis and Management Team

Project Administration Team

Project Management Team

The 23andMe COVID-19 Team


E.P.-C., K. Rawlik, K.M., S.K., C.P.P., J.F.W., V.V., M.A., A.D.L., E.J.P., R.C., A.C., A.F., L.M., K. Rowan, A.C.P., A.L., S.C.H. and J.K.B. contributed to design. E.P.-C., K. Rawlik, A.D.B., T.Q., Y.W., I.N., G.A.M., M.Z., L.K., A.K., A.R., T.M., J.Y., A.L., B.F., S.C.H. and J.K.B. contributed to data analysis. E.P.-C., K. Rawlik, I.N., A.K., A.R., J.M., C.D.R., A.L., B.F. and S.C.H. contributed to bioinformatics. E.P.-C., K. Rawlik, I.N., G.A.M., M.Z., A.K., J.M., C.D.R., R.T., D. McAuley, A.N., M.G.S., B.F., S.C.H. and J.K.B. contributed to writing and reviewing the manuscript. I.N., F.G., W.O., K.M., S.K., D. Maslove, A.N., M.G.S., J.K., M.S.-H., C.S., C.H., P.H., L.L., D. McAuley, H.M., P.J.M.O., C.B., T.W., A.T., C.F., J.A.R., A.R.-M., P.L., C.P.P., A.F., L.M., K. Rowan, A.L., B.F. and S.C.H. contributed to oversight. F.G. and W.O. contributed to project management. F.G., W.O. and J.K.B. contributed to ethics and governance. K.M., A.F. and L.M. contributed to sample handling and sequencing. C.P.P., K. Rowan, S.C.H. and J.K.B. contributed to conception. C.P.P., J.F.W., V.V., M.A., A.D.L., E.J.P., R.C., A.C., K. Rowan and A.C.P. contributed to reviewing the manuscript. K. Rowan and A.L. contributed to clinical data management. J.K.B. contributed to scientific leadership.

Corresponding author

Correspondence to J. Kenneth Baillie .

Ethics declarations

Competing interests.

The authors declare no competing interests.

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Nature thanks Jacques Fellay and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended data fig. 1 pipeline of meta-analysis and post-gwas analyses..

Red border indicates that the data is only available for the hospitalized phenotype, while a black border indicates that the analysis was performed for the critical illness phenotype.

Extended Data Fig. 2 Miami plots.

Meta-analysis results are shown for a) critical and b) hospitalized phenotypes. In each plot results obtained using all cohorts are shown at the top and using GenOMICC data only at the bottom. Independent lead variants in the analyses of all cohorts are annotated with associated genes. Genome-wide significant associations that have not been previously reported are indicated in bold.

Extended Data Fig. 3 Comparison of effect size estimates.

GenOMICC is compared with the critical and hospitalized phenotype definitions in the SCOURGE, 23andMe, and HGI analyses. The black line indicates the best linear fit, given by the equation in each plot, obtained using Orthogonal Distance Regression to account for estimation errors in both sets of effects in the comparison.

Extended Data Fig. 4 Study weightings for (a) critical and (b) hospitalized COVID-19.

Mean +/− standard deviation of weights assigned to each data source in meta-analyses for all significant SNPs.

Extended Data Fig. 5 Cartoon showing postulated roles for genes and mediators implicated in the pathogenesis of critical COVID-19 by GenOMICC GWAS, TWAS and Mendelian randomization.

Postulated roles for genetic variants are shown in a highly simplified format to illustrate potential roles in pathogenesis, with the shaded background indicating the hypothetical impact of the host immune response over time 17 . Host immune processes are divided into those that are thought to play a role in controlling viral replication early in disease (orange section, showing “adaptive” response), and those implicated in driving hypoxaemic respiratory failure later in disease (green section, showing “maladaptive” response). Bold type gene names indicate a higher level of confidence in both the gene identification and the biological role.

Extended Data Fig. 6 Functional genomics analyses for TYK2.

(a) Effect size plot for effect of multiple variants on TYK2 expression (eQTLgen, x-axis) against increasing susceptibility to critical COVID-19 ( \({\beta }_{{xy}}\) = 0.53; \({P}_{{xy}}=1.2\times {10}^{-23}\) ). Colour shows linkage disequilibrium (LD) with the missense variant rs34536443. (b) Crystal structure of TYK2 kinase domain (Protein Data Bank ID 4GVJ 39 ) in two views that differ by a 45° rotation around a horizontal axis. The side chain of P1104 is shown as connected spheres with a nitrogen atom coloured in blue. Carbon, oxygen, nitrogen and phosphorus atoms of ATP are shown as magenta, red, blue and orange connected spheres, respectively. The N-terminal region of the kinase domain is not shown in the second view for clarity. The right-most panel shows a close view of P1104 and neighbouring residues with their side chains shown as sticks. Numbering of residues corresponds to UniProtKB entry P29597 . P1104 is in the catalytic kinase domain and proximal to the ATP-binding site; TYK2 P1104A is catalytically impaired 40 .

Extended Data Fig. 7 Steroid treatment and vaccination status.

Data are shown for a subset of GenOMICC cases who were also recruited to the ISARIC4C study in the UK.

Supplementary information

Supplementary information.

Supplementary Sections 1–13, including Supplementary Figs. 1–54 and Supplementary Tables 7–17.

Reporting Summary

Peer review file, supplementary table 1.

Description of the cohorts used in critical and hospitalized meta-analyses. Cohorts are divided by ancestry and genotyping method (whole-genome sequencing or microarray genotyping). In cohorts in which data are available, the median age with s.d. in parentheses, percentage of female cases, number of female and male cases and controls is shown. NA, data are not available for the cohort. Country of origin indicates the country in which individuals in the cohort were recruited; GenotypingPlatform indicates the array or WGS platform used for genotyping; reference indicates the reference of the publication (if the data have already been published). ‘In GenOMICC v2’ is an indication of whether the dataset was included in a previous GenOMICC paper 1 .

Supplementary Table 2

Full results for colocalization and TWAS analyses in lungs, blood, monocytes and across multiple tissue types (metaTWAS). Colocalization results are reported between significant TWAS genes and eQTLs in GTExv8 in lungs, blood and monocytes, and in eqtlGEN. rsid indicates chromosome, position, reference and alternative alleles; gene.tested is the significant TWAS gene, and is the Ensembl ID corresponding to the significant gene. PP.H3.1e-5 is the posterior probability of independent signals with a prior for colocalization of 5 × 10 −5 , PP.H4.1e-5 is the posterior probability of colocalization with a prior of 5 × 10 −5 , PP.H3.5e-5 is the posterior probability of independent signals with a prior for colocalization of 5 × 10 −5 and PP.H4.5e-5 is the probability of colocalization with a prior of 5 × 10 −5 . Colocalization was considered to be significant when PP.H3 was lower than 0.5 and PP.H4 was the highest posterior probability. Significant genes after Bonferroni correction in a TWAS meta-analysis of lungs, blood, monocytes and all tissues in GTExv8 and the ‘all critical cohorts’ GWAS. Ensembl ID, gene name, P value of the meta-analysis, number of SNPs used in to model gene expression in all tissues, mean z score for all tissues and its s.d. are shown.

Supplementary Table 3

The full results from GSMR analysis for protein level. Exposure indicates the protein name used as exposure for the analysis, bxy is the effect size, se is the standard error, p is the P value of the analysis, nsnp is the number of SNPs used as instruments and multi_snp_based_heidi_outlier is the P value of the Heidi test.

Supplementary Table 4

Full results from GSMR analysis for RNA-seq data from eQTLGEN. Exposure indicates the gene name used as exposure for the analysis, bxy is the effect size, se is the standard error, p is the P value of the analysis, nsnp is the number of SNPs used as instruments and multi_snp_based_heidi_outlier is the P value of the Heidi test.

Supplementary Table 5

Table of variants in credible sets with 95% probability of containing the causal SNP. Credible set ID is the credible set index to which the variant belongs; posterior is the posterior probability of causality for the variant. Variant indicates chromosome, position, reference and alternative alleles; beta is the effect, is the error and P is the P value.

Supplementary Table 6

The full results of the gene-level analysis performed using the mBAT-combo method. Gene indicates ensembl ID, gene_name indicates name of the gene, gene_p is the P value of the gene-level test, nsps is the number of SNPs used for the test, lead_snp is the chromosome, position, reference and alternative alleles for the SNPs with lowest P value in the region, and lead_snp_p indicates the P value of this lead SNP.

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Pairo-Castineira, E., Rawlik, K., Bretherick, A.D. et al. GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19. Nature 617 , 764–768 (2023).

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Received : 22 November 2022

Accepted : 27 March 2023

Published : 17 May 2023

Issue Date : 25 May 2023


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