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  • How to Write a Summary | Guide & Examples

How to Write a Summary | Guide & Examples

Published on November 23, 2020 by Shona McCombes . Revised on May 31, 2023.

Summarizing , or writing a summary, means giving a concise overview of a text’s main points in your own words. A summary is always much shorter than the original text.

There are five key steps that can help you to write a summary:

  • Read the text
  • Break it down into sections
  • Identify the key points in each section
  • Write the summary
  • Check the summary against the article

Writing a summary does not involve critiquing or evaluating the source . You should simply provide an accurate account of the most important information and ideas (without copying any text from the original).

Table of contents

When to write a summary, step 1: read the text, step 2: break the text down into sections, step 3: identify the key points in each section, step 4: write the summary, step 5: check the summary against the article, other interesting articles, frequently asked questions about summarizing.

There are many situations in which you might have to summarize an article or other source:

  • As a stand-alone assignment to show you’ve understood the material
  • To keep notes that will help you remember what you’ve read
  • To give an overview of other researchers’ work in a literature review

When you’re writing an academic text like an essay , research paper , or dissertation , you’ll integrate sources in a variety of ways. You might use a brief quote to support your point, or paraphrase a few sentences or paragraphs.

But it’s often appropriate to summarize a whole article or chapter if it is especially relevant to your own research, or to provide an overview of a source before you analyze or critique it.

In any case, the goal of summarizing is to give your reader a clear understanding of the original source. Follow the five steps outlined below to write a good summary.

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example of summary of scientific article

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You should read the article more than once to make sure you’ve thoroughly understood it. It’s often effective to read in three stages:

  • Scan the article quickly to get a sense of its topic and overall shape.
  • Read the article carefully, highlighting important points and taking notes as you read.
  • Skim the article again to confirm you’ve understood the key points, and reread any particularly important or difficult passages.

There are some tricks you can use to identify the key points as you read:

  • Start by reading the abstract . This already contains the author’s own summary of their work, and it tells you what to expect from the article.
  • Pay attention to headings and subheadings . These should give you a good sense of what each part is about.
  • Read the introduction and the conclusion together and compare them: What did the author set out to do, and what was the outcome?

To make the text more manageable and understand its sub-points, break it down into smaller sections.

If the text is a scientific paper that follows a standard empirical structure, it is probably already organized into clearly marked sections, usually including an introduction , methods , results , and discussion .

Other types of articles may not be explicitly divided into sections. But most articles and essays will be structured around a series of sub-points or themes.

Now it’s time go through each section and pick out its most important points. What does your reader need to know to understand the overall argument or conclusion of the article?

Keep in mind that a summary does not involve paraphrasing every single paragraph of the article. Your goal is to extract the essential points, leaving out anything that can be considered background information or supplementary detail.

In a scientific article, there are some easy questions you can ask to identify the key points in each part.

If the article takes a different form, you might have to think more carefully about what points are most important for the reader to understand its argument.

In that case, pay particular attention to the thesis statement —the central claim that the author wants us to accept, which usually appears in the introduction—and the topic sentences that signal the main idea of each paragraph.

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Now that you know the key points that the article aims to communicate, you need to put them in your own words.

To avoid plagiarism and show you’ve understood the article, it’s essential to properly paraphrase the author’s ideas. Do not copy and paste parts of the article, not even just a sentence or two.

The best way to do this is to put the article aside and write out your own understanding of the author’s key points.

Examples of article summaries

Let’s take a look at an example. Below, we summarize this article , which scientifically investigates the old saying “an apple a day keeps the doctor away.”

Davis et al. (2015) set out to empirically test the popular saying “an apple a day keeps the doctor away.” Apples are often used to represent a healthy lifestyle, and research has shown their nutritional properties could be beneficial for various aspects of health. The authors’ unique approach is to take the saying literally and ask: do people who eat apples use healthcare services less frequently? If there is indeed such a relationship, they suggest, promoting apple consumption could help reduce healthcare costs.

The study used publicly available cross-sectional data from the National Health and Nutrition Examination Survey. Participants were categorized as either apple eaters or non-apple eaters based on their self-reported apple consumption in an average 24-hour period. They were also categorized as either avoiding or not avoiding the use of healthcare services in the past year. The data was statistically analyzed to test whether there was an association between apple consumption and several dependent variables: physician visits, hospital stays, use of mental health services, and use of prescription medication.

Although apple eaters were slightly more likely to have avoided physician visits, this relationship was not statistically significant after adjusting for various relevant factors. No association was found between apple consumption and hospital stays or mental health service use. However, apple eaters were found to be slightly more likely to have avoided using prescription medication. Based on these results, the authors conclude that an apple a day does not keep the doctor away, but it may keep the pharmacist away. They suggest that this finding could have implications for reducing healthcare costs, considering the high annual costs of prescription medication and the inexpensiveness of apples.

However, the authors also note several limitations of the study: most importantly, that apple eaters are likely to differ from non-apple eaters in ways that may have confounded the results (for example, apple eaters may be more likely to be health-conscious). To establish any causal relationship between apple consumption and avoidance of medication, they recommend experimental research.

An article summary like the above would be appropriate for a stand-alone summary assignment. However, you’ll often want to give an even more concise summary of an article.

For example, in a literature review or meta analysis you may want to briefly summarize this study as part of a wider discussion of various sources. In this case, we can boil our summary down even further to include only the most relevant information.

Using national survey data, Davis et al. (2015) tested the assertion that “an apple a day keeps the doctor away” and did not find statistically significant evidence to support this hypothesis. While people who consumed apples were slightly less likely to use prescription medications, the study was unable to demonstrate a causal relationship between these variables.

Citing the source you’re summarizing

When including a summary as part of a larger text, it’s essential to properly cite the source you’re summarizing. The exact format depends on your citation style , but it usually includes an in-text citation and a full reference at the end of your paper.

You can easily create your citations and references in APA or MLA using our free citation generators.

APA Citation Generator MLA Citation Generator

Finally, read through the article once more to ensure that:

  • You’ve accurately represented the author’s work
  • You haven’t missed any essential information
  • The phrasing is not too similar to any sentences in the original.

If you’re summarizing many articles as part of your own work, it may be a good idea to use a plagiarism checker to double-check that your text is completely original and properly cited. Just be sure to use one that’s safe and reliable.

If you want to know more about ChatGPT, AI tools , citation , and plagiarism , make sure to check out some of our other articles with explanations and examples.

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  • ChatGPT citations
  • Is ChatGPT trustworthy?
  • Using ChatGPT for your studies
  • What is ChatGPT?
  • Chicago style
  • Paraphrasing

 Plagiarism

  • Types of plagiarism
  • Self-plagiarism
  • Avoiding plagiarism
  • Academic integrity
  • Consequences of plagiarism
  • Common knowledge

A summary is a short overview of the main points of an article or other source, written entirely in your own words. Want to make your life super easy? Try our free text summarizer today!

A summary is always much shorter than the original text. The length of a summary can range from just a few sentences to several paragraphs; it depends on the length of the article you’re summarizing, and on the purpose of the summary.

You might have to write a summary of a source:

  • As a stand-alone assignment to prove you understand the material
  • For your own use, to keep notes on your reading
  • To provide an overview of other researchers’ work in a literature review
  • In a paper , to summarize or introduce a relevant study

To avoid plagiarism when summarizing an article or other source, follow these two rules:

  • Write the summary entirely in your own words by paraphrasing the author’s ideas.
  • Cite the source with an in-text citation and a full reference so your reader can easily find the original text.

An abstract concisely explains all the key points of an academic text such as a thesis , dissertation or journal article. It should summarize the whole text, not just introduce it.

An abstract is a type of summary , but summaries are also written elsewhere in academic writing . For example, you might summarize a source in a paper , in a literature review , or as a standalone assignment.

All can be done within seconds with our free text summarizer .

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. (2023, May 31). How to Write a Summary | Guide & Examples. Scribbr. Retrieved February 24, 2024, from https://www.scribbr.com/working-with-sources/how-to-summarize/

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How To Write A Research Summary

Deeptanshu D

It’s a common perception that writing a research summary is a quick and easy task. After all, how hard can jotting down 300 words be? But when you consider the weight those 300 words carry, writing a research summary as a part of your dissertation, essay or compelling draft for your paper instantly becomes daunting task.

A research summary requires you to synthesize a complex research paper into an informative, self-explanatory snapshot. It needs to portray what your article contains. Thus, writing it often comes at the end of the task list.

Regardless of when you’re planning to write, it is no less of a challenge, particularly if you’re doing it for the first time. This blog will take you through everything you need to know about research summary so that you have an easier time with it.

How to write a research summary

What is a Research Summary?

A research summary is the part of your research paper that describes its findings to the audience in a brief yet concise manner. A well-curated research summary represents you and your knowledge about the information written in the research paper.

While writing a quality research summary, you need to discover and identify the significant points in the research and condense it in a more straightforward form. A research summary is like a doorway that provides access to the structure of a research paper's sections.

Since the purpose of a summary is to give an overview of the topic, methodology, and conclusions employed in a paper, it requires an objective approach. No analysis or criticism.

Research summary or Abstract. What’s the Difference?

They’re both brief, concise, and give an overview of an aspect of the research paper. So, it’s easy to understand why many new researchers get the two confused. However, a research summary and abstract are two very different things with individual purpose. To start with, a research summary is written at the end while the abstract comes at the beginning of a research paper.

A research summary captures the essence of the paper at the end of your document. It focuses on your topic, methods, and findings. More like a TL;DR, if you will. An abstract, on the other hand, is a description of what your research paper is about. It tells your reader what your topic or hypothesis is, and sets a context around why you have embarked on your research.

Getting Started with a Research Summary

Before you start writing, you need to get insights into your research’s content, style, and organization. There are three fundamental areas of a research summary that you should focus on.

  • While deciding the contents of your research summary, you must include a section on its importance as a whole, the techniques, and the tools that were used to formulate the conclusion. Additionally, there needs to be a short but thorough explanation of how the findings of the research paper have a significance.
  • To keep the summary well-organized, try to cover the various sections of the research paper in separate paragraphs. Besides, how the idea of particular factual research came up first must be explained in a separate paragraph.
  • As a general practice worldwide, research summaries are restricted to 300-400 words. However, if you have chosen a lengthy research paper, try not to exceed the word limit of 10% of the entire research paper.

How to Structure Your Research Summary

The research summary is nothing but a concise form of the entire research paper. Therefore, the structure of a summary stays the same as the paper. So, include all the section titles and write a little about them. The structural elements that a research summary must consist of are:

It represents the topic of the research. Try to phrase it so that it includes the key findings or conclusion of the task.

The abstract gives a context of the research paper. Unlike the abstract at the beginning of a paper, the abstract here, should be very short since you’ll be working with a limited word count.

Introduction

This is the most crucial section of a research summary as it helps readers get familiarized with the topic. You should include the definition of your topic, the current state of the investigation, and practical relevance in this part. Additionally, you should present the problem statement, investigative measures, and any hypothesis in this section.

Methodology

This section provides details about the methodology and the methods adopted to conduct the study. You should write a brief description of the surveys, sampling, type of experiments, statistical analysis, and the rationality behind choosing those particular methods.

Create a list of evidence obtained from the various experiments with a primary analysis, conclusions, and interpretations made upon that. In the paper research paper, you will find the results section as the most detailed and lengthy part. Therefore, you must pick up the key elements and wisely decide which elements are worth including and which are worth skipping.

This is where you present the interpretation of results in the context of their application. Discussion usually covers results, inferences, and theoretical models explaining the obtained values, key strengths, and limitations. All of these are vital elements that you must include in the summary.

Most research papers merge conclusion with discussions. However, depending upon the instructions, you may have to prepare this as a separate section in your research summary. Usually, conclusion revisits the hypothesis and provides the details about the validation or denial about the arguments made in the research paper, based upon how convincing the results were obtained.

The structure of a research summary closely resembles the anatomy of a scholarly article . Additionally, you should keep your research and references limited to authentic and  scholarly sources only.

Tips for Writing a Research Summary

The core concept behind undertaking a research summary is to present a simple and clear understanding of your research paper to the reader. The biggest hurdle while doing that is the number of words you have at your disposal. So, follow the steps below to write a research summary that sticks.

1. Read the parent paper thoroughly

You should go through the research paper thoroughly multiple times to ensure that you have a complete understanding of its contents. A 3-stage reading process helps.

a. Scan: In the first read, go through it to get an understanding of its basic concept and methodologies.

b. Read: For the second step, read the article attentively by going through each section, highlighting the key elements, and subsequently listing the topics that you will include in your research summary.

c. Skim: Flip through the article a few more times to study the interpretation of various experimental results, statistical analysis, and application in different contexts.

Sincerely go through different headings and subheadings as it will allow you to understand the underlying concept of each section. You can try reading the introduction and conclusion simultaneously to understand the motive of the task and how obtained results stay fit to the expected outcome.

2. Identify the key elements in different sections

While exploring different sections of an article, you can try finding answers to simple what, why, and how. Below are a few pointers to give you an idea:

  • What is the research question and how is it addressed?
  • Is there a hypothesis in the introductory part?
  • What type of methods are being adopted?
  • What is the sample size for data collection and how is it being analyzed?
  • What are the most vital findings?
  • Do the results support the hypothesis?

Discussion/Conclusion

  • What is the final solution to the problem statement?
  • What is the explanation for the obtained results?
  • What is the drawn inference?
  • What are the various limitations of the study?

3. Prepare the first draft

Now that you’ve listed the key points that the paper tries to demonstrate, you can start writing the summary following the standard structure of a research summary. Just make sure you’re not writing statements from the parent research paper verbatim.

Instead, try writing down each section in your own words. This will not only help in avoiding plagiarism but will also show your complete understanding of the subject. Alternatively, you can use a summarizing tool (AI-based summary generators) to shorten the content or summarize the content without disrupting the actual meaning of the article.

SciSpace Copilot is one such helpful feature! You can easily upload your research paper and ask Copilot to summarize it. You will get an AI-generated, condensed research summary. SciSpace Copilot also enables you to highlight text, clip math and tables, and ask any question relevant to the research paper; it will give you instant answers with deeper context of the article..

4. Include visuals

One of the best ways to summarize and consolidate a research paper is to provide visuals like graphs, charts, pie diagrams, etc.. Visuals make getting across the facts, the past trends, and the probabilistic figures around a concept much more engaging.

5. Double check for plagiarism

It can be very tempting to copy-paste a few statements or the entire paragraphs depending upon the clarity of those sections. But it’s best to stay away from the practice. Even paraphrasing should be done with utmost care and attention.

Also: QuillBot vs SciSpace: Choose the best AI-paraphrasing tool

6. Religiously follow the word count limit

You need to have strict control while writing different sections of a research summary. In many cases, it has been observed that the research summary and the parent research paper become the same length. If that happens, it can lead to discrediting of your efforts and research summary itself. Whatever the standard word limit has been imposed, you must observe that carefully.

7. Proofread your research summary multiple times

The process of writing the research summary can be exhausting and tiring. However, you shouldn’t allow this to become a reason to skip checking your academic writing several times for mistakes like misspellings, grammar, wordiness, and formatting issues. Proofread and edit until you think your research summary can stand out from the others, provided it is drafted perfectly on both technicality and comprehension parameters. You can also seek assistance from editing and proofreading services , and other free tools that help you keep these annoying grammatical errors at bay.

8. Watch while you write

Keep a keen observation of your writing style. You should use the words very precisely, and in any situation, it should not represent your personal opinions on the topic. You should write the entire research summary in utmost impersonal, precise, factually correct, and evidence-based writing.

9. Ask a friend/colleague to help

Once you are done with the final copy of your research summary, you must ask a friend or colleague to read it. You must test whether your friend or colleague could grasp everything without referring to the parent paper. This will help you in ensuring the clarity of the article.

Once you become familiar with the research paper summary concept and understand how to apply the tips discussed above in your current task, summarizing a research summary won’t be that challenging. While traversing the different stages of your academic career, you will face different scenarios where you may have to create several research summaries.

In such cases, you just need to look for answers to simple questions like “Why this study is necessary,” “what were the methods,” “who were the participants,” “what conclusions were drawn from the research,” and “how it is relevant to the wider world.” Once you find out the answers to these questions, you can easily create a good research summary following the standard structure and a precise writing style.

example of summary of scientific article

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Writing Article Summaries

  • Understanding Article Summaries 

Common Problems in Article Summaries

Read carefully and closely, structure of the summary, writing the summary.

  • Sample Outlines and Paragraphs

Understanding Article Summaries

An article summary is a short, focused paper about one scholarly article that is informed by a critical reading of that article. For argumentative articles, the summary identifies, explains, and analyses the thesis and supporting arguments; for empirical articles, the summary identifies, explains, and analyses the research questions, methods, findings, and implications of the study.

Although article summaries are often short and rarely account for a large portion of your grade, they are a strong indicator of your reading and writing skills. Professors ask you to write article summaries to help you to develop essential skills in critical reading, summarizing, and clear, organized writing. Furthermore, an article summary requires you to read a scholarly article quite closely, which provides a useful introduction to the conventions of writing in your discipline (e.g. Political Studies, Biology, or Anthropology).

The most common problem that students have when writing an article summary is that they misunderstand the goal of the assignment. In an article summary, your job is to write about the article, not about the actual topic of the article. For example, if you are summarizing Smith’s article about the causes of the Bubonic plague in Europe, your summary should be about Smith’s article: What does she want to find out about the plague? What evidence does she use? What is her argument? You are not writing a paper about the actual causes of Bubonic plague in Europe.

Further, as a part of critical reading, you will often consider your own position on a topic or an argument; it is tempting to include an assessment or opinion about the thesis or findings, but this is not the goal of an article summary. Rather, you must identify, explain, and analyse the main point and how it is supported.

Your key to success in writing an article summary is your understanding of the article; therefore, it is essential to read carefully and closely. The Academic Skills Centre offers helpful instruction on the steps for critical reading: pre-reading, active and analytical reading, and reflection.

Argumentative Articles

As you read an argumentative article, consider the following questions:

  • What is the topic?
  • What is the research question? In other words, what is the author trying to find out about that topic?
  • How does the author position his/her article in relation to other studies of the topic?
  • What is the thesis or position? What are the supporting arguments?
  • How are supporting arguments developed? What kind of evidence is used?
  • What is the significance of the author’s thesis? What does it help you to understand about the topic?

Empirical Articles

As you read an empirical article, consider the following questions:

  • What is the research question?
  • What are the predictions and the rationale for these predictions?
  • What methods were used (participants, sampling, materials, procedure)? What were the variables and controls?
  • What were the main results?
  • Are the findings supported by previous research?
  • What are the limitations of the study?
  • What are the implications or applications of the findings?

Create a Reverse Outline

Creating a reverse outline is one way to ensure that you fully understand the article. Pre-read the article (read the abstract, introduction, and/or conclusion). Summarize the main question(s) and thesis or findings. Skim subheadings and topic sentences to understand the organization; make notes in the margins about each section. Read each paragraph within a section; make short notes about the main idea or purpose of each paragraph. This strategy will help you to see how parts of the article connect to the main idea or the whole of the article.

A summary is written in paragraph form and generally does not include subheadings. An introduction is important to clearly identify the article, the topic, the question or purpose of the article, and its thesis or findings. The body paragraphs for a summary of an argumentative article will explain how arguments and evidence support the thesis. Alternatively, the body paragraphs of an empirical article summary may explain the methods and findings, making connections to predictions. The conclusion explains the significance of the argument or implications of the findings. This structure ensures that your summary is focused and clear.

Professors will often give you a list of required topics to include in your summary and/or explain how they want you to organize your summary. Make sure you read the assignment sheet with care and adapt the sample outlines below accordingly.

One significant challenge in writing an article summary is deciding what information or examples from the article to include. Remember, article summaries are much shorter than the article itself. You do not have the space to explain every point the author makes. Instead, you will need to explain the author’s main points and find a few excellent examples that illustrate these points.

You should also keep in mind that article summaries need to be written in your own words. Scholarly writing can use complex terminology to explain complicated ideas, which makes it difficult to understand and to summarize correctly. In the face of difficult text, many students tend to use direct quotations, saving them the time and energy required to understand and reword it. However, a summary requires you to summarize, which means “to state briefly or succinctly” (Oxford English Dictionary) the main ideas presented in a text. The brevity must come from you, in your own words, which demonstrates that you understand the article.

Sample Outlines and Paragraph

Sample outline for an argumentative article summary.

  • General topic of article
  • Author’s research question or approach to the topic
  • Author’s thesis
  • Explain some key points and how they support the thesis
  • Provide a key example or two that the author uses as evidence to support these points
  • Review how the main points work together to support the thesis?
  • How does the author explain the significance or implications of his/her article?

Sample Outline for an Empirical Article Summary

  • General topic of study
  • Author’s research question
  • Variables and hypotheses
  • Participants
  • Experiment design
  • Materials used
  • Key results
  • Did the results support the hypotheses?
  • Implications or applications of the study
  • Major limitations of the study

Sample Paragraph

The paragraph below is an example of an introductory paragraph from a summary of an empirical article:

Tavernier and Willoughby’s (2014) study explored the relationships between university students’ sleep and their intrapersonal, interpersonal, and educational development. While the authors cited many scholars who have explored these relationships, they pointed out that most of these studies focused on unidirectional correlations over a short period of time. In contrast, Tavernier and Willoughby tested whether there was a bidirectional or unidirectional association between participants’ sleep quality and duration and several psychosocial factors including intrapersonal adjustment, friendship quality, and academic achievement. Further they conducted a longitudinal study over a period of three years in order to determine whether there were changes in the strength or direction of these associations over time. They predicted that sleep quality would correlate with measures of intrapersonal adjustment, friendship quality, and academic achievement; they further hypothesized that this correlation would be bidirectional: sleep quality would predict psychosocial measures and at the same time, psychosocial measures would predict sleep quality.

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  • How to Write a Summary | Guide & Examples

How to Write a Summary | Guide & Examples

Published on 25 September 2022 by Shona McCombes . Revised on 12 May 2023.

Summarising , or writing a summary, means giving a concise overview of a text’s main points in your own words. A summary is always much shorter than the original text.

There are five key steps that can help you to write a summary:

  • Read the text
  • Break it down into sections
  • Identify the key points in each section
  • Write the summary
  • Check the summary against the article

Writing a summary does not involve critiquing or analysing the source. You should simply provide an accurate account of the most important information and ideas (without copying any text from the original).

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Table of contents

When to write a summary, step 1: read the text, step 2: break the text down into sections, step 3: identify the key points in each section, step 4: write the summary, step 5: check the summary against the article, frequently asked questions.

There are many situations in which you might have to summarise an article or other source:

  • As a stand-alone assignment to show you’ve understood the material
  • To keep notes that will help you remember what you’ve read
  • To give an overview of other researchers’ work in a literature review

When you’re writing an academic text like an essay , research paper , or dissertation , you’ll integrate sources in a variety of ways. You might use a brief quote to support your point, or paraphrase a few sentences or paragraphs.

But it’s often appropriate to summarize a whole article or chapter if it is especially relevant to your own research, or to provide an overview of a source before you analyse or critique it.

In any case, the goal of summarising is to give your reader a clear understanding of the original source. Follow the five steps outlined below to write a good summary.

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example of summary of scientific article

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You should read the article more than once to make sure you’ve thoroughly understood it. It’s often effective to read in three stages:

  • Scan the article quickly to get a sense of its topic and overall shape.
  • Read the article carefully, highlighting important points and taking notes as you read.
  • Skim the article again to confirm you’ve understood the key points, and reread any particularly important or difficult passages.

There are some tricks you can use to identify the key points as you read:

  • Start by reading the abstract . This already contains the author’s own summary of their work, and it tells you what to expect from the article.
  • Pay attention to headings and subheadings . These should give you a good sense of what each part is about.
  • Read the introduction and the conclusion together and compare them: What did the author set out to do, and what was the outcome?

To make the text more manageable and understand its sub-points, break it down into smaller sections.

If the text is a scientific paper that follows a standard empirical structure, it is probably already organised into clearly marked sections, usually including an introduction, methods, results, and discussion.

Other types of articles may not be explicitly divided into sections. But most articles and essays will be structured around a series of sub-points or themes.

Now it’s time go through each section and pick out its most important points. What does your reader need to know to understand the overall argument or conclusion of the article?

Keep in mind that a summary does not involve paraphrasing every single paragraph of the article. Your goal is to extract the essential points, leaving out anything that can be considered background information or supplementary detail.

In a scientific article, there are some easy questions you can ask to identify the key points in each part.

If the article takes a different form, you might have to think more carefully about what points are most important for the reader to understand its argument.

In that case, pay particular attention to the thesis statement —the central claim that the author wants us to accept, which usually appears in the introduction—and the topic sentences that signal the main idea of each paragraph.

Now that you know the key points that the article aims to communicate, you need to put them in your own words.

To avoid plagiarism and show you’ve understood the article, it’s essential to properly paraphrase the author’s ideas. Do not copy and paste parts of the article, not even just a sentence or two.

The best way to do this is to put the article aside and write out your own understanding of the author’s key points.

Examples of article summaries

Let’s take a look at an example. Below, we summarise this article , which scientifically investigates the old saying ‘an apple a day keeps the doctor away’.

An article summary like the above would be appropriate for a stand-alone summary assignment. However, you’ll often want to give an even more concise summary of an article.

For example, in a literature review or research paper, you may want to briefly summarize this study as part of a wider discussion of various sources. In this case, we can boil our summary down even further to include only the most relevant information.

Citing the source you’re summarizing

When including a summary as part of a larger text, it’s essential to properly cite the source you’re summarizing. The exact format depends on your citation style , but it usually includes an in-text citation and a full reference at the end of your paper.

You can easily create your citations and references in APA or MLA using our free citation generators.

APA Citation Generator MLA Citation Generator

Finally, read through the article once more to ensure that:

  • You’ve accurately represented the author’s work
  • You haven’t missed any essential information
  • The phrasing is not too similar to any sentences in the original.

If you’re summarising many articles as part of your own work, it may be a good idea to use a plagiarism checker to double-check that your text is completely original and properly cited. Just be sure to use one that’s safe and reliable.

A summary is a short overview of the main points of an article or other source, written entirely in your own words.

Save yourself some time with the free summariser.

A summary is always much shorter than the original text. The length of a summary can range from just a few sentences to several paragraphs; it depends on the length of the article you’re summarising, and on the purpose of the summary.

With the summariser tool you can easily adjust the length of your summary.

You might have to write a summary of a source:

  • As a stand-alone assignment to prove you understand the material
  • For your own use, to keep notes on your reading
  • To provide an overview of other researchers’ work in a literature review
  • In a paper , to summarise or introduce a relevant study

To avoid plagiarism when summarising an article or other source, follow these two rules:

  • Write the summary entirely in your own words by   paraphrasing the author’s ideas.
  • Reference the source with an in-text citation and a full reference so your reader can easily find the original text.

An abstract concisely explains all the key points of an academic text such as a thesis , dissertation or journal article. It should summarise the whole text, not just introduce it.

An abstract is a type of summary , but summaries are also written elsewhere in academic writing . For example, you might summarise a source in a paper , in a literature review , or as a standalone assignment.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. (2023, May 12). How to Write a Summary | Guide & Examples. Scribbr. Retrieved 22 February 2024, from https://www.scribbr.co.uk/working-sources/how-to-write-a-summary/

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Diana Ribeiro

How to write a summary of a research paper (with template)

by Diana Ribeiro Last updated Jul 20, 2020 | Published on Jun 27, 2020 Writing Skills 0 comments

In our daily work as medical writers, we have to read many scholarly articles and extract the main information from them. Having a process to retrieve that information and create a short summary that you can easily access will save you precious time. That’s why I decided to guide you through my process of summarising a research article and created a handy template.

Having short summaries of academic papers is useful to create news articles, press releases, social media posts, blog articles, or curated news reports, like the one I write weekly for my newsletter subscribers .

example of summary of scientific article

What’s the importance of summarising research articles?

If you don’t have a system to extract the main information from a scholarly paper, you may have to re-read it repeatedly, looking for that piece of information you know it’s there. Sure, you can use a highlighter pen to mark the main points, but sometimes what happens is that you end up with yellow walls of text. Or green. Or even a rainbow. Which may be pretty, but it’s quite useless as a retrieval system.

What also happens when you highlight text is that you end up with a diverse array of writing styles, none of them being your own. This way, when you try to write a text with information from multiple sources, you have to search for the information and write it in a consistent style.

In this article, I’ll show you how to retrieve the most relevant information from a scientific paper, how to write it in a compelling way, and how to present it in a news-worthy style that’s easily adaptable to your audience. Ready?

example of summary of scientific article

Three steps to summarise a research paper

1. scan and extract the main points.

First things first, so you have to read the paper. But that doesn’t mean you have to read it from start to finish. Start by scanning the article for its main points.

Here’s the essential information to extract from the research paper you have in front of you:

  • Authors, year, doi
  • Study question: look in the introduction for a phrase like “the aim of this study was”
  • Hypothesis tested
  • Study methods: design, participants, materials, procedure, what was manipulated (independent variables), what was measured (dependent variables), how data were analysed.
  • Findings: from the results section; fill this before you look at the discussion section, if possible. Write bullet points.
  • Interpretation: how did the authors interpreted their findings? Use short sentences, in your own words.

After extracting the key information , revisit the article and read it more attentively, to see if you missed something. Add some notes to your summary, but take care to avoid plagiarism. Write notes in your own words. If you can’t do that at this moment, use quotation marks to indicate that your note came straight from the study. You can rewrite it later, when you have a better grasp of the study.

2. Use a journalistic approach for the first draft

Some sources advise you to keep the same structure as the scientific article, but I like to use the journalistic approach of news articles and flush out the more relevant information first, followed by the details. This is more enticing for readers, making them want to continue reading. Yes, I know that your reader may be just you, but I know I have lost myself in some of the things I’ve written, so…keep it interesting, even for a future self 😊.

This is the main information you have to put together:

Title of the article: I like to keep the original article title for the summary, because it’s easier to refer back to the original article if I need to. Sometimes I add a second title, just for me, if the article title is too obscure or long.

  • 1 st paragraph: Answer the 5 W’s in 3-4 sentences.

Who? (the authors)

What? (main finding)

When and where? (journal, date of publication)

Why? (relevance)

This should be a standalone paragraph, meaning that the reader should be able to take out the main information even if they just read this paragraph.

  • Subsequent paragraphs: In 2-3 paragraphs or less, provide context and more information about the research done. If you’re not sure if a detail is important or not, you can include it here and edit it out in the next step.

3. Polish the rough edges

In this stage, you’re going to make a quick edit, checking for completeness and accuracy. Make sure you’ve included all the main points without repeating yourself. Double-check all the numbers. Stay focused on the research questions to avoid tangents. Avoid using jargon and the passive voice whenever possible.

Final summary

Using this approach, you’ll end up with a short summary of your article that you can use to craft other types of writing, such as press releases, news articles, social media blurbs, and many others.

The advantages of summarising research articles are that you can better understand what the article is about, and you’ll have a text written by you, so it’s easier to adapt and you avoid unintentional plagiarism.

That’s it! My guide to write a research paper summary 😊

I’ve created a handout with all the information in this blog post plus a fill-in-the-blanks template that you can use to summarise research articles, you can download it using the form below. You’ll be signed up to my mailing list, and receive a weekly roundup of news in the biomedical industry as a bonus!

If you have any comments or questions, please let me know in the comment box below.

example of summary of scientific article

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And subscribe to the biopharma newsletter 🙂

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About Diana Ribeiro

Diana Ribeiro  is a pharmacist and  freelance medical writer based in Cascais, Portugal.  Before starting her career in medical writing, Diana worked 10+ years in hospital and community pharmacies, where she helped patients and healthcare professionals with drug management and information. Nowadays, she helps pharma, biotech, and meddev companies communicate with their audiences in a clear, accurate, and compelling way. Diana is an active member of the European Medical Writers Association, where she volunteers for the webinar team. You can find more about her on  LinkedIn .

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How to Summarize a Journal Article

Last Updated: February 21, 2024 Approved

Reading Article

Planning draft, writing summary, sample summaries.

This article was co-authored by Richard Perkins . Richard Perkins is a Writing Coach, Academic English Coordinator, and the Founder of PLC Learning Center. With over 24 years of education experience, he gives teachers tools to teach writing to students and works with elementary to university level students to become proficient, confident writers. Richard is a fellow at the National Writing Project. As a teacher leader and consultant at California State University Long Beach's Global Education Project, Mr. Perkins creates and presents teacher workshops that integrate the U.N.'s 17 Sustainable Development Goals in the K-12 curriculum. He holds a BA in Communications and TV from The University of Southern California and an MEd from California State University Dominguez Hills. wikiHow marks an article as reader-approved once it receives enough positive feedback. This article has 24 testimonials from our readers, earning it our reader-approved status. This article has been viewed 1,405,393 times.

Summarizing a journal article is presenting a focused overview of a research study published in a peer-reviewed, scholarly source. A journal article summary provides readers with a short descriptive commentary, giving them some insight into the article's focus. Writing and summarizing a journal article is a common task for college students and research assistants alike. With a little practice, you can learn to read the article effectively with an eye for summary, plan a successful summary, and write it to completion.

Step 1 Read the abstract.

  • The purpose of an abstract is to allow researchers to quickly scan a journal and see if specific research articles are applicable to the work they are doing. If you're collecting research on immune system responses in rodents, you'll be able to know in 100 words not only whether or not the research is in your field, but whether the conclusions back up your own findings, or differ from it.
  • Remember that an abstract and an article summary are two different things, so an article summary that looks just like the abstract is a poor summary. [1] X Research source An abstract is highly condensed and cannot provide the same level of detail regarding the research and its conclusions that a summary can.

Step 2 Understand the context of the research.

  • You still need to go back and actually read the article after coming to the conclusion, but only if the research is still applicable. If you're collecting research, you may not need to digest another source that backs up your own if you're looking for some dissenting opinions.

Step 4 Identify the main argument or position of the article.

  • Look for words like hypothesis, results, typically, generally, or clearly to give you hints about which sentence is the thesis.
  • Underline, highlight, or rewrite the main argument of the research in the margins. Keep yourself focused on this main point, so you'll be able to connect the rest of the article back to that idea and see how it works together.
  • In the humanities, it's sometimes more difficult to get a clear and concise thesis for an article because they are often about complex, abstract ideas (like class in post-modern poetics, or feminist film, for example). If it's unclear, try to articulate it for yourself, as best as you can understand the author's ideas and what they're attempting to prove with their analysis.
  • Try to analyze the author's tone, looking at some of the keywords that really tells you what they are trying to get across to you.

Step 5 Scan the argument.

  • Different areas of focus within a journal article will usually be marked with subsection titles that target a specific step or development during the course of the research study. The titles for these sub-sections are usually bold and in a larger font than the remaining text.
  • Keep in mind that academic journals are often dry reading. Is it absolutely necessary to read through the author's 500 word proof of the formulas used in the glycerine solution fed to the frogs in the research study? Maybe, but probably not. It's usually not essential to read research articles word-for-word, as long as you're picking out the main idea, and why the content is there in the first place.

Step 6 Take notes while you read.

  • These segments will usually include an introduction, methodology, research results, and a conclusion in addition to a listing of references.

Step 1 Write down a brief description of the research.

  • When you're first getting started, it's helpful to turn your filter off and just quickly write out what you remember from the article. These will help you discover the main points necessary to summarize.

Step 2 Decide what aspects of the article are most important.

  • Depending on the research, you may want to describe the theoretical background of the research, or the assumptions of the researchers. In scientific writing, it's important to clearly summarize the hypotheses the researchers outlined before undertaking the research, as well as the procedures used in following through with the project. Summarize briefly any statistical results and include a rudimentary interpretation of the data for your summary.
  • In humanities articles, it's usually good to summarize the fundamental assumptions and the school of thought from which the author comes, as well as the examples and the ideas presented throughout the article.

Step 3 Identify key vocabulary to use in the summary.

  • Any words or terms that the author coins need to be included and discussed in your summary.

Step 4 Aim to keep it brief.

  • As a general rule of thumb, you can probably make one paragraph per main point, ending up with no more than 500-1000 words, for most academic articles. For most journal summaries, you'll be writing several short paragraphs that summarize each separate portion of the journal article.

Step 1 Do not use personal pronouns (I, you, us, we, our, your, my).

  • In scientific articles, usually there is an introduction which establishes the background for the experiment or study, and won't provide you with much to summarize. It will be followed by the development of a research question and testing procedures, though, which are key in dictating the content for the rest of the article.

Step 4 Discuss the methodology used by the authors.

  • The specifics of the testing procedures don't usually need to be included in your summary in their entirety; they should be reduced to a simple idea of how the research question was addressed. The results of the study will usually be processed data, sometimes accompanied by raw, pre-process data. Only the processed data needs to be included in the summary.

Step 5 Describe the results.

  • Make sure your summary covers the research question, the conclusions/results, and how those results were achieved. These are crucial parts of the article and cannot be left out.

Step 6 Connect the main ideas presented in the article.

  • This is sometimes more important in summaries dealing with articles in the humanities. For example, it might be helpful to unpack dense arguments about poet George Herbert's relationship to the divine with more pedestrian summaries: "The author seeks to humanize Herbert by discussing his daily routines, as opposed to his philosophies."

Step 7 Don't draw your own conclusions.

  • This can be difficult for some inexperienced research writers to get the hang of at first, but remember to keep the "I" out of it.

Step 8 Refrain from using direct quotations of text from the journal article.

  • Check verbs after writing. If you're using the same ones over and over, your reader will get bored. In this case, try to go back and really see if you can make really efficient choices.

example of summary of scientific article

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  • ↑ https://owl.english.purdue.edu/owl/owlprint/930/
  • ↑ https://student.unsw.edu.au/writing-critical-review
  • ↑ http://web.pdx.edu/~jduh/courses/faq/JouranlArticleSearch.htm
  • ↑ http://web.cortland.edu/hendrick/journalarticle.pdf

About This Article

Richard Perkins

To summarize a journal article, start by reading the author's abstract, which tells you the main argument of the article. Next, read the article carefully, highlighting portions, identifying key vocabulary, and taking notes as you go. In your summary, define the research question, indicate the methodology used, and focus mostly on the results of the research. Use your notes to help you stay focused on the main argument and always keep your tone objective—avoid using personal pronouns and drawing your own conclusions. For tips on how to read through the journal article thoroughly, such as starting with the conclusion, keep reading! Did this summary help you? Yes No

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  • Getting Started
  • Finding Journal Articles
  • Reading a Scientific Article
  • Summarizing Scientific Articles
  • Using and Evaluating Web Resources
  • Annotated Bibliography
  • Citing Sources / Create Your Bibliography
  • How to Avoid Plagiarizing

How Do You Summarize an Article

A summary has  two aims :

  • to reproduce the overarching ideas in a text, identifying the  general  concepts that run through the entire piece, and
  • to express these overarching ideas using  precise , specific language.

Your Summary Should Include :

1. Introduction

  • Start with an overview of the article which includes the author’s name and the title of the article.
  • Include a sentence that states the main idea of the article.

​​2. Body 

  • Each paragraph should begin with a topic sentence.
  • The number of paragraphs in your summary depends on the length of the original article.
  • Each paragraph focuses on a separate main idea and just the most important details from the article.

​3. Conclusion

  • Summarize the main idea and the underlying meaning of the article.

Adapted from " Guidelines for Writing a Summary " by Christine Bauer-Ramazani, Saint Michael's College.

Tips on Summarizing

Please see the video  Tips on Summarizing  on the  Ohio State Flipped ESL  YouTube channel.  This video investigates the basic elements needed to create an effective one-sentence summary and a summary paragraph.

Additional Resources on How to Summarize

Here are some additional resources on how to summarize an academic article:

  • Summarizing (U of T)
  • How to Summarize a Research Article (UConn)
  • Guidelines for Summarizing an article (Andrews University)
  • Guidelines for using In-text citations in a summary or research paper (Saint Michael's College)
  • << Previous: Reading a Scientific Article
  • Next: Using and Evaluating Web Resources >>
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How to Write Article Summaries, Reviews & Critiques

  • Writing an article SUMMARY
  • Writing an article REVIEW

Writing an article CRITIQUE

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A critique asks you to evaluate an article and the author’s argument. You will need to look critically at what the author is claiming, evaluate the research methods, and look for possible problems with, or applications of, the researcher’s claims.

Introduction

Give an overview of the author’s main points and how the author supports those points. Explain what the author found and describe the process they used to arrive at this conclusion.

Body Paragraphs

Interpret the information from the article:

  • Does the author review previous studies? Is current and relevant research used?
  • What type of research was used – empirical studies, anecdotal material, or personal observations?
  • Was the sample too small to generalize from?
  • Was the participant group lacking in diversity (race, gender, age, education, socioeconomic status, etc.)
  • For instance, volunteers gathered at a health food store might have different attitudes about nutrition than the population at large.
  • How useful does this work seem to you? How does the author suggest the findings could be applied and how do you believe they could be applied?
  • How could the study have been improved in your opinion?
  • Does the author appear to have any biases (related to gender, race, class, or politics)?
  • Is the writing clear and easy to follow? Does the author’s tone add to or detract from the article?
  • How useful are the visuals (such as tables, charts, maps, photographs) included, if any? How do they help to illustrate the argument? Are they confusing or hard to read?
  • What further research might be conducted on this subject?

Try to synthesize the pieces of your critique to emphasize your own main points about the author’s work, relating the researcher’s work to your own knowledge or to topics being discussed in your course.

From the Center for Academic Excellence (opens in a new window), University of Saint Joseph Connecticut

Additional Resources

All links open in a new window.

Writing an Article Critique (from The University of Arizona Global Campus Writing Center)

How to Critique an Article (from Essaypro.com)

How to Write an Article Critique (from EliteEditing.com.au)

How to Write an Article Critique Like a Pro (from Citetotal.com)

  • << Previous: Writing an article REVIEW
  • Next: Citing Sources >>
  • Last Updated: Aug 16, 2023 11:47 AM
  • URL: https://libguides.randolph.edu/summaries

Incorporate STEM journalism in your classroom

  • Exercise type: Discussion
  • Topic: Science & Society
  • Category: Literacy Practices

How to write a summary

  • Download Student Worksheet

Directions for teachers:

Discuss Begin by introducing your students to the concept of summarizing. Merriam-Webster defines a summary as “a short restatement of the main points (as of an argument) for easier remembering, for better understanding or for showing the relation of points.” Class assignments often ask students to summarize, but students also summarize in conversations with friends and family members.

Divide your students into pairs or small groups and ask them to use the following prompts to think about when and how they summarize information.

1. When do you summarize or interact with summaries from others? Be sure to consider examples outside of class assignments.

2. For each of the scenarios you described above, what is the goal of the summary? How does the goal affect the information included in the summary?

3. For each of the scenarios described above, who is the summary for? How does the information included in a summary depend on the audience?

4. How might your goal or audience affect the length of your summary and the language you choose to use?

5. When you’ve encountered complex information in the past (in a story, presentation or conversation), what techniques have helped you turn that info into a summary?

Read and take notes Ask each pair or small group to choose one of the Science News ’ Top 10 articles of the year to read. Make sure students know that they will have to summarize the article after reading. You can ask students to identify a note-taking technique in advance and/or encourage them to identify the following key points as they read. If time is available, consider having students answer the associated comprehension questions  to aid in understanding.

Key points to look for

As you read an article, identify the following:

  • The main point and any details that support the main point
  • A secondary idea and any supporting details
  • The who, what, where when, how and why of the article
  • Important events and the timeline of those events
  • Problems and their resolutions
  • Any caveats or counterpoints to the main or secondary ideas
  • Any questions that come up along the way or remain unanswered at the end

Brainstorm and outline Before students write their summaries individually, ask them to consider the prompts that follow.

1. What is the goal of your summary?

2. Who is your audience?

3. Given your goal and audience, how long should your summary be?

4. What was the main point of the article? That should be the start of your summary.

5. Given the length you’ve chosen, what information can you include and what must you leave out? Refer back to your notes to identify the most important information to include.

Write and review Students should now write their summaries. After writing the summary, students should review the summary they’ve written using the prompts that follow. Then, students can revise the summary based on the answers.

1. Have I been brief?

2. Have I restated the essential information without repeating the exact words and phrases used in the original article — or, have I “used my own words”?

3. Have I missed any key points that I identified under the “Read and take notes” header that should be included?

4. What specific facts have I used from the original article? Have I incorporated those facts correctly?

5. Have I attributed information where necessary?

Share and reflect Now have students read their summaries aloud in their small groups and answer the following prompts.

1. How were the summaries similar? Was there information that every group member thought was essential?

2. How were they different? What did some group members choose to leave out that others included? Why?

3. Could your summary be improved? What would you change about your summary after hearing other summaries?

4. How might you write your summary differently if you had chosen a different audience and/or goal?

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  • Int J Sports Phys Ther
  • v.7(5); 2012 Oct

HOW TO WRITE A SCIENTIFIC ARTICLE

Barbara j. hoogenboom.

1 Grand Valley State University, Grand Rapids, MI, USA

Robert C. Manske

2 University of Wichita, Wichita, KS, USA

Successful production of a written product for submission to a peer‐reviewed scientific journal requires substantial effort. Such an effort can be maximized by following a few simple suggestions when composing/creating the product for submission. By following some suggested guidelines and avoiding common errors, the process can be streamlined and success realized for even beginning/novice authors as they negotiate the publication process. The purpose of this invited commentary is to offer practical suggestions for achieving success when writing and submitting manuscripts to The International Journal of Sports Physical Therapy and other professional journals.

INTRODUCTION

“The whole of science is nothing more than a refinement of everyday thinking” Albert Einstein

Conducting scientific and clinical research is only the beginning of the scholarship of discovery. In order for the results of research to be accessible to other professionals and have a potential effect on the greater scientific community, it must be written and published. Most clinical and scientific discovery is published in peer‐reviewed journals, which are those that utilize a process by which an author's peers, or experts in the content area, evaluate the manuscript. Following this review the manuscript is recommended for publication, revision or rejection. It is the rigor of this review process that makes scientific journals the primary source of new information that impacts clinical decision‐making and practice. 1 , 2

The task of writing a scientific paper and submitting it to a journal for publication is a time‐consuming and often daunting task. 3 , 4 Barriers to effective writing include lack of experience, poor writing habits, writing anxiety, unfamiliarity with the requirements of scholarly writing, lack of confidence in writing ability, fear of failure, and resistance to feedback. 5 However, the very process of writing can be a helpful tool for promoting the process of scientific thinking, 6 , 7 and effective writing skills allow professionals to participate in broader scientific conversations. Furthermore, peer review manuscript publication systems requiring these technical writing skills can be developed and improved with practice. 8 Having an understanding of the process and structure used to produce a peer‐reviewed publication will surely improve the likelihood that a submitted manuscript will result in a successful publication.

Clear communication of the findings of research is essential to the growth and development of science 3 and professional practice. The culmination of the publication process provides not only satisfaction for the researcher and protection of intellectual property, but also the important function of dissemination of research results, new ideas, and alternate thought; which ultimately facilitates scholarly discourse. In short, publication of scientific papers is one way to advance evidence‐based practice in many disciplines, including sports physical therapy. Failure to publish important findings significantly diminishes the potential impact that those findings may have on clinical practice. 9

BASICS OF MANUSCRIPT PREPARATION & GENERAL WRITING TIPS

To begin it might be interesting to learn why reviewers accept manuscripts! Reviewers consider the following five criteria to be the most important in decisions about whether to accept manuscripts for publication: 1) the importance, timeliness, relevance, and prevalence of the problem addressed; 2) the quality of the writing style (i.e., that it is well‐written, clear, straightforward, easy to follow, and logical); 3) the study design applied (i.e., that the design was appropriate, rigorous, and comprehensive); 4) the degree to which the literature review was thoughtful, focused, and up‐to‐date; and 5) the use of a sufficiently large sample. 10 For these statements to be true there are also reasons that reviewers reject manuscripts. The following are the top five reasons for rejecting papers: 1) inappropriate, incomplete, or insufficiently described statistics; 2) over‐interpretation of results; 3) use of inappropriate, suboptimal, or insufficiently described populations or instruments; 4) small or biased samples; and 5) text that is poorly written or difficult to follow. 10 , 11 With these reasons for acceptance or rejection in mind, it is time to review basics and general writing tips to be used when performing manuscript preparation.

“Begin with the end in mind” . When you begin writing about your research, begin with a specific target journal in mind. 12 Every scientific journal should have specific lists of manuscript categories that are preferred for their readership. The IJSPT seeks to provide readership with current information to enhance the practice of sports physical therapy. Therefore the manuscript categories accepted by IJSPT include: Original research; Systematic reviews of literature; Clinical commentary and Current concept reviews; Case reports; Clinical suggestions and unique practice techniques; and Technical notes. Once a decision has been made to write a manuscript, compose an outline that complies with the requirements of the target submission journal and has each of the suggested sections. This means carefully checking the submission criteria and preparing your paper in the exact format of the journal to which you intend to submit. Be thoughtful about the distinction between content (what you are reporting) and structure (where it goes in the manuscript). Poor placement of content confuses the reader (reviewer) and may cause misinterpretation of content. 3 , 5

It may be helpful to follow the IMRaD format for writing scientific manuscripts. This acronym stands for the sections contained within the article: Introduction, Methods, Results, and Discussion. Each of these areas of the manuscript will be addressed in this commentary.

Many accomplished authors write their results first, followed by an introduction and discussion, in an attempt to “stay true” to their results and not stray into additional areas. Typically the last two portions to be written are the conclusion and the abstract.

The ability to accurately describe ideas, protocols/procedures, and outcomes are the pillars of scientific writing . Accurate and clear expression of your thoughts and research information should be the primary goal of scientific writing. 12 Remember that accuracy and clarity are even more important when trying to get complicated ideas across. Contain your literature review, ideas, and discussions to your topic, theme, model, review, commentary, or case. Avoid vague terminology and too much prose. Use short rather than long sentences. If jargon has to be utilized keep it to a minimum and explain the terms you do use clearly. 13

Write with a measure of formality, using scientific language and avoiding conjunctions, slang, and discipline or regionally specific nomenclature or terms (e.g. exercise nicknames). For example, replace the term “Monster walks” with “closed‐chain hip abduction with elastic resistance around the thighs”. You may later refer to the exercise as “also known as Monster walks” if you desire.

Avoid first person language and instead write using third person language. Some journals do not ascribe to this requirement, and allow first person references, however, IJSPT prefers use of third person. For example, replace “We determined that…” with “The authors determined that….”.

For novice writers, it is really helpful to seek a reading mentor that will help you pre‐read your submission. Problems such as improper use of grammar, tense, and spelling are often a cause of rejection by reviewers. Despite the content of the study these easily fixed errors suggest that the authors created the manuscript with less thought leading reviewers to think that the manuscript may also potentially have erroneous findings as well. A review from a second set of trained eyes will often catch these errors missed by the original authors. If English is not your first language, the editorial staff at IJSPT suggests that you consult with someone with the relevant expertise to give you guidance on English writing conventions, verb tense, and grammar. Excellent writing in English is hard, even for those of us for whom it is our first language!

Use figures and graphics to your advantage . ‐ Consider the use of graphic/figure representation of data and important procedures or exercises. Tables should be able to stand alone and be completely understandable at a quick glance. Understanding a table should not require careful review of the manuscript! Figures dramatically enhance the graphic appeal of a scientific paper. Many formats for graphic presentation are acceptable, including graphs, charts, tables, and pictures or videos. Photographs should be clear, free of clutter or extraneous background distractions and be taken with models wearing simple clothing. Color photographs are preferred. Digital figures (Scans or existing files as well as new photographs) must be at least 300dpi. All photographs should be provided as separate files (jpeg or tif preferred) and not be embedded in the paper. Quality and clarity of figures are essential for reproduction purposes and should be considered before taking images for the manuscript.

A video of an exercise or procedure speaks a thousand words. Please consider using short video clips as descriptive additions to your paper. They will be placed on the IJSPT website and accompany your paper. The video clips must be submitted in MPEG‐1, MPEG‐2, Quicktime (.mov), or Audio/Video Interface (.avi) formats. Maximum cumulative length of videos is 5 minutes. Each video segment may not exceed 50 MB, and each video clip must be saved as a separate file and clearly identified. Formulate descriptive figure/video and Table/chart/graph titles and place them on a figure legend document. Carefully consider placement of, naming of, and location of figures. It makes the job of the editors much easier!

Avoid Plagiarism and inadvertent lack of citations. Finally, use citations to your benefit. Cite frequently in order to avoid any plagiarism. The bottom line: If it is not your original idea, give credit where credit is due . When using direct quotations, provide not only the number of the citation, but the page where the quote was found. All citations should appear in text as a superscripted number followed by punctuation. It is the authors' responsibility to fully ensure all references are cited in completed form, in an accurate location. Please carefully follow the instructions for citations and check that all references in your reference list are cited in the paper and that all citations in the paper appear correctly in the reference list. Please go to IJSPT submission guidelines for full information on the format for citations.

Sometimes written as an afterthought, the abstract is of extreme importance as in many instances this section is what is initially previewed by readership to determine if the remainder of the article is worth reading. This is the authors opportunity to draw the reader into the study and entice them to read the rest of the article. The abstract is a summary of the article or study written in 3 rd person allowing the readers to get a quick glance of what the contents of the article include. Writing an abstract is rather challenging as being brief, accurate and concise are requisite. The headings and structure for an abstract are usually provided in the instructions for authors. In some instances, the abstract may change slightly pending content revisions required during the peer review process. Therefore it often works well to complete this portion of the manuscript last. Remember the abstract should be able to stand alone and should be as succinct as possible. 14

Introduction and Review of Literature

The introduction is one of the more difficult portions of the manuscript to write. Past studies are used to set the stage or provide the reader with information regarding the necessity of the represented project. For an introduction to work properly, the reader must feel that the research question is clear, concise, and worthy of study.

A competent introduction should include at least four key concepts: 1) significance of the topic, 2) the information gap in the available literature associated with the topic, 3) a literature review in support of the key questions, 4) subsequently developed purposes/objectives and hypotheses. 9

When constructing a review of the literature, be attentive to “sticking” or “staying true” to your topic at hand. Don't reach or include too broad of a literature review. For example, do not include extraneous information about performance or prevention if your research does not actually address those things. The literature review of a scientific paper is not an exhaustive review of all available knowledge in a given field of study. That type of thorough review should be left to review articles or textbook chapters. Throughout the introduction (and later in the discussion!) remind yourself that a paper, existing evidence, or results of a paper cannot draw conclusions, demonstrate, describe, or make judgments, only PEOPLE (authors) can. “The evidence demonstrates that” should be stated, “Smith and Jones, demonstrated that….”

Conclude your introduction with a solid statement of your purpose(s) and your hypothesis(es), as appropriate. The purpose and objectives should clearly relate to the information gap associated with the given manuscript topic discussed earlier in the introduction section. This may seem repetitive, but it actually is helpful to ensure the reader clearly sees the evolution, importance, and critical aspects of the study at hand See Table 1 for examples of well‐stated purposes.

Examples of well-stated purposes by submission type.

The methods section should clearly describe the specific design of the study and provide clear and concise description of the procedures that were performed. The purpose of sufficient detail in the methods section is so that an appropriately trained person would be able to replicate your experiments. 15 There should be complete transparency when describing the study. To assist in writing and manuscript preparation there are several checklists or guidelines that are available on the IJSPT website. The CONSORT guidelines can be used when developing and reporting a randomized controlled trial. 16 The STARD checklist was developed for designing a diagnostic accuracy study. 17 The PRISMA checklist was developed for use when performing a meta‐analyses or systematic review. 18 A clear methods section should contain the following information: 1) the population and equipment used in the study, 2) how the population and equipment were prepared and what was done during the study, 3) the protocol used, 4) the outcomes and how they were measured, 5) the methods used for data analysis. Initially a brief paragraph should explain the overall procedures and study design. Within this first paragraph there is generally a description of inclusion and exclusion criteria which help the reader understand the population used. Paragraphs that follow should describe in more detail the procedures followed for the study. A clear description of how data was gathered is also helpful. For example were data gathered prospectively or retrospectively? Who if anyone was blinded, and where and when was the actual data collected?

Although it is a good idea for the authors to have justification and a rationale for their procedures, these should be saved for inclusion into the discussion section, not to be discussed in the methods section. However, occasionally studies supporting components of the methods section such as reliability of tests, or validation of outcome measures may be included in the methods section.

The final portion of the methods section will include the statistical methods used to analyze the data. 19 This does not mean that the actual results should be discussed in the methods section, as they have an entire section of their own!

Most scientific journals support the need for all projects involving humans or animals to have up‐to‐date documentation of ethical approval. 20 The methods section should include a clear statement that the researchers have obtained approval from an appropriate institutional review board.

Results, Discussion, and Conclusions

In most journals the results section is separate from the discussion section. It is important that you clearly distinguish your results from your discussion. The results section should describe the results only. The discussion section should put those results into a broader context. Report your results neutrally, as you “found them”. Again, be thoughtful about content and structure. Think carefully about where content is placed in the overall structure of your paper. It is not appropriate to bring up additional results, not discussed in the results section, in the discussion. All results must first be described/presented and then discussed. Thus, the discussion should not simply be a repeat of the results section. Carefully discuss where your information is similar or different from other published evidence and why this might be so. What was different in methods or analysis, what was similar?

As previously stated, stick to your topic at hand, and do not overstretch your discussion! One of the major pitfalls in writing the discussion section is overstating the significance of your findings 4 or making very strong statements. For example, it is better to say: “Findings of the current study support….” or “these findings suggest…” than, “Findings of the current study prove that…” or “this means that….”. Maintain a sense of humbleness, as nothing is without question in the outcomes of any type of research, in any discipline! Use words like “possibly”, “likely” or “suggests” to soften findings. 12

Do not discuss extraneous ideas, concepts, or information not covered by your topic/paper/commentary. Be sure to carefully address all relevant results, not just the statistically significant ones or the ones that support your hypotheses. When you must resort to speculation or opinion, be certain to state that up front using phrases such as “we therefore speculate” or “in the authors' opinion”.

Remember, just as in the introduction and literature review, evidence or results cannot draw conclusions, just as previously stated, only people, scientists, researchers, and authors can!

Finish with a concise, 3‐5 sentence conclusion paragraph. This is not just a restatement of your results, rather is comprised of some final, summative statements that reflect the flow and outcomes of the entire paper. Do not include speculative statements or additional material; however, based upon your findings a statement about potential changes in clinical practice or future research opportunities can be provided here.

CONCLUSIONS

Writing for publication can be a challenging yet satisfying endeavor. The ability to examine, relate, and interlink evidence, as well as to provide a peer‐reviewed, disseminated product of your research labors can be rewarding. A few suggestions have been offered in this commentary that may assist the novice or the developing writer to attempt, polish, and perfect their approach to scholarly writing.

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Biology library

Course: biology library   >   unit 1, the scientific method.

  • Controlled experiments
  • The scientific method and experimental design

Introduction

  • Make an observation.
  • Ask a question.
  • Form a hypothesis , or testable explanation.
  • Make a prediction based on the hypothesis.
  • Test the prediction.
  • Iterate: use the results to make new hypotheses or predictions.

Scientific method example: Failure to toast

1. make an observation..

  • Observation: the toaster won't toast.

2. Ask a question.

  • Question: Why won't my toaster toast?

3. Propose a hypothesis.

  • Hypothesis: Maybe the outlet is broken.

4. Make predictions.

  • Prediction: If I plug the toaster into a different outlet, then it will toast the bread.

5. Test the predictions.

  • Test of prediction: Plug the toaster into a different outlet and try again.
  • If the toaster does toast, then the hypothesis is supported—likely correct.
  • If the toaster doesn't toast, then the hypothesis is not supported—likely wrong.

6. Iterate.

  • Iteration time!
  • If the hypothesis was supported, we might do additional tests to confirm it, or revise it to be more specific. For instance, we might investigate why the outlet is broken.
  • If the hypothesis was not supported, we would come up with a new hypothesis. For instance, the next hypothesis might be that there's a broken wire in the toaster.

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Incredible Answer

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  • Published: 19 February 2024

Genomic data in the All of Us Research Program

The all of us research program genomics investigators.

Nature ( 2024 ) Cite this article

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  • Genetic variation
  • Genome-wide association studies

Comprehensively mapping the genetic basis of human disease across diverse individuals is a long-standing goal for the field of human genetics 1 , 2 , 3 , 4 . The All of Us Research Program is a longitudinal cohort study aiming to enrol a diverse group of at least one million individuals across the USA to accelerate biomedical research and improve human health 5 , 6 . Here we describe the programme’s genomics data release of 245,388 clinical-grade genome sequences. This resource is unique in its diversity as 77% of participants are from communities that are historically under-represented in biomedical research and 46% are individuals from under-represented racial and ethnic minorities. All of Us identified more than 1 billion genetic variants, including more than 275 million previously unreported genetic variants, more than 3.9 million of which had coding consequences. Leveraging linkage between genomic data and the longitudinal electronic health record, we evaluated 3,724 genetic variants associated with 117 diseases and found high replication rates across both participants of European ancestry and participants of African ancestry. Summary-level data are publicly available, and individual-level data can be accessed by researchers through the All of Us Researcher Workbench using a unique data passport model with a median time from initial researcher registration to data access of 29 hours. We anticipate that this diverse dataset will advance the promise of genomic medicine for all.

Comprehensively identifying genetic variation and cataloguing its contribution to health and disease, in conjunction with environmental and lifestyle factors, is a central goal of human health research 1 , 2 . A key limitation in efforts to build this catalogue has been the historic under-representation of large subsets of individuals in biomedical research including individuals from diverse ancestries, individuals with disabilities and individuals from disadvantaged backgrounds 3 , 4 . The All of Us Research Program (All of Us) aims to address this gap by enrolling and collecting comprehensive health data on at least one million individuals who reflect the diversity across the USA 5 , 6 . An essential component of All of Us is the generation of whole-genome sequence (WGS) and genotyping data on one million participants. All of Us is committed to making this dataset broadly useful—not only by democratizing access to this dataset across the scientific community but also to return value to the participants themselves by returning individual DNA results, such as genetic ancestry, hereditary disease risk and pharmacogenetics according to clinical standards, to those who wish to receive these research results.

Here we describe the release of WGS data from 245,388 All of Us participants and demonstrate the impact of this high-quality data in genetic and health studies. We carried out a series of data harmonization and quality control (QC) procedures and conducted analyses characterizing the properties of the dataset including genetic ancestry and relatedness. We validated the data by replicating well-established genotype–phenotype associations including low-density lipoprotein cholesterol (LDL-C) and 117 additional diseases. These data are available through the All of Us Researcher Workbench, a cloud platform that embodies and enables programme priorities, facilitating equitable data and compute access while ensuring responsible conduct of research and protecting participant privacy through a passport data access model.

The All of Us Research Program

To accelerate health research, All of Us is committed to curating and releasing research data early and often 6 . Less than five years after national enrolment began in 2018, this fifth data release includes data from more than 413,000 All of Us participants. Summary data are made available through a public Data Browser, and individual-level participant data are made available to researchers through the Researcher Workbench (Fig. 1a and Data availability).

figure 1

a , The All of Us Research Hub contains a publicly accessible Data Browser for exploration of summary phenotypic and genomic data. The Researcher Workbench is a secure cloud-based environment of participant-level data in a Controlled Tier that is widely accessible to researchers. b , All of Us participants have rich phenotype data from a combination of physical measurements, survey responses, EHRs, wearables and genomic data. Dots indicate the presence of the specific data type for the given number of participants. c , Overall summary of participants under-represented in biomedical research (UBR) with data available in the Controlled Tier. The All of Us logo in a is reproduced with permission of the National Institutes of Health’s All of Us Research Program.

Participant data include a rich combination of phenotypic and genomic data (Fig. 1b ). Participants are asked to complete consent for research use of data, sharing of electronic health records (EHRs), donation of biospecimens (blood or saliva, and urine), in-person provision of physical measurements (height, weight and blood pressure) and surveys initially covering demographics, lifestyle and overall health 7 . Participants are also consented for recontact. EHR data, harmonized using the Observational Medical Outcomes Partnership Common Data Model 8 ( Methods ), are available for more than 287,000 participants (69.42%) from more than 50 health care provider organizations. The EHR dataset is longitudinal, with a quarter of participants having 10 years of EHR data (Extended Data Fig. 1 ). Data include 245,388 WGSs and genome-wide genotyping on 312,925 participants. Sequenced and genotyped individuals in this data release were not prioritized on the basis of any clinical or phenotypic feature. Notably, 99% of participants with WGS data also have survey data and physical measurements, and 84% also have EHR data. In this data release, 77% of individuals with genomic data identify with groups historically under-represented in biomedical research, including 46% who self-identify with a racial or ethnic minority group (Fig. 1c , Supplementary Table 1 and Supplementary Note ).

Scaling the All of Us infrastructure

The genomic dataset generated from All of Us participants is a resource for research and discovery and serves as the basis for return of individual health-related DNA results to participants. Consequently, the US Food and Drug Administration determined that All of Us met the criteria for a significant risk device study. As such, the entire All of Us genomics effort from sample acquisition to sequencing meets clinical laboratory standards 9 .

All of Us participants were recruited through a national network of partners, starting in 2018, as previously described 5 . Participants may enrol through All of Us - funded health care provider organizations or direct volunteer pathways and all biospecimens, including blood and saliva, are sent to the central All of Us Biobank for processing and storage. Genomics data for this release were generated from blood-derived DNA. The programme began return of actionable genomic results in December 2022. As of April 2023, approximately 51,000 individuals were sent notifications asking whether they wanted to view their results, and approximately half have accepted. Return continues on an ongoing basis.

The All of Us Data and Research Center maintains all participant information and biospecimen ID linkage to ensure that participant confidentiality and coded identifiers (participant and aliquot level) are used to track each sample through the All of Us genomics workflow. This workflow facilitates weekly automated aliquot and plating requests to the Biobank, supplies relevant metadata for the sample shipments to the Genome Centers, and contains a feedback loop to inform action on samples that fail QC at any stage. Further, the consent status of each participant is checked before sample shipment to confirm that they are still active. Although all participants with genomic data are consented for the same general research use category, the programme accommodates different preferences for the return of genomic data to participants and only data for those individuals who have consented for return of individual health-related DNA results are distributed to the All of Us Clinical Validation Labs for further evaluation and health-related clinical reporting. All participants in All of Us that choose to get health-related DNA results have the option to schedule a genetic counselling appointment to discuss their results. Individuals with positive findings who choose to obtain results are required to schedule an appointment with a genetic counsellor to receive those findings.

Genome sequencing

To satisfy the requirements for clinical accuracy, precision and consistency across DNA sample extraction and sequencing, the All of Us Genome Centers and Biobank harmonized laboratory protocols, established standard QC methodologies and metrics, and conducted a series of validation experiments using previously characterized clinical samples and commercially available reference standards 9 . Briefly, PCR-free barcoded WGS libraries were constructed with the Illumina Kapa HyperPrep kit. Libraries were pooled and sequenced on the Illumina NovaSeq 6000 instrument. After demultiplexing, initial QC analysis is performed with the Illumina DRAGEN pipeline (Supplementary Table 2 ) leveraging lane, library, flow cell, barcode and sample level metrics as well as assessing contamination, mapping quality and concordance to genotyping array data independently processed from a different aliquot of DNA. The Genome Centers use these metrics to determine whether each sample meets programme specifications and then submits sequencing data to the Data and Research Center for further QC, joint calling and distribution to the research community ( Methods ).

This effort to harmonize sequencing methods, multi-level QC and use of identical data processing protocols mitigated the variability in sequencing location and protocols that often leads to batch effects in large genomic datasets 9 . As a result, the data are not only of clinical-grade quality, but also consistent in coverage (≥30× mean) and uniformity across Genome Centers (Supplementary Figs. 1 – 5 ).

Joint calling and variant discovery

We carried out joint calling across the entire All of Us WGS dataset (Extended Data Fig. 2 ). Joint calling leverages information across samples to prune artefact variants, which increases sensitivity, and enables flagging samples with potential issues that were missed during single-sample QC 10 (Supplementary Table 3 ). Scaling conventional approaches to whole-genome joint calling beyond 50,000 individuals is a notable computational challenge 11 , 12 . To address this, we developed a new cloud variant storage solution, the Genomic Variant Store (GVS), which is based on a schema designed for querying and rendering variants in which the variants are stored in GVS and rendered to an analysable variant file, as opposed to the variant file being the primary storage mechanism (Code availability). We carried out QC on the joint call set on the basis of the approach developed for gnomAD 3.1 (ref.  13 ). This included flagging samples with outlying values in eight metrics (Supplementary Table 4 , Supplementary Fig. 2 and Methods ).

To calculate the sensitivity and precision of the joint call dataset, we included four well-characterized samples. We sequenced the National Institute of Standards and Technology reference materials (DNA samples) from the Genome in a Bottle consortium 13 and carried out variant calling as described above. We used the corresponding published set of variant calls for each sample as the ground truth in our sensitivity and precision calculations 14 . The overall sensitivity for single-nucleotide variants was over 98.7% and precision was more than 99.9%. For short insertions or deletions, the sensitivity was over 97% and precision was more than 99.6% (Supplementary Table 5 and Methods ).

The joint call set included more than 1 billion genetic variants. We annotated the joint call dataset on the basis of functional annotation (for example, gene symbol and protein change) using Illumina Nirvana 15 . We defined coding variants as those inducing an amino acid change on a canonical ENSEMBL transcript and found 272,051,104 non-coding and 3,913,722 coding variants that have not been described previously in dbSNP 16 v153 (Extended Data Table 1 ). A total of 3,912,832 (99.98%) of the coding variants are rare (allelic frequency < 0.01) and the remaining 883 (0.02%) are common (allelic frequency > 0.01). Of the coding variants, 454 (0.01%) are common in one or more of the non-European computed ancestries in All of Us, rare among participants of European ancestry, and have an allelic number greater than 1,000 (Extended Data Table 2 and Extended Data Fig. 3 ). The distributions of pathogenic, or likely pathogenic, ClinVar variant counts per participant, stratified by computed ancestry, filtered to only those variants that are found in individuals with an allele count of <40 are shown in Extended Data Fig. 4 . The potential medical implications of these known and new variants with respect to variant pathogenicity by ancestry are highlighted in a companion paper 17 . In particular, we find that the European ancestry subset has the highest rate of pathogenic variation (2.1%), which was twice the rate of pathogenic variation in individuals of East Asian ancestry 17 .The lower frequency of variants in East Asian individuals may be partially explained by the fact the sample size in that group is small and there may be knowledge bias in the variant databases that is reducing the number of findings in some of the less-studied ancestry groups.

Genetic ancestry and relatedness

Genetic ancestry inference confirmed that 51.1% of the All of Us WGS dataset is derived from individuals of non-European ancestry. Briefly, the ancestry categories are based on the same labels used in gnomAD 18 . We trained a classifier on a 16-dimensional principal component analysis (PCA) space of a diverse reference based on 3,202 samples and 151,159 autosomal single-nucleotide polymorphisms. We projected the All of Us samples into the PCA space of the training data, based on the same single-nucleotide polymorphisms from the WGS data, and generated categorical ancestry predictions from the trained classifier ( Methods ). Continuous genetic ancestry fractions for All of Us samples were inferred using the same PCA data, and participants’ patterns of ancestry and admixture were compared to their self-identified race and ethnicity (Fig. 2 and Methods ). Continuous ancestry inference carried out using genome-wide genotypes yields highly concordant estimates.

figure 2

a , b , Uniform manifold approximation and projection (UMAP) representations of All of Us WGS PCA data with self-described race ( a ) and ethnicity ( b ) labels. c , Proportion of genetic ancestry per individual in six distinct and coherent ancestry groups defined by Human Genome Diversity Project and 1000 Genomes samples.

Kinship estimation confirmed that All of Us WGS data consist largely of unrelated individuals with about 85% (215,107) having no first- or second-degree relatives in the dataset (Supplementary Fig. 6 ). As many genomic analyses leverage unrelated individuals, we identified the smallest set of samples that are required to be removed from the remaining individuals that had first- or second-degree relatives and retained one individual from each kindred. This procedure yielded a maximal independent set of 231,442 individuals (about 94%) with genome sequence data in the current release ( Methods ).

Genetic determinants of LDL-C

As a measure of data quality and utility, we carried out a single-variant genome-wide association study (GWAS) for LDL-C, a trait with well-established genomic architecture ( Methods ). Of the 245,388 WGS participants, 91,749 had one or more LDL-C measurements. The All of Us LDL-C GWAS identified 20 well-established genome-wide significant loci, with minimal genomic inflation (Fig. 3 , Extended Data Table 3 and Supplementary Fig. 7 ). We compared the results to those of a recent multi-ethnic LDL-C GWAS in the National Heart, Lung, and Blood Institute (NHLBI) TOPMed study that included 66,329 ancestrally diverse (56% non-European ancestry) individuals 19 . We found a strong correlation between the effect estimates for NHLBI TOPMed genome-wide significant loci and those of All of Us ( R 2  = 0.98, P  < 1.61 × 10 −45 ; Fig. 3 , inset). Notably, the per-locus effect sizes observed in All of Us are decreased compared to those in TOPMed, which is in part due to differences in the underlying statistical model, differences in the ancestral composition of these datasets and differences in laboratory value ascertainment between EHR-derived data and epidemiology studies. A companion manuscript extended this work to identify common and rare genetic associations for three diseases (atrial fibrillation, coronary artery disease and type 2 diabetes) and two quantitative traits (height and LDL-C) in the All of Us dataset and identified very high concordance with previous efforts across all of these diseases and traits 20 .

figure 3

Manhattan plot demonstrating robust replication of 20 well-established LDL-C genetic loci among 91,749 individuals with 1 or more LDL-C measurements. The red horizontal line denotes the genome wide significance threshold of P = 5 × 10 –8 . Inset, effect estimate ( β ) comparison between NHLBI TOPMed LDL-C GWAS ( x  axis) and All of Us LDL-C GWAS ( y  axis) for the subset of 194 independent variants clumped (window 250 kb, r2 0.5) that reached genome-wide significance in NHLBI TOPMed.

Genotype-by-phenotype associations

As another measure of data quality and utility, we tested replication rates of previously reported phenotype–genotype associations in the five predicted genetic ancestry populations present in the Phenotype/Genotype Reference Map (PGRM): AFR, African ancestry; AMR, Latino/admixed American ancestry; EAS, East Asian ancestry; EUR, European ancestry; SAS, South Asian ancestry. The PGRM contains published associations in the GWAS catalogue in these ancestry populations that map to International Classification of Diseases-based phenotype codes 21 . This replication study specifically looked across 4,947 variants, calculating replication rates for powered associations in each ancestry population. The overall replication rates for associations powered at 80% were: 72.0% (18/25) in AFR, 100% (13/13) in AMR, 46.6% (7/15) in EAS, 74.9% (1,064/1,421) in EUR, and 100% (1/1) in SAS. With the exception of the EAS ancestry results, these powered replication rates are comparable to those of the published PGRM analysis where the replication rates of several single-site EHR-linked biobanks ranges from 76% to 85%. These results demonstrate the utility of the data and also highlight opportunities for further work understanding the specifics of the All of Us population and the potential contribution of gene–environment interactions to genotype–phenotype mapping and motivates the development of methods for multi-site EHR phenotype data extraction, harmonization and genetic association studies.

More broadly, the All of Us resource highlights the opportunities to identify genotype–phenotype associations that differ across diverse populations 22 . For example, the Duffy blood group locus ( ACKR1 ) is more prevalent in individuals of AFR ancestry and individuals of AMR ancestry than in individuals of EUR ancestry. Although the phenome-wide association study of this locus highlights the well-established association of the Duffy blood group with lower white blood cell counts both in individuals of AFR and AMR ancestry 23 , 24 , it also revealed genetic-ancestry-specific phenotype patterns, with minimal phenotypic associations in individuals of EAS ancestry and individuals of EUR ancestry (Fig. 4 and Extended Data Table 4 ). Conversely, rs9273363 in the HLA-DQB1 locus is associated with increased risk of type 1 diabetes 25 , 26 and diabetic complications across ancestries, but only associates with increased risk of coeliac disease in individuals of EUR ancestry (Extended Data Fig. 5 ). Similarly, the TCF7L2 locus 27 strongly associates with increased risk of type 2 diabetes and associated complications across several ancestries (Extended Data Fig. 6 ). Association testing results are available in Supplementary Dataset 1 .

figure 4

Results of genetic-ancestry-stratified phenome-wide association analysis among unrelated individuals highlighting ancestry-specific disease associations across the four most common genetic ancestries of participant. Bonferroni-adjusted phenome-wide significance threshold (<2.88 × 10 −5 ) is plotted as a red horizontal line. AFR ( n  = 34,037, minor allele fraction (MAF) 0.82); AMR ( n  = 28,901, MAF 0.10); EAS ( n  = 32,55, MAF 0.003); EUR ( n  = 101,613, MAF 0.007).

The cloud-based Researcher Workbench

All of Us genomic data are available in a secure, access-controlled cloud-based analysis environment: the All of Us Researcher Workbench. Unlike traditional data access models that require per-project approval, access in the Researcher Workbench is governed by a data passport model based on a researcher’s authenticated identity, institutional affiliation, and completion of self-service training and compliance attestation 28 . After gaining access, a researcher may create a new workspace at any time to conduct a study, provided that they comply with all Data Use Policies and self-declare their research purpose. This information is regularly audited and made accessible publicly on the All of Us Research Projects Directory. This streamlined access model is guided by the principles that: participants are research partners and maintaining their privacy and data security is paramount; their data should be made as accessible as possible for authorized researchers; and we should continually seek to remove unnecessary barriers to accessing and using All of Us data.

For researchers at institutions with an existing institutional data use agreement, access can be gained as soon as they complete the required verification and compliance steps. As of August 2023, 556 institutions have agreements in place, allowing more than 5,000 approved researchers to actively work on more than 4,400 projects. The median time for a researcher from initial registration to completion of these requirements is 28.6 h (10th percentile: 48 min, 90th percentile: 14.9 days), a fraction of the weeks to months it can take to assemble a project-specific application and have it reviewed by an access board with conventional access models.

Given that the size of the project’s phenotypic and genomic dataset is expected to reach 4.75 PB in 2023, the use of a central data store and cloud analysis tools will save funders an estimated US$16.5 million per year when compared to the typical approach of allowing researchers to download genomic data. Storing one copy per institution of this data at 556 registered institutions would cost about US$1.16 billion per year. By contrast, storing a central cloud copy costs about US$1.14 million per year, a 99.9% saving. Importantly, cloud infrastructure also democratizes data access particularly for researchers who do not have high-performance local compute resources.

Here we present the All of Us Research Program’s approach to generating diverse clinical-grade genomic data at an unprecedented scale. We present the data release of about 245,000 genome sequences as part of a scalable framework that will grow to include genetic information and health data for one million or more people living across the USA. Our observations permit several conclusions.

First, the All of Us programme is making a notable contribution to improving the study of human biology through purposeful inclusion of under-represented individuals at scale 29 , 30 . Of the participants with genomic data in All of Us, 45.92% self-identified as a non-European race or ethnicity. This diversity enabled identification of more than 275 million new genetic variants across the dataset not previously captured by other large-scale genome aggregation efforts with diverse participants that have submitted variation to dbSNP v153, such as NHLBI TOPMed 31 freeze 8 (Extended Data Table 1 ). In contrast to gnomAD, All of Us permits individual-level genotype access with detailed phenotype data for all participants. Furthermore, unlike many genomics resources, All of Us is uniformly consented for general research use and enables researchers to go from initial account creation to individual-level data access in as little as a few hours. The All of Us cohort is significantly more diverse than those of other large contemporary research studies generating WGS data 32 , 33 . This enables a more equitable future for precision medicine (for example, through constructing polygenic risk scores that are appropriately calibrated to diverse populations 34 , 35 as the eMERGE programme has done leveraging All of Us data 36 , 37 ). Developing new tools and regulatory frameworks to enable analyses across multiple biobanks in the cloud to harness the unique strengths of each is an active area of investigation addressed in a companion paper to this work 38 .

Second, the All of Us Researcher Workbench embodies the programme’s design philosophy of open science, reproducible research, equitable access and transparency to researchers and to research participants 26 . Importantly, for research studies, no group of data users should have privileged access to All of Us resources based on anything other than data protection criteria. Although the All of Us Researcher Workbench initially targeted onboarding US academic, health care and non-profit organizations, it has recently expanded to international researchers. We anticipate further genomic and phenotypic data releases at regular intervals with data available to all researcher communities. We also anticipate additional derived data and functionality to be made available, such as reference data, structural variants and a service for array imputation using the All of Us genomic data.

Third, All of Us enables studying human biology at an unprecedented scale. The programmatic goal of sequencing one million or more genomes has required harnessing the output of multiple sequencing centres. Previous work has focused on achieving functional equivalence in data processing and joint calling pipelines 39 . To achieve clinical-grade data equivalence, All of Us required protocol equivalence at both sequencing production level and data processing across the sequencing centres. Furthermore, previous work has demonstrated the value of joint calling at scale 10 , 18 . The new GVS framework developed by the All of Us programme enables joint calling at extreme scales (Code availability). Finally, the provision of data access through cloud-native tools enables scalable and secure access and analysis to researchers while simultaneously enabling the trust of research participants and transparency underlying the All of Us data passport access model.

The clinical-grade sequencing carried out by All of Us enables not only research, but also the return of value to participants through clinically relevant genetic results and health-related traits to those who opt-in to receiving this information. In the years ahead, we anticipate that this partnership with All of Us participants will enable researchers to move beyond large-scale genomic discovery to understanding the consequences of implementing genomic medicine at scale.

The All of Us cohort

All of Us aims to engage a longitudinal cohort of one million or more US participants, with a focus on including populations that have historically been under-represented in biomedical research. Details of the All of Us cohort have been described previously 5 . Briefly, the primary objective is to build a robust research resource that can facilitate the exploration of biological, clinical, social and environmental determinants of health and disease. The programme will collect and curate health-related data and biospecimens, and these data and biospecimens will be made broadly available for research uses. Health data are obtained through the electronic medical record and through participant surveys. Survey templates can be found on our public website: https://www.researchallofus.org/data-tools/survey-explorer/ . Adults 18 years and older who have the capacity to consent and reside in the USA or a US territory at present are eligible. Informed consent for all participants is conducted in person or through an eConsent platform that includes primary consent, HIPAA Authorization for Research use of EHRs and other external health data, and Consent for Return of Genomic Results. The protocol was reviewed by the Institutional Review Board (IRB) of the All of Us Research Program. The All of Us IRB follows the regulations and guidance of the NIH Office for Human Research Protections for all studies, ensuring that the rights and welfare of research participants are overseen and protected uniformly.

Data accessibility through a ‘data passport’

Authorization for access to participant-level data in All of Us is based on a ‘data passport’ model, through which authorized researchers do not need IRB review for each research project. The data passport is required for gaining data access to the Researcher Workbench and for creating workspaces to carry out research projects using All of Us data. At present, data passports are authorized through a six-step process that includes affiliation with an institution that has signed a Data Use and Registration Agreement, account creation, identity verification, completion of ethics training, and attestation to a data user code of conduct. Results reported follow the All of Us Data and Statistics Dissemination Policy disallowing disclosure of group counts under 20 to protect participant privacy without seeking prior approval 40 .

At present, All of Us gathers EHR data from about 50 health care organizations that are funded to recruit and enrol participants as well as transfer EHR data for those participants who have consented to provide them. Data stewards at each provider organization harmonize their local data to the Observational Medical Outcomes Partnership (OMOP) Common Data Model, and then submit it to the All of Us Data and Research Center (DRC) so that it can be linked with other participant data and further curated for research use. OMOP is a common data model standardizing health information from disparate EHRs to common vocabularies and organized into tables according to data domains. EHR data are updated from the recruitment sites and sent to the DRC quarterly. Updated data releases to the research community occur approximately once a year. Supplementary Table 6 outlines the OMOP concepts collected by the DRC quarterly from the recruitment sites.

Biospecimen collection and processing

Participants who consented to participate in All of Us donated fresh whole blood (4 ml EDTA and 10 ml EDTA) as a primary source of DNA. The All of Us Biobank managed by the Mayo Clinic extracted DNA from 4 ml EDTA whole blood, and DNA was stored at −80 °C at an average concentration of 150 ng µl −1 . The buffy coat isolated from 10 ml EDTA whole blood has been used for extracting DNA in the case of initial extraction failure or absence of 4 ml EDTA whole blood. The Biobank plated 2.4 µg DNA with a concentration of 60 ng µl −1 in duplicate for array and WGS samples. The samples are distributed to All of Us Genome Centers weekly, and a negative (empty well) control and National Institute of Standards and Technology controls are incorporated every two months for QC purposes.

Genome Center sample receipt, accession and QC

On receipt of DNA sample shipments, the All of Us Genome Centers carry out an inspection of the packaging and sample containers to ensure that sample integrity has not been compromised during transport and to verify that the sample containers correspond to the shipping manifest. QC of the submitted samples also includes DNA quantification, using routine procedures to confirm volume and concentration (Supplementary Table 7 ). Any issues or discrepancies are recorded, and affected samples are put on hold until resolved. Samples that meet quality thresholds are accessioned in the Laboratory Information Management System, and sample aliquots are prepared for library construction processing (for example, normalized with respect to concentration and volume).

WGS library construction, sequencing and primary data QC

The DNA sample is first sheared using a Covaris sonicator and is then size-selected using AMPure XP beads to restrict the range of library insert sizes. Using the PCR Free Kapa HyperPrep library construction kit, enzymatic steps are completed to repair the jagged ends of DNA fragments, add proper A-base segments, and ligate indexed adapter barcode sequences onto samples. Excess adaptors are removed using AMPure XP beads for a final clean-up. Libraries are quantified using quantitative PCR with the Illumina Kapa DNA Quantification Kit and then normalized and pooled for sequencing (Supplementary Table 7 ).

Pooled libraries are loaded on the Illumina NovaSeq 6000 instrument. The data from the initial sequencing run are used to QC individual libraries and to remove non-conforming samples from the pipeline. The data are also used to calibrate the pooling volume of each individual library and re-pool the libraries for additional NovaSeq sequencing to reach an average coverage of 30×.

After demultiplexing, WGS analysis occurs on the Illumina DRAGEN platform. The DRAGEN pipeline consists of highly optimized algorithms for mapping, aligning, sorting, duplicate marking and haplotype variant calling and makes use of platform features such as compression and BCL conversion. Alignment uses the GRCh38dh reference genome. QC data are collected at every stage of the analysis protocol, providing high-resolution metrics required to ensure data consistency for large-scale multiplexing. The DRAGEN pipeline produces a large number of metrics that cover lane, library, flow cell, barcode and sample-level metrics for all runs as well as assessing contamination and mapping quality. The All of Us Genome Centers use these metrics to determine pass or fail for each sample before submitting the CRAM files to the All of Us DRC. For mapping and variant calling, all Genome Centers have harmonized on a set of DRAGEN parameters, which ensures consistency in processing (Supplementary Table 2 ).

Every step through the WGS procedure is rigorously controlled by predefined QC measures. Various control mechanisms and acceptance criteria were established during WGS assay validation. Specific metrics for reviewing and releasing genome data are: mean coverage (threshold of ≥30×), genome coverage (threshold of ≥90% at 20×), coverage of hereditary disease risk genes (threshold of ≥95% at 20×), aligned Q30 bases (threshold of ≥8 × 10 10 ), contamination (threshold of ≤1%) and concordance to independently processed array data.

Array genotyping

Samples are processed for genotyping at three All of Us Genome Centers (Broad, Johns Hopkins University and University of Washington). DNA samples are received from the Biobank and the process is facilitated by the All of Us genomics workflow described above. All three centres used an identical array product, scanners, resource files and genotype calling software for array processing to reduce batch effects. Each centre has its own Laboratory Information Management System that manages workflow control, sample and reagent tracking, and centre-specific liquid handling robotics.

Samples are processed using the Illumina Global Diversity Array (GDA) with Illumina Infinium LCG chemistry using the automated protocol and scanned on Illumina iSCANs with Automated Array Loaders. Illumina IAAP software converts raw data (IDAT files; 2 per sample) into a single GTC file per sample using the BPM file (defines strand, probe sequences and illumicode address) and the EGT file (defines the relationship between intensities and genotype calls). Files used for this data release are: GDA-8v1-0_A5.bpm, GDA-8v1-0_A1_ClusterFile.egt, gentrain v3, reference hg19 and gencall cutoff 0.15. The GDA array assays a total of 1,914,935 variant positions including 1,790,654 single-nucleotide variants, 44,172 indels, 9,935 intensity-only probes for CNV calling, and 70,174 duplicates (same position, different probes). Picard GtcToVcf is used to convert the GTC files to VCF format. Resulting VCF and IDAT files are submitted to the DRC for ingestion and further processing. The VCF file contains assay name, chromosome, position, genotype calls, quality score, raw and normalized intensities, B allele frequency and log R ratio values. Each genome centre is running the GDA array under Clinical Laboratory Improvement Amendments-compliant protocols. The GTC files are parsed and metrics are uploaded to in-house Laboratory Information Management System systems for QC review.

At batch level (each set of 96-well plates run together in the laboratory at one time), each genome centre includes positive control samples that are required to have >98% call rate and >99% concordance to existing data to approve release of the batch of data. At the sample level, the call rate and sex are the key QC determinants 41 . Contamination is also measured using BAFRegress 42 and reported out as metadata. Any sample with a call rate below 98% is repeated one time in the laboratory. Genotyped sex is determined by plotting normalized x versus normalized y intensity values for a batch of samples. Any sample discordant with ‘sex at birth’ reported by the All of Us participant is flagged for further detailed review and repeated one time in the laboratory. If several sex-discordant samples are clustered on an array or on a 96-well plate, the entire array or plate will have data production repeated. Samples identified with sex chromosome aneuploidies are also reported back as metadata (XXX, XXY, XYY and so on). A final processing status of ‘pass’, ‘fail’ or ‘abandon’ is determined before release of data to the All of Us DRC. An array sample will pass if the call rate is >98% and the genotyped sex and sex at birth are concordant (or the sex at birth is not applicable). An array sample will fail if the genotyped sex and the sex at birth are discordant. An array sample will have the status of abandon if the call rate is <98% after at least two attempts at the genome centre.

Data from the arrays are used for participant return of genetic ancestry and non-health-related traits for those who consent, and they are also used to facilitate additional QC of the matched WGS data. Contamination is assessed in the array data to determine whether DNA re-extraction is required before WGS. Re-extraction is prompted by level of contamination combined with consent status for return of results. The arrays are also used to confirm sample identity between the WGS data and the matched array data by assessing concordance at 100 unique sites. To establish concordance, a fingerprint file of these 100 sites is provided to the Genome Centers to assess concordance with the same sites in the WGS data before CRAM submission.

Genomic data curation

As seen in Extended Data Fig. 2 , we generate a joint call set for all WGS samples and make these data available in their entirety and by sample subsets to researchers. A breakdown of the frequencies, stratified by computed ancestries for which we had more than 10,000 participants can be found in Extended Data Fig. 3 . The joint call set process allows us to leverage information across samples to improve QC and increase accuracy.

Single-sample QC

If a sample fails single-sample QC, it is excluded from the release and is not reported in this document. These tests detect sample swaps, cross-individual contamination and sample preparation errors. In some cases, we carry out these tests twice (at both the Genome Center and the DRC), for two reasons: to confirm internal consistency between sites; and to mark samples as passing (or failing) QC on the basis of the research pipeline criteria. The single-sample QC process accepts a higher contamination rate than the clinical pipeline (0.03 for the research pipeline versus 0.01 for the clinical pipeline), but otherwise uses identical thresholds. The list of specific QC processes, passing criteria, error modes addressed and an overview of the results can be found in Supplementary Table 3 .

Joint call set QC

During joint calling, we carry out additional QC steps using information that is available across samples including hard thresholds, population outliers, allele-specific filters, and sensitivity and precision evaluation. Supplementary Table 4 summarizes both the steps that we took and the results obtained for the WGS data. More detailed information about the methods and specific parameters can be found in the All of Us Genomic Research Data Quality Report 36 .

Batch effect analysis

We analysed cross-sequencing centre batch effects in the joint call set. To quantify the batch effect, we calculated Cohen’s d (ref.  43 ) for four metrics (insertion/deletion ratio, single-nucleotide polymorphism count, indel count and single-nucleotide polymorphism transition/transversion ratio) across the three genome sequencing centres (Baylor College of Medicine, Broad Institute and University of Washington), stratified by computed ancestry and seven regions of the genome (whole genome, high-confidence calling, repetitive, GC content of >0.85, GC content of <0.15, low mappability, the ACMG59 genes and regions of large duplications (>1 kb)). Using random batches as a control set, all comparisons had a Cohen’s d of <0.35. Here we report any Cohen’s d results >0.5, which we chose before this analysis and is conventionally the threshold of a medium effect size 44 .

We found that there was an effect size in indel counts (Cohen’s d of 0.53) in the entire genome, between Broad Institute and University of Washington, but this was being driven by repetitive and low-mappability regions. We found no batch effects with Cohen’s d of >0.5 in the ratio metrics or in any metrics in the high-confidence calling, low or high GC content, or ACMG59 regions. A complete list of the batch effects with Cohen’s d of >0.5 are found in Supplementary Table 8 .

Sensitivity and precision evaluation

To determine sensitivity and precision, we included four well-characterized control samples (four National Institute of Standards and Technology Genome in a Bottle samples (HG-001, HG-003, HG-004 and HG-005). The samples were sequenced with the same protocol as All of Us. Of note, these samples were not included in data released to researchers. We used the corresponding published set of variant calls for each sample as the ground truth in our sensitivity and precision calculations. We use the high-confidence calling region, defined by Genome in a Bottle v4.2.1, as the source of ground truth. To be called a true positive, a variant must match the chromosome, position, reference allele, alternate allele and zygosity. In cases of sites with multiple alternative alleles, each alternative allele is considered separately. Sensitivity and precision results are reported in Supplementary Table 5 .

Genetic ancestry inference

We computed categorical ancestry for all WGS samples in All of Us and made these available to researchers. These predictions are also the basis for population allele frequency calculations in the Genomic Variants section of the public Data Browser. We used the high-quality set of sites to determine an ancestry label for each sample. The ancestry categories are based on the same labels used in gnomAD 18 , the Human Genome Diversity Project (HGDP) 45 and 1000 Genomes 1 : African (AFR); Latino/admixed American (AMR); East Asian (EAS); Middle Eastern (MID); European (EUR), composed of Finnish (FIN) and Non-Finnish European (NFE); Other (OTH), not belonging to one of the other ancestries or is an admixture; South Asian (SAS).

We trained a random forest classifier 46 on a training set of the HGDP and 1000 Genomes samples variants on the autosome, obtained from gnomAD 11 . We generated the first 16 principal components (PCs) of the training sample genotypes (using the hwe_normalized_pca in Hail) at the high-quality variant sites for use as the feature vector for each training sample. We used the truth labels from the sample metadata, which can be found alongside the VCFs. Note that we do not train the classifier on the samples labelled as Other. We use the label probabilities (‘confidence’) of the classifier on the other ancestries to determine ancestry of Other.

To determine the ancestry of All of Us samples, we project the All of Us samples into the PCA space of the training data and apply the classifier. As a proxy for the accuracy of our All of Us predictions, we look at the concordance between the survey results and the predicted ancestry. The concordance between self-reported ethnicity and the ancestry predictions was 87.7%.

PC data from All of Us samples and the HGDP and 1000 Genomes samples were used to compute individual participant genetic ancestry fractions for All of Us samples using the Rye program. Rye uses PC data to carry out rapid and accurate genetic ancestry inference on biobank-scale datasets 47 . HGDP and 1000 Genomes reference samples were used to define a set of six distinct and coherent ancestry groups—African, East Asian, European, Middle Eastern, Latino/admixed American and South Asian—corresponding to participant self-identified race and ethnicity groups. Rye was run on the first 16 PCs, using the defined reference ancestry groups to assign ancestry group fractions to individual All of Us participant samples.

Relatedness

We calculated the kinship score using the Hail pc_relate function and reported any pairs with a kinship score above 0.1. The kinship score is half of the fraction of the genetic material shared (ranges from 0.0 to 0.5). We determined the maximal independent set 41 for related samples. We identified a maximally unrelated set of 231,442 samples (94%) for kinship scored greater than 0.1.

LDL-C common variant GWAS

The phenotypic data were extracted from the Curated Data Repository (CDR, Control Tier Dataset v7) in the All of Us Researcher Workbench. The All of Us Cohort Builder and Dataset Builder were used to extract all LDL cholesterol measurements from the Lab and Measurements criteria in EHR data for all participants who have WGS data. The most recent measurements were selected as the phenotype and adjusted for statin use 19 , age and sex. A rank-based inverse normal transformation was applied for this continuous trait to increase power and deflate type I error. Analysis was carried out on the Hail MatrixTable representation of the All of Us WGS joint-called data including removing monomorphic variants, variants with a call rate of <95% and variants with extreme Hardy–Weinberg equilibrium values ( P  < 10 −15 ). A linear regression was carried out with REGENIE 48 on variants with a minor allele frequency >5%, further adjusting for relatedness to the first five ancestry PCs. The final analysis included 34,924 participants and 8,589,520 variants.

Genotype-by-phenotype replication

We tested replication rates of known phenotype–genotype associations in three of the four largest populations: EUR, AFR and EAS. The AMR population was not included because they have no registered GWAS. This method is a conceptual extension of the original GWAS × phenome-wide association study, which replicated 66% of powered associations in a single EHR-linked biobank 49 . The PGRM is an expansion of this work by Bastarache et al., based on associations in the GWAS catalogue 50 in June 2020 (ref.  51 ). After directly matching the Experimental Factor Ontology terms to phecodes, the authors identified 8,085 unique loci and 170 unique phecodes that compose the PGRM. They showed replication rates in several EHR-linked biobanks ranging from 76% to 85%. For this analysis, we used the EUR-, and AFR-based maps, considering only catalogue associations that were P  < 5 × 10 −8 significant.

The main tools used were the Python package Hail for data extraction, plink for genomic associations, and the R packages PheWAS and pgrm for further analysis and visualization. The phenotypes, participant-reported sex at birth, and year of birth were extracted from the All of Us CDR (Controlled Tier Dataset v7). These phenotypes were then loaded into a plink-compatible format using the PheWAS package, and related samples were removed by sub-setting to the maximally unrelated dataset ( n  = 231,442). Only samples with EHR data were kept, filtered by selected loci, annotated with demographic and phenotypic information extracted from the CDR and ancestry prediction information provided by All of Us, ultimately resulting in 181,345 participants for downstream analysis. The variants in the PGRM were filtered by a minimum population-specific allele frequency of >1% or population-specific allele count of >100, leaving 4,986 variants. Results for which there were at least 20 cases in the ancestry group were included. Then, a series of Firth logistic regression tests with phecodes as the outcome and variants as the predictor were carried out, adjusting for age, sex (for non-sex-specific phenotypes) and the first three genomic PC features as covariates. The PGRM was annotated with power calculations based on the case counts and reported allele frequencies. Power of 80% or greater was considered powered for this analysis.

Reporting summary

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

Data availability

The All of Us Research Hub has a tiered data access data passport model with three data access tiers. The Public Tier dataset contains only aggregate data with identifiers removed. These data are available to the public through Data Snapshots ( https://www.researchallofus.org/data-tools/data-snapshots/ ) and the public Data Browser ( https://databrowser.researchallofus.org/ ). The Registered Tier curated dataset contains individual-level data, available only to approved researchers on the Researcher Workbench. At present, the Registered Tier includes data from EHRs, wearables and surveys, as well as physical measurements taken at the time of participant enrolment. The Controlled Tier dataset contains all data in the Registered Tier and additionally genomic data in the form of WGS and genotyping arrays, previously suppressed demographic data fields from EHRs and surveys, and unshifted dates of events. At present, Registered Tier and Controlled Tier data are available to researchers at academic institutions, non-profit institutions, and both non-profit and for-profit health care institutions. Work is underway to begin extending access to additional audiences, including industry-affiliated researchers. Researchers have the option to register for Registered Tier and/or Controlled Tier access by completing the All of Us Researcher Workbench access process, which includes identity verification and All of Us-specific training in research involving human participants ( https://www.researchallofus.org/register/ ). Researchers may create a new workspace at any time to conduct any research study, provided that they comply with all Data Use Policies and self-declare their research purpose. This information is made accessible publicly on the All of Us Research Projects Directory at https://allofus.nih.gov/protecting-data-and-privacy/research-projects-all-us-data .

Code availability

The GVS code is available at https://github.com/broadinstitute/gatk/tree/ah_var_store/scripts/variantstore . The LDL GWAS pipeline is available as a demonstration project in the Featured Workspace Library on the Researcher Workbench ( https://workbench.researchallofus.org/workspaces/aou-rw-5981f9dc/aouldlgwasregeniedsubctv6duplicate/notebooks ).

The 1000 Genomes Project Consortium et al. A global reference for human genetic variation. Nature 526 , 68–74 (2015).

Article   Google Scholar  

Claussnitzer, M. et al. A brief history of human disease genetics. Nature 577 , 179–189 (2020).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Wojcik, G. L. et al. Genetic analyses of diverse populations improves discovery for complex traits. Nature 570 , 514–518 (2019).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Lewis, A. C. F. et al. Getting genetic ancestry right for science and society. Science 376 , 250–252 (2022).

All of Us Program Investigators. The “All of Us” Research Program. N. Engl. J. Med. 381 , 668–676 (2019).

Ramirez, A. H., Gebo, K. A. & Harris, P. A. Progress with the All of Us Research Program: opening access for researchers. JAMA 325 , 2441–2442 (2021).

Article   PubMed   Google Scholar  

Ramirez, A. H. et al. The All of Us Research Program: data quality, utility, and diversity. Patterns 3 , 100570 (2022).

Article   PubMed   PubMed Central   Google Scholar  

Overhage, J. M., Ryan, P. B., Reich, C. G., Hartzema, A. G. & Stang, P. E. Validation of a common data model for active safety surveillance research. J. Am. Med. Inform. Assoc. 19 , 54–60 (2012).

Venner, E. et al. Whole-genome sequencing as an investigational device for return of hereditary disease risk and pharmacogenomic results as part of the All of Us Research Program. Genome Med. 14 , 34 (2022).

Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536 , 285–291 (2016).

Tiao, G. & Goodrich, J. gnomAD v3.1 New Content, Methods, Annotations, and Data Availability ; https://gnomad.broadinstitute.org/news/2020-10-gnomad-v3-1-new-content-methods-annotations-and-data-availability/ .

Chen, S. et al. A genomic mutational constraint map using variation in 76,156 human genomes. Nature 625 , 92–100 (2022).

Zook, J. M. et al. An open resource for accurately benchmarking small variant and reference calls. Nat. Biotechnol. 37 , 561–566 (2019).

Krusche, P. et al. Best practices for benchmarking germline small-variant calls in human genomes. Nat. Biotechnol. 37 , 555–560 (2019).

Stromberg, M. et al. Nirvana: clinical grade variant annotator. In Proc. 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics 596 (Association for Computing Machinery, 2017).

Sherry, S. T. et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 29 , 308–311 (2001).

Venner, E. et al. The frequency of pathogenic variation in the All of Us cohort reveals ancestry-driven disparities. Commun. Biol. https://doi.org/10.1038/s42003-023-05708-y (2024).

Karczewski, S. et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581 , 434–443 (2020).

Selvaraj, M. S. et al. Whole genome sequence analysis of blood lipid levels in >66,000 individuals. Nat. Commun. 13 , 5995 (2022).

Wang, X. et al. Common and rare variants associated with cardiometabolic traits across 98,622 whole-genome sequences in the All of Us research program. J. Hum. Genet. 68 , 565–570 (2023).

Bastarache, L. et al. The phenotype-genotype reference map: improving biobank data science through replication. Am. J. Hum. Genet. 110 , 1522–1533 (2023).

Bianchi, D. W. et al. The All of Us Research Program is an opportunity to enhance the diversity of US biomedical research. Nat. Med. https://doi.org/10.1038/s41591-023-02744-3 (2024).

Van Driest, S. L. et al. Association between a common, benign genotype and unnecessary bone marrow biopsies among African American patients. JAMA Intern. Med. 181 , 1100–1105 (2021).

Chen, M.-H. et al. Trans-ethnic and ancestry-specific blood-cell genetics in 746,667 individuals from 5 global populations. Cell 182 , 1198–1213 (2020).

Chiou, J. et al. Interpreting type 1 diabetes risk with genetics and single-cell epigenomics. Nature 594 , 398–402 (2021).

Hu, X. et al. Additive and interaction effects at three amino acid positions in HLA-DQ and HLA-DR molecules drive type 1 diabetes risk. Nat. Genet. 47 , 898–905 (2015).

Grant, S. F. A. et al. Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nat. Genet. 38 , 320–323 (2006).

Article   CAS   PubMed   Google Scholar  

All of Us Research Program. Framework for Access to All of Us Data Resources v1.1 (2021); https://www.researchallofus.org/wp-content/themes/research-hub-wordpress-theme/media/data&tools/data-access-use/AoU_Data_Access_Framework_508.pdf .

Abul-Husn, N. S. & Kenny, E. E. Personalized medicine and the power of electronic health records. Cell 177 , 58–69 (2019).

Mapes, B. M. et al. Diversity and inclusion for the All of Us research program: A scoping review. PLoS ONE 15 , e0234962 (2020).

Taliun, D. et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 590 , 290–299 (2021).

Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562 , 203–209 (2018).

Halldorsson, B. V. et al. The sequences of 150,119 genomes in the UK Biobank. Nature 607 , 732–740 (2022).

Kurniansyah, N. et al. Evaluating the use of blood pressure polygenic risk scores across race/ethnic background groups. Nat. Commun. 14 , 3202 (2023).

Hou, K. et al. Causal effects on complex traits are similar for common variants across segments of different continental ancestries within admixed individuals. Nat. Genet. 55 , 549– 558 (2022).

Linder, J. E. et al. Returning integrated genomic risk and clinical recommendations: the eMERGE study. Genet. Med. 25 , 100006 (2023).

Lennon, N. J. et al. Selection, optimization and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse US populations. Nat. Med. https://doi.org/10.1038/s41591-024-02796-z (2024).

Deflaux, N. et al. Demonstrating paths for unlocking the value of cloud genomics through cross cohort analysis. Nat. Commun. 14 , 5419 (2023).

Regier, A. A. et al. Functional equivalence of genome sequencing analysis pipelines enables harmonized variant calling across human genetics projects. Nat. Commun. 9 , 4038 (2018).

Article   ADS   PubMed   PubMed Central   Google Scholar  

All of Us Research Program. Data and Statistics Dissemination Policy (2020); https://www.researchallofus.org/wp-content/themes/research-hub-wordpress-theme/media/2020/05/AoU_Policy_Data_and_Statistics_Dissemination_508.pdf .

Laurie, C. C. et al. Quality control and quality assurance in genotypic data for genome-wide association studies. Genet. Epidemiol. 34 , 591–602 (2010).

Jun, G. et al. Detecting and estimating contamination of human DNA samples in sequencing and array-based genotype data. Am. J. Hum. Genet. 91 , 839–848 (2012).

Cohen, J. Statistical Power Analysis for the Behavioral Sciences (Routledge, 2013).

Andrade, C. Mean difference, standardized mean difference (SMD), and their use in meta-analysis. J. Clin. Psychiatry 81 , 20f13681 (2020).

Cavalli-Sforza, L. L. The Human Genome Diversity Project: past, present and future. Nat. Rev. Genet. 6 , 333–340 (2005).

Ho, T. K. Random decision forests. In Proc. 3rd International Conference on Document Analysis and Recognition (IEEE Computer Society Press, 2002).

Conley, A. B. et al. Rye: genetic ancestry inference at biobank scale. Nucleic Acids Res. 51 , e44 (2023).

Mbatchou, J. et al. Computationally efficient whole-genome regression for quantitative and binary traits. Nat. Genet. 53 , 1097–1103 (2021).

Denny, J. C. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat. Biotech. 31 , 1102–1111 (2013).

Buniello, A. et al. The NHGRI-EBI GWAS catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 47 , D1005–D1012 (2019).

Bastarache, L. et al. The Phenotype-Genotype Reference Map: improving biobank data science through replication. Am. J. Hum. Genet. 10 , 1522–1533 (2023).

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Acknowledgements

The All of Us Research Program is supported by the National Institutes of Health, Office of the Director: Regional Medical Centers (OT2 OD026549; OT2 OD026554; OT2 OD026557; OT2 OD026556; OT2 OD026550; OT2 OD 026552; OT2 OD026553; OT2 OD026548; OT2 OD026551; OT2 OD026555); Inter agency agreement AOD 16037; Federally Qualified Health Centers HHSN 263201600085U; Data and Research Center: U2C OD023196; Genome Centers (OT2 OD002748; OT2 OD002750; OT2 OD002751); Biobank: U24 OD023121; The Participant Center: U24 OD023176; Participant Technology Systems Center: U24 OD023163; Communications and Engagement: OT2 OD023205; OT2 OD023206; and Community Partners (OT2 OD025277; OT2 OD025315; OT2 OD025337; OT2 OD025276). In addition, the All of Us Research Program would not be possible without the partnership of its participants. All of Us and the All of Us logo are service marks of the US Department of Health and Human Services. E.E.E. is an investigator of the Howard Hughes Medical Institute. We acknowledge the foundational contributions of our friend and colleague, the late Deborah A. Nickerson. Debbie’s years of insightful contributions throughout the formation of the All of Us genomics programme are permanently imprinted, and she shares credit for all of the successes of this programme.

Author information

Authors and affiliations.

Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

Alexander G. Bick & Henry R. Condon

Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA

Ginger A. Metcalf, Eric Boerwinkle, Richard A. Gibbs, Donna M. Muzny, Eric Venner, Kimberly Walker, Jianhong Hu, Harsha Doddapaneni, Christie L. Kovar, Mullai Murugan, Shannon Dugan, Ziad Khan & Eric Boerwinkle

Vanderbilt Institute of Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA

Kelsey R. Mayo, Jodell E. Linder, Melissa Basford, Ashley Able, Ashley E. Green, Robert J. Carroll, Jennifer Zhang & Yuanyuan Wang

Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA

Lee Lichtenstein, Anthony Philippakis, Sophie Schwartz, M. Morgan T. Aster, Kristian Cibulskis, Andrea Haessly, Rebecca Asch, Aurora Cremer, Kylee Degatano, Akum Shergill, Laura D. Gauthier, Samuel K. Lee, Aaron Hatcher, George B. Grant, Genevieve R. Brandt, Miguel Covarrubias, Eric Banks & Wail Baalawi

Verily, South San Francisco, CA, USA

Shimon Rura, David Glazer, Moira K. Dillon & C. H. Albach

Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA

Robert J. Carroll, Paul A. Harris & Dan M. Roden

All of Us Research Program, National Institutes of Health, Bethesda, MD, USA

Anjene Musick, Andrea H. Ramirez, Sokny Lim, Siddhartha Nambiar, Bradley Ozenberger, Anastasia L. Wise, Chris Lunt, Geoffrey S. Ginsburg & Joshua C. Denny

School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA

I. King Jordan, Shashwat Deepali Nagar & Shivam Sharma

Neuroscience Institute, Institute of Translational Genomic Medicine, Morehouse School of Medicine, Atlanta, GA, USA

Robert Meller

Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA

Mine S. Cicek, Stephen N. Thibodeau & Mine S. Cicek

Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA

Kimberly F. Doheny, Michelle Z. Mawhinney, Sean M. L. Griffith, Elvin Hsu, Hua Ling & Marcia K. Adams

Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA

Evan E. Eichler, Joshua D. Smith, Christian D. Frazar, Colleen P. Davis, Karynne E. Patterson, Marsha M. Wheeler, Sean McGee, Mitzi L. Murray, Valeria Vasta, Dru Leistritz, Matthew A. Richardson, Aparna Radhakrishnan & Brenna W. Ehmen

Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA

Evan E. Eichler

Broad Institute of MIT and Harvard, Cambridge, MA, USA

Stacey Gabriel, Heidi L. Rehm, Niall J. Lennon, Christina Austin-Tse, Eric Banks, Michael Gatzen, Namrata Gupta, Katie Larsson, Sheli McDonough, Steven M. Harrison, Christopher Kachulis, Matthew S. Lebo, Seung Hoan Choi & Xin Wang

Division of Medical Genetics, Department of Medicine, University of Washington School of Medicine, Seattle, WA, USA

Gail P. Jarvik & Elisabeth A. Rosenthal

Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

Dan M. Roden

Department of Pharmacology, Vanderbilt University Medical Center, Nashville, TN, USA

Center for Individualized Medicine, Biorepository Program, Mayo Clinic, Rochester, MN, USA

Stephen N. Thibodeau, Ashley L. Blegen, Samantha J. Wirkus, Victoria A. Wagner, Jeffrey G. Meyer & Mine S. Cicek

Color Health, Burlingame, CA, USA

Scott Topper, Cynthia L. Neben, Marcie Steeves & Alicia Y. Zhou

School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA

Eric Boerwinkle

Laboratory for Molecular Medicine, Massachusetts General Brigham Personalized Medicine, Cambridge, MA, USA

Christina Austin-Tse, Emma Henricks & Matthew S. Lebo

Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA, USA

Christina M. Lockwood, Brian H. Shirts, Colin C. Pritchard, Jillian G. Buchan & Niklas Krumm

Manuscript Writing Group

  • Alexander G. Bick
  • , Ginger A. Metcalf
  • , Kelsey R. Mayo
  • , Lee Lichtenstein
  • , Shimon Rura
  • , Robert J. Carroll
  • , Anjene Musick
  • , Jodell E. Linder
  • , I. King Jordan
  • , Shashwat Deepali Nagar
  • , Shivam Sharma
  •  & Robert Meller

All of Us Research Program Genomics Principal Investigators

  • Melissa Basford
  • , Eric Boerwinkle
  • , Mine S. Cicek
  • , Kimberly F. Doheny
  • , Evan E. Eichler
  • , Stacey Gabriel
  • , Richard A. Gibbs
  • , David Glazer
  • , Paul A. Harris
  • , Gail P. Jarvik
  • , Anthony Philippakis
  • , Heidi L. Rehm
  • , Dan M. Roden
  • , Stephen N. Thibodeau
  •  & Scott Topper

Biobank, Mayo

  • Ashley L. Blegen
  • , Samantha J. Wirkus
  • , Victoria A. Wagner
  • , Jeffrey G. Meyer
  •  & Stephen N. Thibodeau

Genome Center: Baylor-Hopkins Clinical Genome Center

  • Donna M. Muzny
  • , Eric Venner
  • , Michelle Z. Mawhinney
  • , Sean M. L. Griffith
  • , Elvin Hsu
  • , Marcia K. Adams
  • , Kimberly Walker
  • , Jianhong Hu
  • , Harsha Doddapaneni
  • , Christie L. Kovar
  • , Mullai Murugan
  • , Shannon Dugan
  • , Ziad Khan
  •  & Richard A. Gibbs

Genome Center: Broad, Color, and Mass General Brigham Laboratory for Molecular Medicine

  • Niall J. Lennon
  • , Christina Austin-Tse
  • , Eric Banks
  • , Michael Gatzen
  • , Namrata Gupta
  • , Emma Henricks
  • , Katie Larsson
  • , Sheli McDonough
  • , Steven M. Harrison
  • , Christopher Kachulis
  • , Matthew S. Lebo
  • , Cynthia L. Neben
  • , Marcie Steeves
  • , Alicia Y. Zhou
  • , Scott Topper
  •  & Stacey Gabriel

Genome Center: University of Washington

  • Gail P. Jarvik
  • , Joshua D. Smith
  • , Christian D. Frazar
  • , Colleen P. Davis
  • , Karynne E. Patterson
  • , Marsha M. Wheeler
  • , Sean McGee
  • , Christina M. Lockwood
  • , Brian H. Shirts
  • , Colin C. Pritchard
  • , Mitzi L. Murray
  • , Valeria Vasta
  • , Dru Leistritz
  • , Matthew A. Richardson
  • , Jillian G. Buchan
  • , Aparna Radhakrishnan
  • , Niklas Krumm
  •  & Brenna W. Ehmen

Data and Research Center

  • Lee Lichtenstein
  • , Sophie Schwartz
  • , M. Morgan T. Aster
  • , Kristian Cibulskis
  • , Andrea Haessly
  • , Rebecca Asch
  • , Aurora Cremer
  • , Kylee Degatano
  • , Akum Shergill
  • , Laura D. Gauthier
  • , Samuel K. Lee
  • , Aaron Hatcher
  • , George B. Grant
  • , Genevieve R. Brandt
  • , Miguel Covarrubias
  • , Melissa Basford
  • , Alexander G. Bick
  • , Ashley Able
  • , Ashley E. Green
  • , Jennifer Zhang
  • , Henry R. Condon
  • , Yuanyuan Wang
  • , Moira K. Dillon
  • , C. H. Albach
  • , Wail Baalawi
  •  & Dan M. Roden

All of Us Research Demonstration Project Teams

  • Seung Hoan Choi
  • , Elisabeth A. Rosenthal

NIH All of Us Research Program Staff

  • Andrea H. Ramirez
  • , Sokny Lim
  • , Siddhartha Nambiar
  • , Bradley Ozenberger
  • , Anastasia L. Wise
  • , Chris Lunt
  • , Geoffrey S. Ginsburg
  •  & Joshua C. Denny

Contributions

The All of Us Biobank (Mayo Clinic) collected, stored and plated participant biospecimens. The All of Us Genome Centers (Baylor-Hopkins Clinical Genome Center; Broad, Color, and Mass General Brigham Laboratory for Molecular Medicine; and University of Washington School of Medicine) generated and QCed the whole-genomic data. The All of Us Data and Research Center (Vanderbilt University Medical Center, Broad Institute of MIT and Harvard, and Verily) generated the WGS joint call set, carried out quality assurance and QC analyses and developed the Researcher Workbench. All of Us Research Demonstration Project Teams contributed analyses. The other All of Us Genomics Investigators and NIH All of Us Research Program Staff provided crucial programmatic support. Members of the manuscript writing group (A.G.B., G.A.M., K.R.M., L.L., S.R., R.J.C. and A.M.) wrote the first draft of this manuscript, which was revised with contributions and feedback from all authors.

Corresponding author

Correspondence to Alexander G. Bick .

Ethics declarations

Competing interests.

D.M.M., G.A.M., E.V., K.W., J.H., H.D., C.L.K., M.M., S.D., Z.K., E. Boerwinkle and R.A.G. declare that Baylor Genetics is a Baylor College of Medicine affiliate that derives revenue from genetic testing. Eric Venner is affiliated with Codified Genomics, a provider of genetic interpretation. E.E.E. is a scientific advisory board member of Variant Bio, Inc. A.G.B. is a scientific advisory board member of TenSixteen Bio. The remaining authors declare no competing interests.

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Nature thanks Timothy Frayling and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended data fig. 1 historic availability of ehr records in all of us v7 controlled tier curated data repository (n = 413,457)..

For better visibility, the plot shows growth starting in 2010.

Extended Data Fig. 2 Overview of the Genomic Data Curation Pipeline for WGS samples.

The Data and Research Center (DRC) performs additional single sample quality control (QC) on the data as it arrives from the Genome Centers. The variants from samples that pass this QC are loaded into the Genomic Variant Store (GVS), where we jointly call the variants and apply additional QC. We apply a joint call set QC process, which is stored with the call set. The entire joint call set is rendered as a Hail Variant Dataset (VDS), which can be accessed from the analysis notebooks in the Researcher Workbench. Subsections of the genome are extracted from the VDS and rendered in different formats with all participants. Auxiliary data can also be accessed through the Researcher Workbench. This includes variant functional annotations, joint call set QC results, predicted ancestry, and relatedness. Auxiliary data are derived from GVS (arrow not shown) and the VDS. The Cohort Builder directly queries GVS when researchers request genomic data for subsets of samples. Aligned reads, as cram files, are available in the Researcher Workbench (not shown). The graphics of the dish, gene and computer and the All of Us logo are reproduced with permission of the National Institutes of Health’s All of Us Research Program.

Extended Data Fig. 3 Proportion of allelic frequencies (AF), stratified by computed ancestry with over 10,000 participants.

Bar counts are not cumulative (eg, “pop AF < 0.01” does not include “pop AF < 0.001”).

Extended Data Fig. 4 Distribution of pathogenic, and likely pathogenic ClinVar variants.

Stratified by ancestry filtered to only those variants that are found in allele count (AC) < 40 individuals for 245,388 short read WGS samples.

Extended Data Fig. 5 Ancestry specific HLA-DQB1 ( rs9273363 ) locus associations in 231,442 unrelated individuals.

Phenome-wide (PheWAS) associations highlight ancestry specific consequences across ancestries.

Extended Data Fig. 6 Ancestry specific TCF7L2 ( rs7903146 ) locus associations in 231,442 unrelated individuals.

Phenome-wide (PheWAS) associations highlight diabetic consequences across ancestries.

Supplementary information

Supplementary information.

Supplementary Figs. 1–7, Tables 1–8 and Note.

Reporting Summary

Supplementary dataset 1.

Associations of ACKR1, HLA-DQB1 and TCF7L2 loci with all Phecodes stratified by genetic ancestry.

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The All of Us Research Program Genomics Investigators. Genomic data in the All of Us Research Program. Nature (2024). https://doi.org/10.1038/s41586-023-06957-x

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DOI : https://doi.org/10.1038/s41586-023-06957-x

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What Is a Species, Anyway?

Some of the best known species on Earth may not be what they seem.

Credit... Steve Holroyd/Alamy

Supported by

Carl Zimmer

By Carl Zimmer

  • Feb. 19, 2024

Naturalists have been trying for centuries to catalog all of the species on Earth, and the effort remains one of the great unfinished jobs in science. So far, researchers have named about 2.3 million species , but there are millions — perhaps even billions — left to be discovered.

As if this quest isn’t hard enough, biologists cannot agree on what a species is. A 2021 survey found that practicing biologists used 16 different approaches to categorizing species. Any two of the scientists picked at random were overwhelmingly likely to use different ones.

“Everyone uses the term, but no one knows what it is,” said Michal Grabowski, a biologist at the University of Lodz in Poland.

The debate over species is more than an academic pastime. In the current extinction crisis , scientists urgently need to take stock of the world’s biological diversity. But even some of the best known species on Earth may not be what they seem.

Take the giraffe.

In 1758, the Swedish taxonomist Carl Linnaeus described a single species of giraffe: Giraffa camelopardalis. Although the species has declined in recent decades, 117,000 giraffes still survive across Africa, prompting an international conservation group to designate the species as vulnerable, rather than endangered.

But some conservation biologists argue that giraffes are in great peril, because what looks like one species is actually four. Genetic studies have found that giraffe DNA falls into four distinct clusters: the Northern giraffe, the reticulated giraffe, the Masai giraffe and the Southern giraffe.

example of summary of scientific article

Giraffa camelopardalis

Northern giraffe

Giraffa reticulata

Reticulated giraffe

Historic range

Giraffa giraffa

Southern giraffe

Giraffa tippelskirchi

Masai giraffe

example of summary of scientific article

The Northern giraffe, which lives in pockets from Niger to Ethiopia, has suffered catastrophic losses from civil wars, poaching and the destruction of its wild habitat. If the Northern giraffe were considered a separate species, it would be “one of the most threatened large mammals in the world,” said Stephanie Fennessy, the executive director of the Giraffe Conservation Foundation, a nongovernmental conservation organization.

For Linnaeus, species were divinely created forms of life, each with its own distinctive traits. A century later, Charles Darwin recognized that living species had evolved, like young branches sprouting off from the tree of life. That realization made it harder to say exactly when a new group became a species of its own, instead of just a subspecies of an old one.

In the 1940s, Ernst Mayr, a German ornithologist, tried solving this problem with a new definition of species based on how animals breed. If two animals couldn’t breed with each other, Mayr argued, then they were separate species.

The biological species concept, as it came to be known, had a huge influence on later generations of researchers.

In recent years, Christophe Dufresnes, a herpetologist at Nanjing Forestry University in China, has used this concept to classify different species of frogs in Europe.

Some of the groups of frogs interbred a lot, whereas others had no hybrids at all. By analyzing their DNA, Dr. Dufresnes found that groups with a recent ancestor — that is, those that were more closely related — readily produced hybrids. He estimates that it takes about six million years of diverging evolution for two groups of frogs to become unable to interbreed — in other words, to become two distinct species.

“This is very cool,” Dr. Dufresnes said. “Now we know what the threshold is to deem them species or not.”

example of summary of scientific article

Single historical species

Species divergence

Populations start to diverge into

subspecies, which freely interbreed

Gray zone: fewer hybrids

between subspecies,

gene exchange slows

Separate species form

after about 6 million years

Present day

example of summary of scientific article

Populations start to

diverge into subspecies,

which freely interbreed

Gray zone: fewer

hybrids between

subspecies, gene

exchange slows

species form

after a few

million years

Dr. Dufresnes’s method for finding new species takes a lot of work in the field. Other researchers have looked for more efficient ways to identify species. One popular method is to sequence DNA from organisms and observe the differences in their genetic code.

This search can yield a lot of surprises, as illustrated by the giraffes in Africa. Dr. Grabowski’s team has discovered an even more dramatic diversity hiding among European crustaceans, a group of aquatic creatures that includes lobsters, shrimp and crabs. The researchers have shown that animals that look identical to each other and appear to belong to a single species may actually be dozens of new species.

For example, a species of common freshwater shrimp called Gammarus fossarum split 25 million years ago into separate lineages that are still alive today. Depending on how researchers classify their DNA differences, the single species of Gammarus fossarum might in fact be 32 species — or as many as 152.

example of summary of scientific article

Gammarus fossarum

a European freshwater shrimp

example of summary of scientific article

“For us, it’s mind-blowing,” Dr. Grabowski said.

As scientists gather more genetic data, fresh questions are emerging about what seem, on the surface, to be obviously separate species.

You don’t have to be a mammalogist to understand that polar bears and brown bears are different. Just one look at their white and brown coats will do.

The difference in their colors is the result of their ecological adaptations. White polar bears blend into their Arctic habitats, where they hunt for seals and other prey. Brown bears adapted for life on land further south. The differences are so distinct that paleontologists can distinguish fossils of the two species going back hundreds of thousands of years.

And yet the DNA inside those ancient bones is revealing an astonishing history of interbreeding between polar bears and brown bears. After the two lineages split about half a million years ago, they exchanged DNA for thousands of years. They then became more distinct, but about 120,000 years ago they underwent another extraordinary exchange of genes.

Between 25,000 and 10,000 years ago, the bears interbred in several parts of their range. The exchanges have left a significant imprint on bears today: About 10 percent of the DNA in brown bears comes from polar bears.

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thousand years ago

Ursus arctos

Ursus maritimus

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Beth Shapiro, a paleogeneticist at the University of California, Santa Cruz, said that the interbreeding most likely occurred when swings in the climate forced polar bears down from the Arctic and into brown bear territory.

But the exchange of DNA did not blur the bears into one species. Some of the traits that benefit polar bears in their own environment can become a burden for brown bears, and vice versa.

“They clearly demand separate strategies for conservation management,” Dr. Shapiro said. “It makes sense to me to consider them distinct species.”

The uncertainties about what makes a species have left taxonomists with countless conflicts. Separate groups of ornithologists have created their own lists of all the bird species on Earth, for example, and those lists often clash.

Even a common species like the barn owl — found on every continent except Antarctica, as well as remote islands — is a source of disagreement.

The conservation group BirdLife International recognizes barn owls as a species, Tyto alba, that lives across the world. But another influential inventory, called the Clements Checklist of Birds of the World, carves off the barn owls that live on an Indian Ocean island chain as their own species, Tyto deroepstorffi. Yet another recognizes the barn owls in Australia and New Guinea as Tyto delicatula. And a fourth splits Tyto alba into four species, each covering its own broad swath of the planet.

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Common barn owl

As recognized by Bird Life International

example of summary of scientific article

Some ornithologists are trying to resolve these conflicts with a low-tech approach: voting.

In 2021, the International Ornithologists’ Union formed a working group to replace the four leading bird checklists with a single catalog. Nine experts are working their way through the lists and voting on more than 11,000 potential species.

“The discussions can get very heated,” said Leslie Christidis, the group’s chair. Some of the experts tend to lump species together, while others split them. “We’re just trying to negotiate a peaceful system.”

Thomas Wells, a botanist at the University of Oxford, is concerned that debates about the nature of species are slowing down the work of discovering new ones. Taxonomy is traditionally a slow process, especially for plants. It can take decades for a new species of plant to be formally named in a scientific publication after it is first discovered. That sluggish pace is unacceptable, he said, when three out of four undescribed species of plants are already threatened with extinction .

Dr. Wells and his colleagues are developing a new method to speed up the process. They are taking photographs of plants both in the wild and in museum collections and using computer programs to spot samples that seem to cluster together because they have similar shapes. They’re also rapidly sequencing DNA from the samples to see if they cluster together genetically.

If they get clear clusters from approaches such as these, they call the plants a new species. The method — which Dr. Wells calls a “rough and ready” triage in our age of extinctions — may make it possible for his team to describe more than 100 new species of plants each year.

“We don’t really have the luxury of agonizing over, ‘Is this a species, or is this a subspecies?’” he said. “We need to make decisions quickly and as accurately as possible, based on the evidence we have at hand.”

An earlier version of this article misstated the range of the barn owl. It is found on every continent except Antarctica.

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Carl Zimmer covers news about science for The Times and writes the Origins column . More about Carl Zimmer

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8 facts about atheists.

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Atheists make up 4% of U.S. adults, according to our 2023 National Public Opinion Reference Survey . That compares with 3% who described themselves as atheists in 2014 and 2% who did so in 2007 .

Here are some key facts about atheists in the United States and around the world, based on several Pew Research Center surveys.

This analysis draws on several Pew Research Center studies. Data on the share of atheists in the United States is from the  2023 National Public Opinion Reference Survey , as well as the Center’s  2007  and  2014 Religious Landscape Studies .

Other data on U.S. atheists comes from various waves of the American Trends Panel, collected in  September and December 2017 ,  February 2019 ,  September 2022 , and  July and August 2023 .

For data from countries other than the U.S., this analysis draws on nationally representative surveys conducted in 2019, 2022 and 2023. Read more details about our  international survey methodology and country-specific sample designs .

For the purposes of this analysis, “wealthy nations” are those that were classified as “high income” according to the  World Bank Income Classifications .

In the U.S., atheists are mostly men and are relatively young,  according to a Center survey conducted in summer 2023 . Around six-in-ten U.S. atheists are men (64%). And seven-in-ten are ages 49 or younger, compared with about half of U.S. adults overall (52%).

Atheists also are more likely than the general public to be White (77% vs. 62%) and have a college degree (48% vs. 34%). Roughly eight-in-ten atheists identify with or lean toward the Democratic Party.

Almost all U.S. atheists (98%) say religion is not too or not at all important in their lives, according to the same summer 2023 survey. An identical share say that they seldom or never pray.

At the same time, 79% of American atheists say they feel a deep sense of wonder about the universe at least several times a year. And 36% feel a deep sense of spiritual peace and well-being at least that often.

U.S. atheists and religiously affiliated Americans find meaning in their lives in some of the same ways. In a 2017 survey , we asked an open-ended question about this. Like a majority of Americans, most atheists mentioned family as a source of meaning.

However, atheists (26%) were far more likely than Christians (10%) to describe their hobbies as meaningful or satisfying. Atheists were also more likely than Americans overall to describe finances and money, creative pursuits, travel, and leisure activities as meaningful. Very few atheists (4%) said they found life’s meaning in spirituality.

A map showing that western Europeans are more likely than Americans to identify as atheists.

Atheists make up a larger share of the population in many Western European countries than in the U.S.,  according to a spring 2023 Center survey that included 10 European countries. For example, nearly a quarter of French adults (23%) identify as atheists, as do 18% of adults in Sweden, 17% in the Netherlands and 12% in the United Kingdom.

Most U.S. atheists express concerns about the role religion plays in society. An overwhelming majority of atheists (94%) say that the statement “religion causes division and intolerance” describes their views a great deal or a fair amount, according to our summer 2023 survey. And 91% say the same about the statement “religion encourages superstition and illogical thinking.” Nearly three-quarters (73%) say religion does more harm than good in American society.

At the same time, 41% of atheists say religion helps society by giving people meaning and purpose in their lives, and 33% say it encourages people to treat others well.

Atheists may not believe religious teachings, but they are  quite informed about religion . In our 2019 religious knowledge survey , atheists were among the best-performing groups. On average, they answered about 18 out of 32 fact-based questions correctly, while U.S. adults overall got roughly 14 questions right. In particular, atheists were twice as likely as Americans overall to know that the U.S. Constitution says no religious test is necessary to hold public office.

Atheists were also at least as knowledgeable as Christians on Christianity-related questions. For example, roughly eight-in-ten in both groups knew that Easter commemorates the resurrection of Jesus.

Most Americans don’t think believing in God is necessary to be a good person, according to the summer 2023 survey. When we asked people which statement came closer to their views, 73% selected “it is possible to be moral and have good values without believing in God,” while 25% picked “it is necessary to believe in God in order to be moral and have good values.”

Adults in some other wealthy countries tend to agree with this sentiment, based on responses to a similar question we asked in 2019 and 2022 . For example, nine-in-ten Swedish adults say belief in God is not necessary to be moral and have good values, while 85% in Australia, 80% in the Czech Republic and 77% in France say this.

However, fewer than one-in-ten adults in some other countries surveyed say that a person can be moral without believing in God. That includes 5% of adults in Kenya, 4% in the Philippines and 2% in Indonesia. In all three nations, more than nine-in-ten say instead that a person must believe in God to be a moral person.

About three-quarters of U.S. atheists (77%) do not believe in God or a higher power  or in a spiritual force of any kind, according to our summer 2023 survey. At the same time, 23% say they do believe in a higher power of some kind, though fewer than 1% of U.S. atheists say they believe in “God as described in the Bible.”

This shows that not all self-described atheists fit the literal definition of “atheist,” which is “a person who does not believe in the existence of a god or any gods,”  according to Merriam-Webster .

Note: This is an update of a post originally published on Nov. 5, 2015. It was last updated Dec. 6, 2019.

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Among religious ‘nones,’ atheists and agnostics know the most about religion

Why america’s ‘nones’ don’t identify with a religion, key findings about americans’ belief in god, unlike their central and eastern european neighbors, most czechs don’t believe in god, why people with no religion are projected to decline as a share of the world’s population, most popular.

About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

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    1 of 5 Summary and Analysis of Scientific Research Articles Being able to summarize and analyze a research article is important not only for showing your professor that you have understood your assigned reading, but it also is the first step to learning how to write your own research papers and literature reviews.

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    How to Write a Summary | Guide & Examples Published on November 23, 2020 by Shona McCombes . Revised on May 31, 2023. Summarizing, or writing a summary, means giving a concise overview of a text's main points in your own words. A summary is always much shorter than the original text. There are five key steps that can help you to write a summary:

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    EXAMPLE RESEARCH SUMMARY . Danielle Wilson . Psych 100 Section 005 . Tuesday Thursday 1:00PM . Ms. Trich Kremer . ... Psychological Science, 17(5), 313-317. You will need a complete reference page for your article. This can be done in APA ... Examples of articles that are NOT peer reviewed are from TIME, Forbes, and Scientific

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    1. When do you summarize or interact with summaries from others? Be sure to consider examples outside of class assignments. 2. For each of the scenarios you described above, what is the goal of...

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    Symmetries can stabilize degenerate subspace that corresponds to higher-dimensional irreducible representations. For example, degenerate zero modes can be created if the number of sites in different sublattices differs. See the example in Fig. 1, bottom left: There are three sites in the black sublattice and one site in the white sublattice ...

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    A version of this article appears in print on , Section D, Page 1 of the New York edition with the headline: Defining A Species Is Open To Debate. Order Reprints | Today's Paper | Subscribe 207

  28. 8 facts about atheists

    This analysis draws on several Pew Research Center studies. Data on the share of atheists in the United States is from the 2023 National Public Opinion Reference Survey, as well as the Center's 2007 and 2014 Religious Landscape Studies.. Other data on U.S. atheists comes from various waves of the American Trends Panel, collected in September and December 2017, February 2019, September 2022 ...