types of literature review methods

Literature Review Type

Narrative

Systematic

Integrative

Rapid

Scoping

Approach

The traditional approach lacks a structured methodology

Systematic search, including structured methodology

Combines diverse methodologies for a comprehensive understanding

Quick review within time constraints

Preliminary study of existing literature

How Exhaustive is the process?

May or may not be comprehensive

Exhaustive and comprehensive search

A comprehensive search for integration

Time-limited search

Determined by time or scope constraints

Data Synthesis

Narrative

Narrative with tabular accompaniment

Integration of various sources or methodologies

Narrative and tabular

Narrative and tabular

Purpose

Provides description of meta analysis and conceptualization of the review

Comprehensive evidence synthesis

Holistic understanding

Quick policy or practice guidelines review

Preliminary literature review

Key characteristics

Storytelling, chronological presentation

Rigorous, traditional and systematic techniques approach

Diverse source or method integration

Time-constrained, systematic approach

Identifies literature size and scope

Example Use Case

Historical exploration

Effectiveness evaluation

Quantitative, qualitative, and mixed  combination

Policy summary

Research literature overview

Aims to demonstrate writer has extensively researched literature and critically evaluated its quality. Goes beyond mere description to include degree of analysis and conceptual innovation. Typically results in hypothesis or mode

Seeks to identify most significant items in the field

No formal quality assessment. Attempts to evaluate according to contribution

Typically narrative, perhaps conceptual or chronological

Significant component: seeks to identify conceptual contribution to embody existing or derive new theory

Generic term: published materials that provide examination of recent or current literature. Can cover wide range of subjects at various levels of completeness and comprehensiveness. May include research findings

May or may not include comprehensive searching

May or may not include quality assessment

Typically narrative

Analysis may be chronological, conceptual, thematic, etc.

Map out and categorize existing literature from which to commission further reviews and/or primary research by identifying gaps in research literature

Completeness of searching determined by time/scope constraints

No formal quality assessment

May be graphical and tabular

Characterizes quantity and quality of literature, perhaps by study design and other key features. May identify need for primary or secondary research

Technique that statistically combines the results of quantitative studies to provide a more precise effect of the results

Aims for exhaustive, comprehensive searching. May use funnel plot to assess completeness

Quality assessment may determine inclusion/ exclusion and/or sensitivity analyses

Graphical and tabular with narrative commentary

Numerical analysis of measures of effect assuming absence of heterogeneity

Refers to any combination of methods where one significant component is a literature review (usually systematic). Within a review context it refers to a combination of review approaches for example combining quantitative with qualitative research or outcome with process studies

Requires either very sensitive search to retrieve all studies or separately conceived quantitative and qualitative strategies

Requires either a generic appraisal instrument or separate appraisal processes with corresponding checklists

Typically both components will be presented as narrative and in tables. May also employ graphical means of integrating quantitative and qualitative studies

Analysis may characterise both literatures and look for correlations between characteristics or use gap analysis to identify aspects absent in one literature but missing in the other

Generic term: summary of the [medical] literature that attempts to survey the literature and describe its characteristics

May or may not include comprehensive searching (depends whether systematic overview or not)

May or may not include quality assessment (depends whether systematic overview or not)

Synthesis depends on whether systematic or not. Typically narrative but may include tabular features

Analysis may be chronological, conceptual, thematic, etc.

Method for integrating or comparing the findings from qualitative studies. It looks for ‘themes’ or ‘constructs’ that lie in or across individual qualitative studies

May employ selective or purposive sampling

Quality assessment typically used to mediate messages not for inclusion/exclusion

Qualitative, narrative synthesis

Thematic analysis, may include conceptual models

Assessment of what is already known about a policy or practice issue, by using systematic review methods to search and critically appraise existing research

Completeness of searching determined by time constraints

Time-limited formal quality assessment

Typically narrative and tabular

Quantities of literature and overall quality/direction of effect of literature

Preliminary assessment of potential size and scope of available research literature. Aims to identify nature and extent of research evidence (usually including ongoing research)

Completeness of searching determined by time/scope constraints. May include research in progress

No formal quality assessment

Typically tabular with some narrative commentary

Characterizes quantity and quality of literature, perhaps by study design and other key features. Attempts to specify a viable review

Tend to address more current matters in contrast to other combined retrospective and current approaches. May offer new perspectives

Aims for comprehensive searching of current literature

No formal quality assessment

Typically narrative, may have tabular accompaniment

Current state of knowledge and priorities for future investigation and research

Seeks to systematically search for, appraise and synthesis research evidence, often adhering to guidelines on the conduct of a review

Aims for exhaustive, comprehensive searching

Quality assessment may determine inclusion/exclusion

Typically narrative with tabular accompaniment

What is known; recommendations for practice. What remains unknown; uncertainty around findings, recommendations for future research

Combines strengths of critical review with a comprehensive search process. Typically addresses broad questions to produce ‘best evidence synthesis’

Aims for exhaustive, comprehensive searching

May or may not include quality assessment

Minimal narrative, tabular summary of studies

What is known; recommendations for practice. Limitations

Attempt to include elements of systematic review process while stopping short of systematic review. Typically conducted as postgraduate student assignment

May or may not include comprehensive searching

May or may not include quality assessment

Typically narrative with tabular accompaniment

What is known; uncertainty around findings; limitations of methodology

Specifically refers to review compiling evidence from multiple reviews into one accessible and usable document. Focuses on broad condition or problem for which there are competing interventions and highlights reviews that address these interventions and their results

Identification of component reviews, but no search for primary studies

Quality assessment of studies within component reviews and/or of reviews themselves

Graphical and tabular with narrative commentary

What is known; recommendations for practice. What remains unknown; recommendations for future research

Generic term: published materials that provide an examination of recent or current literature. Can cover a wide range of subjects at various levels of completeness and comprehensiveness. May include research findings

May or may not include comprehensive

searching

May or may not include quality

assessment

Typically narrative

Analysis may be chronological, conceptual, thematic, etc.

Seeks to systematically search for, appraise and synthesize research evidence, often adhering to guidelines on the conduct of a review

Aims for exhaustive,

Comprehensive searching

Quality assessment

may determine

inclusion/exclusion

Typically narrative

with tabular

accompaniment

What is known; recommendations

for practice. What remains unknown; uncertainty around findings, recommendations for

future research

Technique that statistically combines the results of quantitative studies to provide a more precise effect of the results

Aims for exhaustive searching. May use funnel plot to assess completeness

Quality assessment may determine inclusion/exclusion and/or sensitivity analyses

Graphical and tabular with narrative commentary

Numerical analysis of measures of effect assuming absence of heterogeneity

Preliminary assessment of potential size and scope of available research literature. Aims to identify the nature and extent of research evidence (usually including ongoing research)

Completeness of searching determined by time/scope constraints. May include research in progress

No formal quality assessment

Typically tabular with some narrative commentary

Characterizes quantity and quality of literature, perhaps by study design and other key features. Attempts to specify a viable review

Refers to any combination of methods where one significant component is a literature review (usually systematic). Within a review context, it refers to a combination of review approaches for example combining quantitative with qualitative research or outcome with process studies

Requires either very sensitive search to retrieve all studies or separately conceived quantitative and qualitative strategies

Requires either a generic appraisal instrument or separate appraisal processes with corresponding checklists

Typically both components will be presented as narrative and in tables. May also employ graphical means of integrating quantitative and qualitative studies

Analysis may characterize both works of literature and look for correlations between characteristics or use gap analysis to identify aspects absent in one literature but missing in the other

Specifically refers to review compiling evidence from multiple reviews into one accessible and usable document. Focuses on a broad condition or problem for which there are competing interventions and highlights reviews that address these interventions and their results

Identification of component reviews, but no search for primary studies

Quality assessment of studies within component reviews and/or of reviews themselves

Graphical and tabular with narrative commentary

What is known; recommendations for practice. What remains unknown; recommendations for future research

Reproduced from Grant MJ, Booth A. A typology of reviews: an analysis of 14 review types and associated methodologies . Health Info Libr J. 2009 Jun;26(2):91-108. doi: 10.1111/j.1471-1842.2009.00848.x

• Last Updated: Sep 6, 2024 12:39 PM
• URL: https://guides.lib.utexas.edu/systematicreviews

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• v.9(7); 2013 Jul

Ten Simple Rules for Writing a Literature Review

Marco pautasso.

1 Centre for Functional and Evolutionary Ecology (CEFE), CNRS, Montpellier, France

2 Centre for Biodiversity Synthesis and Analysis (CESAB), FRB, Aix-en-Provence, France

Literature reviews are in great demand in most scientific fields. Their need stems from the ever-increasing output of scientific publications [1] . For example, compared to 1991, in 2008 three, eight, and forty times more papers were indexed in Web of Science on malaria, obesity, and biodiversity, respectively [2] . Given such mountains of papers, scientists cannot be expected to examine in detail every single new paper relevant to their interests [3] . Thus, it is both advantageous and necessary to rely on regular summaries of the recent literature. Although recognition for scientists mainly comes from primary research, timely literature reviews can lead to new synthetic insights and are often widely read [4] . For such summaries to be useful, however, they need to be compiled in a professional way [5] .

When starting from scratch, reviewing the literature can require a titanic amount of work. That is why researchers who have spent their career working on a certain research issue are in a perfect position to review that literature. Some graduate schools are now offering courses in reviewing the literature, given that most research students start their project by producing an overview of what has already been done on their research issue [6] . However, it is likely that most scientists have not thought in detail about how to approach and carry out a literature review.

Reviewing the literature requires the ability to juggle multiple tasks, from finding and evaluating relevant material to synthesising information from various sources, from critical thinking to paraphrasing, evaluating, and citation skills [7] . In this contribution, I share ten simple rules I learned working on about 25 literature reviews as a PhD and postdoctoral student. Ideas and insights also come from discussions with coauthors and colleagues, as well as feedback from reviewers and editors.

Rule 1: Define a Topic and Audience

How to choose which topic to review? There are so many issues in contemporary science that you could spend a lifetime of attending conferences and reading the literature just pondering what to review. On the one hand, if you take several years to choose, several other people may have had the same idea in the meantime. On the other hand, only a well-considered topic is likely to lead to a brilliant literature review [8] . The topic must at least be:

• interesting to you (ideally, you should have come across a series of recent papers related to your line of work that call for a critical summary),
• an important aspect of the field (so that many readers will be interested in the review and there will be enough material to write it), and
• a well-defined issue (otherwise you could potentially include thousands of publications, which would make the review unhelpful).

Ideas for potential reviews may come from papers providing lists of key research questions to be answered [9] , but also from serendipitous moments during desultory reading and discussions. In addition to choosing your topic, you should also select a target audience. In many cases, the topic (e.g., web services in computational biology) will automatically define an audience (e.g., computational biologists), but that same topic may also be of interest to neighbouring fields (e.g., computer science, biology, etc.).

Rule 2: Search and Re-search the Literature

After having chosen your topic and audience, start by checking the literature and downloading relevant papers. Five pieces of advice here:

• keep track of the search items you use (so that your search can be replicated [10] ),
• keep a list of papers whose pdfs you cannot access immediately (so as to retrieve them later with alternative strategies),
• use a paper management system (e.g., Mendeley, Papers, Qiqqa, Sente),
• define early in the process some criteria for exclusion of irrelevant papers (these criteria can then be described in the review to help define its scope), and
• do not just look for research papers in the area you wish to review, but also seek previous reviews.

The chances are high that someone will already have published a literature review ( Figure 1 ), if not exactly on the issue you are planning to tackle, at least on a related topic. If there are already a few or several reviews of the literature on your issue, my advice is not to give up, but to carry on with your own literature review,

The bottom-right situation (many literature reviews but few research papers) is not just a theoretical situation; it applies, for example, to the study of the impacts of climate change on plant diseases, where there appear to be more literature reviews than research studies [33] .

• discussing in your review the approaches, limitations, and conclusions of past reviews,
• trying to find a new angle that has not been covered adequately in the previous reviews, and
• incorporating new material that has inevitably accumulated since their appearance.

When searching the literature for pertinent papers and reviews, the usual rules apply:

• be thorough,
• use different keywords and database sources (e.g., DBLP, Google Scholar, ISI Proceedings, JSTOR Search, Medline, Scopus, Web of Science), and
• look at who has cited past relevant papers and book chapters.

Rule 3: Take Notes While Reading

If you read the papers first, and only afterwards start writing the review, you will need a very good memory to remember who wrote what, and what your impressions and associations were while reading each single paper. My advice is, while reading, to start writing down interesting pieces of information, insights about how to organize the review, and thoughts on what to write. This way, by the time you have read the literature you selected, you will already have a rough draft of the review.

Of course, this draft will still need much rewriting, restructuring, and rethinking to obtain a text with a coherent argument [11] , but you will have avoided the danger posed by staring at a blank document. Be careful when taking notes to use quotation marks if you are provisionally copying verbatim from the literature. It is advisable then to reformulate such quotes with your own words in the final draft. It is important to be careful in noting the references already at this stage, so as to avoid misattributions. Using referencing software from the very beginning of your endeavour will save you time.

Rule 4: Choose the Type of Review You Wish to Write

After having taken notes while reading the literature, you will have a rough idea of the amount of material available for the review. This is probably a good time to decide whether to go for a mini- or a full review. Some journals are now favouring the publication of rather short reviews focusing on the last few years, with a limit on the number of words and citations. A mini-review is not necessarily a minor review: it may well attract more attention from busy readers, although it will inevitably simplify some issues and leave out some relevant material due to space limitations. A full review will have the advantage of more freedom to cover in detail the complexities of a particular scientific development, but may then be left in the pile of the very important papers “to be read” by readers with little time to spare for major monographs.

There is probably a continuum between mini- and full reviews. The same point applies to the dichotomy of descriptive vs. integrative reviews. While descriptive reviews focus on the methodology, findings, and interpretation of each reviewed study, integrative reviews attempt to find common ideas and concepts from the reviewed material [12] . A similar distinction exists between narrative and systematic reviews: while narrative reviews are qualitative, systematic reviews attempt to test a hypothesis based on the published evidence, which is gathered using a predefined protocol to reduce bias [13] , [14] . When systematic reviews analyse quantitative results in a quantitative way, they become meta-analyses. The choice between different review types will have to be made on a case-by-case basis, depending not just on the nature of the material found and the preferences of the target journal(s), but also on the time available to write the review and the number of coauthors [15] .

Rule 5: Keep the Review Focused, but Make It of Broad Interest

Whether your plan is to write a mini- or a full review, it is good advice to keep it focused 16 , 17 . Including material just for the sake of it can easily lead to reviews that are trying to do too many things at once. The need to keep a review focused can be problematic for interdisciplinary reviews, where the aim is to bridge the gap between fields [18] . If you are writing a review on, for example, how epidemiological approaches are used in modelling the spread of ideas, you may be inclined to include material from both parent fields, epidemiology and the study of cultural diffusion. This may be necessary to some extent, but in this case a focused review would only deal in detail with those studies at the interface between epidemiology and the spread of ideas.

While focus is an important feature of a successful review, this requirement has to be balanced with the need to make the review relevant to a broad audience. This square may be circled by discussing the wider implications of the reviewed topic for other disciplines.

Rule 6: Be Critical and Consistent

Reviewing the literature is not stamp collecting. A good review does not just summarize the literature, but discusses it critically, identifies methodological problems, and points out research gaps [19] . After having read a review of the literature, a reader should have a rough idea of:

• the major achievements in the reviewed field,
• the main areas of debate, and
• the outstanding research questions.

It is challenging to achieve a successful review on all these fronts. A solution can be to involve a set of complementary coauthors: some people are excellent at mapping what has been achieved, some others are very good at identifying dark clouds on the horizon, and some have instead a knack at predicting where solutions are going to come from. If your journal club has exactly this sort of team, then you should definitely write a review of the literature! In addition to critical thinking, a literature review needs consistency, for example in the choice of passive vs. active voice and present vs. past tense.

Rule 7: Find a Logical Structure

Like a well-baked cake, a good review has a number of telling features: it is worth the reader's time, timely, systematic, well written, focused, and critical. It also needs a good structure. With reviews, the usual subdivision of research papers into introduction, methods, results, and discussion does not work or is rarely used. However, a general introduction of the context and, toward the end, a recapitulation of the main points covered and take-home messages make sense also in the case of reviews. For systematic reviews, there is a trend towards including information about how the literature was searched (database, keywords, time limits) [20] .

How can you organize the flow of the main body of the review so that the reader will be drawn into and guided through it? It is generally helpful to draw a conceptual scheme of the review, e.g., with mind-mapping techniques. Such diagrams can help recognize a logical way to order and link the various sections of a review [21] . This is the case not just at the writing stage, but also for readers if the diagram is included in the review as a figure. A careful selection of diagrams and figures relevant to the reviewed topic can be very helpful to structure the text too [22] .

Rule 8: Make Use of Feedback

Reviews of the literature are normally peer-reviewed in the same way as research papers, and rightly so [23] . As a rule, incorporating feedback from reviewers greatly helps improve a review draft. Having read the review with a fresh mind, reviewers may spot inaccuracies, inconsistencies, and ambiguities that had not been noticed by the writers due to rereading the typescript too many times. It is however advisable to reread the draft one more time before submission, as a last-minute correction of typos, leaps, and muddled sentences may enable the reviewers to focus on providing advice on the content rather than the form.

Feedback is vital to writing a good review, and should be sought from a variety of colleagues, so as to obtain a diversity of views on the draft. This may lead in some cases to conflicting views on the merits of the paper, and on how to improve it, but such a situation is better than the absence of feedback. A diversity of feedback perspectives on a literature review can help identify where the consensus view stands in the landscape of the current scientific understanding of an issue [24] .

Rule 9: Include Your Own Relevant Research, but Be Objective

In many cases, reviewers of the literature will have published studies relevant to the review they are writing. This could create a conflict of interest: how can reviewers report objectively on their own work [25] ? Some scientists may be overly enthusiastic about what they have published, and thus risk giving too much importance to their own findings in the review. However, bias could also occur in the other direction: some scientists may be unduly dismissive of their own achievements, so that they will tend to downplay their contribution (if any) to a field when reviewing it.

In general, a review of the literature should neither be a public relations brochure nor an exercise in competitive self-denial. If a reviewer is up to the job of producing a well-organized and methodical review, which flows well and provides a service to the readership, then it should be possible to be objective in reviewing one's own relevant findings. In reviews written by multiple authors, this may be achieved by assigning the review of the results of a coauthor to different coauthors.

Rule 10: Be Up-to-Date, but Do Not Forget Older Studies

Given the progressive acceleration in the publication of scientific papers, today's reviews of the literature need awareness not just of the overall direction and achievements of a field of inquiry, but also of the latest studies, so as not to become out-of-date before they have been published. Ideally, a literature review should not identify as a major research gap an issue that has just been addressed in a series of papers in press (the same applies, of course, to older, overlooked studies (“sleeping beauties” [26] )). This implies that literature reviewers would do well to keep an eye on electronic lists of papers in press, given that it can take months before these appear in scientific databases. Some reviews declare that they have scanned the literature up to a certain point in time, but given that peer review can be a rather lengthy process, a full search for newly appeared literature at the revision stage may be worthwhile. Assessing the contribution of papers that have just appeared is particularly challenging, because there is little perspective with which to gauge their significance and impact on further research and society.

Inevitably, new papers on the reviewed topic (including independently written literature reviews) will appear from all quarters after the review has been published, so that there may soon be the need for an updated review. But this is the nature of science [27] – [32] . I wish everybody good luck with writing a review of the literature.

Acknowledgments

Many thanks to M. Barbosa, K. Dehnen-Schmutz, T. Döring, D. Fontaneto, M. Garbelotto, O. Holdenrieder, M. Jeger, D. Lonsdale, A. MacLeod, P. Mills, M. Moslonka-Lefebvre, G. Stancanelli, P. Weisberg, and X. Xu for insights and discussions, and to P. Bourne, T. Matoni, and D. Smith for helpful comments on a previous draft.

Funding Statement

This work was funded by the French Foundation for Research on Biodiversity (FRB) through its Centre for Synthesis and Analysis of Biodiversity data (CESAB), as part of the NETSEED research project. The funders had no role in the preparation of the manuscript.

ON YOUR 1ST ORDER

Different Types of Literature Review: Which One Fits Your Research?

By Laura Brown on 13th October 2023

You might not have heard that there are multiple kinds of literature review. However, with the progress in your academic career you will learn these classifications and may need to use different types of them. However, there is nothing to worry if you aren’t aware of them now, as here we are going to discuss this topic in detail.

There are approximately 14 types of literature review on the basis of their specific objectives, methodologies, and the way they approach and analyse existing literature in academic research. Of those 14, there are 4 major types. But before we delve into the details of each one of them and how they are useful in academics, let’s first understand the basics of literature review.

What is Literature Review?

A literature review is a critical and systematic summary and evaluation of existing research. It is an essential component of academic and research work, providing an overview of the current state of knowledge in a particular field.

In easy words, a literature review is like making a big, organised summary of all the important research and smart books or articles about a particular topic or question. It’s something scholars and researchers do, and it helps everyone see what we already know about that topic. It’s kind of like taking a snapshot of what we understand right now in a certain field.

It serves with some specific purpose in the research.

• Provides a comprehensive understanding of existing research on a topic.
• Identifies gaps, trends, and inconsistencies in the literature.
• Contextualise your own research within the broader academic discourse.
• Supports the development of theoretical frameworks or research hypotheses.

4 Major Types Of Literature Review

The four major types include, Narrative Review, Systematic Review, Meta-Analysis, and Scoping Review. These are known as the major ones because they’re like the “go-to” methods for researchers in academic and research circles. Think of them as the classic tools in the researcher’s toolbox. They’ve earned their reputation because they have a unique style for literature review introduction , clear steps and specific qualities that make them super handy for different research needs.

1. Narrative Review

Narrative reviews present a well-structured narrative that reads like a cohesive story, providing a comprehensive overview of a specific topic. These reviews often incorporate historical context and offer a broad understanding of the subject matter, making them valuable for researchers looking to establish a foundational understanding of their area of interest. They are particularly useful when a historical perspective or a broad context is necessary to comprehend the current state of knowledge in a field.

2. Systematic Review

Systematic reviews are renowned for their methodological rigour. They involve a meticulously structured process that includes the systematic selection of relevant studies, comprehensive data extraction, and a critical synthesis of their findings. This systematic approach is designed to minimise bias and subjectivity, making systematic reviews highly reliable and objective. They are considered the gold standard for evidence-based research as they provide a clear and rigorous assessment of the available evidence on a specific research question.

3. Meta Analysis

Meta analysis is a powerful method for researchers who prefer a quantitative and statistical perspective. It involves the statistical synthesis of data from various studies, allowing researchers to draw more precise and generalisable conclusions by combining data from multiple sources. Meta analyses are especially valuable when the aim is to quantitatively measure the effect size or impact of a particular intervention, treatment, or phenomenon.

4. Scoping Review

Scoping reviews are invaluable tools, especially for researchers in the early stages of exploring a topic. These reviews aim to map the existing literature, identifying gaps and helping clarify research questions. Scoping reviews provide a panoramic view of the available research, which is particularly useful when researchers are embarking on exploratory studies or trying to understand the breadth and depth of a subject before conducting more focused research.

Different Types Of Literature review In Research

There are some more approaches to conduct literature review. Let’s explore these classifications quickly.

5. Critical Review

Critical reviews provide an in-depth evaluation of existing literature, scrutinising sources for their strengths, weaknesses, and relevance. They offer a critical perspective, often highlighting gaps in the research and areas for further investigation.

6. Theoretical Review

Theoretical reviews are centred around exploring and analysing the theoretical frameworks, concepts, and models present in the literature. They aim to contribute to the development and refinement of theoretical perspectives within a specific field.

7. Integrative Review

Integrative reviews synthesise a diverse range of studies, drawing connections between various research findings to create a comprehensive understanding of a topic. These reviews often bridge gaps between different perspectives and provide a holistic overview.

8. Historical Review

Historical reviews focus on the evolution of a topic over time, tracing its development through past research, events, and scholarly contributions. They offer valuable context for understanding the current state of research.

9. Methodological Review

Among the different kinds of literature reviews, methodological reviews delve into the research methods and methodologies employed in existing studies. Researchers assess these approaches for their effectiveness, validity, and relevance to the research question at hand.

10. Cross-Disciplinary Review

Cross-disciplinary reviews explore a topic from multiple academic disciplines, emphasising the diversity of perspectives and insights that each discipline brings. They are particularly useful for interdisciplinary research projects and uncovering connections between seemingly unrelated fields.

11. Descriptive Review

Descriptive reviews provide an organised summary of existing literature without extensive analysis. They offer a straightforward overview of key findings, research methods, and themes present in the reviewed studies.

12. Rapid Review

Rapid reviews expedite the literature review process, focusing on summarising relevant studies quickly. They are often used for time-sensitive projects where efficiency is a priority, without sacrificing quality.

13. Conceptual Review

Conceptual reviews concentrate on clarifying and developing theoretical concepts within a specific field. They address ambiguities or inconsistencies in existing theories, aiming to refine and expand conceptual frameworks.

14. Library Research

Library research reviews rely primarily on library and archival resources to gather and synthesise information. They are often employed in historical or archive-based research projects, utilising library collections and historical documents for in-depth analysis.

Each type of literature review serves distinct purposes and comes with its own set of strengths and weaknesses, allowing researchers to choose the one that best suits their research objectives and questions.

Choosing the Ideal Literature Review Approach in Academics

In order to conduct your research in the right manner, it is important that you choose the correct type of review for your literature. Here are 8 amazing tips we have sorted for you in regard to literature review help so that you can select the best-suited type for your research.

• Clarify Your Research Goals: Begin by defining your research objectives and what you aim to achieve with the literature review. Are you looking to summarise existing knowledge, identify gaps, or analyse specific data?
• Understand Different Review Types: Familiarise yourself with different kinds of literature reviews, including systematic reviews, narrative reviews, meta-analyses, scoping reviews, and integrative reviews. Each serves a different purpose.
• Consider Available Resources: Assess the resources at your disposal, including time, access to databases, and the volume of literature on your topic. Some review types may be more resource-intensive than others.
• Alignment with Research Question: Ensure that the chosen review type aligns with your research question or hypothesis. Some types are better suited for answering specific research questions than others.
• Scope and Depth: Determine the scope and depth of your review. For a broad overview, a narrative review might be suitable, while a systematic review is ideal for an in-depth analysis.
• Consult with Advisors: Seek guidance from your academic advisors or mentors. They can provide valuable insights into which review type best fits your research goals and resources.
• Consider Research Field Standards: Different academic fields have established standards and preferences for different forms of literature review. Familiarise yourself with what is common and accepted in your field.
• Pilot Review: Consider conducting a small-scale pilot review of the literature to test the feasibility and suitability of your chosen review type before committing to a larger project.

Bonus Tip: Crafting an Effective Literature Review

Now, since you have learned all the literature review types and have understood which one to prefer, here are some bonus tips for you to structure a literature review of a dissertation .

• Clearly Define Your Research Question: Start with a well-defined and focused research question to guide your literature review.
• Thorough Search Strategy: Develop a comprehensive search strategy to ensure you capture all relevant literature.
• Critical Evaluation: Assess the quality and credibility of the sources you include in your review.
• Synthesise and Organise: Summarise the key findings and organise the literature into themes or categories.
• Maintain a Systematic Approach: If conducting a systematic review, adhere to a predefined methodology and reporting guidelines.
• Engage in Continuous Review: Regularly update your literature review to incorporate new research and maintain relevance.

Some Useful Tools And Resources For You

Effective literature reviews demand a range of tools and resources to streamline the process.

• Reference management software like EndNote, Zotero, and Mendeley helps organise, store, and cite sources, saving time and ensuring accuracy.
• Academic databases such as PubMed, Google Scholar, and Web of Science provide access to a vast array of scholarly articles, with advanced search and citation tracking features.
• Research guides from universities and libraries offer tips and templates for structuring reviews.
• Research networks like ResearchGate and Academia.edu facilitate collaboration and access to publications. Literature review templates and research workshops provide additional support.

Some Common Mistakes To Avoid

Avoid these common mistakes when crafting literature reviews.

• Unclear research objectives result in unfocused reviews, so start with well-defined questions.
• Biased source selection can compromise objectivity, so include diverse perspectives.
• Never miss on referencing; proper citation and referencing are essential for academic integrity.
• Don’t overlook older literature, which provides foundational insights.
• Be mindful of scope creep, where the review drifts from the research question; stay disciplined to maintain focus and relevance.

While Summing Up On Various Types Of Literature Review

As we conclude this classification of fourteen distinct approaches to conduct literature reviews, it’s clear that the world of research offers a multitude of avenues for understanding, analysing, and contributing to existing knowledge.

Whether you’re a seasoned scholar or a student beginning your academic journey, the choice of review type should align with your research objectives and the nature of your topic. The versatility of these approaches empowers you to tailor your review to the demands of your project.

Remember, your research endeavours have the potential to shape the future of knowledge, so choose wisely and dive into the world of literature reviews with confidence and purpose. Happy reviewing!

Laura Brown, a senior content writer who writes actionable blogs at Crowd Writer.

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• Expanding a Search
• Refining/Narrowing a Search
• Saving Searches
• Critical Appraisal & Levels of Evidence
• Citing & Managing References
• Database Tutorials
• Types of Literature Reviews
• Finding Full Text
• Term Glossary

Choosing a Review Type

For guidance related to choosing a review type, see:

• "What Type of Review is Right for You?" - Decision Tree (PDF) This decision tree, from Cornell University Library, highlights key difference between narrative, systematic, umbrella, scoping and rapid reviews.
• Reviewing the literature: choosing a review design Noble, H., & Smith, J. (2018). Reviewing the literature: Choosing a review design. Evidence Based Nursing, 21(2), 39–41. https://doi.org/10.1136/eb-2018-102895
• What synthesis methodology should I use? A review and analysis of approaches to research synthesis Schick-Makaroff, K., MacDonald, M., Plummer, M., Burgess, J., & Neander, W. (2016). What synthesis methodology should I use? A review and analysis of approaches to research synthesis. AIMS Public Health, 3 (1), 172-215. doi:10.3934/publichealth.2016.1.172 More information less... ABSTRACT: Our purpose is to present a comprehensive overview and assessment of the main approaches to research synthesis. We use "research synthesis" as a broad overarching term to describe various approaches to combining, integrating, and synthesizing research findings.
• Right Review - Decision Support Tool Not sure of the most suitable review method? Answer a few questions and be guided to suitable knowledge synthesis methods. Updated in 2022 and featured in the Journal of Clinical Epidemiology 10.1016/j.jclinepi.2022.03.004

Types of Evidence Synthesis / Literature Reviews

Literature reviews are comprehensive summaries and syntheses of the previous research on a given topic.  While narrative reviews are common across all academic disciplines, reviews that focus on appraising and synthesizing research evidence are increasingly important in the health and social sciences.  

Most evidence synthesis methods use formal and explicit methods to identify, select and combine results from multiple studies, making evidence synthesis a form of meta-research.  

The review purpose, methods used and the results produced vary among different kinds of literature reviews; some of the common types of literature review are detailed below.

Common Types of Literature Reviews 1

Narrative (literature) review.

• A broad term referring to reviews with a wide scope and non-standardized methodology
• Search strategies, comprehensiveness of literature search, time range covered and method of synthesis will vary and do not follow an established protocol

Integrative Review

• A type of literature review based on a systematic, structured literature search
• Often has a broadly defined purpose or review question
• Seeks to generate or refine and theory or hypothesis and/or develop a holistic understanding of a topic of interest
• Relies on diverse sources of data (e.g. empirical, theoretical or methodological literature; qualitative or quantitative studies)

Systematic Review

• Systematically and transparently collects and categorize existing evidence on a question of scientific, policy or management importance
• Follows a research protocol that is established a priori
• Some sub-types of systematic reviews include: SRs of intervention effectiveness, diagnosis, prognosis, etiology, qualitative evidence, economic evidence, and more.
• Time-intensive and often takes months to a year or more to complete 
• The most commonly referred to type of evidence synthesis; sometimes confused as a blanket term for other types of reviews

Meta-Analysis

• Statistical technique for combining the findings from disparate quantitative studies
• Uses statistical methods to objectively evaluate, synthesize, and summarize results
• Often conducted as part of a systematic review

Scoping Review

• Systematically and transparently collects and categorizes existing evidence on a broad question of scientific, policy or management importance
• Seeks to identify research gaps, identify key concepts and characteristics of the literature and/or examine how research is conducted on a topic of interest
• Useful when the complexity or heterogeneity of the body of literature does not lend itself to a precise systematic review
• Useful if authors do not have a single, precise review question
• May critically evaluate existing evidence, but does not attempt to synthesize the results in the way a systematic review would 
• May take longer than a systematic review

Rapid Review

• Applies a systematic review methodology within a time-constrained setting
• Employs methodological "shortcuts" (e.g., limiting search terms and the scope of the literature search), at the risk of introducing bias
• Useful for addressing issues requiring quick decisions, such as developing policy recommendations

Umbrella Review

• Reviews other systematic reviews on a topic
• Often defines a broader question than is typical of a traditional systematic review
• Most useful when there are competing interventions to consider

1. Adapted from:

Eldermire, E. (2021, November 15). A guide to evidence synthesis: Types of evidence synthesis. Cornell University LibGuides. https://guides.library.cornell.edu/evidence-synthesis/types

Nolfi, D. (2021, October 6). Integrative Review: Systematic vs. Scoping vs. Integrative. Duquesne University LibGuides. https://guides.library.duq.edu/c.php?g=1055475&p=7725920

Delaney, L. (2021, November 24). Systematic reviews: Other review types. UniSA LibGuides. https://guides.library.unisa.edu.au/SystematicReviews/OtherReviewTypes

Further Reading: Exploring Different Types of Literature Reviews

• A typology of reviews: An analysis of 14 review types and associated methodologies Grant, M. J., & Booth, A. (2009). A typology of reviews: An analysis of 14 review types and associated methodologies. Health Information and Libraries Journal, 26 (2), 91-108. doi:10.1111/j.1471-1842.2009.00848.x More information less... ABSTRACT: The expansion of evidence-based practice across sectors has lead to an increasing variety of review types. However, the diversity of terminology used means that the full potential of these review types may be lost amongst a confusion of indistinct and misapplied terms. The objective of this study is to provide descriptive insight into the most common types of reviews, with illustrative examples from health and health information domains.
• Clarifying differences between review designs and methods Gough, D., Thomas, J., & Oliver, S. (2012). Clarifying differences between review designs and methods. Systematic Reviews, 1 , 28. doi:10.1186/2046-4053-1-28 More information less... ABSTRACT: This paper argues that the current proliferation of types of systematic reviews creates challenges for the terminology for describing such reviews....It is therefore proposed that the most useful strategy for the field is to develop terminology for the main dimensions of variation.
• Are we talking the same paradigm? Considering methodological choices in health education systematic review Gordon, M. (2016). Are we talking the same paradigm? Considering methodological choices in health education systematic review. Medical Teacher, 38 (7), 746-750. doi:10.3109/0142159X.2016.1147536 More information less... ABSTRACT: Key items discussed are the positivist synthesis methods meta-analysis and content analysis to address questions in the form of "whether and what" education is effective. These can be juxtaposed with the constructivist aligned thematic analysis and meta-ethnography to address questions in the form of "why." The concept of the realist review is also considered. It is proposed that authors of such work should describe their research alignment and the link between question, alignment and evidence synthesis method selected.
• Meeting the review family: Exploring review types and associated information retrieval requirements Sutton, A., Clowes, M., Preston, L., & Booth, A. (2019). Meeting the review family: Exploring review types and associated information retrieval requirements. Health Information & Libraries Journal, 36(3), 202–222. doi: 10.1111/hir.12276

Integrative Reviews

"The integrative review method is an approach that allows for the inclusion of diverse methodologies (i.e. experimental and non-experimental research)." (Whittemore & Knafl, 2005, p. 547).

• The integrative review: Updated methodology Whittemore, R., & Knafl, K. (2005). The integrative review: Updated methodology. Journal of Advanced Nursing, 52 (5), 546–553. doi:10.1111/j.1365-2648.2005.03621.x More information less... ABSTRACT: The aim of this paper is to distinguish the integrative review method from other review methods and to propose methodological strategies specific to the integrative review method to enhance the rigour of the process....An integrative review is a specific review method that summarizes past empirical or theoretical literature to provide a more comprehensive understanding of a particular phenomenon or healthcare problem....Well-done integrative reviews present the state of the science, contribute to theory development, and have direct applicability to practice and policy.

• Conducting integrative reviews: A guide for novice nursing researchers Dhollande, S., Taylor, A., Meyer, S., & Scott, M. (2021). Conducting integrative reviews: A guide for novice nursing researchers. Journal of Research in Nursing, 26(5), 427–438. https://doi.org/10.1177/1744987121997907
• Rigour in integrative reviews Whittemore, R. (2007). Rigour in integrative reviews. In C. Webb & B. Roe (Eds.), Reviewing Research Evidence for Nursing Practice (pp. 149–156). John Wiley & Sons, Ltd. https://doi.org/10.1002/9780470692127.ch11

Scoping Reviews

Scoping reviews are evidence syntheses that are conducted systematically, but begin with a broader scope of question than traditional systematic reviews, allowing the research to 'map' the relevant literature on a given topic.

• Scoping studies: Towards a methodological framework Arksey, H., & O'Malley, L. (2005). Scoping studies: Towards a methodological framework. International Journal of Social Research Methodology, 8 (1), 19-32. doi:10.1080/1364557032000119616 More information less... ABSTRACT: We distinguish between different types of scoping studies and indicate where these stand in relation to full systematic reviews. We outline a framework for conducting a scoping study based on our recent experiences of reviewing the literature on services for carers for people with mental health problems.
• Scoping studies: Advancing the methodology Levac, D., Colquhoun, H., & O'Brien, K. K. (2010). Scoping studies: Advancing the methodology. Implementation Science, 5 (1), 69. doi:10.1186/1748-5908-5-69 More information less... ABSTRACT: We build upon our experiences conducting three scoping studies using the Arksey and O'Malley methodology to propose recommendations that clarify and enhance each stage of the framework.
• Methodology for JBI scoping reviews Peters, M. D. J., Godfrey, C. M., McInerney, P., Baldini Soares, C., Khalil, H., & Parker, D. (2015). The Joanna Briggs Institute reviewers’ manual: Methodology for JBI scoping reviews [PDF]. Retrieved from The Joanna Briggs Institute website: http://joannabriggs.org/assets/docs/sumari/Reviewers-Manual_Methodology-for-JBI-Scoping-Reviews_2015_v2.pdf More information less... ABSTRACT: Unlike other reviews that address relatively precise questions, such as a systematic review of the effectiveness of a particular intervention based on a precise set of outcomes, scoping reviews can be used to map the key concepts underpinning a research area as well as to clarify working definitions, and/or the conceptual boundaries of a topic. A scoping review may focus on one of these aims or all of them as a set.

Systematic vs. Scoping Reviews: What's the Difference? 

YouTube Video 4 minutes, 45 seconds

Rapid Reviews

Rapid reviews are systematic reviews that are undertaken under a tighter timeframe than traditional systematic reviews. 

• Evidence summaries: The evolution of a rapid review approach Khangura, S., Konnyu, K., Cushman, R., Grimshaw, J., & Moher, D. (2012). Evidence summaries: The evolution of a rapid review approach. Systematic Reviews, 1 (1), 10. doi:10.1186/2046-4053-1-10 More information less... ABSTRACT: Rapid reviews have emerged as a streamlined approach to synthesizing evidence - typically for informing emergent decisions faced by decision makers in health care settings. Although there is growing use of rapid review "methods," and proliferation of rapid review products, there is a dearth of published literature on rapid review methodology. This paper outlines our experience with rapidly producing, publishing and disseminating evidence summaries in the context of our Knowledge to Action (KTA) research program.
• What is a rapid review? A methodological exploration of rapid reviews in Health Technology Assessments Harker, J., & Kleijnen, J. (2012). What is a rapid review? A methodological exploration of rapid reviews in Health Technology Assessments. International Journal of Evidence‐Based Healthcare, 10 (4), 397-410. doi:10.1111/j.1744-1609.2012.00290.x More information less... ABSTRACT: In recent years, there has been an emergence of "rapid reviews" within Health Technology Assessments; however, there is no known published guidance or agreed methodology within recognised systematic review or Health Technology Assessment guidelines. In order to answer the research question "What is a rapid review and is methodology consistent in rapid reviews of Health Technology Assessments?", a study was undertaken in a sample of rapid review Health Technology Assessments from the Health Technology Assessment database within the Cochrane Library and other specialised Health Technology Assessment databases to investigate similarities and/or differences in rapid review methodology utilised.
• Rapid Review Guidebook Dobbins, M. (2017). Rapid review guidebook. Hamilton, ON: National Collaborating Centre for Methods and Tools.
• NCCMT Summary and Tool for Dobbins' Rapid Review Guidebook National Collaborating Centre for Methods and Tools. (2017). Rapid review guidebook. Hamilton, ON: McMaster University. Retrieved from http://www.nccmt.ca/knowledge-repositories/search/308
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Review Comparison Chart

A selection of the common review types found in the literature is presented and compared in the following table using the SALSA framework developed by Grant and Booth (2009).

Adapted from:

Grant, M.J. and Booth, A. (2009), A typology of reviews: an analysis of 14 review types and associated methodologies. Health Information & Libraries Journal, 26: 91-108.  https://doi.org/10.1111/j.1471-1842.2009.00848.x

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"Review Comparison Chart" from  Which review is that? A guide to review types  by the University of Melbourne Library is used with permission.

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Title: Trends in Organizational Behavior: A Systematic Review and Research Directions

Authors : Shilpi Kalwani; Jayashree Mahesh

Addresses : N/A ' N/A

Abstract : Purpose - The purpose of this paper is to present a step-by-step guide to facilitate understanding of emerging trends in the discipline of Organizational Behavior using the technique of Systematic Literature Review. Method - Literature review is done by systematically collecting the existing literature over the period of 1990- 2019. The literature is categorized according to the Journal Name and Ranking, Database, and Geographical Distribution (country wise). Literature is also categorized on the basis of type of study (empirical/conceptual), variables used, scales used, sample studies and sub area of study (Leadership/Motivation etc). This classification can serve as a base for researchers who wish to conduct meta-analysis on emerging trends in Organizational Behavior. Findings - A disciplined screening process resulted in 81 relevant research papers appropriate for the study. These papers explain the emerging trends in the discipline since 1990. Limitations - Due to the vast areas and sub-areas covered under Organizational Behavior, it is not possible to study the entire discipline since 1990 in a single study. Hence the study only focuses on relevant and emerging trends in Organizational Behavior. Implications - The study aims to fill the gap of unavailability of a structured systematic literature review in the discipline of Organizational Behaviour. This may serve as an important source of information for Academicians, Practitioners. The study postulates new avenues for future research. Originality - The study contributes to the methodology for conducting Systematic Literature Reviews in the field of management, specifically in Organizational Behaviour. It highlights an effective method for mapping out thematically, and viewing holistically, emerging research trends.

Keywords : Future workplaces; systematic literature review; organizational behavior.

DOI : 10.1504/JBM.2020.141279

Journal of Business and Management, 2020 Vol.26 No.1, pp.40 - 78

Published online: 05 Sep 2024 *

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• Published: 05 September 2024

The goldmine of GWAS summary statistics: a systematic review of methods and tools

• Panagiota I. Kontou 1 &
• Pantelis G. Bagos 2  

BioData Mining volume  17 , Article number:  31 ( 2024 ) Cite this article

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Genome-wide association studies (GWAS) have revolutionized our understanding of the genetic architecture of complex traits and diseases. GWAS summary statistics have become essential tools for various genetic analyses, including meta-analysis, fine-mapping, and risk prediction. However, the increasing number of GWAS summary statistics and the diversity of software tools available for their analysis can make it challenging for researchers to select the most appropriate tools for their specific needs. This systematic review aims to provide a comprehensive overview of the currently available software tools and databases for GWAS summary statistics analysis. We conducted a comprehensive literature search to identify relevant software tools and databases. We categorized the tools and databases by their functionality, including data management, quality control, single-trait analysis, and multiple-trait analysis. We also compared the tools and databases based on their features, limitations, and user-friendliness. Our review identified a total of 305 functioning software tools and databases dedicated to GWAS summary statistics, each with unique strengths and limitations. We provide descriptions of the key features of each tool and database, including their input/output formats, data types, and computational requirements. We also discuss the overall usability and applicability of each tool for different research scenarios. This comprehensive review will serve as a valuable resource for researchers who are interested in using GWAS summary statistics to investigate the genetic basis of complex traits and diseases. By providing a detailed overview of the available tools and databases, we aim to facilitate informed tool selection and maximize the effectiveness of GWAS summary statistics analysis.

Peer Review reports

Genome-wide association studies (GWAS) enable the simultaneous testing of thousands of genetic variants, usually SNPs, across the genome in order to find variants associated with a trait or a disease [ 1 ]. The GWAS methodology, so far, has generated many robust associations for various traits and diseases and has revolutionized our understanding of the genetic architecture of complex traits. With increasing sample sizes, new sequencing technologies and the accumulation of large biobanks it is expected that our ability to investigate the effects of human genetic variation in complex traits will increase in the near future [ 2 ]. In the first years of the development of the field, efforts were oriented towards the statistical aspects of the analysis [ 3 ], which involved thousands of SNPs simultaneously, including the methodology for multiple testing and quality control. This task was successful and enabled the discovery of associations replicated in subsequent studies, and in several cases, validated experimentally and functionally using a wide variety of methods [ 4 ]. However, it was soon clear that most variants discovered via GWAS have small overall effects on disease susceptibility [ 5 ]. Thus, it became evident that integrating data from multiple sources and developing reliable bioinformatics tools was a necessary step in order to address the complexity of the underlying genetic basis of common human diseases [ 5 ].

Soon after the publication of the first GWAS it also became evident that, at least theoretically, individuals could be identified in such cohorts even if only the summary statistics are available [ 6 ]. This led to imposing strict control access for sharing individual patients’ data (IPD) from GWAS. Subsequent works found that privacy attacks are possible in theory but unsuccessful and unconvincing in real practice. For instance, even sharing 1,000 SNPs for datasets with more than 500 individuals generally leads to a low power of the “attack” [ 7 ]. A more thorough investigation is given in [ 8 ]. In practice, however, not all studies share their data, at least when it comes to the studies published in the first decade of GWAS. It has been estimated that the proportion is only 13%, which increased from 3% in 2010 to 23% in 2017 [ 9 ]. On the contrary, researchers sharing their summary data has been shown to receive on average 81.8% more citations, an effect that probably is related, at least partially, to the usability of the data in downstream analyses [ 10 ]. Summary statistics do not only offer the additional protection of privacy, but also offer significant advantages in computational cost when using the data in downstream analyses, which does not scale with the number of participants in the study [ 11 ]. Thus, it is of no surprise that during the last years a large variety of methods have been developed to perform a so-called post-GWAS analysis using the summary results of a single study, or of several studies, and in most cases integrating data from other sources [ 11 ]. The majority of these methods use the summary data in the form of per-allele SNP effect sizes (log odds ratios or betas) along with their standard errors, or equivalently the z-scores (per-allele effect sizes divided by their standard errors). These methods seek to go a step further from the simple analysis, or re-analysis of a study, and aim to improve our understanding about the functional role of the identified variants [ 12 ]. The most important factors that played significant role in the development of such methods, in this so-called post-GWAS era, is the linkage disequilibrium (LD) information from a population reference panel such as HapMap or 1000 Genomes Project, the gene expression variation in the form of eQTL, and the integration of functional information on biological pathways [ 13 , 14 , 15 ].

The methods developed so far cover a broad range of different types of analysis, either in the study of a single trait or in the combined analysis of multiple traits. For a single trait, we may have methods for meta-analysis [ 16 , 17 ], methods for inferring heritability [ 18 , 19 ], gene-based tests [ 20 ], methods for Gene Set (or Pathway) Analysis [ 21 ], or methods for fine-mapping causal variants [ 22 ]. Regarding the analysis of multiple traits there is also a variety of methods [ 23 ], ranging from those that estimate the genetic correlation between traits [ 24 ], the joint analysis of multiple traits [ 25 ], or the methods that try to estimate causality between traits such as Mendelian Randomization [ 26 ], transcriptome-wide association studies [ 27 ], or colocalization [ 28 ]. Of course, the data standards [ 29 ] used to facilitate these analyses and the databases that the results are stored in, are also of great importance for the community.

In order to provide a comprehensive overview of the currently available software tools and databases for GWAS summary statistics we performed a systematic review following the PRISMA guidelines [ 30 ]. We conducted a comprehensive search of the literature to identify relevant software tools and databases. We categorized the tools and databases by their functionality, in categories related to data, single-trait analysis, and multiple-trait analysis, along with their sub-categories mentioned in the previous paragraph. We also compared the tools and databases based on their features, limitations, and user-friendliness. Our review identified a wide range of software tools and databases for GWAS summary statistics analysis, each with unique strengths and limitations. We provide descriptions of the key features of each tool and database, including their input/output formats, data types, and computational requirements. We also discuss the overall usability and applicability of each tool for different research scenarios. This comprehensive review will serve as a valuable resource for researchers who are interested in using GWAS summary statistics to investigate the genetic basis of complex traits and diseases. By providing a detailed overview of the available tools and databases, we aim to facilitate informed tool selection and maximize the effectiveness of using GWAS summary statistics.

The systematic review

In order to collect all the available published papers, we performed a systematic review of the literature following the PRISMA guidelines [ 30 ]. The search was performed in PubMed ( https://pubmed.ncbi.nlm.nih.gov ) with the following query: ("Summary Statistics" OR "Summary Data" OR "Summary Association Statistics" OR "Summary Association Data") AND (GWAS OR genomewide OR genome-wide ). The abstracts initially, and then the full articles were scrutinized in order to collect the necessary information. The inclusion criteria state that methods, software tools and databases, suitable for the analysis of GWAS summary data are suitable for inclusion. Methods papers that do not report software, or software pages not currently available are excluded. Additional searches were performed in the reference lists of the identified articles in order to identify additional studies that were missing. In many cases multiple articles regarding a single tool were found, so we kept only one. We decided to include reports deposited in preprint servers like medRxiv/bioRxiv, but some of these papers were eventually published in peer-review journals, so in such cases we retained only the latter reference. Tools regarding Polygenic Risk Scores (PRSs) and visualization were excluded. For all included tools we recorded the URL, the PMID, and the main functionality/es along with comments regarding its main methodological features. The initial search identified 2942 articles (22/12/2023).

In total we identified 305 tools and databases (Fig.  1 ). We classified them in three broad categories: data , tools for single traits and tools for multiple traits , along with the various sub-categories. The total breakdown is given in Table  1 . Several tools may perform different tasks and thus they can be considered for more than one category; so, we classified them to the one most closely related to the primary goal of the analysis they claim to perform. Other tools do not fit exactly to the general description of the category, but we nevertheless classified them to the most similar one. The largest sub-category consists of the tools for pleiotropy analysis, whereas the smallest one is related to reconstruction of genotypes and effect sizes. Most tools are written in R (56.4%) with the largest proportion being in the multiple traits category, followed by Python (12.5%) and C/C +  + (8.2%) (Fig.  2 ). Apart from the publicly available databases only a handful of tools are offered as webservers (6.95%). Most of the tools were published after 2015 (Fig.  3 ). Nearly 60% of the tools and databases were published in: Bioinformatics, American Journal of Human Genetics, Nature Genetics, Nature Communications, Nucleic Acids Research and PloS Genetics (Fig.  4 ). In the following sections we proceed with the detailed description of the various tools identified, classified in the different categories and sub-categories. The complete list of identified tools along with the relevant information is given in Supplementary Table 1.

PRISMA Flow diagram for systematic review

Number of Tools and Databases included in the review Published Per Year

The programming languages used in the various categories of identified tools

The journals in which the studies including in the review were published

Firstly, we are going to present the tools dedicated to the data themselves. We include here tools for quality control of GWA summary statistics, tools for imputation and genotype reconstruction as well as the publicly available databases of summary results.

Standards and quality control

The need for sharing and re-using GWAS summary statistics has been an issue for the community during the last years. Generally, it is acceptable that the minimum information (“mandatory”) contained in GWAS summary statistics should include: the chromosome and the base-pair location, the p-value of the association, the risk allele and the other allele, the risk allele frequency, and an estimate of the effect size (odds ratio or beta) along with its standard error [ 29 ]. Other important summary statistics that nevertheless termed as “encouraged” ones include the sample size, the variant ID, the rsID, the confidence interval of the effect size and so on. Such specifications were considered for the GWAS-SSF format [ 31 ], which was developed to meet the requirements settled by the community. GWAS-SSF consists of a tab-separated data file with well-defined fields and an accompanying metadata file. Most repositories and programs use some variant of the GWAS-SSF. However, such tabular formats in several cases lead to ambiguity or incomplete storage of information, or other times lack essential metadata. This leads to poor performance and increased risk of possible errors in downstream analyses. To address these issues, an adaptation of the well-known variant call format [ 32 ] was developed, capable of storing GWAS summary statistics which was called GWAS-VCF along with software tools to apply it in downstream analyses [ 33 ]. The VCF contains a file header with metadata and a main file containing variant-level (one locus per row with one or more alternative alleles/variants) and sample-level (one sample per column) information. This way, the VCF was adapted to include GWAS-specific metadata utilizing the sample column to store variant-trait association data. The GWAS-VCF is the standard used by the MRC-IEU OpenGWAS database [ 34 ] and it comes with appropriate tools to map GWAS summary statistics to VCF with on-the-fly harmonization ( https://github.com/mrcieu/gwas2vcf ).

Despite these efforts, not all available data are in line with the standards, especially when dealing with data from older studies. Thus, there is a need for additional tools to harmonize the data, as well as to identify and correct errors. Tools belonging to the former class were developed early and were focused mainly on harmonizing data in preparation of a meta-analysis. These include QCGWAS [ 35 ], GWAtoolbox [ 36 ] and EasyQC [ 37 ]. GEAR [ 38 ] is very interesting in that it incorporates ideas from population genetics which allow verification of the genetic origin and geographic location of each cohort and identifying significant sample overlap. More recent tools like MungeSumstats [ 39 ] and GWASlab [ 40 ] perform standardization and quality control handling the most common formats, SumStatsRehab [ 41 ] can be used for data validation, restoration of missing data, correction of errors or formatting, and GWASinspector [ 42 ] provides extensive QC reports and perform harmonization being compatible with recent reference panels and by handling insertion/deletion and multi-allelic variants. The latter class of methods, additionally, leverages information from the LD among SNPs. One such tool is GQS [ 43 ] which identifies suspicious regions and prevents erroneous interpretations by comparing the significance of the association for each SNP to its LD value for the reported index SNP. Similar functionalities are offered by DENTIST [ 44 ] which uses LD to detect and eliminate errors and disagreements between GWAS data and the LD reference panel. EXTminus23andMe [ 45 ] evaluates the quality of summary statistics after data removal and the suitability of the down sampled summary statistics for typical follow-up genetic analyses.

The publicly available biological databases played and continue to play a central role in bioinformatics and in biological research in general [ 46 , 47 , 48 ]. The same is the case for databases related to human research [ 49 ] and in particular those involved in GWAS [ 50 ]. The databases we identified can be roughly divided in two categories: databases that contain summary statistics from GWAS and databases that contain important secondary analyses on those data with some of the methods that we will describe in later sections.

Regarding the databases of the first category, NCBI’s dbGAP [ 51 ] was developed to contain the results of studies investigating the interaction of genotype and phenotype, which include GWAS. One of the dbGAP’s primary objectives was to house individual level GWAS data, but the database also contains summary data as well. Summary statistics are generally available to the public, whereas access to IPD requires varying levels of authorization. The NHGRI-EBI GWAS Catalog [ 52 ], which was established in 2008 is considered for years the central repository of GWAS summary statistics. It is a high-quality curated collection of all published GWAS and as of 2023–12-20, contains 6,680 publications, 566,798 top associations and 66,825 full summary statistics (Fig.  5 ). The database played an important role in the community efforts leading to the development of GWAS-SSF format. GWAScentral [ 53 ] previously known as the Human Genome Variation (HGV) database of Genotype-to-Phenotype information is a database that contains over 72.5 million P-values for over 5,000 studies, with over 7.4 million unique genetic markers involved in more than 1,700 unique phenotypes. The database contains data from several sources (including NHGRI-EBI GWAS Catalog, OpenGWAS, Japanese GWASdb, dbGaP, WTCCC and so on). The IEU MRC OpenGWAS [ 34 ] is a new addition and contains 346 million genetic associations from 50,037 GWAS summary datasets. It contains complete data from various consortia and the UK Biobank and comes with a lot of tools for harmonizing the data and storing them in the GWAS-VCF format. At the time of writing there are 4,126 binary traits, 725 metabolites, 3,371 proteins, 3,143 brain imaging phenotypes, and 3,217 other continuous phenotypes. In addition to the complete GWAS summary data, it also contains independent top hits for every dataset, totaling 116,918 independent signals in which 7,109 datasets have at least one hit. GeneATLAS [ 54 ] and GBE [ 55 ] contain associations from the UK Biobank cohort. GeneATLAS currently contains data for 452,264 individuals, 778 traits and 30 million variants, whereas GBE contains summary statistics from over 750,000 individuals combining data from the UK Biobank, the Million Veterans Program and the Biobank Japan. GTEx [ 56 ] and QTLbase [ 57 ] are the primary resources for xQTL data. The GTEx project has been expanded over time, and currently contains data of genetic associations for gene expression and splicing in 838 individuals in 49 tissues. QTLbase, similarly, contains genome-wide QTL summary statistics for many molecular traits across 95 tissue/cell types and multiple conditions. Contains tens of millions of significant genotype-molecular trait associations under different conditions. Other resources of this category, related to various large consortia (GIANT, WTCC, PGC etc.) as well as other biobanks (FinnGen etc.) can be found in Supplementary Table 2.

A snapshot of the data. A A view of the Type 2 Diabetes Mellitus studies deposited in NHGRI-EBI GWAS Catalog. B Type 2 Diabetes Mellitus studies contained in GWAS Central, depicting the significant hits in the chromosomes. C The SFF format

The second category contains databases of important secondary analyses performed on GWAS summary statistics with some of the methods that we describe in detail in later sections, such as gene-based tests, heritability analysis, TWAS, colocalization and so on. TSEA-DB [ 58 ] and PCGA [ 59 ] use information from gene-expression in various tissues to perform tissue or cell-type enrichment analysis of the GWAS association statistics. webTWAS [ 60 ] and COLOCdb [ 61 ] also use information on eQTL but in different fashion. webTWAS currently contains data for over 1,389 full GWAS for which it calculates the causal genes using single tissue expression imputation (using MetaXcan and FUSION), or cross-tissue expression imputation (using UTMOST). COLOCdb on the other hand is the most comprehensive colocalization analysis by integrating publicly available GWASs with different types of xQTL and different algorithms (COLOC, SMR). GWAS ATLAS [ 62 ] contains results of 4,756 GWAS from 473 unique studies across 3,302 unique traits accompanied by useful information obtained from downstream analysis. Each study is accompanied by MAGMA results (see also “gene-based tests”), SNP heritability estimation and genetic correlations with other traits in the database. GWASROCS [ 63 ], on the other hand, contains a large and comprehensive set of SNP-derived AUROCs and heritabilities. Currently includes 579 simulated populations (corresponding to 219 traits) and SNP data (odds ratio, risk allele frequency, and p-values) for 2,886 unique SNPs. Phenome-wide association studies (PheWAS) invert the idea of a GWAS by searching for phenotypes associated with specific variants across the range of thousands of human phenotypes, or the “phenome [ 64 , 65 , 66 ]. Thus, it is expected that a PheWAS will need large databases of GWAS results. PhenoScanner [ 67 ] is the most complete such database with publicly available results from over 65 billion associations and more than 150 million unique genetic variants. Similar functionalities are offered also by OpenGWAS, GWAS ATLAS and PheWAS Catalog [ 68 ]. Lastly, we need to mention LD Hub [ 69 ], a centralized database of publicly available GWAS results for 173 diseases/traits which offers a web interface that automates the LD score regression (LDSC) analysis pipeline (see also “Genetic correlation”).

Imputation and genotype reconstruction

Although some of the methods for quality control mentioned previously can correct errors and alter the data, the methods used for imputation go one step further. As expected, imputation methods were developed initially for individual data for handling studies genotyped with different platforms [ 70 , 71 , 72 ]. Such methods can infer missing genotypes using LD information from reference samples genotyped using denser arrays or sequencing. Genotype imputation increases the coverage of SNPs and thus can be used to increase statistical power, increase the accuracy of fine-mapping and harmonize the data in order to facilitate meta-analysis [ 70 ]. Several factors can influence the imputation accuracy: the sample size, the suitability of the reference panel for the particular sample, the genotyping chip and the allele frequency [ 71 ]. In general, however, these methods are time-consuming since they process individuals one at a time, and thus methods that impute directly the summary statistics were developed. These methods utilize only the information provided in the sample regarding the studied population (p-value, z-score or odds-ratio/beta) and require additional information regarding the LD structure. Nearly all methods perform a kind of multiple regression assuming the multivariate normal distribution for the test statistics and utilizing the theoretical result pointing that the correlation of such test statistics equals the correlation of the corresponding variables [ 73 ], that is the genotype correlation, available through the reference panel. Such methods include FAPI [ 74 ], ImpG [ 75 ], RAISS [ 76 ], DIST [ 77 ] and SSimp [ 78 ] with most of the differences lying in the choice of the reference panel and the exact details of the mathematical methods used to handle matrix inversions in the multivariate normal. DISSCO [ 79 ] uses a similar framework but allows for covariates. Such methods may perform poorly in cases where the sample has a different LD structure compared to the reference panel. Thus, extensions such as DISTMIX [ 80 ] and ARDISS [ 81 ] were developed to handle mixed ethnicity cohorts, improving the imputation performance. Adapt-Mix [ 82 ] estimates the correlation structure in both admixed and non-admixed individuals using simulated and real data and allows the use of this matrix with other imputation methods. Other methods such LS-meta [ 83 ] and LSimputing [ 84 ] offer additional advantages; LS-meta imputes both genetic and environmental components using information from additional omics-trait association summary data, whereas LSimputing implements a non-parametric method that allows for nonlinear SNP-trait associations and predictions in case a sample of IPD is available. Using the same principles, simGWAS [ 84 ] allows simulation of whole GWAS summary data, without generating individual data as an intermediate step.

Genotype reconstruction methods take a different approach. Given the summary statistics for a SNP (either directly measured or imputed), one can reconstruct the genotype counts that produced it. This will offer many advantages, since with the reconstructed genotypes the researchers could perform additional analyses using other statistical methods suitable for grouped data and test different hypotheses [ 85 ]. For instance, one can calculate grouped Polygenic Risk Scores (PRS) [ 85 ], perform logistic regression for grouped data [ 85 , 86 ], perform multivariate meta-analysis [ 87 ], or implement robust tests for association that is expected to work better when the underlying model of inheritance deviates from the additive which is usually assumed [ 88 , 89 ]. The details and the success of the reconstruction depend heavily on available summary statistics. As one can easily understand, p-values and z-scores cannot be used, and one must rely on available effect sizes such as the odds ratio (OR). When the OR, the standard error and the sample size is given, methods are available in epidemiology that allow the reconstruction of the allelic 2X2 table [ 90 ]. If z-scores, confidence intervals or p-values are available one can use them to obtain the standard error. React [ 85 ] uses an equivalent method relying on solving a system of nonlinear equations. If the allele frequency in one group (usually the controls) is also known, the allelic counts may easily be obtained with a simple calculation. In all cases the accuracy of the reconstruction may depend on the precision of the available summary statistics. After the allelic 2X2 table is reconstructed, it is straightforward to obtain the genotype counts, assuming HWE (which as one might expect adds another source of potential bias). MetaSustract [ 91 ] is a tool that recreates analytically the results of the validation cohort from meta-analysis summary statistics, allowing the researchers to compute meta-analysis summary statistics that are independent of the validation cohort, without requiring access to the IPD. Spkmt [ 92 ] works in similar fashion but in families; it can be used to derive the summary statistics of one parent from the data of the offspring and the other parent. Finally, we need to mention two tools that work in somewhat different modes. OATH [ 93 ] is used to reproduce reported results from a GWAS and recover underreported results from other alternative models with a different combination of nuisance parameters, whereas LMOR [ 94 ] performs transformations from the genetic effects estimated under the Linear Mixed Model to the Odds Ratio that only rely on summary statistics.

Analysis of a single trait

In this section we are going to present the various types of methods and tools dedicated to the analysis of a single trait. These include tools for meta-analysis , tools for the estimation of heritability , tools for implementing gene-based tests , gene set methods and fine mapping methods.

Meta -analysis

One of the most obvious uses of GWAS summary data is to combine them and perform a meta-analysis. Meta-analysis is the statistical procedure used to combine evidence from multiple studies in order to increase statistical power and it is a methodology widely used in medical research for decades [ 95 ]. A meta-analysis can be performed with various methods [ 16 ] using IPD or summary data; the former offers many advantages, but the latter is far more easy to be performed taking into account the various restrictions imposed on sharing GWAS IPD and the difficulties in the logistics of such a project [ 17 ]. Moreover, given the large samples usually encountered in GWAS it has been shown, both theoretically and empirically, that meta-analysis using summary statistics has the same efficiency as the joint analysis of IPD [ 96 ]. A compromise between these two extremes arises when a research group has access to individual-level genotype data of a limited sample size and wants to integrate these with existing summary data available in the databases. Such methods are in use in epidemiology for years [ 97 ] and several tools have been developed especially for handling GWAS data, for instance IGESS [ 98 ], metaGIM [ 99 ] and LEP [ 100 ]. PolyGIM [ 101 ] can be applied with or without IPD and uses polytomous logistic regression to investigate disease subtype heterogeneity in situations when only summary data is available.

Regarding summary-data meta-analysis of GWAS, the most commonly used methods includes standard methods, such as combining p-values, z-statistics or effects sizes like Odds Ratio (for binary traits) or mean differences (for continuous traits) using fixed or random effects models [ 16 , 102 ]. These statistical methods are straightforward to implement, and are available in general purpose statistical packages such as STATA and R. However, there are several specialized tools that facilitate the process and provide integration with useful bioinformatics or visualization functions. Such widely used tools include METAL [ 103 ], GWAMA [ 104 ] and PLINK [ 105 ]. Other tools are oriented to more specialized cases offering advanced options. For instance, YAMAS performs meta-analysis including missing SNPs identified with LD without performing imputation [ 106 ] and rareMETALS [ 107 ] uses a partial correlation based score to perform meta-analysis in the presence of large amounts of missing values. There is also a class of tools which focus on the replication of GWAS and the combined analysis of data from primary and replication studies. Such tools include rfdr [ 108 ] and Jlfdr [ 109 ] which control for False Discovery Rate (FDR), Rrate [ 110 ], which determines the sample size of the replication study and checks the consistency between the primary and the replication study, and MAJAR [ 111 ] which jointly test prognostic and predictive effects in meta-analysis without the need of using an independent cohort. metaGAP [ 112 ] is an online tool for calculating the statistical power of a meta-analysis of GWAS (Fig.  6 ). METACARPA works with overlapping or related samples, even when details of the overlap or relatedness are unknown [ 113 ], MAGENTA [ 114 ] performs meta-analysis with gene set enrichment analysis (GSEA), whereas GWASmeta [ 115 ] and MetABF [ 116 ] work in a bayesian framework calculating the Approximate Bayes Factor (ABF). Other tools offer more advanced options such as meta-analysis with multiple traits (see also “multiple traits”), like nGWAMA [ 117 ], metaCCA [ 118 ], CPASSOC [ 119 ], metaUSAT [ 120 ] and CPBayes [ 114 ] (and its extension GCPBayes [ 121 ]), and others are designed for meta-analysis under different genetic models, like GWAR [ 89 ] which uses robust methods (like MIN2 or MAX) in order to handle the uncertainty in the underlying genetic model, or like the simulation tool [ 122 ] which implements an alternate strategy for the additive genetic model simulating data for the individual studies. Finally, we need to mention sPLINK [ 123 ] which performs privacy-aware GWAS on distributed datasets, and XPEB [ 124 ] which is an empirical Bayes approach designed to improve the power GWAS in minority populations by exploiting information from GWASs performed in populations of different origin.

Tools for meta-analysis. A GWASmeta (SMetABF) for performing Bayesian meta-analysis. B The MetaGAP power calculator. C GWAR for robust analysis and meta-analysis of GWAS

Inferring heritability

Heritability is generally defined as the fraction of phenotypic variation explained by genetic variation. Heritability is a dimensionless parameter of the population, and it was introduced by Sewall Wright and Ronald Fisher in the previous century. Traditionally, heritability is estimated using family-based designs such as twin studies. However, there are controversies regarding the various methodologies for estimation and interpretation of the results [ 125 ]. Despite all these, heritability is an important aspect of research in modern genetics, and regarding the prediction of disease risk from genomic data [ 126 ]. The technological advancements have facilitated the development of methods that use large samples of unrelated, or related, individuals. Thus, family-based designs using genomic data (trio-genome-wide complex trait analysis, and so on) have emerged. Such methods are discussed and compared in [ 127 ]. Of course, heritability can also be estimated via the results obtained in a traditional GWAS using unrelated individuals. The gap between these estimates and those obtained from classical heritability estimation methods has been termed the "missing heritability problem" and it is an important open question in current research [ 128 ]. Recent reviews of the methods that use GWAS data, are given in [ 18 , 19 ] focusing on their modeling assumptions, their similarities, and their applicability.

One of the first and simplest methods to calculate heritability from allele frequency, odds ratio and prevalence of the disease was implemented in the SumVg package [ 129 ]. This method, however, utilizes only the significant SNPs. The same authors extended the method later in order to allow calculation using the z-statistics from the whole GWAS sample [ 130 ]. A disadvantage of this method is that LD is not taken care of, and highly correlated SNPs need to be filtered manually. AVENGEME [ 131 ] is a tool that treats causal effect sizes as fixed effects and models the genotypes as random correlated variables. HESS [ 132 ] which was presented later built upon the same ideas and can be viewed as a weighted sum of the squares of the projection of effect sizes onto the eigenvectors of the LD matrix at the particular locus, with weights inversely proportional to the corresponding eigenvalues. LD Score Regression (LDSC) has been frequently applied to summary statistics from GWAS and one of its functionalities is to estimate the SNP heritability of a trait [ 133 ]. LDER [ 134 ] extends LDSC making full use of the information from the LD matrix providing more accurate estimates, whereas s-LDSC [ 135 ] is an extension suitable for partitioning heritability. SumHer [ 136 ] presented later and offers the same functionalities, with the main difference being that it allows for different so called “heritability models”. According to these, a SNP with high MAF is expected to contribute more to the total heritability compared to one with low MAF, whereas on the other hand, a SNP in a region of low LD is expected to contribute more compared to one in a region of high LD. On the contrary, LDSC estimates are obtained by assuming that all SNPs contribute equally. HEELS [ 137 ] is a new tool using REML to produce accurate and precise local heritability estimates and RSS, is a multiple regression-based fine-mapping tool (see “Fine-mapping”), can also calculate SNP heritability from the regression model. VarExp [ 138 ] and GxESum [ 139 ] are methods for estimating the phenotypic variance explained by genome-wide gene-environment (GxE) interactions. There are also tools like GWIZ [ 63 ] and SummaryAUC [ 140 ] that calculate the Receivers Operator’s Characteristic (ROC) curve and the associated Area Under the Curve (AUC). GWIZ generates ROC curves and the AUC using simulations and then estimates heritability using the square of the Somers’ rank correlation D. SummaryAUC on the other hand approximates the AUC of a PRS and its variance. HAMSTA [ 141 ] is a tool that, among others, estimates heritability explained by local ancestry using data from admixture mapping studies. Estimating the Effect size distribution is also a related important concept. GENESIS [ 142 ] uses LD and a Likelihood-based approach to estimate effect-size distributions. It also allows predictions regarding yield of future GWAS with larger sample sizes. GWEHS [ 143 ] calculates the distribution of effect sizes of SNPs, as well as their contribution to trait heritability. Furthermore, it performs predictions for the change in the effect size as well as in the heritability when new variants are identified. FMR [ 144 ] is a method-of-moments for calculating the effect-size distribution and GWAS-Causal-Effects-Model [ 145 ] is a random effects model for estimating the causal variants and their effect size distribution. Finally, there are tools to implicate gene-expression in heritability analysis: MESC [ 146 ] which estimates the proportion of heritability mediated by gene expression levels using linkage disequilibrium (LD) scores and eQTL, and GCSC [ 147 ] which uses results from a TWAS (see “TWAS and Colocalization”) in the so-called gene co-regulation score regression, to identify gene sets enriched for disease heritability.

Gene-based tests

Historically, association tests are oriented towards single variants, and this was the case for both traditional association studies as well as for GWAS. However this approach has some limitations that were noted earlier and a call for a shift towards gene-based tests was made [ 148 ]. Gene-based tests aggregate individual variant associations within a gene, providing a more comprehensive assessment of the gene's overall contribution to a trait or disease. This approach helps prioritize genes with multiple associated variants, enhancing the biological relevance of findings, and it has proven to be useful particularly in case of low frequency variants [ 148 ]. There are plenty of different methods for combining the association statistics or p-values within a gene, ranging from simple Fisher’s method or the minimum p-value approach, to more advanced methods like the Burden Test (BT) [ 149 ] or quadratic tests like SKAT [ 150 ] with variations in power [ 151 ]. Nevertheless, there is a consensus regarding the importance of incorporating LD information of the nearby variants into the methods for controlling the type I error rate at the desired level [ 20 ].

VEGAS, GATES, fastBAT and GCTA are among the oldest tools available for summary data, which remain efficient and widely used. SKAT (Sequence Kernel Association Test) is a well-known regression method for testing association between variants and traits adjusting for covariates. As a score-based variance-component test, it calculates p-values analytically by fitting the null model containing only the covariates [ 150 ]. The original SKAT method uses only IPD, but later implementations like metaSKAT or SKAT-O have been extended to handle summary data. GCTA and VEGAS also use the multivariate normal framework adjusting the estimates for LD using a reference panel [ 152 , 153 ]. Of note, GCTA also offers methods for conditional analysis (see “Fine mapping”), and same also holds for KGG [ 154 ], whereas VEGAS’s new version allows for mixed ethnicity populations. GATES [ 155 ], on the other hand, uses an extended Simes procedure that integrates functional information and association evidence to combine p-values, whereas fastBAT [ 156 ] offers fast analytical p-value computations. The gene analysis in MAGMA (Multi-marker Analysis of GenoMic Annotation) is based on a multiple linear principal components’ regression model to account for LD and uses an F-test to compute the overall gene p-value [ 157 ]. Its extension, nMAGMA, extends the lists of genes that can be annotated by integrating local signals, long-range regulation signals, and tissue-specific gene networks. It also provides tissue-specific risk signals, which are useful for understanding disorders with multi-tissue origins [ 158 ]. H-MAGMA [ 159 ] and eMAGMA [ 160 ] are two other extensions. The former integrates 3D chromatin configuration, whereas the latter leverages significant tissue-specific cis-eQTL information to assign SNPs to putative genes. EPIC [ 161 ] and GAMBIT [ 162 ] also utilize functional data for gene-based analysis; the former using cell-type-specific gene expression data obtained from single-cell RNA sequencing and the latter using coding and tissue-specific regulatory annotations. Such methods share several features in common with TWAS methods (see respective section). AgglomerativLD [ 163 ] also captures LD between SNPs of nearby genes, which induces correlation of the gene-based test statistics. DOT [ 164 ] is one of the few methods that applies a decorrelation-based approach before combining SNP-level statistics or p-values. Tools like GPA [ 165 ], oTFisher [ 166 ], TS [ 167 ] and aSPU [ 168 ] implement some type of so-called adaptive tests (AT), that is, they account for possibly varying association patterns across SNPs, whereas some modern tools like MKATR [ 169 ], COMBAT [ 170 ], MCA [ 171 ], OWC [ 172 ], FST [ 173 ], ACAT [ 174 ], HYST [ 175 ], GBJ [ 176 ] and sumFREGAT [ 177 ] perform analysis with multiple statistical methods and test and combine the results. Notably, tools like aSPU [ 168 ], snpGeneSets [ 178 ], Pascal/PascalX [ 179 , 180 ], MAGMA, chromMAGMA [ 181 ] and FUMA [ 182 ], also offer the option of performing gene-set analysis after performing the gene-based analysis (see next section), whereas HSVS-M [ 168 , 183 ] tests the association of a gene with multiple correlated traits.

Gene Set analysis

Gene set analysis (GSA), or Pathway Analysis, extends the concept of gene-based methods by jointly analyzing groups of functionally related genes and identifying biological pathways enriched with trait-associated genes. By considering the collective impact of multiple genes within a pathway, researchers can obtain a clearer picture of the underlying biological mechanisms influencing the phenotype under investigation. The first applications of such methods borrowed ideas from the microarray data analysis literature, and since then they became widespread in analysis of GWAS [ 184 ]. Any GSA method needs to address some issues. Firstly, how to handle SNPs of the same gene; secondly, how to define the appropriate gene-set or pathway, and finally how to combine the effects from multiple SNPs/genes within the same set/pathway [ 185 ]. Thus, the choices made by different methods can be very diverse leading to a wide variety of different approaches. For instance, some methods operate with SNP-level statistics (effect sizes, z, or p-values) assigning the SNP to the closest gene (usually within a range of ± 20 K bases), whereas others take as input a gene-level statistic or simply a gene list obtained by a gene-based method (of course, several tools allow for both a gene-based and a GSA approach). Regarding the choice of set there is a plethora of databases containing biological pathways (KEGG, PANTHER etc.), or other types of gene-set representation like PPI interactions, ontologies and so on [ 186 ]. Finally, regarding the statistical method used to aggregate evidence there is also a wide range of different methods that handle with different approaches the gene set size and gene length, the LD patterns and the presence of overlapping genes within pathways, or apply different statistical approaches such as those using the so-called competitive null hypothesis, or those using the self-containing one [ 14 , 187 ]. A tutorial regarding the use of such methods is given in [ 21 ].

Among the most easily used and frequently cited are the tools that utilize a webserver. FUMA [ 182 ] and iGSE4GWAS [ 188 ] are tools specialized in GWAS and use SNP-level statistics as inputs, differing in the subsequent analyses: FUMA uses MAGMA for gene-based testing and allows for ORA and Kologorov-Smirnov test (GSEA), whereas iGSE4GWAS maps the most significant SNP to a gene and then performs an improved GSEA with label permutation to obtain accurate p-values. Tools like Enrichr [ 189 ], g:Profiler [ 190 ], DAVID [ 191 ], WebGestalt [ 192 ] and PANTHER [ 193 ] are general purpose enrichment tools that provide functionalities for different types of omics data (Fig.  7 ). They accept gene or SNP-list as input and provide Application Programming Interface (API) ensuring interoperability, whereas for the statistical analysis they all use some version of ORA and/or GSEA (WebGestalt also uses Network Topology-based Analysis). A major feature of these tools is that they incorporate a large number of biological and pathway databases, with g:Profiler and Enrichr offering the most complete collection. GSA-SNP2 is one of the first methods to be developed for GWAS and has seen several improvements regarding the calculation of the combined gene score and the execution time, being among the fastest methods [ 194 ]. aSPUpath2 [ 195 ] and GIGSEA [ 196 ] are two methods that integrate expression data (eQTL) in the pathway analysis. The former uses an adaptive test that extends the aSPU methodology based on chi-square, whereas the latter uses a regression-based approach coupled with permutations to calculate accurate p-values. In a similar fashion, deTS [ 197 ] and PGCA perform tissue-specific enrichment analysis (TSEA) for detecting tissue-specific genes and for enrichment test of different forms of query data. Other methods use different definitions of the gene-sets, in some cases utilizing additional information. For instance, dmGWAS [ 198 ] integrates PPI networks and uses a search method to identify subnetworks. Compared with standard pathway methods it offers to the users the flexibility in the definition of a gene set and can utilize local PPI information. GEMB [ 199 ] defines the gene-sets using gene weights from model predictions and gene ranks from GWAS, and GENOMICper [ 200 ] uses permutations of the identified SNPs by rotation with respect to the genomic locations. GWAB [ 201 ] uses network connections to reprioritize candidate genes by integrating the GWAS and network data, whereas GenToS [ 202 ] searches for trait-associated variants in existing human GWAS. We also need to mention PAPA [ 203 ] which is a flexible tool for pleiotropic pathway analysis. As we already mentioned, aSPU, snpGeneSets, PascalX/PASCAL and MAGMA/chromMAGMA are gene-based methods that also perform GSA, whereas MAGENTA is a tool that performs meta-analysis and subsequently GSA (see “meta-analysis”). Lastly, we need to mention Inferno [ 204 ] and Mergeomics [ 205 ] which are webservers offering a variety of options, extending typical GSA applications. Inferno integrates a variety of functional genomics sources to identify causal noncoding variants using COLOC, WebGestalt, LDSC and MetaXcan. Mergeomics uses summary statistics of multi-omics association studies (GWAS, EWAS, TWAS, PWAS, etc.) and performs correction for LD, GSEA, meta-analysis and identification of regulators of disease-associated pathways and networks.

Enrichment. A Summary view in g:Profiler of the significant SNPs for Type 2 Diabetes Mellitus. B Enrichr results for the same set. C Output of GWAB for Type 2 Diabetes Mellitus SNPs. D Detailed results from g:Profiler

Fine-mapping

While GWAS can identify broad genomic regions associated with the trait, it doesn't pinpoint the exact causal variant within those regions. Fine mapping, working in the opposite direction of that of the gene-based approaches, is a process aimed at narrowing down and identifying causal variants, that is the specific genetic variants responsible for the observed associations between genomic regions and traits of interest. The plethora of statistical methods and study designs makes it difficult to choose an optimal approach. The different approaches that have been proposed to perform fine-mapping can be divided in three broad categories: heuristic methods that select SNPs based on LD patterns, conditional or penalized regression models that perform variable selection, and Bayesian methods that calculate posterior probabilities or Bayes Factors. Based on theoretical and empirical evidence it seems that Bayesian methods have superior performance [ 22 ]. Several factors may influence the performance of fine-mapping approaches, including the true number of causal SNPs in a region and their effect sizes, the local LD structure, the sample size, and the SNP density [ 22 , 206 ]. Functional annotations are also of great importance leading to the so-called functionally informed fine-mapping (FIFM) methods [ 206 ]. The hypothesis of a single causal variant is also very restrictive, and several methods have been developed to allow multiple causal variants in a region as well as to incorporate additional layers of functional annotations, like eQTL [ 207 ]. Moreover, methods for fine-mapping of multiple datasets have been proposed, either exploiting different LD patterns across ethnic groups or borrowing information between different traits [ 207 ].

As we already noted Bayesian methods seem to have superior performance [ 22 ] and thus it is of no surprise that most of the currently available methods operate in a Bayesian framework calculating Posterior Inclusion Probabilities (PIP) and/or Bayes Factors (BFs) in various settings: PAINTOR [ 208 ], DAP [ 209 ], fgwas [ 210 ], FINEMAP [ 211 ], flashfm [ 212 ], FINMOM [ 213 ], CARMA [ 214 ] and CAVIAR/CAVIARBF [ 215 ]. MsCAVIAR [ 216 ] is an extension of the latter method leveraging information from multiple studies, useful in trans-ethnic fine mapping. Similarly, XMAP [ 217 ] performs cross-population fine-mapping by leveraging genetic diversity and accounting for confounding bias. BEATRICE [ 218 ] is a unique method that combines a hierarchical Bayesian model with a deep learning-based inference procedure, whereas RIVIERA-beta [ 219 ] performs Bayesian fine-mapping using Epigenomic Reference Annotation. On a different level, PolyFun/PolyLoc [ 220 ] do not perform fine-mapping per se but are used for estimating the prior causal probabilities of SNPs, which can then be used by other Bayesian fine-mapping methods. SusieR [ 221 ], BVS-PICA [ 222 ] and JAM [ 223 ], operate also in a Bayesian regression framework performing variable selection and penalized regression. Other regression-based methods, like SOJO [ 224 ] and ANNORE [ 225 ] work in a frequentist framework and perform lasso-type and differential shrinkage via random effects, respectively, whereas GSR utilizes a gene score regression approach [ 226 ] and RSS performs multiple regression utilizing the so-called summary statistics likelihood [ 227 ]. AHIUT [ 228 ] performs an intersection–union test based on a joint/conditional regression model with all the SNPs in a region. Lastly, we need to mention PICS2 [ 229 ], which performs probabilistic identification of causal SNPs and is the only of the methods that is available as a web-server, and echocolatoR [ 230 ] which requires minimal input from users and integrates a suite of fine-mapping tools to identify consensus variants, test enrichment and visualize the results.

Analysis of multiple traits

In this section we analyze methods developed for handling multiple traits. Depending on the type of data and the purpose of the analysis the methods can be divided into pleiotropy methods, methods that calculate the genetic correlation , methods for mendelian randomization, transcriptome-wide association and colocalization methods.

Pleiotropy is the phenomenon in which a single variant influences several traits [ 231 ]. Such methods are of great importance in genetic research and several methods have been developed during the last years. A major goal of such methods is to increase the statistical power over single trait methods. Imagine for instance a variant that produces a near-significant effect when analyzed separately for two or three traits. A method that can combine these estimates may produce significant results. Another application of a joint analysis would be to identify variants that influence both traits, or variants that influence only one of them. When all the relevant variants are considered, one can also estimate the kind of relationship between the traits (see “genetic correlation”). A review of the statistical methods to detect pleiotropy in complex traits can be found in [ 25 ]. Usually, the methods that allow for multiple trait analysis are oriented toward quantitative traits like BMI, SBP, DBP and so on, that traditionally are measured on a single cohort, resulting in the existence of cross-trait correlation that needs to be taken into account in the analysis. However, there are also methods for performing the same analysis with summary estimates derived from different cohorts, as well as methods that allow for binary traits with the case–control design, using overlapped or non-overlapped controls.

All methods base their inference on the assumption that the z-statistics follow a multivariate normal distribution (MVN) and perform different types of tests and/or different procedures to estimate or approximate the correlation structure. ACA [ 232 ] one of the first methods, estimates the traits covariance from a subset of the phenotypic data or from published studies, p_ACT [ 233 ] integrates the MVN using the trait correlation, PAT [ 234 ] uses a likelihood-ratio test, and PLEI [ 235 ] uses the union-intersection testing method, but in addition to the likelihood ratio test, it also applies generalized estimating equations under the working independence model; it can be applied for both marginal analysis and conditional analysis. USAT [ 236 ] uses a score-based test, JaSPU [ 237 ] uses an adaptive test which is robust to violations of the MVN assumptions and MTAR [ 238 ] uses a Principal Components (PC)-based test. BMASS [ 239 ] on the other hand is a Bayesian multivariate method, whereas TWT [ 240 ], MTAFS [ 241 ] and EBMMT [ 242 ], which are among the newer tools, perform a Cauchy Combined Test (CCT) to handle the correlation structure and obtain accurate p-values. SHAHER [ 243 ] uses a linear combination of traits by maximizing the proportion of its genetic variance explained by the shared variants and allows both shared and unshared variants to be effectively analyzed and HIPO [ 244 ] performs heritability-informed power optimization for conducting multi-trait association analysis. HOPS [ 245 ] computes a horizontal pleiotropy score by removing correlations between traits caused by vertical pleiotropy and normalizing effect sizes across all traits and PDR [ 246 ] performs a pleiotropic decomposition regression to identify shared components and their underlying genetic variants. We also need to mention methods like MTAG [ 247 ] and PLEIO [ 248 ] which use LDSC and apart from sample overlap also allow data from multiple studies, something that can be considered meta-analysis and methods like MSKAT [ 249 ], multiSKAT [ 250 ], MGAS [ 251 ], MAIUP [ 252 ] and MTAR (multi-trait analysis of rare variants) [ 253 ] which are gene-based methods specialized for multiple traits. Finally, methods like iMAP [ 254 ] and graphGPA2 [ 255 ] use graphical models and are capable of performing analysis of large number of traits.

On the other hand, there are several methods that assume independence of the studied samples. Most of them are designed for larger analyses of many traits from multiple studies, for instance PolarMorphism [ 256 ], JASS [ 257 ], gwas-pw [ 258 ] and FactorGo [ 259 ], sumDAG [ 260 ], combGWAS [ 261 ] and GCPBayes pipeline [ 262 ]. GCPBayes_pipeline uses the functionality of GCPBayes to perform cross-phenotype gene-set analysis between two traits. gwas-pw is used for the joint analysis of two GWAS in order to identify variants influencing both traits. PolarMorphism is based on a transform from Cartesian to polar coordinates and reports a per variant degree of 'sharedness' across traits, whereas FactorGo provides scalable variational factor analysis model that is computationally efficient for large number of traits. JASS provides interactive exploration and visualization of the results of comparison of many traits through a web interface (Fig.  8 A-C), sumDAG goes one step further and constructs phenotype networks by using a Gaussian linear model and a directed acyclic graph, and combGWAS identifies susceptibility variants for comorbid disorders and calculate genetic correlations. EPS [ 263 ] and GPA [ 264 ] differ in integrating Pleiotropy and functional annotation from eQTL.

Analysis of multiple traits. A JASS analysis for Type 2 Diabetes Mellitus (T2DM), Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (SBP), indicating the pairwise genetic correlations between the traits. B Manhattan Plot from JASS for the combined analysis of the three traits. C Pairwise analysis of the SNPs identified as significant in the univariate analysis and in the combined analysis. D Two-sample Mendelian Randomization analysis for the association of SBP and T2DM obtained by MR-BASE

Genetic correlation

Genetic correlation is related to pleiotropy and describes the relationship between two traits, that is, the extent to which the genetic variants influencing one trait overlap with the genetic variants associated with the other. It thus can quantify the overall genetic similarity and provide insights into the polygenic genetic architecture of complex traits [ 23 ]. As we already saw, analyzing simultaneously multiple traits may increase power in case of horizontal pleiotropy; an additional potential application is to use the estimated correlation in order to establish causality between traits in case of vertical pleiotropy (see also next sections). Since heritability is the proportion of the phenotypic variance explained by genotypic variation it is of no surprise that genetic correlation (or, the genetic covariance) is related to the traits’ heritabilities. Thus, several of the methods for estimating heritability discussed earlier, like HESS and SumHer can also calculate the correlation between traits. The most commonly used method, however, for calculating genetic correlation is LDSC (LD Score Regression). The method originally developed for distinguishing polygenicity from bias by examining the relationship between test statistics and LD score, but it is also used for estimating heritability and genetic correlation [ 133 ]. LDSC is also available through the LD Hub server. PCGC-s [ 265 ] is an adaptation of stratified LDSC for case–control studies and can also estimate genetic heritability, genetic correlation, and functional enrichment. Another popular tool is GNOVA [ 266 ] which calculates annotation-stratified covariance using the method of moments and allows for sample overlap. Its extension, SUPERGNOVA [ 267 ] identifies global and local genetic correlations that could provide new insights into the shared genetic basis of many phenotypes. Local correlations, among others, can be also computed using LAVA [ 268 ]. HDL [ 269 ] is a likelihood-based method which produces more precise estimates. A recent comparison found that LDSC and GNOVA are more similar and robust to LD and sample overlap compared to HDL. HDL provides biased estimates of the genetic covariance in most cases and could not distinguish genetic from non-genetic correlation. Moreover, HDL restricts the users to using the built-in reference panel, and its performs poorly when the number of shared SNPs between reference panel and GWAS is small [ 24 ]. Other tools provide somewhat different types of analyses. For instance Popcorn [ 270 ] estimates transethnic genetic correlation, GECKO [ 271 ] estimates both genetic and environmental covariances, PhenoSD [ 272 ] uses LDSC for estimating phenotypic correlations and then performs correction for multiple testing using the spectral decomposition of matrices, whereas LPM [ 273 ] is a latent probit model scalable to hundreds of annotations and phenotypes that integrates functional annotations. ccGWAS [ 274 ] is a tool for comparing two different disorders with small genetic correlation providing a case-case association test, and RHOGE [ 275 ] estimates the genetic correlation between two traits as a function of predicted gene expression effect. LOGOdetect [ 276 ] uses scan statistics with an LD score-weighted inner product of local z-scores to identify small segments that harbor local genetic correlation between two traits. DONUTS [ 277 ] is a unique method since it operates on summary statistics from families.

Mendelian randomization

Mendelian Randomization (MR) is a method suggested in the pre-GWAS era to investigate causal relationships between two traits, usually a phenotype and a disease [ 278 ] using genotype–trait associations to make inferences about environmentally modifiable causes of the traits. In technical terms, MR uses genetic variants as instrumental variables [ 279 ] to mimic the random assignment of exposures in a randomized controlled trial, similar to the way Mendel's laws of inheritance dictate the random assortment of alleles during gamete formation. By utilizing the natural randomization of genetic inheritance, MR aims to minimize biases introduced by confounding factors that usually affect observational studies when investigating the association of two traits. Usually, we are interested in a disease and some other intermediate phenotype, or another disease. For instance, the MR approach may involve the relationship between hypertension and BMI, or between hypertension and diabetes. Traditionally MR was performed with one sample (1SMR) using a single variant (usually referred to IPD methods), and subsequently multivariate methods for MR meta-analysis were developed [ 280 ]. With the emergence of GWAS these methods evolved to the most commonly used two-sample MR (2SMR) methods that utilize summary data estimates from several variants regarding the genotype–phenotype and genotype-disease association from different samples [ 26 , 281 ]. To establish connection with the previous sections, MR seeks to analyze correlated traits [ 282 ] and to provide evidence for causation, in other words to distinguish vertical from horizontal pleiotropy.

Several standard methods for MR in GWAS with summary data have been made available during the last years: the inverse-variance weighted method (IVW), the various types of median estimators (simple or weighted) and the MR-Egger regression approach. IVW gives consistent estimates only if all the genetic variants in the analysis are valid instruments. The median estimator is consistent even when up to 50% of the information comes from invalid instrumental variables, whereas MR-Egger performs equally well but provides somewhat less precise estimates [ 283 ]. These methods are readily available in standard packages like TwoSampleMR [ 284 ] and MR [ 285 ]. The functionalities of TwoSampleMR are also offered, at least partially, through the webserver of MRBASE [ 284 ], which is the only method available as such (see Fig.  8 , D). BWMR [ 286 ] is a tool that performs MR in a Bayesian framework. Besides the issue of weak instruments which is of importance, most modern methods also aim to perform the MR analysis accounting or correcting for horizontal pleiotropy. For instance, pIVW [ 287 ] is an extension of the IVW that accounts simultaneously for weak instruments and balanced horizontal pleiotropy and MRmix [ 288 ] uses a mixture approach allowing a fraction of the instruments to have pleiotropic effect on the outcome. Similarly, MRcML [ 289 ], MR-LDP [ 290 ], MR-Corr2 [ 291 ] and MR-PRESSO [ 292 ] provide functionalities to account for horizontal pleiotropy, whereas IMRP [ 293 ] takes a different approach and searches iteratively for horizontally pleiotropic variants and causal effects. MR-APSS [ 294 ] differs in that it performs MR accounting for both pleiotropy and sample structure which seems to be another important confounder (and includes population stratification, cryptic relatedness, and sample overlap); MRlap [ 295 ] considers both weak instrument bias and winner's curse, accounting for sample overlap. MR.CUE [ 296 ] and TS_LMM [ 297 ] offer additional functionality for handling variability of the estimates. LCV [ 298 ] is a method that estimates causal associations between traits avoiding confounding by genetic correlation, whereas OMR [ 299 ] uses information from all GWAS SNPs for causal inference and JAM-MR [ 300 ] performs variable selection and causal effect estimation in MR. CS [ 301 ], BiDirectCausal [ 302 ], MRCI [ 303 ] and LHC-MR [ 304 ] constitute another important class of methods since they can identify bidirectional causal effects. Another important extension is offered by methods like MR2 [ 305 ], MV-MR [ 306 ], MRBEE [ 307 ], MVMR-cML [ 308 ] and adOMICs [ 309 ] which extend the MR framework in the multivariate setting allowing more than one exposures or outcomes, as well as MR-BMA [ 310 ] which go one step further performing multivariate MR in a Bayesian framework. Finally, other methods like hJAM [ 311 ], MR.RAPS [ 312 ] and MRPEA [ 313 ] offer more advanced options. hJAM unifies the framework of MR and TWAS and can be applied to correlated instruments and multiple intermediates, MR.RAPS uses a three-sample genome-wide design with many independent genetic instruments across the genome to handle many weak genetic instruments and pleiotropy, whereas MRPEA uses pathway association MR analysis approach using data of environmental exposures.

Colocalization and TWAS

As we already described, the MR approach involves the combination of two types of data, a genotype-disease association, and a genotype–phenotype association. If the phenotype involves gene-expression, that is the result of an eQTL study, then we have two distinct but fundamentally related methods, the Transcriptome-wide association study (TWAS) and the colocalization approach (Fig.  9 ). TWAS is based on the idea that genetic variants can influence gene expression, which subsequently can affect complex traits or diseases [ 27 ]. Thus, the approach uses information from eQTL to identify associations between predicted gene expression levels and complex traits/diseases [ 314 ]. Even though there are several different methods, the resemblance to MR is obvious; in fact several methods like SMR that uses a single variant [ 315 ], GSMR that uses multiple variants [ 310 ], and PMR [ 316 ] which can account for correlated instruments, horizontal pleiotropy, and can accommodate both single traits and multiple correlated outcomes, all use the term MR, whereas the authors of TScML [ 317 ], which uses two-stage constrained maximum likelihood, which is an extension of 2SLS, explicitly state that can be used for both MR and TWAS analyses. FUSION and S-PrediXcan are the oldest and most widely known methods. FUSION is the current implementation of the first TWAS method [ 318 ], whereas S-PrediXcan [ 319 ] is the summary-data version of PrediXcan. Xu et al. [ 320 ] noted that PrediXcan and TWAS can be viewed as a special case of general association testing with multiple SNPs in a GLM and proposed the so-called sum of powered score (SPU) test implemented in aSPU-TWAS [ 320 ]. A subsequent evaluation has shown that the original TWAS statistic is equivalent to an LD-aware version of standard MR [ 321 ]. iFunMed [ 322 ] and sMIST [ 323 ] formulate the problem within the framework of mediator analysis, and similarly PTWAS [ 324 ] applies principles from instrumental variables analysis. Comm-S* [ 325 ] uses a variational Bayesian EM algorithm and a likelihood ratio test to assess expression-trait association. Its extension Tiss-Comm [ 326 ] leverages the co-regulation of genetic variations across different tissues explicitly via a unified probabilistic model and also detects the tissue-specific role of candidate target genes in complex traits. Similar multi-tissue approaches are followed by fQTL [ 327 ], sCCA [ 328 ] and UTMOST [ 329 ]. Primo [ 330 ], and OPERA [ 331 ] extend further the integration by allowing different types of xQTL data (eQTL, pQTL, mQTL etc.) to allow estimation under different conditions, whereas SUMMIT [ 332 ] uses a large eQTL summary-level dataset, penalized regression and Cauchy Combination Test and HMAT [ 333 ] aggregates TWAS association tests obtained across multiple gene expression prediction models using the harmonic mean P-value combination (HMP). BGW [ 334 ] and ARCHIE [ 335 ] are two methods that utilize trans-regulated eQTLs. Other tools use combination of methods, like TIGAR [ 336 ] which combines DPR and PrediXcan, whereas others, like JEPEGMIX2‐P [ 337 ] or FOCUS [ 338 ], perform TWAS using pathway information, or use LD to perform fine-mapping over the gene–trait association signals obtained from TWAS, respectively. Even though the various methods discussed here have different modeling assumptions and many were initially developed to answer different biological questions, a recent technical review of the TWAS methods showed that all can be viewed as versions of the two-sample MR analysis [ 339 ]. Indeed, several recent tools like MRLocus [ 340 ], TWMR [ 341 ], and Mr.MtRobin [ 342 ] make explicit use of the MR methodology and jargon in order to perform a sophisticated TWAS. MRLocus performs first a colocalization step to each nearly-LD-independent eQTL, and then performs an MR analysis step across eQTLs. TWMR performs a multi-gene multi-instrument MR approach to identify genes whose expression influence the phenotype. Finally, Mr.MtRobin uses multi-tissue eQTL and a reverse regression random slope mixed model to infer whether a gene is associated with a complex trait. As we have already noticed, webTWAS, apart from the database, also offers a webserver for accessing S-PrediXcan, SMR and UTMOST with user supplied datasets.

Incorporation of eQTL data. A Overview of the gene-expression patterns in T2DM obtained by PCGA. B Top associated tissues and cells for T2DM (PCGA). C An example of colocalization output perform by LocusFocus. D TSEA-DB view of the analysis of significant SNPs involved in T2DM. E Heat-map for the tissues involved in T2DM significant hits obtained by COLOC. F Plots of the genome-wide significant hits obtained from GWAS and eQTL (COLOC). G Heat-map for the tissues involved in T2DM (TSEA-DB). H Example of fine-mapping regarding a SNP indicated in T2DM obtained by PICS2

Another method that also uses GWAS results along with eQTL data is colocalization. Colocalization approaches are used to assess whether two different traits or diseases share a common causal genetic variant or set of variants at a specific genomic locus [ 13 ]. Colocalization analysis identifies genetic variants that show significant association in both GWAS and eQTL studies. However, unlike TWAS, it does not perform gene expression prediction and gene-trait association tests, but it focuses on the colocalized SNPs [ 28 ]. TWAS and colocalization are related approaches but not identical, since it has been shown that may give different results under different conditions (for instance in case of horizontal pleiotropy) and thus it has been suggested that they should be used complementary [ 28 , 343 ]. COLOC was one of the first methods for colocalization and has seen several improvements [ 344 , 345 ] (see also Fig.  9 ). The latest version uses SuSiE and allows evidence for association at multiple causal variants to be evaluated simultaneously, while at the same time separating the statistical support for each variant conditional on the causal signal being considered. MOLOC [ 346 ] is multiple-trait version of COLOC, operating in a Bayesian framework that integrates GWAS summary data with multiple xQTL data to identify regulatory effects, HyPrColoc [ 347 ] is a deterministic Bayesian method that detects colocalization across large numbers of traits, and SS2 [ 348 ] operates across any number of gene-tissue pairs allowing for sample overlap. LLR [ 349 ] works for colocalizing genetic risk variants in multiple GWAS and phenotypes, whereas POEMcoloc [ 350 ] is an approximation to the COLOC method that can be applied when limited data are available. SparkINFERNO [ 351 ], PwCoCo [ 352 ] and ColocQuiaL [ 353 ] are pipelines offering additional functionalities, all using COLOC. eCAVIAR is another popular method [ 354 ] that uses a probabilistic model that accounts for more than one causal variant at a given locus. MSG [ 355 ] increases the power using a spliced gene approach and SharePro [ 356 ] integrates LD modeling and colocalization assessment to account for multiple causal variants in colocalization analysis. PESCA [ 357 ] uses estimates of LD that are ancestry-matched, in order to infer proportions of population-specific and shared causal variants in two populations. These estimates are then used as priors in an empirical Bayes framework for colocalization and test for enrichment of these causal variants in loci of interest. Lastly, we have to mention the methods that operate as webservers offering ease of use. Sherlock [ 358 ] which is also one of the oldest methods, uses a database of eQTL associations from different tissues to identify genetic signatures that match those for specific genes. Unlike other methods it incorporates information from both cis- and trans- eQTL SNPs. LocusFocus [ 359 ] is a web-based colocalization tool that tests colocalization using the Simple Sum method to identify relevant genes and tissues for a particular GWAS locus in the presence of high linkage disequilibrium and/or allelic heterogeneity. Regarding the analysis of eQTL data, ezQTL [ 360 ] is a webserver performing various tasks like data quality control for variants matched between different datasets, LD visualization, and colocalization analysis using eCAVIAR and HyPrColoc, whereas BAGEA [ 361 ] uses a variational Bayes framework to model cis-eQTLs using directed and undirected genomic annotations.

Conclusions

Summary statistics offer protection of privacy over IPD, as well as significant advantages in computational cost, which does not scale with the number of individuals in the study [ 11 ]. Naturally, in the post-GWAS era it is expected that a large number of methods would be developed to perform analysis using the summary results of GWAS [ 11 ]. The particular methods, integrating data from multiple sources such as LD, gene expression and biological pathways, aim to provide biological insight and improve our understanding about the functional role of identified variants [ 12 , 13 , 14 , 15 ]. One thing which we should emphasize is the fact that GWAS summary statistics are not mere replacements for IPD. Of course, some types of analysis can be applied using both summary data or IPD, like meta-analysis, heritability analysis, fine-mapping and so on. In such cases the summary data methods greatly enhance the applicability and the ease of use overcoming the limitations of IPD mentioned earlier. However, methods for other types of analysis, and particularly those that use multiple datasets, like TWAS, colocalization or Mendelian Randomization were designed having in mind the summary data and the integration of data from multiple sources. This is exactly the spirit of the so-called post-GWAS analysis that brought bioinformatics into a central role in genetics research [ 11 ]. Most of the “success stories” in GWAS during the last years can be attributed to the development and the application of such methods in identifying new variants, in functional annotation, causal discovery or even in medical applications [ 2 , 12 , 362 ].

In this work we conducted, for the first time in the literature, a systematic review in order to identify software tools and databases dedicated to GWAS summary data analysis. We categorized the tools and databases by their functionality, in categories related to data, single-trait analysis, and multiple-trait analysis, along with their sub-categories which we analyzed and reviewed. We also compared the tools and databases based on their features, limitations, and user-friendliness. Our review identified a wide range of tools, each with unique strengths and limitations. We provided descriptions of the key features of each tool and database, including their input/output formats, data types, and computational requirements. We also discussed the overall usability and applicability of each tool for different research scenarios. We identified families of related tools for performing different or complementary tasks, for instance the CAVIAR tools (CAVIAR, CAVIARBF, msCAVIAR, eCAVIAR), the EpiXcan tools (S-MultiXcan, S-PrediXcan), the LDAK programs (SumHer, GBAT), the MAGMA tools (nMAGMA, H-MAGMA, eMAGMA) and so on. We need to emphasize that in many cases a tool, originally developed for IPD, is later adapted to handle summary data, whereas in other cases a tool is succeeded by a newer version with added capabilities. For instance, the original PrediXcan method uses only IPD, but it is now considered deprecated. S-PrediXcan and S-MultiXcan are later versions that are designed to be used with summary data. The same is the case regarding SKAT. The original method uses only IPD, but later implementations like metaSKAT or SKAT-O allow for summary data as well. At the same time, it is of importance that there are several tools that combine different functionalities. For instance there are tools that can perform meta-analysis and GSA (MAGENTA), gene-based methods that also offer functionalities for conditional analysis (GCTA), methods for analysis of multiple traits with gene-based tests (multiSKAT, MSKAT), methods that can be seen both as methods for multiple-traits or as meta-analysis (PLEIO, PASCAL), methods that perform both GSA and gene-based tests (aSPU, snpGeneSets, PascalX, PASCAL,MAGMA, FUMA). Of course, there are several single-purpose methods that use and combine different statistical tests or different methods (OWC, MCA, TWT, EBMMT, COMBAT, sumFREGAT, MKATR), and we may not forget methods like LDSC, with its variants, which was originally developed for distinguishing polygenicity from bias, but it is also used for estimating heritability and genetic correlation being integrated in many other tools and pipelines.

As we already mentioned, the tools and databases included in the study were those with a functioning URL. In many publications identified through the literature search the URL was not working. In some situations, we recovered a valid link by performing google searches, or by identifying the authors’ websites, but in many cases, this was not enough. Similarly, several tools deposited in CRAN had been removed or archived. This kind of problem is something already known in the scientific community for years [ 363 , 364 , 365 ]. However, there is more to it. Even for the tools included in the review we could not verify without proper testing that they all work seamlessly, especially for the older ones [ 366 ]. Operating systems evolve, programming languages change, and with these the dependencies of each software also change. Even though there are available best practices [ 367 ], it is not always realistic to expect complex software to work forever without maintenance. Even for some of the tools having valid URLs, for instance deposited on GitHub, or on personal web pages, we found statements by the authors indicating that the software is no longer maintained and that it is not easy to provide technical support. It is clear that more advanced solutions should be pursued. For instance, among the tools we identified the majority are written in R and Python, but only a handful is available as a webserver: ten of the tools for GSA, three tools for colocalization, two tools for meta-analysis, and one for pleiotropy analysis, MR and fine-mapping. Of course, several of the secondary databases we identified also provide the functionality of performing the analysis using data provided by the user (webTWAS, TSEA-DB, PCGA), but even counting these the proportion of web-tools is rather low (< 10%). Web servers and web services have become of high relevance to the field of bioinformatics during the last 20 years [ 368 ], so it is expected to have an increasing number of relevant webservers in the near future as relevant tools are available to facilitate the incorporation of existing applications [ 369 , 370 , 371 , 372 ]. On the other hand, some tools may be too computationally demanding, so other solutions must be found. Container-based applications [ 373 , 374 ] such as Docker can simplify maintenance procedures and add to the reproducibility of research [ 375 ]. Community efforts such as udocker [ 376 ] may promote usability of complex software tools by non-experts in multi-user environments.

As data accumulates it is unavoidable to head to analyses on an even larger scale. Traditionally the large-scale analysis of many gene-disease associations is modeled by the so-called diseasome [ 377 , 378 ] using graph theoretic methods [ 379 , 380 ]. The gene-disease network is composed of pairwise associations obtained from public databases and is a bipartite network [ 379 ] consisting of two separate sets of nodes and the interactions between nodes belonging to the different sets. The projection to the one or the other of the sets may lead to the gene–gene or the disease-disease projected networks that inform us about the associations between members of the same set (for instance, two diseases are connected if they share common genes, and so on). Such methods are available for years, but they treat the associations as fixed inputs to the graph. As data accumulate and even more complex statistical methods are developed that allow cross-trait comparisons and combined analyses of multiple traits, along with the integration of different types of data such as xQTL, it is tempting to speculate that a fusion of these two traditions may come, in which the statistical formalism of the tools presented in this review will merge with the graph theoretic approaches developed in the systems biology literature. For instance, we may see network approaches leading to causal analyses (similar to MR) that consider simultaneously all the diseases and traits for which we have GWAS summary data, or similar approaches that integrate xQTL data of various types, different tissues and so on.

We hope that this comprehensive review will serve as a valuable resource for researchers who are interested in using GWAS summary statistics to investigate the genetic basis of complex traits and diseases, as well as to methodologists that develop and test relevant methods. We provided a detailed overview of the available tools and databases, and we hope that this work will facilitate informed tool selection and will maximize the effectiveness of using GWAS summary statistics.

Availability of data and materials

The data collected in this study are available in Supplementary Material. Supplementary Table 1 contains the list with the identified tools along with the URLs, the references and the descriptions. Supplementary Table 2 contains the list with the additional datasets identified in various consortia.

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Acknowledgements

The authors would like to thank the anonymous reviewers whose comments and constructive criticism helped in improving the quality of the manuscript.

This work is funded by the project “Bridging big omic, genetic and medical data for Precision Medicine implementation in Greece” (TAEDR-0539180) which is carried out within the framework of the National Recovery and Resilience Plan Greece 2.0, funded by the European Union –NextGenerationEU.

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PK: Investigation, Methodology, Data Curation, Visualization. PB: Conceptualization, Supervision, Investigation, Methodology, Data Curation, Visualization. PK and PB wrote parts of the manuscript and have read and approved the final manuscript.

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Kontou, P.I., Bagos, P.G. The goldmine of GWAS summary statistics: a systematic review of methods and tools. BioData Mining 17 , 31 (2024). https://doi.org/10.1186/s13040-024-00385-x

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Received : 09 February 2024

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Published : 05 September 2024

DOI : https://doi.org/10.1186/s13040-024-00385-x

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