The Goldmine of GWAS Summary Statistics: a Systematic Review of Methods and Tools

Overview

Journal BioData Min

Publisher Biomed Central

Specialty Biology

Date 2024 Sep 5

PMID 39238044

Authors

Panagiota I Kontou

Pantelis G Bagos

Affiliations

Soon will be listed here.

Abstract

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.

References

Ren J, Lin Z, Pan W . Integrating GWAS summary statistics, individual-level genotypic and omic data to enhance the performance for large-scale trait imputation. Hum Mol Genet. 2023; 32(17):2693-2703. PMC: 10460491. DOI: 10.1093/hmg/ddad097. View

Lamparter D, Bhatnagar R, Hebestreit K, Belgard T, Zhang A, Hanson-Smith V . A framework for integrating directed and undirected annotations to build explanatory models of cis-eQTL data. PLoS Comput Biol. 2020; 16(6):e1007770. PMC: 7332077. DOI: 10.1371/journal.pcbi.1007770. View

Hukku A, Sampson M, Luca F, Pique-Regi R, Wen X . Analyzing and reconciling colocalization and transcriptome-wide association studies from the perspective of inferential reproducibility. Am J Hum Genet. 2022; 109(5):825-837. PMC: 9118134. DOI: 10.1016/j.ajhg.2022.04.005. View

Xu Z, Wu C, Wei P, Pan W . A Powerful Framework for Integrating eQTL and GWAS Summary Data. Genetics. 2017; 207(3):893-902. PMC: 5676241. DOI: 10.1534/genetics.117.300270. View

Wang L, Gao B, Fan Y, Xue F, Zhou X . Mendelian randomization under the omnigenic architecture. Brief Bioinform. 2021; 22(6). DOI: 10.1093/bib/bbab322. View

Zhu Z, Zhang F, Hu H, Bakshi A, Robinson M, Powell J . Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet. 2016; 48(5):481-7. DOI: 10.1038/ng.3538. View

Boehm F, Zhou X . Statistical methods for Mendelian randomization in genome-wide association studies: A review. Comput Struct Biotechnol J. 2022; 20:2338-2351. PMC: 9123217. DOI: 10.1016/j.csbj.2022.05.015. View

Benner C, Spencer C, Havulinna A, Salomaa V, Ripatti S, Pirinen M . FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics. 2016; 32(10):1493-501. PMC: 4866522. DOI: 10.1093/bioinformatics/btw018. View

Winkler T, Day F, Croteau-Chonka D, Wood A, Locke A, Magi R . Quality control and conduct of genome-wide association meta-analyses. Nat Protoc. 2014; 9(5):1192-212. PMC: 4083217. DOI: 10.1038/nprot.2014.071. View

10.

Denny J, Bastarache L, Ritchie M, Carroll R, Zink R, Mosley J . Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat Biotechnol. 2013; 31(12):1102-10. PMC: 3969265. DOI: 10.1038/nbt.2749. View

11.

Canela-Xandri O, Rawlik K, Tenesa A . An atlas of genetic associations in UK Biobank. Nat Genet. 2018; 50(11):1593-1599. PMC: 6707814. DOI: 10.1038/s41588-018-0248-z. View

12.

Bowden J, Davey Smith G, Haycock P, Burgess S . Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol. 2016; 40(4):304-14. PMC: 4849733. DOI: 10.1002/gepi.21965. View

13.

Mancuso N, Freund M, Johnson R, Shi H, Kichaev G, Gusev A . Probabilistic fine-mapping of transcriptome-wide association studies. Nat Genet. 2019; 51(4):675-682. PMC: 6619422. DOI: 10.1038/s41588-019-0367-1. View

14.

Gusev A, Ko A, Shi H, Bhatia G, Chung W, Penninx B . Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet. 2016; 48(3):245-52. PMC: 4767558. DOI: 10.1038/ng.3506. View

15.

Ray D, Pankow J, Basu S . USAT: A Unified Score-Based Association Test for Multiple Phenotype-Genotype Analysis. Genet Epidemiol. 2015; 40(1):20-34. PMC: 4785800. DOI: 10.1002/gepi.21937. View

16.

Moore J, Asselbergs F, Williams S . Bioinformatics challenges for genome-wide association studies. Bioinformatics. 2010; 26(4):445-55. PMC: 2820680. DOI: 10.1093/bioinformatics/btp713. View

17.

Naj A . Genotype Imputation in Genome-Wide Association Studies. Curr Protoc Hum Genet. 2019; 102(1):e84. DOI: 10.1002/cphg.84. View

18.

Xu S, Wang P, Fung W, Liu Z . A novel penalized inverse-variance weighted estimator for Mendelian randomization with applications to COVID-19 outcomes. Biometrics. 2022; 79(3):2184-2195. PMC: 9538932. DOI: 10.1111/biom.13732. View

19.

Pers T . Gene set analysis for interpreting genetic studies. Hum Mol Genet. 2016; 25(R2):R133-R140. DOI: 10.1093/hmg/ddw249. View

20.

Li B, Leal S . Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am J Hum Genet. 2008; 83(3):311-21. PMC: 2842185. DOI: 10.1016/j.ajhg.2008.06.024. View