Literature DB >> 23657481

Meta-analysis methods for genome-wide association studies and beyond.

Evangelos Evangelou1, John P A Ioannidis.   

Abstract

Meta-analysis of genome-wide association studies (GWASs) has become a popular method for discovering genetic risk variants. Here, we overview both widely applied and newer statistical methods for GWAS meta-analysis, including issues of interpretation and assessment of sources of heterogeneity. We also discuss extensions of these meta-analysis methods to complex data. Where possible, we provide guidelines for researchers who are planning to use these methods. Furthermore, we address special issues that may arise for meta-analysis of sequencing data and rare variants. Finally, we discuss challenges and solutions surrounding the goals of making meta-analysis data publicly available and building powerful consortia.

Mesh:

Year:  2013        PMID: 23657481     DOI: 10.1038/nrg3472

Source DB:  PubMed          Journal:  Nat Rev Genet        ISSN: 1471-0056            Impact factor:   53.242


  112 in total

1.  Quantifying heterogeneity in a meta-analysis.

Authors:  Julian P T Higgins; Simon G Thompson
Journal:  Stat Med       Date:  2002-06-15       Impact factor: 2.373

2.  Extending rare-variant testing strategies: analysis of noncoding sequence and imputed genotypes.

Authors:  Matthew Zawistowski; Shyam Gopalakrishnan; Jun Ding; Yun Li; Sara Grimm; Sebastian Zöllner
Journal:  Am J Hum Genet       Date:  2010-11-12       Impact factor: 11.025

3.  Required sample size and nonreplicability thresholds for heterogeneous genetic associations.

Authors:  Ramal Moonesinghe; Muin J Khoury; Tiebin Liu; John P A Ioannidis
Journal:  Proc Natl Acad Sci U S A       Date:  2008-01-03       Impact factor: 11.205

4.  A unification of multivariate methods for meta-analysis of genetic association studies.

Authors:  Pantelis G Bagos
Journal:  Stat Appl Genet Mol Biol       Date:  2008-10-24

5.  Discovery properties of genome-wide association signals from cumulatively combined data sets.

Authors:  Tiago V Pereira; Nikolaos A Patsopoulos; Georgia Salanti; John P A Ioannidis
Journal:  Am J Epidemiol       Date:  2009-10-06       Impact factor: 4.897

6.  ARIEL and AMELIA: testing for an accumulation of rare variants using next-generation sequencing data.

Authors:  Jennifer L Asimit; Aaron G Day-Williams; Andrew P Morris; Eleftheria Zeggini
Journal:  Hum Hered       Date:  2012-03-22       Impact factor: 0.444

7.  To stratify or not to stratify: power considerations for population-based genome-wide association studies of quantitative traits.

Authors:  Gundula Behrens; Thomas W Winkler; Mathias Gorski; Michael F Leitzmann; Iris M Heid
Journal:  Genet Epidemiol       Date:  2011-12       Impact factor: 2.135

8.  Genome-wide meta-analyses identify multiple loci associated with smoking behavior.

Authors: 
Journal:  Nat Genet       Date:  2010-04-25       Impact factor: 38.330

Review 9.  Comprehensive literature review and statistical considerations for GWAS meta-analysis.

Authors:  Ferdouse Begum; Debashis Ghosh; George C Tseng; Eleanor Feingold
Journal:  Nucleic Acids Res       Date:  2012-01-12       Impact factor: 16.971

10.  Meta-analysis of sex-specific genome-wide association studies.

Authors:  Reedik Magi; Cecilia M Lindgren; Andrew P Morris
Journal:  Genet Epidemiol       Date:  2010-12       Impact factor: 2.135

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  231 in total

Review 1.  A guide on gene prioritization in studies of psychiatric disorders.

Authors:  Sven Stringer; Kim C Cerrone; Wim van den Brink; Julia F van den Berg; Damiaan Denys; Rene S Kahn; Eske M Derks
Journal:  Int J Methods Psychiatr Res       Date:  2015-07-31       Impact factor: 4.035

2.  Comparing genetic variants detected in the 1000 genomes project with SNPs determined by the International HapMap Consortium.

Authors:  Wenqian Zhang; Hui Wen Ng; Mao Shu; Heng Luo; ZhenQiang Su; Weigong Ge; Roger Perkins; Weida Tong; Huixiao Hong
Journal:  J Genet       Date:  2015-12       Impact factor: 1.166

3.  Meta-analysis of Complex Diseases at Gene Level with Generalized Functional Linear Models.

Authors:  Ruzong Fan; Yifan Wang; Chi-Yang Chiu; Wei Chen; Haobo Ren; Yun Li; Michael Boehnke; Christopher I Amos; Jason H Moore; Momiao Xiong
Journal:  Genetics       Date:  2015-12-29       Impact factor: 4.562

Review 4.  Application of computational methods in genetic study of inflammatory bowel disease.

Authors:  Jin Li; Zhi Wei; Hakon Hakonarson
Journal:  World J Gastroenterol       Date:  2016-01-21       Impact factor: 5.742

Review 5.  Unravelling the human genome-phenome relationship using phenome-wide association studies.

Authors:  William S Bush; Matthew T Oetjens; Dana C Crawford
Journal:  Nat Rev Genet       Date:  2016-02-15       Impact factor: 53.242

6.  BAYESIAN LARGE-SCALE MULTIPLE REGRESSION WITH SUMMARY STATISTICS FROM GENOME-WIDE ASSOCIATION STUDIES.

Authors:  Xiang Zhu; Matthew Stephens
Journal:  Ann Appl Stat       Date:  2017-10-05       Impact factor: 2.083

7.  An Algorithm for Creating Virtual Controls Using Integrated and Harmonized Longitudinal Data.

Authors:  William B Hansen; Shyh-Huei Chen; Santiago Saldana; Edward H Ip
Journal:  Eval Health Prof       Date:  2018-05-03       Impact factor: 2.651

8.  Statistical methods to detect novel genetic variants using publicly available GWAS summary data.

Authors:  Bin Guo; Baolin Wu
Journal:  Comput Biol Chem       Date:  2018-03-01       Impact factor: 2.877

Review 9.  The genetics revolution in rheumatology: large scale genomic arrays and genetic mapping.

Authors:  Stephen Eyre; Gisela Orozco; Jane Worthington
Journal:  Nat Rev Rheumatol       Date:  2017-06-01       Impact factor: 20.543

10.  A meta-analytic framework for detection of genetic interactions.

Authors:  Yulun Liu; Yong Chen; Paul Scheet
Journal:  Genet Epidemiol       Date:  2016-08-15       Impact factor: 2.135

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