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

被引：410

作者：

Evangelou, Evangelos ^{[1
]}

Ioannidis, John P. A. ^{[2
,3
,4
]}

机构：

[1] Univ Ioannina, Sch Med, Dept Hyg & Epidemiol, Clin & Mol Epidemiol Unit, GR-45110 Ioannina, Greece

[2] Stanford Univ, Sch Med, Dept Med, Stanford Prevent Res Ctr, Stanford, CA 94305 USA

[3] Stanford Univ, Sch Med, Dept Hlth Res & Policy, Stanford, CA 94305 USA

[4] Stanford Univ, Sch Human & Sci, Dept Stat, Stanford, CA 94305 USA

来源：

NATURE REVIEWS GENETICS | 2013年 / 14卷 / 06期

关键词：

RANDOM-EFFECTS MODEL; LARGE-SCALE; GENETIC ASSOCIATION; SUSCEPTIBILITY LOCI; RARE VARIANTS; GENOTYPE IMPUTATION; IDENTIFIES; 13; POWER; SEQUENCE; COMMON;

D O I：

10.1038/nrg3472

中图分类号：

Q3 [遗传学];

学科分类号：

071007 ; 090102 ;

摘要：

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.

引用

页码：379 / 389

页数：11

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