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Review
. 2013:14:441-65.
doi: 10.1146/annurev-genom-091212-153520. Epub 2013 May 24.

The power of meta-analysis in genome-wide association studies

Orestis A Panagiotou  1 , Cristen J Willer, Joel N Hirschhorn, John P A Ioannidis
Affiliations
Review

The power of meta-analysis in genome-wide association studies

Orestis A Panagiotou et al. Annu Rev Genomics Hum Genet. 2013.

Abstract

Meta-analysis of multiple genome-wide association (GWA) studies has become common practice over the past few years. The main advantage of this technique is the maximization of power to detect subtle genetic effects for common traits. Moreover, one can use meta-analysis to probe and identify heterogeneity in the effect sizes across the combined studies. In this review, we systematically appraise and evaluate the characteristics of GWA meta-analyses with 10,000 or more subjects published up to June 2012. We provide an overview of the current landscape of variants discovered by GWA meta-analyses, and we discuss and assess with extrapolations from empirical data the value of larger meta-analyses for the discovery of additional genetic associations and new biology in the future. Finally, we discuss some emerging logistical and practical issues related to the conduct of meta-analysis of GWA studies.

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Figures

Figure 1
Figure 1
SNPs associated via GWA studies in > 10,000 individuals were compared to the entire set of SNPs typically examined in a GWA study including imputation and to the HapMap Phase II SNPs. A) The proportion of SNPs in each minor allele frequency bin are shown in and B) the cumulative proportion of SNPs that show a gene within a certain distance (kb) is plotted.
Figure 1
Figure 1
SNPs associated via GWA studies in > 10,000 individuals were compared to the entire set of SNPs typically examined in a GWA study including imputation and to the HapMap Phase II SNPs. A) The proportion of SNPs in each minor allele frequency bin are shown in and B) the cumulative proportion of SNPs that show a gene within a certain distance (kb) is plotted.
Figure 2
Figure 2
The effective sample size (see text for details) is plotted against the number of loci reaching genome wide significance for genome-wide association studies for height (top), the combination of three lipid traits (middle; HDL-cholesterol, LDL-cholesterol, and triglycerides), and the combination of two blood pressure traits (bottom; systolic and diastolic blood pressure). In each case, the largest study is removed, and a line through the origin is fitted to the remaining studies (circles). The number of loci in the largest study (filled triangle) is greater than or equal to that predicted by extrapolating the line to the effective sample size of the largest study (open triangle).
See this image and copyright information in PMC

References

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Annotations to References

    1. Asimit J, Zeggini E. Rare variant association analysis methods for complex traits. Annu Rev Genet. 2010;44:293–308. formula image A comprehensive review on the statistical methods for the analysis of rare variants

    1. McCarthy MI, Abecasis GR, Cardon LR, Goldstein DB, Little J, et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet. 2008;9:356–69. formula image A review on the value of GWA studies for detecting genetic associations and fine mapping.

    1. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7:177–88. formula image The paper introducing the most commonly used random-effects model for meta-analysis

    1. Dickson SP, Wang K, Krantz I, Hakonarson H, Goldstein DB. Rare variants create synthetic genome-wide associations. PLoS Biol. 2010;8:e1000294. formula image A paper supporting the contested hypothesis that signals from common variants may arise from synthetic associations of rare variants.

    1. Evangelou E, Maraganore DM, Ioannidis JP. Meta-analysis in genome-wide association datasets: strategies and application in Parkinson disease. PLoS One. 2007;2:e196. formula image The first study to combine different GWA datasets with different meta-analysis methods (fixed and random effects) showing the merits and disadvantages of each.

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