Methods for Meta‐Analysis of Genetic Data

互联网2013-12-31

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Abstract
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Abstract

Modern genetic association studies, using genome?wide genotype data, are often underpowered. Meta?analyses of multiple studies performing genome?wide genotyping improve power and have led to the identification of thousands of genotype?trait associations. This unit provides an overview of the key concepts required for genetic meta?analyses, and presents strategic approaches and key decisions that must be made in the process of performing genome?wide association study (GWAS) meta?analyses. The commentary discusses the interpretation of GWAS meta?analysis results, complications, and some of the possible next steps once a GWAS meta?analysis has successfully identified regions associated with a trait. Curr. Protoc. Hum. Genet. 77:1.24.1?1.24.8. © 2013 by John Wiley & Sons, Inc.

Keywords: genome?wide association; GWAS; genetic association analysis; meta?analysis; common variants

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Introduction
Key Concepts
Discussion
Literature Cited
Figures

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Materials

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Figures

Figure 1.24.1 Example quantile‐quantile (Q‐Q) plots of ‐log10( p values) for GWAS. (A ) No true effects, and no test statistic inflation. (B ) No true effects, and minor test statistic inflation. (C ) True signal and no test statistic inflation. (D ) No true effects, and stronger test statistic inflation. (E ) True signal and mild test statistic inflation. (F ) Strong true signal and no test statistic inflation.

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Literature Cited

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