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Meta‐analysis of sex‐specific genome‐wide association studies
Author(s) -
Magi Reedik,
Lindgren Cecilia M.,
Morris Andrew P.
Publication year - 2010
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.20540
Subject(s) - biology , meta analysis , genetic association , genome wide association study , heritability , sexual dimorphism , missing heritability problem , genetics , allele , evolutionary biology , gene , single nucleotide polymorphism , genotype , medicine , endocrinology
Despite the success of genome‐wide association studies, much of the genetic contribution to complex human traits is still unexplained. One potential source of genetic variation that may contribute to this “missing heritability” is that which differs in magnitude and/or direction between males and females, which could result from sexual dimorphism in gene expression. Such sex‐differentiated effects are common in model organisms, and are becoming increasingly evident in human complex traits through large‐scale male‐ and female‐specific meta‐analyses. In this article, we review the methodology for meta‐analysis of sex‐specific genome‐wide association studies, and propose a sex‐differentiated test of association with quantitative or dichotomous traits, which allows for heterogeneity of allelic effects between males and females. We perform detailed simulations to compare the power of the proposed sex‐differentiated meta‐analysis with the more traditional “sex‐combined” approach, which is ambivalent to gender. The results of this study highlight only a small loss in power for the sex‐differentiated meta‐analysis when the allelic effects of the causal variant are the same in males and females. However, over a range of models of heterogeneity in allelic effects between genders, our sex‐differentiated meta‐analysis strategy offers substantial gains in power, and thus has the potential to discover novel loci contributing effects to complex human traits with existing genome‐wide association data. Genet. Epidemiol . 34:846–853, 2010. © 2010 Wiley‐Liss, Inc.