## Abstract Genomeβwide association studies have recently identified many new loci associated with human complex diseases. These newly discovered variants typically have weak effects requiring studies with large numbers of individuals to achieve the statistical power necessary to identify them. Lik
Meta-analysis of sex-specific genome-wide association studies
β Scribed by Reedik Magi; Cecilia M. Lindgren; Andrew P. Morris
- Publisher
- John Wiley and Sons
- Year
- 2010
- Tongue
- English
- Weight
- 212 KB
- Volume
- 34
- Category
- Article
- ISSN
- 0741-0395
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β¦ Synopsis
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 βmissing heritabilityβ 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 βsex-combinedβ 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. 34:846β853, 2010. Β© 2010 Wiley-Liss, Inc.
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