A comparison of multivariate genome-wide association methods.

Tessel E Galesloot,Luc L Janss,Kristel Van Steen,Sita H Vermeulen,Lambertus A L M Kiemeney,Yurii S Aulchenko

doi:10.1371/journal.pone.0095923

Abstract

Joint association analysis of multiple traits in a genome-wide association study (GWAS), i.e. a multivariate GWAS, offers several advantages over analyzing each trait in a separate GWAS. In this study we directly compared a number of multivariate GWAS methods using simulated data. We focused on six methods that are implemented in the software packages PLINK, SNPTEST, MultiPhen, BIMBAM, PCHAT and TATES, and also compared them to standard univariate GWAS, analysis of the first principal component of the traits, and meta-analysis of univariate results. We simulated data (N = 1000) for three quantitative traits and one bi-allelic quantitative trait locus (QTL), and varied the number of traits associated with the QTL (explained variance 0.1%), minor allele frequency of the QTL, residual correlation between the traits, and the sign of the correlation induced by the QTL relative to the residual correlation. We compared the power of the methods using empirically fixed significance thresholds (α = 0.05). Our results showed that the multivariate methods implemented in PLINK, SNPTEST, MultiPhen and BIMBAM performed best for the majority of the tested scenarios, with a notable increase in power for scenarios with an opposite sign of genetic and residual correlation. All multivariate analyses resulted in a higher power than univariate analyses, even when only one of the traits was associated with the QTL. Hence, use of multivariate GWAS methods can be recommended, even when genetic correlations between traits are weak.

Highlights

Genome-wide association studies (GWAS) have been very successful in the identification of common genetic variants associated with complex traits
In case of presence of pleiotropy, where a single genetic variant is associated with multiple traits, a multivariate GWAS is more consistent with biology compared to cross-trait comparison of univariate analyses [5]
We used simulated data to compare the performance of six multivariate genome-wide association methods (MV-PLINK, MV-SNPTEST, MultiPhen, MV-BIMBAM, PCHAT and TATES) and standard univariate analysis, univariate PCA, and meta-analysis of univariate analyses

Summary

Introduction

Genome-wide association studies (GWAS) have been very successful in the identification of common genetic variants associated with complex traits. Information on a set of related traits is collected in populations sampled for GWAS These traits are typically analyzed separately, i.e. in a univariate manner, for association to genome-wide DNA markers. This is often followed by an informal comparison of evidence for association at particular loci across the studied traits Most multivariate procedures can perform a single test for association with a set of traits This reduces the number of performed tests and alleviates the multiple testing burden compared to analyzing all traits separately [2,4]. In case of presence of pleiotropy, where a single genetic variant is associated with multiple traits, a multivariate GWAS is more consistent with biology compared to cross-trait comparison of univariate analyses [5]

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Results

Conclusion