Multivariate Gene-Based Association Test on Family Data in MGAS

Cesar-Reyer Vroom, Danielle Posthuma, Miao-Xin Li, Conor V. Dolan, Sophie van der Sluis

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

In analyses of unrelated individuals, the program multivariate gene-based association test by extended Simes (MGAS), which facilitates multivariate gene-based association testing, was shown to have correct Type I error rate and superior statistical power compared to other multivariate gene-based approaches. Here we show, through simulation, that MGAS can also be applied to data including genetically related subjects (e.g., family data), by using p value information obtained in Plink or in generalized estimating equations (with the ‘exchangeable’ working correlation matrix), both of which account for the family structure on a univariate single nucleotide polymorphism-based level by applying a sandwich correction of standard errors. We show that when applied to family-data, MGAS has correct Type I error rate, and given the details of the simulation setup, adequate power. Application of MGAS to seven eye measurement phenotypes showed statistically significant association with two genes that were not discovered in previous univariate analyses of a composite score. We conclude that MGAS is a useful and convenient tool for multivariate gene-based genome-wide association analysis in both unrelated and related individuals.
Original languageEnglish
Pages (from-to)718-725
JournalBehavior Genetics
Volume46
Issue number5
DOIs
Publication statusPublished - Sep 2016

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