Multivariate Gene-Based Association Test on Family Data in MGASReport as inadecuate




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Behavior Genetics

, Volume 46, Issue 5, pp 718–725

First Online: 06 April 2016Received: 14 September 2015Accepted: 12 March 2016DOI: 10.1007-s10519-016-9787-1

Cite this article as: Vroom, CR., Posthuma, D., Li, MX. et al. Behav Genet 2016 46: 718. doi:10.1007-s10519-016-9787-1

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.

KeywordsGWAS Multivariate Gene-based Family data MGAS GATES TATES Edited by Stacey Cherny.

Electronic supplementary materialThe online version of this article doi:10.1007-s10519-016-9787-1 contains supplementary material, which is available to authorized users.

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Author: César-Reyer Vroom - Danielle Posthuma - Miao-Xin Li - Conor V. Dolan - Sophie van der Sluis

Source: https://link.springer.com/







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