Multiple comparisons in genetic association studies: a hierarchical modeling approach

Yi Nengjun (), Xu Shizhong, Lou Xiang-Yang and Mallick Himel
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Yi Nengjun: Department of Biostatistics, Section on Statistical Genetics, University of Alabama at Birmingham, Birmingham, AL 35294, USA
Xu Shizhong: Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA
Lou Xiang-Yang: Department of Biostatistics, Section on Statistical Genetics, University of Alabama at Birmingham, Birmingham, AL 35294, USA
Mallick Himel: Department of Biostatistics, Section on Statistical Genetics, University of Alabama at Birmingham, Birmingham, AL 35294, USA

Statistical Applications in Genetics and Molecular Biology, 2014, vol. 13, issue 1, 35-48

Abstract: Multiple comparisons or multiple testing has been viewed as a thorny issue in genetic association studies aiming to detect disease-associated genetic variants from a large number of genotyped variants. We alleviate the problem of multiple comparisons by proposing a hierarchical modeling approach that is fundamentally different from the existing methods. The proposed hierarchical models simultaneously fit as many variables as possible and shrink unimportant effects towards zero. Thus, the hierarchical models yield more efficient estimates of parameters than the traditional methods that analyze genetic variants separately, and also coherently address the multiple comparisons problem due to largely reducing the effective number of genetic effects and the number of statistically “significant” effects. We develop a method for computing the effective number of genetic effects in hierarchical generalized linear models, and propose a new adjustment for multiple comparisons, the hierarchical Bonferroni correction, based on the effective number of genetic effects. Our approach not only increases the power to detect disease-associated variants but also controls the Type I error. We illustrate and evaluate our method with real and simulated data sets from genetic association studies. The method has been implemented in our freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/).

Keywords: Bayesian inference; effective number of parameters; effective number of hypothesis tests; generalized linear models; genetic association studies; hierarchical modeling; hierarchical Bonferroni correction; multiple comparisons (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1515/sagmb-2012-0040

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