An Efficient Test for Gene-Environment Interaction in Generalized Linear Mixed Models with Family Data

Mauricio A. Mazo Lopera, Brandon J. Coombes and Mariza De Andrade
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Mauricio A. Mazo Lopera: School of Statistics, National University of Colombia, Medellín, Antioquia 050022, Colombia
Brandon J. Coombes: Departament of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
Mariza De Andrade: Departament of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA

IJERPH, 2017, vol. 14, issue 10, 1-13

Abstract: Gene-environment (GE) interaction has important implications in the etiology of complex diseases that are caused by a combination of genetic factors and environment variables. Several authors have developed GE analysis in the context of independent subjects or longitudinal data using a gene-set. In this paper, we propose to analyze GE interaction for discrete and continuous phenotypes in family studies by incorporating the relatedness among the relatives for each family into a generalized linear mixed model (GLMM) and by using a gene-based variance component test. In addition, we deal with collinearity problems arising from linkage disequilibrium among single nucleotide polymorphisms (SNPs) by considering their coefficients as random effects under the null model estimation. We show that the best linear unbiased predictor (BLUP) of such random effects in the GLMM is equivalent to the ridge regression estimator. This equivalence provides a simple method to estimate the ridge penalty parameter in comparison to other computationally-demanding estimation approaches based on cross-validation schemes. We evaluated the proposed test using simulation studies and applied it to real data from the Baependi Heart Study consisting of 76 families. Using our approach, we identified an interaction between BMI and the Peroxisome Proliferator Activated Receptor Gamma ( PPARG ) gene associated with diabetes.

Keywords: gene-environment interaction; generalized linear mixed model; variance component test; score test; ridge regression; best linear unbiased predictor; family data (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2017
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