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A two‐stage mixed‐effects model approach for gene‐set analyses in candidate gene studies
Author(s) -
Tsonaka Roula,
Helmvan Mil Annette H. M.,
HouwingDuistermaat Jeanine J.
Publication year - 2011
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4370
Subject(s) - bayes' theorem , covariate , random effects model , single nucleotide polymorphism , mixed model , computational biology , set (abstract data type) , genetic association , gene , genetics , biology , candidate gene , multiple comparisons problem , computer science , statistics , bayesian probability , mathematics , artificial intelligence , machine learning , meta analysis , genotype , medicine , programming language
In genetic association studies, a gene‐set analysis can be more powerful than the separate analyses of multiple genetic variants and can offer unique insights into the genetic basis of many common human diseases. The goal of such an analysis is to study the joint effect of multiple single‐nucleotide polymorphisms (SNPs) which belong to certain genes, and these genes are assumed to be involved in a common biological function. Currently, few approaches acknowledge the within‐genes and between‐genes correlations when testing for gene‐set effects. Thus, here we propose a two‐stage approach, which in the first stage uses a mixed‐effects model with a general random‐effects structure to capture the correlation between the SNPs and in the second stage tests for gene‐set effects by using the empirical Bayes estimates of the random effects of the first stage as covariates in the model for the longitudinal phenotype. The advantage of this approach is its broad applicability because it can be used for any phenotypic outcome and any genetic model and can be implemented with standard statistical software. Copyright © 2011 John Wiley & Sons, Ltd.