Association mapping of complex trait loci with context-dependent effects and unknown context-variable

A novel method for Bayesian analysis of genetic heterogeneity and multilocus association in random population samples is presented. The method is valid for quantitative and binary traits as well as multiallelic markers. In the method, individuals are stochastically assigned into two etiological groups, each of which can have their own, and possibly different, subsets of trait-associated (disease-predisposing) loci or alleleles. The method is favorable especially in situations when etiological models are stratified by the factors that are unknown or went unmeasured. That is, if genetic heterogeneity is due to for example heterogeneity is due to unknown genes-environment or genes-gene interactions. Additionally, a heterogeneity structure for the phenotype does not need to follow the structure of the general population; it can have a distinct selection history. The performance of the method is illustrated with simulated example of genes-environment interaction (quantitative trait with loosely linked markers) and compared to the results of single group analysis in the presence of missing data. Additionally, example analyses with previously analysed cystic fibrosis and type 2 diabetes data sets (binary traits with closely linked markers) are presented. The implementation (written in WinBUGS) is freely available for research purposes at http://www.rni.helsinki.fi/~mjs/.

Key Words: Association analysis, Bayesian inference, genetic heterogeneity, model selection, stratified analysis

Online ISS 1091-6490
Copyright 2008 by the Genetics Society of America
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