Correcting for relatedness in Bayesian models for genomic data association analysis

Abstract

For small pedigrees, the issue of correcting for known or estimated relatedness structure in population-based Bayesian multilocus association analysis is considered. Two such relatedness corrections: 1 a random term arising from the infinite polygenic model and 2 a fixed covariate following the class D model of Bonney, are compared with the case of no correction using both simulated and real marker and gene-expression data from lymphoblastoid cell lines from four CEPH families. This comparison is performed with clinical quantitative trait locus (cQTL) modelsmultilocus association models where marker data and expression levels of gene transcripts as well as possible genotype × expression interaction terms are jointly used to explain quantitative trait variation. We found out that regardless of having a correction term in the model, the cQTL-models fit a few extra small-effect components (similar to finite polygenic models) which itself serves as a relatedness correction. For small data and small heritability one may use the covariate model, which clearly outperforms the infinite polygenic model in small data examples. © 2009 Macmillan Publishers Limited.