Correcting for relatedness in Bayesian models for genomic data association analysis.
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) models-multilocus association models where marker data and expression levels of gene transcripts as well as possible genotype x 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.
Pikkuhookana
P
P
Department of Mathematics and Statistics, Rolf Nevanlinna Institute, University of Helsinki, Helsinki, Finland.
Sillanpää
M J
MJ
D001499:Bayes Theorem; D045744:Cell Line, Tumor; D016678:Genome; D023281:Genomics; D006801:Humans; D008957:Models, Genetic; D009369:Neoplasms / Q000235:genetics; D040641:Quantitative Trait Loci