A Bayesian partition model for case-control studies on highly polymorphic candidate genes.

Abstract

We present a new statistical model for the analysis of case-control or cohort studies examining a highly polymorphic candidate disease susceptibility gene. Many genotypes are possible for such a gene. Consequently, the average number of subjects having each genotype will be modest. If analyzed separately, the risks associated with most genotypes will be estimated imprecisely. Our Bayesian partition model clusters genotypes according to risk, only allowing partitions that satisfy a particular assumption about the joint effect of the two alleles making up a genotype. This assumption is genetically plausible, imposes structure on the set of genotype risks, and still leaves a highly flexible model. By Bayesian model averaging over partitions, the model becomes, in effect, a semiparametric model for genotype risk. It allows borrowing of strength, i.e., estimates of risk for one genotype are informed by the risk estimates of all the genotypes. We present the results of fitting the model to two datasets, one simulated and one genuine case-control study of the NAT1 gene and lung cancer, and compare it in a simulation study with a haplotype relative risk model. The partition model enables genotype risks to be estimated more accurately and the alleles to be ranked according to risk.