Association testing aims to discover the underlying relationship between genotypes (usually Single Nucleotide Polymorphisms, or SNPs) and phenotypes (attributes, or traits). The typically large data sets used in association testing often contain miss
ing values. Standard statistical methods either impute the missing values using relatively simple assumptions, or delete them, or both, which can generate biased results. Here we describe the Bayesian hierarchical model BAMD (Bayesian Association with Missing Data). BAMD is a Gibbs sampler, in which missing values are multiply imputed based upon all of the available information in the data set. We estimate the parameters and prove that updating one SNP at each iteration preserves the ergodic property of the Markov chain, and at the same time improves computational speed. We also implement a model selection option in BAMD, which enables potential detection of SNP interactions. Simulations show that unbiased estimates of SNP effects are recovered with missing genotype data. Also, we validate associations between SNPs and a carbon isotope discrimination phenotype that were previously reported using a family based method, and discover an additional SNP associated with the trait. BAMD is available as an R-package from http://cran.r-project.org/package=BAMD
