A Bayesian analysis to exploit imputed sequence variants for QTL discovery

Abstract Text: Bayesian genomic prediction methods, commonly used for genomic selection in livestock, are potentially a powerful tool for QTL discovery. However these methods become more computationally challenging as we move from HD SNP to sequence variants. We discuss results from a modified BayesR analysis in which 800K genotypes and subsets of imputed sequence variants were allocated to specific classes based on biological information prior to starting the analysis. The analysis determines if there is enrichment for QTL effects by allowing the distribution of SNP effects to vary between classes. We analysed milk traits from a mixed group of Holstein and Jersey bulls. Using examples of mutations in genes previously associated with milk traits, we demonstrate that this modified Bayesian analysis may provide a powerful approach for short-listing potential causal variants.

Keywords:

BayesRC

dairy cattle

causal mutations