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Activity Number: 242
Type: Contributed
Date/Time: Monday, August 1, 2016 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #318706
Title: A Bayesian GWAS Method Utilizing Haplotype Clusters for a Composite Breed Population
Author(s): Danielle Wilson-Wells* and Stephen D. Kachman
Companies: University of Nebraska - Lincoln and University of Nebraska - Lincoln
Keywords: Bayesian ; GWAS ; Genomic prediction

Commercial beef cattle are often composites of multiple breeds. Current methods used to produce genomic predictors are based on the underlying assumption of animals being sampled from a homogeneous population. As a result, the predictors can perform poorly when used to predict the relative genetic merit of animals whose breed composition are different. In part, this is due to the changes in linkage disequilibrium between the markers and the quantitative trait loci as we move from one breed to the next. An alternative model based on breed specific haplotype clusters was developed to allow for differences in linkage disequilibrium across multiple breeds. The haplotype clusters were modeled as hidden states in a hidden Markov model where the genomic effects are associated with loci located on the unobserved clusters. Similar to the Bayes C model, we can model the genomic effects at the loci using a prior, which consists of a mixture of a multivariate normal and a point mass at zero distribution. The model will be used to construct genomic predictors using records on 5,000 cattle genotyped for 99,827 mapped SNPs representing various fractions of three different breeds.

Authors who are presenting talks have a * after their name.

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