Application of a Bayesian non-linear model hybrid scheme to sequence data for genomic prediction and QTL mapping

Tingting Wang,Michael E Goddard,Ben J Hayes,Jennie E Pryce,Iona M Macleod,Yi-Ping Phoebe Chen

doi:10.1186/s12864-017-4030-x

Abstract

BackgroundUsing whole genome sequence data might improve genomic prediction accuracy, when compared with high-density SNP arrays, and could lead to identification of casual mutations affecting complex traits. For some traits, the most accurate genomic predictions are achieved with non-linear Bayesian methods. However, as the number of variants and the size of the reference population increase, the computational time required to implement these Bayesian methods (typically with Monte Carlo Markov Chain sampling) becomes unfeasibly long.ResultsHere, we applied a new method, HyB_BR (for Hybrid BayesR), which implements a mixture model of normal distributions and hybridizes an Expectation-Maximization (EM) algorithm followed by Markov Chain Monte Carlo (MCMC) sampling, to genomic prediction in a large dairy cattle population with imputed whole genome sequence data. The imputed whole genome sequence data included 994,019 variant genotypes of 16,214 Holstein and Jersey bulls and cows. Traits included fat yield, milk volume, protein kg, fat% and protein% in milk, as well as fertility and heat tolerance. HyB_BR achieved genomic prediction accuracies as high as the full MCMC implementation of BayesR, both for predicting a validation set of Holstein and Jersey bulls (multi-breed prediction) and a validation set of Australian Red bulls (across-breed prediction). HyB_BR had a ten fold reduction in compute time, compared with the MCMC implementation of BayesR (48 hours versus 594 hours). We also demonstrate that in many cases HyB_BR identified sequence variants with a high posterior probability of affecting the milk production or fertility traits that were similar to those identified in BayesR. For heat tolerance, both HyB_BR and BayesR found variants in or close to promising candidate genes associated with this trait and not detected by previous studies.ConclusionsThe results demonstrate that HyB_BR is a feasible method for simultaneous genomic prediction and QTL mapping with whole genome sequence in large reference populations.

Highlights

Using whole genome sequence data might improve genomic prediction accuracy, when compared with high-density single nucleotide polymorphism (SNP) arrays, and could lead to identification of casual mutations affecting complex traits
In some cases enough individuals have been sequenced to serve as a reference panel for imputation of individuals that have been genotyped with SNP arrays to whole genome sequence variant genotypes
As Bayesian models are typically implemented with MCMC (Markov Chain Monte Carlo) sampling, application of BayesR with sequence data is currently not feasible

Summary

Introduction

Using whole genome sequence data might improve genomic prediction accuracy, when compared with high-density SNP arrays, and could lead to identification of casual mutations affecting complex traits. As the resulting data sets will be extremely large (thousands of individuals with millions of imputed genotypes), the algorithms used to derive genomic predictions must be computationally efficient. Wang et al BMC Genomics (2017) 18:618 implement a non-linear model at the level of the SNP effects, including the possibility of excluding some SNPs from the model, as such models have been demonstrated to give higher accuracies of genomic predictions for some traits with high-density genotype data [5, 6]. As Bayesian models are typically implemented with MCMC (Markov Chain Monte Carlo) sampling, application of BayesR with sequence data is currently not feasible

Methods

Results

Conclusion