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

Wang, Tingting, Chen, Yi-Ping Phoebe, MacLeod, Iona M., Pryce, Jennie E., Goddard, Michael E. and Hayes, Ben J. (2017) Application of a Bayesian non-linear model hybrid scheme to sequence data for genomic prediction and QTL mapping. BMC Genomics, 18 1: . doi:10.1186/s12864-017-4030-x

Author Wang, Tingting
Chen, Yi-Ping Phoebe
MacLeod, Iona M.
Pryce, Jennie E.
Goddard, Michael E.
Hayes, Ben J.
Title Application of a Bayesian non-linear model hybrid scheme to sequence data for genomic prediction and QTL mapping
Journal name BMC Genomics   Check publisher's open access policy
ISSN 1471-2164
Publication date 2017-08-15
Sub-type Article (original research)
DOI 10.1186/s12864-017-4030-x
Open Access Status DOI
Volume 18
Issue 1
Total pages 23
Place of publication London, United Kingdom
Publisher BioMed Central
Language eng
Subject 1305 Biotechnology
1311 Genetics
Abstract Background: 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. 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. Results: Here, 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. Conclusions: The results demonstrate that HyB_BR is a feasible method for simultaneous genomic prediction and QTL mapping with whole genome sequence in large reference populations.
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Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
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