A multi-trait Bayesian method for mapping QTL and genomic prediction
AuthorKemper, KE; Bowman, PJ; Hayes, BJ; Visscher, PM; Goddard, ME
Source TitleGenetics Selection Evolution
PublisherBIOMED CENTRAL LTD
University of Melbourne Author/sGoddard, Michael
AffiliationAgriculture and Food Systems
Document TypeJournal Article
CitationsKemper, K. E., Bowman, P. J., Hayes, B. J., Visscher, P. M. & Goddard, M. E. (2018). A multi-trait Bayesian method for mapping QTL and genomic prediction. GENETICS SELECTION EVOLUTION, 50 (1), https://doi.org/10.1186/s12711-018-0377-y.
Access StatusOpen Access
ARC Grant codeARC/DP1093502
BACKGROUND: Genomic prediction and quantitative trait loci (QTL) mapping typically analyze one trait at a time but this may ignore the possibility that one polymorphism affects multiple traits. The aim of this study was to develop a multivariate Bayesian approach that could be used for simultaneously elucidating genetic architecture, QTL mapping, and genomic prediction. Our approach uses information from multiple traits to divide markers into 'unassociated' (no association with any trait) and 'associated' (associated with one or more traits). The effect of associated markers is estimated independently for each trait to avoid the assumption that QTL effects follow a multi-variate normal distribution. RESULTS: Using simulated data, our multivariate method (BayesMV) detected a larger number of true QTL (with a posterior probability > 0.9) and increased the accuracy of genomic prediction compared to an equivalent univariate method (BayesR). With real data, accuracies of genomic prediction in validation sets for milk yield traits with high-density genotypes were approximately equal to those from equivalent single-trait methods. BayesMV tended to select a similar number of single nucleotide polymorphisms (SNPs) per trait for genomic prediction compared to BayesR (i.e. those with non-zero effects), but BayesR selected different sets of SNPs for each trait, whereas BayesMV selected a common set of SNPs across traits. Despite these two dramatically different estimates of genetic architecture (i.e. different SNPs affecting each trait vs. pleiotropic SNPs), both models indicated that 3000 to 4000 SNPs are associated with a trait. The BayesMV approach may be advantageous when the aim is to develop a low-density SNP chip that works well for a number of traits. SNPs for milk yield traits identified by BayesMV and BayesR were also found to be associated with detailed milk composition. CONCLUSIONS: The BayesMV method simultaneously estimates the proportion of SNPs that are associated with a combination of traits. When applied to milk production traits, most of the identified SNPs were associated with all three traits (milk, fat and protein yield). BayesMV aims at exploiting pleiotropic QTL and selects a small number of SNPs that could be used to predict multiple traits.
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