Multiple-trait QTL mapping and genomic prediction for wool traits in sheep
AuthorBolormaa, S; Swan, AA; Brown, DJ; Hatcher, S; Moghaddar, N; van der Werf, JH; Goddard, ME; Daetwyler, HD
Source TitleGenetics Selection Evolution
University of Melbourne Author/sGoddard, Michael
AffiliationAgriculture and Food Systems
Document TypeJournal Article
CitationsBolormaa, S., Swan, A. A., Brown, D. J., Hatcher, S., Moghaddar, N., van der Werf, J. H., Goddard, M. E. & Daetwyler, H. D. (2017). Multiple-trait QTL mapping and genomic prediction for wool traits in sheep. GENETICS SELECTION EVOLUTION, 49 (1), https://doi.org/10.1186/s12711-017-0337-y.
Access StatusOpen Access
BACKGROUND: The application of genomic selection to sheep breeding could lead to substantial increases in profitability of wool production due to the availability of accurate breeding values from single nucleotide polymorphism (SNP) data. Several key traits determine the value of wool and influence a sheep's susceptibility to fleece rot and fly strike. Our aim was to predict genomic estimated breeding values (GEBV) and to compare three methods of combining information across traits to map polymorphisms that affect these traits. METHODS: GEBV for 5726 Merino and Merino crossbred sheep were calculated using BayesR and genomic best linear unbiased prediction (GBLUP) with real and imputed 510,174 SNPs for 22 traits (at yearling and adult ages) including wool production and quality, and breech conformation traits that are associated with susceptibility to fly strike. Accuracies of these GEBV were assessed using fivefold cross-validation. We also devised and compared three approximate multi-trait analyses to map pleiotropic quantitative trait loci (QTL): a multi-trait genome-wide association study and two multi-trait methods that use the output from BayesR analyses. One BayesR method used local GEBV for each trait, while the other used the posterior probabilities that a SNP had an effect on each trait. RESULTS: BayesR and GBLUP resulted in similar average GEBV accuracies across traits (~0.22). BayesR accuracies were highest for wool yield and fibre diameter (>0.40) and lowest for skin quality and dag score (<0.10). Generally, accuracy was higher for traits with larger reference populations and higher heritability. In total, the three multi-trait analyses identified 206 putative QTL, of which 20 were common to the three analyses. The two BayesR multi-trait approaches mapped QTL in a more defined manner than the multi-trait GWAS. We identified genes with known effects on hair growth (i.e. FGF5, STAT3, KRT86, and ALX4) near SNPs with pleiotropic effects on wool traits. CONCLUSIONS: The mean accuracy of genomic prediction across wool traits was around 0.22. The three multi-trait analyses identified 206 putative QTL across the ovine genome. Detailed phenotypic information helped to identify likely candidate genes.
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