Bolormaa et al. Genet Sel Evol (2017) 49:62 DOI 10.1186/s12711-017-0337-y
RESEARCH ARTICLE
Ge n e t i c s Se l e c t i o n Ev o l u t i o n
Open Access
Multiple‑trait QTL mapping and genomic prediction for wool traits in sheep Sunduimijid Bolormaa1,7*, Andrew A. Swan2,7, Daniel J. Brown2,7, Sue Hatcher3,7, Nasir Moghaddar4,7, Julius H. van der Werf4,7, Michael E. Goddard1,5 and Hans D. Daetwyler1,6,7
Abstract 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 (
RESEARCH ARTICLE
Ge n e t i c s Se l e c t i o n Ev o l u t i o n
Open Access
Multiple‑trait QTL mapping and genomic prediction for wool traits in sheep Sunduimijid Bolormaa1,7*, Andrew A. Swan2,7, Daniel J. Brown2,7, Sue Hatcher3,7, Nasir Moghaddar4,7, Julius H. van der Werf4,7, Michael E. Goddard1,5 and Hans D. Daetwyler1,6,7
Abstract 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 (
