Bolormaa S, Brown D J, Swan A A, van der Werf J H J, Hayes B J, Daetwyler H D
AgriBio, Centre for AgriBioscience, Biosciences Research, Agriculture Victoria, Bundoora, Vic, 3083, Australia.
Cooperative Research Centre for Sheep Industry Innovation, Armidale, NSW, 2351, Australia.
Anim Genet. 2017 Jun;48(3):338-348. doi: 10.1111/age.12541. Epub 2017 Feb 17.
Economically important reproduction traits in sheep, such as number of lambs weaned and litter size, are expressed only in females and later in life after most selection decisions are made, which makes them ideal candidates for genomic selection. Accurate genomic predictions would lead to greater genetic gain for these traits by enabling accurate selection of young rams with high genetic merit. The aim of this study was to design and evaluate the accuracy of a genomic prediction method for female reproduction in sheep using daughter trait deviations (DTD) for sires and ewe phenotypes (when individual ewes were genotyped) for three reproduction traits: number of lambs born (NLB), litter size (LSIZE) and number of lambs weaned. Genomic best linear unbiased prediction (GBLUP), BayesR and pedigree BLUP analyses of the three reproduction traits measured on 5340 sheep (4503 ewes and 837 sires) with real and imputed genotypes for 510 174 SNPs were performed. The prediction of breeding values using both sire and ewe trait records was validated in Merino sheep. Prediction accuracy was evaluated by across sire family and random cross-validations. Accuracies of genomic estimated breeding values (GEBVs) were assessed as the mean Pearson correlation adjusted by the accuracy of the input phenotypes. The addition of sire DTD into the prediction analysis resulted in higher accuracies compared with using only ewe records in genomic predictions or pedigree BLUP. Using GBLUP, the average accuracy based on the combined records (ewes and sire DTD) was 0.43 across traits, but the accuracies varied by trait and type of cross-validations. The accuracies of GEBVs from random cross-validations (range 0.17-0.61) were higher than were those from sire family cross-validations (range 0.00-0.51). The GEBV accuracies of 0.41-0.54 for NLB and LSIZE based on the combined records were amongst the highest in the study. Although BayesR was not significantly different from GBLUP in prediction accuracy, it identified several candidate genes which are known to be associated with NLB and LSIZE. The approach provides a way to make use of all data available in genomic prediction for traits that have limited recording.
绵羊中具有经济重要性的繁殖性状，如断奶羔羊数和产仔数，仅在母羊中表现，且在大多数选择决策做出之后才在生命后期体现，这使得它们成为基因组选择的理想候选性状。准确的基因组预测能够通过精准选择具有高遗传价值的年轻公羊，从而在这些性状上实现更大的遗传进展。本研究的目的是设计并评估一种基于父系子代性状偏差（DTD）和母羊表型（当个体母羊进行基因分型时）的绵羊雌性繁殖基因组预测方法的准确性，该方法针对三个繁殖性状：产羔数（NLB）、产仔数（LSIZE）和断奶羔羊数。对5340只绵羊（4503只母羊和837只公羊）进行了这三个繁殖性状的测量，并对510174个单核苷酸多态性（SNP）进行了真实和推算基因型分析，同时进行了基因组最佳线性无偏预测（GBLUP）、贝叶斯R（BayesR）和系谱BLUP分析。利用父系和母系性状记录对育种值进行的预测在美利奴绵羊中得到了验证。通过跨父系家族和随机交叉验证来评估预测准确性。基因组估计育种值（GEBV）的准确性通过输入表型准确性调整后的平均皮尔逊相关性来评估。与在基因组预测或系谱BLUP中仅使用母羊记录相比，在预测分析中加入父系DTD可提高准确性。使用GBLUP，基于综合记录（母羊和父系DTD）的平均准确性在各性状间为0.43，但准确性因性状和交叉验证类型而异。随机交叉验证得到的GEBV准确性（范围为0.17 - 0.61）高于父系家族交叉验证得到的准确性（范围为0.00 - 0.51）。基于综合记录，NLB和LSIZE的GEBV准确性在0.41 - 0.54之间，是本研究中最高的之一。尽管BayesR在预测准确性上与GBLUP没有显著差异，但它识别出了几个已知与NLB和LSIZE相关的候选基因。该方法为在记录有限的性状的基因组预测中利用所有可用数据提供了一种途径。
