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利用泰国奶牛随机回归测试日模型的一步基因组最佳线性无偏预测法对产奶性状和体细胞评分进行基因组预测。

Genomic prediction of milk-production traits and somatic cell score using single-step genomic best linear unbiased predictor with random regression test-day model in Thai dairy cattle.

机构信息

The Bureau of Biotechnology in Livestock Production, Department of Livestock Development, Pathum Thani 12000, Thailand.

Department of Animal Science, Khon Kaen University, Meaung, Khon Kaen 40002, Thailand.

出版信息

J Dairy Sci. 2021 Dec;104(12):12713-12723. doi: 10.3168/jds.2021-20263. Epub 2021 Sep 16.

DOI:10.3168/jds.2021-20263
PMID:34538484
Abstract

Cow genotypes are expected to improve the accuracy of genomic estimated breeding values (GEBV) for young bulls in relatively small populations such as Thai Holstein-Friesian crossbred dairy cattle in Thailand. The objective of this study was to investigate the effect of cow genotypes on the predictive ability and individual accuracies of GEBV for young dairy bulls in Thailand. Test-day data included milk yield (n = 170,666), milk component traits (fat yield, protein yield, total solids yield, fat percentage, protein percentage, and total solids percentage; n = 160,526), and somatic cell score (n = 82,378) from 23,201, 82,378, and 13,737 (for milk yield, milk component traits, and SCS, respectively) cows calving between 1993 and 2017, respectively. Pedigree information included 51,128; 48,834; and 32,743 animals for milk yield, milk component traits, and somatic cell score, respectively. Additionally, 876, 868, and 632 pedigreed animals (for milk yield, milk component traits, and SCS, respectively) were genotyped (152 bulls and 724 cows), respectively, using Illumina Bovine SNP50 BeadChip. We cut off the data in the last 6 yr, and the validation animals were defined as genotyped bulls with no daughters in the truncated set. We calculated GEBV using a single-step random regression test-day model (SS-RR-TDM), in comparison with estimated breed value (EBV) based on the pedigree-based model used as the official method in Thailand (RR-TDM). Individual accuracies of GEBV were obtained by inverting the coefficient matrix of the mixed model equations, whereas validation accuracies were measured by the Pearson correlation between deregressed EBV from the full data set and (G)EBV predicted with the reduced data set. When only bull genotypes were used, on average, SS-RR-TDM increased individual accuracies by 0.22 and validation accuracies by 0.07, compared with RR-TDM. With cow genotypes, the additional increase was 0.02 for individual accuracies and 0.06 for validation accuracies. The inflation of GEBV tended to be reduced using cow genotypes. Genomic evaluation by SS-RR-TDM is feasible to select young bulls for the longitudinal traits in Thai dairy cattle, and the accuracy of selection is expected to be increased with more genotypes. Genomic selection using the SS-RR-TDM should be implemented in the routine genetic evaluation of the Thai dairy cattle population. The genetic evaluation should consider including genotypes of both sires and cows.

摘要

奶牛基因型有望提高泰国荷斯坦-弗里生杂交奶牛等小群体中年轻公牛基因组估计育种值(GEBV)的准确性。本研究的目的是研究奶牛基因型对泰国年轻奶牛 GEBV 预测能力和个体准确性的影响。测试日数据包括产奶量(n=170666)、乳成分性状(脂肪产量、蛋白质产量、总固体产量、脂肪百分比、蛋白质百分比和总固体百分比;n=160526)和体细胞评分(n=82378),分别来自于 1993 年至 2017 年期间产犊的 23201、82378 和 13737 头(产奶量、乳成分性状和 SCS)奶牛。系谱信息包括分别用于产奶量、乳成分性状和体细胞评分的 51128、48834 和 32743 头动物。此外,使用 Illumina Bovine SNP50 BeadChip 对 876、868 和 632 头(分别用于产奶量、乳成分性状和 SCS)有系谱记录的动物进行了基因分型(152 头公牛和 724 头母牛)。我们切断了最后 6 年的数据,验证动物被定义为截断集内没有女儿的基因分型公牛。我们使用单步随机回归测试日模型(SS-RR-TDM)计算 GEBV,与泰国官方方法(RR-TDM)基于系谱模型的估计育种值(EBV)进行比较。通过反转混合模型方程的系数矩阵获得 GEBV 的个体准确性,而通过从完整数据集中回归 EBV 与使用简化数据集预测的(G)EBV 之间的皮尔逊相关来衡量验证准确性。当仅使用公牛基因型时,与 RR-TDM 相比,SS-RR-TDM 平均将个体准确性提高了 0.22,验证准确性提高了 0.07。使用奶牛基因型,个体准确性的额外增加为 0.02,验证准确性的额外增加为 0.06。使用奶牛基因型往往会降低 GEBV 的膨胀。使用 SS-RR-TDM 进行基因组评估可以选择泰国奶牛的纵向性状的年轻公牛,并且随着基因型的增加,选择的准确性有望提高。使用 SS-RR-TDM 的基因组选择应在泰国奶牛群体的常规遗传评估中实施。遗传评估应考虑包括公牛和奶牛的基因型。

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