Agriculture Victoria Research, AgriBio, Centre for AgriBioscience, 5 Ring Road, Bundoora, Victoria, 3083, Australia.
Agriculture Victoria Research, AgriBio, Centre for AgriBioscience, 5 Ring Road, Bundoora, Victoria, 3083, Australia.
J Dairy Sci. 2020 Jul;103(7):6276-6298. doi: 10.3168/jds.2019-17914. Epub 2020 Apr 22.
The reliability of genomic prediction is influenced by several factors, including the size of the reference population, which makes genomic prediction for breeds with a relatively small population size challenging, such as Australian Red dairy cattle. Including other breeds in the reference population may help to increase the size of the reference population, but the reliability of genomic prediction is also influenced by the relatedness between the reference and validation population. Our objective was to optimize the reference population for genomic prediction of Australian Red dairy cattle. A reference population comprising up to 3,248 Holstein bulls, 48,386 Holstein cows, 807 Jersey bulls, 8,734 Jersey cows, and 3,041 Australian Red cows and a validation population with between 208 and 224 Australian Red Bulls were used, with records for milk, fat, and protein yield, somatic cell count, fertility, and survival. Three different analyses were implemented: single-trait genomic best linear unbiased predictor (GBLUP), multi-trait GBLUP, and single-trait Bayes R, using 2 different medium-density SNP panels: the standard 50K chip and a custom array of variants that were expected to be enriched for causative mutations. Various reference populations were constructed containing the Australian Red cows and all Holstein and Jersey bulls and cows, all Holstein and Jersey bulls, all Holstein bulls and cows, all Holstein bulls, and a subset of the Holstein individuals varying the relatedness between Holsteins and Australian Reds and the number of Holsteins. Varying the relatedness between reference and validation populations only led to small changes in reliability. Whereas adding a limited number of closely related Holsteins increased reliabilities compared with within-breed prediction, increasing the number of Holsteins decreased the reliability. The multi-trait GBLUP, which considered the same trait in different breeds as correlated traits, yielded higher reliabilities than the single-trait GBLUP. Bayes R yielded lower reliabilities than multi-trait GBLUP and outperformed single-trait GBLUP for larger reference populations. Our results show that increasing the size of a multi-breed reference population may result in a reference population dominated by one breed and reduce the reliability to predict in other breeds.
基因组预测的可靠性受到多个因素的影响，包括参考群体的大小，这使得对于人口规模相对较小的品种（如澳大利亚红奶牛）进行基因组预测具有挑战性。在参考群体中包含其他品种可以帮助增加参考群体的规模，但基因组预测的可靠性也受到参考群体和验证群体之间的亲缘关系的影响。我们的目标是优化澳大利亚红奶牛的基因组预测参考群体。使用了一个包含多达 3248 头荷斯坦公牛、48386 头荷斯坦奶牛、807 头泽西公牛、8734 头泽西奶牛和 3041 头澳大利亚红牛的参考群体，以及用于牛奶、脂肪和蛋白质产量、体细胞计数、生育力和存活率的记录。实施了三种不同的分析：单性状基因组最佳线性无偏预测（GBLUP）、多性状 GBLUP 和单性状贝叶斯 R，使用了两种不同的中密度 SNP 面板：标准的 50K 芯片和一个预期富含致病突变的变体定制阵列。构建了各种参考群体，其中包含澳大利亚红牛和所有荷斯坦和泽西公牛和奶牛、所有荷斯坦和泽西公牛、所有荷斯坦公牛和奶牛、所有荷斯坦公牛和一个从荷斯坦个体中选择的子集，这些个体的亲缘关系在荷斯坦和澳大利亚红之间变化，以及荷斯坦个体的数量。仅改变参考群体和验证群体之间的亲缘关系只会导致可靠性的微小变化。与在品种内预测相比，添加少量亲缘关系密切的荷斯坦个体可以提高可靠性，而增加荷斯坦个体的数量则会降低可靠性。多性状 GBLUP 将不同品种中的相同性状视为相关性状，产生的可靠性高于单性状 GBLUP。贝叶斯 R 产生的可靠性低于多性状 GBLUP，并且在更大的参考群体中表现优于单性状 GBLUP。我们的结果表明，增加多品种参考群体的规模可能会导致以一个品种为主导的参考群体，并降低对其他品种的预测可靠性。
