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荷斯坦牛、西门塔尔牛和瑞士褐牛乳脂脂肪酸组成相关潜在变量的基因组预测。

Genomic prediction for latent variables related to milk fatty acid composition in Holstein, Simmental and Brown Swiss dairy cattle breeds.

机构信息

Dipartimento Agricoltura, Ambiente e Alimenti, Università degli Studi del Molise, Campobasso, Italy.

Dipartimento di Agronomia, Animali, Alimenti, Risorse naturali e Ambiente (DAFNAE), Università di Padova, Padova, Italy.

出版信息

J Anim Breed Genet. 2021 May;138(3):389-402. doi: 10.1111/jbg.12532. Epub 2020 Dec 17.

Abstract

Genomic selection (GS) reports on milk fatty acid (FA) profiles have been published quite recently and are still few despite this trait represents the most important aspect of milk nutritional and sensory quality. Reasons for this can be found in the high costs of phenotype recording but also in issues related to its nature of complex trait constituted by multiple genetically correlated variables with low heritabilities. One possible strategy to deal with such constraint is represented by the use of dimension reduction methods. We analysed 40 individual FAs from Italian Brown Swiss, Holstein and Simmental milk through multivariate factor analysis (MFA) to study the genetics of milk FA-related latent variables (factors) and assess their potential use in breeding. A total of nine factors were obtained, and their genetic parameters were inferred under a Bayesian framework using two statistical approaches: the classical pedigree best linear unbiased prediction (ABLUP) and the single-step genomic BLUP (ssGBLUP). The resulting factorial solutions were able to represent groups of FAs with common origin and function and can be considered concise pathway-level phenotypes. The heritability (h ) values showed relevant variations across different factors in each breed (0.03 ≤ h  ≤ 0.38). The accuracies of breeding values predicted were low to high, ranging from 0.13 to 0.72 and from 0.18 to 0.74 considering the pedigree and the genomic model, respectively. The gain in accuracy in genetic prediction due to the addition of genomic information was ~30% and ~5% in validation and training groups respectively, confirming the contribution of genomic information in yielding more accurate predictions compared to the traditional ABLUP methodology. Our results suggest that MFA in combination with GS can be a valuable tool in dairy cattle breeding and deserves to be further investigated for use in future breeding programs to improve cow's milk FA-related traits.

摘要

最近才发表了关于牛奶脂肪酸 (FA) 谱的基因组选择 (GS) 报告,尽管这个特征代表了牛奶营养和感官质量最重要的方面,但报告仍然很少。造成这种情况的原因可能是表型记录成本高,也可能与该特征的性质有关,该特征由多个遗传上相关的变量组成,具有低遗传力。应对这种限制的一种可能策略是使用降维方法。我们通过多元因子分析 (MFA) 分析了意大利棕色瑞士牛、荷斯坦牛和西门塔尔牛的 40 种个体 FA,以研究与牛奶 FA 相关的潜在变量(因子)的遗传,并评估它们在育种中的潜在用途。总共获得了九个因子,并在贝叶斯框架下使用两种统计方法(经典系谱最佳线性无偏预测 (ABLUP) 和单步基因组 BLUP (ssGBLUP))推断其遗传参数。所得因子解能够代表具有共同起源和功能的 FA 组,可以被认为是简洁的途径水平表型。各品种不同因子的遗传力 (h) 值差异较大(0.03 ≤ h ≤ 0.38)。预测育种值的准确性从低到高不等,考虑系谱和基因组模型,范围分别为 0.13 到 0.72 和 0.18 到 0.74。由于添加基因组信息,遗传预测准确性的提高在验证组和训练组中分别约为 30%和 5%,这证实了与传统的 ABLUP 方法相比,基因组信息在产生更准确预测方面的贡献。我们的结果表明,MFA 与 GS 相结合可以成为奶牛育种的有价值工具,值得进一步研究,以用于未来的育种计划,以改善奶牛与 FA 相关的特征。

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