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 (h2 ) values showed relevant variations across different factors in each breed (0.03 ≤ h2 ≤ 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.

中文翻译：

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荷斯坦奶牛、西门塔尔奶牛和布朗瑞士奶牛品种中与乳脂肪酸组成相关的潜在变量的基因组预测

关于牛奶脂肪酸 (FA) 谱的基因组选择 (GS) 报告最近才发表，尽管这一特性代表了牛奶营养和感官质量的最重要方面，但仍然很少。其原因可以在表型记录的高成本中找到，也可以在与由多个遗传相关的低遗传性变量构成的复杂性状的性质相关的问题中找到。处理此类约束的一种可能策略是使用降维方法。我们通过多变量因子分析 (MFA) 分析了来自意大利 Brown Swiss、Holstein 和 Simmental 牛奶的 40 个个体 FA，以研究与牛奶 FA 相关的潜在变量（因子）的遗传学并评估它们在育种中的潜在用途。一共得到九个因子，并且它们的遗传参数是在贝叶斯框架下使用两种统计方法推断出来的：经典谱系最佳线性无偏预测 (ABLUP) 和单步基因组 BLUP (ssGBLUP)。由此产生的阶乘解能够代表具有共同起源和功能的 FA 组，并且可以被认为是简明的通路水平表型。遗传力 (h2) 值显示了每个品种不同因素的相关变化 (0.03 ≤ h2 ≤ 0.38)。考虑到系谱和基因组模型，预测的育种值的准确度从低到高，范围分别为 0.13 至 0.72 和 0.18 至 0.74。由于添加了基因组信息，遗传预测的准确性在验证组和训练组中分别提高了约 30% 和约 5%，与传统的 ABLUP 方法相比，确认基因组信息在产生更准确的预测方面的贡献。我们的结果表明，MFA 与 GS 相结合可以成为奶牛育种的宝贵工具，值得进一步研究以用于未来的育种计划，以改善牛奶 FA 相关性状。