Milk fatty acid (FA) profile is a clear example of complex and multiple correlated traits whose genetic basis is difficult to assess. Although genome-wide association (GWA) studies have been successful in the identification of significant genetic variants for complex traits, when correlated phenotypes are analysed separately, the outcomes are difficult to compare and interpret in a metabolic context. Here, we performed a multivariate factor analysis (MFA) on Italian Simmental and Italian Holstein milk fat profiles to extract latent unobserved factors able to explain correlation structure and common metabolic function among different FAs. Individual factor scores obtained by MFA were used to perform a single-SNP based GWA. In both breeds, MFA was able to extract ten latent factors with specific biological meaning, notably: de novo synthesis, desaturation activity and biohydrogenation. The GWA result confirmed the increased power of joint association analysis on multiple correlated traits and allowed us to identify major candidate genes with well-documented function consistent with the metabolic classification of factors obtained, such as DGAT1, FASN and SCD.

中文翻译：

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使用详细的牛奶脂肪酸谱的多变量因子分析在意大利西门塔尔和意大利荷斯坦进行全基因组关联研究。

牛奶脂肪酸 (FA) 谱是复杂和多重相关性状的一个明显例子，其遗传基础难以评估。尽管全基因组关联 (GWA) 研究已成功识别复杂性状的重要遗传变异，但当单独分析相关表型时，结果很难在代谢环境中进行比较和解释。在这里，我们对意大利西门塔尔和意大利荷斯坦奶脂肪分布进行了多变量因子分析 (MFA)，以提取能够解释不同 FA 之间的相关结构和共同代谢功能的潜在的未观察到的因素。通过 MFA 获得的单个因素评分用于执行基于单 SNP 的 GWA。在这两个品种中，MFA 都能够提取出具有特定生物学意义的 10 个潜在因子，特别是：从头合成、去饱和活性和生物氢化。GWA 结果证实了对多个相关性状的联合关联分析的增强能力，并使我们能够识别具有充分记录的功能的主要候选基因，这些基因与所获得的因素的代谢分类一致，例如DGAT1、FASN和SCD。