Milk production is one of the most important characteristics of dairy sheep, and the identification of genes affecting milk production traits is critical to understanding the genetics and improve milk production in future generations. Three statistical techniques, namely GWAS, ridge‐regression BLUP and BayesC , were used to identify SNPs in significant association with three milk production traits (milk yield, fat yield and protein yield) in a crossbred dairy sheep population. The results suggested that chromosomes 1, 3, 4, 5, 7 and 11 were likely to harbor genes important to milk production because these chromosomes had the greatest top‐100‐SNP variance contributions on the three milk production traits. The GWAS analysis identified between 74 and 288 genome‐wide significant SNP (P  < 0.05) whereas the BayesCπ model revealed between six and 63 SNPs, each with >95% posterior probability of inclusion as having a non‐zero association effect on at least one of the three milk production traits. Positional candidate genes for milk production in sheep were searched, based on the sheep genomic assembly OAR version 3.1, such as those which map position coincided with or was located within 0.1 Mbp of a genome‐wide suggestive or significant SNP. These identified SNPs and candidate genes supported some previous findings and also added new information about genetic markers for genetic improvement of lactation in dairy sheep, but keeping in mind that the majority of these positional candidate genes are not necessarily true causative loci for these traits and future validations are thus necessary.

中文翻译：

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全基因组关联研究使用三种统计模型对杂交奶羊种群的牛奶生产性状进行研究。

产奶是奶牛最重要的特征之一，鉴定影响产奶性状的基因对于了解遗传学和改善子孙后代的产奶至关重要。三种统计技术，即GWAS，岭回归BLUP和BayesC被用于确定与杂种奶羊种群中的三个产奶特性（产奶量，脂肪产量和蛋白质产量）显着相关的SNP。结果表明，第1、3、4、5、7和11号染色体可能具有对牛奶生产重要的基因，因为这些染色体在三种牛奶生产性状上对top-100-SNP的贡献最大。GWAS分析确定了74至288个全基因组范围内的重要SNP（P <0.05），而BayesC π模型6首63个SNP，每个之间揭示>夹杂物的95％的后验概率为具有三个产奶性状中的至少一个上的非零关联效果。根据绵羊基因组装配OAR 3.1版，搜索绵羊产奶的位置候选基因，例如那些定位位置与全基因组提示性或重要SNP一致或位于0.1 Mbp之内的基因。这些已鉴定的SNP和候选基因支持了一些先前的发现，并且还添加了有关奶牛泌乳遗传改良的遗传标记的新信息，但请记住，这些位置候选基因中的大多数不一定是这些特征和未来的真正致病基因座。因此，验证是必要的。