Mohammadi Hossein, Farahani Amir Hossein Khaltabadi, Moradi Mohammad Hossein, Mastrangelo Salvatore, Di Gerlando Rosalia, Sardina Maria Teresa, Scatassa Maria Luisa, Portolano Baldassare, Tolone Marco
Department of Animal Sciences, Faculty of Agriculture and Natural Resources, Arak University, Arak 38156-8-8349, Iran.
Dipartimento Scienze Agrarie, Alimentari e Forestali, University of Palermo, 90128 Palermo, Italy.
Animals (Basel). 2022 Apr 29;12(9):1155. doi: 10.3390/ani12091155.
The objective of this study was to uncover genomic regions explaining a substantial proportion of the genetic variance in milk production traits and somatic cell score in a Valle del Belice dairy sheep. Weighted single-step genome-wide association studies (WssGWAS) were conducted for milk yield (MY), fat yield (FY), fat percentage (FAT%), protein yield (PY), protein percentage (PROT%), and somatic cell score (SCS). In addition, our aim was also to identify candidate genes within genomic regions that explained the highest proportions of genetic variance. Overall, the full pedigree consists of 5534 animals, of which 1813 ewes had milk data (15,008 records), and 481 ewes were genotyped with a 50 K single nucleotide polymorphism (SNP) array. The effects of markers and the genomic estimated breeding values (GEBV) of the animals were obtained by five iterations of WssGBLUP. We considered the top 10 genomic regions in terms of their explained genomic variants as candidate window regions for each trait. The results showed that top ranked genomic windows (1 Mb windows) explained 3.49, 4.04, 5.37, 4.09, 3.80, and 5.24% of the genetic variances for MY, FY, FAT%, PY, PROT%, and total SCS, respectively. Among the candidate genes found, some known associations were confirmed, while several novel candidate genes were also revealed, including , , , and for MY; and for FY and FAT%; and for PY and PROT%; and , , , , , and for SCS. These findings increase our understanding of the genetic architecture of six examined traits and provide guidance for subsequent genetic improvement through genome selection.
本研究的目的是在贝利塞谷奶羊中揭示能够解释产奶性状和体细胞评分中很大一部分遗传变异的基因组区域。针对产奶量（MY）、乳脂产量（FY）、乳脂率（FAT%）、蛋白质产量（PY）、蛋白质率（PROT%）和体细胞评分（SCS）进行了加权单步全基因组关联研究（WssGWAS）。此外，我们的目标还包括在解释遗传变异比例最高的基因组区域内鉴定候选基因。总体而言，完整的系谱包含5534只动物，其中1813只母羊有产奶数据（15008条记录），481只母羊用50K单核苷酸多态性（SNP）芯片进行了基因分型。通过WssGBLUP的五次迭代获得了标记的效应和动物的基因组估计育种值（GEBV）。我们将每个性状按解释的基因组变异量排名前10的基因组区域视为候选窗口区域。结果表明，排名靠前的基因组窗口（1兆碱基窗口）分别解释了MY、FY、FAT%、PY、PROT%和总SCS遗传变异的3.49%、4.04%、5.37%、4.09%、3.80%和5.24%。在所发现的候选基因中，一些已知的关联得到了证实，同时还揭示了几个新的候选基因，包括与MY相关的 、 、 、 ；与FY和FAT%相关的 和 ；与PY和PROT%相关的 和 ；以及与SCS相关的 、 、 、 、 、 。这些发现增进了我们对所研究的六个性状遗传结构的理解，并为随后通过基因组选择进行遗传改良提供了指导。
