Department of Animal Science, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil.
Embrapa Dairy Cattle, Juiz de Fora, Minas Gerais, Brazil.
J Appl Genet. 2020 Sep;61(3):465-476. doi: 10.1007/s13353-020-00567-3. Epub 2020 Jul 1.
This study focused on the identification of QTL regions, candidate genes, and network related genes based on the first 3 lactations (LAC3) of milk, fat, and protein yields, and somatic cell score (SCS) in Portuguese Holstein cattle. Additionally, the results were compared with those from only first lactation (LAC1) data. The analyses were performed using the weighted single-step GWAS under an autoregressive test-day (TD) multiple lactations model. A total of 11,434,294 and 4,725,673 TD records from LAC3 and LAC1, respectively, including 38,323 autosomal SNPs and 1338 genotyped animals were used in GWAS analyses. A total of 51 (milk), 5 (fat), 24 (protein), and 4 (SCS) genes were associated to previously annotated relevant QTL regions for LAC3. The CACNA2D1 at BTA4 explained the highest proportion of genetic variance respectively for milk, fat, and protein yields. For SCS, the TRNAG-CCC at BTA14, MAPK10, and PTPN3 genes, both at BTA6 were considered important candidate genes. The accessed network refined the importance of the reported genes. CACNA2D1 regulates calcium density and activation/inactivation kinetics of calcium transport in the mammary gland; whereas TRNAG-CCC, MAPK10, and PTPN3 are directly involved with inflammatory processes widely derived from mastitis. In conclusion, potential candidate genes (TRNAG-CCC, MAPK10, and PTPN3) associated with somatic cell were highlighted, which further validation studies are needed to clarify its mechanism action in response to mastitis. Moreover, most of the candidate genes identified were present in both (LAC3 and LAC1) for milk, fat and protein yields, except for SCS, in which no candidate genes were shared between LAC3 and LAC1. The larger phenotypic information provided by LAC3 dataset was more effective to identify relevant genes, providing a better understanding of the genetic architecture of these traits over all lactations simultaneously.
本研究基于葡萄牙荷斯坦牛的前 3 个泌乳期（LAC3）的牛奶、脂肪和蛋白质产量以及体细胞评分（SCS），重点鉴定了 QTL 区域、候选基因和网络相关基因。此外，还将结果与仅第 1 个泌乳期（LAC1）数据的结果进行了比较。使用自回归测试日（TD）多泌乳模型下的加权单步 GWAS 进行了分析。总共使用了 LAC3 和 LAC1 分别包含 38323 个常染色体 SNPs 和 1338 个基因型动物的 11434294 和 4725673 个 TD 记录进行 GWAS 分析。共有 51 个（牛奶）、5 个（脂肪）、24 个（蛋白质）和 4 个（SCS）基因与 LAC3 的先前注释的相关 QTL 区域相关。在 BTA4 上的 CACNA2D1 分别解释了牛奶、脂肪和蛋白质产量遗传变异的最高比例。对于 SCS，BTA14 上的 TRNAG-CCC、BTA6 上的 MAPK10 和 PTPN3 基因被认为是重要的候选基因。所访问的网络细化了报道基因的重要性。CACNA2D1 调节乳腺中钙密度和钙转运的激活/失活动力学；而 TRNAG-CCC、MAPK10 和 PTPN3 则直接参与乳腺炎引起的广泛炎症过程。总之，突出了与体细胞相关的潜在候选基因（TRNAG-CCC、MAPK10 和 PTPN3），需要进一步的验证研究来阐明其对乳腺炎的作用机制。此外，在牛奶、脂肪和蛋白质产量方面，除了 SCS 之外，大多数候选基因都存在于 LAC3 和 LAC1 中（LAC3 和 LAC1），而在 SCS 中，LAC3 和 LAC1 之间没有共享候选基因。LAC3 数据集提供的更大表型信息更有效地识别了相关基因，同时更好地了解了这些性状在所有泌乳期的遗传结构。
