Lu Xubin, Arbab Abdelaziz Adam Idriss, Abdalla Ismail Mohamed, Liu Dingding, Zhang Zhipeng, Xu Tianle, Su Guosheng, Yang Zhangping
College of Animal Science and Technology, Yangzhou University, Yangzhou, China.
Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark.
Front Genet. 2022 Jan 28;12:799664. doi: 10.3389/fgene.2021.799664. eCollection 2021.
Accurately estimating the genetic parameters and revealing more genetic variants underlying milk production and quality are conducive to the genetic improvement of dairy cows. In this study, we estimate the genetic parameters of five milk-related traits of cows-namely, milk yield (MY), milk fat percentage (MFP), milk fat yield (MFY), milk protein percentage (MPP), and milk protein yield (MPY)-based on a random regression test-day model. A total of 95,375 test-day records of 9,834 cows in the lower reaches of the Yangtze River were used for the estimation. In addition, genome-wide association studies (GWASs) for these traits were conducted, based on adjusted phenotypes. The heritability, as well as the standard errors, of MY, MFP, MFY, MPP, and MPY during lactation ranged from 0.22 ± 0.02 to 0.31 ± 0.04, 0.06 ± 0.02 to 0.15 ± 0.03, 0.09 ± 0.02 to 0.28 ± 0.04, 0.07 ± 0.01 to 0.16 ± 0.03, and 0.14 ± 0.02 to 0.27 ± 0.03, respectively, and the genetic correlations between different days in milk (DIM) within lactations decreased as the time interval increased. Two, six, four, six, and three single nucleotide polymorphisms (SNPs) were detected, which explained 5.44, 12.39, 8.89, 10.65, and 7.09% of the phenotypic variation in MY, MFP, MFY, MPP, and MPY, respectively. Ten Kyoto Encyclopedia of Genes and Genomes pathways and 25 Gene Ontology terms were enriched by analyzing the nearest genes and genes within 200 kb of the detected SNPs. Moreover, 17 genes in the enrichment results that may play roles in milk production and quality were selected as candidates, including , , , , , , , , GST1, , , , , , , , and . We hope that this study will provide useful information for in-depth understanding of the genetic architecture of milk production and quality traits, as well as contribute to the genomic selection work of dairy cows in the lower reaches of the Yangtze River.
准确估计遗传参数并揭示更多影响产奶量和品质的遗传变异，有利于奶牛的遗传改良。在本研究中，我们基于随机回归测定日模型，估计了奶牛五个与牛奶相关性状的遗传参数，即产奶量（MY）、乳脂率（MFP）、乳脂产量（MFY）、乳蛋白率（MPP）和乳蛋白产量（MPY）。共使用了长江下游地区9834头奶牛的95375条测定日记录进行估计。此外，基于调整后的表型对这些性状进行了全基因组关联研究（GWAS）。泌乳期MY、MFP、MFY、MPP和MPY的遗传力以及标准误分别在0.22±0.02至0.31±0.04、0.06±0.02至0.15±0.03、0.09±0.02至0.28±0.04、0.07±0.01至0.16±0.03以及0.14±0.02至0.27±0.03之间，并且泌乳期内不同产奶天数（DIM）之间的遗传相关性随着时间间隔的增加而降低。分别检测到2、6、4、6和3个单核苷酸多态性（SNP），它们分别解释了MY、MFP、MFY、MPP和MPY表型变异的5.44%、12.39%、8.89%、10.65%和7.09%。通过分析检测到的SNP最近的基因以及200 kb范围内的基因，富集了10条京都基因与基因组百科全书通路和25个基因本体学术语。此外，在富集结果中选择了17个可能在产奶量和品质方面发挥作用的基因作为候选基因，包括 、 、 、 、 、 、 、 、GST1、 、 、 、 、 、 、 。我们希望本研究将为深入了解产奶量和品质性状的遗传结构提供有用信息，并有助于长江下游地区奶牛的基因组选择工作。
