Department of Animal Genetics and Breeding, College of Animal Science and Technology, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, China Agricultural University, Beijing, China.
Laboratory of Disease Genomics and Individualized Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.
PLoS One. 2019 Aug 1;14(8):e0220629. doi: 10.1371/journal.pone.0220629. eCollection 2019.
We have sequenced the whole genomes of eight proven Holstein bulls from the four half-sib or full-sib families with extremely high and low estimated breeding values (EBV) for milk protein percentage (PP) and fat percentage (FP) using Illumina re-sequencing technology. Consequently, 2.3 billion raw reads were obtained with an average effective depth of 8.1×. After single nucleotide variant (SNV) calling, total 10,961,243 SNVs were identified, and 57,451 of them showed opposite fixed sites between the bulls with high and low EBVs within each family (called as common differential SNVs). Next, we annotated the common differential SNVs based on the bovine reference genome, and observed that 45,188 SNVs (78.70%) were located in the intergenic region of genes and merely 11,871 SNVs (20.67%) located within the protein-coding genes. Of them, 13,099 common differential SNVs that were within or close to protein-coding genes with less than 5 kb were chosen for identification of candidate genes for milk compositions in dairy cattle. By integrated analysis of the 2,657 genes with the GO terms and pathways related to protein and fat metabolism, and the known quantitative trait loci (QTLs) for milk protein and fat traits, we identified 17 promising candidate genes: ALG14, ATP2C1, PLD1, C3H1orf85, SNX7, MTHFD2L, CDKN2D, COL5A3, FDX1L, PIN1, FIG4, EXOC7, LASP1, PGS1, SAO, GPLD1 and MGEA5. Our findings provided an important foundation for further study and a prompt for molecular breeding of dairy cattle.
我们使用 Illumina 重测序技术对来自四个半同胞或全同胞家系的 8 头经证实的荷斯坦公牛进行了全基因组测序,这些公牛具有极高和极低的牛奶蛋白百分率(PP)和脂肪百分率(FP)估计育种值(EBV)。因此,获得了 23 亿个原始读数,平均有效深度为 8.1×。在单核苷酸变异(SNV)调用后,共鉴定出 10961243 个 SNV,其中 57451 个在每个家系中具有高 EBV 和低 EBV 的公牛之间显示出相反的固定位点(称为共同差异 SNV)。接下来,我们根据牛参考基因组注释了共同差异 SNV,并观察到 45188 个 SNV(78.70%)位于基因的基因间区,只有 11871 个 SNV(20.67%)位于蛋白质编码基因内。其中,选择了 13099 个共同差异 SNV,这些 SNV 位于或靠近蛋白质编码基因,且距离小于 5kb,用于鉴定奶牛牛奶成分的候选基因。通过对与蛋白质和脂肪代谢相关的 GO 术语和途径以及已知的牛奶蛋白和脂肪性状的数量性状基因座(QTL)的 2657 个基因进行综合分析,我们鉴定了 17 个有前途的候选基因:ALG14、ATP2C1、PLD1、C3H1orf85、SNX7、MTHFD2L、CDKN2D、COL5A3、FDX1L、PIN1、FIG4、EXOC7、LASP1、PGS1、SAO、GPLD1 和 MGEA5。我们的研究结果为进一步研究和奶牛的分子育种提供了重要基础。