Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705-2350.
Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705-2350; Department of Animal and Avian Sciences, University of Maryland, College Park 20742; Medical Research Council Human Genetics Unit at the Medical Research Council Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, United Kingdom.
J Dairy Sci. 2019 Dec;102(12):11067-11080. doi: 10.3168/jds.2019-16645. Epub 2019 Sep 25.
Improving feed efficiency (FE) of dairy cattle may boost farm profitability and reduce the environmental footprint of the dairy industry. Residual feed intake (RFI), a candidate FE trait in dairy cattle, can be defined to be genetically uncorrelated with major energy sink traits (e.g., milk production, body weight) by including genomic predicted transmitting ability of such traits in genetic analyses for RFI. We examined the genetic basis of RFI through genome-wide association (GWA) analyses and post-GWA enrichment analyses and identified candidate genes and biological pathways associated with RFI in dairy cattle. Data were collected from 4,823 lactations of 3,947 Holstein cows in 9 research herds in the United States. Of these cows, 3,555 were genotyped and were imputed to a high-density list of 312,614 SNP. We used a single-step GWA method to combine information from genotyped and nongenotyped animals with phenotypes as well as their ancestors' information. The estimated genomic breeding values from a single-step genomic BLUP were back-solved to obtain the individual SNP effects for RFI. The proportion of genetic variance explained by each 5-SNP sliding window was also calculated for RFI. Our GWA analyses suggested that RFI is a highly polygenic trait regulated by many genes with small effects. The closest genes to the top SNP and sliding windows were associated with dry matter intake (DMI), RFI, energy homeostasis and energy balance regulation, digestion and metabolism of carbohydrates and proteins, immune regulation, leptin signaling, mitochondrial ATP activities, rumen development, skeletal muscle development, and spermatogenesis. The region of 40.7 to 41.5 Mb on BTA25 (UMD3.1 reference genome) was the top associated region for RFI. The closest genes to this region, CARD11 and EIF3B, were previously shown to be related to RFI of dairy cattle and FE of broilers, respectively. Another candidate region, 57.7 to 58.2 Mb on BTA18, which is associated with DMI and leptin signaling, was also associated with RFI in this study. Post-GWA enrichment analyses used a sum-based marker-set test based on 4 public annotation databases: Gene Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, Reactome pathways, and medical subject heading (MeSH) terms. Results of these analyses were consistent with those from the top GWA signals. Across the 4 databases, GWA signals for RFI were highly enriched in the biosynthesis and metabolism of amino acids and proteins, digestion and metabolism of carbohydrates, skeletal development, mitochondrial electron transport, immunity, rumen bacteria activities, and sperm motility. Our findings offer novel insight into the genetic basis of RFI and identify candidate regions and biological pathways associated with RFI in dairy cattle.
提高奶牛的饲料效率(FE)可以提高农场的盈利能力,减少乳制品行业的环境足迹。残留饲料摄入量(RFI)是奶牛 FE 的候选特征,可以通过将这些特征的基因组预测传递能力纳入 RFI 的遗传分析中,与主要的能量消耗特征(如产奶量、体重)在遗传上不相关。我们通过全基因组关联(GWA)分析和 GWA 后富集分析来研究 RFI 的遗传基础,并鉴定与奶牛 RFI 相关的候选基因和生物学途径。数据来自美国 9 个研究牧场的 3947 头荷斯坦奶牛的 4823 个泌乳期。其中 3555 头奶牛进行了基因分型,并被推断为 312614 个 SNP 的高密度列表。我们使用单步 GWA 方法将来自基因分型和非基因分型动物的信息与表型及其祖先的信息结合起来。单步基因组 BLUP 估计的基因组育种值被回溯求解,以获得 RFI 的个体 SNP 效应。还计算了每个 5-SNP 滑动窗口解释遗传方差的比例。我们的 GWA 分析表明,RFI 是一种由许多具有小效应的基因调控的高度多基因特征。与顶级 SNP 和滑动窗口最接近的基因与干物质摄入(DMI)、RFI、能量稳态和能量平衡调节、碳水化合物和蛋白质的消化和代谢、免疫调节、瘦素信号、线粒体 ATP 活性、瘤胃发育、骨骼肌发育和精子发生有关。BTA25 上的 40.7 到 41.5 Mb 区域(UMD3.1 参考基因组)是与 RFI 最相关的区域。该区域最接近的基因 CARD11 和 EIF3B 先前被证明与奶牛的 RFI 和肉鸡的饲料效率有关。另一个候选区域是 BTA18 上的 57.7 到 58.2 Mb,与 DMI 和瘦素信号有关,在本研究中也与 RFI 有关。GWA 后富集分析使用基于 4 个公共注释数据库的基于总和的标记集测试:基因本体论、京都基因与基因组百科全书(KEGG)途径、反应途径和医学主题标题(MeSH)术语。这些分析的结果与顶级 GWA 信号一致。在这 4 个数据库中,RFI 的 GWA 信号在氨基酸和蛋白质的生物合成和代谢、碳水化合物的消化和代谢、骨骼发育、线粒体电子传递、免疫、瘤胃细菌活性和精子活力方面高度富集。我们的研究结果为 RFI 的遗传基础提供了新的见解,并确定了与奶牛 RFI 相关的候选区域和生物学途径。