Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture of China, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, 100193, Beijing, China; Beijing Sunlon Livestock Development Co. Ltd., 100029, Beijing, China.
Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture of China, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, 100193, Beijing, China.
J Dairy Sci. 2023 Dec;106(12):9055-9070. doi: 10.3168/jds.2023-23462. Epub 2023 Aug 23.
Understanding the underlying pleiotropic relationships among growth and body size traits is important for refining breeding strategies in dairy cattle for optimal body size and growth rate. Therefore, we performed single-trait GWAS for monthly-recorded body weight (BW), hip height, body length, and chest girth from birth to 12 mo of age in Holstein animals, followed by stepwise multiple regression of independent or lowly-linked markers from GWAS loci using conditional and joint association analyses (COJO). Subsequently, we conducted a multitrait meta-analysis to detect pleiotropic markers. Based on the single-trait GWAS, we identified 170 significant SNPs, in which 59 of them remained significant after the COJO analyses. The most significant SNP, located at BTA7:3,676,741, explained 2.93% of the total phenotypic variance for BW6 (BW at 6 mo of age). We identified 17 SNPs with potential pleiotropic effects based on the multitrait meta-analyses, which resulted in 3 additional SNPs in comparison to those detected based on the single-trait GWAS. The identified quantitative trait loci regions overlap with genes known to influence human growth-related traits. According to positional and functional analyses, we proposed HMGA2, HNF4G, MED13L, BHLHE40, FRZB, DMP1, TRIB3, and GATAD2A as important candidate genes influencing the studied traits. The combination of single-trait GWAS and meta-analyses of GWAS results improved the efficiency of detecting associated SNPs, and provided new insights into the genetic mechanisms of growth and development in Holstein cattle.
了解生长和体型特征之间的潜在多效关系对于优化奶牛体型和生长速度的选育策略非常重要。因此,我们对荷斯坦动物从出生到 12 月龄的每月记录体重(BW)、臀部高度、体长和胸围进行了单性状 GWAS 分析,然后使用条件和联合关联分析(COJO)对 GWAS 位点的独立或低度连锁标记进行逐步多元回归。随后,我们进行了多性状荟萃分析以检测多效性标记。基于单性状 GWAS,我们鉴定了 170 个显著 SNP,其中 59 个 SNP 在 COJO 分析后仍然显著。位于 BTA7:3,676,741 的最显著 SNP 解释了 BW6(6 月龄 BW)总表型方差的 2.93%。我们基于多性状荟萃分析鉴定了 17 个具有潜在多效性效应的 SNP,与基于单性状 GWAS 检测到的 SNP 相比,多出了 3 个 SNP。鉴定的数量性状基因座区域与已知影响人类生长相关性状的基因重叠。根据位置和功能分析,我们提出 HMGA2、HNF4G、MED13L、BHLHE40、FRZB、DMP1、TRIB3 和 GATAD2A 作为影响所研究性状的重要候选基因。单性状 GWAS 和 GWAS 结果的荟萃分析相结合提高了检测相关 SNP 的效率,并为荷斯坦牛生长和发育的遗传机制提供了新的见解。