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Background <p>Genome-wide association studies (GWAS) has identified many genetic variants associated with milk-related traits in dairy cattle. However, the causal variants or genes remain largely unknown. In this study, using a large population (&gt; 10,000 individuals) of Chinese Holstein cattle, we performed GWAS for six milk-related traits (milk yield, fat percentage, protein percentage, fat yield, protein yield, and somatic cell score) and subsequently prioritized putative causal variants by multi-trait Bayesian fine-mapping and examined the causal genes by Mendelian randomization (MR) analysis incorporating GWAS and <i>cis</i>-eQTL summary data from CattleGTEx. We also conducted a colocalization analysis to identify the share putative causal variants behind the milk-related traits and gene expressions.</p> Results <p>A total of 9,688 genome-wide significant SNPs (<i>P</i> &lt; 1.2 × 10<sup>−7</sup>) were identified across the GWAS results for six milk-related traits, and these SNPs were distributed in 25 unique QTL regions. Subsequently, the multi-trait Bayesian fine-mapping identified 211 independent credible sets (CS) containing putative causal variants within these QTL regions. Among these CSs, 189 CSs were significantly associated with at least one trait (average <i>lfsr</i> &lt; 0.01). Notably, the lead SNPs within these significant CSs included 3 missense variants and 62 non-coding transcript variants. The MR analysis detected 268 causal associations between gene expression and milk-related traits. The colocalization analysis identified two regions containing common putative causal variants for one or multiple milk-related traits and the expressions of some genes.</p> Conclusions <p>Our integrative analysis of GWAS, Bayesian fine-mapping, MR, and colocalization further confirmed the well-known causal associations of <i>DGAT1</i> and <i>GHR</i> and the milk-related traits. In addition, we revealed some novel potential causal genes, including <i>AHNAK</i>, <i>ARHGEF2</i>, <i>SOX13</i>, <i>FDPS</i>, <i>SCGB2A2</i>, and <i>MROH2B</i>. These results enhance our understanding of genetic mechanisms underlying the milk-related traits in dairy cattle.</p>

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Integrative analysis of GWAS, Bayesian fine-mapping, Mendelian randomization and colocalization reveals genetic determinants underlying milk-related traits in dairy cattle

  • 期刊论文

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  • 发表日期：

    2026年1月3日

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  • Jun Teng,
  • Xiuxin Zhao,
  • Qingxia Yan,
  • Jian Yang,
  • Fen Pei,
  • Xinyi Zhang,
  • Chongwei Duan,
  • Zhujun Chen,
  • Qianwen Xu,
  • Yan Liu,
  • Guanghui Xue,
  • Shuwen Xia,
  • Huili Wang,
  • Yao Gu,
  • Lingzhao Fang,
  • Huiming Liu,
  • Hongding Gao,
  • Jing An,
  • Li Jiang,
  • Chao Ning,
  • Rongling Li,
  • Yundong Gao,
  • Xiao Wang,
  • Jianbin Li,
  • Qin Zhang

摘要

Background

Genome-wide association studies (GWAS) has identified many genetic variants associated with milk-related traits in dairy cattle. However, the causal variants or genes remain largely unknown. In this study, using a large population (> 10,000 individuals) of Chinese Holstein cattle, we performed GWAS for six milk-related traits (milk yield, fat percentage, protein percentage, fat yield, protein yield, and somatic cell score) and subsequently prioritized putative causal variants by multi-trait Bayesian fine-mapping and examined the causal genes by Mendelian randomization (MR) analysis incorporating GWAS and cis-eQTL summary data from CattleGTEx. We also conducted a colocalization analysis to identify the share putative causal variants behind the milk-related traits and gene expressions.

Results

A total of 9,688 genome-wide significant SNPs (P < 1.2 × 10−7) were identified across the GWAS results for six milk-related traits, and these SNPs were distributed in 25 unique QTL regions. Subsequently, the multi-trait Bayesian fine-mapping identified 211 independent credible sets (CS) containing putative causal variants within these QTL regions. Among these CSs, 189 CSs were significantly associated with at least one trait (average lfsr < 0.01). Notably, the lead SNPs within these significant CSs included 3 missense variants and 62 non-coding transcript variants. The MR analysis detected 268 causal associations between gene expression and milk-related traits. The colocalization analysis identified two regions containing common putative causal variants for one or multiple milk-related traits and the expressions of some genes.

Conclusions

Our integrative analysis of GWAS, Bayesian fine-mapping, MR, and colocalization further confirmed the well-known causal associations of DGAT1 and GHR and the milk-related traits. In addition, we revealed some novel potential causal genes, including AHNAK, ARHGEF2, SOX13, FDPS, SCGB2A2, and MROH2B. These results enhance our understanding of genetic mechanisms underlying the milk-related traits in dairy cattle.