Cross-ancestry meta-genome-wide association studies provide insights to the understanding of semen traits in pigs
H. Cheng,
Z.Y. Zhang,
H. Han,
R. Wei,
W. Zhao,
Y.C. Sun,
B.B. Xu,
X.L. Hou,
J.L. Wang,
Y.Q. He,
Y. Fu,
Q.S. Wang,
Y.C. Pan,
Z. Zhang,
Z. Wang
Affiliations
H. Cheng
College of Animal Sciences, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Livestock and Poultry Resources Evaluation and Utilization, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
Z.Y. Zhang
College of Animal Sciences, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Livestock and Poultry Resources Evaluation and Utilization, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
H. Han
College of Animal Sciences, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Livestock and Poultry Resources Evaluation and Utilization, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
R. Wei
College of Animal Sciences, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Livestock and Poultry Resources Evaluation and Utilization, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
W. Zhao
SciGene Biotechnology Co., Ltd., Hefei 230031, China
Y.C. Sun
Haidian Foreign Language Academy, Beijing 100195, China
B.B. Xu
SciGene Biotechnology Co., Ltd., Hefei 230031, China
X.L. Hou
SciGene Biotechnology Co., Ltd., Hefei 230031, China
J.L. Wang
SciGene Biotechnology Co., Ltd., Hefei 230031, China
Y.Q. He
SciGene Biotechnology Co., Ltd., Hefei 230031, China
Y. Fu
SciGene Biotechnology Co., Ltd., Hefei 230031, China
Q.S. Wang
College of Animal Sciences, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Livestock and Poultry Resources Evaluation and Utilization, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China; Hainan Institute, Zhejiang University, Yongyou Industry Park, Yazhou Bay Sci-Tech City, Sanya 572000, China
Y.C. Pan
College of Animal Sciences, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Livestock and Poultry Resources Evaluation and Utilization, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China; Hainan Institute, Zhejiang University, Yongyou Industry Park, Yazhou Bay Sci-Tech City, Sanya 572000, China
Z. Zhang
College of Animal Sciences, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Livestock and Poultry Resources Evaluation and Utilization, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
Z. Wang
College of Animal Sciences, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Livestock and Poultry Resources Evaluation and Utilization, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China; Corresponding author.
Semen traits play a crucial role in pig reproduction and fertility. However, limited data availability hinder a comprehensive understanding of the genetic mechanisms underlying these traits. In this study, we integrated 597 299 ejaculates and 3 596 sequence data to identify genetic variants and candidate genes related to four semen traits, including sperm progressive motility (MOT), semen volume, sperm concentration (CON), and effective sperm count (SUM). A cross-ancestry meta−genome-wide association study was conducted to detect 163 lead single nucleotide polymorphisms (SNPs) associated with MOT, CON, and SUM. Subsequently, transcriptome-wide association studies and colocalisation analyses were integrated to identify 176 candidate genes, many of which have documented roles in spermatogenesis or male mammal semen traits. Our analysis highlighted the potential involvement of CSM5, PDZD9, and LDAF1 in regulating semen traits through multiple methods. Finally, to validate the function of significant SNPs, we performed genomic feature best linear unbiased prediction in 348 independent pigs using identified trait-related SNP subsets as genomic features. We found that integrating the top 0.1, 1, and 5% significant SNPs as genomic features could enhance genomic prediction accuracy for CON and MOT compared to traditional genomic best linear unbiased prediction. This study contributes to a comprehensive understanding of the genetic mechanisms of boar semen traits and provides insight for developing genomic selection models.
