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跨血统全基因组关联研究为理解猪的精液特征提供了新的视角。

Cross-ancestry meta-genome-wide association studies provide insights to the understanding of semen traits in pigs.

机构信息

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.

SciGene Biotechnology Co., Ltd., Hefei 230031, China.

出版信息

Animal. 2024 Nov;18(11):101331. doi: 10.1016/j.animal.2024.101331. Epub 2024 Sep 19.

Abstract

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.

摘要

精液特征在猪的繁殖和生育能力中起着至关重要的作用。然而,有限的数据可用性阻碍了对这些特征背后遗传机制的全面理解。在这项研究中,我们整合了 597299 个精子和 3596 个序列数据,以鉴定与四个精液特征(精子运动能力(MOT)、精液量、精子浓度(CON)和有效精子数(SUM))相关的遗传变异和候选基因。我们进行了跨血统的全基因组关联研究,以检测与 MOT、CON 和 SUM 相关的 163 个主要单核苷酸多态性(SNP)。随后,我们整合了转录组全基因组关联研究和共定位分析,以鉴定 176 个候选基因,其中许多基因在精子发生或雄性哺乳动物精液特征中具有明确的作用。我们的分析强调了 CSM5、PDZD9 和 LDAF1 通过多种方法参与调节精液特征的潜力。最后,为了验证显著 SNP 的功能,我们使用鉴定出的与性状相关的 SNP 子集作为基因组特征,在 348 头独立猪中进行了基因组特征最佳线性无偏预测。我们发现,与传统的基因组最佳线性无偏预测相比,将前 0.1%、1%和 5%显著 SNP 作为基因组特征进行整合,可以提高 CON 和 MOT 的基因组预测准确性。本研究有助于全面了解公猪精液特征的遗传机制,并为开发基因组选择模型提供了思路。

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