Original Research ARTICLE
Weighted single-step genome-wide association study of semen traits in Holstein bulls of China
- 1China Agricultural University (CAU), China
- 2University of Maryland, United States
Efficient production of high quality semen is a crucial trait in the dairy cattle breeding due to the widespread use of artificial insemination (AI). However, the genetic architecture (e.g., distributions of causal variants and their corresponding effects) underlying such semen quality traits remains unclear. In this study, we performed genome-wide association studies (GWAS) to identify genes associated with five semen quality traits in Chinese Holstein population, including ejaculate volume (VE), progressive sperm motility (SM), sperm concentration (SC), number of sperm (NSP), and number of progressive motile sperm (NMSP). Our dataset consisted of 2,218 Holstein bulls in China with full pedigree information, representing 12 AI centers, with 1,508 genotyped using the Illumina BovineSNP50 BeadChip. We used a weighted single-step genome-wide association method with 10 adjacent SNPs as sliding windows, which can make use of individuals without genotypes. We considered the top 10 genomic regions in terms of their explained genomic variants as candidate window regions for each trait. In total, we detected 36 window regions related to one or multiple semen traits across 19 chromosomes. Promising candidate genes of PSMB5, PRMT5, ACTB, PDE3A, NPC1, FSCN1, NR5A2, IQCG, LHX8, and DMRT1 were identified in these window regions for these five semen traits. Our findings provided a solid basis for further research into genetic mechanisms underlying semen quality traits, which may contribute to their accurate genomic prediction in Chinese Holstein population.
Keywords: Semen traits, weighted single-step GWAS, Chinese Holstein, QTL regions, candidate genes
Received: 09 Jan 2019;
Accepted: 01 Oct 2019.
Copyright: © 2019 Yin, Zhou, Shi, Fang, Liu, Sun, Li and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Prof. Jiang Li, China Agricultural University (CAU), Beijing, 100083, Beijing Municipality, China, email@example.com
Prof. Shengli Zhang, China Agricultural University (CAU), Beijing, 100083, Beijing Municipality, China, firstname.lastname@example.org