Introduction
The liquid chip based on Genotyping by Target Sequencing (GBTS) technology can detect a wide range of variations (e.g., SNPs, InDels) and offers high efficiency, low cost, broad adaptability, and flexible application. Reproductive traits are crucial functional traits in dairy cows. Intense selection for milk production traits, coupled with the negative genetic correlation between reproductive and milk production traits, has led to a decline in reproductive performance, decreased reproductive rates, increased veterinary treatment rates, and higher culling rates. Given the low heritability of reproductive traits, exploring genomic selection methods to enhance the accuracy of reproductive trait predictions is significant. This study estimates genetic parameters and performs weighted single-step genome-wide association studies (wssGWAS) to identify the genes associated with three fertility traits in the Chinese Holstein population: the number of services for heifers (NS_H), the interval from calving to first service (ICF), and the calving interval (CI).
Materials and Methods
The phenotypic records for NS_H, ICF, and CI were 134,141, 345,751, and 238,277, respectively. A total of 5,545 cattle had liquid chips (166,009 SNPs + 7,518 InDels), while 18,226 had 150K chips, which were imputed at the liquid-phase chip level and merged after quality control. Genotype data for 13,690 individuals across 103,262 loci were analyzed. An animal model was used for NS_H, while ICF and CI employed a repeatability model. A weighted single-step genome-wide association method with 20 adjacent SNPs as sliding windows was implemented.
Results
The heritability estimates for NS_H, ICF, and CI were 0.049, 0.029, and 0.064, respectively. Ten window regions associated with fertility traits were identified across seven chromosomes, with candidate genes including OVOS2, TESK1, and HSPA14.
Conclusion
These findings provide a foundation for future research into the genetic mechanisms underlying fertility traits, contributing to accurate genomic predictions in the Chinese Holstein population.
