Abstract
Fertility and reproductive performance are key drivers of dairy farm profitability. Hence, reproduction traits have been included in a large majority of worldwide dairy cattle selection indexes. The reproductive traits are lowly heritable but can be improved through direct genetic selection. However, most scientific studies and dairy cattle breeding programs have focused solely on the genetic effects of the dam (GED) on reproductive performance and, therefore, ignored the contribution of the service sire in the phenotypic outcomes. This study aimed to investigate the service sire effects on female reproductive traits in Holstein cattle from a genomic perspective. Genetic parameter estimation and genome-wide association studies (GWAS) were performed for the genetic effect of service sire (GESS) on conception rate (CR), 56-day non-return rate (NRR56), calving ease (CE), stillbirth (SB), and gestation length (GL). Our findings indicate that the additive genetic effects of both sire and dam contribute to the phenotypic variance of reproductive traits measured in females (0.0196 vs. 0.0109, 0.0237 vs. 0.0133, 0.0040 vs. 0.0289, 0.0782 vs. 0.0083, and 0.1024 vs. 0.1020 for GESS and GED heritability estimates for CR, NRR56, CE, SB, and GL, respectively), and these two genetic effects are positively correlated for SB (0.1394) and GL (0.7871). Interestingly, the breeding values for GESS on insemination success traits (CR and NRR56) are unfavorably and significantly correlated with some production, health, and type breeding values (ranging from −0.449 to 0.274), while the GESS values on calving traits (CE, SB, and GL) are usually favorably associated with those traits (ranging from −0.493 to 0.313). One hundred sixty-two significant single-nucleotide polymorphisms (SNPs) and their surrounding protein-coding genes were identified as significantly associated with GESS and GED, respectively. Six genes overlapped between GESS and GED for calving traits and 10 genes overlapped between GESS for success traits and calving traits. Our findings indicate the importance of considering the GESS when genetically evaluating the female reproductive traits in Holstein cattle.
Highlights
MATERIALS AND METHODSIn recent decades, the genetic selection for functional traits, including reproductive performance, has received great emphasis in dairy cattle selection indexes, aiming to achieve more balanced and sustainable breeding goals (Egger-Danner et al, 2015)
gestation length (GL) is affected by various environmental effects, such as temperature (McClintock et al, 2003) and cow parity number (King et al, 1985), which may have led to the discrepancy of GL between different populations
We found that the genetic effect of service sire (GESS) on conception rate (CR) was unfavorably and significantly related to milk yield (−0.218 ± 0.039), indicating that selection exclusively on milk production might indirectly result in a decline of insemination performance of the service sire
Summary
MATERIALS AND METHODSIn recent decades, the genetic selection for functional traits, including reproductive performance, has received great emphasis in dairy cattle selection indexes, aiming to achieve more balanced and sustainable breeding goals (Egger-Danner et al, 2015). Most dairy cattle breeding programs focus only on the genetic effects of the dam (GED) when genetically evaluating fertility and calving traits, there are evidence that the service sire may have a genetic influence on female reproductive performance (Barton et al, 1984; Van Tassell et al, 2003; Averill et al, 2004)—for instance, the direct effects of the service sire (e.g., semen quality and viability) on female reproductive performance have been considered as indirect effects (Jansen, 1985). SCR is measured as confirmed pregnancy ratio (in percentage) of each service sire They implemented a sire–maternal grandsire (S-MGS) model to estimate the genetic component of service sire in calving performance (Van Tassell et al, 2003; Jiang et al, 2018). Understanding the genetic mechanisms underlying male fertility and developing accurate genomic prediction models are of great importance but still underexplored
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