Predicting male fertility in dairy cattle using markers with large effect and functional annotation data

Area of Science:

• Animal Genetics
• Reproductive Biology
• Quantitative Genetics

Background:

• Dairy cattle fertility is crucial for economic efficiency.
• Genomic prediction for cow fertility is established, but bull fertility remains understudied.
• Sire conception rate (SCR) is a key metric for service sire fertility.

Purpose of the Study:

• To evaluate genomic prediction of dairy bull fertility.
• To utilize markers with large effects and functional annotation data.
• To compare predictive models using various SNP sets.

Main Methods:

• Utilized 11.5k U.S. Holstein bulls with SCR records and ~300k SNP markers.
• Employed single-kernel and multi-kernel predictive models.
• Analyzed all SNPs, large-effect markers, and functional variants (non-synonymous, synonymous, regulatory).

Main Results:

• The full SNP set achieved predictive correlations of 0.340.
• Five major markers significantly increased predictive correlations to 0.403 (19% accuracy gain).
• Functional SNP classes and multi-kernel models showed strong predictive performance, reaching 0.405.

Conclusions:

• Incorporating large-effect markers substantially enhances dairy sire fertility prediction.
• Functional variants offer higher predictive ability than random variants.
• This study provides a basis for genome-guided selection strategies in the dairy industry.