DNA-based paternity analysis and genetic evaluation in a large, commercial cattle ranch setting

Area of Science:

• Animal Genetics
• Reproductive Biology
• Quantitative Genetics

Background:

• Deoxyribonucleic acid (DNA)-based paternity testing is essential for accurate genetic evaluations in livestock.
• Variable reproductive success among sires in multiple-sire breeding pastures complicates parentage assignment.
• The resolution of DNA marker panels directly impacts the accuracy of paternity determination and subsequent genetic analyses.

Purpose of the Study:

• To evaluate the impact of DNA marker panel resolution on paternity assignment accuracy in cattle.
• To compare the accuracy of estimated breeding values (EPDs) derived from different paternity assignment methods.
• To determine the optimal number and characteristics of single nucleotide polymorphism (SNP) markers for reliable genetic evaluations.

Main Methods:

• Utilized DNA-based paternity tests on 625 calves from a multiple-sire breeding pasture.
• Compared paternity results from high (P(E)=0.999) and lower (P(E)=0.956) cumulative parentage exclusion probability DNA marker panels.
• Employed a likelihood-based method for sire assignment and calculated on-farm EPDs based on inferred pedigrees.

Main Results:

• A high-resolution panel achieved higher agreement in paternity assignment compared to a lower-resolution panel.
• Lower resolution panels led to ambiguous paternity assignments in 67% of cases, resolved by likelihood methods in 80%.
• EPDs derived from high-resolution panels showed high correlation (0.94) with weaning weights; lower resolution panels generated inaccurate EPDs for non-siring bulls.

Conclusions:

• High-resolution DNA marker panels are critical for accurate paternity assignment and reliable genetic evaluations in cattle.
• Likelihood-based methods can improve sire assignment accuracy but are dependent on marker panel quality.
• Using a sufficient number of SNP loci in marker panels is essential for accurate on-farm EPDs, especially in complex field scenarios.