A definition of unknown parent groups based on bull usage patterns across herds

Area of Science:

• Animal Genetics
• Quantitative Genetics
• Livestock Breeding

Background:

• Traditional unknown parent group (UPG) definitions rely on time, selection, and founder flows.
• Genetic heterogeneity in populations with large expansions and mixed breeding systems (artificial insemination and natural service) complicates UPG definition.
• A novel UPG definition method was developed to account for beef bull flows in complex populations.

Purpose of the Study:

• To propose and apply a new method for defining unknown parent groups (UPG) in the French Charolais cattle population, accounting for genetic heterogeneity.
• To identify key criteria reflecting national genetic disparities in beef cattle populations.
• To validate the robustness and relevance of the defined UPGs in genetic evaluation models.

Main Methods:

• Utilized a factorial approach combining principal component analysis and Ward clustering to identify 14 distinct genetic regions.
• Clustered herds based on bull origin, birth region, reproduction method (AI/NS), and genetic level differences (<20% σg for muscle and skeleton scores).
• Defined 16 sire UPGs and validated them using sire and animal models, estimating genetic levels for muscle and skeleton scores.

Main Results:

• The new method defined 16 sire UPGs, validated as numerically stable and representing distinct genetic subpopulations.
• Significant genetic differences (up to 0.5 σg) were observed between bull UPGs within a time period across regions.
• Bull UPG genetic estimates for muscle and skeleton scores were generally higher than those of cows (0.30-0.75 σg).
• Inclusion of genetic groups increased estimated genetic trends by 20-30% and re-ranked animals, especially those without pedigree information.

Conclusions:

• The proposed UPG definition method, incorporating herd birth period, region, and AI rate, effectively captures national genetic disparities in beef cattle.
• The identified UPGs are robust and relevant for genetic evaluations, improving accuracy and genetic trend estimation.
• Accounting for genetic groups significantly impacts genetic trend estimates and animal re-ranking, highlighting the importance of accurate UPG definition.