Identifying influential sires and distinct clusters of selection candidates based on genomic relationships to reduce inbreeding in the US Holstein

Abstract

High relatedness in the US Holstein breed can be attributed to the increased rate of inbreeding that resulted from strong selection and the extensive use of a few bulls via reproductive biotechnology. The objectives of this study were to determine whether clustering could separate selected candidates into genetically different groups and whether such clustering could reduce the expected inbreeding of the next generation. A genomic relationship matrix composed of 1,145 sires with the most registered progeny in the breed born after 1985 was used for principal component analysis and k-means clustering. The 5 clusters reduced the variance by 25% and contained 171 (C1), 252 (C2), 200 (C3), 244 (C4), and 278 (C5) animals, respectively. The 2 most predominant families were C1 and C2, while C4 contained the most international animals. On average, C1 and C5 contained older animals; the average birth year per cluster was 1988 (C1), 1996 (C2 and C3), 1999 (C4), and 1990 (C5). Increasing to 10 clusters allowed the separation of the predominant sons. Statistically significant differences were observed for indices (net merit index, cheese merit index, and fluid merit index), daughter pregnancy rate, and production traits (milk, fat, and protein), with older clusters having lower merit for production but higher for reproduction. K-means clustering was also used for 20,099 animals considered as selection candidates. Based on the reduction in variance achieved by clustering, 5 to 7 clusters were appropriate. The number of animals in each cluster was 3,577 (C1), 3,073 (C2), 3,302 (C3), 5,931 (C4), and 4,216 (C5). The expected inbreeding from within or across cluster mating was calculated using the complete pedigree, assuming the mean inbreeding of animals born in the same year when parents are unknown. Generally, inbreeding was highest within cluster mating and lowest across cluster mating. Even when 10 clusters were used, one cluster always gave low inbreeding in all scenarios. This suggests that this cluster contains animals that differ from all other groups but still contains enough diversity within itself. Based on lower across cluster inbreeding, up to 7 clusters were appropriate. Statistically significant differences in genomic estimated breeding values were found between clusters. The rankings of clusters for different traits were mostly the same except for reproduction and fat. Results show that diversity within the population exists and clustering of selection candidates can reduce the expected inbreeding of the next generations.

Keywords: genetic diversity; k-means; principal component analysis.