Genome-wide association study for milk production traits in Dual-Purpose Belgian Blue cows

This study aimed to estimate genetic parameters and identify genomic regions associated with milk production traits during the first three parities in Dual-Purpose Belgian Blue (DPBB) cows. Edited data were 135,646 test-day milk yield (MY), fat yield (FY), protein yield (PY), fat percentage (FP), protein percentage (PP), and somatic cell count (SCC) collected from 1988 to 2020 on 20,744 lactations obtained from 10,345 cows in 128 herds. Data of 28,466 single nucleotide polymorphisms (SNP), located on 29 Bos taurus (BTA) autosomes, for 1,699 animals (639 males and 1,060 females) were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method. The SNP solutions were estimated using a single-step genomic best linear unbiased prediction approach. The proportion of genetic variance explained by windows of 25 adjacent SNPs (with an average size of similar to 2 Mb) was calculated, and regions accounting for at least 1.0 % of the total additive genetic variance were used to search for candidate genes. Mean heritability (h2) estimated for milk yield traits (MY, FY, PY, FP, and PP) ranged from 0.32 (FP) to 0.41 (PP), 0.30 (FP) to 0.41 (PP), and 0.29 (FP) to 0.39 (PP), in the first, second, and third parity, respectively. Mean h2 estimated for SCS in the first three parities ranged from 0.17 to 0.27. The highest genetic correlations were found between daily MY and PY, followed by those found between daily FY and PY. Negative genetic correlations were found between yield traits (MY, FY, PY) and SCS. In total, seven genomic regions (BTA1 (n = 2), BTA2, BTA7, BTA10, BTA22, and BTA26) were identified to be associated with the included traits. The identified genomic regions showed contrasting results between parities and among the different stages of each parity. It suggests that differential sets of candidate genes underlie the phenotypic expression of the considered traits among parities and lactation stages of each parity. The findings of this study may be used for future implementation of genomic evaluation to improve lactation performances in DPBB cows.