Genomic studies of milk-related traits in water buffalo (Bubalus bubalis) based on single-step genomic best linear unbiased prediction and random regression models

Genomic selection has been widely implemented in many livestock breeding programs, but it remains incipient buffalo. Therefore, this study aimed to (1) estimate variance components incorporating genomic information Murrah buffalo; (2) evaluate the performance of prediction for milk-related traits using single- and multitrait random regression models (RRM) single-step best linear unbiased approach; (3) longitudinal SNP effects candidate genes potentially associated with time-dependent variation milk, fat, protein yields, as well somatic cell score (SCS) multiple parities. The data used genetic parameters consisted a total 323,140 test-day records. average daily heritability estimates were moderate (0.35 ± 0.02 milk yield, 0.22 0.03 fat 0.42 0.16 SCS). highest estimates, considering all studied, observed between 20 280 d (DIM). correlation at different DIM among evaluated ranged from ?0.10 (156 185 SCS) 0.61 (36 65 yield). In general, direct any is expected result indirect gains yield also increase SCS certain lactation stages, which undesirable. predicted RRM coefficients derive estimated values (GEBV) each time point (from 5 305 DIM). tuning when constructing hybrid relationship matrices had small effect on GEBV accuracy greater bias estimates. solutions back-solved Legendre coefficients, then 3 stages performed trait parity: 70, 71 150, 151 DIM. Important regions related analyzed parities that explain more than 0.50% additive selected further analyses genes. similar potential found traits, our results suggest evidence differential sets underlying phenotypic expression across These contribute better understanding architecture production dairy buffalo reinforce relevance genetically Furthermore, identified can be target future functional genomics studies.