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

Abstract

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

Unraveling Admixture, Inbreeding, and Recent Selection Signatures in West African Indigenous Cattle Populations in Benin

“…The SCIN gene, also mapped in the region, is involved in residual feed intake in cattle ( Salleh et al, 2017 ). The remaining regions under selection in Adm_cur spanned other candidate genes including ANKRD52 , RNF41 , and MYL6 associated with height and carcass traits ( Cai et al, 2019 ; Moravčíková et al, 2019 ; Feitosa et al, 2021 ), and COQ10A and RNF41 related to milk trait and calf mortality ( Lázaro et al, 2021 ; Marín-Garzón et al, 2021 ). In addition, the SARNP gene is related to animal reproduction ( Labrecque et al, 2014 ), while the DNAJC14 is involved in heat stress ( Bahbahani et al, 2015 ; Rehman et al, 2020 ).…”

Effect of Prepartum Dietary Energy Level on Production and Reproduction in Nili Ravi Buffaloes

“…Among river buffaloes, Nili Ravi is the principal milk producing breed [4] selected for dairy production [5]. The performance of buffalo is controlled by various factors, such as genetics [6], management [7], nutrition [8], and, most importantly, reproduction [9].…”

“…Probability of treatment effects: Treat: Main effect of treatment 3. FFF: First follicular formation,4 OR: Ovarian resumption,5 FCL: First corpus luteum formation,6 UI: Uterine involution. LE ME HE Figure 6.…”

Whole genome-wide analysis of DEP family members in sheep (Ovis aries) reveals their potential roles in regulating lactation

“…Previous reports suggest that some DEP proteins can regulate lactation [23][24][25][26]. However, systematic studies of the roles of sheep DEP family members in regulating lactation are currently lacking.…”