Inbreeding coefficients and runs of homozygosity islands in Brazilian water buffalo.

Characterization of autozygosity is relevant to monitor genetic diversity and manage inbreeding levels in breeding programs. Identification of autozygosity hotspots can unravel genomic regions targeted by selection for economically important traits and can help identify candidate genes for selection. In this study, we estimated the inbreeding levels of a Brazilian population of Murrah buffalo undergoing selection for milk production traits, particularly milk yield. We also studied the distribution of runs of homozygosity (ROH) islands and identified putative genes and quantitative trait loci (QTL) under selection. We genotyped 422 Murrah buffalo for 51,611 SNP; 350 of these had ROH longer than 10 Mb, indicating the occurrence of inbreeding in the last 5 generations. The mean length of the ROH per animal was 4.28 ± 1.85 Mb. Inbreeding coefficients were calculated from the genomic relationship matrix, the pedigree, and the ROH, with estimates varying between 0.242 and 0.035. Inbreeding estimates from the pedigree had a low correlation with the genomic estimates, and estimates from the genomic relationship matrix were much higher than those from the pedigree or the ROH. Signatures of selection were identified in 6 genomic regions, located on chromosomes 1, 2, 3, 5, 16, and 18, encompassing a total of 190 genes and 174 QTL. Many of the genes (e.g., APRT and ACSF3) and QTL identified are related to milk production traits, such as milk yield, milk fat yield and percentage, and milk protein yield and percentage. Other genes are associated with reproduction and immune response traits as well as morphological aspects of the buffalo species. Inbreeding levels in this population are still low but are increasing due to selection and should be managed to avoid future losses due to inbreeding depression. The proximity of genes linked to milk production traits with genes associated with reproduction and immune system traits suggests the need to include these latter genes in the breeding program to avoid negatively affecting them due to selection for production traits.

Genetic association of productive and reproductive traits with stayability in Nellore cattle: analysis using Bayesian models.

We evaluated the genetic association of growth traits [weight adjusted to 205 days of age (W205), 365 days of age (W365), and 550 days of age (W550); weight gain between 205 days of age and 365 days of age (WG1) and between 365 days of age and 550 days of age (WG2)] and reproductive traits [age at first calving (AFC); first calving interval (FCI)] with stayability in the herd (STAY), using Bayesian inference in linear and threshold models. We defined STAY as the probability of a cow calving three or more times before the age of 76 months, given that she had calved at least once. We assigned binary codes (0, failure; 1, success) to each female. We used a sire model for analysis and formed different contemporary groups for the investigated traits. We analyzed the results by applying a two-trait sire model that included STAY (threshold trait) and linear traits (W205, W365, W550, WG1, WG2, AFC, and FCI). We used Gibbs sampling to estimate variance components and heritabilities. In all the analyses, we found that the mean heritability estimates for STAY were of moderate magnitude (0.20-0.25). The mean heritabilities for W205, W365, W550, WG1, WG2, AFC, and FCI were 0.20, 0.23, 0.39, 0.08, 0.14, 0.12, and 0.11, respectively. We observed wide variation in the posterior distributions of genetic correlations; however, with the exception of those obtained for the reproductive traits, the mean estimates were of low magnitude. Selection for WG2 can results in favorable correlated response in STAY.

Genetic evaluation using multi-trait and random regression models in Simmental beef cattle.

The Brazilian Association of Simmental and Simbrasil Cattle Farmers provided 29,510 records from 10,659 Simmental beef cattle; these were used to estimate (co)variance components and genetic parameters for weights in the growth trajectory, based on multi-trait (MTM) and random regression models (RRM). The (co)variance components and genetic parameters were estimated by restricted maximum likelihood. In the MTM analysis, the likelihood ratio test was used to determine the significance of random effects included in the model and to define the most appropriate model. All random effects were significant and included in the final model. In the RRM analysis, different adjustments of polynomial orders were compared for 5 different criteria to choose the best fit model. An RRM of third order for the direct additive genetic, direct permanent environmental, maternal additive genetic, and maternal permanent environment effects was sufficient to model variance structures in the growth trajectory of the animals. The (co)variance components were generally similar in MTM and RRM. Direct heritabilities of MTM were slightly lower than RRM and varied from 0.04 to 0.42 and 0.16 to 0.45, respectively. Additive direct correlations were mostly positive and of high magnitude, being highest at closest ages. Considering the results and that pre-adjustment of the weights to standard ages is not required, RRM is recommended for genetic evaluation of Simmental beef cattle in Brazil.