Genomewide association study for production and meat quality traits in Canchim beef cattle.

The commercial value of the bovine carcass is determined by a set of traits, such as weight, yield, back fat thickness, and marbling; therefore, the genetic improvement of growth, meat, and carcass quality traits is an important tool to add value to the supply chain. Genomewide association studies (GWAS) enable the identification of loci that control phenotypic expression of quantitative traits (QTL). Therefore, the objective of this work was to perform a GWAS to identify genomic regions and genes associated with growth, carcass traits, and meat quality in Canchim beef cattle. These traits were yearling weight (YW), rib eye area (REA), back fat thickness (BFT), and marbling (MARB). To increase sample size and marker density, genotype imputation was performed, and only markers imputed with greater than 95% accuracy were used. Genomewide association study was performed using a Bayesian approach, by the Bayes B statistical method, incorporating genotypes and phenotypes from 614 animals from both the Canchim breed and the MA genetic group (offspring of Charolais bulls and one-half Canchim + one-half Zebu cows). This investigation identified 1 and 4 genomic regions explaining 0.23 and 7.35% of the genetic variance for REA and YW, respectively. These regions harbor a total of 19 genes, 7 of which were classified for biological functions by functional analysis. Significant associations were not observed for BFT and MARB. The identification of QTL that had been previously described in the literature reinforces associations found in this study.

Genetic parameters for carcass traits and body weight using a Bayesian approach in the Canchim cattle.

Correlation between genetic parameters and factors such as backfat thickness (BFT), rib eye area (REA), and body weight (BW) were estimated for Canchim beef cattle raised in natural pastures of Brazil. Data from 1648 animals were analyzed using multi-trait (BFT, REA, and BW) animal models by the Bayesian approach. This model included the effects of contemporary group, age, and individual heterozygosity as covariates. In addition, direct additive genetic and random residual effects were also analyzed. Heritability estimated for BFT (0.16), REA (0.50), and BW (0.44) indicated their potential for genetic improvements and response to selection processes. Furthermore, genetic correlations between BW and the remaining traits were high (P > 0.50), suggesting that selection for BW could improve REA and BFT. On the other hand, genetic correlation between BFT and REA was low (P = 0.39 ± 0.17), and included considerable variations, suggesting that these traits can be jointly included as selection criteria without influencing each other. We found that REA and BFT responded to the selection processes, as measured by ultrasound. Therefore, selection for yearling weight results in changes in REA and BFT.