Genetic variances and covariances of live weight traits in Charolais cattle by multi-trait analysis.

Live weight traits are economically important for beef cattle production systems. Genetic analysis of live weight traits frequently presents a problem due to animal records, in that matter, not all the animals have complete records as many young animals leave the herd because of sale, transfer to another herd, or culling reasons. Therefore, the use of multi-trait genetic analysis might be of assistance to help overcome any possible loss of information for those animals with incomplete records. In this study, genetic variances and covariances were obtained to estimate genetic parameters for birth (BW), weaning (WW), and yearling (YW) live weights in a registered Charolais beef cattle population using a multivariate model, where a considerable reduction of data from birth weight to year weight was observed. Direct and maternal heritabilities for BW, WW, and YW were 0.50, 0.30, and 0.28, and 0.31, 0.25, and 0.14, respectively. Direct and maternal genetic correlations were negative in all live weight traits. Genetic correlations among direct BW with direct WW and YW were low, while genetic correlations among maternal traits were medium or high (r>0.39). Comparison between univariate and multi-trait models with substantial reduction of information revealed important differences, implying that multi-trait analysis is better for the structure of data allowing a better fitting of genetic effects by covariance among evaluated traits. Results support multi-trait analysis implementation for genetic evaluations for live weight traits of Charolais cattle.

Genomewide association analysis of growth traits in Charolais beef cattle.

The objective of this study was to perform a genomewide association study (GWAS) for growth traits in Charolais beef cattle and to identify SNP markers and genes associated with these traits. Our study included 855 animals genotyped using 76,883 SNP from the GeneSeek Genomic Profiler Bovine HD panel. The examined phenotypic data included birth, weaning, and yearling weights as well as pre- and postweaning ADG. After quality control, 68,337 SNP and 823 animals were retained in the analysis. The association analysis was performed using the principal components method via the egscore function of the GenABEL version 1.8-0 package in the R environment. Eighteen SNP located in 13 BTA were associated with growth traits ( < 5 × 10). The most important genes in these genomic regions were (), (), (), (), and ( [angiotensinase C]), due to their relationships with perinatal and postnatal survival, bone growth, cell adhesion, regulation of adipogenesis, and appetite. In conclusion, this study is the first to describe a GWAS conducted in beef cattle in Mexico and represents a basis for further and future research. This study detected new QTL associated with growth traits and identified 5 positional and functional candidate genes that are potentially involved in variations of the analyzed traits. Future analyses of these regions could help to identify useful markers for marker-assisted selection and will contribute to the knowledge of the genetic basis of growth in cattle and be a foundation for genomic predictions in Mexican Charolais cattle.

Genetic trends for live weight traits reflect breeding strategies in registered Charolais Farms in Mexico.

Genetic trends are commonly used to verify genetic improvement; however, there are few reports on beef cattle in Mexico. Data from 1998 to 2013 from four Charolais bull breeding farms were examined to verify the genetic responses to different breeding management and selection criteria. Analysis included the comparison of regression lines of breeding values for birth (BW), weaning (WW) and yearling weights (YW), and maternal weaning weight (MWW) on the year of birth of the animals. Results revealed differential genetic progress for BW and YW and indicated that the overall analysis may have diluted the perception of genetic progress from the farmer's point of view. The use of breeding values as a tool for selection is effective to achieve genetic progress, even in negatively correlated traits, such as birth weight and yearling weight.