The aim of this study was to estimate genetic parameters for BW in meat quail at different ages. A total of 24,382 weight records from 3,652 quail, born between 2009 and 2011, were evaluated. Weekly BW was measured from hatch until 42 d of age. The genetic parameters were estimated by the restricted maximum likelihood method using a multivariate animal model. Heritability of BW ranged from 0.03 to 0.23. Genetic correlations were mainly high and positive. Selection for BW at 28 d of age yielded good indirect genetic progress in BW at 42 d of age.

This study was designed to estimate genetic parameters for the following traits of Brahman cattle in Brazil: age at first calving (AFC), calving interval (CI), rebreeding (REB), and stayability (STAY). For REB, the value 1 was assigned to heifers that rebred and calved after first calving and the value 0 was assigned to heifers that failed to rebreed after first calving. Likewise, for STAY, the value 1 was assigned to cows that calved at least 3 times by the time they reach 6 yr of age; otherwise, the value 0 was assigned. A bivariate analysis was used to estimate covariances components by using linear animal model for CI and AFC and threshold animal model for REB and STAY. The mean h(2) were 0.10, 0.02, 0.22, and 0.10 for AFC, CI, REB, and STAY, respectively. The genetic correlations were –0.13 between AFC and CI, –0.35 between AFC and REB, –0.57 between AFC and STAY, and 0.32 between REB and STAY, which reveal that cows that remain productive for longer periods in the herd also start breeding younger and present greater chances to REB. The selection of Brahman cattle for reproductive traits, such as AFC, CI, REB, and STAY, will render low magnitude and long-term responses.

The objective of this work was to evaluate the genetic diversity of Brahman cattle in Brazil with pedigree analysis. Genealogical records of a subpopulation were used considering all pedigree information (Pt) and the pedigree information divided into two periods (P1, from 1994 to 2004; and P2, from 2005 to 2012) or according to the raising system (Ppt, animals on pasture; or Pst, on stable). Estimates were obtained for average inbreeding coefficients, generation intervals (GI), number of equivalent known generation (CGE), number of founders (Nf), effective number of founders (fe), effective number of ancestors (fa), and founder genome equivalents (fg). The average inbreeding coefficients were 11.97, 7.79, 11.95, 11.74, and 11.31% for Pt, P1, P2, Ppt, and Pst, respectively. Average GI was 4.4 years, whereas CGE was 3.18. The fe values were similar to those of fa, which were greater than those of fg. The fe/fa and fg/fe ratios were close to 1, which indicates no genetic bottleneck and small losses by genetic drift. The genetic diversity in the Brazilian population of Brahman breed is not significantly reduced.

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Background Genome-wide association studies (GWAS) are utilized in cattle to identify regions or genetic variants associated with phenotypes of interest, and thus, to identify design strategies that allow for the increase of the frequency of favorable alleles. Visual scores are important traits of cattle production in Brazil because they are utilized as selection criteria, helping to choose more harmonious animals. Despite its importance, there are still no studies on the genome association for these traits. This study aimed to identify genome regions associated with the traits of conformation, precocity and muscling, based on a visual score measured at weaning. Results Bayesian approaches with BayesC and Bayesian LASSO were utilized with 2873 phenotypes of Nellore cattle for a GWAS. The animals were genotyped with Illumina BovineHD BeadChip, and a total of 309,865 SNPs were utilized after quality control. In the analyses, phenotype and deregressed breeding values were utilized as dependent variables; a threshold model was utilized for the former and a linear model for the latter. The association criterion was the percentage of genetic variance explained by SNPs found in 1 Mb-long windows. The Bayesian approach BayesC was better adjusted to the data because it could explain a larger phenotypic variance for both dependent variables. Conclusions There were no large effects for the visual scores, indicating that they have a polygenic nature; however, regions in chromosomes 1, 3, 5, 7, 14, 15, 16, 19, 20 and 23 were identified and explained a large part of the genetic variance.