Genetic parameters for mouth size and their influence on growth traits in pasture-raised Nelore cattle.

Evaluating traits that allow breeders to increase production efficiency in beef production systems is important. The mouth size (MS) score is a trait easily measured and implemented by breeders. Bite size is related to MS in beef cattle and is a determinant of daily feed intake of pasture-raised animals, influencing their growth. The aim of this study was to estimate genetic parameters for MS, weaning weight (WW) and postweaning weight gain (PWG) of Nelore cattle and to evaluate the influence of the interaction between MS and WW on PWG. Phenotypic records of 134,282 Nelore animals born between 1995 and 2019 were used. Variance components were estimated using multitrait animal model with the Bayesian method. The model included the contemporary group as fixed effect, age at measurement of the trait as linear covariate, and direct additive genetic and residual effects as random effects. For WW, random maternal and maternal permanent environmental effects were added to the model. A Bayesian approach was used to analyze the interaction between WW clusters and MS. The heritability estimates were 0.24, 0.15, and 0.23 for MS, WW, and PWG, respectively. The genetic correlation between variables studied ranged from 0.24 to 0.46. The results suggest that animals with a larger mouth tend to have greater PWG, demonstrating the positive influence of MS score on the postweaning performance of cattle. The direct heritability estimates confirm the possibility of selecting animals for the traits studied.

Evaluating traits that allow breeders to increase production efficiency in beef production systems is important. The mouth size (MS) score is a trait easily measured and implemented by breeders. Our results showed that MS in Nelore cattle is a heritable trait, and it is favorably associated with growth traits, indicating that animals with larger mouth are heavier at weaning and gain more weight after weaning on pasture. MS score should be further explored to evaluate its complexity and inclusion in breeding programs incorporating data collected from cattle raised under pasture conditions.

Genotype-environment interaction for milk production of Gyr cattle in Brazil and Colombia.

BACKGROUND: Genotype by environment interactions (G × E) can play an important role in cattle populations and should be included in breeding programs in order to select the best animals for different environments. OBJECTIVE: The aim of this study was to investigate the G × E for milk production of Gyr cattle in Brazil and Colombia by applying a reaction norm model used genomics information, and to identify genomic regions associated with milk production in the two countries. METHODS: The Brazilian and Colombian database included 464 animals (273 cows and 33 sires from Brazil and 158 cows from Colombia) and 27,505 SNPs. A two-trait animal model was used for milk yield adjusted to 305 days in Brazil and Colombia as a function of country of origin, which included genomic information obtained with a single-step genomic reaction norm model. The GIBBS3F90 and POSTGSf90 programs were used. RESULTS: The results obtained indicate G × E based on the reranking of bulls between Brazil and Colombia, demonstrating environmental differences between the two countries. The findings highlight the importance of considering the environment when choosing breeding animals in order to ensure the adequate performance of their progeny. Within this context, the reranking of bulls and the different SNPs associated with milk production in the two countries suggest that G × E is an important effect that should be included in the genetic evaluation of Dairy Gyr cattle in Brazil and Colombia. CONCLUSION: The Gyr breeding program can be optimized by choosing a selection environment that will allow maximum genetic progress in milk production in different environments within and between countries.

Carcass and meat quality of red-winged tinamou (Rhynchotus rufescens) selected for muscle growth.

The aim of the present study was to evaluate the efficiency of selection for body growth and its association with carcass and meat quality traits in the red-winged tinamou. Two experimental groups were selected based on the selection index: selection group with a higher index (TinamouS) and commercial group with a lower index (TinamouC). Weight at 180 days and slaughter weight were significantly higher (p < 0.05) in the TinamouS group, as were hot carcass weight, skinless breast weight, wing weight, and thigh and drumstick weight. The meat quality traits or sensory attributes did not differ significantly (p > 0.05) between groups. A significant positive correlation (0.59; p < 0.05) was found between shear force and chewability and a significant negative correlation (- 0.59; p < 0.05) between aroma intensity and strange aroma. The present study suggests that the selection index promotes greater body growth and preserves meat quality and sensory traits in red-winged tinamou.

Analysis of residual feed intake in Nellore bulls of different ages, rib eye area, and backfat thickness.

The aim of the present study was to use different models that include body composition phenotypes for the evaluation of residual feed intake (RFI) in Nellore bulls of different ages. Phenotypic and genotypic data of bulls that had participated in feed efficiency tests of a commercial (COM) and an experimental (EXP) herd between 2007 and 2019 were used. The mean entry age in the two herds was 645 and 279 days, respectively. The phenotypes were evaluated: rib eye area (REA), backfat thickness (BFT), residual feed intake (RFIKOCH), RFI adjusted for REA (RFIREA), RFI adjusted for BFT (RFIBFT), and RFI adjusted for REA and BFT (RFIREA BFT). The (co)variance components and prediction of genomic estimated breeding values (GEBV) were obtained by REML using ssGBLUP in single and two-trait analyses. Spearman's correlations were calculated based on the GEBV for RFIKOCH. The RFI phenotypes exhibited moderate heritability estimates in both herds (0.17 ± 0.03 to 0.27 ± 0.04). The genetic correlation between phenotypes was positive and high (0.99) in the two herds, a fact that permitted the creation of a single database (SDB). The heritability estimates of the SDB were also of moderate magnitude for the different definitions of RFI (0.19 ± 0.04 to 0.21 ± 0.04). The genetic correlations were positive and high between RFI traits 0.97 ± 0.01 to 0.99 ± 0.01), and positive and low/moderate between REA and BFT (0.01 ± 0.10 to 0.31 ± 0.12). The selection of animals based on the GEBV for RFIKOCH did not alter the ranking of individuals selected for RFIREA, RFIBFT, and RFIREA BFT. The results of the present study suggest that records of Nellore bulls of different ages and with different body compositions can be combined in a SDB for RFI calculation. Therefore, young animals can be evaluated in feed efficiency tests in order to reduce costs and the generation interval and possibly to obtain a higher response to selection.

Genetic principal components for reproductive and productive traits in Holstein cows reared under tropical conditions.

The objective of this study was to compare the standard multi-trait model and five reduced-rank models fitted to the first principal components and genetic parameter estimates in order to determine the most appropriate method to model the covariance structure of reproductive and productive traits in Brazilian Holstein cows. Individual records of the following traits from 5217 cows were analyzed: 305-day milk yield (MY305), peak yield, milk yield per day of calving interval, days from calving to first estrus, days from calving to last service (CLS), calving interval (CI), and gestation length. Schwarz's Bayesian information criterion was used to compare the different models. The results indicated that four principal components were necessary to model the genetic (co)variance structure, reducing the number of parameters to be estimated. Analysis of genetic and phenotypic correlations showed that milk production-related traits were strongly correlated with each other (ranging from 0.74 to 0.99), while the correlation of these traits with the reproductive traits was weak (ranging from - 0.14 to 0.27). Heritability estimates for the traits ranged from 0.03 to 0.18. The reproductive traits CLS and CI and the production trait MY305 should be included as selection criteria in dairy cattle breeding programs because they are correlated with the first two principal components, retaining 91% of the genetic variability of the data.

Relationship between reproductive and productive traits in Holstein cattle using multivariate analysis.

Multivariate procedures are used for the extraction of variables from the correlation matrix of phenotypes in order to identify those traits that explain the largest proportion of phenotypic variation and to evaluate the relationship structure between these traits. The reproductive traits (days from calving to first estrus [CFE], days from calving to last service [CLS], calving interval [CI] and gestation length [GL]) and milk production traits (milk yield at 305 days of lactation [MY305], peak yield [PY] and milk yield per day of calving interval [MYCI]) of 5,217 Holstein females (primiparous and multiparous) were measured. Principal component analysis (PCA) and factor analysis of the correlation matrix were used to estimate the correlation between traits. Analysis grouped the seven traits into three principal components and four latent factors that retained approximately 81.5% and 88.9% of the total variation of the data, respectively. The production variables exhibited positive phenotypic correlation coefficients of high magnitude (>.67). The phenotypic correlation estimates between the productive and reproductive traits were low, ranging from .13 to .22. A strong association (.99) was observed between CLS and CI. Our results indicate that multivariate analysis was effective in generating correlations between the traits studied, grouping the seven traits in a smaller number of variables that retained approximately 81% of the total variation of the data.