Muscle growth affects the metabolome of the pectoralis major muscle in red-winged tinamou (Rhynchotus rufescens).

The aim of the present study was to identify and quantify the metabolites (metabolome analysis) of the pectoralis major muscle in male red-winged tinamou (Rhynchotus rufescens) selected for growth traits. A selection index was developed for females [body weight (BW), chest circumference (CC), and thigh circumference (TC)] and males [BW, CC, TC, semen volume, and sperm concentration] in order to divide the animals into 2 experimental groups: selection group with a higher index (TinamouS) and commercial group with a lower index (TinamouC). Twenty male offspring of the 2 groups (TinamouS, n = 10; TinamouC, n = 10) were confined for 350 d. The birds were slaughtered and pectoralis major muscle samples were collected, subjected to polar and apolar metabolites extractions and analyzed by proton nuclear magnetic resonance (1H NMR) spectroscopy. Analysis of the polar metabolomic profile identified 65 metabolites; 29 of them were differentially expressed between the experimental groups (P < 0.05). The TinamouS groups exhibited significantly higher concentrations (P < 0.05) of 25 metabolites, including anserine, aspartate, betaine, carnosine, creatine, glutamate, threonine, 3-methylhistidine, NAD+, pyruvate, and taurine. Significantly higher concentrations of cysteine, beta-alanine, lactose, and choline were observed in the TinamouC group (P < 0.05). The metabolites identified in the muscle provided information about the main metabolic pathways (higher impact value and P < 0.05), for example, phenylalanine, tyrosine and tryptophan biosynthesis; alanine, aspartate and glutamate metabolism; D-glutamine and D-glutamate metabolism; ß-alanine metabolism; glycine, serine and threonine metabolism; taurine and hypotaurine metabolism; histidine metabolism; phenylalanine metabolism. The NMR spectra of apolar fraction showed 8 classes of chemical compounds. The metabolome analysis shows that the selection index resulted in the upregulation of polyunsaturated fatty acids, unsaturated fatty acids, phosphocholines, phosphoethanolamines, triacylglycerols, and glycerophospholipids. The present study suggests that, despite few generations, the selection based on muscle growth traits promoted changes in metabolite concentrations in red-winged tinamou.

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.

Assessing the accuracy of imputation in the Gyr breed using different SNP panels.

The aim of this study was to evaluate the accuracy of imputation in a Gyr population using two medium-density panels (Bos taurus - Bos indicus) and to test whether the inclusion of the Nellore breed increases the imputation accuracy in the Gyr population. The database consisted of 289 Gyr females from Brazil genotyped with the GGP Bovine LDv4 chip containing 30 000 SNPs and 158 Gyr females from Colombia genotyped with the GGP indicus chip containing 35 000 SNPs. A customized chip was created that contained the information of 9109 SNPs (9K) to test the imputation accuracy in Gyr populations; 604 Nellore animals with information of LD SNPs tested in the scenarios were included in the reference population. Four scenarios were tested: LD9K_30KGIR, LD9K_35INDGIR, LD9K_30KGIR_NEL, and LD9K_35INDGIR_NEL. Principal component analysis (PCA) was computed for the genomic matrix and sample-specific imputation accuracies were calculated using Pearson's correlation (CS) and the concordance rate (CR) for imputed genotypes. The results of PCA of the Colombian and Brazilian Gyr populations demonstrated the genomic relationship between the two populations. The CS and CR ranged from 0.88 to 0.94 and from 0.93 to 0.96, respectively. Among the scenarios tested, the highest CS (0.94) was observed for the LD9K_30KGIR scenario. The present results highlight the importance of the choice of chip for imputation in the Gyr breed. However, the variation in SNPs may reduce the imputation accuracy even when the chip of the Bos indicus subspecies is used.

Analysis of the Pedigree and Ancestors of the Cutting Population of the Quarter Horse Breed.

The aim of this study was to evaluate the genetic diversity and to identify the most influential ancestors in the population of Brazilian Quarter Horses participating in cutting competitions. Data from 1,590 elite horses born between 1970 and 2015, which participated in cutting competitions between 1981 and 2018, were evaluated based on the pedigree file that contained 5,832 animals born since 1834. Evaluation of the quality of the pedigree data showed a mean number of known equivalent generations of 5.4 ± 1.5 and a mean generation interval of 11.7 ± 8.8 years. The inbreeding coefficient (1.98 ± 7.13%), relatedness coefficient (2.18 ± 0.01%), and individual increase in inbreeding (0.43 ± 0.01%) were low. The effective population size (Ne) was 136 animals. The probability of gene origin indicated effective numbers of founders, ancestors, and founder genomes of 255, 37, and 23 animals, respectively. The number of founders and ancestors was 1,852 and 899 animals, respectively, with 10, 50, and 100 ancestors explaining 39.2%, 67.9%, and 91.6%, respectively, of the genetic diversity of the population. The five most influential ancestors were Doc Bar, Peppy San Badger, Freckles Playboy, Poco Lena, and High Brow Cat, accumulating 30.7% of the marginal genetic contributions of the population. Few ancestors were responsible for the genetic diversity, resulting in the narrowing of the initial genetic base. Breeders depend on a small group of ancestors, which could be corrected by increasing the number of breeding animals and by directed mating using ancestors of different origins.

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.

Estimation of (co)variance components for birth weight in Nellore cattle using alternative data editing criteria.

The aim of this study was to estimate the (co)variance components and breeding values for birthweight (BW) in Nellore cattle by considering or not identical weights that exhibit a high frequency within the contemporary group (CG). A total of 175,258 BW records of Nellore cattle born between 2002 and 2018 were used. The CG was formed by farm, year of birth, sex and feeding regime at birth. CGs with more than 16% of identical BW values were eliminated, generating a data file called BWd. Another file was created without removing these animals (BWt). A mixed linear model was used for statistical analysis, which included fixed and random effects. In both data files analysed, single-trait analysis was performed by Bayesian inference. The mean direct and maternal heritability for BW and the correlation between direct and maternal effects were 0.27, 0.07 and -0.07 for BWt, respectively, and 0.30, 0.093 and -0.07 for BWd. This method should affect the estimation of genetic merits of animals for BW, providing greater safety in the choice of sires.

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.