Comparison of different models for the estimation of genetic parameters in tropical goats.

This study aimed to estimate the variance components and genetic parameters for body weight in tropical goats testing different models using Bayesian approach and investigate the effectiveness of fitting the effects of maternal genetic, permanent environmental, and covariance between direct and maternal effects. Records from 1980 to 2010 of 1453 Anglo-Nubian goats' herd were used. Six performance growth traits: birth weight (BW, kg), at 28 (W28, kg), 56 (W56, kg), 112 (W112, kg), 140 (W140, kg), and 196 (W196; kg) days of age, were evaluated. There was a negative covariance between direct genetic effects and maternal additive for all weights. The effect of maternal permanent environment is an important source of variation for performance characteristics in goats until the 196 days, and must be considered in genetic evaluation models in order to obtain accurate predictions of breeding values of individuals. The importance of inclusion of the additive maternal effect appears to be more dependent on the structure of the data set under evaluation. Given the structure of the data, the described management and criteria for choosing the best model (deviance information criterion and the Bayes factor) should make the estimation of parameters for weights at birth and at 28 and 56 days using model IV, since that will provide more consistent results than the type I (less complex), without the need of accurate representations of knowledge prior to data collection. Over time, the breeding program will have more data and thereby increase the possibility of building a prior distribution confident that would enable the inference of parameters for more complex models. However, these are preferable components for the estimation of the characteristics and weights to 112 at 140 and at 196 days, using model I (less complex).

Selection effect for growth traits on energy requirements in beef Nelore steers.

Growth data of 77,372 Nelore steers were used to estimate the selection effect on energy requirements considering two beef production systems: cow-calf and slaughter cycles. All the animals had measures from 120 days to 7 years old. The parameters necessary to evaluate the selection effect on energy requirements were obtained by random regression analysis using Legendre polynomials. The models included additive direct and maternal effects, and animal and maternal permanent environmental effects as random terms. Contemporary group and dam age at calving (linear and quadratic effect) were included as fixed effects, and orthogonal Legendre polynomials of animal age (cubic regression) were considered random covariables. The coefficients from the model M3353_5 were used to calculate the genetic gains necessary to predict the increase in phenotypes. The selection was simulated for body weight (BW) and weight gain (WG) at different ages and energy requirements were calculated using NRC equations. The cost of feed was calculated for a cow-calf and slaughter cycle of production considering a system of Brachiaria decumbens pasture without supplementation. In slaughter system, the selection for weight of 365 days of age is the best option. In cow-calf systems, the selection W120 is the best choice.

Comparison of methods for predicting genomic breeding values for growth traits in Nellore cattle.

The objective of this study was to evaluate the accuracy of genomic predictions of growth traits in Nellore cattle. Data from 5064 animals belonging to farms that participate in the Conexão DeltaGen and PAINT breeding programs were used. Genotyping was performed with the Illumina BovineHD BeadChip (777,962 SNPs). After quality control of the genomic data, 412,993 SNPs were used. Deregressed EBVs (DEBVs) were calculated using the estimated breeding values (EBVs) and accuracies of birth weight (BW), weight gain from birth to weaning (GBW), postweaning weight gain (PWG), yearling height (YH), and cow weight (CW) provided by GenSys. Three models were used to estimate marker effects: genomic best linear unbiased prediction (GBLUP), BayesCπ, and improved Bayesian least absolute shrinkage and selection operator (IBLASSO). The prediction ability of genomic estimated breeding value (GEBVs) was estimated by the average Pearson correlation between DEBVs and GEBVs, predicted with the different methodologies in the validation populations. The regression coefficients of DEBVs on GEBVs in the validation population were calculated and used as indicators of prediction bias of GEBV. In general, the Bayesian methods provided slightly more accurate predictions of genomic breeding values than GBLUP. The BayesCπ and IBLASSO were similar for all traits (BW, GBW, PWG, and YH), except for CW. Thus, there does not seem to be a more suitable method for the estimation of SNP effects and genomic breeding values. Bayesian regression models are of interest for future applications of genomic selection in this population, but further improvements are needed to reduce deflation of their predictions.

Effect of the X chromosome in genomic evaluations of reproductive traits in beef cattle.

The aim of this study was to evaluate whether there are predictive advantages for breeding values with inclusion of X chromosome genomic markers for reproductive (occurrence of early pregnancy - P16 and age at first calving - AFC) and andrological (scrotal circumference -SC) variables in beef cattle. There were 3263 genotypes of females and males evaluated. There were breeding value estimates for SC, AFC and P16 considering two scenarios: 1) only autosomal markers or 2) autosomal and X chromosome markers. To evaluate effects of inclusion of X chromosome markers on selection, responses to selection were compared including or not including genomic marker information from the X chromosome. There were greater heritability estimates for SC (0.40 and 0.31), AFC (0.11 and 0.09) and P16 (0.43 and 0.38) when analyses included, compared with not including, genomic marker information from the X chromosome. When selection is based on results from analyses that did not include information for the X chromosome, there was about a 7 % lesser mean genomic breeding value for the SC traits for selected animals. For P16, there was an approximate 4% lesser breeding value without inclusion of genomic marker information from the X chromosome, while this inclusion did not have as great an effect on the breeding value for AFC. There was an average predictive correlation of 0.79, 0.98 and 0.84 for SC, AFC and P16, respectively. These estimates indicate inclusion of the X chromosome genomic marker information in the analysis can improve prediction of genomic breeding values, especially for SC.