Genome-enabled prediction of indicator traits of resistance to gastrointestinal nematodes in sheep using parametric models and artificial neural networks.

This study aimed to assess the predictive ability of parametric models and artificial neural network method for genomic prediction of the following indicator traits of resistance to gastrointestinal nematodes in Santa Inês sheep: packed cell volume (PCV), fecal egg count (FEC), and Famacha© method (FAM). After quality control, the number of genotyped animals was 551 (PCV), 548 (FEC), and 565 (FAM), and 41,676 SNP. The average prediction accuracy (ACC) calculated by Pearson correlation between observed and predicted values and mean squared errors (MSE) were obtained using genomic best unbiased linear predictor (GBLUP), BayesA, BayesB, Bayesian least absolute shrinkage and selection operator (BLASSO), and Bayesian regularized artificial neural network (three and four hidden neurons, BRANN_3 and BRANN_4, respectively) in a 5-fold cross-validation technique. The average ACC varied from moderate to high according to the trait and models, ranging between 0.418 and 0.546 (PCV), between 0.646 and 0.793 (FEC), and between 0.414 and 0.519 (FAM). Parametric models presented nearly the same ACC and MSE for the studied traits and provided better accuracies than BRANN. The GBLUP, BayesA, BayesB and BLASSO models provided better accuracies than the BRANN_3 method, increasing by around 23% for PCV, and 18.5% for FEC. In conclusion, parametric models are suitable for genome-enabled prediction of indicator traits of resistance to gastrointestinal nematodes in sheep. Due to the small differences in accuracy found between them, the use of the GBLUP model is recommended due to its lower computational costs.

Economic values and selection index in different Angus-Nellore cross-bred production systems.

Bioeconomic models were developed to calculate economic values (EV) for economically important traits in beef cattle, to evaluate the impact of these traits on production profitability, to assess possible market changes with a payment system and to develop economic selection indexes for Angus cattle for two production systems. Two beef cattle production systems were simulated as follows: a cow-calf cycle (CC) and a complete cycle (CoC). Following selection, positive changes in the EV were observed. In the CC, each 1.0% increment in weaning weight (WW), weaning rate (WR) and pregnancy rate (PR) resulted in increases in US$ 1.30, US$ 3.68 and US$ 3.55 per cow/year in profit, respectively. In the CoC, EV of US$ 1.01, US$ 1.79, US$ 1.19, US$ 1.34, US$ 6.84 and US$ 7.86 per cow/year were obtained for WW, year weight, yearling weight, final weight, WR and PR, respectively. The payment system for carcass quality showed that the scenario considering that 100% of the animals displayed uniform carcasses exhibited the highest EV and was considered optimal. Considering the sensitivity analysis, the price paid per animal was the factor that most affected the EV in both systems. The selection indexes obtained may be used in similar production systems, and the use of EV and selection indexes are important tools for any production system with positive change in profit after selection.

Parameter estimates for reproductive and carcass traits in Nelore beef cattle.

The aim of this study was to estimate genetic parameters for scrotal circumference at 365 (SC365) and 450 (SC450) days of age, age at first calving (AFC), ribeye area (REA), backfat (BF) thickness, and rump fat (RF) thickness, in order to provide information on potential traits for Nelore cattle breeding program. Genetic parameters were estimated using the Average Information Restricted Maximum Likelihood method in single- and multitrait analyses. Four different animal models were tested for SC365, SC450, REA, BF, and RF in single-trait analyses. For SC365 and SC450, the maternal genetic effect was statistically significant (P < 0.01) and was included for multitrait analyses. The direct heritability estimates for SC365, SC450, AFC, REA, BF, and RF were equal to 0.31, 0.38, 0.24, 0.32, 0.16, and 0.19, respectively. Maternal heritability for SC365 and SC450 was equal to 0.06 and 0.08, respectively. The highest genetic correlations were found among the scrotal circumferences. Testing for the inclusion of maternal effects in genetic parameters estimation for scrotal circumference should be evaluated in the Nelore breeding program, mostly for correctly ranking the animal's estimated breeding values. Similar heritability estimates were observed for scrotal circumference, as well as favorable genetic correlations of this trait with AFC and carcass traits. Thus, scrotal circumference measured at 365 days of age could be a target trait for consideration in the Nelore selection index in order to improve most of the traits herein analyzed.

Estimates of genetic parameters and genetic trends for reproductive traits and weaning weight in Tabapuã cattle.

The Tabapuã breed is a beef cattle Brazilian breed known for its sexual precocity and desirable characteristics for tropical conditions. However, this is a newly formed breed and few studies have been conducted regarding genetic parameters and genetic trends for its reproductive traits. The objective of the present study was to estimate the genetic parameters, genetic trends, and relative selection efficiency for weaning weight adjusted to 210 d of age (W210), age at first calving (AFC), average calving interval (ACI), first calving interval (CI1), and accumulated productivity (ACP) among Tabapuã beef cattle. Pedigree data on 15,241 Tabapuã animals born between 1958 and 2011 and phenotype records from 7,340 cows born between 1970 and 2011 were supplied by the National Association of Breeders and Researchers (Associação Nacional de Criadores e Pesquisadores). Analysis through the least squares method assisted in defining the fixed effects that were considered within the models. The estimates for the genetic parameters were obtained through the REML, using a multitrait animal model. The likelihood ratio test applied for W210 was significant ( < 0.05) for the inclusion of maternal additive genetic and permanent environmental effects in the model. Genetic trends were calculated through linear regression of the EBV of the animals, according to the year of birth. The heritability estimates obtained ranged from 0.04 ± 0.03 for CI1 to 0.25 ± 0.05 for W210. The genetic correlations ranged from 0.004 ± 0.19 for W210-AFC and 0.93 ± 0.12 for ACI-CI1. The genetic trend was significant ( < 0.05) and favorable for CI1 and the maternal genetic effect of W210 and was significant ( < 0.05) and unfavorable for AFC, ACI, and ACP. The ACP could be used in the selection index to assist the breeding goal of improved productive and reproductive performance. The genetic trends indicated small and unfavorable genetic changes for AFC, ACI, and ACP in light of the recent implementation of a genetic breeding program for this breed.

Phenotypic, genetic and environmental parameters for traits related to femur bone integrity and body weight at 42 days of age in a broiler population.

Intense selection among broilers, especially for performance and carcass traits, currently favors locomotion problems and bone resistance. Conducting studies relating to development and growth of bone tissue in broilers is necessary to minimize losses. Thus, genetic parameters were estimated for a broiler population's phenotypic traits such as BW at 42 d of age (BW42), chilled femur weight (CFW) and its yield (CFY), and femur measurements: calcium, DM, magnesium, phosphorus, and zinc content; breaking strength; rigidity; length; and thickness. Variance components were estimated through multitrait analyses using the restricted maximum likelihood method. The model included a fixed group effect (sex and hatch) and additive and residual genetic random effects. The heritability estimates we obtained ranged from 0.10 ± 0.05 to 0.50 ± 0.08 for chilled femur yield and BW42, respectively, and indicated that the traits can respond to the selection process, except for CFY, which presented low-magnitude heritability coefficients. Genetic correlation estimates between breaking strength, rigidity, and traits related to mineral content indicated that selection that aims to improve the breaking strength resistance of the femur is highly correlated with mineral content. Given the genetic correlation estimates between BW42 and minerals, it is suggested that in this population, selection for BW42 can be performed with greater intensity without affecting femoral integrity.

Phenotypic and genetic variability for body weight of ostriches (Struthio camelus).

1. objective of the present study was to estimate heritability for hatch weight (HW), body weight at 90 (W90) and 180 (W180) d of age in Brazilian ostriches. 2. The heritability estimates were obtained through the restricted maximum likelihood method for an animal model. Least squares method was used to define possible fixed effects for consideration in the analysis model. 3. The estimates of heritability were 0.42 ± 0.05, 0.16 ± 0.04 and 0.24 ± 0.08 for HW, W90 and W180, respectively. 4. Heritability estimates showed that there are sufficient levels of additive genetic variation present in the traits studied, and these traits may respond to selection.

Estimates of genetic parameters and eigenvector indices for milk production of Holstein cows.

The objectives of the present study were to estimate genetic parameters of monthly test-day milk yield (TDMY) of the first lactation of Brazilian Holstein cows using random regression (RR), and to compare the genetic gains for milk production and persistency, derived from RR models, using eigenvector indices and selection indices that did not consider eigenvectors. The data set contained monthly TDMY of 3,543 first lactations of Brazilian Holstein cows calving between 1994 and 2011. The RR model included the fixed effect of the contemporary group (herd-month-year of test days), the covariate calving age (linear and quadratic effects), and a fourth-order regression on Legendre orthogonal polynomials of days in milk (DIM) to model the population-based mean curve. Additive genetic and nongenetic animal effects were fit as RR with 4 classes of residual variance random effect. Eigenvector indices based on the additive genetic RR covariance matrix were used to evaluate the genetic gains of milk yield and persistency compared with the traditional selection index (selection index based on breeding values of milk yield until 305 DIM). The heritability estimates for monthly TDMY ranged from 0.12 ± 0.04 to 0.31 ± 0.04. The estimates of additive genetic and nongenetic animal effects correlation were close to 1 at adjacent monthly TDMY, with a tendency to diminish as the time between DIM classes increased. The first eigenvector was related to the increase of the genetic response of the milk yield and the second eigenvector was related to the increase of the genetic gains of the persistency but it contributed to decrease the genetic gains for total milk yield. Therefore, using this eigenvector to improve persistency will not contribute to change the shape of genetic curve pattern. If the breeding goal is to improve milk production and persistency, complete sequential eigenvector indices (selection indices composite with all eigenvectors) could be used with higher economic values for persistency. However, if the breeding goal is to improve only milk yield, the traditional selection index is indicated.

Genetic parameters and principal component analysis for egg production from White Leghorn hens.

The objectives of this study were to estimate genetic parameters for accumulated egg production over 3-wk periods and for total egg production over 54 wk of egg-laying, and using principal component analysis (PCA), to explore the relationships among the breeding values of these traits to identify the possible genetic relationships present among them and hence to observe which of them could be used as selection criteria for improving egg production. Egg production was measured among 1,512 females of a line of White Leghorn laying hens. The traits analyzed were the number of eggs produced over partial periods of 3 wk, thus totaling 18 partial periods (P1 to P18), and the total number of eggs produced over the period between the 17 and 70 wk of age (PTOT), thus totaling 54 wk of egg production. Estimates of genetic parameters were obtained by means of the restricted maximum likelihood method, using 2-trait animal models. The PCA was done using the breeding values of partial and total egg production. The heritability estimates ranged from 0.05 ± 0.03 (P1 and P8) to 0.27 ± 0.06 (P4) in the 2-trait analysis. The genetic correlations between PTOT and partial periods ranged from 0.19 ± 0.31 (P1) to 1.00 ± 0.05 (P10, P11, and P12). Despite the high genetic correlation, selection of birds based on P10, P11, and P12 did not result in an increase in PTOT because of the low heritability estimates for these periods (0.06 ± 0.03, 0.12 ± 0.04, and 0.10 ± 0.04, respectively). The PCA showed that egg production can be divided genetically into 4 periods, and that P1 and P2 are independent and have little genetic association with the other periods.

Characterization of the variable cow's age at last calving as a measurement of longevity by using the Kaplan-Meier estimator and the Cox model.

In most studies on beef cattle longevity, only the cows reaching a given number of calvings by a specific age are considered in the analyses. With the aim of evaluating all cows with productive life in herds, taking into consideration the different forms of management on each farm, it was proposed to measure cow longevity from age at last calving (ALC), that is, the most recent calving registered in the files. The objective was to characterize this trait in order to study the longevity of Nellore cattle, using the Kaplan-Meier estimators and the Cox model. The covariables and class effects considered in the models were age at first calving (AFC), year and season of birth of the cow and farm. The variable studied (ALC) was classified as presenting complete information (uncensored = 1) or incomplete information (censored = 0), using the criterion of the difference between the date of each cow's last calving and the date of the latest calving at each farm. If this difference was >36 months, the cow was considered to have failed. If not, this cow was censored, thus indicating that future calving remained possible for this cow. The records of 11 791 animals from 22 farms within the Nellore Breed Genetic Improvement Program ('Nellore Brazil') were used. In the estimation process using the Kaplan-Meier model, the variable of AFC was classified into three age groups. In individual analyses, the log-rank test and the Wilcoxon test in the Kaplan-Meier model showed that all covariables and class effects had significant effects (P < 0.05) on ALC. In the analysis considering all covariables and class effects, using the Wald test in the Cox model, only the season of birth of the cow was not significant for ALC (P > 0.05). This analysis indicated that each month added to AFC diminished the risk of the cow's failure in the herd by 2%. Nonetheless, this does not imply that animals with younger AFC had less profitability. Cows with greater numbers of calvings were more precocious than those with fewer calvings.

Genetic parameter estimates and principal component analysis of breeding values of reproduction and growth traits in female Canchim cattle.

Phenotypic data from female Canchim beef cattle were used to obtain estimates of genetic parameters for reproduction and growth traits using a linear animal mixed model. In addition, relationships among animal estimated breeding values (EBVs) for these traits were explored using principal component analysis. The traits studied in female Canchim cattle were age at first calving (AFC), age at second calving (ASC), calving interval (CI), and bodyweight at 420 days of age (BW420). The heritability estimates for AFC, ASC, CI and BW420 were 0.03±0.01, 0.07±0.01, 0.06±0.02, and 0.24±0.02, respectively. The genetic correlations for AFC with ASC, AFC with CI, AFC with BW420, ASC with CI, ASC with BW420, and CI with BW420 were 0.87±0.07, 0.23±0.02, -0.15±0.01, 0.67±0.13, -0.07±0.13, and 0.02±0.14, respectively. Standardised EBVs for AFC, ASC and CI exhibited a high association with the first principal component, whereas the standardised EBV for BW420 was closely associated with the second principal component. The heritability estimates for AFC, ASC and CI suggest that these traits would respond slowly to selection. However, selection response could be enhanced by constructing selection indices based on the principal components.

Growth curves for ostriches (Struthio camelus) in a Brazilian population.

The objective of this study was to fit growth curves using nonlinear and linear functions to describe the growth of ostriches in a Brazilian population. The data set consisted of 112 animals with BW measurements from hatching to 383 d of age. Two nonlinear growth functions (Gompertz and logistic) and a third-order polynomial function were applied. The parameters for the models were estimated using the least-squares method and Gauss-Newton algorithm. The goodness-of-fit of the models was assessed using R(2) and the Akaike information criterion. The R(2) calculated for the logistic growth model was 0.945 for hens and 0.928 for cockerels and for the Gompertz growth model, 0.938 for hens and 0.924 for cockerels. The third-order polynomial fit gave R(2) of 0.938 for hens and 0.924 for cockerels. Among the Akaike information criterion calculations, the logistic growth model presented the lowest values in this study, both for hens and for cockerels. Nonlinear models are more appropriate for describing the sigmoid nature of ostrich growth.

Egg production curve fitting using nonlinear models for selected and nonselected lines of White Leghorn hens.

Egg production curves describe the laying patterns of hen populations over time. The objectives of this study were to fit the weekly egg production rate of selected and nonselected lines of a White Leghorn hen population, using nonlinear and segmented polynomial models, and to study how the selection process changed the egg-laying patterns between these 2 lines. Weekly egg production rates over 54 wk of egg production (from 17 to 70 wk of age) were measured from 1,693 and 282 laying hens from one selected and one nonselected (control) genetic line, respectively. Six nonlinear and one segmented polynomial models were gathered from the literature to investigate whether they could be used to fit curves for the weekly egg production rate. The goodness of fit of the models was measured using Akaike's information criterion, mean square error, coefficient of determination, graphical analysis of the fitted curves, and the deviations of the fitted curves. The Logistic, Yang, Segmented Polynomial, and Grossman models presented the best goodness of fit. In this population, there were significant differences between the parameter estimates of the curves fitted for the selected and nonselected lines, thus indicating that the effect of selection changed the shape of the egg production curves. The selection for egg production was efficient in modifying the birds' egg production curve in this population, thus resulting in genetic gain from the 5th to the 54th week of egg laying and improved the peak egg production and the persistence of egg laying.

Genetic associations between weight at maturity and maturation rate with ages and weights at first and second calving in Canchim beef cattle.

Genetic parameters for weight at maturity (WM), maturation rate (MR), age and weight at first calving (AFC and WFC) and second calving (ASC and WSC) were estimated for females of the Canchim breed. The number of records per trait ranged from 1440 to 1923. The restricted maximum likelihood method was used and the statistical model included the fixed effect of contemporary group and the additive genetic and residual as random effects. The mean heritability estimate and respective standard errors were 0.34±0.05 (WM), 0.13±0.04 (MR), 0.14±0.04 (AFC), 0.44±0.06 (WFC), 0.16±0.06 (ASC) and 0.39±0.06 (WSC). The heritability estimate for WM and MR suggested that it would be possible to achieve changes in the animals' growth curve through selection, but the genetic correlation between these two traits suggested that there would be antagonism between them. Selection for WM would result in animals with later growth and would also affect individuals' sexual precocity. This was also seen from the genetic correlations between WM and the other traits, which ranged from 0.37 to 0.98; and between MR and the other traits, which ranged from -0.83 to -0.25. Selection to modify WM would be expected to result in correlated responses in AFC, WFC and WSC and a moderate response in ASC. Although the heritability estimate for MR was low, selection aimed for its increase might cause a large decrease in AFC, ASC and WFC and a moderate change in WSC, and this could be an alternative for improving the progeny's overall performance.

Estimates of genetic parameters, and cluster and principal components analyses of breeding values related to egg production traits in a White Leghorn population.

The objectives of this paper were to identify the phenotypic egg-laying patterns in a White Leghorn line mainly selected for egg production, to estimate genetic parameters of traits related to egg production and to evaluate the genetic association between these by principal components analysis to identify trait(s) that could be used as selection criteria to improve egg production. Records of 54 wk of egg production from a White Leghorn population were used. The data set contained records of the length:width ratio of eggs at 32, 37, and 40 wk of age; egg weight at 32, 37, and 40 wk of age; BW at 54 and 62 wk of age; age at first egg; early partial egg production rate from 17 to 30 wk and from 17 to 40 wk of age; late partial egg production rate from 30 to 70 wk and from 40 to 70 wk of age; and total egg production rate (TEP). The estimates of genetic parameters between these traits were estimated by the restricted maximum likelihood method. Multivariate analyses were performed: a hierarchical cluster analysis, a nonhierarchical clustering analysis by the k-means method of weekly egg production rate to describe the egg-laying patterns of hens, and a principal components analysis using the breeding values of all traits. The highest heritability estimates were obtained for BW at 54 wk of age (0.68 ± 0.07) and age at first egg (0.53 ± 0.07). It is recommended that a preliminary clustering analysis be performed to obtain the population structure that takes into account the pattern of egg production, rather than the TEP, because hens may have the same final egg production with different patterns of egg laying. Early partial production periods were not good indicators for use in improving total egg production because these traits presented an overestimated genetic correlation with TEP because of the part-whole genetic correlation component. Egg production might be improved by selecting individuals based on TEP.

Heritabilities and genetic correlations for reproductive traits in an F2 reciprocal cross chicken population.

Studies estimating genetic parameters for reproductive traits in chickens can be useful for understanding and improvement of their genetic architecture. A total of 1276 observations of fertility (FERT), hatchability of fertile eggs (HFE) and hatchability of total eggs (HTE) were used to estimate the genetic and phenotypic parameters of 467 females from an F2 population generated by reciprocal crossing between a broiler line and a layer line, which were developed through a poultry genetics breeding program, maintained by Embrapa Swine and Poultry, Concordia, Santa Catarina, Brazil. Estimates of heritability and genetic and phenotypic correlations were obtained using restricted maximum likelihood calculations under the two-trait animal model, including the fixed effect of group (hatching of birds from the same genetic group) and the random additive genetic and residual effects. The mean percentages for FERT, HFE and HTE were 87.91 ± 19.77, 80.07 ± 26.81 and 70.67 ± 28.55%, respectively. The highest heritability estimate (h(2)) was 0.28 ± 0.04 for HTE. Genetic correlations for FERT with HFE (0.43 ± 0.17), HFE with HTE (0.98 ± 0.02) and FERT with HTE (0.69 ± 0.10) were positive and significant. Individuals with high breeding value for HTE would have high breeding values for HFE and FERT because of the high genetic association between them. These results suggest that HTE should be included as a selection criterion in genetic breeding programs to improve the reproductive performance of chickens, because HTE had the highest heritability estimate and high genetic correlation with FERT and HFE, and it is the easiest to measure.

Comparison of logistic and neural network models to fit to the egg production curve of White Leghorn hens.

Neural networks are capable of modeling any complex function and can be used in the poultry and animal production areas. The aim of this study was to investigate the possibility of using neural networks on an egg production data set and fitting models to the egg production curve by applying 2 approaches, one using a nonlinear logistic model and the other using 2 artificial neural network models [multilayer perceptron (MLP) and radial basis function]. Two data sets from 2 generations of a White Leghorn strain that had been selected mainly for egg production were used. In the first data set, the mean weekly egg-laying rate was ascertained over a 54-wk egg production period. This data set was used to adjust and test the logistic model and to train and test the neural networks. The second data set, covering 52 wk of egg production, was used to validate the models. The mean absolute deviation, mean square error, and R(2) were used to evaluate the fit of the models. The MLP neural network had the best fit in the test and validation phases. The advantage of using neural networks is that they can be fitted to any kind of data set and do not require model assumptions such as those required in the nonlinear methodology. The results confirm that MLP neural networks can be used as an alternative tool to fit to egg production. The benefits of the MLP are the great flexibility and their lack of a priori assumptions when estimating a noisy nonlinear model.