Evaluation of longevity modeling censored records in Nellore.

The aim of the present study was to evaluate the prediction ability of models that cope with longevity phenotypic expression as uncensored and censored in Nellore cattle. Longevity was defined as the difference between the dates of last weaned calf and cow birth. There were information of 77 353 females, being 61 097 cows with uncensored phenotypic information and 16 256 cows with censored records. These data were analyzed considering three different models: (1) Gaussian linear model (LM), in which only uncensored records were considered; and two models that consider both uncensored and censored records: (2) Censored Gaussian linear model (CLM); and (3) Weibull frailty hazard model (WM). For the model prediction ability comparisons, the data set was randomly divided into training and validation sets, containing 80% and 20% of the records, respectively. There were considered 10 repetitions applying the following restrictions: (a) at least three animals per contemporary group in the training set; and (b) sires with more than 10 progenies with uncensored records (352 sires) should have daughters in the training and validation sets. The variance components estimated using the whole data set in each model were used as true values in the prediction of breeding values of the animals in the training set. The WM model showed the best prediction ability, providing the lowest χ 2 average and the highest number of sets in which a model had the smallest value of χ 2 statistics. The CLM and LM models showed prediction abilities 2.6% and 3.7% less efficient than WM, respectively. In addition, the accuracies of sire breeding values for LM and CLM were lower than those obtained for WM. The percentages of bulls in common, considering only 10% of sires with the highest breeding values, were around 75% and 54%, respectively, between LM-CLM and LM-WM models, considering all sires, and 75% between LM-CLM and LM-WM, when only sires with more than 10 progenies with uncensored records were taken into account. These results are indicative of reranking of animals in terms of genetic merit between LM, CLM and WM. The model in which censored records of longevity were excluded from the analysis showed the lowest prediction ability. The WM provides the best predictive performance, therefore this model would be recommended to perform genetic evaluation of longevity in this population.

Inferring phenotypic causal structures among meat quality traits and the application of a structural equation model in Japanese Black cattle.

Meat quality is one of the most important traits determining carcass price in the Japanese beef market. Optimized breeding goals and management practices for the improvement of meat quality traits requires knowledge regarding any potential functional relationships between them. In this context, the objective of this research was to infer phenotypic causal networks involving beef marbling score (BMS), beef color score (BCL), firmness of beef (FIR), texture of beef (TEX), beef fat color score (BFS), and the ratio of MUFA to SFA (MUS) from 11,855 Japanese Black cattle. The inductive causation (IC) algorithm was implemented to search for causal links among these traits and was conditionally applied to their joint distribution on genetic effects. This information was obtained from the posterior distribution of the residual (co)variance matrix of a standard Bayesian multiple trait model (MTM). Apart from BFS, the IC algorithm implemented with 95% highest posterior density (HPD) intervals detected only undirected links among the traits. However, as a result of the application of 80% HPD intervals, more links were recovered and the undirected links were changed into directed ones, except between FIR and TEX. Therefore, 2 competing causal networks resulting from the IC algorithm, with either the arrow FIR → TEX or the arrow FIR ← TEX, were fitted using a structural equation model () to infer causal structure coefficients between the selected traits. Results indicated similar genetic and residual variances as well as genetic correlation estimates from both structural equation models. The genetic variances in BMS, FIR, and TEX from the structural equation models were smaller than those obtained from the MTM. In contrast, the variances in BCL, BFS, and MUS, which were not conditioned on any of the other traits in the causal structures, had no significant differences between the structural equation model and MTM. The structural coefficient for the path from MUS (BCL) to BMS showed that a 1-unit improvement in MUS (BCL) resulted in an increase of 0.85 or 1.45 (an decrease of 0.52 or 0.54) in BMS in the causal structures. The analysis revealed some interesting functional relationships, direct genetic effects, and the magnitude of the causal effects between these traits, for example, indicating that BMS would be affected by interventions on MUS and BCL. In addition, if interventions existed in this scenario, a breeding strategy based only on the MTM would lead to a mistaken selection for BMS.

Accuracy of genomic breeding values for meat tenderness in Polled Nellore cattle.

Zebu () cattle, mostly of the Nellore breed, comprise more than 80% of the beef cattle in Brazil, given their tolerance of the tropical climate and high resistance to ectoparasites. Despite their advantages for production in tropical environments, zebu cattle tend to produce tougher meat than Bos taurus breeds. Traditional genetic selection to improve meat tenderness is constrained by the difficulty and cost of phenotypic evaluation for meat quality. Therefore, genomic selection may be the best strategy to improve meat quality traits. This study was performed to compare the accuracies of different Bayesian regression models in predicting molecular breeding values for meat tenderness in Polled Nellore cattle. The data set was composed of Warner-Bratzler shear force (WBSF) of longissimus muscle from 205, 141, and 81 animals slaughtered in 2005, 2010, and 2012, respectively, which were selected and mated so as to create extreme segregation for WBSF. The animals were genotyped with either the Illumina BovineHD (HD; 777,000 from 90 samples) chip or the GeneSeek Genomic Profiler (GGP Indicus HD; 77,000 from 337 samples). The quality controls of SNP were Hard-Weinberg Proportion -value ≥ 0.1%, minor allele frequency > 1%, and call rate > 90%. The FImpute program was used for imputation from the GGP Indicus HD chip to the HD chip. The effect of each SNP was estimated using ridge regression, least absolute shrinkage and selection operator (LASSO), Bayes A, Bayes B, and Bayes Cπ methods. Different numbers of SNP were used, with 1, 2, 3, 4, 5, 7, 10, 20, 40, 60, 80, or 100% of the markers preselected based on their significance test (-value from genomewide association studies [GWAS]) or randomly sampled. The prediction accuracy was assessed by the correlation between genomic breeding value and the observed WBSF phenotype, using a leave-one-out cross-validation methodology. The prediction accuracies using all markers were all very similar for all models, ranging from 0.22 (Bayes Cπ) to 0.25 (Bayes B). When preselecting SNP based on GWAS results, the highest correlation (0.27) between WBSF and the genomic breeding value was achieved using the Bayesian LASSO model with 15,030 (3%) markers. Although this study used relatively few animals, the design of the segregating population ensured wide genetic variability for meat tenderness, which was important to achieve acceptable accuracy of genomic prediction. Although all models showed similar levels of prediction accuracy, some small advantages were observed with the Bayes B approach when higher numbers of markers were preselected based on their -values resulting from a GWAS analysis.

Comparison of models for the genetic evaluation of reproductive traits with censored data in Nellore cattle.

In typical genetic evaluation, often some females have missing records due to reproductive failure and due to voluntary and involuntary culling before the breeding season. These partially or unobserved phenotypes are known as censored records and their inclusion into genetic evaluations might lead to better inferences and breeding value predictions. Then, the objective was to compare prediction ability of models in which the phenotypic expression of age at the first calving (AFC) and days to calving (DC) were considered to be censored and uncensored in a Nellore cattle population. Age at first calving and days to calving were analyzed as following: uncensored animals (LM); penalization of 21 d (PLM); censored records simulated from truncated normal distributions (CLM); threshold-linear model in which censored records were handled as missing (TLM) or coded as the upper AFC/DC value within contemporary group (PTLM); and Weibull frailty hazard model (WM). Pearson correlations (PC), the percentage of the 10% best bulls in common (pTOP10%), accuracy of estimated breeding values (), and a cross-validation scheme were performed. Heritability estimates for AFC were 0.18, 0.12, 0.12, 0.17, 0.14, and 0.07 for LM, PLM, CLM, TLM, PTLM, and WM, respectively. PC and pTOP10% were higher among linear models and smaller between these models and WM. The models provided similar r of sire breeding values. Heritability estimates for DC were 0.03, 0.08, 0.06, 0.02, 0.07, and 0.10 for LM, PLM, CLM, TLM, PTLM, and WM, respectively. Strongly associated predictions were observed in CLM, PLM, PTLM, and WM. The highest coincidence levels of sires in the TOP10% were between CLM, PLM, and PTLM. The r of sire breeding values obtained applying CLM, PLM, PTLM, and WM were similar and higher than those obtained with LM and TLM. In terms of prediction ability, WM, PLM, TLM, and PTLM showed similar prediction performance for AFC. On the other hand, CLM, PLM, PTLM, and WM showed the similar prediction ability for DC Therefore, these models would be recommended to perform genetic evaluation of age at first calving and days to calving in this Nellore population.

Searching for causal networks involving latent variables in complex traits: Application to growth, carcass, and meat quality traits in pigs.

Structural equation models (SEQM) can be used to model causal relationships between multiple variables in multivariate systems. Among the strengths of SEQM is its ability to consider causal links between latent variables. The use of latent variables allows modeling complex phenomena while reducing at the same time the dimensionality of the data. One relevant aspect in the quantitative genetics context is the possibility of correlated genetic effects influencing sets of variables under study. Under this scenario, if one aims at inferring causality among latent variables, genetic covariances act as confounders if ignored. Here we describe a methodology for assessing causal networks involving latent variables underlying complex phenotypic traits. The first step of the method consists of the construction of latent variables defined on the basis of prior knowledge and biological interest. These latent variables are jointly evaluated using confirmatory factor analysis. The estimated factor scores are then used as phenotypes for fitting a multivariate mixed model to obtain the covariance matrix of latent variables conditional on the genetic effects. Finally, causal relationships between the adjusted latent variables are evaluated using different SEQM with alternative causal specifications. We have applied this method to a data set with pigs for which several phenotypes were recorded over time. Five different latent variables were evaluated to explore causal links between growth, carcass, and meat quality traits. The measurement model, which included 5 latent variables capturing the information conveyed by 19 different phenotypic traits, showed an acceptable fit to data (e.g., χ/df = 1.3, root-mean-square error of approximation = 0.028, standardized root-mean-square residual = 0.041). Causal links between latent variables were explored after removing genetic confounders. Interestingly, we found that both growth (-0.160) and carcass traits (-0.500) have a significant negative causal effect on quality traits (-value ≤ 0.001). This result may have important implications for strategies for pig production improvement. More generally, the proposed method allows further learning regarding phenotypic causal structures underlying complex traits in farm species.

Parâmetros genéticos para características de prolificidade nas raças Landrace e Large White / Genetic parameters for prolificacy in Landrace and Large White breeds

Dados de suínos das raças Landrace (LD) e Large White (LW) foram utilizados para estimar componentes de variância para número total de leitões nascidos (NTN), nascidos vivos (NV) e de leitões vivos ao quinto dia (LV5). Usou-se o método da máxima verossimilhança restrita (REML) para estimar componentes de variância. O modelo misto incluiu os efeitos fixos de mês e ano de nascimento e da inseminação da porca e ordem de parto. Análises unicaracterísticas incluíram os efeitos genético direto, genético materno e de ambiente permanente. Análises multicaracterísticas foram feitas para estimar correlações genéticas. Os modelos unicaracterísticas foram comparados e o que continha apenas o efeito genético direto foi considerado o mais adequado. As estimativas de herdabilidade para NTN foram de 0,15 para LW e de 0,08 a 0,12 para LD, dependendo do modelo, para NV foram de 0,14 para LW e de 0,05 a 0,12 para LD, e para LV5, variaram de 0,11 a 0,12 para LW e de 0,03 a 0,08 para LD. As correlações fenotípicas e genéticas entre as três características foram altas e favoráveis. Conclui-se que a seleção para aumento do LV5 pode ser uma via interessante para o aumento do tamanho da leitegada, da sobrevivência de leitões e da habilidade materna em suínos.

Data from Landrace (LD) and Large White (LW) sows were analyzed to estimate variance components for total number of piglets born (NTN), number of piglets born alive (NV) and number alive on day 5 after birth (LV5). REML mixed model equations included the fixed effects of sow´s month and year of birth and insemination and farrowing order. Univariate analyses included, alternatively, direct genetic, maternal genetic and permanent environmental effects. Multiple trait analysis was performed to estimate genetic correlations among the traits. Comparisons between univariate models indicated that the model containing only direct genetic effect was the most appropriate for parameter estimation. Estimates of heritability for NTN were 0.15 in LW and ranged from 0.08 to .12 in LD, 0.14 in LW, from 0.05 to 0.12 in LD for NV, from 0.11 to 0.12 in LW, and from 0.03 to 0.08 in LD for LV5. Phenotypic and genetic correlations among traits were high and favorable. Results suggest that selection for LV5 is an interesting alternative way to increase litter size, piglet survival and maternal ability in swine.

Assessment of bagging GBLUP for whole-genome prediction of broiler chicken traits.

Bootstrap aggregation (bagging) is a resampling method known to produce more accurate predictions when predictors are unstable or when the number of markers is much larger than sample size, because of variance reduction capabilities. The purpose of this study was to compare genomic best linear unbiased prediction (GBLUP) with bootstrap aggregated sampling GBLUP (Bagged GBLUP, or BGBLUP) in terms of prediction accuracy. We used a 600 K Affymetrix platform with 1351 birds genotyped and phenotyped for three traits in broiler chickens; body weight, ultrasound measurement of breast muscle and hen house egg production. The predictive performance of GBLUP versus BGBLUP was evaluated in different scenarios consisting of including or excluding the TOP 20 markers from a standard genome-wide association study (GWAS) as fixed effects in the GBLUP model, and varying training sample sizes and allelic frequency bins. Predictive performance was assessed via five replications of a threefold cross-validation using the correlation between observed and predicted values, and prediction mean-squared error. GBLUP overfitted the training set data, and BGBLUP delivered a better predictive ability in testing sets. Treating the TOP 20 markers from the GWAS into the model as fixed effects improved prediction accuracy and added advantages to BGBLUP over GBLUP. The performance of GBLUP and BGBLUP at different allele frequency bins and training sample sizes was similar. In general, results of this study confirm that BGBLUP can be valuable for enhancing genome-enabled prediction of complex traits.

Dissection of additive genetic variability for quantitative traits in chickens using SNP markers.

The aim of this study was to separate marked additive genetic variability for three quantitative traits in chickens into components associated with classes of minor allele frequency (MAF), individual chromosomes and marker density using the genomewide complex trait analysis (GCTA) approach. Data were from 1351 chickens measured for body weight (BW), ultrasound of breast muscle (BM) and hen house egg production (HHP), each bird with 354 364 SNP genotypes. Estimates of variance components show that SNPs on commercially available genotyping chips marked a large amount of genetic variability for all three traits. The estimated proportion of total variation tagged by all autosomal SNPs was 0.30 (SE 0.04) for BW, 0.33 (SE 0.04) for BM, and 0.19 (SE 0.05) for HHP. We found that a substantial proportion of this variation was explained by low frequency variants (MAF <0.20) for BW and BM, and variants with MAF 0.10-0.30 for HHP. The marked genetic variance explained by each chromosome was linearly related to its length (R(2) = 0.60) for BW and BM. However, for HHP, there was no linear relationship between estimates of variance and length of the chromosome (R(2) = 0.01). Our results suggest that the contribution of SNPs to marked additive genetic variability is dependent on the allele frequency spectrum. For the sample of birds analysed, it was found that increasing marker density beyond 100K SNPs did not capture additional additive genetic variance.

Effect of allele frequencies, effect sizes and number of markers on prediction of quantitative traits in chickens.

The objective was to assess goodness of fit and predictive ability of subsets of single nucleotide polymorphism (SNP) markers constructed based on minor allele frequency (MAF), effect sizes and varying marker density. Target traits were body weight (BW), ultrasound measurement of breast muscle (BM) and hen house egg production (HHP) in broiler chickens. We used a 600 K Affymetrix platform with 1352 birds genotyped. The prediction method was genomic best linear unbiased prediction (GBLUP) with 354 564 single nucleotide polymorphisms (SNPs) used to derive a genomic relationship matrix (G). Predictive ability was assessed as the correlation between predicted genomic values and corrected phenotypes from a threefold cross-validation. Predictive ability was 0.27 ± 0.002 for BW, 0.33 ± 0.001 for BM and 0.20 ± 0.002 for HHP. For the three traits studied, predictive ability decreased when SNPs with a higher MAF were used to construct G. Selection of the 20% SNPs with the largest absolute effect sizes induced a predictive ability equal to that from fitting all markers together. When density of markers increased from 5 K to 20 K, predictive ability enhanced slightly. These results provide evidence that designing a low-density chip using low-frequency markers with large effect sizes may be useful for commercial usage.

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.

Breeding and Genetics Symposium: inferring causal effects from observational data in livestock.

Data regularly recorded in commercial herds have been used extensively for estimation of disease incidence rates, for inferences regarding genetic and phenotypic associations between traits, or for developing predictive models for economically important traits. Some studies have also used field data to investigate potential causal relationships between variables. However, inferring causal effects from observational data is complex due to potential confounding effects and careful analyses using specific statistical and data mining techniques as well as different sets of assumptions are required. Nonetheless, although virtually unknown in the agricultural research community, such methods are available and have been used in many other fields. In this paper, we review and discuss the analysis of observational data using field-recorded information and its potential utility in the study of causal effects in livestock. It is our postulation that there is much to be learned from such data, which can be used either to explicitly investigate causal relationships between variables or to generate hypotheses for further investigation using controlled experiments or additional field-recorded data.

An assessment of linkage disequilibrium in Holstein cattle using a Bayesian network.

Linkage disequilibrium (LD) is defined as a non-random association of the distributions of alleles at different loci within a population. This association between loci is valuable in prediction of quantitative traits in animals and plants and in genome-wide association studies. A question that arises is whether standard metrics such as D' and r(2) reflect complex associations in a genetic system properly. It seems reasonable to take the view that loci associate and interact together as a system or network, as opposed to in a simple pairwise manner. We used a Bayesian network (BN) as a representation of choice for an LD network. A BN is a graphical depiction of a probability distribution and can represent sets of conditional independencies. Moreover, it provides a visual display of the joint distribution of the set of random variables in question. The usefulness of BN for linkage disequilibrium was explored and illustrated using genetic marker loci found to have the strongest effects on milk protein in Holstein cattle based on three strategies for ranking marker effect estimates: posterior means, standardized posterior means and additive genetic variance. Two different algorithms, Tabu search (a local score-based algorithm) and incremental association Markov blanket (a constraint-based algorithm), coupled with the chi-square test, were used for learning the structure of the BN and were compared with the reference r(2) metric represented as an LD heat map. The BN captured several genetic markers associated as clusters, implying that markers are inter-related in a complicated manner. Further, the BN detected conditionally dependent markers. The results confirm that LD relationships are of a multivariate nature and that r(2) gives an incomplete description and understanding of LD. Use of an LD Bayesian network enables inferring associations between loci in a systems framework and provides a more accurate picture of LD than that resulting from the use of pairwise metrics.

Utilização de modelos de norma de reação com variância residual heterogênea para estudo de valores genéticos de peso de codornas de corte em função de níveis de proteína bruta na dieta / Using reaction norm models with heterogeneous residual variance to study genetic values of meat type quail weight in function of crude protein levels of diet

Avaliou-se a sensibilidade dos valores genéticos do peso de codornas de corte, mensurados ao 21º e 35º dias de idade, a dietas contendo diferentes níveis de proteína bruta. Observações obtidas de animais provenientes de duas linhagens (EV1 e EV2) foram utilizadas no ajuste de modelos de regressão aleatória, considerando-se heterogeneidade de variância residual. Os coeficientes de regressão aleatória do intercepto (b0) e linear (b1) apresentaram correlação positiva entre si em todas as análises, porém a linhagem EV2 apresentou maior magnitude dos valores deste parâmetro para ambas as idades. Houve heterogeneidade de variância genética aditiva e alteração na herdabilidade com a mudança no nível proteico da dieta para ambos os grupos genéticos e em todas as idades avaliadas. As normas de reação do grupo EV1 indicam presença de interação entre genótipo e ambiente (G x E) em ambas as idades, com alteração na ordem dos efeitos genéticos de peso em função do nível proteico da dieta. Modificação da dispersão dos valores genéticos em função do nível de proteína indica presença de G x E na linhagem EV2. Portanto, a avaliação genética de codornas de corte sob dietas contendo determinado nível de proteína bruta não permite a predição de valores genéticos para outros níveis proteicos da dieta.

The sensitivity of genetic values for body weight of meat type quails predicted at 21 and 35 days of age under diets with different crude protein levels was evaluated. Data from subjects belonging to two strains (EV1 and EV2) were used to fit a random regression model under heterogeneity of residual variance assumption. The random regression coefficients for intercept (bo) and slope (b1) were positively correlated in all analyses results, but the correlation was higher in the EV2 data analyses for both ages. Results indicated that additive genetic variance and heritability change as a function of the environment gradient for both genetic strains and ages. The reaction norms for EV1 strain suggest there is genotype by environment interaction (G x E) for both ages as there were remarkable changes in the ranking of body weight breeding values for different crude protein levels. Furthermore, changes in the magnitude of the genetic effects dispersion as a function of protein level of diet indicates there is G x E in EV1 and EV2 strains. Therefore, the prediction of breeding values for body weight of quails under a specific level of crude protein in the diet does not hold for different environments regarding the level of this nutrient.

Uso de redes neurais artificais na predição de valores genéticos para peso aos 205 dias em bovinos da raça Tabapuã / Use of artificial neural networks in breeding values prediction for weight at 205 days in Tabapuã beef cattle

Dados de 19240 animais Tabapuã, provenientes de 152 fazendas localizadas em diversos estados brasileiros, nascidos entre 1976 e 1995, foram utilizados para predição do valor genético do peso aos 205 dias de idade (VG_P205) por meio de redes neurais artificiais (RNAs) e usando o algoritmo LM - Levenberg Marquardt - para treinamento dos dados de entrada. Por se tratar de rede com aprendizado supervisionado, foram utilizados, como saída desejada, os valores genéticos preditos pelo BLUP para a característica P205. Os valores genéticos do P205 obtidos pela RNA e os preditos pelo BLUP foram altamente correlacionados. A ordenação dos valores genéticos do P205 oriundos das RNAs e os valores preditos pelo BLUP (VG_P205_RNA) sugeriram que houve variação na classificação dos animais, indicando riscos no uso de RNAs para avaliação genética dessa característica. Inserções de novos animais necessitam de novo treinamento dos dados, sempre dependentes do BLUP.

Data from 19,240 Tabapuã animals from 152 farms located in different states of Brazil, born from 1976 to 1995, were used to predict the genetic value of body weight at 205 days of age (BV_P205) of Tabapuã beef cattle using Artificial Neural Networks (ANN) and LM algorithm - Levenberg Marquardt training for data entry. Due to the use of networks with supervised learning, the predicted breeding values for P205 from BLUP were used as desired output. The breeding values for P205 obtained from RNA and those predicted by BLUP were highly correlated. The ranked breeding values for body weight at 205 days through RNA and those predicted by BLUP (VG_P205_RNA) showed a variation in the classification of animals indicating risks in the use of ANNs procedure for genetic evaluation of this trait. Insertions of new animals require new training data always dependent on BLUP.

Estimativas de componentes de (co)variância para peso e escores visuais de conformação frigorífica em bovinos Nelore / Variance component estimates for weight and visual scores of slaughter conformation in Nellore cattle

Estimaram-se os componentes de (co)variância e herdabilidade da conformação frigorífica à desmama (CFD), conformação frigorífica ao sobreano (CFS), peso à desmama (PD) e peso ao sobreano (PS) de animais Nelore, e as correlações genéticas entre essas características. Um modelo animal multicaracterística foi proposto para analisar 6.397 informações de peso e escores visuais de conformação frigorífica, obtidas à desmama e ao sobreano. Esse modelo incluiu os efeitos aleatórios genético aditivo direto, genético aditivo materno, ambiente permanente materno e residual, além dos efeitos fixos de grupo contemporâneo e das covariáveis idade da mãe ao parto - para peso e conformação frigorífica à desmama e ao sobreano - e idade do animal à data da avaliação - para conformação frigorífica, à desmama e ao sobreano. As herdabilidades estimadas para CFD, CFS, PD e PS foram, respectivamente, 0,13, 0,25, 0,22 e 0,29. Correlações genéticas positivas e de alta magnitude entre as características de peso e as características de avaliação visual sugerem que a seleção para uma delas pode resultar em resposta indireta na outra. A característica de conformação frigorífica pode ser selecionada em idade mais precoce em razão da correlação genética alta e positiva entre mensurações feitas nas duas idades estudadas.

The aim of this study was to estimate variance components, heritability and genetic correlation for slaughter conformation at weaning (SCW), slaughter conformation at yearling age (SCY), weaning weight (WW) and yearling age weight (YW) of Nellore cattle. A total of 6,397 records of all traits measured at weaning and at yearling age were used in the analysis. A multiple trait animal model which included the direct genetic additive, maternal genetic additive, maternal permanent environmental and residual random effects, as well as the fixed effect of contemporary group and the covariates age at calving (for weight and slaughter conformation at weaning and yearling age) and age at the evaluation time (slaughter conformation at weaning and yearling age) was proposed. The heritability estimates for SCW, SCY, WW and YW were, respectively, 0.13, 0.25, 0.22 and 0.29. Positive and high genetic correlations between body weight traits and visual evaluation traits suggested that direct selection for one trait results in positive indirect response in the remaining trait. Slaughter conformation trait can be selected at earlier age due to the high and positive genetic correlation between conformation scores at different age.

Avaliação da persistência na lactação da raça Guzerá, utilizando modelos de regressão aleatória / Evaluation of lactation persistency of Guzerat cows using random regression models

Estimaram-se a herdabilidade e as correlações genéticas e de ambiente permanente entre seis medidas de persistência da lactação de vacas da raça Guzerá, utilizando modelo de regressão aleatória. Foram considerados 8276 registros de produção de leite no dia do controle, na primeira lactação, de 1021 vacas. A regressão aleatória foi calculada pela função logarítmica de Ali e Schaeffer e pelo polinômio de Legendre, obtendo-se os coeficientes para os efeitos fixos, genético aditivo e de ambiente permanente. A função que mais se adequou aos dados foi a de Ali e Schaeffer, mas apresentou problemas de convergência. Os resultados evidenciaram que a persistência é uma característica com herdabilidade de valor moderado e de baixa correlação com o valor genético para produção de leite aos 305 dias, indicando a possibilidade de se obter resposta à seleção para mudança na curva de lactação sem afetar negativamente a produção total de leite na lactação. A medida de persistência que calcula a diferença de produção de leite entre as fases intermediária e inicial da lactação apresentou alta correlação com a produção aos 305 dias.

The heritability and the genetic and permanent environment correlations were estimated among six different measures of persistency in the lactation of Guzerat cow, using the Random Regression Model. A total of 8,403 records from 1,034 first lactation cows were evaluated. The Random Regression Model was calculated by the logarithmic function of Ali and Schaeffer and Legendre polynomials to get coefficients for fixed, additive genetic and permanent environment effects. Ali and Schaeffer was the function that better fit to the data, but it had convergence problems. The results showed that persistence is a trait with moderate heritability, and low correlation with genetic value for 305-d milk production which allows to select animals in order to alter the format of the curve of production without affecting the total productivity. The measure of persistence that calculates the difference of milk production between the medium and initial phases was highly correlated with 305-d milk production.

Sensibilidade de valores genéticos de codornas de corte em crescimento às modificações de níveis de proteína das dietas / Sensitivity of genetic values of growing meat-type quails to changes in protein levels of diet

Um total de 5240 informações de peso de progênies provenientes de 200 reprodutores e 400 reprodutrizes, alimentadas com dietas com níveis de proteína bruta que variaram de 24 a 30 por cento, e nível de 2900kcal de energia metabolizável, foi utilizado para avaliar a sensibilidade de valores genéticos de duas linhagens de codornas de corte, EV1 e EV2, em relação às mudanças de níveis protéicos das dietas, utilizando-se modelos de regressão aleatória. As codornas com maior valor genético aditivo para peso no ambiente médio (nível protéico igual a zero em uma escala de -1 a 1) respondem de maneira positiva ao aumento do nível protéico da dieta, sendo mais importante para a linhagem EV2 e de pouca expressão para a linhagem EV1, no 21º dia de idade. No 42º dia de idade, codornas da linhagem EV1 apresentam aumento de dispersão dos valores genéticos com o aumento dos níveis protéicos da dieta, indicando heterogeneidade de sensibilidades dos valores genéticos aditivos à mudança ambiente, ou à existência de interação genótipo x ambiente. Codornas EV2 apresentaram aumento de dispersão dos valores genéticos em função do nível protéico em ambas as idades. A interação genótipo x nível protéico interfere em menores idades na expressão fenotípica da linhagem EV2. As herdabilidades estimadas apresentaram alta variação e maior magnitude para maiores níveis de proteína bruta na dieta, indicando maior resposta à seleção para níveis mais elevados de proteína da dieta, à exceção da linhagem EV1, no 21º dia de idade. Avaliações genéticas realizadas para codornas alimentadas com dietas contendo determinado nível protéico não permitiriam a predição de valores genéticos válidos para outros níveis protéicos das dietas, à exceção da linhagem EV1, no 21º dia de idade.

A total of 5,240 body weight records of quail of offsprings from 200 males and 400 females matings, fed crude protein diet levels varying from 24 to 30 percent and 2,900kcal of metabolizable energy, was used to evaluate the sensitivity of genetic values of two meat strains, EV1 and EV2, to changes in crude protein levels of diets, using random regression models. Quails showing higher body weight genetic value in the average environment (crude protein level equal to zero in a scale varying from -1 to 1) respond positively to an increase in crude protein level of diet, this change is more important for EV2 quails (higher slope) and of small magnitude for EV1 quails at 21 days of age. EV1 quails at 42 days of age showed remarked increase in the dispersion for breeding values as crude protein level increased in the diets, suggesting heterogeneity of slope of the breeding values to change in protein level of diets or suggesting a genotype x protein level of diet interaction. EV2 quails showed an increase in the breeding values in function of crude protein level of diet for both ages. Genotype x protein level of diet interferes earlier in the phenotype expression of EV2 quails. Heritability estimates showed a sizeable variation and were of higher values for quails fed higher protein level diets, except for EV1 quails at 21 days of age. Genetic evaluations of quails fed specific crude protein level do not allow a prediction of valid breeding values for quails fed other crude protein level of diet.

Interação genótipo x ambiente e estimativas de parâmetros genéticos dos pesos aos 205 e 365 dias de idade de bovinos Nelore / Genotype by environment interaction and genetic parameter estimates for 205 and 365 body weight of Nelore cattle

Dados de pesos aos 205 (P205) e 365 (P365) dias de idade, de 46.408 animais Nelore, nascidos entre 1976 e 2000, provenientes de 530 rebanhos dos diversos estados brasileiros, foram utilizados para avaliar as interações genótipo x ambiente (IGA) e estimar herdabilidades direta e materna dos pesos pelo método da máxima verossimilhança restrita. O modelo estatístico utilizado incluiu efeitos fixos de grupo contemporâneo, idade da vaca ao parto (covariável) e efeitos aleatórios genéticos aditivo direto e materno. As correlações genéticas, estimadas para as características P205 e P365, cada uma considerada como característica distinta em cada uma das regiões Sul (S), Sudeste (SE), Centro-Oeste (CO), Norte (N) e Nordeste (NE), foram, respectivamente, 0,86 e 0,84, 0,64 e 0,35, 0,75 e 0,42, 0,79 e 0,86, 0,92 e 0,81, 0,95 e 0,83, 0,83 e 1,00, 0,88 e 0,81, 0,33 e 0,85, 0,63 e 0,99 para S e SE, S e CO, S e N, S e NE, SE e CO, SE e N, SE e NE, CO e N, CO e NE e N e NE. As baixas correlações genéticas indicaram que há efeito da interação genótipo x ambiente nas combinações que envolvem as regiões S/CO, S/N, S/NE, CO/NE e N/NE para P205 dias de idade e S/CO e S/N para P365 dias de idade, havendo, portanto, necessidade de avaliação genética regional quando se consideram regiões bastante distintas.

Body weight records at 205 (205BW) and 365 (365BW) days of age of 46,408 Nelore animals, born from 1976 to 2000 period in 530 Nelore herds of several states of Brazil, were used to evaluate genotype by environment interactions and to estimate genetic and maternal heritability by restricted maximum likelihood methodology. The statistical model included the fixed effects of contemporary group and age of cow (covariate), and the random direct and maternal additive genetic effects. The genetic correlation for 205BW and 365BW, each one considered as different traits in each of the South (S), Southeast (SE), Central west (CO), North (N) and Northeast (NE) regions were 0.86 and 0.84, 0.64 and 0.35, 0.75 and 0.42, 0.79 and 0.86, 0.92 and 0.81, 0.95 and 0.83, 0.83 and 1.00, 0.88 and 0.81, 0.33 and 0.85, 0.63 and 0.99 for S/SE, S/CO, S/N, S/NE, SE/CO, SE/N, SE/NE, CO/N, CO/NE and N/NE, respectively. There is a significant genotype by environment interaction for 205BW in the combination involving the S/CO, S/N, S/NE, CO/NE, and N/NE regions. There are significant genotype by environment interaction effects in the combination involving the S/CO and S/N regions for 365BW. Based on genetic x environment interaction results, regional genetic evaluation is recommend for the very distinct regions.

Estruturas de covariância de peso em função da idade de animais Nelore das regiões Sudeste e Centro-Oeste do Brasil / Covariance structure of body weight in function of age for Nellore animals from Southeast and Center West of Brazil

Dados de peso de animais da raça Nelore de 90 a 450 dias de idade das regiões Sudeste (SE) e Centro-Oeste (CO) do Brasil foram utilizados para comparar estruturas de (co)variância de efeitos aleatórios em função de idade estimadas para as duas regiões por meio de modelos de regressão aleatória. Componentes de (co)variância referentes aos coeficientes de regressão aleatória foram estimados por EMREML por meio do programa REMLF90. Os efeitos fixos de grupo contemporâneo e os efeitos aleatórios genético aditivo direto, genético aditivo materno e permanente de ambiente foram modelados por polinômios quadráticos de Legendre. As comparações envolveram estruturas de covariância e de correlação dos efeitos aleatórios, herdabilidades direta e materna e a razão entre variâncias genéticas de diferentes regiões. As herdabilidades e estruturas de covariância e de correlação apresentaram comportamento semelhante nas duas regiões. A variância residual e as variâncias de efeito permanente de ambiente foram menores no CO, bem como a variância genética aditiva materna dos 150 aos 400 dias de idade. Trajetórias dos efeitos fixos em função de idade de diferentes grupos contemporâneos apresentaram diferentes formas, sugerindo a necessidade de estimar um conjunto de coeficientes de regressão específico para cada grupo contemporâneo. A variância do efeito genético aditivo materno apresentou maior heterogeneidade entre regiões do que a variância genética aditiva direta.

Body weight records from 90 to 450 days of age of Nellore animals from the Southeast and Center West of Brazil were used do estimate covariance structures of age dependent random effects for each region using random regression models. Covariance components of the regression coefficients were estimated by EMREML using the software REML90. The fixed effects of contemporary groups and additive genetic, additive maternal and permanent environment random effects were modeled by quadratic Legendre polynomials. The comparisons included structures of covariance and correlation of random effects, direct and maternal heritabitability and the ratio between genetic variances from different regions. The heritability, covariance and correlation structures showed similar patterns for both regions. Residual variance and permanent environment variances were smaller for Center West region as well as the maternal genetic additive from 150 to 400 days of age. Fixed effect trajectories in function of age of different contemporary groups showed different patterns, suggesting the necessity of specific set of regression coefficient estimates for each contemporary group. The maternal additive genetic variance showed higher heterogeneity between regions than the direct additive genetic variance.

Interacão genótipo ambiente e estimativas de parâmetros genéticos de características ponderais de bovinos Tabapuã / Genotype by environment interaction and genetic parameter estimates for growth traits of Tabapuã cattle

Dados de pesos aos 205 (P205) e 365 (P365) dias de idade, de 28.946 animais Tabapuã, provenientes de 152 fazendas dos diversos estados brasileiros, nascidos no período de 1976 a 1995, foram utilizados nesta análise. Foram avaliadas as interacões genótipo-ambiente, bem como estimadas herdabilidades direta e materna pelo método de máxima verossimilhanca restrita em modelo estatístico, que incluiu efeitos fixos de grupo contemporâneo, idade da vaca ao parto (covariável) e efeitos aleatórios genéticos direto e materno. As estimativas de herdabilidade direta e materna para P205 nas regiões Sul (R1), Sudeste (R2), Centro-Oeste (R3) e Nordeste (R4) foram: 0,02 e 0,31 (R1), 0,17 e 0,19 (R2), 0,20 e 0,09 (R3) e 0,06 e 0,16 (R4). Para P365, foram 0,05 e 0,03 (R1), 0,20 e 0,03 (R2), 0,51 e 0,62 (R3) e 0,15 e 0,05 (R4). As correlacões genéticas encontradas para as características P205 e P365, ambas consideradas características distintas nas regiões R1, R2, R3 e R4, foram: 1,00 e 0,99, 0,84 e 0,99, -0,86 e -0,73, 0,98 e 0,93, 0,51 e 0,45, 1,00 e 0,12 para R1/R2, R1/R3, R1/R4, R2/R3, R2/R4 e R3/R4, respectivamente. Esses resultados indicam que, na desmama (P205), o efeito da interacão genótipo x ambiente foi observado somente nas combinacões que envolveram a região Nordeste (R4) e as regiões Sul (R1) e Sudeste (R2). Para pesos pós-desmama (P365), o efeito dessa interacão foi evidenciado em todas as combinacões que incluíram a região Nordeste.