Comparing Bayesian models for the genetic evaluation of oocytes and embryo counts in Dairy Gir cattle.

Count traits are usually explored in livestock breeding programs, and they usually do not fit into normal distribution, requiring alternatives to adjust the phenotype to estimate accurate genetic parameters and breeding values. Alternatively, distribution such as Poisson can be used to evaluate count traits. This study aimed to compare and discuss the genetic evaluation for oocyte and embryo counts considering Gaussian (untransformed variable - LIN; transformed by logarithm - LOG; transformed by Anscombe - ANS) and Poisson (POI) distributions. The data comprised 11,343 total oocytes (TO), viable oocytes (VO), cleaved embryos (CE), and viable embryo (VE) records of ovum pick-up from 1740 Dairy Gir heifers and cows. The genetic parameters and breeding values were estimated by the MCMCglmm package of the R software. The posterior means of heritability varied from 0.40 (LIN) to 0.49 (POI) for TO, 0.39 (LIN) to 0.49 (POI) for VO, 0.30 (LOG) to 0.41 (POI) for cleaved embryos, and 0.19 (LIN) to 0.32 (POI) for viable embryos. The posterior means of repeatability varied from 0.56 (LIN) to 0.65 (POI) for TO, 0.53 (LOG) to 0.63 (POI) for VO, 0.44 (LOG) to 0.60 (POI) for CE, and 0.36 (LOG) to 0.56 (POI) for VE. Deviance information criterion and mean squared residuals indicated that POI model should be used for the genetic evaluation of embryo and oocyte count traits. Spearman's rank correlation between estimated breeding value (EBV) for embryo and oocyte count traits computed by POI, LOG, and ANS models was high (ranging from 0.77 to 0.99), indicating little reranking among the best animals. The POI model is the most adequate for genetic evaluation, resulting in more reliable EBV of oocyte and embryo count traits for Dairy Gir cattle.

Impact of heat stress on genetic evaluation of oocyte and embryo production in Gir dairy cattle.

Identifying and selecting genotypes tolerant to heat stress might improve reproductive traits in dairy cattle, including oocyte and embryo production. The temperature-humidity index (THI) was used, via random regression models, to investigate the impact of heat stress on genetic parameters and breeding values of oocyte and embryo production in Gir dairy cattle. We evaluated records of total oocytes (TO), viable oocytes (VO), cleaved embryos (CE), and viable embryos (VE) from dairy Gir donors. Twenty-four models were tested, considering age at ovum pick-up (AOPU) and THI means as a regressor in the genetic evaluation. We computed THI in eight periods, from 0 to 112 days before ovum pick-up, which were adjusted by different orders of Legendre polynomials (second, third, and fourth). The best-fit model according to Akaike's information criterion (AIC) and Model Posterior Probabilities (MPP) considered Legendre polynomials of third order and THI means of 112 days for TO, fourth order and 56 days for VO, second order and 28 days for CE, and second order and 42 days for VE, respectively. The heritability (h2) estimates across AOPU and THI scales ranged from 0.34 to 0.62 for TO, 0.31 to 0.58 for VO, 0.26 to 0.39 for CE, and 0.15 to 0.26 for VE, respectively. The fraction of the phenotypic variance explained by the permanent environment in different AOPU and THI scales ranged from 0.03 to 0.25 for TO, 0.05 to 0.26 for VO, 0.09 to 0.36 for CE, and 0.15 to 0.27 for VE, respectively. Spearman's rank correlation between the estimated breeding values in different AOPU and THI scale from the top 5% sires and females ranged from 0.18 to 0.90 for TO, 0.31 to 0.95 for VO, 0.14 to 0.85 for CE, and 0.47 to 0.94 for VE, respectively. The h2 estimates for all evaluated traits varied from moderate to high magnitude across AOPU and THI scales, indicating that genetic selection can result in rapid genetic progress for the evaluated traits. There was a reranking among the best animals in different AOPU and THI. It is possible to select dairy Gir cattle tolerant to heat stress to improve oocyte and embryo production.

Impact of different numbers of milk test-day records during lactation on the reliability of estimated breeding values.

The objective of this study was to evaluate the impact of the reduction in the number of test-day records per lactation on genetic parameters of test-day milk yield (TDMY) and the reliability of estimated breeding values for 305-day milk yield in Holstein cattle. Estimates of genetic parameters and breeding values were performed using the animal model of random regression and adjustment of the Legendre polynomial (fourth order). When comparing sires with the same number of daughters, greater reliability was found in the subpopulations with the highest number of milk test-day records per lactation per cow. It was also found that the elimination of at least one test-day record affected the reliability of estimated breeding value for 305-day milk yield in the sires, regardless of the class of number of daughters per sire. When selecting the 5% best sires and 20% best cows, the lowest order correlations were observed between the population with 10 test-day records per lactation (complete lactation) and the other subpopulations with incomplete lactations (4, 5, 6, 7, 8, 9 test-day records per lactation). The reduction in the number of test-day milk records per lactation interferes in the reliability of the estimated breeding value for sires and cows, negatively impacting the precision of selecting genetically superior animals.

Selection for Test-Day Milk Yield and Thermotolerance in Brazilian Holstein Cattle.

Intense selection for milk yield has increased environmental sensitivity in animals, and currently, heat stress is an expensive problem in dairy farming. The objectives were to identify the best model for characterizing environmental sensitivity in Holstein cattle, using the test-day milk yield (TDMY) combined with the temperature-humidity index (THI), and identify sires genetically superior for heat-stress (HS) tolerance and milk yield, through random regression. The data comprised 94,549 TDMYs of 11,294 first-parity Holstein cows in Brazil, collected from 1997 to 2013. The yield data were fitted to Legendre orthogonal polynomials, linear splines and the Wilmink function. The THI (the average of two days before the dairy control) was used as an environmental gradient. An animal model that fitted production using a Legendre polynomials of quartic order for the days in milk and quadratic equations for the THI presented a better quality of fit (Akaike's information criterion (AIC) and Bayesian information criterion (BIC)). The Spearman correlation coefficient of greatest impact was 0.54, between the top 1% for TDMY and top 1% for HS. Only 9% of the sires showed plasticity and an aptitude for joint selection. Thus, despite the small population fraction allowed for joint selection, sufficient genetic variability for selecting more resilient sires was found, which promoted concomitant genetic gains in milk yield and thermotolerance.

Inclusion of bioclimatic variables in genetic evaluations of dairy cattle.

OBJECTIVE: Considering the importance of dairy farming and the negative effects of heat stress, more tolerant genotypes need to be identified. The objective of this study was to investigate the effect of heat stress via temperature-humidity index (THI) and diurnal temperature variation (DTV) in the genetic evaluations for daily milk yield of Holstein dairy cattle, using random regression models. METHODS: The data comprised 94,549 test-day records of 11,294 first parity Holstein cows from Brazil, collected from 1997 to 2013, and bioclimatic data (THI and DTV) from 18 weather stations. Least square linear regression models were used to determine the THI and DTV thresholds for milk yield losses caused by heat stress. In addition to the standard model (SM, without bioclimatic variables), THI and DTV were combined in various ways and tested for different days, totaling 41 models. RESULTS: The THI and DTV thresholds for milk yield losses was THI = 74 (-0.106 kg/d/THI) and DTV = 13 (-0.045 kg/d/DTV). The model that included THI and DTV as fixed effects, considering the two-day average, presented better fit (-2logL, Akaike information criterion, and Bayesian information criterion). The estimated breeding values (EBVs) and the reliabilities of the EBVs improved when using this model. CONCLUSION: Sires are re-ranking when heat stress indicators are included in the model. Genetic evaluation using the mean of two days of THI and DTV as fixed effect, improved EBVs and EBVs reliability.

Genetic and environmental factors that affect tick infestation in Nelore x Angus crossbreed cattle / Fatores genéticos e ambientais que afetam a infestação de carrapatos em bovinos cruzados Nelore x Angus

ABSTRACT: This study evaluated the effects of seasons and latitude on tick counting and determined the best model to estimate genetic parameters for tick count and hair coat. Records of animals naturally exposed to ticks on farms in several Brazilian states and in Paraguay were used. The ANOVA was used to verify the effects of seasons and latitude on the tick count trait. Spring was the season with the highest average, followed by summer and autumn, which showed no differences between them. The winter presented the lowest average values. Latitude -11° had the highest mean value followed by latitude -18°. The Bayesian approach was used to evaluate tick count and hair coat and to identify a suitable model for estimating genetic parameters for use in genetic evaluations. The data were analyzed using an animal model with four different specifications for "fixed" purposes. The inference was based on a Markov chain Monte Carlo (MCMC). The criteria for selection of the Bayesian model indicated that the M1 model, which considered the breed composition in the contemporary group, was superior to the other models, both for tick count and hair coat. Heritability estimates for tick count and hair coat obtained using the M1 model were 0.14 and 0.22, respectively. The rank correlations between the models for tick count and hair coat were estimated and reordering was verified for tick count. The estimated genetic correlation between tick count and hair coat traits was negative (-0.12). These findings suggest that different genes regulate tick count and hair coat.

RESUMO: Os objetivos foram avaliar os efeitos das estações e latitude na contagem de carrapatos e determinar o melhor modelo para estimar parâmetros genéticos para contagem de carrapatos e pelame. Foram utilizados registros de animais expostos naturalmente a carrapatos em fazendas em vários estados brasileiros e no Paraguai. A ANOVA foi utilizada para verificar os efeitos das estações e da latitude na característica de contagem de carrapatos. A primavera foi a estação com a maior média, seguida pelo verão e outono, que não mostraram diferenças entre eles. O inverno apresentou os menores valores médios. A latitude -11° teve o maior valor médio seguido pela latitude -18°. A abordagem bayesiana foi usada para avaliar a contagem de carrapatos e o pelame e identificar o modelo adequado para estimar parâmetros genéticos e para uso em avaliações genéticas. Os dados foram analisados usando um modelo animal com quatro especificações diferentes para efeitos "fixos". A inferência foi baseada em uma cadeia de Markov Monte Carlo (MCMC). Os critérios de seleção do modelo bayesiano indicaram que o modelo M1, que considerou a composição racial no grupo contemporâneo, foi superior aos demais modelos, tanto na contagem de carrapatos e para pelame. As estimativas de herdabilidade para contagem de carrapatos e pelame obtidas usando o modelo M1 foram de 0,14 e 0,22, respectivamente. As correlações de ranking entre os modelos para a contagem de carrapatos e pelame foram estimadas e a reordenação foi verificada para a contagem de carrapatos. A correlação genética estimada entre a contagem de carrapatos e pelame foi negativa (-0,12). Esses achados sugerem que genes diferentes regulam a contagem de carrapatos e pelame.

Random regression models using different functions to estimate genetic parameters for milk production in Holstein Friesians / Modelos de regressão aleatória usando diferentes funções para estimar parâmetros genéticos para produção de leite na raça Holandesa

ABSTRACT: The objective of this study was to compare the functions of Wilmink and Ali and Schaeffer with Legendre polynomials in random regression models using heterogeneous residual variances for modeling genetic parameters during the first lactation in the Holstein Friesian breed. Five thousand eight hundred and eighty biweekly records of test-day milk production were used. The models included the fixed effects of group of contemporaries and cow age at calving as covariable. Statistical criteria indicated that the WF.33_HE2, LEG.33_HE2, and LEG.55_HE4 functions best described the changes in the variances that occur throughout lactation. Heritability estimates using WF.33_HE2 and LEG.33_HE2 models were similar, ranging from 0.31 to 0.50. The LEG.55_HE4 model diverged from these models, with higher estimates at the beginning of lactation and lower estimates after the 16th fortnight. The LEG55_HE4, among the three better models indicated by the index, is the one with highest number of parameters (14 vs 34) and resulted in lower estimation of residual variance at the beginning and at the end of lactation, but overestimated heritability in the first fortnight and presented a greater difficulty to model genetic and permanent environment correlations among controls. Random regression models that used the Wilmink and Legendre polynomials functions with two residual variance classes appropriately described the genetic variation during lactation of Holstein Friesians reared in Rio Grande do Sul.

RESUMO: Objetivou-se comparar as funções de Wilmink e Ali e Schaeffer com polinômios de Legendre em modelos de regressão aleatória, utilizando variâncias residuais heterogêneas, para modelar parâmetros genéticos ao longo da primeira lactação na raça Holandesa. Foram utilizados cinco mil oitocentos e oitenta registros quinzenais de produção de leite no dia do controle. Os modelos incluíram os efeitos fixos de grupo de contemporâneos e a idade da vaca ao parto como covariável. Os critérios estatísticos apontaram as funções WF.33_HE2, LEG.33_HE2 e a LEG.55_HE4 como as melhores em descrever as mudanças nas variâncias que ocorrem ao longo da lactação. As herdabilidades estimadas pelos modelos WF.33_HE2 e LEG.33_HE2 foram semelhantes, variando de 0,31 a 0,50. O LEG.55_HE4 divergiu destes, no início da lactação, com estimativas superiores e, a partir da 16ª quinzena, com estimativas inferiores. O LEG55_HE4, entre os três melhores modelos indicados pelo índice, é o mais parametrizado (14 vs 34) e resultou em menores estimativas de variância residual no início e no final da lactação, mas superestimou a herdabilidade na primeira quinzena e apresentou maior dificuldade em modelar as correlações genéticas e de ambiente permanente entre os controles. Os modelos de regressão aleatória que usaram a função de Wilmink e Polinômios de Legendre com duas classes de variâncias residuais descreveram adequadamente a variação genética ao longo da lactação de vacas da raça Holandesa, criadas no Rio Grande do Sul.

Milk production and percentages of fat and protein in Holstein breed cows raised in Rio Grande do Sul, Brazil / Produção de leite e porcentagens de gordura e proteína de bovinos da raça Holandesa criados no Rio Grande do Sul, Brasil

ABSTRACT: The objective of this study was to identify a nonlinear regression model that better describes the milk production and the percentages of fat and protein curves, and to identify the season and age of calving that result in higher productions. For the analysis, 8,047 records of milk production and percentages of fat and protein obtained from 1,330 Holstein breed cows raised in Rio Grande do Sul, born from 1989 to 2011, were used. After determining the most appropriate nonlinear regression model, the identity of models and the equality of parameter tests for the different classes of season and age of calving were performed. Residual mean square, mean absolute deviation, mean squared prediction error and percentage of estimated curves per animal, indicated the Linear Hyperbolic Function (LHF) as the most appropriate to describe the milk production and the levels of fat and protein curves. Cows calving in the autumn and winter seasons yield higher quantities of milk and lower fat and protein content than those calved in the spring and summer seasons. Cows aged between 46 and 93 months at calving yield higher milk quantities, and those with calving age between 94 and 118 months produced more fat.

RESUMO: Objetivou-se identificar o modelo de regressão não linear que melhor descreve as curvas de produção de leite e de porcentagens de gordura e proteína; a estação e idade ao parto que resultam em maiores produções. Nas análises, foram utilizados 8047 registros de produção de leite e porcentagens de gordura e proteína provenientes de 1330 vacas da raça Holandesa, criadas no Rio Grande do Sul, nascidas de 1989 a 2011. Após a definição do modelo de regressão não linear mais adequado, procedeu-se à realização do teste de identidade de modelos e de igualdade de parâmetros para as diferentes classes de estação e idade da vaca ao parto. O quadrado médio do resíduo, desvio médio absoluto, erro quadrático médio de predição e a porcentagem de curvas estimadas por animal apontaram a Função Linear Hiperbólica (FLH) como a mais adequada para descrever as curvas de produção de leite e teores de gordura e proteína. Vacas com ocorrência de partos nas estações de outono e inverno produzem maior quantidade de leite e menor teor de gordura e proteína do leite do que aquelas que parem nas estações de primavera e verão. Vacas com idades ao parto entre 46 e 93 meses produzem maior quantidade de leite e as com 94 a 118 meses, maior teor de gordura do leite.

Parâmetros genéticos para produção de leite no dia do controle de vacas da raça Holandesa utilizando modelos de análises de fatores e componentes principais / Genetic parameters for test day milk production of Holstein cows using factors and principal components analysis

Objetivou-se comparar um modelo multi-característica padrão com modelos de análise de fatores (AF) e de componentes principais (CP) para estimar parâmetros genéticos para a produção de leite no dia do controle (PLDC) de vacas da raça Holandesa. O arquivo de trabalho constituiu-se de 4.616 registros mensais de PLDC de primeiras lactações de vacas da raça Holandesa. As PLDC foram agrupadas em dez classes mensais, entre o 5o e 305o dia da lactação (PLDC1 a PLDC10). Foram realizadas análises considerando 11 modelos diferentes, como segue: multi-característica padrão (MC); cinco modelos de posto reduzido, para a matriz de covariância genética, ajustando um a cinco (CP1 ... CP5) componentes principais; e dois modelos utilizando análise de fatores (F1, F2, F3, F4 e F5). Para todos os modelos, foram considerados como aleatórios os efeitos genético aditivo e o residual e como fixos os de grupo de contemporâneos, da idade da vaca ao parto (linear e quadrático) e dias em lactação (linear). Os valores de Log L, AIC e BIC melhoraram com o aumento do número de parâmetros até CP4 e AF4. Comparando CP4 e AF4, observa-se que CP4 resultou em melhores valores de Log L, AIC e BIC. As estimativas de herdabilidade e correlações genéticas utilizando os modelos MC, CP4 e AF4 foram similares, variando de 0,06 (PL6) a 0,65 (PL10) e de 0,05 (PL4xPL10) a 0,94 (PL2xPL3), respectivamente, indicando que a estrutura de covariâncias genéticas entre as produções de leite no dia do controle pode ser ajustada utilizando um modelo de posto reduzido, contendo quatro componentes principais ou quatro fatores.

The objective was to compare a standard multi-trait (MT) analysis model with factor (FA) and principal components (PC) analyses models to estimated genetic parameters for Holstein cows test day milk production (TD). The data file was composed by 4.616 TD at first lactation registers. The TD was grouped into ten monthly classes of lactation, from the 5th and the 305th day of lactation (TD1 to TD10). Analyses were performed considering 11 different models: standard multi-traits (MT), five reduced rank models to genetic covariance matrix adjusting one (PC1), two (PC2), three (PC3), four (PC4) and five (PD5) principal components and five models using factor analyses (F1, F2, F3, F4 and F5). To all the models the effects additive genetic and residual were considered as random and the effects of contemporary group, age of cow at parturition (linear and quadratic) and days in lactation (linear) were considered as fixed. The values of Log L, AIC e BIC improved with the augment of the number of parameters until CP4 and AF4. Comparing CP4 and AF4 is possible to verify that CP4 proportioned better values to Log L, AIC e BIC. The heritabilities and genetic correlations estimated to the ten test day milk production using MC, CP4 and AF4 models were similar ranging from 0.06 (PL6) to 0.65 (PL10) and from 0.05 (PL4xPL10) to 0.94 (PL2xPL3), respectively, indicating that the structure of the genetic covariance between the TD milk productions can be adjusted using a reduced rank model with four principal components or four factors.