A Common Dataset for Genomic Analysis of Livestock Populations

Although common datasets are an important resource for the scientific community and can be used to address important questions, genomic datasets of a meaningful size have not generally been available in livestock species. We describe a pig dataset that PIC (a Genus company) has made available for comparing genomic prediction methods. We also describe genomic evaluation of the data using methods that PIC considers best practice for predicting and validating genomic breeding values, and we discuss the impact of data structure on accuracy. The dataset contains 3534 individuals with high-density genotypes, phenotypes, and estimated breeding values for five traits. Genomic breeding values were calculated using BayesB, with phenotypes and de-regressed breeding values, and using a single-step genomic BLUP approach that combines information from genotyped and un-genotyped animals. The genomic breeding value accuracy increased with increased trait heritability and with increased relationship between training and validation. In nearly all cases, BayesB using de-regressed breeding values outperformed the other approaches, but the single-step evaluation performed only slightly worse. This dataset was useful for comparing methods for genomic prediction using real data. Our results indicate that validation approaches accounting for relatedness between populations can correct for potential overestimation of genomic breeding value accuracies, with implications for genotyping strategies to carry out genomic selection programs

Genetic analysis of days to calving in Nelore beef cattle

A Nelore population data were analyzed to estimate genetic parameters for days to calving (DC) and verify the possibility of using it as a selection criterion. There were 49 698 calving observations from 36 196 females. Data were analyzed using a single-trait animal model. The fixed effects were contemporary groups, calf sex and age of dam at joining as a covariable. The contemporary groups were composed of farm, year, season and handling group at joining and mating type (a mating group with multiple sires, a mating group with one sire or artificial insemination). The inclusion of permanent environmental effects of animals did not increase the likelihood function significantly. Heritability estimate was 0.090 ± 0.006, indicating that DC is strongly influenced by the environment

Estimates of heritabilities and correlations for visual scores, weight and height at 550 days of age in Nelore cattle herds

Os objetivos neste trabalho foram avaliar as relações entre os escores visuais de estrutura corporal, precocidade e musculosidade ao sobreano (aproximadamente 550 dias de idade) com características de crescimento para verificar as possibilidades de utilizar essas características como critérios de seleção. Foram obtidas estimativas dos componentes de covariâncias por máxima verossimilhança restrita empregando-se um modelo animal com o efeito fixo de grupo contemporâneo e a idade como covariável (efeitos linear e quadrático). Os grupos contemporâneos foram definidos pelas variáveis: sexo; ano, estação e fazenda de nascimento; e fazenda e grupo de manejo aos 120, 210, 365 e 550 dias de idade. Foram utilizadas 1.367 observações de estrutura corporal, precocidade e musculosidade. As estimativas de herdabilidade foram de 0,24 ± 0,09 para estrutura corporal; 0,63 ± 0,12 para precocidade e 0,48 ± 0,11 para musculosidade, e as estimativas de correlações genéticas entre os escores foram 0,49 entre estrutura corporal e precocidade; 0,63 entre estrutura corporal e musculosidade; e 0,90 entre precocidade e musculosidade. As correlações genéticas entre os escores de estrutura corporal, precocidade e musculosidade, e o peso ao sobreano foram todas positivas (0,83; 0,42 e 0,50, respectivamente), enquanto as estimativas de correlações genéticas entre altura de posterior e os escores de estrutura corporal, precocidade e musculosidade, respectivamente, foram 0,57, -0,29 e -0,33. As características estrutura corporal, precocidade e musculosidade ao sobreano apresentaram variação genética aditiva de moderada a alta. As correlações genéticas dos escores com altura do posterior indicam que a seleção de animais mais altos, ainda que indireta, pode ocasionar aumento da estrutura corporal média dos animais, que poderão ser menos precoces e menos musculosos ao sobreano. A seleção para os escores visuais, principalmente para estrutura corporal, deve promover aumento no peso ao sobreano dos animais.The objectives of this study were to evaluate the associations between visual scores of body structure, precocity and muscling at 550 days of age and growing traits, and verify the possibilities of applying these traits as selection criteria. (Co)variance components were estimated by restricted maximum likelihood, employing an animal model with fixed effects of contemporary group and age as a covariate (linear and quadratic effects). Contemporary groups were defined by variables: sex; year, season and herd of birth; herd and management group at 120, 210, 365 and 550 days of age. Scores from 1,367 animals for body structure, precocity and muscling were evaluated. Heritability estimates for the visual scores were 0.24 ± 0.09 for body structure, 0.63 ± 0.12 for precocity and 0.48 ± 0.11 for muscling. Genetic correlations estimates among the scores were 0.49 for body structure and precocity, 0.63 between body structure and muscling, 0.90 between precocity and muscling. The genetic correlation estimates among the scores of body structure, precocity and muscling and weight at 550 days were all positive (0.83, 0.42 and 0.50, respectively), while the genetic correlation estimates between hip height and body structure, precocity and muscling were 0,57, -0,29 and -0,33, respectively. Scores for body structure, precocity and muscling at 550 days of age presented moderate-to-large additive genetic variability. The genetic correlation estimates between visual scores and hip height indicated that the selection of taller animals, even though indirectly, can increase the body structure of animals and decrease precocity and muscling at 550 days. Selection for visual scores, especially body structure, should increase animal weight at 550 days.Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES

Análise da curva de crescimento de bovinos da raça Nelore utilizando funções não-lineares em análises Bayesianas: Selma Forni. -

O objetivo do presente trabalho foi estimar conjuntamente os parâmetros das curvas de crescimento de animais da raça Nelore, seus componentes de (co)variâncias e os efeitos genéticos e ambientais que atuaram sobre eles. As funções de Brody, Von Bertalanffy, Gompertz e Logística foram empregadas no primeiro estágio de um modelo hierárquico Bayesiano. Os efeitos genéticos e ambientais foram considerados em um modelo animal no segundo estágio de hierarquia. Diferentes abordagens para a variância do erro de ajuste foram avaliadas: constância ao longo da trajetória, aumento linear até os três anos de idade e aumento exponencial. Amostras aleatórias das distribuições marginais foram obtidas aplicando-se os algoritmos de Metropolis-Hastings e amostragem de Gibbs. A presença de animais que não atingiram a maturidade no conjunto de dados não prejudicou a predição dos pesos adultos. Grande parte da variância fenotípica observada neste peso foi devida a efeitos genéticos aditivos. O parâmetro a das curvas de Brody, Von Bertalanffy e Gompertz poderia ser utilizado como critério de seleção para controlar o aumento de peso adulto. O ambiente materno influenciou não somente o crescimento inicial dos animais mas também os pesos maduros e deve ser considerado na avaliação de todas as etapas do crescimento. Os modelos linear e exponencial empregados para a variância do erro de ajuste não representaram de forma adequada este parâmetro no início da curva. A seleção para alterar a pendente da curva de crescimento mantendo o peso adulto constante seria ineficiente, uma vez que, é alta e positiva a correlação genética entre o peso assintótico e a taxa de maturação.The objective of this work was to estimate the joint posterior distribution of Nelore growth curve parameters, their (co)variance components and the environmental and additive genetic components affecting them. The Brody, Von Bertalanffy, Gompertz and Logistic functions were applied in the first stage of a hierarchical Bayesian model. The environmental and genetic effects were described by an animal model in the second stage. Different approaches for describing the adjustment error variance along the growth curve were evaluated: constancy throughout the trajectory, linear increasing until three years of age and exponential increasing. Random samples of the marginal distributions were drawn using Metropolis-Hastings and Gibbs sampling algorithms. Even thought the curve parameters were estimated for animals with records just from the beginning of the growth process, the adult weights were accurately predicted. A high additive genetic variance for mature weight was observed. The parameter a of Brody, Von Bertalanffy and Gompertz models could be used as a selection criterion to control adult weight increases. The effect of maternal environment on growth was carried through to maturity and it should be considered while evaluating all weights. The adjustment error variances at the beginning of growth curve were not adequately described by the linear and exponential models. Selection to change the growth curve slope without modifying adult weight would be inefficient, since their genetic correlation is high.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES

Genome-wide prediction of discrete traits using bayesian regressions and machine learning

Abstract Background Genomic selection has gained much attention and the main goal is to increase the predictive accuracy and the genetic gain in livestock using dense marker information. Most methods dealing with the large p (number of covariates) small n (number of observations) problem have dealt only with continuous traits, but there are many important traits in livestock that are recorded in a discrete fashion (e.g. pregnancy outcome, disease resistance). It is necessary to evaluate alternatives to analyze discrete traits in a genome-wide prediction context. Methods This study shows two threshold versions of Bayesian regressions (Bayes A and Bayesian LASSO) and two machine learning algorithms (boosting and random forest) to analyze discrete traits in a genome-wide prediction context. These methods were evaluated using simulated and field data to predict yet-to-be observed records. Performances were compared based on the models' predictive ability. Results The simulation showed that machine learning had some advantages over Bayesian regressions when a small number of QTL regulated the trait under pure additivity. However, differences were small and disappeared with a large number of QTL. Bayesian threshold LASSO and boosting achieved the highest accuracies, whereas Random Forest presented the highest classification performance. Random Forest was the most consistent method in detecting resistant and susceptible animals, phi correlation was up to 81% greater than Bayesian regressions. Random Forest outperformed other methods in correctly classifying resistant and susceptible animals in the two pure swine lines evaluated. Boosting and Bayes A were more accurate with crossbred data. Conclusions The results of this study suggest that the best method for genome-wide prediction may depend on the genetic basis of the population analyzed. All methods were less accurate at correctly classifying intermediate animals than extreme animals. Among the different alternatives proposed to analyze discrete traits, machine-learning showed some advantages over Bayesian regressions. Boosting with a pseudo Huber loss function showed high accuracy, whereas Random Forest produced more consistent results and an interesting predictive ability. Nonetheless, the best method may be case-dependent and a initial evaluation of different methods is recommended to deal with a particular problem.</p

Avaliação de fatores de ambiente e estimativas de parâmetros genéticos para a característica dias para o parto na raça Nelore

Dados de um rebanho comercial da raça Nelore foram analisados com os objetivos de avaliar a influência de fatores ambientais e estimar parâmetros genéticos para a característica dias para o parto (DPP). Foram utilizados modelos fixos que incluíram os efeitos de grupo de contemporâneos, sexo do bezerro, idade da vaca no início da estação de monta, mês do parto anterior e peso do bezerro desmamado no final da estação de monta (apenas este último efeito não influenciou a característica significativamente). Duas definições de grupo de contemporâneos foram consideradas: a primeira definida pelas informações fazenda, ano e estação de monta, grupos de manejo (nascimento, desmama e reprodução) e tipo de serviço (monta natural, monta controlada ou inseminação artificial); e a segunda, pelas mesmas informações mais o sexo do bezerro. As análises genéticas foram realizadas com conjuntos de dados que incluíram ou não os animais sem registros de parto. Quando incluídos, o valor de DPP atribuído a esses animais foi igual ao maior valor observado dentro do grupo de manejo somado a 21 dias. Os componentes de variância foram estimados por máxima verossimilhança restrita utilizando-se modelos animal unicaracterística. A inclusão do efeito aleatório de ambiente permanente nos modelos foi avaliada mediante o Likelihood Ratio Test. As herdabilidades estimadas variaram entre 0,01 e 0,11 e a inclusão do efeito de ambiente permanente nos modelos foi significativa, de modo que a exclusão deste efeito superestimou a variância genética aditiva. A metodologia aplicada para penalizar os animais que não pariram não melhorou a identificação das diferenças genéticas entre eles. Os resultados indicaram que, como grande parte das características reprodutivas em bovinos, a DPP sofre grande influência do ambiente.Data from a Nelore population was used to evaluate environmental effects and to estimate genetic parameters for days to calving (DPP). The influence of contemporary group, age at breeding season, month of last calving and weaning weight of the calf at the end of the breeding season were evaluated with fixed models. The weaning weight of the calf at the end of the breeding season was the only effect that did not influence DPP. Two different contemporary group (CG) definitions were also evaluated. Herd, year and season of breeding, management groups (birth, weaning and breeding) and mating type (multiple sires, single sire or artificial insemination) defined the first CG and the second CG included the same variables and sex of the calf. Variance components were estimated using restricted maximum likelihood method fitting univariate animal models, including or not non-calvers cows (which were penalized by adding 21 days to the largest DPP record in their management group). Heritability estimates ranged between 0.01 and 0.11. Exclusion of the permanent environmental effect from the model overestimated additive genetic variance. The method of assigning penalties to non-calvers cows was unsuitable; it resulted in reduced additive genetic variance. The results suggest that DPP is strongly influenced by the environment.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP

A Common Dataset for Genomic Analysis of Livestock Populations

Although common datasets are an important resource for the scientific community and can be used to address important questions, genomic datasets of a meaningful size have not generally been available in livestock species. We describe a pig dataset that PIC (a Genus company) has made available for comparing genomic prediction methods. We also describe genomic evaluation of the data using methods that PIC considers best practice for predicting and validating genomic breeding values, and we discuss the impact of data structure on accuracy. The dataset contains 3534 individuals with high-density genotypes, phenotypes, and estimated breeding values for five traits. Genomic breeding values were calculated using BayesB, with phenotypes and de-regressed breeding values, and using a single-step genomic BLUP approach that combines information from genotyped and un-genotyped animals. The genomic breeding value accuracy increased with increased trait heritability and with increased relationship between training and validation. In nearly all cases, BayesB using de-regressed breeding values outperformed the other approaches, but the single-step evaluation performed only slightly worse. This dataset was useful for comparing methods for genomic prediction using real data. Our results indicate that validation approaches accounting for relatedness between populations can correct for potential overestimation of genomic breeding value accuracies, with implications for genotyping strategies to carry out genomic selection programs