RESUMO
RESUMEN Objetivo. Estimar parámetros genéticos para peso a los ocho meses de edad (W8M), edad al primer parto (AFC) y primer intervalo entre partos (FCI) usando parentesco genómico y por pedigrí. Materiales y métodos. Se utilizaron 481, 3063 y 1098 registros fenotípicos para W8M, AFC y FCI, respectivamente. La información genómica estuvo compuesta por una población de 718 animales genotipados con un chip que incluyó 30106 marcadores genéticos tipo polimorfismo de nucleótido simple (SNP). Modelos univariado y bivariado fueron construidos bajo la metodología del mejor predictor lineal insesgado convencional (BLUP) y genómico en una etapa (ssGBLUP). Resultados. Las heredabilidades para W8M, AFC y FCI variaron desde 0.25 a 0.26, 0.20 a 0.22 y 0.04 a 0.08, respectivamente. Los modelos de AFC y FCI con la metodología ssGBLUP disminuyeron ligeramente el error y aumentaron la varianza genética aditiva, respectivamente. Conclusiones. La inclusión de información genómica mejora levemente la precisión de las estimaciones genéticas en esta población. Sin embargo, una población de animales genotipados más grande y con mayor conectividad genética por parentesco permitiría aumentar para los criadores el potencial de la metodología ssGBLUP en ganado Simmental de Colombia.
ABSTRACT Objective. To estimate genetic parameters for weight at eight months of age (W8M), age at first calving (AFC) and first calving interval (FCI) using pedigree and genomic relationship. Materials and methods. Phenotypic data on 481, 3063 and 1098 animals for W8M, AFC and FCI were used, respectively. The genomic information came from a population of 718 genotyped animals with a density chip of 30,106 single nucleotide polymorphism markers (SNP). Univariate and bivariate models were used under the conventional (BLUP) and single step genomic best linear unbiased predictor (ssGBLUP) methodologies. Results. The heritabilities for W8M, AFC and FCI ranged from 0.25 to 0.26, from 0.20 to 0.22 and from 0.04 to 0.08, respectively. The AFC and FCI models under ssGBLUP slightly decreased the error and increased the additive genetic variance, respectively. Conclusions. The inclusion of genomic information slightly increases the accuracy of the genetic estimates in this population. However, a larger amount of genotyped animals and with a higher genetic relationship connectivity would allow breeders to increase the potential of the ssGBLUP methodology in Colombian Simmental cattle.
Assuntos
Animais , Gado , Reprodução , GenômicaRESUMO
Gene-environment (GE) interaction has important implications in the etiology of complex diseases that are caused by a combination of genetic factors and environment variables. Several authors have developed GE analysis in the context of independent subjects or longitudinal data using a gene-set. In this paper, we propose to analyze GE interaction for discrete and continuous phenotypes in family studies by incorporating the relatedness among the relatives for each family into a generalized linear mixed model (GLMM) and by using a gene-based variance component test. In addition, we deal with collinearity problems arising from linkage disequilibrium among single nucleotide polymorphisms (SNPs) by considering their coefficients as random effects under the null model estimation. We show that the best linear unbiased predictor (BLUP) of such random effects in the GLMM is equivalent to the ridge regression estimator. This equivalence provides a simple method to estimate the ridge penalty parameter in comparison to other computationally-demanding estimation approaches based on cross-validation schemes. We evaluated the proposed test using simulation studies and applied it to real data from the Baependi Heart Study consisting of 76 families. Using our approach, we identified an interaction between BMI and the Peroxisome Proliferator Activated Receptor Gamma (PPARG) gene associated with diabetes.
Assuntos
Família , Interação Gene-Ambiente , Desequilíbrio de Ligação , Modelos Genéticos , Humanos , Modelos Lineares , Fenótipo , Polimorfismo de Nucleotídeo ÚnicoRESUMO
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.
RESUMO
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.(AU)
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.(AU)