Estudo de seleção genômica para características de produção e qualidade do leite de búfalas
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Data
2017-07-21
Autores
Barros, Camila da Costa [UNESP]
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Universidade Estadual Paulista (Unesp)
Resumo
Objetivou-se com o presente trabalho comparar diferentes métodos Bayesianos de predição genômica para as características de produção de leite (PL) e as porcentagens de gordura (%G) e proteína (%P) no leite de búfalas e, realizar um estudo de associação genômica ampla, a fim de identificar regiões cromossômicas e genes possivelmente relacionados às mesmas, utilizando informações de indivíduos genotipados e não genotipados. O número de animais com fenótipo foi 3.355, o arquivo de pedigree continha 15.495 animais, dos quais 322 foram genotipados com o 90 K Axiom® Buffalo Genotyping array. Os seguintes critérios de controle de qualidade dos SNPs foram utilizados: MAF < 0,05; Call Rate < 0,95 e Equilíbrio de Hardy-Weinberg p-value < 10-6. Em relação à amostra foi considerado call rate <0,90. Para as predições genômicas, os seguintes modelos Bayesianos foram utilizados: Bayes A (BA), Bayes B (BB), Bayes C (BC) e Bayes LASSO (BL). O fenótipo corrigido para os efeitos fixos (Y*) foi utilizado como variável resposta nas análises genômicas. A habilidade de predição dos diferentes modelos foi avaliada usando o método leave-one-out de validação cruzada. As acurácias de predição foram calculadas através da correlação de Pearson entre o valor genético genômico estimado (GEBV) e a variável resposta (Y*) para cada modelo e característica avaliados. Em relação ao estudo de associação genômica ampla, um processo iterativo foi realizado para calcular os pesos dos marcadores em função do quadrado dos efeitos dos SNPs e das frequências alélicas (ssGWAS). Em geral, todos os modelos Bayesianos demonstraram semelhantes acurácias de predição, variando de 0,41 a 0,42, 0,38 a 0,39 e 0,39 a 0,40 para a PL, %G e %P, respectivamente. Portanto, os métodos BA, BB, BC e BL podem ser utilizados nas predições dos efeitos dos SNPs, obtendo-se, praticamente, as mesmas acurácias de predição. Os dez SNPs de maiores efeitos para a PL, %G e %P explicaram 7,48, 9,94 e 6,56% da proporção da variância genética, respectivamente. Os resultados do ssGWAS revelaram regiões cromossômicas e genes que podem estar relacionados com as características analisadas. Tais regiões e genes identificados poderão contribuir para o melhor entendimento sobre a influência dos mesmos nas características produção de leite e as porcentagens de gordura e proteína no leite de búfalas.
The aim of this study was to compare different Bayesian methods of genomic prediction for milk yield (MY), fat (%F) and protein (%P) percentages in dairy buffaloes in Brazil, and to perform a genome-wide association study for the purpose of identify chromosomal regions and genes possibly related to the these traits, using information from genotyped and non-genotyped individuals. The number of animals with phenotype was 3,355, the pedigree file contained 15,495 animals, of which 322 were genotyped. The animals were genotyped using a 90K SNP panel (Axiom® Buffalo Genotyping Array). The following criteria for quality control of SNPs were used: MAF < 0.05, Call Rate < 0.95 and Hardy-Weinberg Equilibrium p-value < 10-6 . In relation to the sample, a Call Rate <0.90 was used. Four methods for genomic prediction were used: Bayes A (BA), Bayes B (BB), Bayes C (BC) and Bayes LASSO (BL). Phenotypes for the fixed effects (Y*) were used as response variables. The predictive ability of the different models was evaluated using a leave-one-out cross-validation approach. The prediction accuracy was calculated by Pearson's correlation between estimated genomic genetic value (GEBV) and response variable (Y*) for each model. In relation to genome-wide association studies, an iterative process was performed to derive SNP weights as function of squares of SNP effects and allele frequencies (ssGWAS). In general, all Bayesian models showed similar prediction accuracy, ranging from 0.41 to 0.42, 0.38 to 0.39 and 0,39 to 0,40 for MY, %F and %P, respectively. Therefore, the methods BA, BB, BC and BL can be used in the predictions of the effects of SNPs, obtaining, practically, the same prediction accuracy. The proportions of variance explained by the top 10 SNPs for MY, %F and %P were: 7.48, 9.94 and 6.56%, respectively. The results of ssGWAS revealed chromosomal regions and genes that may be related with the analyzed traits. These regions and genes may contribute to a better understanding of their influence on milk yield and fat and protein percentages in buffalo milk.
The aim of this study was to compare different Bayesian methods of genomic prediction for milk yield (MY), fat (%F) and protein (%P) percentages in dairy buffaloes in Brazil, and to perform a genome-wide association study for the purpose of identify chromosomal regions and genes possibly related to the these traits, using information from genotyped and non-genotyped individuals. The number of animals with phenotype was 3,355, the pedigree file contained 15,495 animals, of which 322 were genotyped. The animals were genotyped using a 90K SNP panel (Axiom® Buffalo Genotyping Array). The following criteria for quality control of SNPs were used: MAF < 0.05, Call Rate < 0.95 and Hardy-Weinberg Equilibrium p-value < 10-6 . In relation to the sample, a Call Rate <0.90 was used. Four methods for genomic prediction were used: Bayes A (BA), Bayes B (BB), Bayes C (BC) and Bayes LASSO (BL). Phenotypes for the fixed effects (Y*) were used as response variables. The predictive ability of the different models was evaluated using a leave-one-out cross-validation approach. The prediction accuracy was calculated by Pearson's correlation between estimated genomic genetic value (GEBV) and response variable (Y*) for each model. In relation to genome-wide association studies, an iterative process was performed to derive SNP weights as function of squares of SNP effects and allele frequencies (ssGWAS). In general, all Bayesian models showed similar prediction accuracy, ranging from 0.41 to 0.42, 0.38 to 0.39 and 0,39 to 0,40 for MY, %F and %P, respectively. Therefore, the methods BA, BB, BC and BL can be used in the predictions of the effects of SNPs, obtaining, practically, the same prediction accuracy. The proportions of variance explained by the top 10 SNPs for MY, %F and %P were: 7.48, 9.94 and 6.56%, respectively. The results of ssGWAS revealed chromosomal regions and genes that may be related with the analyzed traits. These regions and genes may contribute to a better understanding of their influence on milk yield and fat and protein percentages in buffalo milk.
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Palavras-chave
Búfalos, GWAS, Predição genômica, SNP