A genomic association and prediction of principal components of growth traits and visual scores in Nellore cattle
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2018-12-18
Autores
Vargas, Giovana [UNESP]
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Universidade Estadual Paulista (Unesp)
Resumo
A análise de componentes principais (ACP) é uma técnica da estatística multivariada usada para avaliar as relações entre diferentes características a fim de eliminar a redundância resultante de suas correlações. No melhoramento genético animal, a ACP tem sido usada para explorar possíveis interpretações biológicas associadas aos componentes principais (CPs) que podem levar a caracterização de diferentes biotipos de animais. Os objetivos do presente estudo foram: i) avaliar as relações entre características de crescimento, escore visual e reprodutiva, por meio de ACP; ii) identificar, por meio de estudo de associação genômica ampla (GWAS), regiões genômicas que diferenciam os animais quanto aos diferentes componentes; e iii) avaliar a habilidade de predição de valores genéticos genômicos (GEBVs) obtidos para os CPs. Foram utilizados dados fenotípicos de 355.524 animais da raça Nelore provenientes da base de dados Aliança Nelore. Destes, foram genotipados 3.382 animais em painel lllumina® BovineHD (HD, ~777.000 SNPs) e 137 animais em painel GeneSeek Genomic Profiler Bovine HD (~76.000 SNPs). Os animais genotipados com o painel GGP-HD tiveram seus genótipos imputados para o painel mais denso (HD). Após o controle de qualidade, 3.519 animais com informações genotípicas de 471.880 SNPs permaneceram nas análises. A ACP foi realizada utilizando-se a matriz de (co)variância genética aditiva (AT) obtida a partir de análise multi-característica. As estimativas dos efeitos dos SNPs foram obtidas utilizando-se as metodologias weighted single-step GBLUP e BayesC. Os genes identificados para as top 10 janelas que explicaram a maior proporção da variância foram usados para realizar as análises funcionais. Os GEBVs foram preditos utilizando como variáveis respostas: EBV das características originais, EBV dos CPs e EBV de um índice de seleção utilizado por programas de melhoramento da raça Nelore. A habilidade de predição da seleção genômica foi calculada pela correlação entre os GEBVs e as variáveis respostas. Os três primeiros CPs explicaram 87,11% da variância genética aditiva total das características. O primeiro CP contrastou animais de acordo com a taxa de crescimento, o segundo CP diferenciou os animais em biotipos tardios e precoces, e o terceiro CP contrastou características mensuradas ao desmame e ao sobreano. Foram identificadas possíveis regiões genômicas associadas a características de crescimento, carcaça, conformação e ácidos graxos, que sugerem possível associação com os CPs. As regiões identificadas ajudam na interpretação biológica dos CPs e seus respectivos biotipos em bovinos Nelore. No estudo de predição genômica, acurácias de magnitude moderada foram obtidas para as nove características estudadas, para os CPs e índice de seleção, indicando que a ACP poderia ser utilizada para a seleção de bovinos Nelore.
Principal component analysis (PCA) is a multivariate statistical technique that allows evaluating relationships among different traits in order to eliminate the redundancy resulting from their correlations. In animal breeding, PCA has been used to explore possible biological interpretations associated with the principal components (PCs) that can lead to the characterization of distinguished animal’s biotype. The objectives of the present study were: i) to evaluate relationships among growth, visual scores, and reproductive traits by performing a PCA; ii) to identify genomic regions associated with PCs by performing a genome-wide association study (GWAS) on the main PCs; and iii) to evaluate the prediction ability of genomic breeding values (GEBVs) obtained for the PCs. Phenotypic data from 355,524 Nellore animals provided by the Alliance Nellore database, were used in this investigation. A total of 3,382 Nellore animals were genotyped using the lllumina® BovineHD chip (HD, ~777,000 SNPs) and 137 animals were genotyped using the GeneSeek Genomic Profiler Bovine HD chip (~76,000 SNPs). The GGP-HD genotypes were imputed to the HD genotypes. After genomic data quality control, 471,880 SNPs from 3,519 animals were available. The PCA was applied on the additive genetic (co)variance matrix (AT) obtained using multi-trait analysis. For GWAS, SNP effects were estimated using the weighted single-step GBLUP and the BayesC methods. The genes identified within the top-10 ranking windows that explained the highest proportion of variance were used for further functional analyses. For the genomic prediction study, the GEBVs were predicted using three distinguish response variables: EBV of the original traits, EBV of the PCs, and EBV of a selection index used by some Nellore cattle commercial breeding programs. The genomic predictive ability was calculated by correlation between GEBVs and response variables. The first three PCs explained 87.11% of the total additive genetic variance for the traits. The first component contrasted the animals according to the growth rate, the second component contrasted the animals with early or late biotype, while the third component differentiate weaning and yearling traits. GWAS detected potential genomic regions associated with growth, carcass traits, conformation, and fatty acid composition traits that may be affecting the PCs. These findings are of relevance to the biological understanding of the PCs and their associated biotypes in Nellore cattle. Genomic predictions with moderate accuracies were obtained for the nine original traits, PCs and selection index, indicating the possibility of using PCA for implementing genomic selection for Nellore cattle.
Principal component analysis (PCA) is a multivariate statistical technique that allows evaluating relationships among different traits in order to eliminate the redundancy resulting from their correlations. In animal breeding, PCA has been used to explore possible biological interpretations associated with the principal components (PCs) that can lead to the characterization of distinguished animal’s biotype. The objectives of the present study were: i) to evaluate relationships among growth, visual scores, and reproductive traits by performing a PCA; ii) to identify genomic regions associated with PCs by performing a genome-wide association study (GWAS) on the main PCs; and iii) to evaluate the prediction ability of genomic breeding values (GEBVs) obtained for the PCs. Phenotypic data from 355,524 Nellore animals provided by the Alliance Nellore database, were used in this investigation. A total of 3,382 Nellore animals were genotyped using the lllumina® BovineHD chip (HD, ~777,000 SNPs) and 137 animals were genotyped using the GeneSeek Genomic Profiler Bovine HD chip (~76,000 SNPs). The GGP-HD genotypes were imputed to the HD genotypes. After genomic data quality control, 471,880 SNPs from 3,519 animals were available. The PCA was applied on the additive genetic (co)variance matrix (AT) obtained using multi-trait analysis. For GWAS, SNP effects were estimated using the weighted single-step GBLUP and the BayesC methods. The genes identified within the top-10 ranking windows that explained the highest proportion of variance were used for further functional analyses. For the genomic prediction study, the GEBVs were predicted using three distinguish response variables: EBV of the original traits, EBV of the PCs, and EBV of a selection index used by some Nellore cattle commercial breeding programs. The genomic predictive ability was calculated by correlation between GEBVs and response variables. The first three PCs explained 87.11% of the total additive genetic variance for the traits. The first component contrasted the animals according to the growth rate, the second component contrasted the animals with early or late biotype, while the third component differentiate weaning and yearling traits. GWAS detected potential genomic regions associated with growth, carcass traits, conformation, and fatty acid composition traits that may be affecting the PCs. These findings are of relevance to the biological understanding of the PCs and their associated biotypes in Nellore cattle. Genomic predictions with moderate accuracies were obtained for the nine original traits, PCs and selection index, indicating the possibility of using PCA for implementing genomic selection for Nellore cattle.
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Palavras-chave
Bovinos de corte, Efeitos dos SNPs, GEBV, WssGBLUP, Beef cattle, GEBV, SNP effects, WssGBLUP