Identificação de regiões cromossômicas, genes e polimorfismos de DNA associados ao desempenho de equinos de corrida da raça quarto de milha
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2017-04-28
Autores
Pereira, Guilherme Luis [UNESP]
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Universidade Estadual Paulista (Unesp)
Resumo
Dentre os equinos selecionados para velocidade, a linhagem de corrida da raça Quarto de Milha se destaca pelo alto desempenho em provas de curtas distâncias, sendo considerados os mais velozes do mundo. Apesar de, no Brasil, o efetivo de animais ser relativamente menor na linhagem de corrida do que nas demais, sua importância econômica é substancial. Tendo em vista o interesse econômico e científico relacionado a esta característica atlética, poucos esforços têm sido realizados para a maior compreensão de seus mecanismos genéticos e fisiológicos. Este trabalho teve como objetivos: 1) realizar a imputação de genótipos em duas vias entre indivíduos de uma amostra populacional relativamente pequena de cavalos de corrida da raça Quarto de Milha genotipados com painéis de 54k ou de 65k, bem como avaliar a acurácia de imputação por meio de simulações; 2) realizar estudo de associação ampla do genoma (GWAS) em cavalos da linhagem de corrida da raça Quarto de Milha por meio da utilização de chips equinos de genotipagem de SNPs, visando a prospecção de regiões cromossômicas, genes e polimorfismos relacionados ao desempenho; 3) analisar exomas de equinos de corrida da raça Quarto de Milha contrastantes para Índice de Velocidade máximo (IV max) em regiões previamente associadas à característica por meio de GWAS, visando a prospecção de polimorfismos gênicos causais, ligados ou em forte desequilíbrio de ligação com o desempenho em corridas. A imputação foi realizada utilizando 116 cavalos genotipados com o arranjo de SNPs de 54k e 233 genotipados com arranjo de 65k. Nas simulações foram escolhidas amostras aleatórias para constituírem as populações imputadas e referências em dois cenários. O cenário A simulou a imputação genótipos na primeira via (65k para 54k) e o cenário B na segunda (54k para 65k). No cenário A foram considerados 113 indivíduos para a população referência e 236 para a imputada, dos quais 116 e 120 foram genotipados com os arranjos de 54k e 65k, respectivamente. No cenário B foram considerados 50 indivíduos para a população referência e 299 para a imputada, dos quais 66 e 233 foram genotipados com os arranjos de 54k e 65k, respectivamente. Com isso, após o controle de qualidade, os painéis de 54k e de 65k contaram com 7.048 e 16.940 marcadores exclusivos, respectivamente. As médias de taxa de concordância para os cenários A e B foram 0,9815 e 0,9751 e para r2 alélico foram 0,9791 e 0,9740, respectivamente. O GWAS foi realizado com base no método single step GBLUP por meio de duas abordagens: ssGWAS1, em que somente efeitos de SNPs são reestimados a cada iteração, e ssGWAS2, em que a cada iteração são reestimados efeitos de SNPs a partir de valores genético genômico (GEBVs) reestimados. Vinte e uma regiões foram encontradas explicando mais que 1% da variância genética total (gVar) da característica índice de velocidade máximo (IV max) para ssGWAS1 e doze parassGWAS2. No total mais de 40% da gVar foi explicada por estas regiões para ssGWAS1 e cerca de 30% para ssGWAS2. Entre os cromossomos que explicaram mais de 1% da variância genética, cinco foram comuns aos dois métodos (ECA 3, 10, 15, 22, 25). Foram identificados 108 genes na primeira abordagem e 59 na segunda. A partir de informações de GEBVs de cada cavalo foram formados dois grupos de animais contrastantes para desempenho em corridas (20 animais de IV max superior e 20 IV max inferior), para ser sequenciados. Foram observadas leituras de boa qualidade para toda extensão das reads sequenciadas (até 100pb) e cobertura média de 43x. Foram identificadas 1.203 variantes (1.105 SNPs e 93 InDels) em 33 regiões de interesse obtidas, anteriormente, por meio de estudo de GWAS, das quais 61,3% não estavam registradas/depositadas no banco de dados de variantes equino. Do total de polimorfismos, 29 (24 SNPs e 5 InDels) foram considerados de importância com base em três abordagens distintas e independentes: escores SIFT classificado como deletério (<0,05), grau de impacto na região consenso de cada polimorfismo, e frequências alélicas diferentes, identificadas pelo teste de Fisher (p< 0,01), entre os grupos de cavalos contrastantes para IV max. Com isso, oito genes descritos como candidatos em trabalhos anteriores (ABCG5, COL11A1, GEN1, SOCS3, MICAL1, SPTBN1, EPB41L3 e SHQ1), e oito genes candidatos novos (AKNA, ARMC2, FKBP15, LHX1, NOL10, TMEM192, ZFP37, FIG4 e HNRNPU) foram relacionados ao desempenho em corridas de cavalos da raça Quarto de Milha. Assim, os resultados obtidos neste trabalho mostraram que o desempenho em corridas na raça Quarto de Milha, dado pelo IV max, é característica quantitativa e que não há ocorrência de major genes.
Among horses selected for speed, the racing line of Quarter Horses is characterized by high performance in sprint races, with these animals being considered the fastest horses in the world. Although in Brazil the effective number of animals in the racing line is relatively smaller compared to the other lines, its economic importance is substantial. Despite economic and scientific interest in this athletic trait, few efforts have been made to better understand the genetic and physiological mechanisms underlying this trait. The objectives of this study were: 1) to perform two-step genotype imputation between individuals in a relatively small population sample of racing Quarter Horses genotyped with the 54k or 65k panel, and to evaluate the accuracy of imputation through simulations; 2) to perform genome-wide association studies (GWAS) in Quarter Horses of the racing line using equine SNP genotyping chips for prospecting chromosome regions, genes and polymorphisms related to performance; 3) analyze exomes and UTRs in regions previously associated with this trait by GWAS in Quarter Horse racehorses with contrasting maximum speed index (SImax), prospecting causal gene polymorphisms that are related to or are in strong linkage disequilibrium with racing performance. Genotypes were imputed using 116 horses genotyped with the 54k SNP array and 233 animals genotyped with the 65k array. For the simulations, random samples were chosen to compose the imputed and reference populations in two scenarios. Scenario A simulated the genotype imputation in the first step (from 65k to 54k) and scenario B in the second step (from 54k to 65k). Thus, after quality control, the 54k and 65k panels contained 7,048 and 16,940 exclusive markers, respectively. The mean concordance rate was 0.9815 and 0.9751 for scenarios A and B, and the mean allelic r2 was 0.9791 and 0.974, respectively. After imputation was performed by the single-step GBLUP method using two approaches: ssGWAS1 in which only SNP effects are recalculated at each iteration, and ssGWAS2 in which SNP effects are recalculated from genomic estimated breeding values (GEBVs) at each iteration. Twenty-one regions that explained more than 1% of the total genetic variance (gVar) in the maximum speed index were identified by ssGWAS1 and 12 by ssGWAS2. More than 40% of gVar was explained by these regions in ssGWAS1 and about 30% in ssGWAS2. Among chromosomes that explained more than 1% of genetic variance, five were common to both methods (ECA 3, 10, 15, 22, 25). A total of 108 genes were identified with the first approach and 59 with the second approach. To exome sequencing, GEBVs were used for the formation of two groups of animals with contrasting racing performance (20 animals with superior SI max and 20 with inferior SI max). Good quality data were obtained throughout the reads sequenced, with an average coverage of 43x. A total of 1,203 variants (1,105 SNPs and 93 InDels) were identified in 33 regions of interest obtained previously by GWAS; of these, 61.3% were not registered/deposited in the horse genomic variant database. Twenty-nine of the polymorphisms (24 SNPs and 5 InDels) were considered to be important based on three different and independent approaches: SIFT scores classified as deleterious (<0.05), degree of impact on the consensus region of each polymorphism, and different allele frequencies identified by Fisher’s exact test (p< 0.01) between the groups of horses with contrasting SImax. Thus, eight genes described as functional and positional candidates in previous studies (ABCG5, COL11A1, GEN1, SOCS3, MICAL1, SPTBN1, EPB41L3, and SHQ1) and eight new candidate genes (AKNA, ARMC2, FKBP15, LHX1, NOL10, TMEM192, ZFP37, FIG4, and HNRNPU), some of them with known function, were related to racing performance in Quarter Horses. Taken together, the present results show that the racing performance of Quarter Horses, given by the maximum speed index, is a quantitative trait and that no major genes exist.
Among horses selected for speed, the racing line of Quarter Horses is characterized by high performance in sprint races, with these animals being considered the fastest horses in the world. Although in Brazil the effective number of animals in the racing line is relatively smaller compared to the other lines, its economic importance is substantial. Despite economic and scientific interest in this athletic trait, few efforts have been made to better understand the genetic and physiological mechanisms underlying this trait. The objectives of this study were: 1) to perform two-step genotype imputation between individuals in a relatively small population sample of racing Quarter Horses genotyped with the 54k or 65k panel, and to evaluate the accuracy of imputation through simulations; 2) to perform genome-wide association studies (GWAS) in Quarter Horses of the racing line using equine SNP genotyping chips for prospecting chromosome regions, genes and polymorphisms related to performance; 3) analyze exomes and UTRs in regions previously associated with this trait by GWAS in Quarter Horse racehorses with contrasting maximum speed index (SImax), prospecting causal gene polymorphisms that are related to or are in strong linkage disequilibrium with racing performance. Genotypes were imputed using 116 horses genotyped with the 54k SNP array and 233 animals genotyped with the 65k array. For the simulations, random samples were chosen to compose the imputed and reference populations in two scenarios. Scenario A simulated the genotype imputation in the first step (from 65k to 54k) and scenario B in the second step (from 54k to 65k). Thus, after quality control, the 54k and 65k panels contained 7,048 and 16,940 exclusive markers, respectively. The mean concordance rate was 0.9815 and 0.9751 for scenarios A and B, and the mean allelic r2 was 0.9791 and 0.974, respectively. After imputation was performed by the single-step GBLUP method using two approaches: ssGWAS1 in which only SNP effects are recalculated at each iteration, and ssGWAS2 in which SNP effects are recalculated from genomic estimated breeding values (GEBVs) at each iteration. Twenty-one regions that explained more than 1% of the total genetic variance (gVar) in the maximum speed index were identified by ssGWAS1 and 12 by ssGWAS2. More than 40% of gVar was explained by these regions in ssGWAS1 and about 30% in ssGWAS2. Among chromosomes that explained more than 1% of genetic variance, five were common to both methods (ECA 3, 10, 15, 22, 25). A total of 108 genes were identified with the first approach and 59 with the second approach. To exome sequencing, GEBVs were used for the formation of two groups of animals with contrasting racing performance (20 animals with superior SI max and 20 with inferior SI max). Good quality data were obtained throughout the reads sequenced, with an average coverage of 43x. A total of 1,203 variants (1,105 SNPs and 93 InDels) were identified in 33 regions of interest obtained previously by GWAS; of these, 61.3% were not registered/deposited in the horse genomic variant database. Twenty-nine of the polymorphisms (24 SNPs and 5 InDels) were considered to be important based on three different and independent approaches: SIFT scores classified as deleterious (<0.05), degree of impact on the consensus region of each polymorphism, and different allele frequencies identified by Fisher’s exact test (p< 0.01) between the groups of horses with contrasting SImax. Thus, eight genes described as functional and positional candidates in previous studies (ABCG5, COL11A1, GEN1, SOCS3, MICAL1, SPTBN1, EPB41L3, and SHQ1) and eight new candidate genes (AKNA, ARMC2, FKBP15, LHX1, NOL10, TMEM192, ZFP37, FIG4, and HNRNPU), some of them with known function, were related to racing performance in Quarter Horses. Taken together, the present results show that the racing performance of Quarter Horses, given by the maximum speed index, is a quantitative trait and that no major genes exist.
Descrição
Palavras-chave
Exoma, GWAS, Imputação de genótipos, InDels, Índice de velocidade, SNPs