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Searching for new loci and candidate genes for economically important traits through gene-based association analysis of Simmental cattle

Single-marker genome-wide association study (GWAS) is a convenient strategy of genetic analysis that has been successful in detecting the association of a number of single-nucleotide polymorphisms (SNPs) with quantitative traits. However, analysis of individual SNPs can only account for a small prop...

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Detalles Bibliográficos
Autores principales: Xia, Jiangwei, Fan, Huizhong, Chang, Tianpeng, Xu, Lingyang, Zhang, Wengang, Song, Yuxin, Zhu, Bo, Zhang, Lupei, Gao, Xue, Chen, Yan, Li, Junya, Gao, Huijiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5294460/
https://www.ncbi.nlm.nih.gov/pubmed/28169328
http://dx.doi.org/10.1038/srep42048
Descripción
Sumario:Single-marker genome-wide association study (GWAS) is a convenient strategy of genetic analysis that has been successful in detecting the association of a number of single-nucleotide polymorphisms (SNPs) with quantitative traits. However, analysis of individual SNPs can only account for a small proportion of genetic variation and offers only limited knowledge of complex traits. This inadequacy may be overcome by employing a gene-based GWAS analytic approach, which can be considered complementary to the single-SNP association analysis. Here we performed an initial single-SNP GWAS for bone weight (BW) and meat pH value with a total of 770,000 SNPs in 1141 Simmental cattle. Additionally, 21836 cattle genes collected from the Ensembl Genes 83 database were analyzed to find supplementary evidence to support the importance of gene-based association study. Results of the single SNP-based association study showed that there were 11 SNPs significantly associated with bone weight (BW) and two SNPs associated with meat pH value. Interestingly, all of these SNPs were located in genes detected by the gene-based association study.