Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Xia J

PROVIDER: S-EPMC5294460 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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

Xia Jiangwei J Fan Huizhong H Chang Tianpeng T Xu Lingyang L Zhang Wengang W Song Yuxin Y Zhu Bo B Zhang Lupei L Gao Xue X Chen Yan Y Li Junya J Gao Huijiang H

Scientific reports 20170207

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 ...[more]

PMID: 28169328

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Project description:BACKGROUND:Little is known about the genetic architecture of economically important traits in Brown Swiss cattle because only few genome-wide association studies (GWAS) have been carried out in this breed. Moreover, most GWAS have been performed for single traits, thus not providing detailed insights into potentially existing pleiotropic effects of trait-associated loci. RESULTS:To compile a comprehensive catalogue of large-effect quantitative trait loci (QTL) segregating in Brown Swiss cattle, we carried out association tests between partially imputed genotypes at 598,016 SNPs and daughter-derived phenotypes for more than 50 economically important traits, including milk production, growth and carcass quality, body conformation, reproduction and calving traits in 4578 artificial insemination bulls from two cohorts of Brown Swiss cattle (Austrian-German and Swiss populations). Across-cohort multi-trait meta-analyses of the results from the single-trait GWAS revealed 25 quantitative trait loci (QTL; P?<?8.36?×?10-?8) for economically relevant traits on 17 Bos taurus autosomes (BTA). Evidence of pleiotropy was detected at five QTL located on BTA5, 6, 17, 21 and 25. Of these, two QTL at BTA6:90,486,780 and BTA25:1,455,150 affect a diverse range of economically important traits, including traits related to body conformation, calving, longevity and milking speed. Furthermore, the QTL at BTA6:90,486,780 seems to be a target of ongoing selection as evidenced by an integrated haplotype score of 2.49 and significant changes in allele frequency over the past 25?years, whereas either no or only weak evidence of selection was detected at all other QTL. CONCLUSIONS:Our findings provide a comprehensive overview of QTL segregating in Brown Swiss cattle. Detected QTL explain between 2 and 10% of the variation in the estimated breeding values and thus may be considered as the most important QTL segregating in the Brown Swiss cattle breed. Multi-trait association testing boosts the power to detect pleiotropic QTL and assesses the full spectrum of phenotypes that are affected by trait-associated variants.

Dataset Information

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

Publications

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

Similar Datasets

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