...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Bioinformatics and Computational Biology >Analysis of multiple related phenotypes in genome-wide association studies
【24h】

Analysis of multiple related phenotypes in genome-wide association studies

机译：基因组关联研究中多种相关表型分析

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Most genome-wide association studies (GWAS) have been conducted by focusing on one phenotype of interest for identifying genetic variants associated with common complex phenotypes. However, despite many successful results from GWAS, only a small number of genetic variants tend to be identified and replicated given a very stringent genome-wide significance criterion, and explain only a small fraction of phenotype heritability. In order to improve power by using more information from data, we propose an alternative multivariate approach, which considers multiple related phenotypes simultaneously. We demonstrate through computer simulation that the multivariate approach can improve power for detecting disease-predisposing genetic variants and pleiotropic variants that have simultaneous effects on multiple related phenotypes. We apply the multivariate approach to a GWA dataset of 8,842 Korean individuals genotyped for 327,872 SNPs, and detect novel genetic variants associated with metabolic syndrome related phenotypes. Considering several related phenotype simultaneously, the multivariate approach provides not only more powerful results than the conventional univariate approach but also clue to identify pleiotropic genes that are important to the pathogenesis of many related complex phenotypes.
机译：通过专注于鉴定与常见复杂表型相关的遗传变异的感兴趣的一种表型来进行大多数基因组关联研究（GWAS）。然而，尽管Gwas的许多成功的结果，但只有少量的遗传变体才能被鉴定并复制，并且给予非常严格的基因组显着性标准，并且仅解释一小部分的表型可遗传性。为了通过使用来自数据的更多信息来提高能力，我们提出一种替代的多变量方法，其同时考虑多种相关表型。我们通过计算机模拟证明多变量方法可以改善检测疾病预测遗传变异的能力和具有对多种相关表型同时效应的疾病预测的能力。我们将多变量的方法应用于8,842个韩国人的GWA数据集进行基因分为327,872 SNP，并检测与代谢综合征相关表型相关的新型遗传变体。同时考虑几种相关的表型，多变量方法不仅提供比传统的单变量方法更强大的结果，而且还提供了鉴定对许多相关复杂表型的发病机制很重要的抗性基因。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号