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Identification of subgroups with large differential treatment effects in genome-wide association studies.

机译:在全基因组关联研究中鉴定具有较大差异治疗效果的亚组。

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摘要

Correct identification of the important genetic markers in a genome-wide association study is a formidable task due to the high chance of false positives. This work focuses on the problem of identifying the subgroups and their associated markers that yield large absolute risk reductions in a placebo-controlled setting. Several promising methods are proposed and examined for detecting subgroups defined by two or more of such markers. The methods employ decision trees, importance scoring, LOWESS smoothing, multi-step searching, screening and random data perturbation. Results from simulation experiments demonstrating the effectiveness of the methods are reported.
机译:在全基因组关联研究中正确识别重要的遗传标记是一项艰巨的任务,因为假阳性的可能性很高。这项工作的重点是确定在安慰剂对照的环境中能够显着降低绝对风险的亚组及其相关标志物。提出并检验了几种有前途的方法来检测由两个或更多个这样的标记物定义的亚组。该方法采用决策树,重要性评分,LOWESS平滑,多步搜索,筛选和随机数据扰动。报告了模拟实验的结果,证明了该方法的有效性。

著录项

  • 作者

    He, Xu.;

  • 作者单位

    The University of Wisconsin - Madison.;

  • 授予单位 The University of Wisconsin - Madison.;
  • 学科 Biology Biostatistics.;Statistics.
  • 学位 Ph.D.
  • 年度 2012
  • 页码 152 p.
  • 总页数 152
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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