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Data-driven integration of epidemiological and toxicological data to select candidate interacting genes and environmentaln factors in association with disease

机译:数据驱动的流行病学和毒理学数据整合,选择与疾病相关的候选相互作用基因和环境因素

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摘要

Motivation: Complex diseases, such as Type 2 Diabetes Mellitus (T2D), result from the interplay of both environmental and genetic factors. However, most studies investigate either the genetics or the environment and there are a few that study their possible interaction in context of disease. One key challenge in documenting interactions between genes and environment includes choosing which of each to test jointly. Here, we attempt to address this challenge through a data-driven integration of epidemiological and toxicological studies. Specifically, we derive lists of candidate interacting genetic and environmental factors by integrating findings from genome-wide and environment-wide association studies. Next, we search for evidence of toxicological relationships between these genetic and environmental factors that may have an etiological role in the disease. We illustrate our method by selecting candidate interacting factors for T2D.
机译:动机:复杂的疾病,例如2型糖尿病(T2D),是环境和遗传因素相互作用的结果。但是,大多数研究都对遗传学或环境进行了研究,很少有人研究其在疾病背景下的可能相互作用。记录基因和环境之间相互作用的一个关键挑战包括选择要共同测试的每个。在这里,我们试图通过数据驱动的流行病学和毒理学研究的整合来应对这一挑战。具体而言,我们通过整合来自全基因组和环境范围的关联研究的结果,得出候选相互作用的遗传和环境因素的列表。接下来,我们寻找这些遗传和环境因素之间的毒理关系的证据,这些因素可能与疾病的病因有关。我们通过选择T2D的候选相互作用因子来说明我们的方法。

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  • 来源
    《Bioinformatics》 |2012年第12期|p.121-126|共6页
  • 作者

    Atul J. Butte;

  • 作者单位
  • 收录信息 美国《科学引文索引》(SCI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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