Discovering pair-wise genetic interactions: an information theory-based approach.

Discovering pair-wise genetic interactions: an information theory-based approach.
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DOI:
10.1371/journal.pone.0092310
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发表时间:
2014
期刊:
影响因子:
3.7
通讯作者:
Galas DJ
Galas DJ
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Ignac TM;Skupin A;Sakhanenko NA;Galas DJ

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表型变异,包括人类健康和疾病的基础,部分是由遗传变异和环境因素之间的多重相互作用造成的。虽然单基因变异引起的疾病或表型可以通过既定的关联方法和基于家庭的方法确定,但由多基因相互作用产生的复杂表型特征仍然很难表征。在这里,我们描述了一种基于信息论的新方法,并展示了它如何改进以前的方法来识别遗传相互作用,包括合成和修饰类型的相互作用。我们将我们的测量,称为相互作用距离,应用于先前分析的酵母产孢效率数据集,脂质相关小鼠数据和几种人类疾病模型来表征该方法。我们展示了相互作用距离如何在实验和模拟数据集中揭示新的基因相互作用候选者,并在几种情况下优于其他措施。该方法还允许我们优化临床研究的病例/对照样本组成。
Phenotypic variation, including that which underlies health and disease in humans, results in part from multiple interactions among both genetic variation and environmental factors. While diseases or phenotypes caused by single gene variants can be identified by established association methods and family-based approaches, complex phenotypic traits resulting from multi-gene interactions remain very difficult to characterize. Here we describe a new method based on information theory, and demonstrate how it improves on previous approaches to identifying genetic interactions, including both synthetic and modifier kinds of interactions. We apply our measure, called interaction distance, to previously analyzed data sets of yeast sporulation efficiency, lipid related mouse data and several human disease models to characterize the method. We show how the interaction distance can reveal novel gene interaction candidates in experimental and simulated data sets, and outperforms other measures in several circumstances. The method also allows us to optimize case/control sample composition for clinical studies.
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