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首页> 外文期刊>PLoS Genetics >Capturing the Spectrum of Interaction Effects in Genetic Association Studies by Simulated Evaporative Cooling Network Analysis
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Capturing the Spectrum of Interaction Effects in Genetic Association Studies by Simulated Evaporative Cooling Network Analysis

机译：通过模拟蒸发冷却网络分析捕获遗传关联研究中相互作用效应的光谱

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Evidence from human genetic studies of several disorders suggests that interactions between alleles at multiple genes play an important role in influencing phenotypic expression. Analytical methods for identifying Mendelian disease genes are not appropriate when applied to common multigenic diseases, because such methods investigate association with the phenotype only one genetic locus at a time. New strategies are needed that can capture the spectrum of genetic effects, from Mendelian to multifactorial epistasis. Random Forests (RF) and Relief-F are two powerful machine-learning methods that have been studied as filters for genetic case-control data due to their ability to account for the context of alleles at multiple genes when scoring the relevance of individual genetic variants to the phenotype. However, when variants interact strongly, the independence assumption of RF in the tree node-splitting criterion leads to diminished importance scores for relevant variants. Relief-F, on the other hand, was designed to detect strong interactions but is sensitive to large backgrounds of variants that are irrelevant to classification of the phenotype, which is an acute problem in genome-wide association studies. To overcome the weaknesses of these data mining approaches, we develop Evaporative Cooling (EC) feature selection, a flexible machine learning method that can integrate multiple importance scores while removing irrelevant genetic variants. To characterize detailed interactions, we construct a genetic-association interaction network (GAIN), whose edges quantify the synergy between variants with respect to the phenotype. We use simulation analysis to show that EC is able to identify a wide range of interaction effects in genetic association data. We apply the EC filter to a smallpox vaccine cohort study of single nucleotide polymorphisms (SNPs) and infer a GAIN for a collection of SNPs associated with adverse events. Our results suggest an important role for hubs in SNP disease susceptibility networks. The software is available at http://sites.google.com/site/McKinneyLab/software.
机译：人类基因研究的几种疾病的证据表明，多个基因的等位基因之间的相互作用在影响表型表达中起着重要作用。当应用于常见的多基因疾病时，鉴定孟德尔疾病基因的分析方法是不合适的，因为此类方法一次仅调查与一个遗传位点的表型的关联。需要新的策略来捕获从孟德尔到多因素上位性的遗传效应。随机森林（RF）和Relief-F是两种功能强大的机器学习方法，已被研究用作遗传病例控制数据的过滤器，因为它们在对单个遗传变异的相关性进行评分时能够考虑多个基因的等位基因背景表型。但是，当变体相互作用强烈时，树节点拆分准则中RF的独立性假设会导致相关变体的重要性得分降低。另一方面，Relief-F旨在检测强烈的相互作用，但对与表型分类无关的变体的大背景敏感，这是全基因组关联研究中的一个严重问题。为了克服这些数据挖掘方法的弱点，我们开发了蒸发冷却（EC）功能选择，这是一种灵活的机器学习方法，可以整合多个重要性评分，同时删除不相关的遗传变异。为了表征详细的相互作用，我们构建了一个遗传联想相互作用网络（GAIN），其边缘量化了表型变异之间的协同作用。我们使用模拟分析表明，EC能够识别遗传关联数据中的多种相互作用效应。我们将EC过滤器应用于单核苷酸多态性（SNP）的天花疫苗队列研究，并推断GAIN收集与不良事件相关的SNP。我们的结果表明枢纽在SNP疾病易感性网络中的重要作用。可以从http://sites.google.com/site/McKinneyLab/software获得该软件。

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