Capturing the spectrum of interaction effects in genetic association studies by simulated evaporative cooling network analysis.
Capturing the spectrum of interaction effects in genetic association studies by simulated evaporative cooling network analysis.
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DOI：
10.1371/journal.pgen.1000432
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发表时间：
2009-03
期刊：
影响因子：
4.5
通讯作者：
Tian D
中科院分区：
文献类型：
--
作者：
McKinney BA;Crowe JE;Guo J;Tian D
来自人类遗传学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，用于鉴定孟德尔疾病基因的分析方法是不合适的，因为这样的方法一次仅研究一个遗传位点与表型的关联。需要新的策略，可以捕获遗传效应的频谱，从孟德尔到多因素上位性。随机森林（RF）和Relief-F是两种强大的机器学习方法，它们被研究为遗传病例对照数据的过滤器，因为它们能够在对个体遗传变异与表型的相关性进行评分时考虑多个基因的等位基因背景。然而，当变体强烈相互作用时，树节点分裂准则中RF的独立性假设导致相关变体的重要性分数降低。另一方面，Relief-F被设计用于检测强相互作用，但对与表型分类无关的变异的大背景敏感，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）特征选择，这是一种灵活的机器学习方法，可以整合多个重要性得分，同时去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关联相互作用网络（GAIN），其边缘量化了变异体之间的协同作用。我们使用模拟分析表明，EC是能够识别广泛的遗传关联数据中的相互作用的影响。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，并推断与不良事件相关的SNP集合的GAIN。我们的研究结果表明，在SNP疾病易感性网络的枢纽的重要作用。该软件可在www.example.com上获得。对许多疾病和病症的易感性是由遗传网络中多个点的故障引起的。这些细分点中的每一个本身可能对疾病风险产生非常温和的影响，但这些点通过彼此之间的统计相互作用可能产生更强的影响。全基因组关联研究提供了鉴定多个基因座上的等位基因的机会，这些等位基因相互作用以影响常见疾病和病症中的表型变异。然而，如果每个SNP都被测试关联性，就好像它独立于基因组的其余部分一样，那么来自基因组上的标记的变异的全部优势将无法实现。在这项研究中，我们说明了一种新的高维遗传关联分析方法的实用性，该方法将SNP的集合视为系统层面的相互作用。这种方法使用机器学习过滤器，然后使用信息论和图论方法来推断相互作用的SNP的表型特异性网络。
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. Susceptibility to many diseases and disorders is caused by breakdown at multiple points in the genetic network. Each of these points of breakdown by itself may have a very modest effect on disease risk but the points may have a much stronger effect through statistical interactions with each other. Genome-wide association studies provide the opportunity to identify alleles at multiple loci that interact to influence phenotypic variation in common diseases and disorders. However, if each SNP is tested for association as though it were independent of the rest of the genome, then the full advantage of the variation from markers across the genome will be unfulfilled. In this study, we illustrate the utility of a new approach to high-dimensional genetic association analysis that treats the collection of SNPs as interacting on a system level. This approach uses a machine-learning filter followed by an information theoretic and graph theoretic approach to infer a phenotype-specific network of interacting SNPs.
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