Comparative analysis of methods for detecting interacting loci.
Comparative analysis of methods for detecting interacting loci.
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DOI：
10.1186/1471-2164-12-344
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发表时间：
2011-07-05
期刊：
影响因子：
4.4
通讯作者：
Wang Y
中科院分区：
文献类型：
--
作者：
Chen L;Yu G;Langefeld CD;Miller DJ;Guy RT;Raghuram J;Yuan X;Herrington DM;Wang Y
遗传位点之间的相互作用被认为在疾病风险中起重要作用。虽然已经提出了许多方法来检测这种相互作用，但它们的相对性能在很大程度上仍然不清楚，主要是因为在介绍这些方法的论文和后续研究中使用了不同的数据来源，检测性能标准和实验方案。此外，很少有研究严格集中在现有方法的比较。鉴于检测基因-基因和基因-环境相互作用的重要性，需要对现有相互作用检测方法的性能和局限性进行严格、全面的比较。我们报告了八种代表性方法的比较，其中七种是专门设计用于检测单核苷酸多态性（SNP）之间的相互作用，最后一种是流行的主效应检验方法，用作性能评估的基线。在大量模拟数据集上比较了所选方法，多因素降维（MDR）、全相互作用模型（FMD）、信息增益（IG）、贝叶斯上位关联映射（BEAM）、SNP收集器（SH）、最大熵条件概率模型（MECPM）、具有相互作用项的逻辑回归（LRIT）和逻辑回归（LR），每种方法均与复杂疾病模型一致，在不同的相互作用模型下嵌入多组相互作用的SNP。评估标准包括几个相关的检测能力的措施，家庭明智的I类错误率，和计算复杂性。这项研究有几个重要的结果。首先，虽然成功地检测到了具有强效应的相互作用中的一些SNP，但大多数方法以可接受的假阳性率错过了许多相互作用的SNP。在这项研究中，表现最好的方法是MECPM。其次，一些方法用于控制I类错误率的统计学显著性评估标准相当保守，从而限制了它们的功效，并且难以对其进行公平比较。第三，正如预期的那样，不同模型的功效是不同的，并且是等位基因频率、次要等位基因频率、连锁不平衡和边际效应的函数。第四，权力和这些因素之间的分析关系，推导出，有助于解释的研究结果。第五，对于这些方法，主效应的大小影响检验的功效。第六，大多数方法可以检测一些真实的SNP，但检测整个相互作用的SNP集的能力有限。这项比较研究提供了新的见解，目前的方法检测相互作用的基因座的优势和局限性。这项研究，沿着与免费提供的模拟工具，我们提供，应有助于支持改进方法的发展。模拟工具可在以下网址获得：http://code.google.com/p/simulation-tool-bmc-ms9169818735220977/downloads/list。
Interactions among genetic loci are believed to play an important role in disease risk. While many methods have been proposed for detecting such interactions, their relative performance remains largely unclear, mainly because different data sources, detection performance criteria, and experimental protocols were used in the papers introducing these methods and in subsequent studies. Moreover, there have been very few studies strictly focused on comparison of existing methods. Given the importance of detecting gene-gene and gene-environment interactions, a rigorous, comprehensive comparison of performance and limitations of available interaction detection methods is warranted. We report a comparison of eight representative methods, of which seven were specifically designed to detect interactions among single nucleotide polymorphisms (SNPs), with the last a popular main-effect testing method used as a baseline for performance evaluation. The selected methods, multifactor dimensionality reduction (MDR), full interaction model (FIM), information gain (IG), Bayesian epistasis association mapping (BEAM), SNP harvester (SH), maximum entropy conditional probability modeling (MECPM), logistic regression with an interaction term (LRIT), and logistic regression (LR) were compared on a large number of simulated data sets, each, consistent with complex disease models, embedding multiple sets of interacting SNPs, under different interaction models. The assessment criteria included several relevant detection power measures, family-wise type I error rate, and computational complexity. There are several important results from this study. First, while some SNPs in interactions with strong effects are successfully detected, most of the methods miss many interacting SNPs at an acceptable rate of false positives. In this study, the best-performing method was MECPM. Second, the statistical significance assessment criteria, used by some of the methods to control the type I error rate, are quite conservative, thereby limiting their power and making it difficult to fairly compare them. Third, as expected, power varies for different models and as a function of penetrance, minor allele frequency, linkage disequilibrium and marginal effects. Fourth, the analytical relationships between power and these factors are derived, aiding in the interpretation of the study results. Fifth, for these methods the magnitude of the main effect influences the power of the tests. Sixth, most methods can detect some ground-truth SNPs but have modest power to detect the whole set of interacting SNPs. This comparison study provides new insights into the strengths and limitations of current methods for detecting interacting loci. This study, along with freely available simulation tools we provide, should help support development of improved methods. The simulation tools are available at: http://code.google.com/p/simulation-tool-bmc-ms9169818735220977/downloads/list.
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DOI：
10.1038/nrg2579
发表时间：
2009-06
期刊：
Nature reviews. Genetics
影响因子：
--
作者：
Cordell HJ
通讯作者：
Cordell HJ
影响因子：
1.9
作者：
Garcia-Magarinos, Manuel;Lopez-de-Ullibarri, Inaki;Salas, Antonio
通讯作者：
Salas, Antonio
DOI：
10.1056/nejmra0808700
发表时间：
2009-04-23
期刊：
The New England journal of medicine
影响因子：
--
作者：
Hardy J;Singleton A
通讯作者：
Singleton A
影响因子：
3.5
作者：
Easton, Douglas F.;Eeles, Rosalind A.
通讯作者：
Eeles, Rosalind A.
影响因子：
6.2
作者：
Hussman JP;Chung RH;Griswold AJ;Jaworski JM;Salyakina D;Ma D;Konidari I;Whitehead PL;Vance JM;Martin ER;Cuccaro ML;Gilbert JR;Haines JL;Pericak-Vance MA
通讯作者：
Pericak-Vance MA