To control false positives in gene-gene interaction analysis: two novel conditional entropy-based approaches.
To control false positives in gene-gene interaction analysis: two novel conditional entropy-based approaches.
复制标题
控制基因间相互作用分析中的假阳性：两种新颖的基于条件熵的方法
DOI：
10.1371/journal.pone.0081984
复制
发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Qin J
中科院分区：
文献类型：
--
作者：
Zuo X;Rao S;Fan A;Lin M;Li H;Zhao X;Qin J
全基因组范围内的基因-基因相互作用分析被认为是一个强有力的途径，以确定丢失的遗传成分，不能使用目前的单点关联分析检测。最近，开发了几种无模型方法（例如，常用的基于信息的度量和几种基于逻辑回归的度量）用于检测遗传基因座之间的非线性依赖性，但是它们潜在地具有夸大的假阳性误差的风险，特别是当一个或两个基因座处的主效应显著时。在这项研究中，我们提出了两个条件熵为基础的指标来挑战这一限制。大量的模拟表明，提出的两个指标，疾病是罕见的，可以保持一致的正确的假阳性率。在常见疾病的情况下，我们提出的指标实现了更好或相当的假阳性错误的控制，相比之前提出的四个无模型指标。在功效方面，我们的方法在一系列常见疾病模型中优于几个竞争指标。此外，在真实的数据分析中，这两个指标都成功地检测到相互作用，并与最初报告的结果或逻辑回归方法竞争。总之，所提出的条件熵为基础的指标是有前途的替代目前基于模型的方法来检测真正的上位效应。
Genome-wide analysis of gene-gene interactions has been recognized as a powerful avenue to identify the missing genetic components that can not be detected by using current single-point association analysis. Recently, several model-free methods (e.g. the commonly used information based metrics and several logistic regression-based metrics) were developed for detecting non-linear dependence between genetic loci, but they are potentially at the risk of inflated false positive error, in particular when the main effects at one or both loci are salient. In this study, we proposed two conditional entropy-based metrics to challenge this limitation. Extensive simulations demonstrated that the two proposed metrics, provided the disease is rare, could maintain consistently correct false positive rate. In the scenarios for a common disease, our proposed metrics achieved better or comparable control of false positive error, compared to four previously proposed model-free metrics. In terms of power, our methods outperformed several competing metrics in a range of common disease models. Furthermore, in real data analyses, both metrics succeeded in detecting interactions and were competitive with the originally reported results or the logistic regression approaches. In conclusion, the proposed conditional entropy-based metrics are promising as alternatives to current model-based approaches for detecting genuine epistatic effects.
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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：
64.5
作者：
Bond, GL;Hu, WW;Levine, AJ
通讯作者：
Levine, AJ
DOI：
10.1155/2007/14741
发表时间：
2007
期刊：
EURASIP journal on bioinformatics & systems biology
影响因子：
--
作者：
Aktulga HM;Kontoyiannis I;Lyznik LA;Szpankowski L;Grama AY;Szpankowski W
通讯作者：
Szpankowski W
DOI：
10.1038/ejhg.2009.38
发表时间：
2009-10
期刊：
European journal of human genetics : EJHG
影响因子：
--
作者：
通讯作者：
--
影响因子：
5.2
作者：
Dong, Changzheng;Chu, Xun;Li, Yixue
通讯作者：
Li, Yixue