Power of multifactor dimensionality reduction for detecting gene-gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity
Power of multifactor dimensionality reduction for detecting gene-gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity
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DOI：
10.1002/gepi.10218
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发表时间：
2003-02-01
影响因子：
2.1
通讯作者：
Moore, JH
中科院分区：
文献类型：
--
作者：
Ritchie, MD;Hahn, LW;Moore, JH
主要通过与其他基因和其他环境因素的相互作用来影响常见、复杂的多因素疾病风险的基因的识别和表征，仍然是遗传流行病学中的统计和计算挑战。这一挑战部分是由于参数统计方法的局限性，检测基因的影响，是完全或部分依赖于与其他基因和环境暴露的相互作用。我们以前介绍了多因素降维（MDR）作为一种方法，用于减少多位点基因型信息的维数，以提高与疾病风险相关的多态性组合的识别。MDR方法是非参数的（即，没有关于统计参数值的假设），是无模型的（即，假设没有特定的遗传模型），并直接适用于病例对照和不一致同胞对研究设计。经验和理论研究都表明，MDR具有识别高阶基因-基因相互作用的优异能力。然而，MDR在常见噪声源存在下识别基因-基因相互作用的能力目前尚不清楚。本研究的目的是评估MDR在存在基因分型错误、缺失数据、表型和遗传或基因座异质性导致的噪声的情况下识别基因-基因相互作用的能力。使用模拟数据，我们表明，MDR有很高的权力，以确定基因-基因的相互作用存在5%的基因分型错误，5%的数据缺失，或两者的组合。然而，MDR在存在50%表型复制的情况下降低了某些模型的功效，并且在存在50%遗传异质性的情况下功效非常有限。扩展MDR以解决遗传异质性应该是这种新方法的持续方法学发展的优先事项。(C)2003 Wiley-Liss，Inc.
The identification and characterization of genes that influence the risk of common, complex multifactorial diseases, primarily through interactions with other genes and other environmental factors, remains a statistical and computational challenge in genetic epidemiology. This challenge is partly due to the limitations of parametric statistical methods for detecting genetic effects that are dependent solely or partially on interactions with other genes and environmental exposures. We previously introduced multifactor dimensionality reduction (MDR) as a method for reducing the dimensionality of multilocus genotype information to improve the identification of polymorphism combinations associated with disease risk. The MDR approach is nonparametric (i.e., no hypothesis about the value of a statistical parameter is made), is model-free (i.e., assumes no particular inheritance model), and is directly applicable to case-control and discordant sib-pair study designs. Both empirical and theoretical studies suggest that MDR has excellent power for identifying high-order gene-gene interactions. However, the power of MDR for identifying gene-gene interactions in the presence of common sources of noise is not currently known. The goal of this study was to evaluate the power of MDR for identifying gene-gene interactions in the presence of noise due to genotyping error, missing data, phenocopy, and genetic or locus heterogeneity. Using simulated data, we show that MDR has high power to identify gene-gene interactions in the presence of 5% genotyping error, 5% missing data, or a combination of both. However, MDR has reduced power for some models in the presence of 50% phenocopy, and very limited power in the presence of 50% genetic heterogeneity. Extending MDR to address genetic heterogeneity should be a priority for the continued methodological development of this new approach. (C) 2003 Wiley-Liss, Inc.