Niu Adan, Zhang Shuanglin, Sha Qiuying
Department of Mathematical Sciences, Michigan Technological University, Houghton, MI 49931, USA.
Ann Hum Genet. 2011 Nov;75(6):742-54. doi: 10.1111/j.1469-1809.2011.00681.x.
Complex diseases are presumed to be the result of multiple genes and environmental factors, which emphasize the importance of gene - gene and gene - environment interactions. Traditional parametric approaches are limited in their ability to detect high-order interactions and handle sparse data, and standard stepwise procedures may miss interactions with undetectable main effects. To address these limitations, the multifactor dimensionality reduction (MDR) method was developed. MDR is well suited for examining high-order interactions and detecting interactions without main effects. Like most statistical methods in genetic association studies, MDR may also lead to a false positive in the presence of population stratification. Although many statistical methods have been proposed to detect main effects and control for population stratification using genomic markers, not many methods are available to detect interactions and control for population stratification at the same time. In this article, we developed a novel test, MDR in structured populations (MDR-SP), to detect the interactions and control for population stratification. MDR-SP is applicable to both quantitative and qualitative traits and can incorporate covariates. We present simulation studies to demonstrate the validity of the test and to evaluate its power.
复杂疾病被认为是多种基因和环境因素共同作用的结果，这凸显了基因-基因以及基因-环境相互作用的重要性。传统的参数方法在检测高阶相互作用和处理稀疏数据方面能力有限，而且标准的逐步程序可能会遗漏那些主效应无法检测到的相互作用。为了解决这些局限性，多因素降维（MDR）方法应运而生。MDR非常适合用于检验高阶相互作用以及检测无主效应的相互作用。与遗传关联研究中的大多数统计方法一样，在存在群体分层的情况下，MDR也可能导致假阳性结果。尽管已经提出了许多统计方法来利用基因组标记检测主效应并控制群体分层，但同时检测相互作用并控制群体分层的方法却不多。在本文中，我们开发了一种新的检验方法——结构化群体中的MDR（MDR-SP），用于检测相互作用并控制群体分层。MDR-SP适用于定量和定性性状，并且可以纳入协变量。我们通过模拟研究来证明该检验方法的有效性并评估其效能。
