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A generalized combinatorial approach for detecting gene-by-gene and gene-by-environment interactions with application to nicotine dependence.

Abstract：

:The determination of gene-by-gene and gene-by-environment interactions has long been one of the greatest challenges in genetics. The traditional methods are typically inadequate because of the problem referred to as the "curse of dimensionality." Recent combinatorial approaches, such as the multifactor dimensionality reduction (MDR) method, the combinatorial partitioning method, and the restricted partition method, have a straightforward correspondence to the concept of the phenotypic landscape that unifies biological, statistical genetics, and evolutionary theories. However, the existing approaches have several limitations, such as not allowing for covariates, that restrict their practical use. In this study, we report a generalized MDR (GMDR) method that permits adjustment for discrete and quantitative covariates and is applicable to both dichotomous and continuous phenotypes in various population-based study designs. Computer simulations indicated that the GMDR method has superior performance in its ability to identify epistatic loci, compared with current methods in the literature. We applied our proposed method to a genetics study of four genes that were reported to be associated with nicotine dependence and found significant joint action between CHRNB4 and NTRK2. Moreover, our example illustrates that the newly proposed GMDR approach can increase prediction ability, suggesting that its use is justified in practice. In summary, GMDR serves the purpose of identifying contributors to population variation better than do the other existing methods.

journal_name

Am J Hum Genet

journal_title

American journal of human genetics

authors

Lou XY,Chen GB,Yan L,Ma JZ,Zhu J,Elston RC,Li MD

doi

10.1086/518312

subject

Has Abstract

pub_date

2007-06-01 00:00:00

pages

1125-37

issue

6

eissn

0002-9297

issn

1537-6605

pii

S0002-9297(07)61030-1

journal_volume

80

pub_type

杂志文章

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