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

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

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

Xiang-Yang Lou,Guo-Bo Chen,Lei Yan,Jennie Z Ma,Jun Zhu,Robert C Elston,Ming D Li,

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.

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

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

For similar genetic phenomena: variation (genetics) research abstracts see: genetic phenomena: variation (genetics) research

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

PUBLICATION TYPE: Research Support, Non-U.S. Gov

Journal: American journal of human genetics

VOLUME: 80

Page Numbers: 1125-37

Journal Abbreviation: Am. J. Hum. Genet.

ISSN: 0002-9297

DAY: 25

MONTH: 04

YEAR: 2007

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

Number of References:

LANGUAGE: eng

NlmUniqueID: 370475

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

KEYWORDS: Variation (Genetics)

MESH TERMS: genetics

Chemical & Substance for Abstract: A generalized combinatorial approach for detecting gene-by-gene and gene-by-environment interactions with application to nicotine dependence. Information

Substance Name: Receptor, trkB

Registry Number: EC 2.7.1.112

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

AFFILIATION: Department of Psychiatry and Neurobehavioral Sciences, University of Virginia, Charlottesville, VA 22911, USA.

Country: United States

AGENCY: United States NIGMS

GRANT: GM28356

ACRONYM: GM

MEDLINETA: Am J Hum Genet

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