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Evaluating Epistatic Interaction Signals In Complex Traits Using Quantitative Traits

O. Mukherjee, K. R. Sanapala, Padmanabhan Anbazhagana, S. Ghosh
Published 2009 · Medicine

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Rheumatoid arthritis (RA) is a complex, chronic inflammatory disease implicated to have several plausible candidate loci; however, these may not account for all the genetic variations underlying RA. Common disorders are hypothesized to be highly complex with interaction among genes and other risk factors playing a major role in the disease process. This complexity is further magnified because such interactions may be with or without a strong independent effect and are thus difficult to detect using traditional statistical methodologies. The main challenge to analyze such gene × gene and gene × environment interaction is attributed to a phenomenon referred to as the "curse of dimensionality." Several combinatorial methodologies have been proposed to tackle this analytical challenge. Because quantitative traits underlie complex phenotypes and contain more information on the trait variation within genotypes than qualitative dichotomy, analyzing quantitative traits correlated with the affection status is a more powerful tool for mapping such trait genes. Recently, a generalized multifactor dimensionality reduction method was proposed that allows for adjustment for discrete and quantitative traits and can be used to analyze qualitative and quantitative phenotypes in a population based study design.In this report, we evaluate the efficiency of the generalized multifactor dimensionality reduction statistical suite to decipher small interacting factors that contribute to RA disease pathogenesis.
This paper references
10.1101/GR.172901
A combinatorial partitioning method to identify multilocus genotypic partitions that predict quantitative trait variation.
M. Nelson (2001)
10.1086/518312
A generalized combinatorial approach for detecting gene-by-gene and gene-by-environment interactions with application to nicotine dependence.
Xiang-Yang Lou (2007)
10.1086/321276
Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer.
M. Ritchie (2001)
10.1093/bioinformatics/btf869
Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions
L. Hahn (2003)
10.1002/ART.10989
Screening the genome for rheumatoid arthritis susceptibility genes: a replication study and combined analysis of 512 multicase families.
D. Jawaheer (2003)
10.1186/1753-6561-1-S1-S30
A two-stage classification approach identifies seven susceptibility genes for a simulated complex disease
N. Pankratz (2007)
10.1086/338759
A perspective on epistasis: limits of models displaying no main effect.
R. Culverhouse (2002)
10.1186/1753-6561-1-S1-S70
Exploring epistasis in candidate genes for rheumatoid arthritis
M. Ritchie (2007)
10.1093/CLINCHEM/47.6.1089
Diagnostic accuracy of the anti-citrulline antibody assay for rheumatoid arthritis.
N. Bizzaro (2001)
Gregersen PK and North American Rheumatoid Arthritis Consortium: Screening the genome for rheumatoid arthritis susceptibility genes
D Jawaheer (2003)
10.1136/ard.57.9.533
Only high disease activity and positive rheumatoid factor indicate poor prognosis in patients with early rheumatoid arthritis treated with “sawtooth” strategy
T. Möttönen (1998)
10.1002/GEPI.20006
Detecting epistatic interactions contributing to quantitative traits
R. Culverhouse (2004)
10.1186/1753-6561-1-S1-S17
Evaluating gene × gene and gene × smoking interaction in rheumatoid arthritis using candidate genes in GAW15
Ling Mei (2007)
10.1002/1529-0131(200001)43:1<155::AID-ANR20>3.0.CO;2-3
The diagnostic properties of rheumatoid arthritis antibodies recognizing a cyclic citrullinated peptide.
G. A. Schellekens (2000)
10.1002/ART.1780301102
The shared epitope hypothesis. An approach to understanding the molecular genetics of susceptibility to rheumatoid arthritis.
P. Gregersen (1987)



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