A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility.

A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. Research Abstract Details 

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A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. Abstract Text:

Jason H Moore,Joshua C Gilbert,Chia-Ti Tsai,Fu-Tien Chiang,Todd Holden,Nate Barney,Bill C White,

Detecting, characterizing, and interpreting gene-gene interactions or epistasis in studies of human disease susceptibility is both a mathematical and a computational challenge. To address this problem, we have previously developed a multifactor dimensionality reduction (MDR) method for collapsing high-dimensional genetic data into a single dimension (i.e. constructive induction) thus permitting interactions to be detected in relatively small sample sizes. In this paper, we describe a comprehensive and flexible framework for detecting and interpreting gene-gene interactions that utilizes advances in information theory for selecting interesting single-nucleotide polymorphisms (SNPs), MDR for constructive induction, machine learning methods for classification, and finally graphical models for interpretation. We illustrate the usefulness of this strategy using artificial datasets simulated from several different two-locus and three-locus epistasis models. We show that the accuracy, sensitivity, specificity, and precision of a naïve Bayes classifier are significantly improved when SNPs are selected based on their information gain (i.e. class entropy removed) and reduced to a single attribute using MDR. We then apply this strategy to detecting, characterizing, and interpreting epistatic models in a genetic study (n = 500) of atrial fibrillation and show that both classification and model interpretation are significantly improved.

A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. Publishing Authors By Initials

JH Moore,JC Gilbert,CT Tsai,FT Chiang,T Holden,N Barney,BC White,

For similar genetic phenomena: variation (genetics): polymorphism, genetic: polymorphism, single nucleotide research abstracts see: genetic phenomena: variation (genetics): polymorphism, genetic: polymorphism, single nucleotide research

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A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. Journal Published:

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

Journal: Journal of theoretical biology

VOLUME: 241

Page Numbers: 252-61

Journal Abbreviation: J. Theor. Biol.

ISSN: 0022-5193

DAY: 2

MONTH: 02

YEAR: 2006

A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. Information

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LANGUAGE: eng

NlmUniqueID: 376342

A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. Keywords Mesh Terms:

KEYWORDS: Polymorphism, Single Nucleotide

MESH TERMS: genetics

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Grant and Affiliation Information for A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility.

AFFILIATION: Computational Genetics Laboratory, Department of Genetics, Dartmouth-Hitchcock Medical Center, One Medical Center Dr., 706 Rubin Bldg, HB7937, Lebanon, NH 03756, USA. jason.h.moore@dartmouth.edu

Country: Netherlands

AGENCY: United States NCRR

GRANT: RR018787

ACRONYM: RR

MEDLINETA: J Theor Biol

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