Dataset Information

An information-gain approach to detecting three-way epistatic interactions in genetic association studies.

ABSTRACT: Epistasis has been historically used to describe the phenomenon that the effect of a given gene on a phenotype can be dependent on one or more other genes, and is an essential element for understanding the association between genetic and phenotypic variations. Quantifying epistasis of orders higher than two is very challenging due to both the computational complexity of enumerating all possible combinations in genome-wide data and the lack of efficient and effective methodologies.In this study, we propose a fast, non-parametric, and model-free measure for three-way epistasis.Such a measure is based on information gain, and is able to separate all lower order effects from pure three-way epistasis.Our method was verified on synthetic data and applied to real data from a candidate-gene study of tuberculosis in a West African population. In the tuberculosis data, we found a statistically significant pure three-way epistatic interaction effect that was stronger than any lower-order associations.Our study provides a methodological basis for detecting and characterizing high-order gene-gene interactions in genetic association studies.

SUBMITTER: Hu T

PROVIDER: S-EPMC3721169 | biostudies-literature | 2013 Jul-Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

An information-gain approach to detecting three-way epistatic interactions in genetic association studies.

Hu Ting T Chen Yuanzhu Y Kiralis Jeff W JW Collins Ryan L RL Wejse Christian C Sirugo Giorgio G Williams Scott M SM Moore Jason H JH

Journal of the American Medical Informatics Association : JAMIA 20130208 4

<h4>Background</h4>Epistasis has been historically used to describe the phenomenon that the effect of a given gene on a phenotype can be dependent on one or more other genes, and is an essential element for understanding the association between genetic and phenotypic variations. Quantifying epistasis of orders higher than two is very challenging due to both the computational complexity of enumerating all possible combinations in genome-wide data and the lack of efficient and effective methodolog ...[more]

PMID: 23396514

Similar Datasets

Project description:BackgroundThe key roles of epistatic interactions between multiple genetic variants in the pathogenesis of complex diseases notwithstanding, the detection of such interactions remains a great challenge in genome-wide association studies. Although some existing multi-locus approaches have shown their successes in small-scale case-control data, the "combination explosion" course prohibits their applications to genome-wide analysis. It is therefore indispensable to develop new methods that are able to reduce the search space for epistatic interactions from an astronomic number of all possible combinations of genetic variants to a manageable set of candidates.ResultsWe studied case-control data from the viewpoint of binary classification. More precisely, we treated single nucleotide polymorphism (SNP) markers as categorical features and adopted the random forest to discriminate cases against controls. On the basis of the gini importance given by the random forest, we designed a sliding window sequential forward feature selection (SWSFS) algorithm to select a small set of candidate SNPs that could minimize the classification error and then statistically tested up to three-way interactions of the candidates. We compared this approach with three existing methods on three simulated disease models and showed that our approach is comparable to, sometimes more powerful than, the other methods. We applied our approach to a genome-wide case-control dataset for Age-related Macular Degeneration (AMD) and successfully identified two SNPs that were reported to be associated with this disease.ConclusionBesides existing pure statistical approaches, we demonstrated the feasibility of incorporating machine learning methods into genome-wide case-control studies. The gini importance offers yet another measure for the associations between SNPs and complex diseases, thereby complementing existing statistical measures to facilitate the identification of epistatic interactions and the understanding of epistasis in the pathogenesis of complex diseases.

Dataset Information

An information-gain approach to detecting three-way epistatic interactions in genetic association studies.

Publications

An information-gain approach to detecting three-way epistatic interactions in genetic association studies.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets