An ensemble learning approach jointly modeling main and interaction effects in genetic association studies.

Genetic Epidemiology
Zhaogong ZhangQiuying Sha

Abstract

Complex diseases are presumed to be the results of interactions of several genes and environmental factors, with each gene only having a small effect on the disease. Thus, the methods that can account for gene-gene interactions to search for a set of marker loci in different genes or across genome and to analyze these loci jointly are critical. In this article, we propose an ensemble learning approach (ELA) to detect a set of loci whose main and interaction effects jointly have a significant association with the trait. In the ELA, we first search for "base learners" and then combine the effects of the base learners by a linear model. Each base learner represents a main effect or an interaction effect. The result of the ELA is easy to interpret. When the ELA is applied to analyze a data set, we can get a final model, an overall P-value of the association test between the set of loci involved in the final model and the trait, and an importance measure for each base learner and each marker involved in the final model. The final model is a linear combination of some base learners. We know which base learner represents a main effect and which one represents an interaction effect. The importance measure of each base learner or marker...Continue Reading

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Citations

May 13, 2009·Nature Reviews. Genetics·Heather J Cordell
Nov 26, 2010·Genetic Epidemiology·Kristin L Ayers, Heather J Cordell
Jul 17, 2013·BMC Medical Genomics·Kyunga KimTaesung Park
Feb 18, 2018·Statistical Applications in Genetics and Molecular Biology·Jean-Eudes DazardPeter Zimmerman
Apr 4, 2013·Human Heredity·Qing LiIngo Ruczinski

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