2014 journal article
Pathway-Guided Identification of Gene-Gene Interactions
ANNALS OF HUMAN GENETICS, 78(6), 478–491.
author keywords: Pathway analysis; gene-gene interactions; bio-knowledge-guided
MeSH headings : Computer Simulation; Epistasis, Genetic; Genotype; Humans; Linear Models; Models, Genetic; Polymorphism, Single Nucleotide; Principal Component Analysis; Regression Analysis
TL;DR:
A regression model with pathway‐guided regularization for detecting interactions among genes using the principal components to summarize the SNP‐SNP interactions between a gene pair, and an L1 penalty that incorporates adaptive weights based on biological guidance and trait supervision to identify important main and interaction effects is proposed.
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open access via europepmc.org (repository)
UN Sustainable Development Goal Categories
3. Good Health and Well-being
(Web of Science)
Source: Web Of Science
Added: August 6, 2018
Assessing gene‐gene interactions (GxG) at the gene level can permit examination of epistasis at biologically functional units with amplified interaction signals from marker‐marker pairs. While current gene‐based GxG methods tend to be designed for two or a few genes, for complex traits, it is often common to have a list of many candidate genes to explore GxG. We propose a regression model with pathway‐guided regularization for detecting interactions among genes. Specifically, we use the principal components to summarize the SNP‐SNP interactions between a gene pair, and use an L1 penalty that incorporates adaptive weights based on biological guidance and trait supervision to identify important main and interaction effects. Our approach aims to combine biological guidance and data adaptiveness, and yields credible findings that may be likely to shed insights in order to formulate biological hypotheses for further molecular studies. The proposed approach can be used to explore the GxG with a list of many candidate genes and is applicable even when sample size is smaller than the number of predictors studied. We evaluate the utility of the proposed method using simulation and real data analysis. The results suggest improved performance over methods not utilizing pathway and trait guidance.