ArticleGenetic epidemiology2013
A kernel regression approach to gene-gene interaction detection for case-control studies.
Article in Genetic epidemiology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- RKMR: A Rapid Kernel Machine Regression Framework for Optimal Marker Detection in Spatial Omics Data.bioRxiv : the preprint server for biology · 2026Article
- CBKMR: A Copula-based Bayesian Kernel Machine Regression Framework for Optimal Marker Detection in Omics Data.bioRxiv : the preprint server for biology · 2025Article
- cLD: Rare-variant linkage disequilibrium between genomic regions identifies novel genomic interactions.PLoS genetics · 2023Article
- Robust genetic interaction analysis.Briefings in bioinformatics · 2019Review
- A review of kernel methods for genetic association studies.Genetic epidemiology · 2019Review
- Statistical software for analyzing the health effects of multiple concurrent exposures via Bayesian kernel machine regression.Environmental health : a global access science source · 2018Article
- A gene-based information gain method for detecting gene-gene interactions in case-control studies.European journal of human genetics : EJHG · 2015Article
- A Fast Multiple-Kernel Method With Applications to Detect Gene-Environment Interaction.Genetic epidemiology · 2015Article
- Jackknife-based gene-gene interactiontests for untyped SNPs.BMC genetics · 2015Article
- Kernel methods for large-scale genomic data analysis.Briefings in bioinformatics · 2015Review
- Complete effect-profile assessment in association studies with multiple genetic and multiple environmental factors.Genetic epidemiology · 2015Article
- A Bayesian Partitioning Model for the Detection of Multilocus Effects in Case-Control Studies.Human heredity · 2015Article
- Pathway-guided identification of gene-gene interactions.Annals of human genetics · 2014Article
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2 authors.
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Abstract
Gene-gene interactions are increasingly being addressed as a potentially important contributor to the variability of complex traits. Consequently, attentions have moved beyond single locus analysis of association to more complex genetic models. Although several single-marker approaches toward interaction analysis have been developed, such methods suffer from very high testing dimensionality and do not take advantage of existing information, notably the definition of genes as functional units. Here, we propose a comprehensive family of gene-level score tests for identifying genetic elements of disease risk, in particular pairwise gene-gene interactions. Using kernel machine methods, we devise score-based variance component tests under a generalized linear mixed model framework. We conducted simulations based upon coalescent genetic models to evaluate the performance of our approach under a variety of disease models. These simulations indicate that our methods are generally higher powered than alternative gene-level approaches and at worst competitive with exhaustive SNP-level (where SNP is single-nucleotide polymorphism) analyses. Furthermore, we observe that simulated epistatic effects resulted in significant marginal testing results for the involved genes regardless of whether or not true main effects were present. We detail the benefits of our methods and discuss potential genome-wide analysis strategies for gene-gene interaction analysis in a case-control study design.
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