ArticleHuman heredity2012
Natural and orthogonal interaction framework for modeling gene-environment interactions with application to lung cancer.
Article in Human heredity, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-analysis identifies gene-by-environment interactions as demonstrated in a study of 4,965 mice.PLoS genetics · 2014Pooled it
- The pursuit of genetic gain in agricultural crops through the application of machine-learning to genomic prediction.Frontiers in genetics · 2023Article
- Joint modeling of eQTLs and parent-of-origin effects using an orthogonal framework with RNA-seq data.Human genetics · 2020Article
- Gene-Environment Interaction in the Era of Precision Medicine - Filling the Potholes Rather Than Starting to Build a New Road.Frontiers in genetics · 2020Article
- Orthogonal Decomposition of the Genetic Variance for Epistatic Traits Under Linkage Disequilibrium-Applications to the Analysis of Bateson-Dobzhansky-Müller Incompatibilities and Sign Epistasis.Frontiers in genetics · 2019Article
- Natural and orthogonal model for estimating gene-gene interactions applied to cutaneous melanoma.Human genetics · 2014Article
- Gene-environment interactions in cancer epidemiology: a National Cancer Institute Think Tank report.Genetic epidemiology · 2013Article
- Gene-environment interactions in genome-wide association studies: current approaches and new directions.Journal of child psychology and psychiatry, and allied disciplines · 2013Review
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Authors and funding
18 authors.
Funding
Abstract
objectivesWe aimed at extending the Natural and Orthogonal Interaction (NOIA) framework, developed for modeling gene-gene interactions in the analysis of quantitative traits, to allow for reduced genetic models, dichotomous traits, and gene-environment interactions. We evaluate the performance of the NOIA statistical models using simulated data and lung cancer data.
methodsThe NOIA statistical models are developed for additive, dominant, and recessive genetic models as well as for a binary environmental exposure. Using the Kronecker product rule, a NOIA statistical model is built to model gene-environment interactions. By treating the genotypic values as the logarithm of odds, the NOIA statistical models are extended to the analysis of case-control data.
resultsOur simulations showed that power for testing associations while allowing for interaction using the NOIA statistical model is much higher than using functional models for most of the scenarios we simulated. When applied to lung cancer data, much smaller p values were obtained using the NOIA statistical model for either the main effects or the SNP-smoking interactions for some of the SNPs tested.
conclusionThe NOIA statistical models are usually more powerful than the functional models in detecting main effects and interaction effects for both quantitative traits and binary traits.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.