ArticleGenetic epidemiology2009
A partial least-square approach for modeling gene-gene and gene-environment interactions when multiple markers are genotyped.
Article in Genetic epidemiology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
27 citing papers in PubMed, 45 citations in OpenAlex.
- Detecting gene-gene interactions from GWAS using diffusion kernel principal components.BMC bioinformatics · 2022Article
- Genetic interactions effects for cancer disease identification using computational models: a review.Medical & biological engineering & computing · 2021Review
- Penalized partial least squares for pleiotropy.BMC bioinformatics · 2021Article
- Gene-Environment Interaction: A Variable Selection Perspective.Methods in molecular biology (Clifton, N.J.) · 2021Review
- Identifying novel associations in GWAS by hierarchical Bayesian latent variable detection of differentially misclassified phenotypes.BMC bioinformatics · 2020Article
- A kernel machine method for detecting higher order interactions in multimodal datasets: Application to schizophrenia.Journal of neuroscience methods · 2018Article
- Methods and results from the genome-wide association group at GAW20.BMC genetics · 2018Article
- Eigen-Epistasis for detecting gene-gene interactions.BMC bioinformatics · 2017Article
- Comparison of dimension reduction-based logistic regression models for case-control genome-wide association study: principal components analysis vs. partial least squares.Journal of biomedical research · 2015Article
- Complete effect-profile assessment in association studies with multiple genetic and multiple environmental factors.Genetic epidemiology · 2015Article
- Powerful Tukey's One Degree-of-Freedom Test for Detecting Gene-Gene and Gene-Environment Interactions.Cancer informatics · 2015Article
- Correcting systematic inflation in genetic association tests that consider interaction effects: application to a genome-wide association study of posttraumatic stress disorder.JAMA psychiatry · 2014Article
- Pathway-guided identification of gene-gene interactions.Annals of human genetics · 2014Article
- Cuckoo search epistasis: a new method for exploring significant genetic interactions.Heredity · 2014Article
- Detecting a weak association by testing its multiple perturbations: a data mining approach.Scientific reports · 2014Article
- Analysis of gene-gene interactions using gene-trait similarity regression.Human heredity · 2014Article
- GEE-based SNP set association test for continuous and discrete traits in family-based association studies.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
- Multivariate detection of gene-gene interactions.Genetic epidemiology · 2012Article
- Roles of genetic variants in the PI3K and RAS/RAF pathways in susceptibility to endometrial cancer and clinical outcomes.Journal of cancer research and clinical oncology · 2012Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Genetic association studies achieve an unprecedented level of resolution in mapping disease genes by genotyping dense single nucleotype polymorphisms (SNPs) in a gene region. Meanwhile, these studies require new powerful statistical tools that can optimally handle a large amount of information provided by genotype data. A question that arises is how to model interactions between two genes. Simply modeling all possible interactions between the SNPs in two gene regions is not desirable because a greatly increased number of degrees of freedom can be involved in the test statistic. We introduce an approach to reduce the genotype dimension in modeling interactions. The genotype compression of this approach is built upon the information on both the trait and the cross-locus gametic disequilibrium between SNPs in two interacting genes, in such a way as to parsimoniously model the interactions without loss of useful information in the process of dimension reduction. As a result, it improves power to detect association in the presence of gene-gene interactions. This approach can be similarly applied for modeling gene-environment interactions. We compare this method with other approaches, the corresponding test without modeling any interaction, that based on a saturated interaction model, that based on principal component analysis, and that based on Tukey's one-degree-of-freedom model. Our simulations suggest that this new approach has superior power to that of the other methods. In an application to endometrial cancer case-control data from the Women's Health Initiative, this approach detected AKT1 and AKT2 as being significantly associated with endometrial cancer susceptibility by taking into account their interactions with body mass index.
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Registered trials
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.