ArticleGenetic epidemiology2010
Screen and clean: a tool for identifying interactions in genome-wide association studies.
Article in Genetic epidemiology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 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
55 citing papers in PubMed, 134 citations in OpenAlex.
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- Potential application of elastic nets for shared polygenicity detection with adapted threshold selection.The international journal of biostatistics · 2023Article
- A hierarchical integrative group least absolute shrinkage and selection operator for analyzing environmental mixtures.Environmetrics · 2021Article
- False discovery rate control in genome-wide association studies with population structure.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- MIDESP: Mutual Information-Based Detection of Epistatic SNP Pairs for Qualitative and Quantitative Phenotypes.Biology · 2021Article
- Variable selection methods for identifying predictor interactions in data with repeatedly measured binary outcomes.Journal of clinical and translational science · 2020Article
- Genomics models in radiotherapy: From mechanistic to machine learning.Medical physics · 2020Review
- Multi-resolution localization of causal variants across the genome.Nature communications · 2020Article
- Efficient Signal Inclusion With Genomic Applications.Journal of the American Statistical Association · 2019Article
- Machine learning identifies interacting genetic variants contributing to breast cancer risk: A case study in Finnish cases and controls.Scientific reports · 2018Article
- Feature selection with interactions in logistic regression models using multivariate synergies for a GWAS application.BMC genomics · 2018Article
- Analysis of genotype by methylation interactions through sparsity-inducing regularized regression.BMC proceedings · 2018Article
- Machine Learning and Radiogenomics: Lessons Learned and Future Directions.Frontiers in oncology · 2018Review
- Backward genotype-transcript-phenotype association mapping.Methods (San Diego, Calif.) · 2017Article
- Article
- Controlling the Rate of GWAS False Discoveries.Genetics · 2017Article
- Assessing statistical significance in multivariable genome wide association analysis.Bioinformatics (Oxford, England) · 2016Article
- Detection of gene-gene interactions using multistage sparse and low-rank regression.Biometrics · 2016Article
- Convex Modeling of Interactions with Strong Heredity.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2016Article
- Chi8: a GPU program for detecting significant interacting SNPs with the Chi-square 8-df test.BMC research notes · 2015Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Epistasis could be an important source of risk for disease. How interacting loci might be discovered is an open question for genome-wide association studies (GWAS). Most researchers limit their statistical analyses to testing individual pairwise interactions (i.e., marginal tests for association). A more effective means of identifying important predictors is to fit models that include many predictors simultaneously (i.e., higher-dimensional models). We explore a procedure called screen and clean (SC) for identifying liability loci, including interactions, by using the lasso procedure, which is a model selection tool for high-dimensional regression. We approach the problem by using a varying dictionary consisting of terms to include in the model. In the first step the lasso dictionary includes only main effects. The most promising single-nucleotide polymorphisms (SNPs) are identified using a screening procedure. Next the lasso dictionary is adjusted to include these main effects and the corresponding interaction terms. Again, promising terms are identified using lasso screening. Then significant terms are identified through the cleaning process. Implementation of SC for GWAS requires algorithms to explore the complex model space induced by the many SNPs genotyped and their interactions. We propose and explore a set of algorithms and find that SC successfully controls Type I error while yielding good power to identify risk loci and their interactions. When the method is applied to data obtained from the Wellcome Trust Case Control Consortium study of Type 1 Diabetes it uncovers evidence supporting interaction within the HLA class II region as well as within Chromosome 12q24.
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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.