ArticleBioinformatics (Oxford, England)2011
A variable selection method for genome-wide association studies.
Article in Bioinformatics (Oxford, England), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 1 of them a synthesis that pooled it.
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Who cites it
62 citing papers in PubMed, 1 synthesis or guideline pooled it, 146 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
- Bayesian bi-level variable selection for genome-wide survival study.Genomics & informatics · 2023Article
- Identification of Driver Epistatic Gene Pairs Combining Germline and Somatic Mutations in Cancer.International journal of molecular sciences · 2023Article
- BGWAS: Bayesian variable selection in linear mixed models with nonlocal priors for genome-wide association studies.BMC bioinformatics · 2023Article
- Deep Learning Framework for Complex Disease Risk Prediction Using Genomic Variations.Sensors (Basel, Switzerland) · 2023Article
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- BICOSS: Bayesian iterative conditional stochastic search for GWAS.BMC bioinformatics · 2022Article
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- Artificial intelligence powered statistical genetics in biobanks.Journal of human genetics · 2021Review
- Implementing a sustainable health insurance system in Cambodia: a study protocol for developing and validating an efficient household income-level assessment model for equitable premium collection.International journal for equity in health · 2020Article
- Integrate Molecular Phenome and Polygenic Interaction to Detect the Genetic Risk of Ischemic Stroke.Frontiers in cell and developmental biology · 2020Article
- JS-MA: A Jensen-Shannon Divergence Based Method for Mapping Genome-Wide Associations on Multiple Diseases.Frontiers in genetics · 2020Article
- Structured Genome-Wide Association Studies with Bayesian Hierarchical Variable Selection.Genetics · 2019Article
- Precision Lasso: accounting for correlations and linear dependencies in high-dimensional genomic data.Bioinformatics (Oxford, England) · 2019Article
- SNP variable selection by generalized graph domination.PloS one · 2019Article
- GWASinlps: non-local prior based iterative SNP selection tool for genome-wide association studies.Bioinformatics (Oxford, England) · 2019Article
- Variable selection in heterogeneous datasets: A truncated-rank sparse linear mixed model with applications to genome-wide association studies.Methods (San Diego, Calif.) · 2018Article
2 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
motivationGenome-wide association studies (GWAS) involving half a million or more single nucleotide polymorphisms (SNPs) allow genetic dissection of complex diseases in a holistic manner. The common practice of analyzing one SNP at a time does not fully realize the potential of GWAS to identify multiple causal variants and to predict risk of disease. Existing methods for joint analysis of GWAS data tend to miss causal SNPs that are marginally uncorrelated with disease and have high false discovery rates (FDRs).
resultsWe introduce GWASelect, a statistically powerful and computationally efficient variable selection method designed to tackle the unique challenges of GWAS data. This method searches iteratively over the potential SNPs conditional on previously selected SNPs and is thus capable of capturing causal SNPs that are marginally correlated with disease as well as those that are marginally uncorrelated with disease. A special resampling mechanism is built into the method to reduce false positive findings. Simulation studies demonstrate that the GWASelect performs well under a wide spectrum of linkage disequilibrium patterns and can be substantially more powerful than existing methods in capturing causal variants while having a lower FDR. In addition, the regression models based on the GWASelect tend to yield more accurate prediction of disease risk than existing methods. The advantages of the GWASelect are illustrated with the Wellcome Trust Case-Control Consortium (WTCCC) data. AVAILABILITY: The software implementing GWASelect is available at http://www.bios.unc.edu/~lin. Access to WTCCC data: http://www.wtccc.org.uk/.
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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.