ArticleStatistics in medicine2013
Integrative variable selection via Bayesian model uncertainty.
Article in Statistics in medicine, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- JAM: A Scalable Bayesian Framework for Joint Analysis of Marginal SNP Effects.Genetic epidemiology · 2016Pooled it
- Gene-environment interactions within a precision environmental health framework.Cell genomics · 2024Review
- Bayesian feature selection for radiomics using reliability metrics.Frontiers in genetics · 2023Article
- A scoping review of smoking cessation pharmacogenetic studies to advance future research across racial, ethnic, and ancestral populations.Frontiers in genetics · 2023Article
- EPISPOT: An epigenome-driven approach for detecting and interpreting hotspots in molecular QTL studies.American journal of human genetics · 2021Article
- Rare Variants in the DNA Repair Pathway and the Risk of Colorectal Cancer.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2021Article
- Learning from a lot: Empirical Bayes for high-dimensional model-based prediction.Scandinavian journal of statistics, theory and applications · 2019Article
- From genome-wide associations to candidate causal variants by statistical fine-mapping.Nature reviews. Genetics · 2018Review
- Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases.American journal of epidemiology · 2017Article
- Bayesian vector autoregressive model for multi-subject effective connectivity inference using multi-modal neuroimaging data.Human brain mapping · 2017Article
- Weibull regression with Bayesian variable selection to identify prognostic tumour markers of breast cancer survival.Statistical methods in medical research · 2017Article
- Incorporating Functional Annotations for Fine-Mapping Causal Variants in a Bayesian Framework Using Summary Statistics.Genetics · 2016Article
- Post hoc Analysis for Detecting Individual Rare Variant Risk Associations Using Probit Regression Bayesian Variable Selection Methods in Case-Control Sequencing Studies.Genetic epidemiology · 2016Article
- Statistical Methods in Integrative Genomics.Annual review of statistics and its application · 2016Article
- Reducing GWAS Complexity.Cell cycle (Georgetown, Tex.) · 2016Review
- A Bayesian model for the identification of differentially expressed genes in Daphnia magna exposed to munition pollutants.Biometrics · 2015Article
- Fine Mapping Causal Variants with an Approximate Bayesian Method Using Marginal Test Statistics.Genetics · 2015Article
- False discovery rates for rare variants from sequenced data.Genetic epidemiology · 2015Article
- Identification of rare causal variants in sequence-based studies: methods and applications to VPS13B, a gene involved in Cohen syndrome and autism.PLoS genetics · 2014Article
- Integrating functional data to prioritize causal variants in statistical fine-mapping studies.PLoS genetics · 2014Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
We are interested in developing integrative approaches for variable selection problems that incorporate external knowledge on a set of predictors of interest. In particular, we have developed an integrative Bayesian model uncertainty (iBMU) method, which formally incorporates multiple sources of data via a second-stage probit model on the probability that any predictor is associated with the outcome of interest. Using simulations, we demonstrate that iBMU leads to an increase in power to detect true marginal associations over more commonly used variable selection techniques, such as least absolute shrinkage and selection operator and elastic net. In addition, iBMU leads to a more efficient model search algorithm over the basic BMU method even when the predictor-level covariates are only modestly informative. The increase in power and efficiency of our method becomes more substantial as the predictor-level covariates become more informative. Finally, we demonstrate the power and flexibility of iBMU for integrating both gene structure and functional biomarker information into a candidate gene study investigating over 50 genes in the brain reward system and their role with smoking cessation from the Pharmacogenetics of Nicotine Addiction and Treatment Consortium.
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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.