ArticleBMC bioinformatics2008
Variable selection for large p small n regression models with incomplete data: mapping QTL with epistases.
Article in BMC bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
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- A network analysis of depression and anxiety symptoms among Chinese elderly living alone: based on the 2017-2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS).BMC psychiatry · 2025Article
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- In situ-crosslinked Zippersomes enhance cardiac repair by increasing accumulation and retention.Bioengineering & translational medicine · 2024Article
- Interconnected mental health symptoms: network analysis of depression, anxiety, stress, and burnout among psychiatric nurses in the context of the COVID-19 pandemic.Frontiers in psychiatry · 2024Article
- Tensor-Decomposition-Based Unsupervised Feature Extraction Applied to Prostate Cancer Multiomics Data.Genes · 2020Article
- SNP variable selection by generalized graph domination.PloS one · 2019Article
- Multivariate Analysis of the Cotton Seed Ionome Reveals a Shared Genetic Architecture.G3 (Bethesda, Md.) · 2018Article
- Genome-wide mapping and prediction suggests presence of local epistasis in a vast elite winter wheat populations adapted to Central Europe.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2017Article
- Multivariate Bayesian variable selection exploiting dependence structure among outcomes: Application to air pollution effects on DNA methylation.Biometrics · 2017Article
- Mapping and validation of quantitative trait loci associated with concentrations of 16 elements in unmilled rice grain.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2014Article
- Explaining microbial phenotypes on a genomic scale: GWAS for microbes.Briefings in functional genomics · 2013Review
- PUMA: a unified framework for penalized multiple regression analysis of GWAS data.PLoS computational biology · 2013Article
- Mouse obesity network reconstruction with a variational Bayes algorithm to employ aggressive false positive control.BMC bioinformatics · 2012Article
- Bayesian shrinkage analysis of QTLs under shape-adaptive shrinkage priors, and accurate re-estimation of genetic effects.Heredity · 2011Article
- New insights into the genetic control of gene expression using a Bayesian multi-tissue approach.PLoS computational biology · 2010Article
- A variational Bayes algorithm for fast and accurate multiple locus genome-wide association analysis.BMC bioinformatics · 2010Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundIdentifying quantitative trait loci (QTL) for both additive and epistatic effects raises the statistical issue of selecting variables from a large number of candidates using a small number of observations. Missing trait and/or marker values prevent one from directly applying the classical model selection criteria such as Akaike's information criterion (AIC) and Bayesian information criterion (BIC).
resultsWe propose a two-step Bayesian variable selection method which deals with the sparse parameter space and the small sample size issues. The regression coefficient priors are flexible enough to incorporate the characteristic of "large p small n" data. Specifically, sparseness and possible asymmetry of the significant coefficients are dealt with by developing a Gibbs sampling algorithm to stochastically search through low-dimensional subspaces for significant variables. The superior performance of the approach is demonstrated via simulation study. We also applied it to real QTL mapping datasets.
conclusionThe two-step procedure coupled with Bayesian classification offers flexibility in modeling "large p small n" data, especially for the sparse and asymmetric parameter space. This approach can be extended to other settings characterized by high dimension and low sample size.
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