Dataset Information

Bayesian variable selection for binary outcomes in high-dimensional genomic studies using non-local priors.

ABSTRACT:

Motivation

The advent of new genomic technologies has resulted in the production of massive data sets. Analyses of these data require new statistical and computational methods. In this article, we propose one such method that is useful in selecting explanatory variables for prediction of a binary response. Although this problem has recently been addressed using penalized likelihood methods, we adopt a Bayesian approach that utilizes a mixture of non-local prior densities and point masses on the binary regression coefficient vectors.

Results

The resulting method, which we call iMOMLogit, provides improved performance in identifying true models and reducing estimation and prediction error in a number of simulation studies. More importantly, its application to several genomic datasets produces predictions that have high accuracy using far fewer explanatory variables than competing methods. We also describe a novel approach for setting prior hyperparameters by examining the total variation distance between the prior distributions on the regression parameters and the distribution of the maximum likelihood estimator under the null distribution. Finally, we describe a computational algorithm that can be used to implement iMOMLogit in ultrahigh-dimensional settings ([Formula: see text]) and provide diagnostics to assess the probability that this algorithm has identified the highest posterior probability model.

Availability and implementation

Software to implement this method can be downloaded at: http://www.stat.tamu.edu/?amir/code.html

Contact

wwang7@mdanderson.org or vjohnson@stat.tamu.edu

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Nikooienejad A

PROVIDER: S-EPMC4848399 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Publications

Bayesian variable selection for binary outcomes in high-dimensional genomic studies using non-local priors.

Nikooienejad Amir A Wang Wenyi W Johnson Valen E VE

Bioinformatics (Oxford, England) 20160106 9

<h4>Motivation</h4>The advent of new genomic technologies has resulted in the production of massive data sets. Analyses of these data require new statistical and computational methods. In this article, we propose one such method that is useful in selecting explanatory variables for prediction of a binary response. Although this problem has recently been addressed using penalized likelihood methods, we adopt a Bayesian approach that utilizes a mixture of non-local prior densities and point masses ...[more]

PMID: 26740524

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Project description:BackgroundThe onset of silent diseases such as type 2 diabetes is often registered through self-report in large prospective cohorts. Self-reported outcomes are cost-effective; however, they are subject to error. Diagnosis of silent events may also occur through the use of imperfect laboratory-based diagnostic tests. In this paper, we describe an approach for variable selection in high dimensional datasets for settings in which the outcome is observed with error.MethodsWe adapt the spike and slab Bayesian Variable Selection approach in the context of error-prone, self-reported outcomes. The performance of the proposed approach is studied through simulation studies. An illustrative application is included using data from the Women's Health Initiative SNP Health Association Resource, which includes extensive genotypic (>900,000 SNPs) and phenotypic data on 9,873 African American and Hispanic American women.ResultsSimulation studies show improved sensitivity of our proposed method when compared to a naive approach that ignores error in the self-reported outcomes. Application of the proposed method resulted in discovery of several single nucleotide polymorphisms (SNPs) that are associated with risk of type 2 diabetes in a dataset of 9,873 African American and Hispanic participants in the Women's Health Initiative. There was little overlap among the top ranking SNPs associated with type 2 diabetes risk between the racial groups, adding support to previous observations in the literature of disease associated genetic loci that are often not generalizable across race/ethnicity populations. The adapted Bayesian variable selection algorithm is implemented in R. The source code for the simulations are available in the Supplement.ConclusionsVariable selection accuracy is reduced when the outcome is ascertained by error-prone self-reports. For this setting, our proposed algorithm has improved variable selection performance when compared to approaches that neglect to account for the error-prone nature of self-reports.

Dataset Information

Bayesian variable selection for binary outcomes in high-dimensional genomic studies using non-local priors.

Motivation

Results

Availability and implementation

Contact

Supplementary information

Publications

Bayesian variable selection for binary outcomes in high-dimensional genomic studies using non-local priors.

Similar Datasets

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