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BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2020

Open Access 01-12-2020 | Type 2 Diabetes | Research article

Bayesian variable selection for high dimensional predictors and self-reported outcomes

Authors: Xiangdong Gu, Mahlet G Tadesse, Andrea S Foulkes, Yunsheng Ma, Raji Balasubramanian

Published in: BMC Medical Informatics and Decision Making | Issue 1/2020

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Abstract

Background

The 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.

Methods

We 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.

Results

Simulation 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.

Conclusions

Variable 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.
Appendix
Available only for authorised users
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Metadata
Title
Bayesian variable selection for high dimensional predictors and self-reported outcomes
Authors
Xiangdong Gu
Mahlet G Tadesse
Andrea S Foulkes
Yunsheng Ma
Raji Balasubramanian
Publication date
01-12-2020
Publisher
BioMed Central
Keyword
Type 2 Diabetes
Published in
BMC Medical Informatics and Decision Making / Issue 1/2020
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-020-01223-w

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