Bayesian variable selection for high dimensional predictors and self-reported outcomes
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(2020) 20:212
RESEARCH ARTICLE
Open Access
Bayesian variable selection for high dimensional predictors and self-reported outcomes Xiangdong Gu1 , Mahlet G Tadesse2 , Andrea S Foulkes3 , Yunsheng Ma4 and Raji Balasubramanian1*
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, selfreported 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. Keywords: Bayesian variable selection, Self-reports, High dimensional data
Background The time to a silent event in several clinical settings can only be assessed through sequentially administered diagnostic tests. For example, diabetes can be detected by measuring levels of fasting blood glucose or glycosylated hemoglobin levels (HbA1c). Although gold standard *Correspondence: [email protected] Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA, USA Full list of author information is available at the end of the article 1
diagnostic tests are often available, the associated cost is prohibitive in large epidemiological studies which often recruit hundreds of thousands participants. Instead, disease incidence is often ascertained throu
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