Bayesian Models for Variable Selection that Incorporate Biological Information*

Vannucci, Marina; Stingo, Francesco C.; Berzuini, Carlo

doi:10.1093/acprof:oso/9780199694587.003.0022

Cited by 13 publications

(14 citation statements)

References 63 publications

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“…Specifically, we use continuous structured spike-and-slab priors (George and McCulloch, 1997;Ishwaran et al, 2005;Andersen et al, 2014) within the regression model for a specific layer to (i) blend prior information about the correlation between the genomic markers, and (ii) borrow information about the selected genomic markers from the estimation at one spherical layer and propagate the information through the prior of the model to the subsequent spherical layer(s). As opposed to existing approaches that incorporate apriori structural information (Li and Zhang, 2010;Vannucci and Stingo, 2010), our prior formulation incorporates structural information not only for the dependence between the predictors, but also for the dependence between the multivariate response and across the spherical layers.…”

Section: Statistical Frameworkmentioning

confidence: 99%

Tumor Radiogenomics with Bayesian Layered Variable Selection

Mohammed,

Kurtek,

Bharath

et al. 2021

Preprint

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We propose a statistical framework to integrate radiological magnetic resonance imaging (MRI) and genomic data to identify the underlying radiogenomic associations in lower grade gliomas (LGG). We devise a novel imaging phenotype by dividing the tumor region into concentric spherical layers that mimics the tumor evolution process. MRI data within each layer is represented by voxel-intensity-based probability density functions which capture the complete information about tumor heterogeneity. Under a Riemannian-geometric framework these densities are mapped to a vector of principal component scores which act as imaging phenotypes. Subsequently, we build Bayesian variable selection models for each layer with the imaging phenotypes as the response and the genomic markers as predictors. Our novel hierarchical prior formulation incorporates the interior-to-exterior structure of the layers, and the correlation between the genomic markers. We employ a computationally-efficient Expectation-Maximization-based strategy for estimation. Simulation studies demonstrate the superior performance of our approach compared to other approaches. With a focus on the cancer driver genes in LGG, we discuss some biologically relevant findings. Genes implicated with survival and oncogenesis are identified as being associated with the spherical layers, which could potentially serve as early-stage diagnostic markers for disease monitoring, prior to routine invasive approaches.

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Section: Statistical Frameworkmentioning

confidence: 99%

Tumor Radiogenomics with Bayesian Layered Variable Selection

Mohammed,

Kurtek,

Bharath

et al. 2021

Preprint

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“…Many Bayesian methods developed for GWA analysis emphasize that they allow the integration of prior information but few publications have focused on the practicalities of this problem. To our knowledge, the work performed so far has focused on including prior information either at the level of the SNP effects (Fridley et al, 2010;Spencer et al, 2016;Stingo, Chen, Tadesse, & Vannucci, 2011;Vannucci & Stingo, 2010) or at the level of the SNP's probability of association with the outcome (Lewinger, Conti, Baurley, Triche, & Thomas, 2007; Thompson et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Inclusion of biological knowledge in a Bayesian shrinkage model for joint estimation of SNP effects

Pereira

Thompson

Weichenberger

et al. 2017

Genetic Epidemiology

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With the aim of improving detection of novel single-nucleotide polymorphisms (SNPs) in genetic association studies, we propose a method of including prior biological information in a Bayesian shrinkage model that jointly estimates SNP effects. We assume that the SNP effects follow a normal distribution centered at zero with variance controlled by a shrinkage hyperparameter. We use biological information to define the amount of shrinkage applied on the SNP effects distribution, so that the effects of SNPs with more biological support are less shrunk towards zero, thus being more likely detected. The performance of the method was tested in a simulation study (1000 datasets, 500 subjects with ~200SNPs in 10 linkage disequilibrium (LD) blocks) using a continuous and a binary outcome. It was further tested in an empirical example on body mass index (continuous) and overweight (binary) in a dataset of 1,829 subjects and 2,614 SNPs from 30 blocks. Biological knowledge was retrieved using the bioinformatics tool Dintor which queried various databases. The joint Bayesian model with inclusion of prior information outperformed the standard analysis: in the simulation study, the mean ranking of the true LD block was 2.8 for the Bayesian model vs. 3.6 for the standard analysis of individual SNPs; in the empirical example, the mean ranking of the six true blocks was 8.5 vs. 9.3 in the standard analysis. These results suggest that our method is more powerful than the standard analysis. We expect its performance to improve further as more biological information about SNPs becomes available.

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“…() is suboptimal because the method selects the same covariates for all responses. Several priors based on an MRF have been proposed to incorporate biological information on the structured dependence between covariates in variable selection for univariate regression models (Li and Zhang, ; Vannucci and Stingo, ; Stingo et al., ). Our approach differs from these existing MRF models in that we leverage the dependence among multiple outcomes in our prior specification for the variable selection indicators, as opposed to specifying it based on the network structure known a priori among covariates.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Bayesian Variable Selection Exploiting Dependence Structure Among Outcomes: Application to Air Pollution Effects on DNA Methylation

et al. 2016

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Summary The analysis of multiple outcomes is becoming increasingly common in modern biomedical studies. It is well-known that joint statistical models for multiple outcomes are more flexible and more powerful than fitting a separate model for each outcome; they yield more powerful tests of exposure or treatment effects by taking into account the dependence among outcomes and pooling evidence across outcomes. It is, however, unlikely that all outcomes are related to the same subset of covariates. Therefore, there is interest in identifying exposures or treatments associated with particular outcomes, which we term outcome-specific variable selection. In this work we propose a variable selection approach for multivariate normal responses that incorporates not only information on the mean model, but also information on the variance-covariance structure of the outcomes. The approach effectively leverages evidence from all correlated outcomes to estimate the effect of a particular covariate on a given outcome. To implement this strategy, we develop a Bayesian method that builds a multivariate prior for the variable selection indicators based on the variance-covariance of the outcomes. We show via simulation that the proposed variable selection strategy can boost power to detect subtle effects without increasing the probability of false discoveries. We apply the approach to the Normative Aging Study (NAS) epigenetic data and identify a subset of five genes in the asthma pathway for which gene-specific DNA methylations are associated with exposures to either black carbon, a marker of traffic pollution, or sulfate, a marker of particles generated by power plants.

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Bayesian Models for Variable Selection that Incorporate Biological Information*

Cited by 13 publications

References 63 publications

Tumor Radiogenomics with Bayesian Layered Variable Selection

Tumor Radiogenomics with Bayesian Layered Variable Selection

Inclusion of biological knowledge in a Bayesian shrinkage model for joint estimation of SNP effects

Multivariate Bayesian Variable Selection Exploiting Dependence Structure Among Outcomes: Application to Air Pollution Effects on DNA Methylation

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