ArticleJournal of the American Statistical Association2013
An Integrative Bayesian Modeling Approach to Imaging Genetics.
Article in Journal of the American Statistical Association, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Computation and resource efficient genome-wide association analysis for large-scale imaging studies.Nature communications · 2026Article
- Computation and resource efficient genome-wide association analysis for large-scale imaging studies.medRxiv : the preprint server for health sciences · 2025Article
- Bayesian hierarchical quantile regression with application to characterizing the immune architecture of lung cancer.Biometrics · 2023Article
- Neuroimaging feature extraction using a neural network classifier for imaging genetics.BMC bioinformatics · 2023Article
- Bayesian feature selection for radiomics using reliability metrics.Frontiers in genetics · 2023Article
- A Bayesian zero-inflated negative binomial regression model for the integrative analysis of microbiome data.Biostatistics (Oxford, England) · 2021Article
- Hierarchical Normalized Completely Random Measures for Robust Graphical Modeling.Bayesian analysis · 2019Article
- A Bayesian hidden Potts mixture model for analyzing lung cancer pathology images.Biostatistics (Oxford, England) · 2019Article
- A Review of Statistical Methods in Imaging Genetics.The Canadian journal of statistics = Revue canadienne de statistique · 2019Article
- RADIO-IBAG: RADIOMICS-BASED INTEGRATIVE BAYESIAN ANALYSIS OF MULTIPLATFORM GENOMIC DATA.The annals of applied statistics · 2019Article
- A structural equation model for imaging genetics using spatial transcriptomics.Brain informatics · 2018Article
- A Bayesian Approach for Estimating Dynamic Functional Network Connectivity in fMRI Data.Journal of the American Statistical Association · 2018Article
- A Bayesian group sparse multi-task regression model for imaging genetics.Bioinformatics (Oxford, England) · 2017Article
- A Hierarchical Bayesian Model for the Identification of PET Markers Associated to the Prediction of Surgical Outcome after Anterior Temporal Lobe Resection.Frontiers in neuroscience · 2017Article
- Generalized reduced rank latent factor regression for high dimensional tensor fields, and neuroimaging-genetic applications.NeuroImage · 2017Article
- Semiparametric Bayes conditional graphical models for imaging genetics applications.Stat (International Statistical Institute) · 2016Article
- Multiple functional linear model for association analysis of RNA-seq with imaging.Quantitative biology (Beijing, China) · 2015Article
- Sparse models for correlative and integrative analysis of imaging and genetic data.Journal of neuroscience methods · 2014Review
- Bayesian Models for fMRI Data Analysis.Wiley interdisciplinary reviews. Computational statisticsArticle
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4 authors.
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Abstract
In this paper we present a Bayesian hierarchical modeling approach for imaging genetics, where the interest lies in linking brain connectivity across multiple individuals to their genetic information. We have available data from a functional magnetic resonance (fMRI) study on schizophrenia. Our goals are to identify brain regions of interest (ROIs) with discriminating activation patterns between schizophrenic patients and healthy controls, and to relate the ROIs' activations with available genetic information from single nucleotide polymorphisms (SNPs) on the subjects. For this task we develop a hierarchical mixture model that includes several innovative characteristics: it incorporates the selection of ROIs that discriminate the subjects into separate groups; it allows the mixture components to depend on selected covariates; it includes prior models that capture structural dependencies among the ROIs. Applied to the schizophrenia data set, the model leads to the simultaneous selection of a set of discriminatory ROIs and the relevant SNPs, together with the reconstruction of the correlation structure of the selected regions. To the best of our knowledge, our work represents the first attempt at a rigorous modeling strategy for imaging genetics data that incorporates all such features.
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