The Annals of Applied Statistics
- Ann. Appl. Stat.
- Volume 10, Number 3 (2016), 1547-1571.
A Bayesian predictive model for imaging genetics with application to schizophrenia
Imaging genetics has rapidly emerged as a promising approach for investigating the genetic determinants of brain mechanisms that underlie an individual’s behavior or psychiatric condition. In particular, for early detection and targeted treatment of schizophrenia, it is of high clinical relevance to identify genetic variants and imaging-based biomarkers that can be used as diagnostic markers, in addition to commonly used symptom-based assessments. By combining single-nucleotide polymorphism (SNP) arrays and functional magnetic resonance imaging (fMRI), we propose an integrative Bayesian risk prediction model that allows us to discriminate between individuals with schizophrenia and healthy controls, based on a sparse set of discriminatory regions of interest (ROIs) and SNPs. Inference on a regulatory network between SNPs and ROI intensities (ROI–SNP network) is used in a single modeling framework to inform the selection of the discriminatory ROIs and SNPs. We use simulation studies to assess the performance of our method and apply it to data collected from individuals with schizophrenia and healthy controls. We found our approach to outperform competing methods that do not link the ROI–SNP network to the selection of discriminatory markers.
Ann. Appl. Stat., Volume 10, Number 3 (2016), 1547-1571.
Received: March 2015
Revised: March 2016
First available in Project Euclid: 28 September 2016
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Chekouo, Thierry; Stingo, Francesco C.; Guindani, Michele; Do, Kim-Anh. A Bayesian predictive model for imaging genetics with application to schizophrenia. Ann. Appl. Stat. 10 (2016), no. 3, 1547--1571. doi:10.1214/16-AOAS948. https://projecteuclid.org/euclid.aoas/1475069618
- Supplement to “A Bayesian predictive model for imaging genetics with application to schizophrenia”. The supplementary material [Chekouo et al. (2016)] contains details about posterior computation, hyperparameter settings and sensitivity, data preprocessing, and additional simulation studies and data analyses. The companion MATLAB code is available on The Annals of Applied Statistics website.