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IEEE Trans Med Imaging. 2016 Jul;35(7):1765-79. doi: 10.1109/TMI.2016.2527784. Epub 2016 Feb 11.

Probabilistic Modeling of Imaging, Genetics and Diagnosis.

Abstract

We propose a unified Bayesian framework for detecting genetic variants associated with disease by exploiting image-based features as an intermediate phenotype. The use of imaging data for examining genetic associations promises new directions of analysis, but currently the most widely used methods make sub-optimal use of the richness that these data types can offer. Currently, image features are most commonly selected based on their relevance to the disease phenotype. Then, in a separate step, a set of genetic variants is identified to explain the selected features. In contrast, our method performs these tasks simultaneously in order to jointly exploit information in both data types. The analysis yields probabilistic measures of clinical relevance for both imaging and genetic markers. We derive an efficient approximate inference algorithm that handles the high dimensionality of image and genetic data. We evaluate the algorithm on synthetic data and demonstrate that it outperforms traditional models. We also illustrate our method on Alzheimer's Disease Neuroimaging Initiative data.

PMID:
26886973
PMCID:
PMC5364030
DOI:
10.1109/TMI.2016.2527784
[Indexed for MEDLINE]
Free PMC Article

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