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Joint Modeling of Imaging and Genetics

Institution:
1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
2Martinos Center for Biomedical Imaging, Charlestown, MA, USA.
Publisher:
Inf Process Med Imaging IPMI 2013
Publication Date:
Jun-2013
Volume Number:
23
Pages:
766-77
Citation:
Inf Process Med Imaging. 2013 Jun; 23:766-77.
PubMed ID:
24684016
PMCID:
PMC3979537
Keywords:
Imaging Genetics, Bayesian Model, Variational Inference, Probabilistic Graphical Model
Appears in Collections:
NAC, NA-MIC
Sponsors:
P41 EB015902/EB/NIBIB NIH HHS/United States
P41 RR013218/RR/NCRR NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
K25 EB013649/EB/NIBIB NIH HHS/United States
AHAF pilot research grant in Alzheimer’s disease A2012333
NSERC CGS-D and Barbara J. Weedon Fellowship
Generated Citation:
Batmanghelich K.N., Dalca A.V., Sabuncu M.R., Golland P. Joint Modeling of Imaging and Genetics. Inf Process Med Imaging. 2013 Jun; 23:766-77. PMID: 24684016. PMCID: PMC3979537.
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We propose a unified Bayesian framework for detecting genetic variants associated with a disease while exploiting image-based features as an intermediate phenotype. Traditionally, imaging genetics methods comprise two separate steps. First, image features are selected based on their relevance to the disease phenotype. Second, a set of genetic variants are identified to explain the selected features. In contrast, our method performs these tasks simultaneously to ultimately assign probabilistic measures of relevance to both genetic and imaging markers. We derive an efficient approximate inference algorithm that handles high dimensionality of imaging genetic data. We evaluate the algorithm on synthetic data and show that it outperforms traditional models. We also illustrate the application of the method on ADNI data.

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