title: Joint modeling of imaging and genetics.
abstract: 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.
authors.forename: Nematollah K
authors.initials: NK
authors.lastname: Batmanghelich
authors.forename: Adrian V
authors.initials: AV
authors.lastname: Dalca
authors.forename: Mert R
authors.initials: MR
authors.lastname: Sabuncu
authors.forename: Golland
authors.initials: G
authors.lastname: Polina
mesh_terms: D000544:Alzheimer Disease / Q000175:diagnosis* / Q000235:genetics*; D001921:Brain / Q000473:pathology* / Q000503:physiopathology; D003198:Computer Simulation; D005819:Genetic Markers / Q000235:genetics; D020022:Genetic Predisposition to Disease / Q000235:genetics; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008957:Models, Genetic; D008959:Models, Neurological; D057054:Molecular Imaging / Q000379:methods; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D005819:Genetic Markers