An increasing number of clinical and imaging research studies is collecting various additional information including genetic data. The goals of the emerging field of imaging genetics can be summarized into two aims: 1) using imaging biomarkers as an intermediate phenotype to uncover underlying biological mechanisms of diseases; 2) phenotype discovery. 

In this talk, we will focus on the first goal, namely using imaging as an intermediate phenotype, and briefly discuss the second goal of discovering image-based phenotypes associated with a disease. We propose a unified Bayesian framework for detecting genetic variants associated with a disease while exploiting image-based features as an intermediate phenotype. Imaging genetics methods typically comprise two separate steps. First, image features are selected based on their relevance to the disease phenotype. Second, a set of genetic variants is 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 in the context of the Alzhemer's disease (ADNI dataset).