2016-Imaging Genomics - A New Frontier for Statistical Methodology


Imaging Genomics - A New Frontier for Statistical Methodology 
Organizer and Chair: Linglong Kong (University of Alberta) 

MICHELE GUINDANI, University of Texas
Bayesian Predictive Modeling for Imaging Genetics with Application to Schizophrenia  [PDF]
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. 
FAROUK NATHOO, University of Victoria
A Bayesian Group Sparse Multi-Task Regression Model for Imaging Genomics  [PDF]
Advances in technology for brain imaging and genotyping have motivated studies examining the relationships between genetic variation and brain structure. Wang et al. (Bioinformatics, 2012) developed an approach for simultaneous regression parameter estimation and SNP selection based on penalized regression with a group l2,1-norm penalty. The group-norm penalty formulation incorporates the biological group structures among SNPs induced from their genetic arrangement and enforces sparsity at the group level. In this paper, we propose a corresponding Bayesian model that allows for full posterior inference for the regression parameters using Gibbs sampling. Properties of our method are investigated using simulation studies and the methodology is applied to a large dataset collected as part of the Alzheimer's Disease Neuroimaging Initiative.