Identification of Disease-Sensitive Brain Imaging Phenotypes and Genetic Factors Using GWAS Summary Statistics

Brain imaging genetics is a rapidly growing neuroscience area that integrates genetic variations and brain imaging phenotypes to investigate the genetic underpinnings of brain disorders. In this field, using multi-modal imaging data can leverage complementary information and thus stands a chance of identifying comprehensive genetic risk factors. Due to privacy and copyright issues, many imaging and genetic data are unavailable, and thus existing imaging genetic methods cannot work. In this paper, we proposed a novel multi-modal brain imaging genetic learning method that can study the associations between imaging phenotypes and genetic variations using genome-wide association study (GWAS) summary statistics. Our method leverages the powerful multi-modal of brain imaging phenotypes and GWAS. More importantly, it does not need to access the imaging and genetic data of each individual. Experimental results on both Alzheimer's Disease Neuroimaging Initiative (ADNI) database and GWAS summary statistics suggested that our method has the same learning ability, including identifying associations between genetic biomarkers and imaging phenotypes and selecting relevant biomarkers, as those counterparts depending on the individual data. Therefore, our learning method provides a novel methodology for brain imaging genetics without individual data.