Abstract:

Genome-wide association studies (GWASs) have been widely applied in biomedicine including neuroimaging field to discover genetic variants associated with brain-related traits. So far, almost all GWASs conducted in neuroimaging genetics are performed on univariate quantitative features summarized from brain images. In this study, we proposed and implemented a novel machine learning strategy for systematically identifying genetic variants that lead to detectable differences on full brain MRI data. The classification-based strategy has the potential to be applied to other multivariate phenotypes.