Title: 

A novel association study framework powered by machine learning

Abstract: 

Genome-wide association studies (GWASs) have been widely applied to discover genetic variants associated with a diverse array of traits. So far, almost all GWASs conducted in neuroimaging genetics are performed on image-derived quantitative features, which are univariate. In this study, we proposed and implemented a novel machine learning strategy for systematically identifying variants that lead to detectable discrepancies on the full-frame brain images. When applied to data collected by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) consortium, we are able to identify novel variants that show strong association with brain phenotypes.