A novel classification framework for genome-wide association study of whole brain MRI images using deep learning.

ABSTRACT: Genome-wide association studies (GWASs) have been widely applied in the 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. On the other hand, powerful deep learning technologies have dramatically improved our ability to classify images. In this study, we proposed and implemented a novel machine learning strategy for systematically identifying genetic variants that lead to detectable nuances on Magnetic Resonance Images (MRI). For a specific single nucleotide polymorphism (SNP), if MRI images labeled by genotypes of this SNP can be reliably distinguished using machine learning, we then hypothesized that this SNP is lik

SUBMITTER: Yu S 

PROVIDER: S-EPMC11508069 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature