An Explainable Deep Learning Framework for Imaging Genetics: Deriving Brain-Genotype Scores From MRI to Link Genetic Variation, Brain Structure, and Cognition

Imaging genetics aims to understand how genetic variation influences brain structure and cognitive function. Traditional approaches often rely on imaging-derived phenotypes (IDPs), which require high-dimensional brain images to be reduced to predefined summary measures and may therefore miss subtle or spatially distributed genotype-related effects. We developed a two-stage framework that integrates deep learning and statistical modelling to derive and exploit brain-genotype scores--continuous, image-based representations of genetic variation learned directly from structural MRI. In the first stage, we trained a multi-task 3D convolutional neural network (CNN) on T1-weighted MRI scans from the UK Biobank, a large, population-based cohort, to predict single-nucleotide polymorphism (SNP) variation, producing brain-genotype scores that capture distributed neuroanatomical patterns associated with specific genetic variants. Unlike conventional IDPs, these scores are learned directly from raw images and are designed to encode genotype-related brain structure without reliance on predefined regional features. Gradient-based saliency maps were used to localise neuroanatomical regions contributing to each score, providing interpretable links between genetic variation and brain anatomy. In the second stage, brain-genotype scores derived from the held-out test set were used as quantitative neuroanatomical markers in association analyses with cognitive performance. These scores showed robust, Bonferroni-corrected associations with multiple cognitive measures, including fluid intelligence, reaction time, and memory performance. In contrast, traditional machine learning models trained on IDPs failed to generate comparably in-formative scores. This integrated framework demonstrates that brain-genotype scores provide a flexible and interpretable representation of genotype-related neuroanatomical variation, enabling the discovery of biologically meaningful links between genetic variation, brain structure, and cognition that are difficult to detect using traditional imaging genetic approaches.