Genetic architecture of white matter microstructure captured by unsupervised deep representation learning of fractional anisotropy maps

Abstract

Abstract Fractional anisotropy (FA) from diffusion MRI is a widely used marker of white matter (WM) integrity, but conventional FA-based genetic studies typically rely on tract- or atlas-defined averages that may obscure spatially distributed WM variation and limit genetic discovery. Here, we propose a deep learning framework, termed unsupervised deep representation of WM (UDR-WM), which uses voxel-wise FA maps to derive brain-wide unsupervised deep imaging phenotypes (UDIP-FA) without prior anatomical assumptions. Compared with traditional FA phenotypes, UDIP-FA shows greater sensitivity to aging and substantially higher SNP-based heritability. Multivariate GWAS identified 939 lead SNPs across 586 loci, mapping to 3,480 UDIP-FA-associated genes. These genes are enriched in glial cells, especially astrocytes and oligodendrocytes, and form disease-relevant modules in protein interaction and co-expression networks implicating myelination and axonal structure. UDIP-FA is genetically associated with multiple brain disorders, cognitive traits, and polygenic risk. Together, our results suggest that UDIP-FA provides a biologically meaningful view of white matter, complementing conventional ROI-based FA measures and offering a more refined way to study its genetic architecture.