Language
English
Publication Date
6-3-2026
Journal
Nature Communications
DOI
10.1038/s41467-026-73996-z
PMID
42236496
PMCID
PMC13396798
PubMedCentral® Posted Date
6-3-2026
PubMedCentral® Full Text Version
Post-print
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.
Keywords
White Matter, Humans, Anisotropy, Polymorphism, Single Nucleotide, Deep Learning, Genome-Wide Association Study, Diffusion Magnetic Resonance Imaging, Oligodendroglia, Brain, Phenotype, Computational biology and bioinformatics, Genome-wide association studies, Predictive markers
Published Open-Access
yes
Recommended Citation
Zhao, Xingzhong; Xie, Ziqian; He, Wei; et al., "Genetic Architecture of White Matter Microstructure Captured by Unsupervised Deep Representation Learning of Fractional Anisotropy Maps" (2026). The Brown Foundation: Institute of Molecular Medicine. 103.
https://digitalcommons.library.tmc.edu/molecular_med/103