Abstract
Scoliosis is the most common developmental spinal deformity, but its genetic underpinnings remain only partially understood. To identify scoliosis-related loci, we utilized dual energy X-ray absorptiometry (DXA) scans from 57,588 individuals in the UK Biobank (UKB), and quantified spinal curvature using deep learning-based vertebral segmentation and landmarking to measure cumulative horizontal displacement. On a subset of 150 individuals, our automated image-derived curvature measurements showed a correlation of 0.83 with clinical Cobb angle assessments, supporting their validity as a proxy for scoliosis severity. To connect spinal curvature to genetics, we conducted a genome-wide association study (GWAS). Our quantitative imaging phenotype identified 2 novel loci associated with scoliosis in a European population. These loci are in SEM1/SHFM1 and on an lncRNA on chr 3 located between EDEM1 and GRM7. Genetic correlation analysis revealed significant overlap between our image-based GWAS and ICD-10-based GWAS in the UKB and the Biobank of Japan. We show that our quantitative GWAS identifies more genome-wide significant loci than a case-control scoliosis dataset with ten times the sample size. Our results illustrate the potential of quantitative imaging phenotypes to uncover genetic associations that are challenging to capture with medical records alone and identify new loci for functional follow-up.
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Data availability
All data used for this study were obtained from the UK Biobank under application number 65439. GWAS summary statistics are currently being uploaded to the GWAS catalog and are available at this Box link: https://utexas.box.com/s/0tyyxhzv2e5x8iucsdcd8dfutobnmcd2.
Code availability
Deep-learning and image processing tools can be found at https://github.com/reb12345/Scoliosis.
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Acknowledgements
V.M.N. was supported on a grant from the Allen Discovery Center program, a Paul G. Allen Frontiers Group-advised program of the Paul G. Allen Family Foundation.
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M.Z. and E.K. wrote the paper with input from all co-authors. M.Z., E.K., S.R., L.X., D.P., J.W., C.L., and N.O. performed the analysis. R.G., C.W. and V.M.N. supervised the analysis.
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Zeosky, M., Kun, E., Reddy, S. et al. Deep learning-based precision phenotyping of spine curvature identifies novel genetic risk loci for scoliosis in the UK Biobank. npj Digit. Med. (2026). https://doi.org/10.1038/s41746-026-02540-6
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DOI: https://doi.org/10.1038/s41746-026-02540-6
