{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T09:38:48Z","timestamp":1793353128080,"version":"4.5.0"},"reference-count":108,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T00:00:00Z","timestamp":1792713600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T00:00:00Z","timestamp":1792713600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000049","name":"U.S. Department of Health & Human Services | NIH | National Institute on Aging","doi-asserted-by":"publisher","award":["5R01AG081398"],"award-info":[{"award-number":["5R01AG081398"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"U.S. Department of Health & Human Services | NIH | National Institute on Aging","doi-asserted-by":"publisher","award":["5U01AG070112"],"award-info":[{"award-number":["5U01AG070112"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000053","name":"U.S. Department of Health & Human Services | NIH | National Eye Institute","doi-asserted-by":"publisher","award":["5R01EY032768"],"award-info":[{"award-number":["5R01EY032768"]}],"id":[{"id":"10.13039\/100000053","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Commun Biol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Biobank-scale imaging-genomic cohorts enable genome-wide association studies (GWASs) of organ structure and function. Traditional approaches rely on expert-defined image-derived phenotypes (IDPs), which capture only a fraction of the biological variation in high-content images. Powered by artificial intelligence, self-supervised learning (SSL) learns representations directly from intrinsic image structures, creating scalable, data-driven traits. Here, we review the technical frameworks and challenges of SSL-based imaging genetics and their applications in brain MRI, cardiac MRI, and retinal imaging. Early studies suggest that these data-driven approaches can complement traditional IDPs and may expand the phenotypic space available for discovery of heritable imaging traits, although broader validation across cohorts, ancestries, scanners, and imaging protocols remains needed.<\/jats:p>","DOI":"10.1038\/s42003-026-11199-4","type":"journal-article","created":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T15:00:51Z","timestamp":1792767651000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A review of self-supervised imaging phenotypes for genome-wide association studies"],"prefix":"10.1038","volume":"9","author":[{"given":"Khush","family":"Patel","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7508-0856","authenticated-orcid":false,"given":"Xingzhong","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6541-1773","authenticated-orcid":false,"given":"Ziqian","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7754-1890","authenticated-orcid":false,"given":"Degui","family":"Zhi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,23]]},"reference":[{"key":"11199_CR1","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1038\/s41586-018-0571-7","volume":"562","author":"LT Elliott","year":"2018","unstructured":"Elliott, L. T. et al. Genome-wide association studies of brain imaging phenotypes in UK Biobank. Nature 562, 210\u2013216 (2018).","journal-title":"Nature"},{"key":"11199_CR2","doi-asserted-by":"publisher","first-page":"792","DOI":"10.1038\/s41588-022-01090-3","volume":"54","author":"JP Pirruccello","year":"2022","unstructured":"Pirruccello, J. P. et al. Genetic analysis of right heart structure and function in 40,000 people. Nat. Genet. 54, 792\u2013803 (2022).","journal-title":"Nat. Genet."},{"key":"11199_CR3","doi-asserted-by":"publisher","first-page":"e1009497","DOI":"10.1371\/journal.pgen.1009497","volume":"17","author":"H Currant","year":"2021","unstructured":"Currant, H. et al. Genetic variation affects morphological retinal phenotypes extracted from UK Biobank optical coherence tomography images. PLoS Genet. 17, e1009497 (2021).","journal-title":"PLoS Genet."},{"key":"11199_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40478-023-01563-4","volume":"11","author":"M Ali","year":"2023","unstructured":"Ali, M. et al. Large multi-ethnic genetic analyses of amyloid imaging identify new genes for Alzheimer disease. Acta Neuropathol. Commun. 11, 1\u201320 (2023).","journal-title":"Acta Neuropathol. Commun."},{"key":"11199_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-58291-1","volume":"10","author":"SSM Elsheikh","year":"2020","unstructured":"Elsheikh, S. S. M., Chimusa, E. R., Mulder, N. J. & Crimi, A. Genome-wide association study of brain connectivity changes for Alzheimer\u2019s disease. Sci. Rep. 10, 1\u201316 (2020).","journal-title":"Sci. Rep."},{"key":"11199_CR6","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1056\/NEJM200008173430701","volume":"343","author":"SY Bookheimer","year":"2000","unstructured":"Bookheimer, S. Y. et al. Patterns of brain activation in people at risk for Alzheimer\u2019s disease. N. Engl. J. Med. 343, 450\u2013456 (2000).","journal-title":"N. Engl. J. Med."},{"key":"11199_CR7","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1126\/science.1071829","volume":"297","author":"AR Hariri","year":"2002","unstructured":"Hariri, A. R. et al. Serotonin transporter genetic variation and the response of the human amygdala. Science 297, 400\u2013403 (2002).","journal-title":"Science"},{"key":"11199_CR8","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1016\/S0092-8674(03)00035-7","volume":"112","author":"MF Egan","year":"2003","unstructured":"Egan, M. F. et al. The BDNF val66met polymorphism affects activity-dependent secretion of BDNF and human memory and hippocampal function. Cell 112, 257\u2013269 (2003).","journal-title":"Cell"},{"key":"11199_CR9","doi-asserted-by":"publisher","first-page":"512","DOI":"10.1038\/mp.2012.19","volume":"18","author":"SE Murphy","year":"2013","unstructured":"Murphy, S. E. et al. The effect of the serotonin transporter polymorphism (5-HTTLPR) on amygdala function: a meta-analysis. Mol. Psychiatry 18, 512\u2013520 (2013).","journal-title":"Mol. Psychiatry"},{"key":"11199_CR10","doi-asserted-by":"publisher","first-page":"802","DOI":"10.1038\/npp.2012.245","volume":"38","author":"AB Hart","year":"2013","unstructured":"Hart, A. B., de Wit, H. & Palmer, A. A. Candidate gene studies of a promising intermediate phenotype: failure to replicate. Neuropsychopharmacology 38, 802\u2013816 (2013).","journal-title":"Neuropsychopharmacology"},{"key":"11199_CR11","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1038\/ng.2250","volume":"44","author":"JL Stein","year":"2012","unstructured":"Stein, J. L. et al. Identification of common variants associated with human hippocampal and intracranial volumes. Nat. Genet. 44, 552\u2013561 (2012).","journal-title":"Nat. Genet."},{"key":"11199_CR12","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1038\/ng.2237","volume":"44","author":"JC Bis","year":"2012","unstructured":"Bis, J. C. et al. Common variants at 12q14 and 12q24 are associated with hippocampal volume. Nat. Genet. 44, 545\u2013551 (2012).","journal-title":"Nat. Genet."},{"key":"11199_CR13","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1038\/ng.2245","volume":"44","author":"MA Ikram","year":"2012","unstructured":"Ikram, M. A. et al. Common variants at 6q22 and 17q21 are associated with intracranial volume. Nat. Genet. 44, 539\u2013544 (2012).","journal-title":"Nat. Genet."},{"key":"11199_CR14","doi-asserted-by":"publisher","first-page":"e1001779","DOI":"10.1371\/journal.pmed.1001779","volume":"12","author":"C Sudlow","year":"2015","unstructured":"Sudlow, C. et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLOS Med. 12, e1001779 (2015).","journal-title":"PLOS Med."},{"key":"11199_CR15","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-15948-9","volume":"11","author":"TJ Littlejohns","year":"2020","unstructured":"Littlejohns, T. J. et al. The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions. Nat. Commun. 11, 2624 (2020).","journal-title":"Nat. Commun."},{"key":"11199_CR16","doi-asserted-by":"publisher","first-page":"1523","DOI":"10.1038\/nn.4393","volume":"19","author":"KL Miller","year":"2016","unstructured":"Miller, K. L. et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat. Neurosci. 19, 1523\u20131536 (2016).","journal-title":"Nat. Neurosci."},{"key":"11199_CR17","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1016\/j.neuroimage.2017.10.034","volume":"166","author":"F Alfaro-Almagro","year":"2018","unstructured":"Alfaro-Almagro, F. et al. Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank. Neuroimage 166, 400\u2013424 (2018).","journal-title":"Neuroimage"},{"key":"11199_CR18","doi-asserted-by":"publisher","first-page":"1107","DOI":"10.1007\/s10654-022-00890-5","volume":"37","author":"A Peters","year":"2022","unstructured":"Peters, A. et al. Framework and baseline examination of the German National Cohort (NAKO). Eur. J. Epidemiol. 37, 1107\u20131124 (2022).","journal-title":"Eur. J. Epidemiol."},{"key":"11199_CR19","first-page":"587","volume":"121","author":"F Bamberg","year":"2024","unstructured":"Bamberg, F. et al. Baseline MRI examination in the NAKO health study\u2014findings on feasibility, participation and dropout rates, comfort, and image quality. Dtsch Arztebl Int 121, 587\u2013593 (2024).","journal-title":"Dtsch Arztebl Int"},{"key":"11199_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12889-015-2522-7","volume":"15","author":"H Al Kuwari","year":"2015","unstructured":"Al Kuwari, H. et al. The Qatar Biobank: background and methods. BMC Public Health 15, 1\u20139 (2015).","journal-title":"BMC Public Health"},{"key":"11199_CR21","doi-asserted-by":"crossref","unstructured":"Weiner, M. W. ADNI: two decades of impact and the path forward. Alzheimers. Dement. 20, e083392 (2024).","DOI":"10.1002\/alz.083392"},{"key":"11199_CR22","doi-asserted-by":"publisher","first-page":"7361","DOI":"10.1002\/alz.14167","volume":"20","author":"PS Aisen","year":"2024","unstructured":"Aisen, P. S. et al. The Alzheimer\u2019s disease neuroimaging initiative clinical core. Alzheimers Dement. 20, 7361\u20137368 (2024).","journal-title":"Alzheimers Dement."},{"key":"11199_CR23","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1007\/s10519-023-10144-z","volume":"53","author":"S Wilson","year":"2023","unstructured":"Wilson, S., Fan, C. C. & Hewitt, J. ABCD behavior genetics: twin, family, and genomic studies using the adolescent brain cognitive development (ABCD) study dataset. Behav. Genet. 53, 155\u2013158 (2023).","journal-title":"Behav. Genet."},{"key":"11199_CR24","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1038\/s41386-020-0736-6","volume":"46","author":"NR Karcher","year":"2020","unstructured":"Karcher, N. R. & Barch, D. M. The ABCD study: understanding the development of risk for mental and physical health outcomes. Neuropsychopharmacology 46, 131\u2013142 (2020).","journal-title":"Neuropsychopharmacology"},{"key":"11199_CR25","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCHEARTFAILURE.122.010289","author":"L Beussink-Nelson","year":"2024","unstructured":"Beussink-Nelson, L. et al. Multi-ethnic study of atherosclerosis early heart failure study: rationale, design, and baseline characteristics. Circ. Heart Fail. https:\/\/doi.org\/10.1161\/CIRCHEARTFAILURE.122.010289 (2024).","journal-title":"Circ. Heart Fail."},{"key":"11199_CR26","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1007\/s10654-020-00640-5","volume":"35","author":"MA Ikram","year":"2020","unstructured":"Ikram, M. A. et al. Objectives, design and main findings until 2020 from the Rotterdam Study. Eur. J. Epidemiol. 35, 483\u2013517 (2020).","journal-title":"Eur. J. Epidemiol."},{"key":"11199_CR27","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1038\/s41569-019-0202-5","volume":"16","author":"C Andersson","year":"2019","unstructured":"Andersson, C., Johnson, A. D., Benjamin, E. J., Levy, D. & Vasan, R. S. 70-year legacy of the Framingham Heart Study. Nat. Rev. Cardiol. 16, 687\u2013698 (2019).","journal-title":"Nat. Rev. Cardiol."},{"key":"11199_CR28","doi-asserted-by":"publisher","first-page":"1800","DOI":"10.1093\/ije\/dyv337","volume":"44","author":"CW Tsao","year":"2015","unstructured":"Tsao, C. W. & Vasan, R. S. Cohort Profile: The Framingham Heart Study (FHS): overview of milestones in cardiovascular epidemiology. Int. J. Epidemiol. 44, 1800\u20131813 (2015).","journal-title":"Int. J. Epidemiol."},{"key":"11199_CR29","doi-asserted-by":"publisher","first-page":"737","DOI":"10.1038\/s41593-021-00826-4","volume":"24","author":"SM Smith","year":"2021","unstructured":"Smith, S. M. et al. An expanded set of genome-wide association studies of brain imaging phenotypes in UK Biobank. Nat. Neurosci. 24, 737\u2013745 (2021).","journal-title":"Nat. Neurosci."},{"key":"11199_CR30","doi-asserted-by":"publisher","DOI":"10.1126\/scitranslmed.adg4517","volume":"16","author":"SM Zekavat","year":"2024","unstructured":"Zekavat, S. M. et al. Phenome- and genome-wide analyses of retinal optical coherence tomography images identify links between ocular and systemic health. Sci. Transl. Med. 16, eadg4517 (2024).","journal-title":"Sci. Transl. Med."},{"key":"11199_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.xops.2023.100288","volume":"3","author":"M Tomasoni","year":"2023","unstructured":"Tomasoni, M. et al. Genome-wide association studies of retinal vessel tortuosity identify numerous novel loci revealing genes and pathways associated with ocular and cardiometabolic diseases. Ophthalmol. Sci. 3, 100288 (2023).","journal-title":"Ophthalmol. Sci."},{"key":"11199_CR32","doi-asserted-by":"crossref","unstructured":"Schoemaker, D. et al. Hippocampus and Amygdala Volumes from Magnetic Resonance Images in Children: Assessing Accuracy of FreeSurfer and FSL Against Manual Segmentation. Neuroimage 129, 1\u201314 (2016).","DOI":"10.1016\/j.neuroimage.2016.01.038"},{"key":"11199_CR33","first-page":"105","volume":"11383","author":"X Han","year":"2019","unstructured":"Han, X. et al. Patient-specific registration of pre-operative and post-recurrence brain tumor MRI scans. Brainlesion 11383, 105\u2013114 (2019).","journal-title":"Brainlesion"},{"key":"11199_CR34","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1038\/s41592-023-02151-z","volume":"21","author":"L Maier-Hein","year":"2024","unstructured":"Maier-Hein, L. et al. Metrics reloaded: recommendations for image analysis validation. Nat. Methods 21, 195\u2013212 (2024).","journal-title":"Nat. Methods"},{"key":"11199_CR35","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1038\/s41551-018-0195-0","volume":"2","author":"R Poplin","year":"2018","unstructured":"Poplin, R. et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat. Biomed. Eng. 2, 158\u2013164 (2018).","journal-title":"Nat. Biomed. Eng."},{"key":"11199_CR36","doi-asserted-by":"publisher","first-page":"e1011273","DOI":"10.1371\/journal.pgen.1011273","volume":"20","author":"Z Xie","year":"2024","unstructured":"Xie, Z. et al. iGWAS: image-based genome-wide association of self-supervised deep phenotyping of retina fundus images. PLoS Genet. 20, e1011273 (2024).","journal-title":"PLoS Genet."},{"key":"11199_CR37","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1038\/s42003-024-06096-7","volume":"7","author":"K Patel","year":"2024","unstructured":"Patel, K. et al. Unsupervised deep representation learning enables phenotype discovery for genetic association studies of brain imaging. Commun. Biol. 7, 414 (2024).","journal-title":"Commun. Biol."},{"key":"11199_CR38","doi-asserted-by":"publisher","first-page":"1562","DOI":"10.1016\/j.ajhg.2025.05.015","volume":"112","author":"Y Zhou","year":"2025","unstructured":"Zhou, Y. et al. Applying multimodal AI to physiological waveforms improves genetic prediction of cardiovascular traits. Am. J. Hum. Genet. 112, 1562\u20131579 (2025).","journal-title":"Am. J. Hum. Genet."},{"key":"11199_CR39","doi-asserted-by":"publisher","first-page":"1604","DOI":"10.1038\/s41588-024-01831-6","volume":"56","author":"T Yun","year":"2024","unstructured":"Yun, T. et al. Unsupervised representation learning on high-dimensional clinical data improves genomic discovery and prediction. Nat. Genet. 56, 1604\u20131613 (2024).","journal-title":"Nat. Genet."},{"key":"11199_CR40","doi-asserted-by":"crossref","unstructured":"Sergouniotis, P. I. et al. Autoencoder-based phenotyping of ophthalmic images highlights genetic loci influencing retinal morphology and provides informative biomarkers. Bioinformatics 41, btae732 (2024).","DOI":"10.1101\/2023.06.15.23291410"},{"key":"11199_CR41","doi-asserted-by":"publisher","first-page":"1346","DOI":"10.1038\/s41551-022-00914-1","volume":"6","author":"R Krishnan","year":"2022","unstructured":"Krishnan, R., Rajpurkar, P. & Topol, E. J. Self-supervised learning in medicine and healthcare. Nat. Biomed. Eng. 6, 1346\u20131352 (2022).","journal-title":"Nat. Biomed. Eng."},{"key":"11199_CR42","doi-asserted-by":"publisher","first-page":"9052","DOI":"10.1109\/TPAMI.2024.3415112","volume":"46","author":"J Gui","year":"2024","unstructured":"Gui, J. et al. A survey on self-supervised learning: algorithms, applications, and future trends. IEEE Trans. Pattern Anal. Mach. Intell. 46, 9052\u20139071 (2024).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"11199_CR43","first-page":"857","volume":"35","author":"X Liu","year":"2023","unstructured":"Liu, X. et al. Self-supervised learning: generative or contrastive. IEEE Trans. Knowl. Data Eng. 35, 857\u2013876 (2023).","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"11199_CR44","doi-asserted-by":"publisher","first-page":"4189","DOI":"10.1002\/mp.14341","volume":"47","author":"M Qiao","year":"2020","unstructured":"Qiao, M. et al. Temporally coherent cardiac motion tracking from cine MRI: traditional registration method and modern CNN method. Med. Phys. 47, 4189\u20134198 (2020).","journal-title":"Med. Phys."},{"key":"11199_CR45","unstructured":"Xie, Y. et al. Genetic InfoMax: exploring mutual information maximization in high-dimensional imaging genetics studies. Trans. Mach. Learn. Res. https:\/\/openreview.net\/forum?id=9UgUMFW67X 2835\u20138856 (ISSN, 2024)."},{"key":"11199_CR46","doi-asserted-by":"publisher","first-page":"e1011332","DOI":"10.1371\/journal.pgen.1011332","volume":"20","author":"A Rakowski","year":"2024","unstructured":"Rakowski, A., Monti, R. & Lippert, C. TransferGWAS of T1-weighted brain MRI data from UK Biobank. PLoS Genet. 20, e1011332 (2024).","journal-title":"PLoS Genet."},{"key":"11199_CR47","doi-asserted-by":"publisher","first-page":"3015","DOI":"10.1038\/s41591-024-03144-x","volume":"30","author":"Z Yang","year":"2024","unstructured":"Yang, Z. et al. Brain aging patterns in a large and diverse cohort of 49,482 individuals. Nat. Med. 30, 3015\u20133026 (2024).","journal-title":"Nat. Med."},{"key":"11199_CR48","doi-asserted-by":"publisher","first-page":"861","DOI":"10.21105\/joss.00861","volume":"3","author":"L McInnes","year":"2018","unstructured":"McInnes, L., Healy, J., Saul, N. & Gro\u00dfberger, L. UMAP: uniform manifold approximation and projection. J. Open Sour. Softw. 3, 861 (2018).","journal-title":"J. Open Sour. Softw."},{"key":"11199_CR49","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1038\/ng.3190","volume":"47","author":"P-R Loh","year":"2015","unstructured":"Loh, P.-R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284\u2013290 (2015).","journal-title":"Nat. Genet."},{"key":"11199_CR50","doi-asserted-by":"publisher","first-page":"1749","DOI":"10.1038\/s41588-019-0530-8","volume":"51","author":"L Jiang","year":"2019","unstructured":"Jiang, L. et al. A resource-efficient tool for mixed model association analysis of large-scale data. Nat. Genet. 51, 1749\u20131755 (2019).","journal-title":"Nat. Genet."},{"key":"11199_CR51","doi-asserted-by":"publisher","DOI":"10.1038\/srep38837","volume":"7","author":"HF Porter","year":"2017","unstructured":"Porter, H. F. & O\u2019Reilly, P. F. Multivariate simulation framework reveals performance of multi-trait GWAS methods. Sci. Rep. 7, 38837 (2017).","journal-title":"Sci. Rep."},{"key":"11199_CR52","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-17368-1","volume":"11","author":"D van der Meer","year":"2020","unstructured":"van der Meer, D. et al. Understanding the genetic determinants of the brain with MOSTest. Nat. Commun. 11, 3512 (2020).","journal-title":"Nat. Commun."},{"key":"11199_CR53","doi-asserted-by":"publisher","unstructured":"Guo, B. et al. Efficient multi-phenotype genome-wide analysis identifies genetic associations for unsupervised deep-learning-derived high-dimensional brain imaging phenotypes. Preprint at https:\/\/doi.org\/10.1101\/2024.12.06.24318618 (2024).","DOI":"10.1101\/2024.12.06.24318618"},{"key":"11199_CR54","doi-asserted-by":"publisher","first-page":"R89","DOI":"10.1093\/hmg\/ddu328","volume":"23","author":"G Davey Smith","year":"2014","unstructured":"Davey Smith, G. & Hemani, G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum. Mol. Genet 23, R89\u2013R98 (2014).","journal-title":"Hum. Mol. Genet"},{"key":"11199_CR55","doi-asserted-by":"publisher","unstructured":"Taleb, A., Kirchler, M., Monti, R. & Lippert, C. ContIG: self-supervised multimodal contrastive learning for medical imaging with genetics. In 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https:\/\/doi.org\/10.1109\/cvpr52688.2022.02024. (IEEE, 2022).","DOI":"10.1109\/cvpr52688.2022.02024"},{"key":"11199_CR56","doi-asserted-by":"publisher","first-page":"53277","DOI":"10.1109\/ACCESS.2024.3387702","volume":"12","author":"AQ Wang","year":"2024","unstructured":"Wang, A. Q. et al. A framework for interpretability in machine learning for medical imaging. IEEE Access 12, 53277\u201353292 (2024).","journal-title":"IEEE Access"},{"key":"11199_CR57","doi-asserted-by":"publisher","unstructured":"Selvaraju, R. R. et al. Grad-CAM: Visual explanations from deep networks via gradient-based localization. In 2017 IEEE International Conference on Computer Vision (ICCV) 618\u2013626 https:\/\/doi.org\/10.1109\/iccv.2017.74. (IEEE, 2017).","DOI":"10.1109\/iccv.2017.74"},{"key":"11199_CR58","unstructured":"Sundararajan, M., Taly, A. & Yan, Q. Axiomatic Attribution for Deep Networks. In Proc. 34th International Conference on Machine Learning vol. 70 3319\u20133328 (PMLR, 2017)."},{"key":"11199_CR59","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1038\/s42256-024-00801-1","volume":"6","author":"R Bonazzola","year":"2024","unstructured":"Bonazzola, R. et al. Unsupervised ensemble-based phenotyping enhances discoverability of genes related to left-ventricular morphology. Nat. Mach. Intell. 6, 291\u2013306 (2024).","journal-title":"Nat. Mach. Intell."},{"key":"11199_CR60","doi-asserted-by":"publisher","first-page":"IMAG.a.987","DOI":"10.1162\/IMAG.a.987","volume":"3","author":"A Dufournet","year":"2025","unstructured":"Dufournet, A. et al. A self-supervised learning framework for discovering cortical folding patterns under genetic influence: application to the Anterior Cingulate Cortex. Imaging Neurosci. 3, IMAG.a.987 (2025).","journal-title":"Imaging Neurosci."},{"key":"11199_CR61","doi-asserted-by":"publisher","unstructured":"Cardoso, M. J. et al. MONAI: an open-source framework for deep learning in healthcare. Preprint at https:\/\/doi.org\/10.48550\/arXiv.2211.02701 (2022).","DOI":"10.48550\/arXiv.2211.02701"},{"key":"11199_CR62","doi-asserted-by":"publisher","first-page":"117012","DOI":"10.1016\/j.neuroimage.2020.117012","volume":"219","author":"L Henschel","year":"2020","unstructured":"Henschel, L. et al. FastSurfer - A fast and accurate deep learning based neuroimaging pipeline. Neuroimage 219, 117012 (2020).","journal-title":"Neuroimage"},{"key":"11199_CR63","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-59440-6","volume":"14","author":"NJ Tustison","year":"2024","unstructured":"Tustison, N. J. et al. ANTsX neuroimaging-derived structural phenotypes of UK Biobank. Sci. Rep. 14, 8848 (2024).","journal-title":"Sci. Rep."},{"key":"11199_CR64","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton, G. E. & Salakhutdinov, R. R. Reducing the dimensionality of data with neural networks. Science 313, 504\u2013507 (2006).","journal-title":"Science"},{"key":"11199_CR65","doi-asserted-by":"publisher","unstructured":"Kingma, D. P. & Welling, M. An introduction to variational autoencoders. Found. Trends Mach. Learn. 12, 307\u2013392 https:\/\/doi.org\/10.1561\/2200000056 (2019).","DOI":"10.1561\/2200000056"},{"key":"11199_CR66","doi-asserted-by":"publisher","first-page":"7154","DOI":"10.1007\/s11263-025-02524-1","volume":"133","author":"V Hondru","year":"2025","unstructured":"Hondru, V., Croitoru, F. A., Minaee, S., Ionescu, R. T. & Sebe, N. Masked image modeling: a survey. Int. J. Comput. Vis. 133, 7154\u20137200 (2025).","journal-title":"Int. J. Comput. Vis."},{"key":"11199_CR67","first-page":"138","volume":"14222","author":"K Ye","year":"2023","unstructured":"Ye, K. et al. Bidirectional mapping with contrastive learning on multimodal neuroimaging data. Med. Image Comput. Comput. Assist. Interv. 14222, 138\u2013148 (2023).","journal-title":"Med. Image Comput. Comput. Assist. Interv."},{"key":"11199_CR68","unstructured":"Chen, T., Kornblith, S., Norouzi, M. & Hinton, G. A simple framework for contrastive learning of visual representations. In Proc. 37th International Conference on Machine Learning vol. 119, 1597\u20131607 (PMLR, 2020)."},{"key":"11199_CR69","doi-asserted-by":"publisher","unstructured":"He, K., Fan, H., Wu, Y., Xie, S. & Girshick, R. Momentum contrast for unsupervised visual representation learning. In 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https:\/\/doi.org\/10.1109\/cvpr42600.2020.00975. (IEEE, 2020).","DOI":"10.1109\/cvpr42600.2020.00975"},{"key":"11199_CR70","unstructured":"Zbontar, J., Jing, L., Misra, I., LeCun, Y. & Deny, S. Barlow Twins: self-supervised learning via redundancy reduction. In Proc. 38th International Conference on Machine Learning (eds Meila, M. & Zhang, T.) PMLR https:\/\/proceedings.mlr.press\/v139\/zbontar21a.html139, 12310\u201312320 (2021)."},{"key":"11199_CR71","unstructured":"Bardes, A., Ponce, J. & LeCun, Y. VICReg: variance-invariance-covariance regularization for self-supervised learning. In International Conference on Learning Representations https:\/\/openreview.net\/forum?id=xm6YD62D1Ub (ICLR, 2022)."},{"key":"11199_CR72","first-page":"21271","volume":"33","author":"J-B Grill","year":"2020","unstructured":"Grill, J.-B. et al. Bootstrap your own latent: a new approach to self-supervised learning. Adv. Neural Inf. Process. Syst. 33, 21271\u201321284 (2020).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"11199_CR73","unstructured":"Radford, A. et al. Learning transferable visual models from natural language supervision. In Proc. 38th International Conference on Machine Learning vol. 139, 8748\u20138763 (PMLR, 2021)."},{"key":"11199_CR74","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-023-38125-0","volume":"14","author":"A Radhakrishnan","year":"2023","unstructured":"Radhakrishnan, A. et al. Cross-modal autoencoder framework learns holistic representations of cardiovascular state. Nat. Commun. 14, 1\u201312 (2023).","journal-title":"Nat. Commun."},{"key":"11199_CR75","doi-asserted-by":"publisher","first-page":"913","DOI":"10.1038\/s42003-022-03880-1","volume":"5","author":"X Zhou","year":"2022","unstructured":"Zhou, X. et al. Choice of voxel-based morphometry processing pipeline drives variability in the location of neuroanatomical brain markers. Commun. Biol. 5, 913 (2022).","journal-title":"Commun. Biol."},{"key":"11199_CR76","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1006\/nimg.2000.0582","volume":"11","author":"J Ashburner","year":"2000","unstructured":"Ashburner, J. & Friston, K. J. Voxel-based morphometry-the methods. Neuroimage 11, 805\u2013821 (2000).","journal-title":"Neuroimage"},{"key":"11199_CR77","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-026-73996-z","author":"X Zhao","year":"2026","unstructured":"Zhao, X. et al. Genetic architecture of white matter microstructure captured by unsupervised deep representation learning of fractional anisotropy maps. Nat. Commun. https:\/\/doi.org\/10.1038\/s41467-026-73996-z (2026).","journal-title":"Nat. Commun."},{"key":"11199_CR78","doi-asserted-by":"publisher","unstructured":"Deng, J. et al. ImageNet: a large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition https:\/\/doi.org\/10.1109\/cvpr.2009.5206848 (IEEE, 2009).","DOI":"10.1109\/cvpr.2009.5206848"},{"key":"11199_CR79","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-78761-4_8","author":"J Laval","year":"2025","unstructured":"Laval, J. et al. Towards a foundation model for cortical folding. Mach. Learn. Clin. Neuroimaging https:\/\/doi.org\/10.1007\/978-3-031-78761-4_8 (2025).","journal-title":"Mach. Learn. Clin. Neuroimaging"},{"key":"11199_CR80","doi-asserted-by":"publisher","first-page":"S163","DOI":"10.1016\/S1053-8119(09)71720-3","volume":"47","author":"D Rivi\u00e8re","year":"2009","unstructured":"Rivi\u00e8re, D., Geffroy, D., Denghien, I., Souedet, N. & Cointepas, Y. BrainVISA: an extensible software environment for sharing multimodal neuroimaging data and processing tools. NeuroImage 47, S163 (2009).","journal-title":"NeuroImage"},{"key":"11199_CR81","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-15823-7","volume":"11","author":"JP Pirruccello","year":"2020","unstructured":"Pirruccello, J. P. et al. Analysis of cardiac magnetic resonance imaging in 36,000 individuals yields genetic insights into dilated cardiomyopathy. Nat. Commun. 11, 2254 (2020).","journal-title":"Nat. Commun."},{"key":"11199_CR82","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1161\/CIRCGENETICS.111.962365","volume":"6","author":"ER Fox","year":"2013","unstructured":"Fox, E. R. et al. Genome-wide association study of cardiac structure and systolic function in African Americans: the Candidate Gene Association Resource (CARe) study. Circ. Cardiovasc Genet. 6, 37\u201346 (2013).","journal-title":"Circ. Cardiovasc Genet."},{"key":"11199_CR83","unstructured":"Defferrard, M., Bresson, X. & Vandergheynst, P. Convolutional neural networks on graphs with fast localized spectral filtering. Adv. Neural Inform. Process. Syst. (eds Lee, D. et al.) 29, 3844\u20133852 https:\/\/proceedings.neurips.cc\/paper\/2016\/hash\/04df4d434d481c5bb723be1b6df1ee65-Abstract.html. (2016)."},{"key":"11199_CR84","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-58212-2","volume":"10","author":"G Tarroni","year":"2020","unstructured":"Tarroni, G. et al. Large-scale quality control of cardiac imaging in population studies: application to UK Biobank. Sci. Rep. 10, 2408 (2020).","journal-title":"Sci. Rep."},{"key":"11199_CR85","doi-asserted-by":"publisher","first-page":"5962","DOI":"10.1109\/TPAMI.2021.3087709","volume":"44","author":"J Deng","year":"2022","unstructured":"Deng, J. et al. ArcFace: additive angular margin loss for deep face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 44, 5962\u20135979 (2022).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"11199_CR86","doi-asserted-by":"publisher","first-page":"3621","DOI":"10.1093\/bioinformatics\/btac369","volume":"38","author":"M Kirchler","year":"2022","unstructured":"Kirchler, M. et al. transferGWAS: GWAS of images using deep transfer learning. Bioinformatics 38, 3621\u20133628 (2022).","journal-title":"Bioinformatics"},{"key":"11199_CR87","unstructured":"Kornblith, S., Norouzi, M., Lee, H. & Hinton, G. Similarity of neural network representations revisited. In Proc. 36th International Conference on Machine Learning 97, 3519\u20133529 (PMLR, 2019)."},{"key":"11199_CR88","unstructured":"Roeder, G., Metz, L. & Kingma, D. On Linear identifiability of learned representations. In Proc. 38th International Conference on Machine Learning (eds. Meila, M. & Zhang, T.) 139, 9030\u20139039 (PMLR, 2021)."},{"key":"11199_CR89","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1214\/09-STS290","volume":"24","author":"P Kraft","year":"2009","unstructured":"Kraft, P., Zeggini, E. & Ioannidis, J. P. A. Replication in genome-wide association studies. Stat. Sci. 24, 561\u2013573 (2009).","journal-title":"Stat. Sci."},{"key":"11199_CR90","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1016\/j.ajhg.2018.04.002","volume":"102","author":"MD Gallagher","year":"2018","unstructured":"Gallagher, M. D. & Chen-Plotkin, A. S. The Post-GWAS Era: from association to function. Am. J. Hum. Genet 102, 717\u2013730 (2018).","journal-title":"Am. J. Hum. Genet"},{"key":"11199_CR91","doi-asserted-by":"publisher","first-page":"424","DOI":"10.3389\/fgene.2020.00424","volume":"11","author":"E Cano-Gamez","year":"2020","unstructured":"Cano-Gamez, E. & Trynka, G. From GWAS to function: using functional genomics to identify the mechanisms underlying complex diseases. Front. Genet. 11, 424 (2020).","journal-title":"Front. Genet."},{"key":"11199_CR92","doi-asserted-by":"publisher","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378","volume":"385","author":"GS Collins","year":"2024","unstructured":"Collins, G. S. et al. TRIPOD\u2009+\u2009AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ 385, e078378 (2024).","journal-title":"BMJ"},{"key":"11199_CR93","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1038\/nrg.2015.36","volume":"17","author":"WS Bush","year":"2016","unstructured":"Bush, W. S., Oetjens, M. T. & Crawford, D. C. Unravelling the human genome-phenome relationship using phenome-wide association studies. Nat. Rev. Genet. 17, 129\u2013145 (2016).","journal-title":"Nat. Rev. Genet."},{"key":"11199_CR94","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1038\/nrg3523","volume":"14","author":"TA Manolio","year":"2013","unstructured":"Manolio, T. A. Bringing genome-wide association findings into clinical use. Nat. Rev. Genet. 14, 549\u2013558 (2013).","journal-title":"Nat. Rev. Genet."},{"key":"11199_CR95","doi-asserted-by":"publisher","first-page":"e200267","DOI":"10.1148\/ryai.2021200267","volume":"3","author":"N Arun","year":"2021","unstructured":"Arun, N. et al. Assessing the trustworthiness of saliency maps for localizing abnormalities in medical imaging. Radio. Artif. Intell. 3, e200267 (2021).","journal-title":"Radio. Artif. Intell."},{"key":"11199_CR96","doi-asserted-by":"publisher","first-page":"vbae135","DOI":"10.1093\/bioadv\/vbae135","volume":"4","author":"S Mukherjee","year":"2024","unstructured":"Mukherjee, S. et al. EmbedGEM: a framework to evaluate the utility of embeddings for genetic discovery. Bioinform. Adv. 4, vbae135 (2024).","journal-title":"Bioinform. Adv."},{"key":"11199_CR97","doi-asserted-by":"publisher","DOI":"10.1038\/s42003-026-10430-6","author":"SR Islam","year":"2026","unstructured":"Islam, S. R., Xia, T., He, W., Xie, Z. & Zhi, D. Vision transformer autoencoders captures local and non-local features in brain imaging to reveal novel genetic associations. Commun. Biol. https:\/\/doi.org\/10.1038\/s42003-026-10430-6 (2026).","journal-title":"Commun. Biol."},{"key":"11199_CR98","unstructured":"Dosovitskiy, A. et al. An image is worth 16\u00d716 words: transformers for image recognition at scale. In International Conference on Learning Representations https:\/\/openreview.net\/forum?id=YicbFdNTTy (ICLR, 2021)."},{"key":"11199_CR99","doi-asserted-by":"publisher","first-page":"vbaf196","DOI":"10.1093\/bioadv\/vbaf196","volume":"5","author":"D Machado Reyes","year":"2025","unstructured":"Machado Reyes, D., Burch, M., Parida, L. & Bose, A. A foundation model for learning genetic associations from brain imaging phenotypes. Bioinform. Adv. 5, vbaf196 (2025).","journal-title":"Bioinform. Adv."},{"key":"11199_CR100","doi-asserted-by":"publisher","first-page":"nwae348","DOI":"10.1093\/nsr\/nwae348","volume":"11","author":"M Chen","year":"2024","unstructured":"Chen, M., Mei, S., Fan, J. & Wang, M. Opportunities and challenges of diffusion models for generative AI. Natl. Sci. Rev. 11, nwae348 (2024).","journal-title":"Natl. Sci. Rev."},{"key":"11199_CR101","doi-asserted-by":"publisher","unstructured":"Arnab, A. et al. ViViT: a video vision transformer. In 2021 IEEE\/CVF International Conference on Computer Vision (ICCV). https:\/\/doi.org\/10.1109\/iccv48922.2021.00676. (IEEE, 2021).","DOI":"10.1109\/iccv48922.2021.00676"},{"key":"11199_CR102","doi-asserted-by":"crossref","unstructured":"Liu, Y. et al. Genetic architecture of 11 organ traits derived from abdominal MRI using deep learning. Elife 10, e65554 (2021).","DOI":"10.7554\/eLife.65554"},{"key":"11199_CR103","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-30931-2","volume":"13","author":"S Agrawal","year":"2022","unstructured":"Agrawal, S. et al. Inherited basis of visceral, abdominal subcutaneous and gluteofemoral fat depots. Nat. Commun. 13, 3771 (2022).","journal-title":"Nat. Commun."},{"key":"11199_CR104","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1093\/jbmr\/zjae002","volume":"39","author":"JH Tobias","year":"2024","unstructured":"Tobias, J. H. et al. Femoral neck width genetic risk score is a novel independent risk factor for hip fractures. J. Bone Min. Res. 39, 241\u2013251 (2024).","journal-title":"J. Bone Min. Res."},{"key":"11199_CR105","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1002\/jbmr.3605","volume":"34","author":"DA Baird","year":"2019","unstructured":"Baird, D. A. et al. Identification of novel loci associated with hip shape: a meta-analysis of Genomewide Association Studies. J. Bone Min. Res. 34, 241\u2013251 (2019).","journal-title":"J. Bone Min. Res."},{"key":"11199_CR106","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1164\/rccm.201306-1061OC","volume":"189","author":"A Manichaikul","year":"2014","unstructured":"Manichaikul, A. et al. Genome-wide study of percent emphysema on computed tomography in the general population. The multi-ethnic study of atherosclerosis lung\/SNP Health Association Resource Study. Am. J. Respir. Crit. Care Med. 189, 408\u2013418 (2014).","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"11199_CR107","doi-asserted-by":"publisher","first-page":"807","DOI":"10.1164\/rccm.202102-0260OC","volume":"204","author":"MC van der Molen","year":"2021","unstructured":"van der Molen, M. C. et al. Determinants of lung fissure completeness. Am. J. Respir. Crit. Care Med. 204, 807\u2013816 (2021).","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"11199_CR108","doi-asserted-by":"publisher","unstructured":"Chaudhary, S. et al. HistoGWAS: an AI-enabled framework for automated genetic analysis of tissue phenotypes in histology cohorts. Genome Biol. https:\/\/doi.org\/10.1186\/s13059-026-04031-z27,122 (2026).","DOI":"10.1186\/s13059-026-04031-z"}],"container-title":["Communications Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s42003-026-11199-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s42003-026-11199-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s42003-026-11199-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T08:06:07Z","timestamp":1793001967000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s42003-026-11199-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,23]]},"references-count":108,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["11199"],"URL":"https:\/\/doi.org\/10.1038\/s42003-026-11199-4","relation":{},"ISSN":["2399-3642"],"issn-type":[{"value":"2399-3642","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,23]]},"assertion":[{"value":"4 February 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 October 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 October 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Ethics"}}],"article-number":"1394"}}