{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T00:54:08Z","timestamp":1792457648467,"version":"4.5.0"},"reference-count":34,"publisher":"MIT Press","license":[{"start":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T00:00:00Z","timestamp":1745539200000},"content-version":"vor","delay-in-days":114,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Medical Research Council","award":["503480"],"award-info":[{"award-number":["503480"]}]},{"DOI":"10.13039\/501100002283","name":"Alzheimer\u2019s Research UK","doi-asserted-by":"crossref","award":["503176"],"award-info":[{"award-number":["503176"]}],"id":[{"id":"10.13039\/501100002283","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100010269","name":"Wellcome Trust","doi-asserted-by":"crossref","award":["082604\/2\/07\/Z"],"award-info":[{"award-number":["082604\/2\/07\/Z"]}],"id":[{"id":"10.13039\/100010269","id-type":"DOI","asserted-by":"crossref"}]},{"name":"German Federal Ministry of Education and Research (BMBF):","award":["01GI0102"],"award-info":[{"award-number":["01GI0102"]}]},{"name":"German Federal Ministry of Education and Research (BMBF):","award":["01GI0711"],"award-info":[{"award-number":["01GI0711"]}]},{"name":"German Federal Ministry of Education and Research (BMBF):","award":["01GI0420"],"award-info":[{"award-number":["01GI0420"]}]},{"name":"NIH\/NIA","award":["R01 AG033193"],"award-info":[{"award-number":["R01 AG033193"]}]},{"name":"NIA","award":["AG081220"],"award-info":[{"award-number":["AG081220"]}]},{"DOI":"10.13039\/501100024705","name":"AGES","doi-asserted-by":"crossref","award":["N01-AG-12100"],"award-info":[{"award-number":["N01-AG-12100"]}],"id":[{"id":"10.13039\/501100024705","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000050","name":"NHLBI","doi-asserted-by":"crossref","award":["R01 HL105756"],"award-info":[{"award-number":["R01 HL105756"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"crossref"}]},{"name":"NIH\/NIA","award":["U01 AG032984"],"award-info":[{"award-number":["U01 AG032984"]}]},{"name":"NIH\/NIA","award":["U24 AG021886"],"award-info":[{"award-number":["U24 AG021886"]}]},{"name":"NIH\/NIA","award":["U01 AG016976"],"award-info":[{"award-number":["U01 AG016976"]}]},{"DOI":"10.13039\/100000957","name":"Alzheimer\u2019s Association","doi-asserted-by":"crossref","award":["ADGC-10-196728"],"award-info":[{"award-number":["ADGC-10-196728"]}],"id":[{"id":"10.13039\/100000957","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100023930","name":"McLaughlin Centre","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100023930","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["RGPIN-2021-03734"],"award-info":[{"award-number":["RGPIN-2021-03734"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["RGPIN-2022-04831"],"award-info":[{"award-number":["RGPIN-2022-04831"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014405","name":"The CAMH Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100014405","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The Koerner Family Foundation"},{"DOI":"10.13039\/501100004089","name":"Krembil Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004089","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000024","name":"Canadian Institutes of Health Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007224","name":"University of Toronto\u2019s Data Science Institute, and the Connaught Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100007224","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,5,16]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The UK Biobank study has produced thousands of brain imaging-derived phenotypes (IDPs) collected from more than 40,000 genotyped individuals so far, facilitating the investigation of genetic and imaging biomarkers for brain disorders. Motivated by efforts in genetics to integrate gene expression levels with genome-wide association studies (GWASs), recent methods in imaging genetics adopted an instrumental variable (IV) approach to identify causal IDPs for brain disorders. However, several methodological challenges arise with existing methods in achieving causality in imaging genetics, including horizontal pleiotropy and high dimensionality of candidate IVs. In this work, we propose testing the causality of each brain modality (i.e., structural, functional, and diffusion magnetic resonance imaging (MRI)) for each gene as a useful alternative, which offers an enhanced understanding of the roles of genetic variants and imaging features on behavior by controlling for the pleiotropic effects of IDPs from other imaging modalities. We demonstrate the utility of the proposed method by using Alzheimer\u2019s GWAS data from the UK Biobank and the International Genomics of Alzheimer\u2019s Project (IGAP) study. Our method is implemented using summary statistics, which is available on GitHub.<\/jats:p>","DOI":"10.1162\/imag_a_00580","type":"journal-article","created":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T15:41:17Z","timestamp":1745595677000},"update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":2,"title":["Leveraging multimodal neuroimaging and GWAS for identifying modality-level causal pathways to Alzheimer\u2019s disease"],"prefix":"10.1162","volume":"3","author":[{"given":"Yuan","family":"Tian","sequence":"first","affiliation":[{"name":"Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Felsky","sequence":"additional","affiliation":[{"name":"Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada"},{"name":"Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada"},{"name":"Institute of Medical Science, University of Toronto, Toronto, ON, Canada"},{"name":"Department of Psychiatry, University of Toronto, Toronto, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jessica","family":"Gronsbell","sequence":"additional","affiliation":[{"name":"Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada"},{"name":"Department of Computer Science, University of Toronto, Toronto, ON, Canada"},{"name":"Department of Family & Community Medicine, University of Toronto, Toronto, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1927-5218","authenticated-orcid":false,"given":"Jun Young","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada"},{"name":"Department of Psychology, University of Toronto, Toronto, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"281","published-online":{"date-parts":[[2025,5,16]]},"reference":[{"issue":"8","key":"2025051616053798000_imag_a_00580-b1","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1002\/gepi.22077","article-title":"Mendelian randomization with fine-mapped genetic data: Choosing from large numbers of correlated instrumental variables","volume":"41","author":"Burgess","year":"2017","journal-title":"Genetic Epidemiology"},{"issue":"7726","key":"2025051616053798000_imag_a_00580-b2","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41586-018-0579-z","article-title":"The UK biobank resource with deep phenotyping and genomic data","volume":"562","author":"Bycroft","year":"2018","journal-title":"Nature"},{"issue":"9","key":"2025051616053798000_imag_a_00580-b3","doi-asserted-by":"crossref","first-page":"1834","DOI":"10.1016\/j.ajhg.2024.07.007","article-title":"A novel multivariable Mendelian randomization framework to disentangle highly correlated exposures with application to metabolomics","volume":"111","author":"Chan","year":"2024","journal-title":"The American Journal of Human Genetics"},{"issue":"3","key":"2025051616053798000_imag_a_00580-b4","first-page":"323","article-title":"The distribution of a linear combination of \u03c72 random variables","volume":"29","author":"Davies","year":"1980","journal-title":"Journal of the Royal Statistical Society Series C: Applied Statistics"},{"issue":"6","key":"2025051616053798000_imag_a_00580-b5","doi-asserted-by":"crossref","first-page":"1350","DOI":"10.1093\/schbul\/sbx174","article-title":"Genetic correlation profile of schizophrenia mirrors epidemiological results and suggests link between polygenic and rare variant (22q11. 2) cases of schizophrenia","volume":"44","author":"Duncan","year":"2018","journal-title":"Schizophrenia Bulletin"},{"issue":"7726","key":"2025051616053798000_imag_a_00580-b7","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1038\/s41586-018-0571-7","article-title":"Genome-wide association studies of brain imaging phenotypes in UK Biobank","volume":"562","author":"Elliott","year":"2018","journal-title":"Nature"},{"issue":"9","key":"2025051616053798000_imag_a_00580-b8","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.1038\/ng.3367","article-title":"A gene-based association method for mapping traits using reference transcriptome data","volume":"47","author":"Gamazon","year":"2015","journal-title":"Nature Genetics"},{"issue":"2","key":"2025051616053798000_imag_a_00580-b9","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1001\/archpsyc.63.2.168","article-title":"Role of genes and environments for explaining Alzheimer disease","volume":"63","author":"Gatz","year":"2006","journal-title":"Archives of General Psychiatry"},{"issue":"3","key":"2025051616053798000_imag_a_00580-b10","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1038\/ng.3506","article-title":"Integrative approaches for large-scale transcriptome-wide association studies","volume":"48","author":"Gusev","year":"2016","journal-title":"Nature Genetics"},{"key":"2025051616053798000_imag_a_00580-b11","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1016\/j.neuroimage.2015.05.043","article-title":"Fvgwas: Fast voxelwise genome wide association analysis of large-scale imaging genetic data","volume":"118","author":"Huang","year":"2015","journal-title":"NeuroImage"},{"key":"2025051616053798000_imag_a_00580-b12","doi-asserted-by":"crossref","first-page":"e80063","DOI":"10.7554\/eLife.80063","article-title":"Sparse dimensionality reduction approaches in mendelian randomisation with highly correlated exposures","volume":"12","author":"Karageorgiou","year":"2023","journal-title":"Elife"},{"key":"2025051616053798000_imag_a_00580-b13","doi-asserted-by":"crossref","first-page":"117347","DOI":"10.1016\/j.neuroimage.2020.117347","article-title":"Implicating causal brain imaging endophenotypes in Alzheimer\u2019s disease using multivariable IWAS and GWAS summary data","volume":"223","author":"Knutson","year":"2020","journal-title":"NeuroImage"},{"issue":"8","key":"2025051616053798000_imag_a_00580-b14","doi-asserted-by":"crossref","first-page":"1178","DOI":"10.1093\/bioinformatics\/btv719","article-title":"Adaptive gene-and pathway-trait association testing with GWAS summary statistics","volume":"32","author":"Kwak","year":"2016","journal-title":"Bioinformatics"},{"issue":"12","key":"2025051616053798000_imag_a_00580-b15","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1038\/ng.2802","article-title":"Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer\u2019s disease","volume":"45","author":"Lambert","year":"2013","journal-title":"Nature Genetics"},{"key":"2025051616053798000_imag_a_00580-b16","doi-asserted-by":"crossref","first-page":"101542","DOI":"10.1016\/j.dcn.2025.101542","article-title":"Brainxcan identifies brain features associated with behavioral and psychiatric traits using large-scale genetic and imaging data","volume":"73","author":"Liang","year":"2025","journal-title":"Developmental Cognitive Neuroscience"},{"key":"2025051616053798000_imag_a_00580-b17","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.jneumeth.2014.09.001","article-title":"Sparse models for correlative and integrative analysis of imaging and genetic data","volume":"237","author":"Lin","year":"2014","journal-title":"Journal of Neuroscience Methods"},{"issue":"1","key":"2025051616053798000_imag_a_00580-b18","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1214\/12-AOAS597","article-title":"Joint and individual variation explained (jive) for integrated analysis of multiple data types","volume":"7","author":"Lock","year":"2013","journal-title":"The Annals of Applied Statistics"},{"issue":"1","key":"2025051616053798000_imag_a_00580-b19","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1214\/21-AOAS1495","article-title":"Bidimensional linked matrix factorization for pan-omics pan-cancer analysis","volume":"16","author":"Lock","year":"2022","journal-title":"The Annals of Applied Statistics"},{"issue":"1","key":"2025051616053798000_imag_a_00580-b20","doi-asserted-by":"crossref","first-page":"899","DOI":"10.1038\/s42003-023-05279-y","article-title":"Transcriptome-wide association studies: Recent advances in methods, applications and available databases","volume":"6","author":"Mai","year":"2023","journal-title":"Communications Biology"},{"key":"2025051616053798000_imag_a_00580-b21","first-page":"73","article-title":"A new mendelian randomization method to estimate causal effects of multivariable brain imaging exposures","volume":"27","author":"Mo","year":"2022","journal-title":"Pacific Symposium on Biocomputing"},{"issue":"4","key":"2025051616053798000_imag_a_00580-b22","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.1534\/genetics.114.165035","article-title":"A powerful and adaptive association test for rare variants","volume":"197","author":"Pan","year":"2014","journal-title":"Genetics"},{"issue":"1","key":"2025051616053798000_imag_a_00580-b23","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1111\/biom.13141","article-title":"Integrative factorization of bidimensionally linked matrices","volume":"76","author":"Park","year":"2020","journal-title":"Biometrics"},{"issue":"4","key":"2025051616053798000_imag_a_00580-b24","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1002\/gepi.22551","article-title":"Robust use of phenotypic heterogeneity at drug target genes for mechanistic insights: Application of cis-multivariable mendelian randomization to glp1r gene region","volume":"48","author":"Patel","year":"2024","journal-title":"Genetic Epidemiology"},{"issue":"6","key":"2025051616053798000_imag_a_00580-b25","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.1016\/j.ajhg.2019.11.001","article-title":"Making the most of clumping and thresholding for polygenic scores","volume":"105","author":"Priv\u00e9","year":"2019","journal-title":"The American Journal of Human Genetics"},{"issue":"1","key":"2025051616053798000_imag_a_00580-b26","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1109\/JPROC.2019.2947272","article-title":"Brain imaging genomics: Integrated analysis and machine learning","volume":"108","author":"Shen","year":"2019","journal-title":"Proceedings of the IEEE"},{"issue":"5","key":"2025051616053798000_imag_a_00580-b27","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1038\/s41593-021-00826-4","article-title":"An expanded set of genome-wide association studies of brain imaging phenotypes in UK biobank","volume":"24","author":"Smith","year":"2021","journal-title":"Nature Neuroscience"},{"issue":"10","key":"2025051616053798000_imag_a_00580-b28","doi-asserted-by":"crossref","first-page":"a006296","DOI":"10.1101\/cshperspect.a006296","article-title":"The genetics of Alzheimer disease","volume":"2","author":"Tanzi","year":"2012","journal-title":"Cold Spring Harbor Perspectives in Medicine"},{"key":"2025051616053798000_imag_a_00580-b29","doi-asserted-by":"crossref","first-page":"119385","DOI":"10.1016\/j.neuroimage.2022.119385","article-title":"Causal inference on neuroimaging data with Mendelian randomisation","volume":"258","author":"Taschler","year":"2022","journal-title":"NeuroImage"},{"key":"2025051616053798000_imag_a_00580-b6","first-page":"1061","article-title":"The 1000 Genomes Project Consortium","volume":"467","year":"2010","journal-title":"Nature"},{"issue":"1","key":"2025051616053798000_imag_a_00580-b30","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.ajhg.2011.05.029","article-title":"Rare-variant association testing for sequencing data with the sequence kernel association test","volume":"89","author":"Wu","year":"2011","journal-title":"The American Journal of Human Genetics"},{"key":"2025051616053798000_imag_a_00580-b31","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.neuroimage.2017.07.036","article-title":"Imaging-wide association study: Integrating imaging endophenotypes in GWAS","volume":"159","author":"Xu","year":"2017","journal-title":"NeuroImage"},{"issue":"3","key":"2025051616053798000_imag_a_00580-b32","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1002\/gepi.22274","article-title":"Some statistical consideration in transcriptome-wide association studies","volume":"44","author":"Xue","year":"2020","journal-title":"Genetic Epidemiology"},{"issue":"6648","key":"2025051616053798000_imag_a_00580-b33","doi-asserted-by":"crossref","first-page":"abn6598","DOI":"10.1126\/science.abn6598","article-title":"Heart-brain connections: Phenotypic and genetic insights from magnetic resonance images","volume":"380","author":"Zhao","year":"2023","journal-title":"Science"},{"issue":"1","key":"2025051616053798000_imag_a_00580-b34","doi-asserted-by":"crossref","first-page":"6064","DOI":"10.1038\/s41467-024-50309-w","article-title":"Eye-brain connections revealed by multimodal retinal and brain imaging genetics","volume":"15","author":"Zhao","year":"2024","journal-title":"Nature Communications"}],"container-title":["Imaging Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/direct.mit.edu\/imag\/article-pdf\/doi\/10.1162\/imag_a_00580\/2514768\/imag_a_00580.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/direct.mit.edu\/imag\/article-pdf\/doi\/10.1162\/imag_a_00580\/2514768\/imag_a_00580.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T16:05:50Z","timestamp":1747411550000},"score":1,"resource":{"primary":{"URL":"https:\/\/direct.mit.edu\/imag\/article\/doi\/10.1162\/imag_a_00580\/128949\/Leveraging-multimodal-neuroimaging-and-GWAS-for"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":34,"URL":"https:\/\/doi.org\/10.1162\/imag_a_00580","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2025.02.27.25322897","asserted-by":"object"}]},"ISSN":["2837-6056"],"issn-type":[{"value":"2837-6056","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025]]},"published":{"date-parts":[[2025]]},"article-number":"imag_a_00580"}}