{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Bledsoe X"],"funding":["Scott Hamilton CARES Foundation","NIA NIH HHS","NHGRI NIH HHS","NIH HHS","NIGMS NIH HHS"],"pagination":["e71217"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12930104"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["22(2)"],"pubmed_abstract":["<h4>Introduction</h4>Neuroimaging genetics has advanced our understanding of Alzheimer's disease (AD); however, frameworks using functional genomics are needed to elucidate mechanisms connecting loci to neurological outcomes. To address this need, we explored relationships between AD-associated variants and disease via their impact on gene expression and neuroanatomical phenotypes.<h4>Methods</h4>We mapped established AD genes to neuroimaging traits using the NeuroimaGene Atlas and predicted transcript-driven neurological features of AD by comparing gene-derived neuroimaging features with clinical neuroimaging data. Genetic covariance analyses were performed to characterize shared genetic architecture between AD endophenotypes and neuroimaging features, and to identify neuroimaging feature"],"journal":["Alzheimer's & dementia : the journal of the Alzheimer's Association"],"pubmed_title":["Neuroimaging PheWAS and molecular phenotyping implicate PSMC3 in Alzheimer's disease."],"pmcid":["PMC12930104"],"funding_grant_id":["R01HG011138","R01AG073439","RF1AG061351","R35HG010718","R56AG068026","R01GM140287"],"pubmed_authors":["Dumitrescu L","Bush WS","Gamazon ER","Chen HH","Naj AC","Below JE","Archer D","Wang TC","Bledsoe X","Wu Y","Hohman TJ"],"additional_accession":[]},"is_claimable":false,"name":"Neuroimaging PheWAS and molecular phenotyping implicate PSMC3 in Alzheimer's disease.","description":"<h4>Introduction</h4>Neuroimaging genetics has advanced our understanding of Alzheimer's disease (AD); however, frameworks using functional genomics are needed to elucidate mechanisms connecting loci to neurological outcomes. To address this need, we explored relationships between AD-associated variants and disease via their impact on gene expression and neuroanatomical phenotypes.<h4>Methods</h4>We mapped established AD genes to neuroimaging traits using the NeuroimaGene Atlas and predicted transcript-driven neurological features of AD by comparing gene-derived neuroimaging features with clinical neuroimaging data. Genetic covariance analyses were performed to characterize shared genetic architecture between AD endophenotypes and neuroimaging features, and to identify neuroimaging feature","dates":{"release":"2026-01-01T00:00:00Z","publication":"2026 Feb","modification":"2026-07-16T20:26:14.192Z","creation":"2026-07-10T03:10:39.704Z"},"accession":"S-EPMC12930104","cross_references":{"pubmed":["41732108"],"doi":["10.1002/alz.71217"]}}