Database for ancestry-specific cross-tissue gene expression models: AGEMdb
Year: 2026
Type: article
Abstract: Background Transcriptome-wide association studies (TWAS) integrate genetically regulated gene expression with genome-wide association study (GWAS) data to identify gene–trait associations. These analyses rely on accurate gene expression prediction models trained using genotype and transcriptomic data from the same set of individuals. However, most publicly available TWAS models are based on resources that predominantly consist of participants of European ancestry. This European ancestry bias limits the transferability and generalizability of gene expression models across diverse populations. Description Using genotype and gene expression data from the Genotype-Tissue Expression (GTEx) Project, we curated datasets comprising 689 European ancestry individuals and 111 African American individuals after quality control and confounder adjustment. We then developed ancestry-specific cross-tissue gene expression imputation models using the UTMOST framework. These models are accessible through AGEMdb, a web-based database that allows users to browse, search, and download gene expression imputation models with flexible filtered options (e.g. by tissue and gene). By incorporating African American-specific models, AGEMdb addresses the European ancestry bias present in existing TWAS resources and supports more equitable gene–trait association analyses. Conclusion AGEMdb provides a valuable resource for ancestry-specific TWAS, which promotes more inclusive studies that support the advancement of equitable genomic research.
Source: BMC Bioinformatics
Institutions: University of Manchester, Arthritis UK
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,990
Funders: Van Andel Research Institute, University of Miami, University of Pennsylvania, Harvard University, North Carolina State University, Broad Institute, Leidos, National Institute for Health and Care Research, University of Manchester, Université de Genève, National Institutes of Health, Medical Research Council, National Heart, Lung, and Blood Institute, National Institute of Mental Health, National Institute on Drug Abuse, National Human Genome Research Institute, NIH Office of the Director, National Cancer Institute, National Institute of Neurological Disorders and Stroke, Common Fund
Awards: Methods for high-resolution analysis of genetic effects on gene expression, G1217436835, G1534570039, G1561323845, G1650773652, Identification and validation of cell specific eQTLs by Bayesian modeling, G1883398070, Using the Transcriptome for SNP and Gene Annotation, G2158443041, MODELING THE EFFECTS OF STRUCTURAL VARIATION IN GTEX DATA AND MENDELIAN DISEASE, G2877753951, G3221727586, G32319252, G3387558946, G3443372292, G347407575, G3503792436, G3547560438, G3793778294, G386607036, G3952396931, Genetic Regulation of Gene Expression and its Impact on Phenotypes, G4110903388, G4124418613, G4382062407, Systems approaches to link tissue-specific expression to disease, Harnessing GTEx to Create Transcriptome Knowledge and Inform Disease Biology, G4478373029, G4715193760, G4729234894, Epistatic and Cross Tissue Analysis for Human Gene Expression Traits, G5097752872, G563538711, G5737582129, Statistical analysis of gene expression quantitative trait loci (eQTL), Methods for high-resolution analysis of genetic effects on gene expression, G6317516893, G6684147844, G7274654584, G7353525908, FACILITATING GTEx, DISEASE, AND GxE ANALYSES VIA FAST EXPRESSION (e)QTL MAPPING, Statistical analysis of gene expression quantitative trait loci (eQTL), CNS Mechanisms in Cocaine-Related Sudden Death, G7665525972, G7761642487, G8719970897, G8788848806, G8906494432
OpenAlex ID: W7165779824