Abstract
Repurposing existing medicines to target disease-associated genes represents a promising strategy for developing effective treatments for complex diseases. However, progress has been hindered by a lack of viable candidate drug targets identified through genome-wide association studies. Gene-based association tests provide a more powerful alternative to traditional SNP-based methods, yet current approaches often fail to leverage shared heritability across populations and to effectively integrate functional genomic data. To address these challenges, we develop GenT and its various extensions, comprising a framework of gene-based tests utilizing summary-level data from genome-wide association studies. Using GenT, we identify 16, 15, 35, and 83 candidate genes linked to Alzheimer’s disease, amyotrophic lateral sclerosis, major depression, and schizophrenia, respectively, not detected by Genome-Wide Association Studies (GWAS). Additionally, we use our multi-ancestry gene-based test (MuGenT) to identify 28 candidate genes associated with type 2 diabetes. By integrating brain expression and protein quantitative trait loci into our analysis, we identify 43 candidate genes associated with Alzheimer’s disease that have supporting xQTL evidence. We also perform experimental assays to demonstrate that the NTRK1 inhibitor GW441756 significantly reduces tau hyper-phosphorylation (including p-tau181 and p-tau217) in Alzheimer’s disease patient-derived iPSC neurons, providing mechanistic support for our predictions.
Data availability
Supporting Data are available from Zenodo [https://doi.org/10.5281/zenodo.18121206]. We provided gene-based association test results from GenT for AD, ALS, MDD, SCZ in Supporting Data 1-4; multi-ancestry gene-based association testing with MuGenT for T2D in Supporting Data 5; gene-based population heterogeneity testing with MuGenT-PH for T2D in Supporting Data 6; brain eQTL- and pQTL-integrated gene-based association testing for AD in Supporting Data 7 and 8, respectively; and all fine-mapped SNPs for AD, ALS, MDD, SCZ, and all T2D populations in Supporting Data 9−17. We created a web application (https://nlorinczcomi.shinyapps.io/gent/) to interactively query and download all gene-based association test and fine-mapping results with GenT and SuSiE for the 32 phenotypes listed in Supporting Data 18. Fine-mapped GenT results for five complex diseases are provided in Supporting Data 19-23. In our main analyses, GWAS summary statistics were downloaded from public repositories. AD GWAS summary statistics are under accession code GCST90027158; GWAS summary statistics for SCZ are available on Figshare [https://doi.org/10.6084/m9.figshare.19426775]; GWAS summary statistics for ALS are available under accession code GCST90027164; GWAS summary statistics for MDD are available on https://datashare.ed.ac.uk/handle/10283/3203; GWAS summary statistics for T2D are available from https://www.diagram-consortium.org/downloads.html (accession label: “Multi-ancestry GWAS meta-analysis summary statistics”). All other repositories from which we downloaded GWAS summary statistics for phenotypes whose GenT results are presented at (https://nlorinczcomi.shinyapps.io/gent/) are available in Supporting Data 18. GWAS summary statistics for xQTL in cortex, cerebellum, frontal cortex, hippocampus, and spinal cord tissues were downloaded from the ‘eQTL Tissue-Specific All SNP Gene Associations’ section of (https://gtexportal.org/home/downloads/adult-gtex/qtl) (v8). ROSMAP data are available at the AD Knowledge portal under restricted access: https://adknowledgeportal.synapse.org. Supplementary Figs. 1–33 provide additional simulation and real data analysis examples to further demonstrate and evaluate the performance of our gene-based testing approaches. Supplementary Figs. 34–37 provide additional diagnostic results for MuGenT testing in T2D. Supplementary Figs. 38–51 display locus-specific plots for a set of genes which were significant in xGenT testing with AD. Supplementary Figs. 52–68 display their local genetic correlations across multiple phenotypes and GTEx v8 tissues, and Supplementary Figs. 69–84 display the distributions of their eQTL effect sizes across all GTEx tissues. Supplementary Fig. 85 demonstrates between-ancestry heterogeneity of LD patterns in the KIF11 locus. Supplementary Fig. 86 displays a cross-tissue network of local genetic correlations in the RIPK2 locus. Supplementary Fig. 87 shows cell-type specific expression of three candidate AD-associated genes using ROSMAP data and identified with xGenT, and Supplementary Fig. 88 shows a set of protein-protein interactions involving NTRK1. Source Data are provided with this article. Source data are provided with this paper.
Code availability
All code used to generate our simulation and real data analysis results is available at (https://github.com/noahlorinczcomi/gent_analysis) and is deposited in Zenodo [https://doi.org/10.5281/zenodo.17449888]. We have created an R package for researchers to apply GenT, MuGenT, MuGenT-PH, MuGenT-Pleio, MuGenT-Sel, and xGenT to their own data which is available from (https://github.com/noahlorinczcomi/gent), where tutorials are available. The R package we used for data analysis is available from Zenodo [https://doi.org/10.5281/zenodo.17449854]. We made all summary gene-based association testing and fine-mapping results from analyses from 32 phenotypes available for query and download from (https://nlorinczcomi.shinyapps.io/gent).
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Acknowledgements
This work was supported by the National Institute on Aging (NIA) under Award Number U01AG073323, R01AG066707, R01AG084250, R01AG076448, R01AG082118, R01AG092462, R01AG092591, RF1AG082211, and R33AG083003, the National Institute of Neurological Disorders and Stroke (NINDS) under Award Number RF1NS133812, National Institutes of Health (NIH) under Award Number 1OT2OD038083-01, the Alzheimer’s Association award (ALZDISCOVERY−1051936), the Alzheimer’s Drug Discovery Foundation (ADDF), and Dr. Keyhan and Dr. Jafar Mobasseri Endowed Chair for Innovative Research to F.C. This work was supported in part by the Rebecca E. Barchas, MD, Professorship in Translational Psychiatry, the Valor Foundation, Project 19PABH134580006-AHA/Allen Initiative in Brain Health and Cognitive Impairment, the Elizabeth Ring Mather & William Gwinn Mather Fund, S. Livingston Samuel Mather Trust, and the Louis Stokes VA Medical Center resources and facilities to A.A.P. This work was supported in part by National Heart, Lung, and Blood Institute (NHLBI) under Award Number P01HL158501 to M.K.C. and F.C. This work was also supported by Global Center for Pathogen and Human Health Research Postdoc Fellowship.
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N.L.C. and F.C. conceived the study design. N.L.C. developed the methodology, performed the formal analysis, and wrote the original draft. Y.Z., J.Z., and W.M. created a visualization. W.S., I.R.P., and X.C. performed experiments. F.C. supervised all activities, provided all resources, and reviewed and edited the manuscript. Y.H., A.P., J.B., J.H., and M.C. edited the manuscript. All authors approved the final manuscript.
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Lorincz-Comi, N., Song, W., Chen, X. et al. Combining xQTL and genome-wide association studies from diverse populations improves druggable gene discovery. Nat Commun (2026). https://doi.org/10.1038/s41467-026-69236-z
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DOI: https://doi.org/10.1038/s41467-026-69236-z
