Evidence map›Paper›PMID 40653482›Full record

ArticleJournal of translational medicine2025

Multi-omics analysis for identifying cell-type-specific and bulk-level druggable targets in Alzheimer's disease.

Shiwei Liu, Minyoung Cho, Yen-Ning Huang, Tamina Park, Soumilee Chaudhuri, Thea J Rosewood, Paula J Bice, Dongjun Chung, David A Bennett, Nilüfer Ertekin-Taner and 2 more

Abstract read
In one paragraph

Article in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Shiwei LiuCenter for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 550 N. University Blvd., Indianapolis, IN, 46202, USA.
Minyoung ChoCenter for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 550 N. University Blvd., Indianapolis, IN, 46202, USA.
Yen-Ning HuangCenter for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 550 N. University Blvd., Indianapolis, IN, 46202, USA.
Tamina ParkCenter for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 550 N. University Blvd., Indianapolis, IN, 46202, USA.
Soumilee ChaudhuriCenter for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 550 N. University Blvd., Indianapolis, IN, 46202, USA.
Thea J RosewoodCenter for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 550 N. University Blvd., Indianapolis, IN, 46202, USA.
Paula J BiceCenter for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 550 N. University Blvd., Indianapolis, IN, 46202, USA.
Dongjun ChungDepartment of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA.
David A BennettDepartment of Neurological Science, Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA.
Nilüfer Ertekin-TanerDepartment of Neuroscience, Mayo Clinic, Jacksonville, FL, 32224, USA.
Andrew J SaykinCenter for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 550 N. University Blvd., Indianapolis, IN, 46202, USA.
Kwangsik NhoCenter for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 550 N. University Blvd., Indianapolis, IN, 46202, USA. knho@iu.edu.ORCID 0000-0002-7624-3872

Funding

Project 1U19AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE/RES/EDU · PI Laurel A Beckett · 2016 to 2026
$226.7M
Peripheral and Central Biomarkers of Alzheimer's Disease in Diverse CohortsU19AG074879 · NIA · MAYO CLINIC JACKSONVILLE · PI Minerva Maria Carrasquillo, NILUFER ERTEKIN-TANER · 2023 to 2026
$42.0M
Research Education ComponentP30AG010133 · NIA · INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS · PI SAYKIN, ANDREW J · 1991 to 2020
$37.3M
Research Education ComponentP30AG072976 · NIA · INDIANA UNIVERSITY INDIANAPOLIS · PI ANDREW J SAYKIN · 2021 to 2026
$24.1M
Ultrascale Machine Learning to Empower Discovery in Alzheimers Disease BiobanksU01AG068057 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Christos Davatzikos, Heng Huang · 2020 to 2026
$20.7M
KBASE2: Korean Brain Aging Study, Longitudinal Endophenotypes and Systems BiologyU01AG072177 · NIA · INDIANA UNIVERSITY INDIANAPOLIS · PI LEE, DONG YOUNG, NHO, KWANGSIK TIMOTHY · 2021 to 2025
$11.2M
Memory Circuitry in MCI and Early Alzheimer’s Disease Prodrome: Molecular DriversR01AG019771 · NIA · INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS · PI SAYKIN, ANDREW J · 2001 to 2021
$6.9M
Leveraging Neuroimaging Biomarkers to Understand the Role of Social Networks in Alzheimer's DiseaseR01AG057739 · NIA · TRUSTEES OF INDIANA UNIVERSITY · PI APOSTOLOVA, LIANA G, PERRY, BREA LOUISE · 2018 to 2022
$3.5M
Cognitive Aging, Alzheimers disease, and Cancer-related Cognitive DeclineR01AG068193 · NIA · GEORGETOWN UNIVERSITY · PI MANDELBLATT, JEANNE, SAYKIN, ANDREW J · 2020 to 2023
$2.8M
Training Grant on Alzheimer's Disease and ADRD at Indiana UniversityT32AG071444 · NIA · INDIANA UNIVERSITY INDIANAPOLIS · PI GARY E. LANDRETH, Bruce T Lamb · 2021 to 2026
$2.8M
Integrating Neuroimaging, Multi-omics, and Clinical Data in Complex DiseaseR01LM012535 · NLM · INDIANA UNIVERSITY INDIANAPOLIS · PI NHO, KWANGSIK TIMOTHY · 2017 to 2021
$1.7M
Informatics Algorithms for Genomic Analysis of Brain Imaging DataR01LM013463 · NLM · UNIVERSITY OF PENNSYLVANIA · PI MOORE, JASON H., SAYKIN, ANDREW J · 2020 to 2023
$1.4M
NIA NIH HHS P30 AG010133NIA NIH HHS P30 AG072976NIA NIH HHS R01 AG019771NIA NIH HHS R01 AG057739NIA NIH HHS R01 AG068193NIA NIH HHS T32 AG071444NIA NIH HHS U01 AG068057NIA NIH HHS U01 AG072177NIA NIH HHS U19 AG024904NIA NIH HHS U19 AG074879NIH HHS Alzheimer's Disease Neuroimaging Initiative Health Enhancement Scientific Program (ADNI HESP) subaward from U19 AG024904NIH HHS CLEAR-AD Scholarship from U19 AG074879NIH HHS P30 AG010133NIH HHS P30 AG072976NIH HHS R01 AG019771NIH HHS R01 AG057739NIH HHS R01 AG068193NIH HHS R01 LM012535NIH HHS R01 LM013463NIH HHS T32 AG071444NIH HHS U01 AG068057NIH HHS U01 AG072177NIH HHS U19 AG024904NIH HHS U19 AG074879NIH HHS U19 AG0748790NLM NIH HHS R01 LM012535NLM NIH HHS R01 LM013463
6 · The paper itself

Abstract

backgroundAnalyzing disease-linked genetic variants via expression quantitative trait loci (eQTLs) helps identify potential disease-causing genes. Previous research prioritized genes by integrating Genome-Wide Association Study (GWAS) results with tissue-level eQTLs. Recent studies have explored brain cell type-specific eQTLs, but a systematic analysis across multiple Alzheimer's disease (AD) genome-wide association study (GWAS) datasets or comparisons between tissue-level and cell type-specific effects remain limited. Here, we integrated brain cell type-level and bulk-level eQTL datasets with AD GWAS datasets to identify potential causal genes.

methodsWe used Summary Data-Based Mendelian Randomization (SMR) and Bayesian Colocalization (COLOC) to integrate AD GWAS summary statistics with eQTLs datasets. Combining data from five AD GWAS, two single-cell eQTL datasets, and one bulk eQTL dataset, we identified novel candidate causal genes and further confirmed known ones. We investigated gene regulation through enhancer activity using H3K27ac and ATAC-seq data, performed protein-protein interaction (PPI) and pathway enrichment, and conducted a drug/compound enrichment analysis with Drug Signatures Database (DSigDB) to support drug repurposing for AD.

resultsWe identified 28 candidate causal genes for AD, of which 12 were uniquely detected at the cell-type level, 9 were exclusive to the bulk level and 7 detected in both. Among the 19 cell-type level candidate causal genes, microglia contributed the highest number of candidate genes, followed by excitatory neurons, astrocytes, inhibitory neurons, oligodendrocytes, and oligodendrocyte precursor cells (OPCs). PABPC1 emerged as a novel candidate causal gene in astrocytes. We generated PPI networks for the candidate causal genes and found that pathways such as membrane organization, cell migration, and ERK1/2 and PI3K/AKT signaling were enriched. The AD-risk variant associated with candidate causal gene PABPC1 is located near or within enhancers only active in astrocytes. We classified the 28 genes into three drug tiers and identified druggable interactions, with imatinib mesylate emerging as a key candidate. A drug-target gene network was created to explore potential drug targets for AD.

conclusionsWe systematically prioritized AD candidate causal genes based on cell type-level and bulk level molecular evidence. The integrative approach enhances our understanding of molecular mechanisms of AD-related genetic variants and facilitates interpretation of AD GWAS results.

Indexed as

Alzheimer DiseaseGenomicsMolecular Targeted TherapyBayes TheoremGene Expression RegulationGene Regulatory NetworksGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansMendelian Randomization AnalysisMultiomicsOrgan SpecificityPolymorphism, Single NucleotideProtein Interaction MapsQuantitative Trait LociAlzheimer’s diseaseAstrocytesCausal genesCell typeDrug repurposingeQTLGene expressionGenetic variantGWASSNP

Identifiers

PMID40653482
PMCPMC12257706

What Socratic holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.