Evidence map›Paper›PMID 40166559›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Bayesian inference of genetic pleiotropy identifies drug targets and repurposable medicines for human complex diseases.

Noah Lorincz-Comi, Feixiong Cheng

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Noah Lorincz-ComiCleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA.ORCID 0000-0002-0517-2499
Feixiong ChengCleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA.ORCID 0000-0002-1736-2847

Funding

Endophenotype Network-based Approaches to Prediction and Population-based Validation of In Silico Drug Repurposing for Alzheimer's DiseaseR01AG066707 · NIA · CLEVELAND CLINIC LERNER COM-CWRU · PI Feixiong Cheng · 2020 to 2026
$4.9M
TREM2 Genotype-Informed Drug Repurposing and Combination Therapy Design for Alzheimer’s DiseaseR01AG076448 · NIA · CLEVELAND CLINIC LERNER COM-CWRU · PI Feixiong Cheng, Li Gan · 2022 to 2026
$4.0M
Alzheimer's MultiOme Data Repurposing: Artificial Intelligence, Network Medicine, and Therapeutics DiscoveryU01AG073323 · NIA · CLEVELAND CLINIC LERNER COM-CWRU · PI BEKRIS, LYNN, CHENG, FEIXIONG · 2021 to 2025
$4.0M
Alzheimer's Disease and Related Dementia-like Sequelae of SARS-CoV-2 Infection: Virus-Host Interactome, Neuropathobiology, and Drug RepurposingRF1AG082211 · NIA · CLEVELAND CLINIC LERNER COM-CWRU · PI CHENG, FEIXIONG, PIEPER, ANDREW A · 2023 to 2023
$2.4M
Characterize neuronal and glial cell-specific vulnerability to proteinopathies in Alzheimer's disease using multimodal single-nuclei genomic and epigenomic approachesR01AG082118 · NIA · CLEVELAND CLINIC LERNER COM-CWRU · PI BONAKDARPOUR, BORNA, CHENG, FEIXIONG · 2023 to 2025
$2.4M
Dark GPCR signaling underlying the Microbiome-Gut-Brain Axis for Alzheimer's Disease and Related DementiaRF1NS133812 · NINDS · CLEVELAND CLINIC LERNER COM-CWRU · PI BROWN, JONATHAN MARK, CHENG, FEIXIONG · 2023 to 2023
$2.3M
Microglial Activation and Inflammatory Endophenotypes Underlying Sex Differences of Alzheimer’s DiseaseR01AG084250 · NIA · CLEVELAND CLINIC LERNER COM-CWRU · PI Feixiong Cheng, Justin D. Lathia · 2023 to 2026
$2.2M
Multimodal single-cell genomic and epigenomic analyses elucidate Alzheimer’s sexual dimorphism in human immune systems agingR56AG074001 · NIA · CLEVELAND CLINIC LERNER COM-CWRU · PI BEKRIS, LYNN, CHENG, FEIXIONG · 2021 to 2021
$1.2M
Precision Medicine Digital Twins for Alzheimer’s Target and Drug Discovery and LongevityR21AG083003 · NIA · CLEVELAND CLINIC LERNER COM-CWRU · PI CHENG, FEIXIONG · 2023 to 2023
$483k
NIA NIH HHS R01 AG066707NIA NIH HHS R01 AG076448NIA NIH HHS R01 AG082118NIA NIH HHS R01 AG084250NIA NIH HHS R21 AG083003NIA NIH HHS R56 AG074001NIA NIH HHS RF1 AG082211NIA NIH HHS U01 AG073323NINDS NIH HHS RF1 NS133812
6 · The paper itself

Abstract

Complex diseases share heritable components which can be leveraged to identify drug targets with low side effect or high repurposing potential, but current methods cannot efficiently make these inferences at scale using public data. We introduce a Bayesian model to estimate the polygenic structure of a trait using GWAS summary data (BPACT). Across 32 complex traits, we estimated that 69.5 to 97.5% of disease-associated druggable genes are shared between multiple traits. We observed that targeting

Indexed as

complex diseasedruggable genesdrug repurposinggene association testinggenome-wide association studypleiotropypolygenicPolygenicity

Identifiers

PMID40166559
PMCPMC11957083

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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 OpenQuestion graph.