Evidence map›Paper›PMID 41057938›Full record

SynthesisHuman genomics2025

Identification of genetically-supported new drug targets for osteomyelitis based on druggable genomes.

Ruotong Yao, Yangguang Lu, Di Lu, Haiyong Ren, Xiang Wang, Bingyuan Lin, Siyao Chen, Yusheng Zhu, Feng Chen, Yukai Wang and 4 more

Abstract readMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Human genomics, 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
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0citing papers in PubMed
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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

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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

14 authors.

Ruotong Yao *The First School of Medicine, School of Information and Engineering, Wenzhou Medical University, Wenzhou, China.
Yangguang Lu *The First School of Medicine, School of Information and Engineering, Wenzhou Medical University, Wenzhou, China.
Di Lu *Department of Orthopedics, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China.
Haiyong RenDepartment of Orthopedics, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China.
Xiang WangDepartment of Orthopedics, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China.
Bingyuan LinDepartment of Orthopedics, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China.
Siyao ChenThe First School of Medicine, School of Information and Engineering, Wenzhou Medical University, Wenzhou, China.
Yusheng ZhuThe Second School of Medicine, Wenzhou Medical University, Wenzhou, China.
Feng ChenThe Second School of Medicine, Wenzhou Medical University, Wenzhou, China.
Yukai WangThe Second School of Medicine, Wenzhou Medical University, Wenzhou, China.
Yi GaoSchool of Public Health, Wenzhou Medical University, Wenzhou, China.
Jiawen ShenThe First School of Medicine, School of Information and Engineering, Wenzhou Medical University, Wenzhou, China.
Qiaofeng GuoDepartment of Orthopedics, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China. hzgqf1971@163.com.
Kai HuangDepartment of Orthopedics, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China. zjhzhuangkai@163.com.

Funding

National Health Commission Scientific Research Fund-Major Health Science and Technology Plan of Zhejiang Province WKJ-ZJ-2419Student Research Project Funding Program of Wenzhou Medical University wyx2024101043Student Research Project Funding Program of Wenzhou Medical University wyx2024101120
6 · The paper itself

Abstract

backgroundLimited drug treatment data are available for osteomyelitis (OM), an inflammatory bone condition secondary to infection. Given its genetic characteristics, it is necessary to integrate genetics into drug development for osteomyelitis. This study applied pharmacogenomics to identify new drug targets for osteomyelitis using Mendelian randomization (MR).

methodsFollowing the Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization guidelines, expression and protein quantitative trait loci (QTL) analysis was applied to simulate drug exposure. Single nucleotide polymorphisms were selected as instrumental variables for MR analysis using blood QTL data and independent osteomyelitis genome-wide association study datasets from UK Biobank and FinnGen R10. A random-effects model meta-analysis combining the results from two datasets was performed. Bayesian co-localization analysis was conducted to validate the targets. Sensitivity analyses were performed using various MR methods, with MR-Egger regression and Cochran's Q test being conducted to assess the horizontal pleiotropy and heterogeneity of the instrumental variables.

resultsAt α = 1 × 10

conclusionsThis study identified 12 new genetically supported drug targets for osteomyelitis, thereby providing a genetic foundation for new drug development, repurposing existing drugs, and personalized treatment.

Indexed as

OsteomyelitisGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansMendelian Randomization AnalysisObservational Studies as TopicPharmacogeneticsPolymorphism, Single NucleotideQuantitative Trait LociDrug targetsGenetic epidemiologyMendelian randomizationOsteomyelitis

Identifiers

PMID41057938
PMCPMC12505852

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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 OpenQuestion graph.