Evidence map›Paper›PMID 39495296›Full record

ArticleHuman genetics2025

Integrative analysis of transcriptome and proteome wide association studies prioritized functional genes for obesity.

Qi-Gang Zhao, Xin-Ling Ma, Qian Xu, Zi-Tong Song, Fan Bu, Kuan Li, Bai-Xue Han, Shan-Shan Yan, Lei Zhang, Yuan Luo and 1 more

Abstract read
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In one paragraph

Article in Human genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

11 authors.

Qi-Gang ZhaoDepartment of Orthopedics, Taicang Affiliated Hospital of Soochow University, 58 Changsheng Rd., Suzhou Taicang City, 215400, Jiangsu Province, PR China.
Xin-Ling MaDepartment of Epidemiology and Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, 199 Ren-ai Rd., Suzhou City, 215123, Jiangsu Province, PR China.
Qian XuDepartment of Epidemiology and Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, 199 Ren-ai Rd., Suzhou City, 215123, Jiangsu Province, PR China.
Zi-Tong SongDepartment of Epidemiology and Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, 199 Ren-ai Rd., Suzhou City, 215123, Jiangsu Province, PR China.
Fan BuDepartment of Epidemiology and Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, 199 Ren-ai Rd., Suzhou City, 215123, Jiangsu Province, PR China.
Kuan LiDepartment of Epidemiology and Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, 199 Ren-ai Rd., Suzhou City, 215123, Jiangsu Province, PR China.
Bai-Xue HanDepartment of Epidemiology and Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, 199 Ren-ai Rd., Suzhou City, 215123, Jiangsu Province, PR China.
Shan-Shan YanDepartment of Epidemiology and Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, 199 Ren-ai Rd., Suzhou City, 215123, Jiangsu Province, PR China.
Lei ZhangCenter for Genetic Epidemiology and Genomics, School of Public Health, Suzhou Medical College of Soochow University, Suzhou City, Jiangsu, PR China.
Yuan LuoDepartment of Orthopedics, Taicang Affiliated Hospital of Soochow University, 58 Changsheng Rd., Suzhou Taicang City, 215400, Jiangsu Province, PR China. ly8046@suda.edu.cn.ORCID http://orcid.org/0009-0007-8102-6710
Yu-Fang PeiDepartment of Epidemiology and Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, 199 Ren-ai Rd., Suzhou City, 215123, Jiangsu Province, PR China. ypei@suda.edu.cn.ORCID http://orcid.org/0000-0002-9157-0759

Funding

National Natural Science Foundation of China 32170670
6 · The paper itself

Abstract

backgroundGenome-wide association studies have identified dozens of genomic loci for obesity. However, functional genes and their detailed genetic mechanisms underlying these loci are mainly unknown. In this study, we conducted an integrative study to prioritize plausibly functional genes by combining information from genome-, transcriptome- and proteome-wide association analyses.

methodsWe first conducted proteome-wide association analyses and transcriptome-wide association analyses for the six obesity-related traits. We then performed colocalization analysis on the identified loci shared between the proteome- and transcriptome-association analyses. Finally, we validated the identified genes with other plasma/blood reference panels. The highlighted genes were assessed for expression of other tissues, single-cell and tissue specificity, and druggability.

resultsWe prioritized 4 high-confidence genes (FASN, ICAM1, PDCD6IP, and YWHAB) by proteome-wide association studies, transcriptome-wide association studies, and colocalization analyses, which consistently influenced the variation of obesity traits at both mRNA and protein levels. These 4 genes were successfully validated using other plasma/blood reference panels. These 4 genes shared regulatory structures in obesity-related tissues. Single-cell and tissue-specific analyses showed that FASN and ICAM1 were explicitly expressed in metabolism- and immunity-related tissues and cells. Furthermore, FASN and ICAM1 had been developed as drug targets.

conclusionOur study provided novel promising protein targets for further mechanistic and therapeutic studies of obesity.

Indexed as

ObesityProteomeTranscriptomeGene Expression ProfilingGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansIntercellular Adhesion Molecule-1Polymorphism, Single NucleotideQuantitative Trait LociICAM1 protein, humanIntercellular Adhesion Molecule-1ProteomeDrug utilizationFunctional geneObesityProteome-wide association studyTranscriptome-wide association study

Identifiers

PMID39495296

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