Evidence map›Paper›PMID 39003413›Full record

ArticleMolecular psychiatry2025

Application of GWAS summary data and drug-induced gene expression profiles of neural progenitor cells in psychiatric drug prioritization analysis.

Xiangyi Li, Chao Xue, Zheng Zhu, Xuegao Yu, Qi Yang, Liqian Cui, Miaoxin Li

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Article in Molecular psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Xiangyi LiProgram in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China.ORCID 0000-0002-9408-3016
Chao XueProgram in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China.
Zheng ZhuProgram in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China.
Xuegao YuProgram in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China.
Qi YangProgram in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China.
Liqian CuiDepartment of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China. cuiliqian@mail.sysu.edu.cn.ORCID 0000-0002-7477-3626
Miaoxin LiProgram in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China. limiaoxin@mail.sysu.edu.cn.ORCID 0000-0002-4733-0109

Funding

National Natural Science Foundation of China (National Science Foundation of China) 32170637National Natural Science Foundation of China (National Science Foundation of China) 32300500
6 · The paper itself

Abstract

Common psychiatric disorders constitute one of the most substantial healthcare burdens worldwide. However, drug development in psychiatry remains hampered partially due to the lack of approaches to estimating drugs that can simultaneously modulate the expression of a nontrivial fraction of disease susceptibility genes. We proposed a new drug prioritization strategy under the framework of our previously proposed phenotype-associated tissues estimation approach (DESE) by investigating the drugs' selective perturbation effect on disease susceptibility genes. Based on the genome-wide association study summary data and drug-induced gene expression profiles of neural progenitor cells, we applied this strategy to prioritize candidate drugs for schizophrenia, depression and bipolar I disorder and identified several known therapeutic drugs among the top-ranked drug candidates. Also, our results revealed that the disease susceptibility genes involved in the selective gene perturbation analysis were enriched with many biologically sensible function terms and interacted with known therapeutic drugs. Our results suggested that selective gene perturbation analysis could be a promising starting point to prioritize biologically sensible drug candidates under the "one drug, multiple targets" paradigm for the drug development of common psychiatric disorders.

Indexed as

Bipolar DisorderGenome-Wide Association StudyMental DisordersNeural Stem CellsSchizophreniaGene Expression ProfilingGenetic Predisposition to DiseaseHumansPolymorphism, Single NucleotidePsychotropic DrugsTranscriptomePsychotropic Drugs

Identifiers

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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.