Evidence map›Paper›PMID 41053005›Full record

ArticleTranslational psychiatry2025

Integrating genetic regulation and schizophrenia-specific splicing quantitative expression with GWAS prioritizes novel risk genes for schizophrenia.

Xiaoyan Li, Lingli Fan, Yiran Zhao, Yuanyuan Li, Junyang Wang, Shengmin Xu, Junfeng Xia

Abstract read
In one paragraph

Article in Translational psychiatry, 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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. The Regulatory Potential of Long Non-Coding RNAs in Bipolar Disorder.International journal of molecular sciences · 2026
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4 · The record

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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

7 authors.

Xiaoyan Li *Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institute of Health Sciences and Technology, Anhui University, Hefei, Anhui, China. lixiaoyan@ahu.edu.cn.ORCID http://orcid.org/0000-0002-8967-4663
Lingli Fan *Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institute of Health Sciences and Technology, Anhui University, Hefei, Anhui, China.
Yiran Zhao *Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institute of Health Sciences and Technology, Anhui University, Hefei, Anhui, China.
Yuanyuan LiInformation Materials and Intelligent Sensing Laboratory of Anhui Province, Institute of Health Sciences and Technology, Anhui University, Hefei, Anhui, China.
Junyang WangDepartment of Human Anatomy, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, Henan, China.
Shengmin XuInformation Materials and Intelligent Sensing Laboratory of Anhui Province, Institute of Health Sciences and Technology, Anhui University, Hefei, Anhui, China.
Junfeng XiaInformation Materials and Intelligent Sensing Laboratory of Anhui Province, Institute of Health Sciences and Technology, Anhui University, Hefei, Anhui, China. jfxia@ahu.edu.cn.

Funding

National Natural Science Foundation of China (National Science Foundation of China) 32271283National Natural Science Foundation of China (National Science Foundation of China) 82101611National Natural Science Foundation of China (National Science Foundation of China) 82401765National Natural Science Foundation of China (National Science Foundation of China) U22A2038
6 · The paper itself

Abstract

Alternative splicing (AS) plays a vital role in the pathogenesis of schizophrenia (SCZ). Previous studies have linked the genetic signals from genome-wide association studies (GWAS) with expression quantitative trait loci (eQTL), but the interplay with other genetic regulatory mechanisms, particularly splicing QTL (sQTL), remains unclear. Here, we constructed a comprehensive disease-specific sQTL map to provide genetic variants that could alter gene activity through RNA splicing in SCZ. We analyzed data from 539 SCZ patients, identifying a total of 24,810 significant sQTLs (FDR < 0.05) involving in AS events of 7083 unique genes. By combining this with a large-scale SCZ GWAS, we employed Mendelian randomization (MR) and colocalization analyses to pinpoint 27 significant risk genes with genetic AS regulation that may play a causal role in SCZ. Additional differential splicing analysis of these genes in 539 cases and 754 controls revealed 12 significant genes that may increase SCZ risk due to their AS dysregulation. Notably, five genes (DPYD, LACC1, CCDC122, ANAPC7, and DGKZ) showed consistent splicing regulation effects in both MR analysis and differential splicing analysis. Pathway enrichment analysis of differentially spliced genes revealed potential biologically pathways relevant to SCZ, particularly in synaptic transmission and microtubule movement. Furthermore, single-cell RNA-seq analysis revealed that several genes were preferentially expressed in specific brain cell types, including oligodendrocytes, microglia, and excitatory neurons. Overall, our findings highlight several susceptibility genes that may contribute to SCZ risk by AS regulation. Further characterization of these genes could advance mechanistic understanding and therapeutic discovery for SCZ.

Indexed as

Alternative SplicingGenetic Predisposition to DiseaseQuantitative Trait LociSchizophreniaGenome-Wide Association StudyHumansMendelian Randomization AnalysisPolymorphism, Single Nucleotide

Identifiers

PMID41053005
PMCPMC12501019

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