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Updated: Nov 11, 2025

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Published on: December 2, 2015
Integrative Analyses Followed by Functional Characterization Reveal TMEM180 as a Schizophrenia Risk Gene
Jun-Yang Wang1,2, Xiao-Yan Li1,2, Hui-Juan Li1,2
1Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China.
Abstract:
Recent large-scale integrative analyses (including Transcriptome-Wide Association Study [TWAS] and Summary-data-based Mendelian Randomization [SMR]) have identified multiple genes whose cis-regulated expression changes may confer risk of schizophrenia. Nevertheless, expression quantitative trait loci (eQTL) data and genome-wide associations used for integrative analyses were mainly from populations of European ancestry, resulting in potential missing of pivotal biological insights in other continental populations due to population heterogeneity. Here we conducted TWAS and SMR integrative analyses using blood eQTL (from 162 subjects) and GWAS data (22 778 cases and 35 362 controls) of schizophrenia in East Asian (EAS) populations. Both TWAS (P = 2.89 × 10-14) and SMR (P = 6.04 × 10-5) analyses showed that decreased TMEM180 mRNA expression was significantly associated with risk of schizophrenia. We further found that TMEM180 was significantly down-regulated in the peripheral blood of schizophrenia cases compared with controls (P = 8.63 × 10-4 in EAS sample), and its expression was also significantly lower in the brain tissues of schizophrenia cases compared with controls (P = 1.87 × 10-5 in European sample from PsychENCODE). Functional explorations suggested that Tmem180 knockdown affected neurodevelopment, ie, proliferation and differentiation of neural stem cells. RNA sequencing showed that pathways regulated by Tmem180 were significantly enriched in brain development and synaptic transmission. In conclusion, our study provides convergent lines of evidence for the involvement of TMEM180 in schizophrenia, and highlights the potential and importance of resource integration and sharing at this big data era in bio-medical research.
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