Immune cell-based transcriptomic Mendelian randomization and colocalization study on type 1 diabetes
Open Access
- 26 November 2025
- journal article
- research article
- Published by Springer Nature in BMC Medicine
- Vol. 23 (1)
Abstract
Background: Interventions to prevent type 1 diabetes (T1D), an immune-mediated disease requiring lifelong treatment, remain limited. We sought to identify novel therapeutic targets for T1D through Mendelian randomization and colocalization using immune cell-derived instruments. Methods: We selected locally acting genetic variants from 14 transcriptomic studies to instrument expression of 8998 genes measured in immune cell types. Outcome associations were obtained from a T1D genome-wide association study that included 18,942 cases and 501,638 controls. Follow-up analyses included assessments of horizontal pleiotropy and novelty, as well as phenome-wide scans with colocalization (PheWAS-coloc) of instrumental variants. Results: We prioritized 21 genes ( CLNK , EED , LZTFL1 , MGAT4A , NAA38 , NFKB1 , PHACTR4 , PHLPP2 , PLEKHA1 , P2RY12 , REST , RGS14 , SERPINB6 , SESN3 , SLC25A29 , SPAG1 , STIM2 , THEMIS , TMEM80 , VSIR , ZNF217 ) that have not been identified in previous T1D genome-wide association studies. Notably, higher genetically predicted VSIR (encoding the immune checkpoint protein VISTA) was associated with decreased risk of T1D, providing human genetic support that complements existing animal-model evidence for VISTA’s protective role in autoimmune diseases. We also prioritized P2RY12 , which is targeted by several approved drugs, revealing a possible repurposing opportunity. PheWAS-coloc analyses further linked P2RY12 expression to Epstein-Barr virus EBNA-1 antibody levels, implicating a pathway relevant to autoimmunity. Conclusions: Our findings provide novel avenues for drug development and repurposing to prevent or delay T1D onset.Keywords
Funding Information
- British Heart Foundation (FKZ 81X2100281)
- NIHR Cambridge Biomedical Research Centre (NIHR203312)
- Health Data Research UK (R2071CNR)
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