Investigating Overlapping Genetic Factors and Novel Causal Genes in Autoimmune Diseases: A Transcriptome-Wide Association and Multiomics Study

Leihua Fu; Jieni Yu; Xin Wang; Zhe Chen; Jiaying Sun; Feidan Gao; Zhijian Zhang; Jiaping Fu; Pan Hong; Weiying Feng

doi:10.1155/ijog/9595651

Investigating Overlapping Genetic Factors and Novel Causal Genes in Autoimmune Diseases: A Transcriptome-Wide Association and Multiomics Study

Int J Genomics. 2025 Jun 24:2025:9595651. doi: 10.1155/ijog/9595651. eCollection 2025.

Authors

Leihua Fu^{1

2}, Jieni Yu¹, Xin Wang³, Zhe Chen¹, Jiaying Sun³, Feidan Gao¹, Zhijian Zhang¹, Jiaping Fu¹, Pan Hong¹, Weiying Feng¹

Affiliations

¹ Department of Hematology, Shaoxing People's Hospital, Shaoxing City, Zhejiang Province, China.
² Department of Genome Science and Microbiology, Faculty of Medical Sciences, University of Fukui, Fukui, Japan.
³ Department of Rheumatology, Shaoxing People's Hospital, Shaoxing City, Zhejiang Province, China.

Abstract

Background: Autoimmune diseases exhibit familial clustering and co-occurrence, suggesting the presence of shared genetic risk factors. However, the overlapping genetic factors across these diseases have yet to be fully elucidated. This study aimed to identify shared genetic factors across five autoimmune diseases: systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), ankylosing spondylitis (AS), Sjögren's syndrome (SS), and polymyalgia rheumatica (PMR). Methods: A blood tissue-based transcriptome-wide association study (TWAS) was conducted to identify candidate genes. Bayesian colocalization analysis was employed to pinpoint genetic variants shared across diseases. Multiomics summary data-based Mendelian randomization (SMR) was used to identify causal risk genes, while transcriptomic analysis, gene set variation analysis (GSVA), and weighted gene coexpression network analysis (WGCNA) were applied to further investigate the functional roles of these genes. Results: The TWAS identified 78 candidate genes across the five autoimmune diseases. Bayesian colocalization analysis revealed five genes, GTF2H4, FLOT1, HCP5, IER3, and STK19, that share genetic variants across these disorders. Specifically, RA and AS shared independent variants of GTF2H4 (rs2230365 and rs147708689, respectively). HCP5 variants were shared with SS (rs1800628) and SLE (rs1150757), and rs1800628 was also identified as a shared locus in FLOT1 for SLE. SMR analysis highlighted FLOT1 as a strong causal risk gene for SLE. Transcriptomic analysis showed that FLOT1 is highly expressed in T cells and platelets, with involvement in multiple metabolic pathways. WGCNA identified four key neighboring genes, EHD1, SLC10A3, LMNA, and STXBP2, associated with FLOT1. Conclusion: This study uncovers shared genetic factors across five autoimmune diseases, with FLOT1 identified as a novel causal risk gene for SLE. These findings suggest that platelet-mediated pathogenic mechanisms may contribute to SLE, providing a potential target for future therapeutic interventions.

Keywords: SLE; autoimmune disease; flotillin-1 (FLOT1); platelet; summary data–based Mendelian randomization (SMR).