ArticleAnnals of the rheumatic diseases2025
Integrative exome sequencing and machine learning identify MICB and interferon pathway genes as contributors to SSc risk.
Article in Annals of the rheumatic diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Novel HLA class I and II insights into the pathogenesis of systemic sclerosis-associated interstitial lung disease.Annals of the rheumatic diseases · 2026Article
- Advancing drug development for systemic sclerosis by prioritizing findings from human genetic association studies.Rheumatology (Oxford, England) · 2026Review
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Authors and funding
15 authors.
Funding
Abstract
objectivesSystemic sclerosis (SSc) is a complex autoimmune disease with both known and unidentified genetic contributors. While genome-wide association studies (GWAS) have implicated multiple loci, many reside in noncoding regions. We aimed to identify novel protein-coding variants and pathogenic pathways using exome sequencing (ES) integrated with an Evolutionary Action-Machine Learning (EAML) framework, single-cell RNA sequencing (scRNA-seq), and expression quantitative trait locus (eQTL) analysis.
methodsGWAS was conducted in 2,559 SSc cases and 893 controls of Caucasian ancestry, with replication in 9,846 cases and 18,333 controls of European ancestry. EAML prioritized genes with high-impact missense variants predictive of disease. Public scRNA-seq data from SSc and control skin biopsies were analyzed to localize gene expression across cell types. Whole blood eQTL data were used to identify regulatory effects of risk variants.
resultsA novel SSc risk locus at MICB (rs2516497, P = 3.66 × 10
conclusionsThis integrative genomic study identifies novel risk loci and mechanistic pathways in SSc, highlighting MICB, NOTCH4, and interferon-related genes. The findings provide insight into the cellular and regulatory architecture of SSc and support the utility of combining ES, machine learning, scRNA-seq, and eQTL data in complex disease genetics.
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