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.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.