Integrative exome sequencing and machine learning identify MICB and interferon pathway genes as contributors to SSc risk.

ABSTRACT:

Objectives

Systemic 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.

Methods

GWAS 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 co

SUBMITTER: Ketkar S 

PROVIDER: S-EPMC12715722 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature