rs1005714

This review article by Ota and Kuwana synthesizes genetic studies on systemic sclerosis (SSc), a complex autoimmune disease. Multiple genetic association studies, including GWAS and candidate gene approaches, have identified SSc susceptibility genes primarily involved in innate immunity (IRF4, IRF5, IRF7, IRF8, TNFAIP3), adaptive immune response (TNFSF4, CD247, PTPN22, CSK, STAT4, BLK), IL-12 signaling (IL-12A, IL-12RB1, IL-12RB2, TYK2), apoptosis/autophagy (ATG5, GSDMA, GSDMB, NOTCH4), and vascular homeostasis/fibrosis (PPARG). The review emphasizes that identified risk variants are predominantly located in non-coding regulatory regions and influence gene expression rather than protein structure.

This is a comprehensive review of genetic factors in systemic sclerosis (SSc), a complex autoimmune disease. The review synthesizes findings from candidate gene analysis and genome-wide association studies identifying numerous SNPs and genetic variants associated with SSc susceptibility, primarily in genes involved in innate immunity (IRF4, IRF5, IRF7, IRF8, TNFAIP3), adaptive immunity (TNFSF4, PTPN22, STAT4, BLK, PRDM1), and cell death pathways (ATG5, DNASE1L3, GSDMA/B, NOTCH4). HLA class II genes are associated with SSc-related autoantibodies rather than SSc itself, with DRB1 alleles carrying the FLEDR amino acid sequence critical for anti-topo I antibody responses.

This review updates knowledge on genetic factors in systemic sclerosis (SSc) susceptibility and disease expression. GWAS and candidate gene studies have identified multiple SSc-associated genetic variants primarily located in non-coding regions that influence gene expression through eQTL effects. Major risk genes include those involved in innate immunity (IRF4, IRF5, IRF7, IRF8, TNFAIP3), adaptive immune response (PTPN22, STAT4, TNFSF4, CD247), and cell death pathways (ATG5), while few genes directly involve fibrosis or vascular homeostasis. HLA class II genes associate with SSc-related autoantibodies rather than SSc itself. Multi-omics approaches are needed to characterize the complex molecular architecture and identify biomarkers.