Purpose of review: Systemic sclerosis (SSc) is a complex autoimmune disorder that affects the connective tissue and causes severe vascular damage and fibrosis of the skin and internal organs. There are recent advances in the field that apply novel methods to high throughput genotype information of thousands of patients with SSc and provide promising results towards the use of genomic data to help SSc diagnosis and clinical care.
Recent findings: This review addresses the development of the first SSc genomic risk score, which can contribute to differentiating SSc patients from healthy controls and other immune-mediated diseases. Moreover, we explore the implementation of data mining strategies on the results of genome-wide association studies to highlight subtype-specific HLA class II associations and a strong association of the HLA class I locus with SSc for the first time. Finally, the combination of genomic data with transcriptomics informed drug repurposing and genetic association studies in well characterized SSc patient cohorts identified markers of severe complications of the disease.
Summary: Early diagnosis and clinical management of SSc and SSc-related complications are still challenging for rheumatologists. The development of predictive models and tools using genotype data may help to finally deliver personalized clinical care and treatment for patients with SSc in the near future.
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