Integrative exome sequencing and machine learning identify new genes contributing to systemic sclerosis risk

Systemic sclerosis (SSc) is a severe autoimmune disease with complex genetic causes. Some genetic contributors have been identified, but others remain unknown, which has impeded development of targeted treatments. In a new study published in Annals of the Rheumatic Diseases, researchers at Baylor College of Medicine and collaborating institutions used complementary approaches that integrate exome sequencing and evolutionary action machine learning to identify protein changes and their associated mechanisms in SSc.