Discovery of new myositis genetic associations through leveraging other immune-mediated diseases

• Published in: HGG advances 2024-10, Vol.5 (4), p.100336, Article 100336
• Main Authors: Reales, Guillermo, Amos, Christopher I., Benveniste, Olivier, Chinoy, Hector, De Bleecker, Jan, De Paepe, Boel, Doria, Andrea, Gregersen, Peter K., Lamb, Janine A., Limaye, Vidya, Lundberg, Ingrid E., Machado, Pedro M., Maurer, Britta, Miller, Frederick W., Molberg, Øyvind, Pachman, Lauren M., Padyukov, Leonid, Radstake, Timothy R., Reed, Ann M., Rider, Lisa G., Rothwell, Simon, Selva-O'Callaghan, Albert, Vencovský, Jiri, Wedderburn, Lucy R., Wallace, Chris
• Format: Article
• Language: English
• ISSN: 2666-2477; 2666-2477
• DOI: 10.1016/j.xhgg.2024.100336

Genome-wide association studies (GWASs) have been successful at finding associations between genetic variants and human traits, including the immune-mediated diseases (IMDs). However, the requirement of large sample sizes for discovery poses a challenge for learning about less common diseases, where increasing volunteer numbers might not be feasible. An example of this is myositis (or idiopathic inflammatory myopathies [IIM]s), a group of rare, heterogeneous autoimmune diseases affecting skeletal muscle and other organs, severely impairing life quality. Here, we applied a feature engineering method to borrow information from larger IMD GWASs to find new genetic associations with IIM and its subgroups. Combining this approach with two clustering methods, we found 17 IMDs genetically close to IIM, including some common comorbid conditions, such as systemic sclerosis and Sjögren’s syndrome, as well as hypo- and hyperthyroidism. All IIM subtypes were genetically similar within this framework. Next, we colocalized IIM signals that overlapped IMD signals, and found seven potentially novel myositis associations mapped to immune-related genes, including BLK, IRF5/TNPO3, and ITK/HAVCR2, implicating a role for both B and T cells in IIM. This work proposes a new paradigm of genetic discovery in rarer diseases by leveraging information from more common IMD, and can be expanded to other conditions and traits beyond IMD. Finding relevant genetic variants is challenging for rare diseases due to decreased statistical power. We used a feature engineering approach that borrows information from common IMDs, followed by colocalization, to find seven additional associations in myositis, implicating a role for both T cells and B cells.