Genetic interactions affect lung function in patients with systemic sclerosis

Abstract Scleroderma, or systemic sclerosis (SSc), is an autoimmune disease characterized by progressive fibrosis of the skin and internal organs. The most common cause of death in people with SSc is lung disease, but the pathogenesis of lung disease in SSc is insufficiently understood to devise specific treatment strategies. Developing targeted treatments requires not only the identification of molecular processes involved in SSc-associated lung disease, but also understanding of how these processes interact to drive pathology. One potentially powerful approach is to identify alleles that interact genetically to influence lung outcomes in patients with SSc. Analysis of interactions, rather than individual allele effects, has the potential to delineate molecular interactions that are important in SSc-related lung pathology. However, detecting genetic interactions, or epistasis, in human cohorts is challenging. Large numbers of variants with low minor allele frequencies, paired with heterogeneous disease presentation, reduce power to detect epistasis. Here we present an analysis that increases power to detect epistasis in human genome-wide association studies (GWAS). We tested for genetic interactions influencing lung function and autoantibody status in a cohort of 416 SSc patients. Using Matrix Epistasis to filter SNPs followed by the Combined Analysis of Pleiotropy and Epistasis (CAPE), we identified a network of interacting alleles influencing lung function in patients with SSc. In particular, we identified a three-gene network comprising WNT5A, RBMS3, and MSI2, which in combination influenced multiple pulmonary pathology measures. The associations of these genes with lung outcomes in SSc are novel and high-confidence. Furthermore, gene coexpression analysis suggested that the interactions we identified are tissue-specific, thus differentiating SSc-related pathogenic processes in lung from those in skin. Author summarySystemic sclerosis (SSc), or scleroderma, is a devastating autoimmune disease. Patients experience progressive fibrosis of their skin and internal organs, reduced quality of life, and increased risk of death. Lung disease associated with SSc is particularly dangerous and is currently the leading cause of death in SSc patients. There are no specific treatments for SSc or SSc-related lung disease, but promising work in the genetics of this disease has identified more than 200 genetic variants that influence SSc [1]. Piecing together how genetic variants interact with each other to influence disease may provide clues for targeted therapies. Here we present a novel analytical approach for identifying genetic interactions in a human disease cohort. In this approach we first filtered SNPs to those that are most likely to interact to influence the disease traits. We then applied the Combined Analysis of Pleiotropy and Epistasis (CAPE), which combines information across multiple traits to increase power to detect genetic interactions. Using this approach, we identified a three-gene network among MSI2, WNT5A, and RBMS3 that influenced autoantibody status and lung function in a cohort of 416 SSc patients. Gene expression data suggest that this interaction network is tissue- and disease-specific, and may thus provide a specific target for SSc therapy.