Identifying shared genetic architecture between rheumatoid arthritis and other conditions: a phenome-wide association study with genetic risk scoresResearch in context
Harrison G. Zhang,
Greg McDermott,
Thany Seyok,
Sicong Huang,
Kumar Dahal,
Sehi L’Yi,
Clara Lea-Bonzel,
Jacklyn Stratton,
Dana Weisenfeld,
Paul Monach,
Soumya Raychaudhuri,
Kun-Hsing Yu,
Tianrun Cai,
Jing Cui,
Chuan Hong,
Tianxi Cai,
Katherine P. Liao
Affiliations
Harrison G. Zhang
Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
Greg McDermott
Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
Thany Seyok
Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
Sicong Huang
Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
Kumar Dahal
Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
Sehi L’Yi
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
Clara Lea-Bonzel
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
Jacklyn Stratton
Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
Dana Weisenfeld
Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
Paul Monach
Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; VA Boston Healthcare System, Boston, MA, USA
Soumya Raychaudhuri
Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA; Center for Data Science, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
Kun-Hsing Yu
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
Tianrun Cai
Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
Jing Cui
Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
Chuan Hong
Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
Tianxi Cai
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA; VA Boston Healthcare System, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
Katherine P. Liao
Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA; VA Boston Healthcare System, Boston, MA, USA; Corresponding author. Division of Rheumatology, Inflammation, and Immunity, 60 Fenwood Road, Boston, MA, 02115, USA.
Summary: Background: Rheumatoid arthritis (RA) shares genetic variants with other autoimmune conditions, but existing studies test the association between RA variants with a pre-defined set of phenotypes. The objective of this study was to perform a large-scale, systemic screen to determine phenotypes that share genetic architecture with RA to inform our understanding of shared pathways. Methods: In the UK Biobank (UKB), we constructed RA genetic risk scores (GRS) incorporating human leukocyte antigen (HLA) and non-HLA risk alleles. Phenotypes were defined using groupings of International Classification of Diseases (ICD) codes. Patients with an RA code were excluded to mitigate the possibility of associations being driven by the diagnosis or management of RA. We performed a phenome-wide association study, testing the association between the RA GRS with phenotypes using multivariate generalized estimating equations that adjusted for age, sex, and first five principal components. Statistical significance was defined using Bonferroni correction. Results were replicated in an independent cohort and replicated phenotypes were validated using medical record review of patients. Findings: We studied n = 316,166 subjects from UKB without evidence of RA and screened for association between the RA GRS and n = 1317 phenotypes. In the UKB, 20 phenotypes were significantly associated with the RA GRS, of which 13 (65%) were immune mediated conditions including polymyalgia rheumatica, granulomatosis with polyangiitis (GPA), type 1 diabetes, and multiple sclerosis. We further identified a novel association in Celiac disease where the HLA and non-HLA alleles had strong associations in opposite directions. Strikingly, we observed that the non-HLA GRS was exclusively associated with greater risk of the validated conditions, suggesting shared underlying pathways outside the HLA region. Interpretation: This study replicated and identified novel autoimmune phenotypes verified by medical record review that share immune pathways with RA and may inform opportunities for shared treatment targets, as well as risk assessment for conditions with a paucity of genomic data, such as GPA. Funding: This research was funded by the US National Institutes of Health (P30AR072577, R21AR078339, R35GM142879, T32AR007530) and the Harold and DuVal Bowen Fund.
