Autoantibodies, autoimmune risk alleles and clinical associations in rheumatoid arthritis cases and non-RA controls in the electronic medical records
By Katherine P. Liao, F. Kurreeman, Gang Li, Grant E. Duclos, Shawn N. Murphy, R. Guzmán, Tianxi Cai, N. Gupta, Vivian S. Gainer, P. Schur and 7 more. 2013
99 citations recorded by the source corpus.
Objectives The significance of non-RA autoantibodies in patients with rheumatoid arthritis (RA) is unclear. We studied associations between autoimmune risk alleles and autoantibodies in RA cases and non-RA controls, and autoantibodies and clinical diagnoses from the electronic medical records (EMR). Methods We studied 1,290 RA cases and 1,236 non-RA controls of European genetic ancestry from the EMR from two large academic centers. We measured antibodies to citrullinated peptides (ACPA), anti-nuclear antibodies (ANA), antibodies to tissue transglutaminase (anti-tTG), antibodies to thyroid peroxidase (anti-TPO). We genotyped subjects for autoimmune risk alleles, and studied the association between number of autoimmune risk alleles and number of types of autoantibodies present. We conducted a phenome-wide association study (PheWAS) to study potential associations between autoantibodies and clinical diagnoses among RA cases and controls. Results Mean age was 60.7 in RA and 64.6 years in controls, and both were 79% female. The prevalence of ACPA and ANA was higher in RA cases compared to controls (p<0.0001, both); we observed no difference in anti-TPO and anti-tTG. Carriage of higher numbers of autoimmune risk alleles was associated with increasing types of autoantibodies in RA cases (p=4.4x10−6) and controls (p=0.002). From the PheWAS, ANA was significantly associated with Sjogren’s/siccain RA cases. Conclusion The increased frequency of autoantibodies in RA cases and controls was associated with the number of autoimmune risk alleles carried by an individual. PheWAS analyses within the EMR linked to blood samples provide a novel method to test for the clinical significance of biomarkers in disease.
Subfields: Genetics, Rheumatology
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Citations in the corpus
- Natural Language Processing for EHR-Based Computational Phenotyping (2018, background): “Much research [66-69] has explored EHR phenotyping algorithms to facilitate GWAS.”