Genetic basis of autoantibody positive and negative rheumatoid arthritis risk in a multi-ethnic cohort derived from electronic health records

Abstract

Discovering and following up on genetic associations with complex phenotypes require large patient cohorts. This is particularly true for patient cohorts of diverse ancestry and clinically relevant subsets of disease. The ability to mine the electronic health records (EHRs) of patients followed as part of routine clinical care provides a potential opportunity to efficiently identify affected cases and unaffected controls for appropriate-sized genetic studies. Here, we demonstrate proof-of-concept that it is possible to use EHR data linked with biospecimens to establish a multi-ethnic case-control cohort for genetic research of a complex disease, rheumatoid arthritis (RA). In 1,515 EHR-derived RA cases and 1,480 controls matched for both genetic ancestry and disease-specific autoantibodies (anti-citrullinated protein antibodies [ACPA]), we demonstrate that the odds ratios and aggregate genetic risk score (GRS) of known RA risk alleles measured in individuals of European ancestry within our EHR cohort are nearly identical to those derived from a genome-wide association study (GWAS) of 5,539 autoantibody-positive RA cases and 20,169 controls. We extend this approach to other ethnic groups and identify a large overlap in the GRS among individuals of European, African, East Asian, and Hispanic ancestry. We also demonstrate that the distribution of a GRS based on 28 non-HLA risk alleles in ACPA+ cases partially overlaps with ACPA- subgroup of RA cases. Our study demonstrates that the genetic basis of rheumatoid arthritis risk is similar among cases of diverse ancestry divided into subsets based on ACPA status and emphasizes the utility of linking EHR clinical data with biospecimens for genetic studies.

Figure 1

Assessment of Population Structure and Assignment of EHR Subjects into Four Ancestry Groups of European, African, East Asian, and Hispanic Descent Population structure of the EHR (A) cases and (B) controls are plotted on PCs 1 and 2 (filled circles). These subjects were projected onto reference populations from Phase 3 of the HapMap Project via 144 ancestry-informative markers (dotted lines). The centroids indicate the outer bounds of the three major HapMap continental populations, and the darker filled circles indicate the center of each centroid. Individuals who were not assigned to these major continental populations were classified as “other” and were predominantly of EHR-reported Hispanic origin (gray circles).

Figure 2

Overlap of Odds Ratio and 95% Confidence Intervals between Previous GWAS Meta-Analysis Dataset and ACPA+ European Subset from EHR Cohort Asterisks indicate TNFAIP3 SNP rs6920220 and CCL21 SNP rs951005. EU indicates individuals of European descent from our EHR cohort. GWAS represents samples from the previously published GWAS meta-analysis.

Figure 3

Distribution of the Aggregate Genetic-Risk Score from 29 RA Risk Alleles in ACPA+ Cases versus Controls Samples represented in the respective panels are (A) controls and ACPA+ cases in our EHR study and controls (orange lines) and seropositive (ACPA+ and or RF+) and healthy individuals from the GWAS meta-analysis, all of European descent (EU) (blue lines); (B) controls and ACPA+ cases of African descent (AF) (green lines), (C) controls and ACPA+ cases of East Asian descent (AS) (purple lines), and (D) controls and ACPA+ cases of Hispanic descent (HIS) (gray lines).

Figure 4

Overlap of Odds Ratio and 95% Confidence Intervals between European ACPA+ and ACPA− Subsets from the EHR Cohort Asterisks indicate TNFAIP3 SNP rs6920220 and CCL21 SNP rs951005. EU indicates individuals of European descent from our EHR cohort.

Figure 5

Distribution of the Aggregate Genetic-Risk Score from 28 non-HLA RA Risk Alleles in Controls, ACPA+ Cases, and ACPA− Cases in Individuals of European Ancestry