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

Fina Kurreeman, Katherine Liao, Lori Chibnik, Brendan Hickey, Eli Stahl, Vivian Gainer, Gang Li, Lynn Bry, Scott Mahan, Kristin Ardlie, Brian Thomson, Peter Szolovits, Susanne Churchill, Shawn N. Murphy, Tianxi Cai, Soumya Raychaudhuri, Isaac Kohane, Elizabeth Karlson, Robert M. Plenge

Research output: Contribution to journalArticlepeer-review

103 Scopus citations

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.

Original languageEnglish
Pages (from-to)57-69
Number of pages13
JournalAmerican Journal of Human Genetics
Volume88
Issue number1
DOIs
StatePublished - 7 Jan 2011
Externally publishedYes

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