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This article is part of the supplement: Genetic Analysis Workshop 16

Open Access Proceedings

Adjusting for HLA-DRβ1 in a genome-wide association analysis of rheumatoid arthritis and related biomarkers

Abigail G Matthews1*, Jia Li2, Chunsheng He1, Jurg Ott13 and Mariza de Andrade2

Author Affiliations

1 Laboratory of Statistical Genetics, Rockefeller University, Box 192, 1230 York Avenue, New York, NY 10065, USA

2 Department of Health Sciences Research, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA

3 Beijing Institute of Genomics, Chinese Academy of Sciences, Number 7 Bei Tu Cheng West Road, Chaoyang District, Beijing 100029, PR China

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BMC Proceedings 2009, 3(Suppl 7):S12  doi:

Published: 15 December 2009

Abstract

Background

There is a long-established association between rheumatoid arthritis and HLA-DRβ1. The shared epitope (SE) allele is an indicator of the presence of any of the HLA-DRβ1 alleles associated with RA. Other autoantibodies are also associated with RA, specifically rheumatoid factor IgM (RFUW) and anti-cyclic citrullinated peptide (anti-CCP).

Methods

Using the Genetic Analysis Workshop 16 North American Rheumatoid Arthritis Consortium genome-wide association data, we sought to find non-HLA-DRβ1 genetic associations by stratifying across SE status, and using the continuous biomarker phenotypes of RFUW and anti-CCP. To evaluate the binary RA phenotype, we applied the recently developed FP test and compared it to logistic regression or a genotype count-based test. We adjusted for multiple testing using the Bonferroni correction, the Q value approach, or permutation-based p-values. A case-only analysis of the biomarkers RFUW and anti-CCP used linear regression and ANOVAs.

Results

The initial genome-wide association analysis using all cases and controls provides substantial evidence of an association on chromosomes 9 and 2 within the immune system-related gene UBXD2. In SE-positive subjects, many single-nucleotide polymorphisms were significant, including some on chromosome 6. Due to very few SE negative cases, we had limited power to detect associations in SE negative subjects. We were also unable to find genetic associations with either RFUW or anti-CCP.

Conclusion

Our analyses have confirmed previous findings for genes PTPN22 and C5. We also identified a novel candidate gene on chromosome 2, UBXD2. Results suggest FP test may be more powerful than the genotype count-based test.