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

Open Access Proceedings

Data for Genetic Analysis Workshop 16 Problem 1, association analysis of rheumatoid arthritis data

Christopher I Amos1*, Wei Vivien Chen1, Michael F Seldin2, Elaine F Remmers3, Kimberly E Taylor4, Lindsey A Criswell4, Annette T Lee5, Robert M Plenge6, Daniel L Kastner3 and Peter K Gregersen5

Author Affiliations

1 Departments of Epidemiology and Biomathematics, University of Texas, MD Anderson Cancer Center, 1155 Pressler Street, Houston, Texas 77030, USA

2 Rowe Program of Human Genetics, Department of Medicine, University of California, One Shields Avenue, 4303 Tupper Hall, Davis, California 95616, USA

3 Genetics and Genomics Branch, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 6N226, Bethesda, Maryland 20892, USA

4 The Rosalind Russell Medical Research Center for Arthritis, Department of Medicine, Division of Rheumatology, University of California at San Francisco, 374 Parnassus Avenue, Box 0500, San Francisco, California 94143-0500, USA

5 Center for Genomics and Human Genetics, North Shore-Feinstein Medical Research Institute, 350 Community Drive, Manhasset, New York 11030, USA

6 Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, 320 Charles Street, Cambridge, Massachusetts 02141, USA

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BMC Proceedings 2009, 3(Suppl 7):S2  doi:

Published: 15 December 2009


For Genetic Analysis Workshop 16 Problem 1, we provided data for genome-wide association analysis of rheumatoid arthritis. Single-nucleotide polymorphism (SNP) genotype data were provided for 868 cases and 1194 controls that had been assayed using an Illumina 550 k platform. In addition, phenotypic data were provided from genotyping DRB1 alleles, which were classified according to the rheumatoid arthritis shared epitope, levels of anti-cyclic citrullinated peptide, and levels of rheumatoid factor IgM. Several questions could be addressed using the data, including analysis of genetic associations using single SNPs or haplotypes, as well as gene-gene and genetic analysis of SNPs for qualitative and quantitative factors.