Whole-Genome Detection of Disease-Associated Deletions or Excess Homozygosity in a Case-Control Study of Rheumatoid Arthritis. C. C. Wu1, S. Shete2, E. J. Jo3, Y. Xu4, E. Y. Lu5, W. V. Chen5, C. I. Amos5, 6 1) Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan; 2) Biostatistics, MD Anderson Cancer Center, Houston, Texas; 3) Duncan Cancer Center, Baylor College of Medicine, Houston, Texas; 4) Biostatistics, School of Public Health, Yale University, New Haven, Connecticut; 5) Genetics, MD Anderson Cancer Center, Houston, Texas; 6) Community and Family Medicine, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire.

   Copy number variations are abundant in humans and represent one of the least well studied classes of genetic variants. Few comprehensive studies have been performed of copy number variations contribution to complex human disease susceptibility. Because known rheumatoid arthritis susceptibility loci explain only a portion of familial clustering, we performed a genome-wide study of association between deletions and rheumatoid arthritis using high-density 550K SNP genotype data. We recently developed a statistical method for detecting deletions or excess homozygosity associated with complex disease in case-control studies, using SNPs in genome-wide association studies. We used this method and tested each contiguous SNP locus between the 868 cases and 1194 controls to detect deletion variants or excess homozygosity that influence susceptibility. Our method is designed to detect statistically significant evidence of deletions or homozygosity at individual SNPs for SNP-by-SNP analyses and to combine the information among neighboring SNPs for cluster analyses. In addition to successfully detecting known deleterious deletion variants on HLA-DRB1 and C4 genes on MHC, we identified additional 4.3-kb and 28-kb clusters on chromosomes 10p (5,316,846-5,321,159) and 13q (20,783,404-20,811,429), respectively, which were significant at a corrected 0.05 nominal significance level, adjusted for multiple comparison procedures. Independently, we performed analyses using the PennCNV method and identified cases and controls that had chromosomal segments with copy number < 2. PennCNV is an algorithm for identifying and cataloging copy numbers for individuals on the basis of a hidden Markov model. Using Fishers exact test to compare the numbers of cases and controls per SNP, we identified 26 neighboring significant SNPs (protective; more controls than cases) that jointly showed evidence of deletions on chromosome 14 with p-values < 10-8. We further extended our method to logistic regression frame work, which allows us to adjust for the population structure using eigen vectors. This approach supported our previous findings.

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