Rheumatology Advance Access originally published online on November 8, 2005
Rheumatology 2006 45(2):166-170; doi:10.1093/rheumatology/kei128
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Genome scan meta-analysis of rheumatoid arthritis
Division of Rheumatology, Department of Internal Medicine, Korea University Medical Center, College of Medicine, Korea University, Seoul, Korea.
Correspondence to: Y. H. Lee, Division of Rheumatology, Department of Internal Medicine, Korea University Medical Center, 126-1 Ka, Anam-Dong, Seongbuk-Ku, Seoul, 136-705, Korea. E-mail: lyhcgh{at}korea.ac.kr
Objective. Genome scans for rheumatoid arthritis (RA) have yielded inconsistent results. The absence of replication of linkage might be due to lack of power of individual studies. We performed a genome scan meta-analysis of published data to increase statistical power and to assess evidence for linkage of RA across genome scan studies.
Methods. Four RA whole-genome scans containing 767 families with 964 sibling pairs were included for the genome scan meta-analysis (GSMA). The GSMA method was applied to pool the results obtained from four genome scans. For each study, 120 genomic bins of 
30 centimorgans were defined and ranked according to maximum evidence for linkage within each bin. Bin ranks were weighted and summed across all studies. The summed rank for each bin was assessed empirically for significance using permutation methods.
Results. A total of nine bins lay above the 95% confidence level (P=0.05) and four bins were above the 99% confidence level (P=0.01) in the RA GSMA, suggesting that these bins contain RA-linked loci: bins 6.2, 6.4, 8.1, 18.3, 12.3, 12.2, 1.5, 6.3 and 16.2. The strongest evidence for linkage occurred on chromosome 6p22.3-6p21.1 (bin 6.2), containing the HLA region (Psumrnk=0.0000008).
Conclusion. This RA GSMA confirmed the evidence for HLA loci as the greatest susceptibility factor to RA and showed evidence for linkage at non-HLA loci, such as chromosomes 1p, 6, 8p, 12, 16 and 18q, across studies. These data may provide a basis to carry out targeted linkage and candidate gene studies, particularly in the regions.
KEY WORDS: Rheumatoid arthritis, Genome scan, Linkage, Meta-analysis
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