Abstract
Rheumatoid arthritis (RA) is a chronic, complex autoimmune inflammatory disorder with poorly known etiology. Approximately 1% of the adult population is afflicted with RA. Linkage analysis of RA can be complicated by the presence of phenotypic and genetic heterogeneity. It is shown that the ordered-subset analysis (OSA) technique reduces heterogeneity, increases statistical power for detecting linkage and helps to define the most informative data set for follow-up analysis. We applied OSA to the family data from the North American Rheumatoid Arthritis Consortium study as part of the Genetic Analysis Workshop 15 (GAW15). We have incorporated two continuous covariates, 'age of onset' and 'anti-CCP level' (anti-cyclic citrinullated peptide), into our genome-wide ordered-subset linkage analysis using 809 Illumina SNP markers in 5713 individuals from 606 Caucasian RA families. A statistically significant increase in nonparametric linkage (NPL) scores was observed with covariate 'age of onset' in chromosomes 4 (p = 0.000003) and 9 (p = 0.002). With the covariate 'anti-CCP level', statistically significant increases in NPL scores were observed in chromosomes 2 (p = 0.0001), 18 (p = 0.00007), and 19 (p = 0.0003). Once we identified the linked genomic region, we then attempted to identify the best plausible parametric model at that linked locus. Our results show significant improvement in evidence for linkage and demonstrate that OSA is a useful technique to detect linkage under heterogeneity.