In this study, we analyzed anti-CCP levels as dichotomized, categorical, and continuous traits in linkage analyses. On chromosome 7, we observed improved linkage signals and a narrower linkage peak for dichotomized anti-CCP levels as compared to a clinical diagnosis of RA alone, although neither analysis reached statistical significance. Chromosome 7 previously showed stronger evidence for linkage for RA with positive anti-CCP [6
]. However, for the three chromosomes we selected that demonstrated evidence for linkage with RA, analyzing anti-CCP levels as quantitative traits did not further improve power to detect linkage compared to RA. In addition, categorization and log transformation of the continuous phenotype resulted in significantly reduced power to detect linkage.
Mapping susceptibility genes is challenging for clinically and genetically heterogeneous diseases such as RA. Examining a more homogenous disease phenotype might increase the power of linkage analysis. Anti-CCP is highly specific for RA and its levels are correlated with disease severity [9
]. A previous study suggested genetic variability according to anti-CCP status on chromosomes 4, 5, 6, and 7 [6
]. Our data on chromosome 7 supports the hypothesis that anti-CCP status might represent a more genetically homogeneous phenotype of RA. In addition, although linkage signals on chromosome 11 were less significant for the CCP_binary phenotype than RA, linkage peak regions on both chromosomes 7 and 11 were narrower, suggesting that analyzing anti-CCP levels as a dichotomous phenotype might be helpful in narrowing candidate regions in fine mapping.
For naturally occurring continuous or polychotomous traits, dichotomization could lead to power loss for linkage analysis [7
]. In particular, if the underlying gene confers not only disease susceptibility but also disease severity, treating disease phenotypes as quantitative traits could provide additional information in linkage analysis. We hypothesized that performing quantitative linkage analyses might increase the power to detect linkage for anti-CCP levels. However, analyses of none of the three quantitative phenotypes provided improved linkage signals compared to NPL analysis of RA. In particular, regression-based quantitative linkage analyses of log-transformed or ordinal anti-CCP levels, with or without covariate adjustment, appeared to have reduced power to detect linkage compared to NPL analysis of dichotomized anti-CCP status. We also used the variance components and quantitative trait locus analysis options implemented in Merlin for CCP_cat and Log_CCP phenotypes and observed similar results. For this particular trait, transforming the phenotype to achieve normality reduced the signal due to the major locus. Alternate approaches to handle non-normality are needed for quantitative trait linkage analysis in selected samples. Consistent with another report [13
], we found loss of power when treating CCP_cat as continuous. Novel approaches, such as the recently proposed proportional odds latent variable model by Feng et al. [14
], need to be further explored. Finally, all these quantitative linkage analyses are very sensitive to the specification of population parameters, such as population mean and variance, chosen for the regress
analyses. Replacement of the estimated population mean by the sample mean or default values set by the programs resulted in completely different LOD scores. These results further highlight the importance of precise estimates and appropriate analytical approaches in linkage analysis of quantitative traits.