ABSTRACT
Although several genes (including a strong effect in the human leukocyte antigen (HLA) region) and some environmental factors have been implicated to cause susceptibility to rheumatoid arthritis (RA), the etiology of the disease is not completely understood. The ability to screen the entire genome for association to complex diseases has great potential for identifying gene effects. However, the efficiency of gene detection in this situation may be improved by methods specifically designed for high-dimensional data. The aim of this study was to compare how three different statistical approaches, multifactor dimensionality reduction (MDR), random forests (RF), and an omnibus approach, worked in identifying gene effects (including gene-gene interaction) associated with RA. We developed a test set of genes based on previous linkage and association findings and tested all three methods. In the presence of the HLA shared-epitope factor, other genes showed weaker effects. All three methods detected SNPs in PTPN22 and TRAF1-C5 as being important. But we did not detect any new genes in this study. We conclude that the three high-dimensional methods are useful as an initial screening for gene associations to identify promising genes for further modeling and additional replication studies.
