In this study, we developed a systematic method to find risk alleles and relative gene for rheumatoid arthritis (RA). The method consists of three steps: 1) genome-wide case-control association studying based on haplotypes; 2) genome-wide association mapping based on directly mining haplotypes produced from case-control data via a density-based clustering algorithm; 3) candidate genes within 1 Mb of the interesting haplotype blocks prioritizing underlying biological processes or diseases, based on their similarity to known genes involved in these phenomena. By analyzing the dataset of 5393 informative single-nucleotide polymorphisms (SNPs) markers containing 822 uncorrelated individuals which obtained from the North American Rheumatoid Arthritis Consortium (NARAC), we found 25 haplotypes in 18 haplotype blocks and 33 genes will be increase the risk of RA. 9 of the genes have been identified by previous studies, while novel genes may be risk genes for RA. The genes PTPRC (p =1.15E-04) and F12 (1.36E-02) have the highest risk of RA. In summary, the results of our analysis will provide fundamental new insights into the pathogenesis of RA, and the systematic analysis method combining the genome-wide association study based on haplotype and the prioritizing study of candidate genes based on their similarity to known genes will help to comprehend the genetic architecture underlying other complex human diseases.