A combinatorial approach for detecting gene-gene interaction using multiple traits of Genetic Analysis Workshop 16 rheumatoid arthritis data

Rheumatoid arthritis is inherited in a complex manner. So far several single susceptibility genes, such as PTPN22, STAT4, and TRAF1-C5, have been identified. However, it is presumed that some genes may interact to have a significant effect on the disease, while each of them only plays a modest role. We propose a new combinatorial association test to detect the gene-gene interaction in the rheumatoid arthritis data using multiple traits: disease status, anti-cyclic citrullinated peptide, and immunoglobulin M. Existing gene-gene interaction tests only use the information on a single trait at a time. In this article, we propose a new multivariate combinatorial searching method that utilizes multiple traits at the same time. Multivariate combinatorial searching method is conducted by incorporating the multiple traits with various techniques of feature selection to search for a set of disease-susceptibility genes that may interact. By analyzing three panels of markers, we have identified a significant gene-gene interaction between PTPN22 and TRAF1-C5.