High throughput multiple combination extraction from large scale polymorphism data by exact tree method

Abstract

Single nucleotide polymorphisms (SNPs) are increasingly becoming important in clinical settings as useful genetic markers. For the evaluation of genetic risk factors of multifactorial diseases, it is not sufficient to focus on individual SNPs. It is preferable to evaluate combinations of multiple markers, because it allows us to examine the interactions between multiple factors. If all the combinations possible were evaluated round-robin, the number of calculations would rapidly explode as the number of markers analyzed increased. To overcome this limitation, we devised the exact tree method based on decision tree analysis and applied it to 14 SNP data from 68 Japanese stroke patients and 189 healthy controls. From the obtained tree models, we succeeded in extracting multiple statistically significant combinations that elevate the risk of stroke. From this result, we inferred that this method would work more efficiently in the whole genome study, which handles thousands of genetic markers. This exploratory data mining method will facilitate the extraction of combinations from large-scale genetic data and provide a good foothold for further verificatory research.