MegaSNPHunter: a learning approach to detect disease predisposition SNPs and high level interactions in genome wide association study.

ABSTRACT:

Background

The interactions of multiple single nucleotide polymorphisms (SNPs) are highly hypothesized to affect an individual's susceptibility to complex diseases. Although many works have been done to identify and quantify the importance of multi-SNP interactions, few of them could handle the genome wide data due to the combinatorial explosive search space and the difficulty to statistically evaluate the high-order interactions given limited samples.

Results

Three comparative experiments are designed to evaluate the performance of MegaSNPHunter. The first experiment uses synthetic data generated on the basis of epistasis models. The second one uses a genome wide study on Parkinson disease (data acquired by using Illumina HumanHap300 SNP chips). The third one chooses the rh

SUBMITTER: Wan X 

PROVIDER: S-EPMC2642772 | biostudies-literature | 2009 Jan

REPOSITORIES: biostudies-literature