This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Genome-wide association studies (GWAS) involve 500,000 to 1 million DNA sequence variants called single nucleotide polymorphisms (SNPs), which are the most common form of variation in the human genome. A univariate filter has the risk of removing variants that are important to disease risk due to their interactions with other variants in the gene network. We have developed a computational approach that takes the context of all SNPs into account when ranking the importance to disease risk of an individual SNP. However, the advantage of context dependence comes at the cost of large memory requirements. The large shared memory of the Blacklight supercomputer will give us the opportunity to discover knew knowledge hidden in these large data sets.