Big Data Contrast Mining for Genetic Distinctions between Disease Subtypes Matt Spencer and Chi-Ren Shyu – University of Missouri Informatics Institute Manhattan Plot by Ikram, M. K., et al. (2010). Complex Disease Etiology Genome-Wide Association Studies find genetic loci (SNPs) that are enriched in a disease-affected population. However, the traditional GWAS approach is not suitable for complex diseases. This is due to the addition of two major challenges when searching for the genetic etiology of a complex disease: We are specifically interested in autism, which may involve a combination of these attributes. We need a way to examine associations between diseases and multiple genes, and to identify genetic differences between subgroups of the affected population. Etiology The cause of a disease or abnormal condition Complex Disease A disease or disorder which cannot be attributed to a single gene • Interactions among genes – e.g. two genes both have to be mutated in order to cause the disease • Multiple causes of the disease – e.g. two genes both cause disease regardless of the state of the other GWAS Associated Genome Regions Frequent Pattern Mining In this research, the items are SNPs within the genomes of autistic individuals. The resulting combinations indicate groups of rare-form SNPs most prevalent in children with autism. B C E H K M N O R U Y BOUNCY 1 1 1 1 1 1 BUYER 1 1 1 1 1 CHUNKY 1 1 1 1 1 1 HOCKEY 1 1 1 1 1 1 HONEY 1 1 1 1 1 MERCY 1 1 1 1 1 MONEY 1 1 1 1 1 MONKEY 1 1 1 1 1 1 RHYME 1 1 1 1 1 Transactions Items Sup (U, Y) = 3/9 No combination including M can possibly have a support higher than 4/9. Sup (M) = 4/9 Eliminating just one item (A) significantly reduces the number of combinations that need to be examined. A data mining technique that identifies common trends within a dataset. Items that don’t achieve a certain population prevalence (“Support”, or just “Sup”) are eliminated from future combinations. To address the challenge that complex diseases may have multiple causes, we use Contrast Mining. Contrast Mining 1. The population is divided into subpops based on one or more phenotypes. Nondysmorphic vs Dysmorphic : 464 : 80 FPM Contrast Mining 2. Frequent Pattern Mining identifies prevalent SNP combos for both subpops. 3. Prevalent combinations are compared to identify major discrepancies. High-Contrast Itemsets An itemset may be highly prevalent in a relevant subpop, even when that itemset does not pass the Min Sup threshold in the general population. This demonstrates the importance of Contrast Mining within heterogeneous populations. We plan to apply this Genomic Contrast Mining procedure to the SFARI dataset, searching for genetic distinctions between several known and suspected autism subgroups. We hope to identify genes that can ultimately be diagnostic for specific autism subtypes. This would lead to earlier and more specific diagnoses, allowing “Precision Medicine” treatments to be developed and administered to people with specific disease subtypes. We also plan to use SNP combinations to connect autism-related genes from animal models to genes previously unaffiliated with autism. The combined knowledge of genes relevant to autism in humans and those relevant to autism in rats can lead to discoveries unobtainable through the study of either organism individually. Future Work Acknowledgements This work is supported by the NIH 1T32LM012410-01 Training Grant. The computing infrastructure is supported by the NSF CNS- 1429294 Grant. The SFARI SSC data is provided by the Simons Foundation (SFARI award #26021565-08C000066 to J.M.). We use Apache Hadoop to store and manage genotype and intermediate data in a distributed fashion. We also use Apache Spark to perform computations, using the shared memory pool of many machines. To find associations between complex disease and multiple genes, we must generate combinations of SNPs to test for association. However, this leads to a explosion of SNP combinations. He