Seminar Abstract

“Determining the feasibility and value of federated data integration combining logical and probabilistic inference for SNP annotation”

Most common and complex diseases are influenced at some level by variation in the genome. The future work of statistical geneticists, molecular biologists, and physician-scientists with interests in genetics or genomics must thus take genetics into consideration. Research done in public health genetics, specifically in the area of single nucleotide polymorphisms (SNPs), is the first step to understanding human genetic variation. Functional uncertainty, volume of information, and cost-effectiveness result in the prioritization of SNPs to be an important research question. SNP Integration Tool (SNPit) is a data integration system tool that looks at all the possible predictors of functional SNPs and provides the user with integrated information and decision making capability. Determining the feasibility and value of SNPit with rules and probabilistic inference, thus, represents challenges from both the biological and biomedical informatics standpoint concerning how to represent, integrate, and conduct inference over disparate biological data sources.

I will discuss how to determine the feasibility and value of creating a federated integration system combining logical and probabilistic inference for functional SNP annotation. Preliminary studies include the prototype SNPit system which consolidates information on a variety of functional annotation predictors: everything ranging from gene expression, protein function, and evolutionary conservation through to known functional mutations.