Determining the feasibility and value of federated data integration with combinations of 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 regarding SNP function, volume of information, and cost of investigating potential causative SNPs result in the prioritization of SNPs to be an important driving problem for this dissertation. SNP Integration Tool (SNPit) is a data integration system 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 a data integtration system combinations of probabilistic and logical inference for functional SNP annotation is the main focus of this dissertation. The dissertation describes challenges from both the biological and biomedical informatics standpoint regarding how to represent, integrate, and conduct inference over disparate biological data sources. Through development of these systems, this dissertation studies the feasibility of this form of federated data integration for SNP annotation and also assesses its' accuracy for SNP annotation. The work characterizes the suitability of combinations of logical and probabilistic inference with federated data integration for both point and regional SNP annotation. This dissertation contributes to our knowledge in the area of data integration and biological applications by describing the design, implementation, and evaluation of combinations of logical, probabilistic, and both logical and probabilistic inference applied to the domain of functional SNP annotation.