Evaluation of probabilistic and logical inference for a SNP annotation system

Authors:
Terry H. Shen;Peter Tarczy-Hornoch;Landon T. Detwiler;Eithon Cadag;Christopher S. Carlson
Affiliations:
Department of Biomedical and Health Informatics, University of Washington, Seattle, WA, USA;Department of Biomedical and Health Informatics, University of Washington, Seattle, WA, USA and Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA and Depar ...;Department of Biological Structure, University of Washington, Seattle, WA, USA;Department of Biomedical and Health Informatics, University of Washington, Seattle, WA, USA and Biomedical Research Institute, Seattle, WA, USA;Department of Epidemiology, University of Washington, Seattle, WA, USA and Fred Hutchinson Cancer Research Center, Seattle, WA, USA
Venue:
Journal of Biomedical Informatics
Year:
2010

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Abstract

Genome wide association studies (GWAS) are an important approach to understanding the genetic mechanisms behind human diseases. Single nucleotide polymorphisms (SNPs) are the predominant markers used in genome wide association studies, and the ability to predict which SNPs are likely to be functional is important for both a priori and a posteriori analyses of GWA studies. This article describes the design, implementation and evaluation of a family of systems for the purpose of identifying SNPs that may cause a change in phenotypic outcomes. The methods described in this article characterize the feasibility of combinations of logical and probabilistic inference with federated data integration for both point and regional SNP annotation and analysis. Evaluations of the methods demonstrate the overall strong predictive value of logical, and logical with probabilistic, inference applied to the domain of SNP annotation.