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Two Birds, One Stone: Selecting Functionally Informative Tag SNPs for Disease Association Studies

  • Phil Hyoun Lee
  • Hagit Shatkay
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4645)

Abstract

Selecting an informative subset of SNPs, generally referred to as tag SNPs, to genotype and analyze is considered to be an essential step toward effective disease association studies. However, while the selected informative tag SNPs may characterize the allele information of a target genomic region, they are not necessarily the ones directly associated with disease or with functional impairment. To address this limitation, we present a first integrative SNP selection system that simultaneously identifies SNPs that are both informative and carry a deleterious functional effect – which in turn means that they are likely to be directly associated with disease. We formulate the problem of selecting functionally informative tag SNPs as a multi-objective optimization problem and present a heuristic algorithm for addressing it. We also present the system we developed for assessing the functional significance of SNPs. To evaluate our system, we compare it to other state-of-the-art SNP selection systems, which conduct both information-based tag SNP selection and function-based SNP selection, but do so in two separate consecutive steps. Using 14 datasets, based on disease-related genes curated by the OMIM database, we show that our system consistently improves upon current systems.

Keywords

Single Nucleotide Polymorphism Nucleic Acid Research Exonic Splice Enhancer Single Nucleotide Polymorphism Effect Single Nucleotide Polymorphism Selection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Phil Hyoun Lee
    • 1
  • Hagit Shatkay
    • 1
  1. 1.Computational Biology and Machine Learning Lab, School of Computing, Queen’s University, Kingston, ONCanada

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