Abstract
Selecting a representative set of single nucleotide polymorphism (SNP) markers for facilitating association studies is an important step to uncover the genetic basis of human disease. Tag SNP selection and functional SNP selection are the two main approaches for addressing the SNP selection problem. However, little was done so far to effectively combine these distinct and possibly competing approaches. Here, we present a new multiobjective optimization framework for identifying SNPs that are both informative tagging and have functional significance (FS). Our selection algorithm is based on the notion of Pareto optimality, which has been extensively used for addressing multiobjective optimization problems in game theory, economics, and engineering. We applied our method to 34 disease-susceptibility genes for lung cancer and compared the performance with that of other systems which support both tag SNP selection and functional SNP selection methods. The comparison shows that our algorithm always finds a subset of SNPs that improves upon the subset selected by other state-of-the-art systems with respect to both selection objectives.
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Acknowledgments
This work was supported by HS's NSERC Discovery grant 298292-04 and CFI New Opportunities Award 10437.
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Lee, P.H., Jung, JY., Shatkay, H. (2010). Functionally Informative Tag SNP Selection Using a Pareto-Optimal Approach. In: Arabnia, H. (eds) Advances in Computational Biology. Advances in Experimental Medicine and Biology, vol 680. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-5913-3_20
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DOI: https://doi.org/10.1007/978-1-4419-5913-3_20
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