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.
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Lee, P.H., Shatkay, H. (2007). Two Birds, One Stone: Selecting Functionally Informative Tag SNPs for Disease Association Studies. In: Giancarlo, R., Hannenhalli, S. (eds) Algorithms in Bioinformatics. WABI 2007. Lecture Notes in Computer Science(), vol 4645. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74126-8_7
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DOI: https://doi.org/10.1007/978-3-540-74126-8_7
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