How to Select Tag SNPs in Genetic Association Studies? The CLONTagger Method with Parameter Optimization

dc.contributor.authorIlhan, Ilhan
dc.contributor.authorTezel, Gulay
dc.date.accessioned2020-03-26T18:42:07Z
dc.date.available2020-03-26T18:42:07Z
dc.date.issued2013
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractSelection of genetic variants is a crucial first step in the rational design of studies aimed at explaining individual differences in susceptibility to complex human diseases or health intervention outcomes; for example, in the emerging fields of pharmacogenomics, nutrigenomics, and vaccinomics. While single nucleotide polymorphisms (SNPs) are frequently employed in these studies, the cost of genotyping a huge number of SNPs remains a limiting factor, particularly in low and middle income countries. Therefore, it is important to detect a subset of SNPs to represent the rest of SNPs with maximum possible accuracy. The present study introduces a new method, CLONTagger with parameter optimization, which uses Support Vector Machine (SVM) to predict the rest of SNPs and Clonal Selection Algorithm (CLONALG) to select tag SNPs. Furthermore, the Particle Swarm Optimization algorithm is preferred for the optimization of C and gamma parameters of the Support Vector Machine. Additionally, using many datasets, we compared the proposed new method with the tag SNP selection algorithms present in literature. Our results suggest that the CLONTagger with parameter optimization can identify tag SNPs with better prediction accuracy than other methods. Application-oriented studies are warranted to evaluate the utility of this method in future research in human genetics and study of the genetic components of variable responses to drugs, nutrition, and vaccines.en_US
dc.description.sponsorshipSelcuk University Scientific Research Projects Coordinatorship, Konya, TurkeySelcuk Universityen_US
dc.description.sponsorshipThis study is supported by Selcuk University Scientific Research Projects Coordinatorship, Konya, Turkey. The authors would like to thank the OMICS editors and anonymous reviewers of this manuscript for their very helpful suggestions and constructive critique.en_US
dc.identifier.doi10.1089/omi.2012.0100en_US
dc.identifier.endpage383en_US
dc.identifier.issn1536-2310en_US
dc.identifier.issn1557-8100en_US
dc.identifier.issue7en_US
dc.identifier.pmid23758474en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage368en_US
dc.identifier.urihttps://dx.doi.org/10.1089/omi.2012.0100
dc.identifier.urihttps://hdl.handle.net/20.500.12395/29556
dc.identifier.volume17en_US
dc.identifier.wosWOS:000321624500002en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherMARY ANN LIEBERT, INCen_US
dc.relation.ispartofOMICS-A JOURNAL OF INTEGRATIVE BIOLOGYen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.titleHow to Select Tag SNPs in Genetic Association Studies? The CLONTagger Method with Parameter Optimizationen_US
dc.typeArticleen_US

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