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Article: Use of an artificial neural network to detect association between a disease and multiple marker genotypes

TitleUse of an artificial neural network to detect association between a disease and multiple marker genotypes
Authors
Issue Date2001
PublisherBlackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/AHG
Citation
Annals Of Human Genetics, 2001 n. 1, p. 95-107 How to Cite?
AbstractSingle nucleotide polymorphisms (SNPs) are very common throughout the genome and hence are potentially valuable for mapping disease susceptibility loci by detecting association between SNP markers and disease. However as SNPs are biallelic they may have relatively little power in association studies compared with the information that would be obtainable if marker haplotypes were available and could be used efficiently. Modelling the evolutionary events leading to linkage disequilibrium is very complex and many methods that seek to use information from multiple markers simultaneously need to make simplifying assumptions and may only be applicable when marker haplotypes, rather than genotypes, are available for analysis. We explore the properties of a simple application of a standard artificial neural network to this problem. The pattern-recognition properties of the network are used in the hope that marker haplotypes implicit in the genotypes will differ between cases and controls in a way which will lead to the network being able to classify the subjects correctly, according to their marker genotype. This method makes no assumptions at all regarding population history or the marker map, and can be applied to genotypes, as would be available from a simple case-control sample, without any need to determine haplotypes. Through application to data simulated under a very wide range of assumptions we show that such an analysis produces a useful augmentation in power above that which would be achieved by testing each marker individually, in particular when more than one mutation has occurred in a disease gene at different points in evolution. The application of neural networks to such problems shows considerable promise and further work could usefully be directed towards optimising the design and implementation of such networks.
Persistent Identifierhttp://hdl.handle.net/10722/175831
ISSN
2015 Impact Factor: 1.889
2015 SCImago Journal Rankings: 1.191
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorCurtis, Den_US
dc.contributor.authorNorth, BVen_US
dc.contributor.authorSham, PCen_US
dc.date.accessioned2012-11-26T09:01:39Z-
dc.date.available2012-11-26T09:01:39Z-
dc.date.issued2001en_US
dc.identifier.citationAnnals Of Human Genetics, 2001 n. 1, p. 95-107en_US
dc.identifier.issn0003-4800en_US
dc.identifier.urihttp://hdl.handle.net/10722/175831-
dc.description.abstractSingle nucleotide polymorphisms (SNPs) are very common throughout the genome and hence are potentially valuable for mapping disease susceptibility loci by detecting association between SNP markers and disease. However as SNPs are biallelic they may have relatively little power in association studies compared with the information that would be obtainable if marker haplotypes were available and could be used efficiently. Modelling the evolutionary events leading to linkage disequilibrium is very complex and many methods that seek to use information from multiple markers simultaneously need to make simplifying assumptions and may only be applicable when marker haplotypes, rather than genotypes, are available for analysis. We explore the properties of a simple application of a standard artificial neural network to this problem. The pattern-recognition properties of the network are used in the hope that marker haplotypes implicit in the genotypes will differ between cases and controls in a way which will lead to the network being able to classify the subjects correctly, according to their marker genotype. This method makes no assumptions at all regarding population history or the marker map, and can be applied to genotypes, as would be available from a simple case-control sample, without any need to determine haplotypes. Through application to data simulated under a very wide range of assumptions we show that such an analysis produces a useful augmentation in power above that which would be achieved by testing each marker individually, in particular when more than one mutation has occurred in a disease gene at different points in evolution. The application of neural networks to such problems shows considerable promise and further work could usefully be directed towards optimising the design and implementation of such networks.en_US
dc.languageengen_US
dc.publisherBlackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/AHGen_US
dc.relation.ispartofAnnals of Human Geneticsen_US
dc.subject.meshAllelesen_US
dc.subject.meshBiological Evolutionen_US
dc.subject.meshDatabases, Factualen_US
dc.subject.meshGenetic Diseases, Inbornen_US
dc.subject.meshGenetic Markersen_US
dc.subject.meshGenotypeen_US
dc.subject.meshHaplotypesen_US
dc.subject.meshHumansen_US
dc.subject.meshModels, Geneticen_US
dc.subject.meshMutationen_US
dc.subject.meshNeural Networks (Computer)en_US
dc.subject.meshPolymorphism, Geneticen_US
dc.subject.meshPolymorphism, Single Nucleotideen_US
dc.subject.meshRecombination, Geneticen_US
dc.titleUse of an artificial neural network to detect association between a disease and multiple marker genotypesen_US
dc.typeArticleen_US
dc.identifier.emailSham, PC: pcsham@hku.hken_US
dc.identifier.authoritySham, PC=rp00459en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1046/j.1469-1809.2001.6510095.x-
dc.identifier.pmid11415525-
dc.identifier.scopuseid_2-s2.0-0034744364en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0034744364&selection=ref&src=s&origin=recordpageen_US
dc.identifier.issue1en_US
dc.identifier.spage95en_US
dc.identifier.epage107en_US
dc.identifier.isiWOS:000168307900007-
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridCurtis, D=14633020700en_US
dc.identifier.scopusauthoridNorth, BV=7005058731en_US
dc.identifier.scopusauthoridSham, PC=34573429300en_US

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