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Article: Monte Carlo tests for associations between disease and alleles at highly polymorphic loci

TitleMonte Carlo tests for associations between disease and alleles at highly polymorphic loci
Authors
Issue Date1995
PublisherBlackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/AHG
Citation
Annals Of Human Genetics, 1995, v. 59 n. 1, p. 97-105 How to Cite?
AbstractIn an association analysis comparing cases and controls with respect to allele frequencies at a highly polymorphic locus, a potential problem is that the conventional chi-squared test may not be valid for a large, sparse contingency table. However, reliance on statistics with known asymptotic distribution is now unnecessary, as Monte Carlo simulations can be performed to estimate the significance level of any test statistic. We have implemented a Monte Carlo method for four 'chi-squared' test statistics, three of which involved combination of alleles, and evaluated their performance on a real data set. Combining rare alleles to avoid small expected cell counts, and considering each allele in turn against the rest, reduced the power to detect a genuine association when the number of alleles was very large. We should either not combine alleles at all, or combine them in such a way that preserves the evidence for an association.
Persistent Identifierhttp://hdl.handle.net/10722/175718
ISSN
2015 Impact Factor: 1.889
2015 SCImago Journal Rankings: 1.191
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorSham, PCen_US
dc.contributor.authorCurtis, Den_US
dc.date.accessioned2012-11-26T09:00:43Z-
dc.date.available2012-11-26T09:00:43Z-
dc.date.issued1995en_US
dc.identifier.citationAnnals Of Human Genetics, 1995, v. 59 n. 1, p. 97-105en_US
dc.identifier.issn0003-4800en_US
dc.identifier.urihttp://hdl.handle.net/10722/175718-
dc.description.abstractIn an association analysis comparing cases and controls with respect to allele frequencies at a highly polymorphic locus, a potential problem is that the conventional chi-squared test may not be valid for a large, sparse contingency table. However, reliance on statistics with known asymptotic distribution is now unnecessary, as Monte Carlo simulations can be performed to estimate the significance level of any test statistic. We have implemented a Monte Carlo method for four 'chi-squared' test statistics, three of which involved combination of alleles, and evaluated their performance on a real data set. Combining rare alleles to avoid small expected cell counts, and considering each allele in turn against the rest, reduced the power to detect a genuine association when the number of alleles was very large. We should either not combine alleles at all, or combine them in such a way that preserves the evidence for an association.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.meshCase-Control Studiesen_US
dc.subject.meshChi-Square Distributionen_US
dc.subject.meshGenetic Markersen_US
dc.subject.meshGenetic Predisposition To Diseaseen_US
dc.subject.meshHaplotypes - Geneticsen_US
dc.subject.meshHumansen_US
dc.subject.meshMonte Carlo Methoden_US
dc.subject.meshPolymorphism, Geneticen_US
dc.titleMonte Carlo tests for associations between disease and alleles at highly polymorphic locien_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.doi10.1111/j.1469-1809.1995.tb01608.x-
dc.identifier.pmid7762987-
dc.identifier.scopuseid_2-s2.0-0028909113en_US
dc.identifier.volume59en_US
dc.identifier.issue1en_US
dc.identifier.spage97en_US
dc.identifier.epage105en_US
dc.identifier.isiWOS:A1995QG05600008-
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridSham, PC=34573429300en_US
dc.identifier.scopusauthoridCurtis, D=14633020700en_US

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