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Article: Extension of conditional model-free likelihood-based linkage analysis to additive and other models

TitleExtension of conditional model-free likelihood-based linkage analysis to additive and other models
Authors
Issue Date2002
PublisherBlackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/AHG
Citation
Annals Of Human Genetics, 2002, v. 66 n. 2, p. 157-167 How to Cite?
AbstractWe have previously described extending our method of 'model-free' linkage analysis, implemented in the MFLINK program, in order to deal with liability classes. This allows a new form of conditional two-locus linkage analysis, meaning that the genotypes of a known risk locus can be used to define liability classes so that their effects can be incorporated in tests for linkage at additional loci. In this method, relationships between transmission models for different liability classes were constrained so that there was a constant multiplicative effect on penetrance values. Here we present further extensions to the method to allow for different relationships. In particular, rather than only having a multiplicative effect on risk of affection we now allow specification of a multiplicative effect on risk of non-affection, or a combination of both relationships, across liability classes. We now also allow specification of an additive effect on penetrance. By way of example, we apply these methods to genome scan data for Alzheimer's disease using apolipoprotein E genotype to define liability classes. We show that, although in general the different methods produce results which tend to be quite highly correlated, certain markers can produce quite different results according to the method applied and that these could well lead to differences of interpretation. Without knowing a priori which relationship is likely to be most appropriate to describe the overall combined effect of the two loci one might be obliged to apply a number of different methods. This in turn may lead to the familiar difficulties associated with multiple testing. Nevertheless, the new method allows researchers greater flexibility in analysing linkage data for disease in which one or more risk polymorphisms have already been identified.
Persistent Identifierhttp://hdl.handle.net/10722/175862
ISSN
2015 Impact Factor: 1.889
2015 SCImago Journal Rankings: 1.191
References

 

DC FieldValueLanguage
dc.contributor.authorCurtis, Den_US
dc.contributor.authorNorth, BVen_US
dc.contributor.authorSham, PCen_US
dc.date.accessioned2012-11-26T09:01:53Z-
dc.date.available2012-11-26T09:01:53Z-
dc.date.issued2002en_US
dc.identifier.citationAnnals Of Human Genetics, 2002, v. 66 n. 2, p. 157-167en_US
dc.identifier.issn0003-4800en_US
dc.identifier.urihttp://hdl.handle.net/10722/175862-
dc.description.abstractWe have previously described extending our method of 'model-free' linkage analysis, implemented in the MFLINK program, in order to deal with liability classes. This allows a new form of conditional two-locus linkage analysis, meaning that the genotypes of a known risk locus can be used to define liability classes so that their effects can be incorporated in tests for linkage at additional loci. In this method, relationships between transmission models for different liability classes were constrained so that there was a constant multiplicative effect on penetrance values. Here we present further extensions to the method to allow for different relationships. In particular, rather than only having a multiplicative effect on risk of affection we now allow specification of a multiplicative effect on risk of non-affection, or a combination of both relationships, across liability classes. We now also allow specification of an additive effect on penetrance. By way of example, we apply these methods to genome scan data for Alzheimer's disease using apolipoprotein E genotype to define liability classes. We show that, although in general the different methods produce results which tend to be quite highly correlated, certain markers can produce quite different results according to the method applied and that these could well lead to differences of interpretation. Without knowing a priori which relationship is likely to be most appropriate to describe the overall combined effect of the two loci one might be obliged to apply a number of different methods. This in turn may lead to the familiar difficulties associated with multiple testing. Nevertheless, the new method allows researchers greater flexibility in analysing linkage data for disease in which one or more risk polymorphisms have already been identified.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.meshGenetic Linkageen_US
dc.subject.meshHumansen_US
dc.subject.meshLikelihood Functionsen_US
dc.subject.meshLod Scoreen_US
dc.subject.meshModels, Geneticen_US
dc.subject.meshPolymorphism, Geneticen_US
dc.subject.meshRisken_US
dc.titleExtension of conditional model-free likelihood-based linkage analysis to additive and other modelsen_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.doi10.1017/S0003480002001069en_US
dc.identifier.pmid12174219-
dc.identifier.scopuseid_2-s2.0-0036520778en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0036520778&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume66en_US
dc.identifier.issue2en_US
dc.identifier.spage157en_US
dc.identifier.epage167en_US
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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