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- Publisher Website: 10.1159/000085222
- Scopus: eid_2-s2.0-21044450087
- PMID: 15838177
- WOS: WOS:000229033000002
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Article: Application of logistic regression to case-control association studies involving two causative loci
Title | Application of logistic regression to case-control association studies involving two causative loci |
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Authors | |
Keywords | Association Case-control Logistic regression |
Issue Date | 2005 |
Publisher | S Karger AG. The Journal's web site is located at http://www.karger.com/HHE |
Citation | Human Heredity, 2005, v. 59 n. 2, p. 79-87 How to Cite? |
Abstract | Models in which two susceptibility loci jointly influence the risk of developing disease can be explored using logistic regression analysis. Comparison of likelihoods of models incorporating different sets of disease model parameters allows inferences to be drawn regarding the nature of the joint effect of the loci.We have simulated case-control samples generated assuming different two-locus models and then analysed them using logistic regression. We show that this method is practicable and that, for the models we have used, it can be expected to allow useful inferences to be drawn from sample sizes consisting of hundreds of subjects. Interactions between loci can be explored, but interactive effects do not exactly correspond with classical definitions of epistasis. We have particularly examined the issue of the extent to which it is helpful to utilise information from a previously identified locus when investigating a second, unknown locus. We show that for some models conditional analysis can have substantially greater power while for others unconditional analysis can be more powerful. Hence we conclude that in general both conditional and unconditional analyses should be performed when searching for additional loci. Copyright © 2005 S. Karger AG. |
Persistent Identifier | http://hdl.handle.net/10722/175931 |
ISSN | 2023 Impact Factor: 1.1 2023 SCImago Journal Rankings: 0.483 |
ISI Accession Number ID | |
References |
DC Field | Value | Language |
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dc.contributor.author | North, BV | en_US |
dc.contributor.author | Curtis, D | en_US |
dc.contributor.author | Sham, PC | en_US |
dc.date.accessioned | 2012-11-26T09:02:36Z | - |
dc.date.available | 2012-11-26T09:02:36Z | - |
dc.date.issued | 2005 | en_US |
dc.identifier.citation | Human Heredity, 2005, v. 59 n. 2, p. 79-87 | en_US |
dc.identifier.issn | 0001-5652 | en_US |
dc.identifier.uri | http://hdl.handle.net/10722/175931 | - |
dc.description.abstract | Models in which two susceptibility loci jointly influence the risk of developing disease can be explored using logistic regression analysis. Comparison of likelihoods of models incorporating different sets of disease model parameters allows inferences to be drawn regarding the nature of the joint effect of the loci.We have simulated case-control samples generated assuming different two-locus models and then analysed them using logistic regression. We show that this method is practicable and that, for the models we have used, it can be expected to allow useful inferences to be drawn from sample sizes consisting of hundreds of subjects. Interactions between loci can be explored, but interactive effects do not exactly correspond with classical definitions of epistasis. We have particularly examined the issue of the extent to which it is helpful to utilise information from a previously identified locus when investigating a second, unknown locus. We show that for some models conditional analysis can have substantially greater power while for others unconditional analysis can be more powerful. Hence we conclude that in general both conditional and unconditional analyses should be performed when searching for additional loci. Copyright © 2005 S. Karger AG. | en_US |
dc.language | eng | en_US |
dc.publisher | S Karger AG. The Journal's web site is located at http://www.karger.com/HHE | en_US |
dc.relation.ispartof | Human Heredity | en_US |
dc.subject | Association | - |
dc.subject | Case-control | - |
dc.subject | Logistic regression | - |
dc.subject.mesh | Case-Control Studies | en_US |
dc.subject.mesh | Genetic Predisposition To Disease | en_US |
dc.subject.mesh | Humans | en_US |
dc.subject.mesh | Likelihood Functions | en_US |
dc.subject.mesh | Logistic Models | en_US |
dc.subject.mesh | Models, Genetic | en_US |
dc.subject.mesh | Sample Size | en_US |
dc.subject.mesh | Software | en_US |
dc.title | Application of logistic regression to case-control association studies involving two causative loci | en_US |
dc.type | Article | en_US |
dc.identifier.email | Sham, PC: pcsham@hku.hk | en_US |
dc.identifier.authority | Sham, PC=rp00459 | en_US |
dc.description.nature | link_to_subscribed_fulltext | en_US |
dc.identifier.doi | 10.1159/000085222 | en_US |
dc.identifier.pmid | 15838177 | - |
dc.identifier.scopus | eid_2-s2.0-21044450087 | en_US |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-21044450087&selection=ref&src=s&origin=recordpage | en_US |
dc.identifier.volume | 59 | en_US |
dc.identifier.issue | 2 | en_US |
dc.identifier.spage | 79 | en_US |
dc.identifier.epage | 87 | en_US |
dc.identifier.isi | WOS:000229033000002 | - |
dc.publisher.place | Switzerland | en_US |
dc.identifier.scopusauthorid | North, BV=7005058731 | en_US |
dc.identifier.scopusauthorid | Curtis, D=14633020700 | en_US |
dc.identifier.scopusauthorid | Sham, PC=34573429300 | en_US |
dc.identifier.issnl | 0001-5652 | - |