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Article: Single marker association analysis for unrelated samples

TitleSingle marker association analysis for unrelated samples
Authors
Zheng, GXu, JYuan, AGastwirth, JL
KeywordsAdditive
Anova
Association
Binary Trait
Case-Control Design
Dominant
Genetic Model
Genotype Relative Risks
Max3
Mode of Inheritance
Penetrance
Quantitative Trait
Rassoc
Recessive
Robustness
Issue Date2012
PublisherHumana Press, Inc. The Journal's web site is located at http://link.springer.com/bookseries/7651
Citation
Methods in Molecular Biology, 2012, v. 850, p. 347-358 How to Cite?
DOI: http://dx.doi.org/10.1007/978-1-61779-555-8_18
AbstractMethods for single marker association analysis are presented for binary and quantitative traits. For a binary trait, we focus on the analysis of retrospective case-control data using Pearson's chi-squared test, the trend test, and a robust test. For a continuous trait, typical methods are based on a linear regression model or the analysis of variance. We illustrate how these tests can be applied using a public available R package "Rassoc" and some existing R functions. Guidelines for choosing these test statistics are provided. © 2012 Springer Science+Business Media, LLC.
Persistent Identifierhttp://hdl.handle.net/10722/221675
ISSN
1064-3745
2020 SCImago Journal Rankings: 0.711
PubMed Central ID
PMC3652252

 

DC FieldValueLanguage
dc.contributor.authorZheng, G-
dc.contributor.authorXu, J-
dc.contributor.authorYuan, A-
dc.contributor.authorGastwirth, JL-
dc.date.accessioned2015-12-04T15:29:00Z-
dc.date.available2015-12-04T15:29:00Z-
dc.date.issued2012-
dc.identifier.citationMethods in Molecular Biology, 2012, v. 850, p. 347-358-
dc.identifier.issn1064-3745-
dc.identifier.urihttp://hdl.handle.net/10722/221675-
dc.description.abstractMethods for single marker association analysis are presented for binary and quantitative traits. For a binary trait, we focus on the analysis of retrospective case-control data using Pearson's chi-squared test, the trend test, and a robust test. For a continuous trait, typical methods are based on a linear regression model or the analysis of variance. We illustrate how these tests can be applied using a public available R package "Rassoc" and some existing R functions. Guidelines for choosing these test statistics are provided. © 2012 Springer Science+Business Media, LLC.-
dc.languageeng-
dc.publisherHumana Press, Inc. The Journal's web site is located at http://link.springer.com/bookseries/7651-
dc.relation.ispartofMethods in Molecular Biology-
dc.subjectAdditive-
dc.subjectAnova-
dc.subjectAssociation-
dc.subjectBinary Trait-
dc.subjectCase-Control Design-
dc.subjectDominant-
dc.subjectGenetic Model-
dc.subjectGenotype Relative Risks-
dc.subjectMax3-
dc.subjectMode of Inheritance-
dc.subjectPenetrance-
dc.subjectQuantitative Trait-
dc.subjectRassoc-
dc.subjectRecessive-
dc.subjectRobustness-
dc.titleSingle marker association analysis for unrelated samples-
dc.typeArticle-
dc.identifier.emailXu, J: xujf@hku.hk-
dc.identifier.authorityXu, J=rp02086-
dc.identifier.doi10.1007/978-1-61779-555-8_18-
dc.identifier.pmid22307707-
dc.identifier.pmcidPMC3652252-
dc.identifier.scopuseid_2-s2.0-84863247440-
dc.identifier.volume850-
dc.identifier.spage347-
dc.identifier.epage358-
dc.identifier.issnl1064-3745-

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