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Article: Robust tests for gene–environment interaction in case-control and case-only designs

TitleRobust tests for gene–environment interaction in case-control and case-only designs
Authors
KeywordsCase-control design
Case-only design
Genetic modelgene–environment interaction
Robust test
Issue Date2019
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/csda
Citation
Computational Statistics & Data Analysis, 2019, v. 129, p. 79-92 How to Cite?
AbstractThe case-control and case-only designs are commonly used to detect the gene-environment (G-E) interaction. In principle, the tests based on these two designs require a pre-specified genetic model to achieve an expected power of detecting the G-E interaction. Unfortunately, for most complex diseases the underlying genetic models are unknown. It is well known that mis-specification of the genetic model can result in a substantial loss of power in the detection of the main genetic effect. However, limited effort has been dedicated to the study of G-E interaction. This issue has been investigated in this article with a conclusion that the genetic model mis-specification can not only undermine the power of detecting G-E interaction in both case-control and case-only designs but also distort the type I error rate in case-control design. To tackle this problem, a class of robust tests, namely MAX3, have been proposed for both the case-control and case-only designs. The proposed tests can well control the type I error rate and yield satisfactory power even when the genetic model is mis-specified. The asymptotic distribution and the p-value formula for MAX3 have also been derived. Comprehensive simulation studies and a real data application on the genome-wide association study (GWAS) have been conducted using these analytical tools and the results demonstrate desirable operating characteristics of the proposed robust tests. (C) 2018 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/272974
ISSN
2019 Impact Factor: 1.186
2015 SCImago Journal Rankings: 1.283
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZang, Y-
dc.contributor.authorFung, TWK-
dc.contributor.authorCao, S-
dc.contributor.authorNg, HK-
dc.contributor.authorZhang, C-
dc.date.accessioned2019-08-06T09:20:12Z-
dc.date.available2019-08-06T09:20:12Z-
dc.date.issued2019-
dc.identifier.citationComputational Statistics & Data Analysis, 2019, v. 129, p. 79-92-
dc.identifier.issn0167-9473-
dc.identifier.urihttp://hdl.handle.net/10722/272974-
dc.description.abstractThe case-control and case-only designs are commonly used to detect the gene-environment (G-E) interaction. In principle, the tests based on these two designs require a pre-specified genetic model to achieve an expected power of detecting the G-E interaction. Unfortunately, for most complex diseases the underlying genetic models are unknown. It is well known that mis-specification of the genetic model can result in a substantial loss of power in the detection of the main genetic effect. However, limited effort has been dedicated to the study of G-E interaction. This issue has been investigated in this article with a conclusion that the genetic model mis-specification can not only undermine the power of detecting G-E interaction in both case-control and case-only designs but also distort the type I error rate in case-control design. To tackle this problem, a class of robust tests, namely MAX3, have been proposed for both the case-control and case-only designs. The proposed tests can well control the type I error rate and yield satisfactory power even when the genetic model is mis-specified. The asymptotic distribution and the p-value formula for MAX3 have also been derived. Comprehensive simulation studies and a real data application on the genome-wide association study (GWAS) have been conducted using these analytical tools and the results demonstrate desirable operating characteristics of the proposed robust tests. (C) 2018 Elsevier B.V. All rights reserved.-
dc.languageeng-
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/csda-
dc.relation.ispartofComputational Statistics & Data Analysis-
dc.subjectCase-control design-
dc.subjectCase-only design-
dc.subjectGenetic modelgene–environment interaction-
dc.subjectRobust test-
dc.titleRobust tests for gene–environment interaction in case-control and case-only designs-
dc.typeArticle-
dc.identifier.emailFung, TWK: wingfung@hkucc.hku.hk-
dc.identifier.authorityFung, TWK=rp00696-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.csda.2018.08.014-
dc.identifier.scopuseid_2-s2.0-85053005960-
dc.identifier.hkuros300030-
dc.identifier.volume129-
dc.identifier.spage79-
dc.identifier.epage92-
dc.identifier.isiWOS:000447571400006-
dc.publisher.placeNetherlands-

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