Literature DB >> 21778736

Testing gene-gene interactions in the case-parents design.

Zhaoxia Yu1.   

Abstract

The case-parents design has been widely used to detect genetic associations as it can prevent spurious association that could occur in population-based designs. When examining the effect of an individual genetic locus on a disease, logistic regressions developed by conditioning on parental genotypes provide complete protection from spurious association caused by population stratification. However, when testing gene-gene interactions, it is unknown whether conditional logistic regressions are still robust. Here we evaluate the robustness and efficiency of several gene-gene interaction tests that are derived from conditional logistic regressions. We found that in the presence of SNP genotype correlation due to population stratification or linkage disequilibrium, tests with incorrectly specified main-genetic-effect models can lead to inflated type I error rates. We also found that a test with fully flexible main genetic effects always maintains correct test size and its robustness can be achieved with negligible sacrifice of its power. When testing gene-gene interactions is the focus, the test allowing fully flexible main effects is recommended to be used.
Copyright © 2011 S. Karger AG, Basel.

Entities:  

Mesh:

Year:  2011        PMID: 21778736      PMCID: PMC3153343          DOI: 10.1159/000327355

Source DB:  PubMed          Journal:  Hum Hered        ISSN: 0001-5652            Impact factor:   0.444


  22 in total

1.  The use of case-parent triads to study joint effects of genotype and exposure.

Authors:  D M Umbach; C R Weinberg
Journal:  Am J Hum Genet       Date:  2000-01       Impact factor: 11.025

2.  A unified stepwise regression procedure for evaluating the relative effects of polymorphisms within a gene using case/control or family data: application to HLA in type 1 diabetes.

Authors:  Heather J Cordell; David G Clayton
Journal:  Am J Hum Genet       Date:  2001-11-21       Impact factor: 11.025

3.  Testing haplotype-environment interactions using case-parent triads.

Authors:  Min Shi; David M Umbach; Clarice R Weinberg
Journal:  Hum Hered       Date:  2010-04-23       Impact factor: 0.444

4.  Tests for compositional epistasis under single interaction-parameter models.

Authors:  Tyler J VanderWeele; Nan M Laird
Journal:  Ann Hum Genet       Date:  2010-08-20       Impact factor: 1.670

5.  Testing and estimating gene-environment interactions in family-based association studies.

Authors:  Stijn Vansteelandt; Dawn L Demeo; Jessica Lasky-Su; Jordan W Smoller; Amy J Murphy; Matt McQueen; Kady Schneiter; Juan C Celedon; Scott T Weiss; Edwin K Silverman; Christoph Lange
Journal:  Biometrics       Date:  2007-10-25       Impact factor: 2.571

6.  Exploiting gene-environment independence for analysis of case-control studies: an empirical Bayes-type shrinkage estimator to trade-off between bias and efficiency.

Authors:  Bhramar Mukherjee; Nilanjan Chatterjee
Journal:  Biometrics       Date:  2007-12-20       Impact factor: 2.571

7.  Detecting gene-environment interactions using a combined case-only and case-control approach.

Authors:  Dalin Li; David V Conti
Journal:  Am J Epidemiol       Date:  2008-12-13       Impact factor: 4.897

8.  A doubly robust test for gene-environment interaction in family-based studies of affected offspring.

Authors:  Beatrijs Moerkerke; Stijn Vansteelandt; Christoph Lange
Journal:  Biostatistics       Date:  2010-02-12       Impact factor: 5.899

9.  On the robustness of tests of genetic associations incorporating gene-environment interaction when the environmental exposure is misspecified.

Authors:  Eric J Tchetgen Tchetgen; Peter Kraft
Journal:  Epidemiology       Date:  2011-03       Impact factor: 4.822

10.  Gene-environment interaction tests for dichotomous traits in trios and sibships.

Authors:  Thomas J Hoffmann; Christoph Lange; Stijn Vansteelandt; Nan M Laird
Journal:  Genet Epidemiol       Date:  2009-12       Impact factor: 2.135

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  1 in total

1.  Genome-Wide Analysis of Gene-Gene and Gene-Environment Interactions Using Closed-Form Wald Tests.

Authors:  Zhaoxia Yu; Michael Demetriou; Daniel L Gillen
Journal:  Genet Epidemiol       Date:  2015-06-10       Impact factor: 2.135

  1 in total

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