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Vol. 70, No. 4, 2010
Issue release date: February 2011
Section title: Original Paper
Free Access
Hum Hered 2010;70:292–300
(DOI:10.1159/000323318)

Genome-Wide Meta-Analysis of Joint Tests for Genetic and Gene-Environment Interaction Effects

Aschard H.a · Hancock D.B.b · London S.J.b · Kraft P.a
aProgram in Molecular and Genetic Epidemiology, Harvard School of Public Health, Boston, Mass., bEpidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, N.C., USA
email Corresponding Author

Abstract

Background: There is growing interest in the study of gene-environment interactions in the context of genome-wide association studies (GWASs). These studies will likely require meta-analytic approaches to have sufficient power. Methods: We describe an approach for meta-analysis of a joint test for genetic main effects and gene-environment interaction effects. Using simulation studies based on a meta-analysis of five studies (total n = 10,161), we compare the power of this test to the meta-analysis of marginal test of genetic association and the meta-analysis of standard 1 d.f. interaction tests across a broad range of genetic main effects and gene-environment interaction effects. Results: We show that the joint meta-analysis is valid and can be more powerful than classical meta-analytic approaches, with a potential gain of power over 50% compared to the marginal test. The standard interaction test had less than 1% power in almost all the situations we considered. We also show that regardless of the test used, sample sizes far exceeding those of a typical individual GWAS will be needed to reliably detect genes with subtle gene-environment interaction patterns. Conclusion: The joint meta-analysis is an attractive approach to discover markers which may have been missed by initial GWASs focusing on marginal marker-trait associations.

© 2011 S. Karger AG, Basel


  

Key Words

  • Gene-environment interaction
  • Genome-wide scan
  • Meta-analysis
  • Case-control association analysis
  • Complex disease

References

  1. Khoury MJ, Wacholder S: Invited commentary: from genome-wide association studies to gene-environment-wide interaction studies – challenges and opportunities. Am J Epidemiol 2009;169:227–230, discussion 234–235.
  2. Chatterjee N, Kalaylioglu Z, Carroll RJ: Exploiting gene-environment independence in family-based case-control studies: increased power for detecting associations, interactions and joint effects. Genet Epidemiol 2005;28:138–156.
  3. Murcray CE, Lewinger JP, Gauderman WJ: Gene-environment interaction in genome-wide association studies. Am J Epidemiol 2009;169:219–226.
  4. Umbach DM, Weinberg CR: Designing and analysing case-control studies to exploit independence of genotype and exposure. Stat Med 1997;16:1731–1743.
  5. Kraft P, Yen YC, Stram DO, Morrison J, Gauderman WJ: Exploiting gene-environment interaction to detect genetic associations. Hum Hered 2007;63:111–119.
  6. de Bakker PI, Ferreira MA, Jia X, Neale BM, Raychaudhuri S, Voight BF: Practical aspects of imputation-driven meta-analysis of genome-wide association studies. Hum Mol Genet 2008;17:R122–R128.
  7. Zeggini E, Ioannidis JP: Meta-analysis in genome-wide association studies. Pharmacogenomics 2009;10:191–201.
  8. Cornelis MC, Tchetgen Tchetgen EJ, Liang L, Qi L, Chatterjee N, Hu FB, Kraft P: Gene-environment interactions in genome-wide association studies: a comparative study of tests applied to empirical studies of type 2 diabetes. Under review.
  9. Tchetgen Tchetgen EJ, Kraft P: On the robustness of tests of genetic associations incorporating gene-environment interaction when the environmental exposure is mis-specified. Epidemiology, in press.
  10. Aulchenko YS, Struchalin MV, van Duijn CM: ProbABEL package for genome-wide association analysis of imputed data. BMC Bioinformatics 2010;11:134.
  11. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, Sham PC: PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007;81:559–575.
  12. van Houwelingen HC, Arends LR, Stijnen T: Advanced methods in meta-analysis: multivariate approach and meta-regression. Stat Med 2002;21:589–624.
  13. Hunter DJ, Kraft P, Jacobs KB, Cox DG, Yeager M, Hankinson SE, Wacholder S, Wang Z, Welch R, Hutchinson A, Wang J, Yu K, Chatterjee N, Orr N, Willett WC, Colditz GA, Ziegler RG, Berg CD, Buys SS, McCarty CA, Feigelson HS, Calle EE, Thun MJ, Hayes RB, Tucker M, Gerhard DS, Fraumeni JF Jr, Hoover RN, Thomas G, Chanock SJ: A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer. Nat Genet 2007;39:870–874.
  14. Qi L, Cornelis MC, Kraft P, Stanya KJ, Linda Kao WH, Pankow JS, Dupuis J, Florez JC, Fox CS, Pare G, Sun Q, Girman CJ, Laurie CC, Mirel DB, Manolio TA, Chasman DI, Boerwinkle E, Ridker PM, Hunter DJ, Meigs JB, Lee CH, van Dam RM, Hu FB: Genetic variants at 2q24 are associated with susceptibility to type 2 diabetes. Hum Mol Genet 2010;19:2706–2715.
  15. Li Y, Abecasis GR: Mach 1.0: Rapid haplotype reconstruction and missing genotype inference. Am J Hum Genet 2006;S79:2290.
  16. Lindstrom S, Yen YC, Spiegelman D, Kraft P: The impact of gene-environment dependence and misclassification in genetic association studies incorporating gene-environment interactions. Hum Hered 2009;68:171–181.
  17. Weiss NS: Subgroup-specific associations in the face of overall null results: should we rush in or fear to tread? Cancer Epidemiol Biomarkers Prev 2008;17:1297–1299.
  18. Greenland S: Interactions in epidemiology: relevance, identification, and estimation. Epidemiology 2009;20:14–17.
  19. Kraft P, Hunter DJ: The challenge of assessing gene-environment and gene-gene interactions; in Khouy MJ, Bedrosian SR, Gwinn M, et al (eds): Human Genome Epidemiology. New York, Oxford University Press, 2010, pp 165–187.
  20. Siemiatycki J, Thomas DC: Biological models and statistical interactions: an example from multistage carcinogenesis. Int J Epidemiol 1981;10:383–387.
  21. Manning AK, Lavalley M, Liu CT, Rice K, An P, Liu Y, Miljkovic I, Rasmussen-Torvik L, Harris TB, Province MA, Borecki IB, Florez JC, Meigs JB, Cupples LA, Dupuis J: Meta-analysis of gene-environment interaction: joint estimation of SNP and SNP x environment regression coefficients. Genet Epidemiol 2011;35:11–18.

  

Author Contacts

Hugues Aschard
Harvard School of Public Health, Department of Epidemiology
Building 2, Room 205, 665 Huntington Avenue
Boston, MA 02115 (USA)
Tel. +1 617 432 5900, Fax +1 617 432 1722, E-Mail haschard@hsph.harvard.edu

  

Article Information

Received: June 16, 2010
Accepted after revision: December 2, 2010
Published online: February 3, 2011
Number of Print Pages : 9
Number of Figures : 3, Number of Tables : 2, Number of References : 21

  

Publication Details

Human Heredity (International Journal of Human and Medical Genetics)

Vol. 70, No. 4, Year 2010 (Cover Date: February 2011)

Journal Editor: Devoto M. (Philadelphia, Pa./Rome)
ISSN: 0001-5652 (Print), eISSN: 1423-0062 (Online)

For additional information: http://www.karger.com/HHE


Copyright / Drug Dosage / Disclaimer

Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher or, in the case of photocopying, direct payment of a specified fee to the Copyright Clearance Center.
Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in goverment regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.
Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.

Abstract

Background: There is growing interest in the study of gene-environment interactions in the context of genome-wide association studies (GWASs). These studies will likely require meta-analytic approaches to have sufficient power. Methods: We describe an approach for meta-analysis of a joint test for genetic main effects and gene-environment interaction effects. Using simulation studies based on a meta-analysis of five studies (total n = 10,161), we compare the power of this test to the meta-analysis of marginal test of genetic association and the meta-analysis of standard 1 d.f. interaction tests across a broad range of genetic main effects and gene-environment interaction effects. Results: We show that the joint meta-analysis is valid and can be more powerful than classical meta-analytic approaches, with a potential gain of power over 50% compared to the marginal test. The standard interaction test had less than 1% power in almost all the situations we considered. We also show that regardless of the test used, sample sizes far exceeding those of a typical individual GWAS will be needed to reliably detect genes with subtle gene-environment interaction patterns. Conclusion: The joint meta-analysis is an attractive approach to discover markers which may have been missed by initial GWASs focusing on marginal marker-trait associations.

© 2011 S. Karger AG, Basel


  

Author Contacts

Hugues Aschard
Harvard School of Public Health, Department of Epidemiology
Building 2, Room 205, 665 Huntington Avenue
Boston, MA 02115 (USA)
Tel. +1 617 432 5900, Fax +1 617 432 1722, E-Mail haschard@hsph.harvard.edu

  

Article Information

Received: June 16, 2010
Accepted after revision: December 2, 2010
Published online: February 3, 2011
Number of Print Pages : 9
Number of Figures : 3, Number of Tables : 2, Number of References : 21

  

Publication Details

Human Heredity (International Journal of Human and Medical Genetics)

Vol. 70, No. 4, Year 2010 (Cover Date: February 2011)

Journal Editor: Devoto M. (Philadelphia, Pa./Rome)
ISSN: 0001-5652 (Print), eISSN: 1423-0062 (Online)

For additional information: http://www.karger.com/HHE


Article / Publication Details

First-Page Preview
Abstract of Original Paper

Received: 6/18/2010
Accepted: 12/2/2010
Published online: 2/3/2011
Issue release date: February 2011

Number of Print Pages: 9
Number of Figures: 3
Number of Tables: 2

ISSN: 0001-5652 (Print)
eISSN: 1423-0062 (Online)

For additional information: http://www.karger.com/HHE


Copyright / Drug Dosage

Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher or, in the case of photocopying, direct payment of a specified fee to the Copyright Clearance Center.
Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in goverment regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.
Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.

References

  1. Khoury MJ, Wacholder S: Invited commentary: from genome-wide association studies to gene-environment-wide interaction studies – challenges and opportunities. Am J Epidemiol 2009;169:227–230, discussion 234–235.
  2. Chatterjee N, Kalaylioglu Z, Carroll RJ: Exploiting gene-environment independence in family-based case-control studies: increased power for detecting associations, interactions and joint effects. Genet Epidemiol 2005;28:138–156.
  3. Murcray CE, Lewinger JP, Gauderman WJ: Gene-environment interaction in genome-wide association studies. Am J Epidemiol 2009;169:219–226.
  4. Umbach DM, Weinberg CR: Designing and analysing case-control studies to exploit independence of genotype and exposure. Stat Med 1997;16:1731–1743.
  5. Kraft P, Yen YC, Stram DO, Morrison J, Gauderman WJ: Exploiting gene-environment interaction to detect genetic associations. Hum Hered 2007;63:111–119.
  6. de Bakker PI, Ferreira MA, Jia X, Neale BM, Raychaudhuri S, Voight BF: Practical aspects of imputation-driven meta-analysis of genome-wide association studies. Hum Mol Genet 2008;17:R122–R128.
  7. Zeggini E, Ioannidis JP: Meta-analysis in genome-wide association studies. Pharmacogenomics 2009;10:191–201.
  8. Cornelis MC, Tchetgen Tchetgen EJ, Liang L, Qi L, Chatterjee N, Hu FB, Kraft P: Gene-environment interactions in genome-wide association studies: a comparative study of tests applied to empirical studies of type 2 diabetes. Under review.
  9. Tchetgen Tchetgen EJ, Kraft P: On the robustness of tests of genetic associations incorporating gene-environment interaction when the environmental exposure is mis-specified. Epidemiology, in press.
  10. Aulchenko YS, Struchalin MV, van Duijn CM: ProbABEL package for genome-wide association analysis of imputed data. BMC Bioinformatics 2010;11:134.
  11. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, Sham PC: PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007;81:559–575.
  12. van Houwelingen HC, Arends LR, Stijnen T: Advanced methods in meta-analysis: multivariate approach and meta-regression. Stat Med 2002;21:589–624.
  13. Hunter DJ, Kraft P, Jacobs KB, Cox DG, Yeager M, Hankinson SE, Wacholder S, Wang Z, Welch R, Hutchinson A, Wang J, Yu K, Chatterjee N, Orr N, Willett WC, Colditz GA, Ziegler RG, Berg CD, Buys SS, McCarty CA, Feigelson HS, Calle EE, Thun MJ, Hayes RB, Tucker M, Gerhard DS, Fraumeni JF Jr, Hoover RN, Thomas G, Chanock SJ: A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer. Nat Genet 2007;39:870–874.
  14. Qi L, Cornelis MC, Kraft P, Stanya KJ, Linda Kao WH, Pankow JS, Dupuis J, Florez JC, Fox CS, Pare G, Sun Q, Girman CJ, Laurie CC, Mirel DB, Manolio TA, Chasman DI, Boerwinkle E, Ridker PM, Hunter DJ, Meigs JB, Lee CH, van Dam RM, Hu FB: Genetic variants at 2q24 are associated with susceptibility to type 2 diabetes. Hum Mol Genet 2010;19:2706–2715.
  15. Li Y, Abecasis GR: Mach 1.0: Rapid haplotype reconstruction and missing genotype inference. Am J Hum Genet 2006;S79:2290.
  16. Lindstrom S, Yen YC, Spiegelman D, Kraft P: The impact of gene-environment dependence and misclassification in genetic association studies incorporating gene-environment interactions. Hum Hered 2009;68:171–181.
  17. Weiss NS: Subgroup-specific associations in the face of overall null results: should we rush in or fear to tread? Cancer Epidemiol Biomarkers Prev 2008;17:1297–1299.
  18. Greenland S: Interactions in epidemiology: relevance, identification, and estimation. Epidemiology 2009;20:14–17.
  19. Kraft P, Hunter DJ: The challenge of assessing gene-environment and gene-gene interactions; in Khouy MJ, Bedrosian SR, Gwinn M, et al (eds): Human Genome Epidemiology. New York, Oxford University Press, 2010, pp 165–187.
  20. Siemiatycki J, Thomas DC: Biological models and statistical interactions: an example from multistage carcinogenesis. Int J Epidemiol 1981;10:383–387.
  21. Manning AK, Lavalley M, Liu CT, Rice K, An P, Liu Y, Miljkovic I, Rasmussen-Torvik L, Harris TB, Province MA, Borecki IB, Florez JC, Meigs JB, Cupples LA, Dupuis J: Meta-analysis of gene-environment interaction: joint estimation of SNP and SNP x environment regression coefficients. Genet Epidemiol 2011;35:11–18.