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Table of Contents
Vol. 63, No. 2, 2007
Issue release date: February 2007
Section title: Original Paper
Hum Hered 2007;63:111–119
(DOI:10.1159/000099183)

Exploiting Gene-Environment Interaction to Detect Genetic Associations

Kraft P.a, b · Yen Y.-C.a · Stram D.O.c · Morrison J.c · Gauderman W.J.c
Departments of aEpidemiology and bBiostatistics, Harvard School of Public Health, Boston, Mass., cDepartment of Preventive Medicine, University of Southern California Keck School of Medicine, Los Angeles, Calif., USA
email Corresponding Author

Abstract

Complex disease by definition results from the interplay of genetic and environmental factors. However, it is currently unclear how gene-environment interaction can best be used to locate complex disease susceptibility loci, particularly in the context of studies where between 1,000 and 1,000,000 markers are scanned for association with disease. We present a joint test of marginal association and gene-environment interaction for case-control data. We compare the power and sample size requirements of this joint test to other analyses: the marginal test of genetic association, the standard test for gene-environment interaction based on logistic regression, and the case-only test for interaction that exploits gene-environment independence. Although for many penetrance models the joint test of genetic marginal effect and interaction is not the most powerful, it is nearly optimal across all penetrance models we considered. In particular, it generally has better power than the marginal test when the genetic effect is restricted to exposed subjects and much better power than the tests of gene-environment interaction when the genetic effect is not restricted to a particular exposure level. This makes the joint test an attractive tool for large-scale association scans where the true gene-environment interaction model is unknown.

© 2007 S. Karger AG, Basel


  

Key Words

  • Gene-environment interaction
  • Power and sample size calculations
  • Genome-wide association scans

References

  1. Haiman CA, Stram DO, Wilkens LR, Pike MC, Kolonel LN, Henderson BE, Le Marchand L: Ethnic and racial differences in the smoking-related risk of lung cancer. N Engl J Med 2006;354:333–342.
  2. Risch N: Dissecting racial and ethnic differences. N Engl J Med 2006;354:408–411.
  3. McConnell R, Berhane K, Yao L, Jerrett M, Lurmann F, Gilliland F, Kunzli N, Gauderman J, Avol E, Thomas D, Peters J: Traffic, susceptibility, and childhood asthma. Environ Health Perspect 2006;114:766–772.
  4. Han J, Hankinson SE, Colditz GA, Hunter DJ: Genetic variation in XRCC1, sun exposure, and risk of skin cancer. Br J Cancer 2004;91:1604–1609.
  5. Clayton D, McKeigue PM: Epidemiological methods for studying genes and environmental factors in complex diseases. Lancet 2001;358:1356–1360.
  6. Botto L, Khoury M: Facing the challenge of complex genotypes and gene-environment interaction: the basic epidemiologic units in case-control and case-only designs; in Khoury M, Little J, Burke W (eds): Human Genome Epidemiology: A Scientific Foundation for Using Genetic Information to Improve Health and Prevent Disease. Oxford, Oxford University Press, 2004.
  7. Cupples LA, Bailey J, Cartier KC, Falk CT, Liu KY, Ye Y, Yu R, Zhang H, Zhao H: Data mining. Genet Epidemiol 2005;29(suppl 1):S103–109.

    External Resources

  8. Hoh J, Ott J: Mathematical multi-locus approaches to localizing complex human trait genes. Nat Rev Genet 2003;4:701–709.
  9. Ritchie MD, Hahn LW, Roodi N, Bailey LR, Dupont WD, Parl FF, Moore JH: Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum Genet 2001;69:138–147.
  10. Kooperberg C, Ruczinski I: Identifying interacting SNPs using Monte Carlo logic regression. Genet Epidemiol 2005;28:157–170.
  11. Selinger-Leneman H, Genin E, Norris J, Khlat M: Does accounting for gene-environment (GxE) interaction increase the power to detect the effect of a gene in a multifactorial disease? Genet Epidemiol 2003;24:200–207.
  12. Millstein J, Conti DV, Gilliland FD, Gauderman WJ: A testing framework for identifying susceptibility genes in the presence of epistasis. Am J Hum Genet 2006;78:15–27.
  13. Pepe MS, Janes H, Longton G, Leisenring W, Newcomb P: Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. Am J Epidemiol 2004;159:882–890.
  14. Longmate J: Complexity and power in case-control association studies. Am J Hum Genet 2001;68:1229–1237.
  15. Piegorsch W, Weinberg C, Taylor J: Non-hierarchical logistic models and case-only designs for assessing susceptibility in population-based case-control studies. Stat Med 1994;13:153–162.
  16. Yang Q, Khoury MJ, Sun F, Flanders WD: Case-only design to measure gene-gene interaction. Epidemiology 1999;10:167–170.
  17. Yang Q, Khoury MJ, Flanders WD: Sample size requirements in case-only designs to detect gene-environment interaction. Am J Epidemiol 1997;146:713–720.
  18. Gauderman W: Sample size requirements for association studies of gene-gene interaction. Am J Epidemiol 2002;155:478–484.
  19. Thomas DC: Are we ready for genome-wide association studies? Cancer Epidemiol Biomarkers Prev 2006;15:595–598.
  20. Kraft P: Efficient two-stage genome-wide association designs based on false positive report probabilities. Pac Symp Biocomput 2006, pp 523–534.
  21. Wang H, Thomas DC, Pe’er I, Stram DO: Optimal two-stage genotyping designs for genome-wide association scans. Genet Epidemiol 2006;30:356–368.
  22. Umbach D, Weinberg C: Designing and analysing case-control studies to exploit independence of genotype and exposure. Stat Med 1997;16:1731–1743.
  23. Chatterjee N, Carroll R: Semiparametric maximum likelihood estimation exploiting gene-environment independence. Biometrika 2005;92:399–418.

    External Resources

  24. Liu X, Fallin MD, Kao WH: Genetic dissection methods: designs used for tests of gene-environment interaction. Curr Opin Genet Dev 2004;14:241–245.
  25. Chatterjee N, Kalaylioglu Z, Carroll R: Exploiting gene-environment independence in family-based case-control studies: Increased power for detecting associations, interactions and joint effects. Genet Epidemiol 2005.
  26. Langholz B, Rothman N, Wacholder S, Thomas D: Cohort studies for characterizing measured genes. Monogr Natl Cancer Inst 1999;26:39–42.
  27. Garcia-Closas M, Wacholder S, Caporaso N, Rothman N: Inference issues in cohort and case-control studies of genetic effects and gene-environment interactions; in Khoury M, Little J, Burke W (eds): Human Genome Epidemiology: A Scientific Foundation for Using Genetic Information to Improve Health and Prevent Disease. Oxford, Oxford University Press, 2004.
  28. Garcia-Closas M, Thompson WD, Robins JM: Differential misclassification and the assessment of gene-environment interactions in case-control studies. Am J Epidemiol 1998;147:426–433.
  29. Morimoto LM, White E, Newcomb PA: Selection bias in the assessment of gene-environment interaction in case-control studies. Am J Epidemiol 2003;158:259–263.
  30. Garcia-Closas M, Rothman N, Lubin J: Misclassification in case-control studies of gene-environment interactions: assessment of bias and sample size. Cancer Epidemiol Biomarkers Prev 1999;8:1043–1050.
  31. Thompson W: Effect modification and the limits of biological inference from epidemiologic data. J Clin Epidemiol 1991;44:221–232.
  32. Thomas D: Gene characterization studies: an overview. Monogr Natl Cancer Inst 1999;26:17–23.
  33. Gauderman WJ: Sample size calculations for matched case-control studies of gene-environment interaction. Stat Med 2002;21:35–50.

  

Author Contacts

Peter Kraft
Departments of Epidemiology and Biostatistics, Harvard School of Public Health
Building 2 Room 207, 665 Huntington Avenue
Boston, MA 02115 (USA)
Tel. +1 617 432 4271, Fax +1 617 432 1722, E-Mail pkraft@hsph.harvard.edu

  

Article Information

Published online: February 2, 2007
Number of Print Pages : 9
Number of Figures : 2, Number of Tables : 1, Number of References : 33

  

Publication Details

Human Heredity (International Journal of Human and Medical Genetics)

Vol. 63, No. 2, Year 2007 (Cover Date: February 2007)

Journal Editor: Devoto, M. (Philadelphia, Pa.)
ISSN: 0001–5652 (print), 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

Complex disease by definition results from the interplay of genetic and environmental factors. However, it is currently unclear how gene-environment interaction can best be used to locate complex disease susceptibility loci, particularly in the context of studies where between 1,000 and 1,000,000 markers are scanned for association with disease. We present a joint test of marginal association and gene-environment interaction for case-control data. We compare the power and sample size requirements of this joint test to other analyses: the marginal test of genetic association, the standard test for gene-environment interaction based on logistic regression, and the case-only test for interaction that exploits gene-environment independence. Although for many penetrance models the joint test of genetic marginal effect and interaction is not the most powerful, it is nearly optimal across all penetrance models we considered. In particular, it generally has better power than the marginal test when the genetic effect is restricted to exposed subjects and much better power than the tests of gene-environment interaction when the genetic effect is not restricted to a particular exposure level. This makes the joint test an attractive tool for large-scale association scans where the true gene-environment interaction model is unknown.

© 2007 S. Karger AG, Basel


  

Author Contacts

Peter Kraft
Departments of Epidemiology and Biostatistics, Harvard School of Public Health
Building 2 Room 207, 665 Huntington Avenue
Boston, MA 02115 (USA)
Tel. +1 617 432 4271, Fax +1 617 432 1722, E-Mail pkraft@hsph.harvard.edu

  

Article Information

Published online: February 2, 2007
Number of Print Pages : 9
Number of Figures : 2, Number of Tables : 1, Number of References : 33

  

Publication Details

Human Heredity (International Journal of Human and Medical Genetics)

Vol. 63, No. 2, Year 2007 (Cover Date: February 2007)

Journal Editor: Devoto, M. (Philadelphia, Pa.)
ISSN: 0001–5652 (print), 1423–0062 (Online)

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


Article / Publication Details

First-Page Preview
Abstract of Original Paper

Received: 7/31/2006
Accepted: 8/31/2006
Published online: 2/2/2007
Issue release date: February 2007

Number of Print Pages: 9
Number of Figures: 2
Number of Tables: 1

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. Haiman CA, Stram DO, Wilkens LR, Pike MC, Kolonel LN, Henderson BE, Le Marchand L: Ethnic and racial differences in the smoking-related risk of lung cancer. N Engl J Med 2006;354:333–342.
  2. Risch N: Dissecting racial and ethnic differences. N Engl J Med 2006;354:408–411.
  3. McConnell R, Berhane K, Yao L, Jerrett M, Lurmann F, Gilliland F, Kunzli N, Gauderman J, Avol E, Thomas D, Peters J: Traffic, susceptibility, and childhood asthma. Environ Health Perspect 2006;114:766–772.
  4. Han J, Hankinson SE, Colditz GA, Hunter DJ: Genetic variation in XRCC1, sun exposure, and risk of skin cancer. Br J Cancer 2004;91:1604–1609.
  5. Clayton D, McKeigue PM: Epidemiological methods for studying genes and environmental factors in complex diseases. Lancet 2001;358:1356–1360.
  6. Botto L, Khoury M: Facing the challenge of complex genotypes and gene-environment interaction: the basic epidemiologic units in case-control and case-only designs; in Khoury M, Little J, Burke W (eds): Human Genome Epidemiology: A Scientific Foundation for Using Genetic Information to Improve Health and Prevent Disease. Oxford, Oxford University Press, 2004.
  7. Cupples LA, Bailey J, Cartier KC, Falk CT, Liu KY, Ye Y, Yu R, Zhang H, Zhao H: Data mining. Genet Epidemiol 2005;29(suppl 1):S103–109.

    External Resources

  8. Hoh J, Ott J: Mathematical multi-locus approaches to localizing complex human trait genes. Nat Rev Genet 2003;4:701–709.
  9. Ritchie MD, Hahn LW, Roodi N, Bailey LR, Dupont WD, Parl FF, Moore JH: Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum Genet 2001;69:138–147.
  10. Kooperberg C, Ruczinski I: Identifying interacting SNPs using Monte Carlo logic regression. Genet Epidemiol 2005;28:157–170.
  11. Selinger-Leneman H, Genin E, Norris J, Khlat M: Does accounting for gene-environment (GxE) interaction increase the power to detect the effect of a gene in a multifactorial disease? Genet Epidemiol 2003;24:200–207.
  12. Millstein J, Conti DV, Gilliland FD, Gauderman WJ: A testing framework for identifying susceptibility genes in the presence of epistasis. Am J Hum Genet 2006;78:15–27.
  13. Pepe MS, Janes H, Longton G, Leisenring W, Newcomb P: Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. Am J Epidemiol 2004;159:882–890.
  14. Longmate J: Complexity and power in case-control association studies. Am J Hum Genet 2001;68:1229–1237.
  15. Piegorsch W, Weinberg C, Taylor J: Non-hierarchical logistic models and case-only designs for assessing susceptibility in population-based case-control studies. Stat Med 1994;13:153–162.
  16. Yang Q, Khoury MJ, Sun F, Flanders WD: Case-only design to measure gene-gene interaction. Epidemiology 1999;10:167–170.
  17. Yang Q, Khoury MJ, Flanders WD: Sample size requirements in case-only designs to detect gene-environment interaction. Am J Epidemiol 1997;146:713–720.
  18. Gauderman W: Sample size requirements for association studies of gene-gene interaction. Am J Epidemiol 2002;155:478–484.
  19. Thomas DC: Are we ready for genome-wide association studies? Cancer Epidemiol Biomarkers Prev 2006;15:595–598.
  20. Kraft P: Efficient two-stage genome-wide association designs based on false positive report probabilities. Pac Symp Biocomput 2006, pp 523–534.
  21. Wang H, Thomas DC, Pe’er I, Stram DO: Optimal two-stage genotyping designs for genome-wide association scans. Genet Epidemiol 2006;30:356–368.
  22. Umbach D, Weinberg C: Designing and analysing case-control studies to exploit independence of genotype and exposure. Stat Med 1997;16:1731–1743.
  23. Chatterjee N, Carroll R: Semiparametric maximum likelihood estimation exploiting gene-environment independence. Biometrika 2005;92:399–418.

    External Resources

  24. Liu X, Fallin MD, Kao WH: Genetic dissection methods: designs used for tests of gene-environment interaction. Curr Opin Genet Dev 2004;14:241–245.
  25. Chatterjee N, Kalaylioglu Z, Carroll R: Exploiting gene-environment independence in family-based case-control studies: Increased power for detecting associations, interactions and joint effects. Genet Epidemiol 2005.
  26. Langholz B, Rothman N, Wacholder S, Thomas D: Cohort studies for characterizing measured genes. Monogr Natl Cancer Inst 1999;26:39–42.
  27. Garcia-Closas M, Wacholder S, Caporaso N, Rothman N: Inference issues in cohort and case-control studies of genetic effects and gene-environment interactions; in Khoury M, Little J, Burke W (eds): Human Genome Epidemiology: A Scientific Foundation for Using Genetic Information to Improve Health and Prevent Disease. Oxford, Oxford University Press, 2004.
  28. Garcia-Closas M, Thompson WD, Robins JM: Differential misclassification and the assessment of gene-environment interactions in case-control studies. Am J Epidemiol 1998;147:426–433.
  29. Morimoto LM, White E, Newcomb PA: Selection bias in the assessment of gene-environment interaction in case-control studies. Am J Epidemiol 2003;158:259–263.
  30. Garcia-Closas M, Rothman N, Lubin J: Misclassification in case-control studies of gene-environment interactions: assessment of bias and sample size. Cancer Epidemiol Biomarkers Prev 1999;8:1043–1050.
  31. Thompson W: Effect modification and the limits of biological inference from epidemiologic data. J Clin Epidemiol 1991;44:221–232.
  32. Thomas D: Gene characterization studies: an overview. Monogr Natl Cancer Inst 1999;26:17–23.
  33. Gauderman WJ: Sample size calculations for matched case-control studies of gene-environment interaction. Stat Med 2002;21:35–50.