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Vol. 70, No. 3, 2010
Issue release date: October 2010
Free Access
Hum Hered 2010;70:219–225
(DOI:10.1159/000319175)

A Simple and Computationally Efficient Sampling Approach to Covariate Adjustment for Multifactor Dimensionality Reduction Analysis of Epistasis

Gui J.c · Andrew A.S.c · Andrews P.a, b · Nelson H.M.i · Kelsey K.T.g · Karagas M.R.c · Moore J.H.a-f, h
aComputational Genetics Laboratory, and Departments of bGenetics and cCommunity and Family Medicine, Norris-Cotton Cancer Center, Dartmouth Medical School, Lebanon, N.H., dDepartment of Computer Science, University of New Hampshire, Durham, N.H., eDepartment of Computer Science, University of Vermont, Burlington, Vt., fDepartment of Psychiatry and Human Behavior, and gDepartment of Community Health, Brown University, Providence, R.I., hTranslational Genomics Research Institute, Phoenix, Ariz., and iDivision of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, Minn., USA
email Corresponding Author

Abstract

Epistasis or gene-gene interaction is a fundamental component of the genetic architecture of complex traits such as disease susceptibility. Multifactor dimensionality reduction (MDR) was developed as a nonparametric and model-free method to detect epistasis when there are no significant marginal genetic effects. However, in many studies of complex disease, other covariates like age of onset and smoking status could have a strong main effect and may potentially interfere with MDR’s ability to achieve its goal. In this paper, we present a simple and computationally efficient sampling method to adjust for covariate effects in MDR. We use simulation to show that after adjustment, MDR has sufficient power to detect true gene-gene interactions. We also compare our method with the state-of-art technique in covariate adjustment. The results suggest that our proposed method performs similarly, but is more computationally efficient. We then apply this new method to an analysis of a population-based bladder cancer study in New Hampshire.


 goto top of outline Key Words

  • Covariate adjustment
  • Multifactor dimensionality reduction
  • Epistasis

 goto top of outline Abstract

Epistasis or gene-gene interaction is a fundamental component of the genetic architecture of complex traits such as disease susceptibility. Multifactor dimensionality reduction (MDR) was developed as a nonparametric and model-free method to detect epistasis when there are no significant marginal genetic effects. However, in many studies of complex disease, other covariates like age of onset and smoking status could have a strong main effect and may potentially interfere with MDR’s ability to achieve its goal. In this paper, we present a simple and computationally efficient sampling method to adjust for covariate effects in MDR. We use simulation to show that after adjustment, MDR has sufficient power to detect true gene-gene interactions. We also compare our method with the state-of-art technique in covariate adjustment. The results suggest that our proposed method performs similarly, but is more computationally efficient. We then apply this new method to an analysis of a population-based bladder cancer study in New Hampshire.

Copyright © 2010 S. Karger AG, Basel


 goto top of outline References
  1. Andrew AS, Nelson HN, Kelsey KT, Moore JH, Meng A, Casella DP, Tosterson TD, Schned AR, Karagas MR: Concordance of multiple analytical approaches demonstrates a complex relationship between DNA repair gene SNPs, smoking, and bladder cancer susceptibility. Carcinogenesis 2006;27:1030–1037.
  2. Greene CS, Himmelstein DS, Kelsey KT, Williams SM, Andrew AS, Karagas MR, Moore JH: Enabling personal genomics with an explicit test of epistasis. Pac Symp Biocomput 2010;327–336.

    External Resources

  3. Hahn LW, Ritchie MD, Moore JH: Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions. Bioinformatics 2003;19:376–382.
  4. Hahn LW, Moore JH: Ideal discrimination of discrete clinical endpoints using multilocus genotypes. In Silico Biol 2004;4:183–194.
  5. Lou X, Chen G, Yan L, Ma JZ, Zhu J, Elston RC, Li MD: A generalized combinatorial approach for detecting gene by gene and gene by environment interactions with application to nicotine dependence. Am J Hum Genet 2007;80:1125–1137.
  6. Michalski RS: A theory and methodology of inductive learning. Artif Intel 1983;20:111–161.

    External Resources

  7. Moore JH, Williams SM: New strategies for identifying gene-gene interactions in hypertension. Ann Med 2002;34:88–95.
  8. Moore JH: The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Hum Hered 2003;56:73–82.
  9. Moore JH: Computational analysis of gene-gene interactions in common human diseases using multifactor dimensionality reduction. Expert Rev Mol Diagn 2004;4:795–803.
  10. Moore JH: Global view of epistasis. Nat Genet 2005;37:13–14.
  11. Moore JH, Williams SM: Traversing the conceptual divide between biological and statistical epistasis: Systems biology and a more modern synthesis. BioEssays 2005;27:637–646.
  12. Moore JH, Gilbert JC, Tsai CT, Chiang FT, Holden W, Barney N, White BC: A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. J Theor Biol 2006;241:252–261.
  13. Moore JH: Genome-wide analysis of epistasis using multifactor dimensionality reduction: feature selection and construction in the domain of human genetics; in Zhu X, Davidson I (eds): Knowledge Discovery and Data Mining: Challenges and Realities with Real World Data. Hershey: IGI Press, 2007, pp 17–30.
  14. Pattin KA, White BC, Barney N, Gui J, Nelson HH, Kelsey KR Andrew AS, Karagas MR, Moore JH: A computationally efficient hypothesis testing method for epistasis analysis using multifactor dimensionality reduction. Genet Epidemiol 2008;33:87–94.

    External Resources

  15. 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.
  16. Ritchie MD, Hahn LW, Moore JH: Power of multifactor dimensionality reduction for detecting gene-gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity. Genet Epidemiol 2003;24:150–157.
  17. Velez DR, White BC, Motsinger AA, Bush WS, Ritchie MD, Williams SM, Moore JH: A balanced accuracy metric for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction. Genet Epidemiol 2007;31:306–315.

 goto top of outline Author Contacts

Jason H. Moore, PhD
Computational Genetics Laboratory, Norris-Cotton Cancer Center
Dartmouth Medical School, 706 Rubin Bldg, HB7937, One Medical Center Dr.
Lebanon, NH 03756 (USA)
Tel. +1 603 653 9939, Fax +1 603 653 9900, E-Mail jason.h.moore@dartmouth.edu


 goto top of outline Article Information

Received: February 1, 2010
Accepted after revision: July 13, 2010
Published online: October 1, 2010
Number of Print Pages : 7
Number of Figures : 1, Number of Tables : 2, Number of References : 17


 goto top of outline Publication Details

Human Heredity (International Journal of Human and Medical Genetics)

Vol. 70, No. 3, Year 2010 (Cover Date: October 2010)

Journal Editor: Devoto M. (Philadelphia, Pa.)
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

Epistasis or gene-gene interaction is a fundamental component of the genetic architecture of complex traits such as disease susceptibility. Multifactor dimensionality reduction (MDR) was developed as a nonparametric and model-free method to detect epistasis when there are no significant marginal genetic effects. However, in many studies of complex disease, other covariates like age of onset and smoking status could have a strong main effect and may potentially interfere with MDR’s ability to achieve its goal. In this paper, we present a simple and computationally efficient sampling method to adjust for covariate effects in MDR. We use simulation to show that after adjustment, MDR has sufficient power to detect true gene-gene interactions. We also compare our method with the state-of-art technique in covariate adjustment. The results suggest that our proposed method performs similarly, but is more computationally efficient. We then apply this new method to an analysis of a population-based bladder cancer study in New Hampshire.



 goto top of outline Author Contacts

Jason H. Moore, PhD
Computational Genetics Laboratory, Norris-Cotton Cancer Center
Dartmouth Medical School, 706 Rubin Bldg, HB7937, One Medical Center Dr.
Lebanon, NH 03756 (USA)
Tel. +1 603 653 9939, Fax +1 603 653 9900, E-Mail jason.h.moore@dartmouth.edu


 goto top of outline Article Information

Received: February 1, 2010
Accepted after revision: July 13, 2010
Published online: October 1, 2010
Number of Print Pages : 7
Number of Figures : 1, Number of Tables : 2, Number of References : 17


 goto top of outline Publication Details

Human Heredity (International Journal of Human and Medical Genetics)

Vol. 70, No. 3, Year 2010 (Cover Date: October 2010)

Journal Editor: Devoto M. (Philadelphia, Pa.)
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. Andrew AS, Nelson HN, Kelsey KT, Moore JH, Meng A, Casella DP, Tosterson TD, Schned AR, Karagas MR: Concordance of multiple analytical approaches demonstrates a complex relationship between DNA repair gene SNPs, smoking, and bladder cancer susceptibility. Carcinogenesis 2006;27:1030–1037.
  2. Greene CS, Himmelstein DS, Kelsey KT, Williams SM, Andrew AS, Karagas MR, Moore JH: Enabling personal genomics with an explicit test of epistasis. Pac Symp Biocomput 2010;327–336.

    External Resources

  3. Hahn LW, Ritchie MD, Moore JH: Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions. Bioinformatics 2003;19:376–382.
  4. Hahn LW, Moore JH: Ideal discrimination of discrete clinical endpoints using multilocus genotypes. In Silico Biol 2004;4:183–194.
  5. Lou X, Chen G, Yan L, Ma JZ, Zhu J, Elston RC, Li MD: A generalized combinatorial approach for detecting gene by gene and gene by environment interactions with application to nicotine dependence. Am J Hum Genet 2007;80:1125–1137.
  6. Michalski RS: A theory and methodology of inductive learning. Artif Intel 1983;20:111–161.

    External Resources

  7. Moore JH, Williams SM: New strategies for identifying gene-gene interactions in hypertension. Ann Med 2002;34:88–95.
  8. Moore JH: The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Hum Hered 2003;56:73–82.
  9. Moore JH: Computational analysis of gene-gene interactions in common human diseases using multifactor dimensionality reduction. Expert Rev Mol Diagn 2004;4:795–803.
  10. Moore JH: Global view of epistasis. Nat Genet 2005;37:13–14.
  11. Moore JH, Williams SM: Traversing the conceptual divide between biological and statistical epistasis: Systems biology and a more modern synthesis. BioEssays 2005;27:637–646.
  12. Moore JH, Gilbert JC, Tsai CT, Chiang FT, Holden W, Barney N, White BC: A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. J Theor Biol 2006;241:252–261.
  13. Moore JH: Genome-wide analysis of epistasis using multifactor dimensionality reduction: feature selection and construction in the domain of human genetics; in Zhu X, Davidson I (eds): Knowledge Discovery and Data Mining: Challenges and Realities with Real World Data. Hershey: IGI Press, 2007, pp 17–30.
  14. Pattin KA, White BC, Barney N, Gui J, Nelson HH, Kelsey KR Andrew AS, Karagas MR, Moore JH: A computationally efficient hypothesis testing method for epistasis analysis using multifactor dimensionality reduction. Genet Epidemiol 2008;33:87–94.

    External Resources

  15. 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.
  16. Ritchie MD, Hahn LW, Moore JH: Power of multifactor dimensionality reduction for detecting gene-gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity. Genet Epidemiol 2003;24:150–157.
  17. Velez DR, White BC, Motsinger AA, Bush WS, Ritchie MD, Williams SM, Moore JH: A balanced accuracy metric for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction. Genet Epidemiol 2007;31:306–315.