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Table of Contents
Vol. 56, No. 1-3, 2003
Issue release date: November 2003
Section title: Original Paper
Hum Hered 2003;56:73–82
(DOI:10.1159/000073735)

The Ubiquitous Nature of Epistasis in Determining Susceptibility to Common Human Diseases

Moore J.H.
Program in Human Genetics, Department of Molecular Physiology and Biophysics, Vanderbilt University Medical School, Nashville, Tenn., USA
email Corresponding Author

Abstract

There is increasing awareness that epistasis or gene-gene interaction plays a role in susceptibility to common human diseases. In this paper, we formulate a working hypothesis that epistasis is a ubiquitous component of the genetic architecture of common human diseases and that complex interactions are more important than the independent main effects of any one susceptibility gene. This working hypothesis is based on several bodies of evidence. First, the idea that epistasis is important is not new. In fact, the recognition that deviations from Mendelian ratios are due to interactions between genes has been around for nearly 100 years. Second, the ubiquity of biomolecular interactions in gene regulation and biochemical and metabolic systems suggest that relationship between DNA sequence variations and clinical endpoints is likely to involve gene-gene interactions. Third, positive results from studies of single polymorphisms typically do not replicate across independent samples. This is true for both linkage and association studies. Fourth, gene-gene interactions are commonly found when properly investigated. We review each of these points and then review an analytical strategy called multifactor dimensionality reduction for detecting epistasis. We end with ideas of how hypotheses about biological epistasis can be generated from statistical evidence using biochemical systems models. If this working hypothesis is true, it suggests that we need a research strategy for identifying common disease susceptibility genes that embraces, rather than ignores, the complexity of the genotype to phenotype relationship.

© 2003 S. Karger AG, Basel


  

Key Words

  • Gene-gene interactions
  • Multifactor dimensionality reduction
  • Biological systems moduling

References

  1. Altmuller J, Palmer LJ, Fischer G, Scherb H, Wjst M: Genomewide scans of complex human diseases: true linkage is hard to find. Am J Hum Genet 2001;69:936–950.
  2. Bateson W: Mendel’s Principles of Heredity. Cambridge, Cambridge University Press, 1909.
  3. Bellman R: Adaptive Control Processes. Princeton, Princeton University Press, 1961.
  4. Bernard C: Introduction à l’étude de la médecine expérimentale, par M. Claude Bernard. Paris, J.B. Baillière et fils, 1865.
  5. Cheverud JM, Routman EJ: Epistasis and its contribution to genetic variance components. Genetics 1995;139:1455–1461.
  6. Coffey CS, Hebert PR, Ritchie MD, Krumholz HM, Morgan TM, Gaziano JM, Ridker PM, Moore JH: An application of conditional logistic regression and multifactor dimensionality reduction for detecting gene-gene interactions on risk of myocardial infarction: The importance of model validation. Submitted, 2003a.
  7. Coffey CS, Hebert PR, Krumholz HM, Williams SM, Moore JH: Reporting of model validation procedures in studies of genetic interactions. Submitted, 2003b.
  8. Concato J, Feinstein AR, Holford TR: The risk of determining risk with multivariable models. Ann Intern Med 1993;118:201–210.
  9. Desel J, Juhas G: What is a Petri net? Informal answers for the informed reader; in Ehrig H, Juhas G (eds): Lecture Notes in Computer Science 2128. Berlin, Springer, 2001, pp 1–25.
  10. Di Paolo EA, Noble J, Bullock S: Simulation models as opaque thought experiments; in Dedau MA, McCaskill JS, Packard NH, Rasmussen S (eds): Artificial Life VII: Proceedings of the Seventh International Conference on Artificial Life. Cambridge, The MIT Press, 2000.
  11. Dipple KM, McCabe ER: Modifier genes convert ‘simple’ Mendelian disorders to complex traits. Mol Genet Metab 2000;71:43–50.
  12. Finckh U: The future of genetic association studies in Alzheimer disease. J Neural Transm 2003;110:253–266.
  13. Fisher RA. The correlation between relatives on the supposition of Mendelian inheritance. Trans R Soc Edinburgh 1918;52:399–433.
  14. Freitas AA: Understanding the crucial role of attribute interaction in data mining. Artif Intel Rev 2001;16:177–199.

    External Resources

  15. Gallie DR: Protein-protein interactions required during translation. Plant Mol Biol 2002;50:949–970.
  16. Gibson G: Epistasis and pleiotropy as natural properties of transcriptional regulation. Theor Popul Biol 1996;49:58–89.
  17. Gibson G, Wagner G: Canalization in evolutionary genetics: a stabilizing theory? Bioessays 2000;22:372–380.
  18. Goss PJ, Peccoud J: Quantitative modeling of stochastic systems in molecular biology by using stochastic Petri nets. Proc Natl Acad Sci USA 1998;95:6750–6755.
  19. Griffiths AJF, Miller JH, Suzuki DT, Lewontin RC, Gelbart WM: An Introduction to Genetic Analysis. New York, WH Freeman, 2000.
  20. Hahn LW, Ritchie MD, Moore JH: Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions. Bioinformatics 2003;19:376–382.
  21. Hansen TF: Is modularity necessary for evolvability? Remarks on the relationship between pleiotropy and evolvability. BioSystems 2003;69:83–94.
  22. Hirschhorn JN, Lohmueller K, Byrne E, Hirschhorn K: A comprehensive review of genetic association studies. Genet Med 2002;4:45–61.
  23. Hoh J, Ott J: A train of thoughts on gene mapping. Theor Popul Biol 2001;60:149–153.
  24. Hoh J, Wille A, Zee R, Cheng S, Reynolds R, Lindpaintner K, Ott J: Selecting SNPs in two-stage analysis of disease association data: A model-free approach. Ann Hum Genet 2000;64:413–417.
  25. Hollander WF: Epistasis and hypostasis. J Hered 1955;46:222–225.
  26. Hosmer DW, Lemeshow S: Applied Logistic Regression. New York, John Wiley & Sons Inc., 2000.
  27. Jansen RC: Studying complex biological systems using multifactorial perturbation. Nat Rev Genet 2003;4:145–151.
  28. Kerem E, Corey M, Kerem BS, Rommens J, Markiewicz D, Levison H, Tsui LC, Durie P: The relation between genotype and phenotype in cystic fibrosis-analysis of the most common mutation (delta F508). N Engl J Med 1990;323:1517–1522.
  29. Kooperberg C, Ruczinski I, LeBlanc ML, Hsu L: Sequence analysis using logic regression. Genet Epidemiol 2001;21:S626–S631.
  30. Leamy LJ, Routman EJ, Cheverud JM: An epistatic genetic basis for fluctuating asymmetry of mandible size in mice. Evolution 2002;56:642–653.
  31. Martinez E: Multi-protein complexes in eukaryotic gene transcription. Plant Mol Biol 2002;50:925–947.
  32. Michal G: Biochemical Pathways: An Atlas of Biochemistry and Molecular Biology. New York, Wiley, 1999.
  33. Moore JH: Cross validation consistency for the assessment of genetic programming results in microarray studies; in Raidl G, Meyer J-A, Middendorf M, Cagnoni S, Cardalda JJR, Corne DW, Gottlieb J, Guillot A, Hart E, Johnson CG, Marchiori E (eds): Lecture Notes in Computer Science 2611. Berlin, Springer-Verlag, 2003, pp 99–106.
  34. Moore JH, Hahn LW: A cellular automata approach to detecting interactions among single-nucleotide polymorphisms in complex multifactorial diseases. Pac Symp Biocomput 2002;7:53–64.
  35. Moore JH, Hahn LW: Cellular automata and genetic algorithms for parallel problem solving in human genetics; in Merelo JJ, Panagiotis A, Beyer H-G (eds): Lecture Notes in Computer Science 2439. Berlin, Springer-Verlag, 2002, pp 821–830.
  36. Moore JH, Hahn LW: Grammatical evolution for the discovery of Petri net models of complex genetic systems; in Cantu-Paz E, et al (eds): Lecture Notes in Computer Science, Berlin, Springer-Verlag, 2003, pp 2412–2413.
  37. Moore JH, Hahn LW: Evaluation of a discrete dynamic systems approach for modeling the hierarchical relationship between genes, biochemistry, and disease susceptibility. Discrete Contin Dyn Sys, 2003b, in press.
  38. Moore JH, Hahn LW: Petri net modeling of high-order genetic systems using grammatical evolution. BioSystems, 2003c, in press.
  39. Moore JH, Lamb JM, Brown NJ, Vaughan DE: A comparison of combinatorial partitioning and linear regression for the detection of epistatic effects of the ACE I/D and PAI-1 4G/5G polymorphisms on plasma PAI-1 levels. Clin Genet 2002a;62:74–79.
  40. Moore JH, Parker JS, Olsen NJ, Aune T: Symbolic discriminant analysis of microarray data in autoimmune disease. Genet Epidemiol 2002b;23:57–69.
  41. Moore JH, Smolkin ME, Lamb JM, Brown NJ, Vaughan DE: The relationship between plasma t-PA and PAI-1 levels is dependent on epistatic effects of the ACE I/D and PAI-1 4G/5G polymorphisms. Clin Genet 2002;62:53–59.
  42. Moore JH, Williams SM: New strategies for identifying gene-gene interactions in hypertension. Ann Med 2002;34:88–95.
  43. Morch ET: Chondrodystrophic Dwarfs in Denmark. Copenhagen, Munksgaard, 1941.
  44. Neel JV, Schull WJ: Human Heredity. Chicago, University of Chicago Press, 1954.
  45. Nelson MR, Kardia SL, Ferrell RE, Sing CF: A combinatorial partitioning method to identify multilocus genotypic partitions that predict quantitative trait variation. Genome Res 2001;11:458–470.
  46. Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR: A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol 1996;49:1373–1379.
  47. Phillips PC: The language of gene interaction. Genetics 1998;149:1167–1171.
  48. Pigliucci M: Phenotypic Plasticity. Baltimore, The Johns Hopkins Press, 2001.
  49. Pogun S: Are attractors ‘strange’, or is life more complicated than the simple laws of physics? BioSystems 2001;63:101–114.
  50. Rice SH: The evolution of canalization and the breaking of von Baer’s laws: Modeling the evolution of development with epistasis. Evolution 1998;52:647–656.
  51. 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.
  52. 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.
  53. Ritchie MD, White BC, Parker JS, Hahn LW, Moore JH: Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases. BMC Bioinformatics 2003, in press.
  54. Roberts DF: Fertility, mortality and culture: the changing pattern of natural selection. In: The Role of Natural Selection in Human Evolution. New York, American Elsevier Publishing Company, 1975.
  55. Salvatore F, Scudiero O, Castaldo G: Genotype-phenotype correlation in cystic fibrosis: The role of modifier genes. Am J Med Genet 2002;111:88–95.
  56. Shull GH: Duplicate genes for capsule form in BURSA bursa Bastoris. J Ind Abst Vererb 1914;12:97–149.
  57. Smith JM: Evolutionary Genetics. New York, Oxford University Press, 1998.
  58. Templeton AR: Epistasis and complex traits; in Wolf J, Brodie III B, Wade M (eds): Epistasis and the Evolutionary Process. New York, Oxford University Press, 2000.
  59. Tyson JJ, Chen KC, Novak B: Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell. Curr Opin Cell Biol 2003;15:221–223.
  60. Waddington CH: Canalization of development and the inheritance of acquired characters. Nature 1942;150:563–565.
  61. Waddington CH: The Strategy of the Genes. New York, MacMillan, 1957.
  62. Wade MJ, Winther RG, Agrawal AF, Goodnight CJ: Alternative definitions of epistasis: dependence and interaction. Trends Ecol Evol 2001;16:498–504.

    External Resources

  63. Wagner A: Robustness against mutations in genetic networks of yeast. Nat Genet 2000;24:355–361.
  64. Williams SM, Addy JH, Phillips JA 3rd, Dai M, Kpodonu J, Afful J, Jackson H, Joseph K, Eason F, Murray MM, Epperson P, Aduonum A, Wong LJ, Jose PA, Felder RA: Combinations of variations in multiple genes are associated with hypertension. Hypertension 2000;36:2–6.
  65. Wolf JB, Brofie III ED, Wade MJ: Epistasis and the Evolutionary Process. New York, Oxford University Press, 2000.
  66. Wright S: The role of mutation, inbreeding, crossbreeding and selection in evolution. Proc 6th Intl Congr Genet 1932;1:356–366.
  67. Wright S: Physiological and evolutionary theories of codominance. Am Nat 1934;68:25–53.
  68. Zee RY, Hoh J, Cheng S, Reynolds R, Grow MA, Silbergleit A, Walker K, Steiner L, Zangenberg G, Fernandez-Ortiz A, Macaya C, Pintor E, Fernandez-Cruz A, Ott J, Lindpainter K: Multi-locus interactions predict risk for post-PTCA restenosis: An approach to the genetic analysis of common complex disease. Pharmacogenomics J 2002;2:197–201.

  

Author Contacts

Jason H. Moore, PhD
Program in Human Genetics, Department of Molecular Physiology and Biophysics
519 Light Hall, Vanderbilt University Medical School
Nashville, TN 37232-0700 (USA)
Tel. +1 615 343 5852, Fax +1 615 343 8619, E-Mail moore@phg.mc.vanderbilt.edu

  

Article Information

Received: April 22, 2003
Accepted after revision: June 17, 2003
Number of Print Pages : 10
Number of Figures : 2, Number of Tables : 0, Number of References : 68

  

Publication Details

Human Heredity (International Journal of Human and Medical Genetics)
Founded 1950 as Acta Genetica et Statistica Medica by Gunnar Dahlberg; Continued by M. Hauge (1965–1983)

Vol. 56, No. 1-3, Year 2003 (Cover Date: Released November 2003)

Journal Editor: J. Ott, New York, N.Y.
ISSN: 0001–5652 (print), 1423–0062 (Online)

For additional information: http://www.karger.ch/journals/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

There is increasing awareness that epistasis or gene-gene interaction plays a role in susceptibility to common human diseases. In this paper, we formulate a working hypothesis that epistasis is a ubiquitous component of the genetic architecture of common human diseases and that complex interactions are more important than the independent main effects of any one susceptibility gene. This working hypothesis is based on several bodies of evidence. First, the idea that epistasis is important is not new. In fact, the recognition that deviations from Mendelian ratios are due to interactions between genes has been around for nearly 100 years. Second, the ubiquity of biomolecular interactions in gene regulation and biochemical and metabolic systems suggest that relationship between DNA sequence variations and clinical endpoints is likely to involve gene-gene interactions. Third, positive results from studies of single polymorphisms typically do not replicate across independent samples. This is true for both linkage and association studies. Fourth, gene-gene interactions are commonly found when properly investigated. We review each of these points and then review an analytical strategy called multifactor dimensionality reduction for detecting epistasis. We end with ideas of how hypotheses about biological epistasis can be generated from statistical evidence using biochemical systems models. If this working hypothesis is true, it suggests that we need a research strategy for identifying common disease susceptibility genes that embraces, rather than ignores, the complexity of the genotype to phenotype relationship.

© 2003 S. Karger AG, Basel


  

Author Contacts

Jason H. Moore, PhD
Program in Human Genetics, Department of Molecular Physiology and Biophysics
519 Light Hall, Vanderbilt University Medical School
Nashville, TN 37232-0700 (USA)
Tel. +1 615 343 5852, Fax +1 615 343 8619, E-Mail moore@phg.mc.vanderbilt.edu

  

Article Information

Received: April 22, 2003
Accepted after revision: June 17, 2003
Number of Print Pages : 10
Number of Figures : 2, Number of Tables : 0, Number of References : 68

  

Publication Details

Human Heredity (International Journal of Human and Medical Genetics)
Founded 1950 as Acta Genetica et Statistica Medica by Gunnar Dahlberg; Continued by M. Hauge (1965–1983)

Vol. 56, No. 1-3, Year 2003 (Cover Date: Released November 2003)

Journal Editor: J. Ott, New York, N.Y.
ISSN: 0001–5652 (print), 1423–0062 (Online)

For additional information: http://www.karger.ch/journals/hhe


Article / Publication Details

First-Page Preview
Abstract of Original Paper

Received: 4/22/2003
Accepted: 6/17/2003
Published online: 11/14/2003
Issue release date: November 2003

Number of Print Pages: 10
Number of Figures: 2
Number of Tables: 0

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. Altmuller J, Palmer LJ, Fischer G, Scherb H, Wjst M: Genomewide scans of complex human diseases: true linkage is hard to find. Am J Hum Genet 2001;69:936–950.
  2. Bateson W: Mendel’s Principles of Heredity. Cambridge, Cambridge University Press, 1909.
  3. Bellman R: Adaptive Control Processes. Princeton, Princeton University Press, 1961.
  4. Bernard C: Introduction à l’étude de la médecine expérimentale, par M. Claude Bernard. Paris, J.B. Baillière et fils, 1865.
  5. Cheverud JM, Routman EJ: Epistasis and its contribution to genetic variance components. Genetics 1995;139:1455–1461.
  6. Coffey CS, Hebert PR, Ritchie MD, Krumholz HM, Morgan TM, Gaziano JM, Ridker PM, Moore JH: An application of conditional logistic regression and multifactor dimensionality reduction for detecting gene-gene interactions on risk of myocardial infarction: The importance of model validation. Submitted, 2003a.
  7. Coffey CS, Hebert PR, Krumholz HM, Williams SM, Moore JH: Reporting of model validation procedures in studies of genetic interactions. Submitted, 2003b.
  8. Concato J, Feinstein AR, Holford TR: The risk of determining risk with multivariable models. Ann Intern Med 1993;118:201–210.
  9. Desel J, Juhas G: What is a Petri net? Informal answers for the informed reader; in Ehrig H, Juhas G (eds): Lecture Notes in Computer Science 2128. Berlin, Springer, 2001, pp 1–25.
  10. Di Paolo EA, Noble J, Bullock S: Simulation models as opaque thought experiments; in Dedau MA, McCaskill JS, Packard NH, Rasmussen S (eds): Artificial Life VII: Proceedings of the Seventh International Conference on Artificial Life. Cambridge, The MIT Press, 2000.
  11. Dipple KM, McCabe ER: Modifier genes convert ‘simple’ Mendelian disorders to complex traits. Mol Genet Metab 2000;71:43–50.
  12. Finckh U: The future of genetic association studies in Alzheimer disease. J Neural Transm 2003;110:253–266.
  13. Fisher RA. The correlation between relatives on the supposition of Mendelian inheritance. Trans R Soc Edinburgh 1918;52:399–433.
  14. Freitas AA: Understanding the crucial role of attribute interaction in data mining. Artif Intel Rev 2001;16:177–199.

    External Resources

  15. Gallie DR: Protein-protein interactions required during translation. Plant Mol Biol 2002;50:949–970.
  16. Gibson G: Epistasis and pleiotropy as natural properties of transcriptional regulation. Theor Popul Biol 1996;49:58–89.
  17. Gibson G, Wagner G: Canalization in evolutionary genetics: a stabilizing theory? Bioessays 2000;22:372–380.
  18. Goss PJ, Peccoud J: Quantitative modeling of stochastic systems in molecular biology by using stochastic Petri nets. Proc Natl Acad Sci USA 1998;95:6750–6755.
  19. Griffiths AJF, Miller JH, Suzuki DT, Lewontin RC, Gelbart WM: An Introduction to Genetic Analysis. New York, WH Freeman, 2000.
  20. Hahn LW, Ritchie MD, Moore JH: Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions. Bioinformatics 2003;19:376–382.
  21. Hansen TF: Is modularity necessary for evolvability? Remarks on the relationship between pleiotropy and evolvability. BioSystems 2003;69:83–94.
  22. Hirschhorn JN, Lohmueller K, Byrne E, Hirschhorn K: A comprehensive review of genetic association studies. Genet Med 2002;4:45–61.
  23. Hoh J, Ott J: A train of thoughts on gene mapping. Theor Popul Biol 2001;60:149–153.
  24. Hoh J, Wille A, Zee R, Cheng S, Reynolds R, Lindpaintner K, Ott J: Selecting SNPs in two-stage analysis of disease association data: A model-free approach. Ann Hum Genet 2000;64:413–417.
  25. Hollander WF: Epistasis and hypostasis. J Hered 1955;46:222–225.
  26. Hosmer DW, Lemeshow S: Applied Logistic Regression. New York, John Wiley & Sons Inc., 2000.
  27. Jansen RC: Studying complex biological systems using multifactorial perturbation. Nat Rev Genet 2003;4:145–151.
  28. Kerem E, Corey M, Kerem BS, Rommens J, Markiewicz D, Levison H, Tsui LC, Durie P: The relation between genotype and phenotype in cystic fibrosis-analysis of the most common mutation (delta F508). N Engl J Med 1990;323:1517–1522.
  29. Kooperberg C, Ruczinski I, LeBlanc ML, Hsu L: Sequence analysis using logic regression. Genet Epidemiol 2001;21:S626–S631.
  30. Leamy LJ, Routman EJ, Cheverud JM: An epistatic genetic basis for fluctuating asymmetry of mandible size in mice. Evolution 2002;56:642–653.
  31. Martinez E: Multi-protein complexes in eukaryotic gene transcription. Plant Mol Biol 2002;50:925–947.
  32. Michal G: Biochemical Pathways: An Atlas of Biochemistry and Molecular Biology. New York, Wiley, 1999.
  33. Moore JH: Cross validation consistency for the assessment of genetic programming results in microarray studies; in Raidl G, Meyer J-A, Middendorf M, Cagnoni S, Cardalda JJR, Corne DW, Gottlieb J, Guillot A, Hart E, Johnson CG, Marchiori E (eds): Lecture Notes in Computer Science 2611. Berlin, Springer-Verlag, 2003, pp 99–106.
  34. Moore JH, Hahn LW: A cellular automata approach to detecting interactions among single-nucleotide polymorphisms in complex multifactorial diseases. Pac Symp Biocomput 2002;7:53–64.
  35. Moore JH, Hahn LW: Cellular automata and genetic algorithms for parallel problem solving in human genetics; in Merelo JJ, Panagiotis A, Beyer H-G (eds): Lecture Notes in Computer Science 2439. Berlin, Springer-Verlag, 2002, pp 821–830.
  36. Moore JH, Hahn LW: Grammatical evolution for the discovery of Petri net models of complex genetic systems; in Cantu-Paz E, et al (eds): Lecture Notes in Computer Science, Berlin, Springer-Verlag, 2003, pp 2412–2413.
  37. Moore JH, Hahn LW: Evaluation of a discrete dynamic systems approach for modeling the hierarchical relationship between genes, biochemistry, and disease susceptibility. Discrete Contin Dyn Sys, 2003b, in press.
  38. Moore JH, Hahn LW: Petri net modeling of high-order genetic systems using grammatical evolution. BioSystems, 2003c, in press.
  39. Moore JH, Lamb JM, Brown NJ, Vaughan DE: A comparison of combinatorial partitioning and linear regression for the detection of epistatic effects of the ACE I/D and PAI-1 4G/5G polymorphisms on plasma PAI-1 levels. Clin Genet 2002a;62:74–79.
  40. Moore JH, Parker JS, Olsen NJ, Aune T: Symbolic discriminant analysis of microarray data in autoimmune disease. Genet Epidemiol 2002b;23:57–69.
  41. Moore JH, Smolkin ME, Lamb JM, Brown NJ, Vaughan DE: The relationship between plasma t-PA and PAI-1 levels is dependent on epistatic effects of the ACE I/D and PAI-1 4G/5G polymorphisms. Clin Genet 2002;62:53–59.
  42. Moore JH, Williams SM: New strategies for identifying gene-gene interactions in hypertension. Ann Med 2002;34:88–95.
  43. Morch ET: Chondrodystrophic Dwarfs in Denmark. Copenhagen, Munksgaard, 1941.
  44. Neel JV, Schull WJ: Human Heredity. Chicago, University of Chicago Press, 1954.
  45. Nelson MR, Kardia SL, Ferrell RE, Sing CF: A combinatorial partitioning method to identify multilocus genotypic partitions that predict quantitative trait variation. Genome Res 2001;11:458–470.
  46. Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR: A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol 1996;49:1373–1379.
  47. Phillips PC: The language of gene interaction. Genetics 1998;149:1167–1171.
  48. Pigliucci M: Phenotypic Plasticity. Baltimore, The Johns Hopkins Press, 2001.
  49. Pogun S: Are attractors ‘strange’, or is life more complicated than the simple laws of physics? BioSystems 2001;63:101–114.
  50. Rice SH: The evolution of canalization and the breaking of von Baer’s laws: Modeling the evolution of development with epistasis. Evolution 1998;52:647–656.
  51. 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.
  52. 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.
  53. Ritchie MD, White BC, Parker JS, Hahn LW, Moore JH: Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases. BMC Bioinformatics 2003, in press.
  54. Roberts DF: Fertility, mortality and culture: the changing pattern of natural selection. In: The Role of Natural Selection in Human Evolution. New York, American Elsevier Publishing Company, 1975.
  55. Salvatore F, Scudiero O, Castaldo G: Genotype-phenotype correlation in cystic fibrosis: The role of modifier genes. Am J Med Genet 2002;111:88–95.
  56. Shull GH: Duplicate genes for capsule form in BURSA bursa Bastoris. J Ind Abst Vererb 1914;12:97–149.
  57. Smith JM: Evolutionary Genetics. New York, Oxford University Press, 1998.
  58. Templeton AR: Epistasis and complex traits; in Wolf J, Brodie III B, Wade M (eds): Epistasis and the Evolutionary Process. New York, Oxford University Press, 2000.
  59. Tyson JJ, Chen KC, Novak B: Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell. Curr Opin Cell Biol 2003;15:221–223.
  60. Waddington CH: Canalization of development and the inheritance of acquired characters. Nature 1942;150:563–565.
  61. Waddington CH: The Strategy of the Genes. New York, MacMillan, 1957.
  62. Wade MJ, Winther RG, Agrawal AF, Goodnight CJ: Alternative definitions of epistasis: dependence and interaction. Trends Ecol Evol 2001;16:498–504.

    External Resources

  63. Wagner A: Robustness against mutations in genetic networks of yeast. Nat Genet 2000;24:355–361.
  64. Williams SM, Addy JH, Phillips JA 3rd, Dai M, Kpodonu J, Afful J, Jackson H, Joseph K, Eason F, Murray MM, Epperson P, Aduonum A, Wong LJ, Jose PA, Felder RA: Combinations of variations in multiple genes are associated with hypertension. Hypertension 2000;36:2–6.
  65. Wolf JB, Brofie III ED, Wade MJ: Epistasis and the Evolutionary Process. New York, Oxford University Press, 2000.
  66. Wright S: The role of mutation, inbreeding, crossbreeding and selection in evolution. Proc 6th Intl Congr Genet 1932;1:356–366.
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