Skip to main content
SpringerLink
Log in

Classical and Dominance-Based Rough Sets in the Search for Genes under Balancing Selection

  • Chapter
Transactions on Rough Sets XI

Part of the book series: Lecture Notes in Computer Science ((TRS,volume 5946))

Abstract

Since the time of Kimura’s theory of neutral evolution at molecular level the search for genes under natural selection is one of the crucial problems in population genetics. There exists quite a number of statistical tests designed for it, however, the interpretation of the results is often hard due to the existence of extra-selective factors, such as population growth, migration and recombination. The author, in his earlier work, has proposed the idea of multi-null hypotheses methodology applied for testing the selection in ATM, RECQL, WRN and BLM genes - the foursome implicated in human familial cancer. However, because of high computational effort required for estimating the critical values under nonclassical null hypotheses, mentioned strategy is not an appropriate tool for selection screening. The current article presents novel, rough set based methodology, helpful in the interpretation of the tests outcomes applied only versus classical nulls. The author considers for this purpose both classical and dominance based rough set frameworks. None of rough set based methods requires long-lasting simulations and, as it is shown in a paper, both give reliable results. The advantage of dominance based approach over classical one is more natural treatment of statistical test outcomes, resulting in better generalization without necessity of manual incorporating the domain-dependent reasoning to the process of knowledge processing. However, in testing this gain in generalization proved to be at the price of a slight loss of accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Kimura, M.: The Neutral Theory of Molecular Evolution. Cambridge University Press, Cambridge (1983)

    Google Scholar 

  2. Zhang, J.: Evolution of the Human ASPM Gene, a Major Determinant of Brain Size. Genetics 165, 2063–2070 (2003)

    Google Scholar 

  3. Evans, P.D., Anderson, J.R., Vallender, E.J., Gilbert, S.L., Malcom, Ch.M., et al.: Adaptive Evolution of ASPM, a Major Determinant of Cerebral Cortical Size in Humans. Human Molecular Genetics 13, 489–494 (2004)

    Article  Google Scholar 

  4. Bamshad, M.J., Mummidi, S., Gonzalez, E., Ahuja, S.S., Dunn, D.M., et al.: A strong signature of balancing selection in the 5’ cis-regulatory region of CCR5. Proc. Nat. Acad. Sci. USA 99(16), 10539–10544 (2002)

    Article  Google Scholar 

  5. Gilad, Y., Rosenberg, S., Przeworski, M., Lancet, D., Skorecki, K.: Evidence for positive selection and population structure at the human MAO-A gene. Proc. Natl. Acad. Sci. USA 99, 862–867 (2002)

    Article  Google Scholar 

  6. Toomajian, C., Kreitman, M.: Sequence Variation and Haplotype Structure at the Human HFE Locus. Genetics 161, 1609–1623 (2002)

    Google Scholar 

  7. Wooding, S.P., Watkins, W.S., Bamshad, M.J., Dunn, D.M., Weiss, R.B., Jorde, L.B.: DNA sequence variation in a 3.7-kb noncoding sequence 5’ of the CYP1A2 Gene: Implications for Human Population History and Natural Selection. Am. J. Hum. Genet. 71, 528–542 (2002)

    Article  Google Scholar 

  8. Fu, Y.X., Li, W.H.: Statistical Tests of Neutrality of Mutations. Genetics 133, 693–709 (1993)

    Google Scholar 

  9. Fu, Y.X.: Statistical Tests of Neutrality of Mutations Against Population Growth, Hitchhiking and Background Selection. Genetics 147, 915–925 (1997)

    Google Scholar 

  10. Kelly, J.K.: A Test of Neutrality Based on Interlocus Associations. Genetics 146, 1197–1206 (1997)

    Google Scholar 

  11. Wall, J.D.: Recombination and the Power of Statistical Tests of Neutrality. Genet. Res. 74, 65–79 (1999)

    Article  Google Scholar 

  12. Nielsen, R.: Statistical Tests of Selective Neutrality in the Age of Genomics. Heredity 86, 641–647 (2001)

    Article  Google Scholar 

  13. Cyran, K.A., Polañska, J., Kimmel, M.: Testing for Signatures of Natural Selection at Molecular Genes Level. J. Med. Inf. Techn. 8, 31–39 (2004)

    Google Scholar 

  14. Dhillon, K.K., Sidorova, J., Saintigny, Y., Poot, M., Gollahon, K., Rabinovitch, P.S., Mon-nat Jr., R.J.: Functional Role of the Werner Syndrome RecQ Helicase in Human Fibroblasts. Aging Cell 6, 53–61 (2007)

    Article  Google Scholar 

  15. Karmakar, P., Seki, M., Kanamori, M., Hashiguchi, K., Ohtsuki, M., Murata, E., Inoue, E., Tada, S., Lan, L., Yasui, A., Enomoto, T.: BLM is an Early Responder to DNA Double-strand Breaks. Biochem. Biophys. Res. Commun. 348, 62–69 (2006)

    Article  Google Scholar 

  16. Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11(5), 341–356 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  17. Pawlak, Z.: Rough sets: theoretical aspects of reasoning about data. Kluwer Academic, Dordrecht (1991)

    MATH  Google Scholar 

  18. Greco, S., Matarazzo, B., Sowiñski, R.: Rough sets theory for multicriteria decision analysis. European Journal of Operational Research 129(1), 1–47 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  19. Greco, S., Matarazzo, B., Sowiñski, R.: Multicriteria classification by dominance-based rough set approach. In: Kloesgen, W., Zytkow, J. (eds.) Handbook of Data Mining and Knowledge Discovery. Oxford University Press, New York (2002)

    Google Scholar 

  20. Sowiñski, R., Greco, S., Matarazzo, B.: Rough set based decision support. In: Burke, E.K., Kendall, G. (eds.) Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, ch. 16, pp. 475–527. Springer, New York (2005)

    Google Scholar 

  21. Cyran, K.A.: Rough Sets in the Interpretation of Statistical Tests Outcomes for Genes under Hypothetical Balancing Selection. In: Kryszkiewicz, M., Peters, J.F., Rybiński, H., Skowron, A. (eds.) RSEISP 2007. LNCS (LNAI), vol. 4585, pp. 716–725. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  22. Bonnen, P.E., Story, M.D., Ashorn, C.L., Buchholz, T.A., Weil, M.M., Nelson, D.L.: Haplotypes at ATM identify coding-sequence variation and indicate a region of extensive linkage disequilibrium. Am. J. Hum. Genet. 67, 1437–1451 (2000)

    Article  Google Scholar 

  23. Bonnen, P.E., Wang, P.J., Kimmel, M., Chakraborty, R., Nelson, D.L.: Haplotype and linkage disequilibrium architecture for human cancer-associated genes. Genome Res. 12, 1846–1853 (2002)

    Article  Google Scholar 

  24. Trikka, D., Fang, Z., Renwick, A., Jones, S.H., Chakraborty, R., et al.: Complex SNP-based haplotypes in three human helicases: implications for cancer association studies. Genome Res. 12, 627–639 (2002)

    Article  Google Scholar 

  25. Uziel, T., Savitsky, K., Platzer, M., Ziv, Y., Helbitz, T., et al.: Genomic organization of the ATM gene. Genomics 33, 317–320 (1996)

    Article  Google Scholar 

  26. Teraoka, S.N., Telatar, M., Becker-Catania, S., Liang, T., Onengut, S., et al.: Splicing defects in the ataxia-telangiectasia gene, ATM: underlying mutations and consequences. Am. J. Hum. Genet. 64, 1617–1631 (1999)

    Article  Google Scholar 

  27. Li, A., Swift, M.: Mutations at the ataxia-telangiectasia locus and clinical phenotypes of A-T patients. Am. J. Med. Genet. 92, 170–177 (2000)

    Article  Google Scholar 

  28. Golding, S.E., Rosenberg, E., Neill, S., Dent, P., Povirk, L.F., Valerie, K.: Extracellular Signal-Related Kinase Positively Regulates Ataxia Telangiectasia Mutated, Homologous Recombination Repair, and the DNA Damage Response. Cancer Res. 67, 1046–1053 (2007)

    Article  Google Scholar 

  29. Schneider, J., Philipp, M., Yamini, P., Dork, T., Woitowitz, H.J.: ATM Gene Mutations in Former Uranium Miners of SDAG Wismut: a Pilot Study. Oncol. Rep. 17, 477–482 (2007)

    Google Scholar 

  30. Siitonen, H.A., Kopra, O., Haravuori, H., Winter, R.M., Saamanen, A.M., et al.: Molecular defect of RAPADILINO syndrom expands the phenotype spectrum of RECQL diseases. Hum. Mol. Genet. 12(21), 2837–2844 (2003)

    Article  Google Scholar 

  31. Yusa, K., Horie, K., Kondoh, G., Kouno, M., Maeda, Y., et al.: Genome-wide phenotype analysis in ES cells by regulated disruption of Bloom’s syndrome gene. Nature 429, 896–899 (2004)

    Article  Google Scholar 

  32. Karow, J.K., Constantinou, A., Li, J.-L., West, S.C., Hickson, I.D.: The Bloom’s syndrome gene product promotes branch migration of Holliday junctions. Proc. Nat. Acad. Sci. USA 97, 6504–6508 (2000)

    Article  Google Scholar 

  33. Wu, L., Hickson, I.D.: The Bloom’s syndrome helicase suppresses crossing over during homologous recombination. Nature 426, 870–874 (2003)

    Article  Google Scholar 

  34. Adams, M.D., McVey, M., Sekelsky, J.J.: Drosophila BLM in double-strand break repair by synthesis-dependent strand annealing. Science 299, 265–267 (2003)

    Article  Google Scholar 

  35. Yu, C.-E., Oshima, J., Wijsman, E.M., Nakura, J., Miki, T., Piussan, C., et al.: Werner’s Syndrome Collaborative Group: Mutations in the consensus helicase domains of the Werner syndrome gene. Am. J. Hum. Genet. 60, 330–341 (1997)

    Google Scholar 

  36. Sinclair, D.A., Mills, K., Guarente, L.: Accelerated aging and nucleolar fragmentation in yeast sgs1 mutants. Science 277, 1313–1316 (1997)

    Article  Google Scholar 

  37. Huang, S., Li, B., Gray, M.D., Oshima, J., Mian, I.S., Campisi, J.: The premature ageing syndrome protein, WRN, is a 3-prime-5-prime exonuclease. Nature Genet. 20, 114–115 (1998)

    Article  Google Scholar 

  38. Ellis, N.A., Roe, A.M., Kozloski, J., Proytcheva, M., Falk, C., German, J.: Linkage disequilibrium between the FES, D15S127, and BLM loci in Ashkenazi Jews with Bloom syndrome. Am. J. Hum. Genet. 55, 453–460 (1994)

    Google Scholar 

  39. McDonald, J.H., Kreitman, M.: Adaptive protein evolution at the Adh locus in Drosophila. Nature 351, 652–654 (1991)

    Article  Google Scholar 

  40. Akashi, H.: Inferring weak selection from pattern of polymorphism and divergence at ’silent’ sites in Drosophila DNA. Genetics 139, 1067–1076 (1995)

    Google Scholar 

  41. Nielsen, R., Weinreich, D.M.: The Age of Nonsynonymous and Synonymous Mutations and Implications for the Slightly Deleterious Theory. Genetics 153, 497–506 (1999)

    Google Scholar 

  42. Hudson, R.R., Kreitman, M., Aguade, M.: A test of neutral molecular evolution based on nucleotide data. Genetics 116, 153–159 (1987)

    Google Scholar 

  43. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. With discussion. J. Roy. Stat. Soc. Ser. B 39, 1–38 (1977)

    MATH  MathSciNet  Google Scholar 

  44. Excoffier, L., Slatkin, M.: Maximum-likelihood estimation of molecular haplotype frequencies in a diploid population. Mol. Biol. Evol. 12, 921–927 (1995)

    Google Scholar 

  45. Polanska, J.: The EM Algorithm and its Implementation for the Estimation of the Frequencies of SNP-Haplotypes. Int. J. Appl. Math. Comp. Sci. 13, 419–429 (2003)

    MATH  MathSciNet  Google Scholar 

  46. Greco, S., Matarazzo, B., Slowinski, R.: The use of rough sets and fuzzy sets in MCDM. In: Gal, T., Hanne, T., Stewart, T. (eds.) Advances in Multiple Criteria Decision Making, vol. 14. Kluwer Academic Publishers, Dordrecht (1999)

    Google Scholar 

  47. Greco, S., Matarazzo, B., Slowinski, R.: Handling missing values in rough set analysis of multi-attribute and multi-criteria decision problems. In: Zhong, N., Skowron, A., Ohsuga, S. (eds.) RSFDGrC 1999. LNCS (LNAI), vol. 1711, pp. 146–157. Springer, Heidelberg (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland

    Krzysztof A. Cyran

Authors
  1. Krzysztof A. Cyran
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. University of Manitoba, Department of Electrical and Computer Engineering, , , ,, Winnipeg, R3T 5V6, Manitoba, Canada

    James F. Peters

  2. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Cyran, K.A. (2010). Classical and Dominance-Based Rough Sets in the Search for Genes under Balancing Selection. In: Peters, J.F., Skowron, A. (eds) Transactions on Rough Sets XI. Lecture Notes in Computer Science, vol 5946. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11479-3_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-11479-3_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-11478-6

  • Online ISBN: 978-3-642-11479-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation