Advertisement

PNN for Molecular Level Selection Detection.

  • Krzysztof A. Cyran
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 27)

Abstract

Contemporary population genetics has developed several statistical tests designed for the detection of natural selection at the molecular level. However, the appropriate interpretation of the test results is often hard. This is because such factors as population growth, migration, and recombination can produce similar values for some of these tests. To overcome these difficulties, the author has proposed a so-called multi-null methodology, and he has used it in search of natural selection in ATM, RECQL, WRN, and BLM, i.e., in four human familial cancer genes. However, this methodology is not appropriate for fast detection because of the long-lasting computer simulations required for estimating critical values under nonclassical null hypotheses. Here the author presents the results of another study based on the application of probabilistic neural networks for the detection of natural selection at the molecular level. The advantage of the proposed method is that it not so time-consuming and, because of the good recognition abilities of probabilistic neural networks, it gives low decision error levels in cross validation.

Keywords

Natural Selection Single Nucleotide Polymorphism Ataxia Telangiectasia Mutate Probabilistic Neural Network Ataxia Telangiectasia 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The author would like to acknowledge his financial support under the habilitation grant number BW/RGH-5/Rau-0/2007, under SUT statutory activities BK2007, and under MNiSW grant number 3T11F 010 29. Also the author would like to thank Prof. M. Kimmel from the Department of Statistics at Rice University in Houston TX, USA, for advice and long discussions concerning the statistical and biological aspects of the research using non-neutrality tests for the detection of natural selection operating at the molecular level.

References

  1. 1.
    Biton S, Gropp M, Itsykson P, Pereg Y, Mittelman L, Johe K, Reubinoff B, Shiloh Y (2007) ATM-mediated response to DNA double strand breaks in human neurons derived from stem cells. DNA Repair (Amst) 6:128–134CrossRefGoogle Scholar
  2. 2.
    Cyran K (2007) Rough sets in the interpretation of statistical tests outcomes for genes under hypothetical balancing selection. In: Kryszkiewicz M, Peters J, Rybinski H, Skowron A (eds) Springer-Verlag, Lecture notes in artificial intelligence, pp 716–725Google Scholar
  3. 3.
    Cyran KA, Polańska J, Chakraborty R, Nelson D, Kimmel M (2004) Signatures of selection at molecular level in two genes implicated in human familial cancers. In: 12th international conference on intelligent systems for molecular biology and 3rd European conference on computational biology. Glasgow UK, pp 162–162Google Scholar
  4. 4.
    Cyran KA, Polańska J, Kimmel M (2004) Testing for signatures of natural selection at molecular genes level. J Med Info Technol 8:31–39Google Scholar
  5. 5.
    Dhillon KK, Sidorova J, Saintigny Y, Poot M, Gollahon K, Rabinovitch PS, Monnat RJ Jr (2007) Functional role of the Werner syndrome RecQ helicase in human fibroblasts. Aging Cell 6:53–61CrossRefGoogle Scholar
  6. 6.
    Evans PD, Anderson JR, Vallender EJ, Gilbert SL, Malcom ChM, et al (2004) Adaptive evolution of ASPM, a major determinant of cerebral cortical size in humans. Hum Mol Genet 13:489–494CrossRefGoogle Scholar
  7. 7.
    Fu YX (1997) Statistical tests of neutrality of mutations against population growth, hitchhiking and background selection. Genetics 147:915–925Google Scholar
  8. 8.
    Fu YX, Li WH (1993) Statistical tests of neutrality of mutations. Genetics 133: 693–709Google Scholar
  9. 9.
    Golding SE, Rosenberg E, Neill S, Dent P, Povirk LF, Valerie K (2007) Extracellular signal-related kinase positively regulates ataxia telangiectasia mutated, homologous recombination repair, and the DNA damage response. Cancer Res. 67:1046–1053CrossRefGoogle Scholar
  10. 10.
    Karmakar P, Seki M, Kanamori M, Hashiguchi K, Ohtsuki M, Murata E, Inoue E, Tada S, Lan L, Yasui A, Enomoto T (2006) BLM is an early responder to DNA double-strand breaks. Biochem Biophys Res Commun 348:62–69CrossRefGoogle Scholar
  11. 11.
    Kelly JK (1997) A test of neutrality based on interlocus associations. Genetics 146:1197–1206Google Scholar
  12. 12.
    Kimura M (1983) The neutral theory of molecular evolution. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  13. 13.
    Nielsen R (2001) Statistical tests of selective neutrality in the age of genomics. Heredity 86:641–647CrossRefGoogle Scholar
  14. 14.
    Polańska J (2003) The EM algorithm and its implementation for the estimation of the frequencies of SNP-haplotypes. Int J Appl Math Comput Sci 13:419–429MATHMathSciNetGoogle Scholar
  15. 15.
    Schneider J, Philipp M, Yamini P, Dork T, Woitowitz HJ (2007) ATM gene mutations in former uranium miners of SDAG Wismut: a pilot study. Oncol Rep 17:477–482Google Scholar
  16. 16.
    Wall JD (1999) Recombination and the power of statistical tests of neutrality. Genet Res 74:65–79CrossRefGoogle Scholar
  17. 17.
    Zhang J (2003) Evolution of the human ASPM gene, a major determinant of brain size. Genetics 165:2063–2070Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Krzysztof A. Cyran
    • 1
  1. 1.Institute of InformaticsSilesian University of TechnologyPoland

Personalised recommendations