Advertisement

Linear Reduction for Haplotype Inference

  • Jingwu He
  • Alex Zelikovsky
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3240)

Abstract

Haplotype inference problem asks for a set of haplotypes explaining a given set of genotypes. Popular software tools for haplotype inference (e.g., PHASE, HAPLOTYPER) as well as new algorithms recently proposed for perfect phylogeny inference (DPPH) are often not well scalable. When the number of sites (SNP’s) comes to thousands these tools often cannot deliver answer in reasonable time even if the number of haplotypes is small. In this paper we propose a new linear algebra based method which drastically reduces the number of sites in the original data. After solving a reduced instance, linear decoding allows to recover haplotypes of full length for given genotypes. Experiments show that our method significantly speeds up popular haplotype inference tools while finding almost the same solution practically in all cases thus not compromising the quality of the known haplotype inference methods.

Keywords

Haplotype inference linear independence perfect phylogeny 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bafna, V., Gusfield, D., Lancia, G., Yooseph, S.: Haplotyping as perfect phylogeny: A direct approach. Technical report, UC Davis, Department of Computer Science (2002)Google Scholar
  2. 2.
    Bafna, V., Gusfield, D., Lancia, G., Yooseph, S.: Haplotyping as perfect phylogeny: A direct approach. J. Computational Biology 10, 323–340 (2003)CrossRefGoogle Scholar
  3. 3.
    Chung, R.H., Gusfield, D.: Empirical exploration of perfect phylogeny haplotyping and haplotypers. In: Warnow, T.J., Zhu, B. (eds.) COCOON 2003. LNCS, vol. 2697, pp. 5–19. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Chung, R.H., Gusfield, D.: Perfect phylogeny haplotyper: Haplotype inferral using a tree model. Bioinformatics 19(6), 780–781 (2003)CrossRefGoogle Scholar
  5. 5.
    Clark, A.: Inference of haplotypes from PCR-amplified samples of diploid populations. Mol. Biol. Evol. 7, 111–122 (1990)Google Scholar
  6. 6.
    Clark, A., Weiss, K., Nickerson, D., et al.: Haplotype structure and population genetic inferences from nucleotide-sequence variation in human lipoprotein lipase. Am. J. Human Genetics 63, 595–612 (1998)CrossRefGoogle Scholar
  7. 7.
    Daly, M., Rioux, J., Schaffner, S., Hudson, T., Lander, E.: High resolution haplotype structure in the human genome. Nature Genetics 29, 229–232 (2001)CrossRefGoogle Scholar
  8. 8.
    Eskin, E., Halperin, E., Karp, R.: Efficient reconstruction of haplotype structure via perfect phylogeny. Technical report, UC Berkeley, Computer Science Division (EECS) (2002)Google Scholar
  9. 9.
    Fullerton, M., Clark, A., Sing, C., et al.: Apolipoprotein E variation at the sequence haplotype level: implications for the origin and maintenance of a major human polymorphism. Am. J. of Human Genetics, 881–900 (2000)Google Scholar
  10. 10.
    Gabriel, G., Schaffner, S., Nguyen, H., Moore, J., Roy, J., Blumenstiel, B., Higgins, J., DeFelice, M., Lochner, A., Faggart, M., Liu-Cordero, S., Rotimi, C., Adeyemo, A., Cooper, R., Ward, R., Lander, E., Daly, M., Altshuler, D.: The structure of haplotype blocks in the human genome. Science 296, 2225–2229 (2002)CrossRefGoogle Scholar
  11. 11.
    Gusfield, D.: Inference of haplotypes from samples of diploid populations: complexity and algorithms. Journal of computational biology 8(3) (2001)Google Scholar
  12. 12.
    Gusfield, D.: Haplotyping as Perfect Phylogeny: Conceptual Framework and Efficient Solutions (Extended Abstract). In: Proceedings of RECOMB 2002: The Sixth Annual International Conference on Computational Biology, pp. 166–175 (2002)Google Scholar
  13. 13.
    Halperin, E., Eskin, E.: Haplotype reconstruction from genotype data using imperfect phylogeny. Bioinformatics. Advance Access published on (February 26, 2004)Google Scholar
  14. 14.
    Hudson, R.: Gene genealogies and the coalescent process. Oxford Survey of Evolutionary Biology 7, 1–44 (1990)Google Scholar
  15. 15.
    Lin, S., Cutler, D., Zwick, M., Cahkravarti, A.: Haplotype inference in random population samples. Am. J. of Hum. Genet. 71, 1129–1137 (2003)CrossRefGoogle Scholar
  16. 16.
    Niu, T., Qin, Z., Xu, X., Liu, J.S.: Bayesian haplotype inference for multiple linked single-nucleotide polymorphisms. Am. J. Hum. Genet 70, 157–169 (2002)CrossRefGoogle Scholar
  17. 17.
    Orzack, S., Gusfield, D., Stanton, V.: The absolute and relative accuracy of haplotype inferral methods and a consensus approach to haplotype inferral. Abstract Nr 115 in Am. Society of Human Genetics, Supplement (2001)Google Scholar
  18. 18.
    Patil, N., Berno, A., Hinds, D., Barrett, W., Doshi, J., Hacker, C., Kautzer, C., Lee, D., Marjoribanks, C., McDonough, D., Nguyen, B., Norris, M., Sheehan, J., Shen, N., Stern, D., Stokowski, R., Thomas, D., Trulson, M., Vyas, K., Frazer, K., Fodor, S., Cox, D.: Blocks of limited haplotype diversity revealed by high-resolution scanning of human chromosome 21. Science 294, 1719–1723 (2001)CrossRefGoogle Scholar
  19. 19.
    Stephens, M., Smith, N., Donnelly, P.: A new statistical method for haplotype reconstruction from population data. Am. J. Human Genetics 68, 978–989 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Jingwu He
    • 1
  • Alex Zelikovsky
    • 1
  1. 1.Department of Computer ScienceGeorgia State UniversityAtlantaUSA

Personalised recommendations