Haplotypes versus Genotypes on Pedigrees

  • Bonnie Kirkpatrick
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6293)


Genome sequencing will soon produce haplotype data for individuals. For pedigrees of related individuals, sequencing appears to be an attractive alternative to genotyping. However, methods for pedigree analysis with haplotype data have not yet been developed, and the computational complexity of such problems has been an open question. Furthermore, it is not clear in which scenarios haplotype data would provide better estimates than genotype data for quantities such as recombination rates.To answer these questions, a reduction is given from genotype problem instances to haplotype problem instances, and it is shown that solving the haplotype problem yields the solution to the genotype problem, up to constant factors or coefficients. The pedigree analysis problems we will consider are the likelihood, maximum probability haplotype, and minimum recombination haplotype problems.

Two algorithms are introduced: an exponential-time hidden Markov model (HMM) for haplotype data where some individuals are untyped, and a linear-time algorithm for pedigrees having haplotype data for all individuals. Recombination estimates from the general haplotype HMM algorithm are compared to recombination estimates produced by a genotype HMM. Having haplotype data on all individuals produces better estimates. However, having several untyped individuals can drastically reduce the utility of haplotype data.


Hide Markov Model Genotype Data Pedigree Analysis Haplotype Data Haplotype Allele 
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.


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  1. 1.
    Barrett, J.C., Hansoul, S., Nicolae, D.L., Cho, J.H., Duerr, R.H., Rioux, J.D., Brant, S.R., Silverberg, M.S., Taylor, K.D., Barmada, M.M., et al.: Genome-wide association defines more than 30 distinct susceptibility loci for crohn’s disease. Nature Genetics 40, 955–962 (2008)CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Burdick, J.T., Chen, W., Abecasis, G.R., Cheung, V.G.: In silico method for inferring genotyeps in pedigrees. Nature Genetics 38, 1002–1004 (2006)CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Chen, W.-M., Abecasis, G.R.: Family-based association tests for genomewide association scans. American Journal of Human Genetics 81, 913–926 (2007)CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Coop, G., Wen, X., Ober, C., Pritchard, J.K., Przeworski, M.: High-Resolution Mapping of Crossovers Reveals Extensive Variation in Fine-Scale Recombination Patterns Among Humans. Science 319(5868), 1395–1398 (2008)CrossRefPubMedGoogle Scholar
  5. 5.
    Elston, R.C., Stewart, J.: A general model for the analysis of pedigree data. Human Heredity 21, 523–542 (1971)CrossRefPubMedGoogle Scholar
  6. 6.
    Eid, J., et al.: Real-Time DNA Sequencing from Single Polymerase Molecules. Science 323(5910), 133–138 (2009)CrossRefPubMedGoogle Scholar
  7. 7.
    Geiger, D., Meek, C., Wexler, Y.: Speeding up HMM algorithms for genetic linkage analysis via chain reductions of the state space. Bioinformatics 25(12), i196 (2009)Google Scholar
  8. 8.
    Kirkpatrick, B., Halperin, E., Karp, R.M.: Haplotype inference in complex pedigrees. Journal of Computational Biology (2010) (in press)Google Scholar
  9. 9.
    Lander, E.S., Green, P.: Construction of multilocus genetic linkage maps in humans. Proceedings of the National Academy of Science 84(5), 2363–2367 (1987)CrossRefGoogle Scholar
  10. 10.
    Li, J., Jiang, T.: An exact solution for finding minimum recombinant haplotype configurations on pedigrees with missing data by integer linear programming. In: Proceedings of the 7th Annual International Conference on Research in Computational Molecular Biology, pp. 101–110 (2003)Google Scholar
  11. 11.
    Ng, M.Y., Levinson, D.F., et al.: Meta-analysis of 32 genome-wide linkage studies of schizophrenia. Mol. Psychiatry 14, 774–785 (2009)CrossRefPubMedGoogle Scholar
  12. 12.
    Piccolboni, A., Gusfield, D.: On the complexity of fundamental computational problems in pedigree analysis. Journal of Computational Biology 10(5), 763–773 (2003)CrossRefPubMedGoogle Scholar
  13. 13.
    Romero, I.G., Ober, C.: CFTR mutations and reproductive outcomes in a population isolate. Human Genet. 122, 583–588 (2008)CrossRefGoogle Scholar
  14. 14.
    Sobel, E., Lange, K.: Descent graphs in pedigree analysis: Applications to haplotyping, location scores, and marker-sharing statistics. American Journal of Human Genetics 58(6), 1323–1337 (1996)PubMedPubMedCentralGoogle Scholar
  15. 15.
    Thatte, B.D.: Combinatorics of pedigrees (2006)Google Scholar
  16. 16.
    Xiao, J., Liu, L., Xia, L., Jiang, T.: Efficient Algorithms for Reconstructing Zero-Recombinant Haplotypes on a Pedigree Based on Fast Elimination of Redundant Linear Equations. SIAM Journal on Computing 38, 2198 (2009)CrossRefGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Bonnie Kirkpatrick
    • 1
  1. 1.Electrical Engineering and Computer SciencesUniversity of California Berkeley and International Computer Science InstituteUSA

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