Haplotype Inference in Complex Pedigrees

  • Bonnie Kirkpatrick
  • Javier Rosa
  • Eran Halperin
  • Richard M. Karp
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5541)


Despite the desirable information contained in complex pedigree datasets, analysis methods struggle to efficiently process these datasets. The attractiveness of pedigree data sets is their power for detecting rare variants, particularly in comparison with studies of unrelated individuals. In addition, rather than assuming individuals in a study are unrelated, knowledge of their relationships can avoid spurious results due to confounding population structure effects. However, a major challenge for the applicability of pedigree methods is the ability handle complex pedigrees, having multiple founding lineages, inbreeding, and half-sibling relationships.

A key ingredient in association studies is imputation and inference of haplotypes from genotype data. Existing haplotype inference methods either do not efficiently scales to complex pedigrees or their accuracy is limited. In this paper, we present algorithms for efficient haplotype inference and imputation in complex pedigrees. Our method, PhyloPed, leverages the perfect phylogeny model, resulting in an efficient method with high accuracy. In addition, PhyloPed effectively combines the founder haplotype information from different lineages and is immune to inaccuracies in prior information about the founders.


Recombination Rate Haplotype Inference Founder Haplotype Complex Pedigree Perfect Phylogeny 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Abney, M., Ober, C., McPeek, M.S.: Quantitative-trait homozygosity and association mapping and empirical genomewide significance in large, complex pedigrees: Fasting serum-insulin level in the hutterites. The American Journal of Human Genetics 70(4), 920–934 (2002)CrossRefPubMedGoogle Scholar
  2. 2.
    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
  3. 3.
    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
  4. 4.
    Cannings, C., Sheehan, N.A.: On a Misconception About Irreducibility of the Single-Site Gibbs Sampler in a Pedigree Application. Genetics 162(2), 993–996 (2002)PubMedPubMedCentralGoogle Scholar
  5. 5.
    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
  6. 6.
    Daly, M.J., Rioux, J.D., Schaffner, S.F., Hudson, T.J., Lander, E.S.: High-resolution haplotype structure in the human genome. Nature Genetics 29(2), 229–232 (2001)CrossRefPubMedGoogle Scholar
  7. 7.
    Ding, Z., Filkov, V., Gusfield, D.: A linear-time algorithm for perfect phylogeny haplotyping. Journal of Computational Biology 2, 522–553 (2006)CrossRefGoogle Scholar
  8. 8.
    Elston, R.C., Stewart, J.: A general model for the analysis of pedigree data. Human Heredity 21, 523–542 (1971)CrossRefPubMedGoogle Scholar
  9. 9.
    Eskin, E., Halperin, E., Karp, R.: Efficient reconstruction of haplotype structure via perfect phylogeny. Journal of Bioinformatics and Computational Biology 1(1), 1–20 (2003)CrossRefPubMedGoogle Scholar
  10. 10.
    Fishelson, M., Dovgolevsky, N., Geiger, D.: Maximum likelihood haplotyping for general pedigrees. Human Heredity 59, 41–60 (2005)CrossRefPubMedGoogle Scholar
  11. 11.
    Gusfield. Haplotyping as perfect phylogeny: Conceptual framework and efficient solutions. In: Proceedings of the 6th Annual International Conference on (Research in) Computational (Molecular) Biology (2002)Google Scholar
  12. 12.
    Halperin, E., Kimmel, G., Shamir, R.: Tag SNP selection in genotype data for maximizing SNP prediction accuracy. Bioinformatics 21(suppl. 1), i195–i203 (2005)CrossRefGoogle Scholar
  13. 13.
    Jensen, C.S., Kong, A.: Blocking gibbs sampling for linkage analysis in large pedigrees with many loops. American Journal of Human Genetics 65 (1999)Google Scholar
  14. 14.
    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
  15. 15.
    Lauritzen, S.L., Sheehan, N.A.: Graphical models for genetic analysis. Statistical Science 18(4), 489–514 (2003)CrossRefGoogle Scholar
  16. 16.
    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
  17. 17.
    Sutter, N.B., Bustamante, C.D., Chase, K., Gray, M.M., Zhao, K., Zhu, L., Padhukasahasram, B., Karlins, E., Davis, S., Jones, P.G., Quignon, P., Johnson, G.S., Parker, H.G., Fretwell, N., Mosher, D.S., Lawler, D.F., Satyaraj, E., Nordborg, M., Lark, K.G., Wayne, R.K., Ostrander, E.A.: A Single IGF1 Allele Is a Major Determinant of Small Size in Dogs. Science 316(5821), 112–115 (2007)CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Thomas, A., Abkevich, V., Gutin, A., Bansal, A.: Multilocus linkage analysis by blocked gibbs sampling. Statistics and Computing 10(3), 259–269 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Bonnie Kirkpatrick
    • 1
  • Javier Rosa
    • 2
  • Eran Halperin
    • 3
  • Richard M. Karp
    • 4
  1. 1.Computer Science DeptUniversity of CaliforniaBerkeleyUSA
  2. 2.Computer Science Dept, RutgersThe State University of New JerseyNew BrunswickUSA
  3. 3.School of Computer Science and the Dept. of BiotechnologyTel-Aviv University, and the International Computer Science InstituteBerkeleyIsrael
  4. 4.Computer Science DeptUniversity of California, Berkeley and the International Computer Science InstituteUSA

Personalised recommendations