A Spatial-Aware Haplotype Copying Model with Applications to Genotype Imputation

  • Wen-Yun Yang
  • Farhad Hormozdiari
  • Eleazar Eskin
  • Bogdan Pasaniuc
Conference paper

DOI: 10.1007/978-3-319-05269-4_30

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8394)
Cite this paper as:
Yang WY., Hormozdiari F., Eskin E., Pasaniuc B. (2014) A Spatial-Aware Haplotype Copying Model with Applications to Genotype Imputation. In: Sharan R. (eds) Research in Computational Molecular Biology. RECOMB 2014. Lecture Notes in Computer Science, vol 8394. Springer, Cham

Abstract

Ever since its introduction, the haplotype copy model has proven to be one of the most successful approaches for modeling genetic variation in human populations with applications ranging from ancestry inference to genotype phasing and imputation. Motivated by coalescent theory, this approach assumes that any chromosome (haplotype) can be modeled as a mosaic of segments copied from a set of chromosomes sampled from the same population. At the core of the model is the assumption that any chromosome from the sample is equally likely to contribute a priori to the copying process. Motivated by recent works that model genetic variation in a geographic continuum, we propose a new spatial-aware haplotype copy model that jointly models geography and the haplotype copying process. We extend hidden Markov models of haplotype diversity such that at any given location, haplotypes that are closest in the genetic-geographic continuum map are a priori more likely to contribute to the copying process than distant ones. Through simulations starting from the 1000 Genomes data, we show that our model achieves superior accuracy in genotype imputation over the standard spatial-unaware haplotype copy model. In addition, we show the utility of our model in selecting a small personalized reference panel for imputation that leads to both improved accuracy as well as to a lower computational runtime than the standard approach. Finally, we show our proposed model can be used to localize individuals on the genetic-geographical map on the basis of their genotype data.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Wen-Yun Yang
    • 1
  • Farhad Hormozdiari
    • 1
  • Eleazar Eskin
    • 1
    • 2
  • Bogdan Pasaniuc
    • 2
    • 3
  1. 1.Department of Computer ScienceUniversity of California Los AngelesUSA
  2. 2.Department of Human GeneticsUniversity of California Los AngelesUSA
  3. 3.Department of Pathology and Laboratory MedicineUniversity of California Los AngelesUSA

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