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Bioinformatics Advance Access originally published online on September 25, 2007
Bioinformatics 2007 23(22):3048-3055; doi:10.1093/bioinformatics/btm435
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© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

HAPLOPOOL: improving haplotype frequency estimation through DNA pools and phylogenetic modeling

Bonnie Kirkpatrick 1,*, Carlos Santos Armendariz 2, Richard M. Karp 1,3 and Eran Halperin 3

1Department of Electrical Engineering and Computer Sciences, UC Berkeley, CA, 2Computer Science Department, Universidad Rey Juan Carlos, Madrid, Spain and 3International Computer Science Institute, Berkeley, CA, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: The search for genetic variants that are linked to complex diseases such as cancer, Parkinson's;, or Alzheimer's; disease, may lead to better treatments. Since haplotypes can serve as proxies for hidden variants, one method of finding the linked variants is to look for case-control associations between the haplotypes and disease. Finding these associations requires a high-quality estimation of the haplotype frequencies in the population. To this end, we present, HAPLOPOOL, a method of estimating haplotype frequencies from blocks of consecutive SNPs.

Results: HAPLOPOOL leverages the efficiency of DNA pools and estimates the population haplotype frequencies from pools of disjoint sets, each containing two or three unrelated individuals. We study the trade-off between pooling efficiency and accuracy of haplotype frequency estimates. For a fixed genotyping budget, HAPLOPOOL performs favorably on pools of two individuals as compared with a state-of-the-art non-pooled phasing method, PHASE. Of independent interest, HAPLOPOOL can be used to phase non-pooled genotype data with an accuracy approaching that of PHASE.

We compared our algorithm to three programs that estimate haplotype frequencies from pooled data. HAPLOPOOL is an order of magnitude more efficient (at least six times faster), and considerably more accurate than previous methods. In contrast to previous methods, HAPLOPOOL performs well with missing data, genotyping errors and long haplotype blocks (of between 5 and 25 SNPs).

Availability: The HAPLOPOOL software is available at: http://haplopool.icsi.berkeley.edu/haplopool/

Contact: bbkirk{at}eecs.berkeley.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Keith Crandall

Received on June 7, 2007; revised on August 3, 2007; accepted on August 16, 2007

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