Literature DB >> 15290779

Efficient reconstruction of haplotype structure via perfect phylogeny.

Eleazar Eskin1, Eran Halperin, Richard M Karp.   

Abstract

Each person's genome contains two copies of each chromosome, one inherited from the father and the other from the mother. A person's genotype specifies the pair of bases at each site, but does not specify which base occurs on which chromosome. The sequence of each chromosome separately is called a haplotype. The determination of the haplotypes within a population is essential for understanding genetic variation and the inheritance of complex diseases. The haplotype mapping project, a successor to the human genome project, seeks to determine the common haplotypes in the human population. Since experimental determination of a person's genotype is less expensive than determining its component haplotypes, algorithms are required for computing haplotypes from genotypes. Two observations aid in this process: first, the human genome contains short blocks within which only a few different haplotypes occur; second, as suggested by Gusfield, it is reasonable to assume that the haplotypes observed within a block have evolved according to a perfect phylogeny, in which at most one mutation event has occurred at any site, and no recombination occurred at the given region. We present a simple and efficient polynomial-time algorithm for inferring haplotypes from the genotypes of a set of individuals assuming a perfect phylogeny. Using a reduction to 2-SAT we extend this algorithm to handle constraints that apply when we have genotypes from both parents and child. We also present a hardness result for the problem of removing the minimum number of individuals from a population to ensure that the genotypes of the remaining individuals are consistent with a perfect phylogeny. Our algorithms have been tested on real data and give biologically meaningful results. Our webserver (http://www.cs.columbia.edu/compbio/hap/) is publicly available for predicting haplotypes from genotype data and partitioning genotype data into blocks.

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Year:  2003        PMID: 15290779     DOI: 10.1142/s0219720003000174

Source DB:  PubMed          Journal:  J Bioinform Comput Biol        ISSN: 0219-7200            Impact factor:   1.122


  17 in total

1.  Inference and analysis of haplotypes from combined genotyping studies deposited in dbSNP.

Authors:  Noah A Zaitlen; Hyun Min Kang; Michael L Feolo; Stephen T Sherry; Eran Halperin; Eleazar Eskin
Journal:  Genome Res       Date:  2005-11       Impact factor: 9.043

2.  Accounting for decay of linkage disequilibrium in haplotype inference and missing-data imputation.

Authors:  Matthew Stephens; Paul Scheet
Journal:  Am J Hum Genet       Date:  2005-01-31       Impact factor: 11.025

3.  A coalescence-guided hierarchical Bayesian method for haplotype inference.

Authors:  Yu Zhang; Tianhua Niu; Jun S Liu
Journal:  Am J Hum Genet       Date:  2006-06-28       Impact factor: 11.025

4.  Haplotype reconstruction using perfect phylogeny and sequence data.

Authors:  Anatoly Efros; Eran Halperin
Journal:  BMC Bioinformatics       Date:  2012-04-19       Impact factor: 3.169

5.  A comparison of phasing algorithms for trios and unrelated individuals.

Authors:  Jonathan Marchini; David Cutler; Nick Patterson; Matthew Stephens; Eleazar Eskin; Eran Halperin; Shin Lin; Zhaohui S Qin; Heather M Munro; Goncalo R Abecasis; Peter Donnelly
Journal:  Am J Hum Genet       Date:  2006-01-26       Impact factor: 11.025

6.  On the genealogy of asexual diploids.

Authors:  Fumei Lam; Charles H Langley; Yun S Song
Journal:  J Comput Biol       Date:  2011-03       Impact factor: 1.479

7.  Hap-seq: an optimal algorithm for haplotype phasing with imputation using sequencing data.

Authors:  Dan He; Buhm Han; Eleazar Eskin
Journal:  J Comput Biol       Date:  2013-02       Impact factor: 1.479

Review 8.  Linkage disequilibrium--understanding the evolutionary past and mapping the medical future.

Authors:  Montgomery Slatkin
Journal:  Nat Rev Genet       Date:  2008-06       Impact factor: 53.242

9.  Angiotensin-converting enzyme gene polymorphism predicts the time-course of blood pressure response to angiotensin converting enzyme inhibition in the AASK trial.

Authors:  Vibha Bhatnagar; Daniel T O'Connor; Nicholas J Schork; Rany M Salem; Caroline M Nievergelt; Brinda K Rana; Douglas W Smith; George L Bakris; John P Middleton; Keith C Norris; Jackson T Wright; Deanna Cheek; Leena Hiremath; Gabriel Contreras; Lawrence J Appel; Michael S Lipkowitz
Journal:  J Hypertens       Date:  2007-10       Impact factor: 4.844

10.  Genome-wide compatible SNP intervals and their properties.

Authors:  Jeremy Wang; Fernando Pardo-Manual de Villena; Kyle J Moore; Wei Wang; Qi Zhang; Leonard McMillan
Journal:  ACM Int Conf Bioinform Comput Biol (2010)       Date:  2010-08
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