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A Comparison of Phasing Algorithms for Trios and Unrelated Individuals

Knowledge of haplotype phase is valuable for many analysis methods in the study of disease, population, and evolutionary genetics. Considerable research effort has been devoted to the development of statistical and computational methods that infer haplotype phase from genotype data. Although a subst... Full description

Journal Title: American journal of human genetics 2006, Vol.78 (3), p.437-450
Main Author: Marchini, Jonathan
Other Authors: Cutler, David , Patterson, Nick , Stephens, Matthew , Eskin, Eleazar , Halperin, Eran , Lin, Shin , Qin, Zhaohui S. , Munro, Heather M. , Abecasis, Gonçalo R. , Donnelly, Peter
Format: Electronic Article Electronic Article
Language: English
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Quelle: Alma/SFX Local Collection
Publisher: Chicago, IL: Elsevier Inc
ID: ISSN: 0002-9297
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recordid: cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_1380287
title: A Comparison of Phasing Algorithms for Trios and Unrelated Individuals
format: Article
creator:
  • Marchini, Jonathan
  • Cutler, David
  • Patterson, Nick
  • Stephens, Matthew
  • Eskin, Eleazar
  • Halperin, Eran
  • Lin, Shin
  • Qin, Zhaohui S.
  • Munro, Heather M.
  • Abecasis, Gonçalo R.
  • Donnelly, Peter
subjects:
  • Algorithms
  • Analysis
  • Biological and medical sciences
  • Chromosome mapping
  • Computer Simulation
  • General aspects. Genetic counseling
  • Genetic research
  • Genetic variation
  • Genetics
  • Genetics(clinical)
  • Genetics, Behavioral - methods
  • Haplotypes - genetics
  • Humans
  • Medical genetics
  • Medical sciences
  • Parent-Child Relations
  • Polymorphism, Single Nucleotide
  • Population genetics
  • Statistical methods
ispartof: American journal of human genetics, 2006, Vol.78 (3), p.437-450
description: Knowledge of haplotype phase is valuable for many analysis methods in the study of disease, population, and evolutionary genetics. Considerable research effort has been devoted to the development of statistical and computational methods that infer haplotype phase from genotype data. Although a substantial number of such methods have been developed, they have focused principally on inference from unrelated individuals, and comparisons between methods have been rather limited. Here, we describe the extension of five leading algorithms for phase inference for handling father-mother-child trios. We performed a comprehensive assessment of the methods applied to both trios and to unrelated individuals, with a focus on genomic-scale problems, using both simulated data and data from the HapMap project. The most accurate algorithm was PHASE (v2.1). For this method, the percentages of genotypes whose phase was incorrectly inferred were 0.12%, 0.05%, and 0.16% for trios from simulated data, HapMap Centre d'Etude du Polymorphisme Humain (CEPH) trios, and HapMap Yoruban trios, respectively, and 5.2% and 5.9% for unrelated individuals in simulated data and the HapMap CEPH data, respectively. The other methods considered in this work had comparable but slightly worse error rates. The error rates for trios are similar to the levels of genotyping error and missing data expected. We thus conclude that all the methods considered will provide highly accurate estimates of haplotypes when applied to trio data sets. Running times differ substantially between methods. Although it is one of the slowest methods, PHASE (v2.1) was used to infer haplotypes for the 1 million–SNP HapMap data set. Finally, we evaluated methods of estimating the value of r 2 between a pair of SNPs and concluded that all methods estimated r 2 well when the estimated value was ⩾0.8.
language: eng
source: Alma/SFX Local Collection
identifier: ISSN: 0002-9297
fulltext: fulltext
issn:
  • 0002-9297
  • 1537-6605
url: Link


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titleA Comparison of Phasing Algorithms for Trios and Unrelated Individuals
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creatorMarchini, Jonathan ; Cutler, David ; Patterson, Nick ; Stephens, Matthew ; Eskin, Eleazar ; Halperin, Eran ; Lin, Shin ; Qin, Zhaohui S. ; Munro, Heather M. ; Abecasis, Gonçalo R. ; Donnelly, Peter
creatorcontribMarchini, Jonathan ; Cutler, David ; Patterson, Nick ; Stephens, Matthew ; Eskin, Eleazar ; Halperin, Eran ; Lin, Shin ; Qin, Zhaohui S. ; Munro, Heather M. ; Abecasis, Gonçalo R. ; Donnelly, Peter ; for the International HapMap Consortium ; International HapMap Consortium
descriptionKnowledge of haplotype phase is valuable for many analysis methods in the study of disease, population, and evolutionary genetics. Considerable research effort has been devoted to the development of statistical and computational methods that infer haplotype phase from genotype data. Although a substantial number of such methods have been developed, they have focused principally on inference from unrelated individuals, and comparisons between methods have been rather limited. Here, we describe the extension of five leading algorithms for phase inference for handling father-mother-child trios. We performed a comprehensive assessment of the methods applied to both trios and to unrelated individuals, with a focus on genomic-scale problems, using both simulated data and data from the HapMap project. The most accurate algorithm was PHASE (v2.1). For this method, the percentages of genotypes whose phase was incorrectly inferred were 0.12%, 0.05%, and 0.16% for trios from simulated data, HapMap Centre d'Etude du Polymorphisme Humain (CEPH) trios, and HapMap Yoruban trios, respectively, and 5.2% and 5.9% for unrelated individuals in simulated data and the HapMap CEPH data, respectively. The other methods considered in this work had comparable but slightly worse error rates. The error rates for trios are similar to the levels of genotyping error and missing data expected. We thus conclude that all the methods considered will provide highly accurate estimates of haplotypes when applied to trio data sets. Running times differ substantially between methods. Although it is one of the slowest methods, PHASE (v2.1) was used to infer haplotypes for the 1 million–SNP HapMap data set. Finally, we evaluated methods of estimating the value of r 2 between a pair of SNPs and concluded that all methods estimated r 2 well when the estimated value was ⩾0.8.
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subjectAlgorithms ; Analysis ; Biological and medical sciences ; Chromosome mapping ; Computer Simulation ; General aspects. Genetic counseling ; Genetic research ; Genetic variation ; Genetics ; Genetics(clinical) ; Genetics, Behavioral - methods ; Haplotypes - genetics ; Humans ; Medical genetics ; Medical sciences ; Parent-Child Relations ; Polymorphism, Single Nucleotide ; Population genetics ; Statistical methods
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descriptionKnowledge of haplotype phase is valuable for many analysis methods in the study of disease, population, and evolutionary genetics. Considerable research effort has been devoted to the development of statistical and computational methods that infer haplotype phase from genotype data. Although a substantial number of such methods have been developed, they have focused principally on inference from unrelated individuals, and comparisons between methods have been rather limited. Here, we describe the extension of five leading algorithms for phase inference for handling father-mother-child trios. We performed a comprehensive assessment of the methods applied to both trios and to unrelated individuals, with a focus on genomic-scale problems, using both simulated data and data from the HapMap project. The most accurate algorithm was PHASE (v2.1). For this method, the percentages of genotypes whose phase was incorrectly inferred were 0.12%, 0.05%, and 0.16% for trios from simulated data, HapMap Centre d'Etude du Polymorphisme Humain (CEPH) trios, and HapMap Yoruban trios, respectively, and 5.2% and 5.9% for unrelated individuals in simulated data and the HapMap CEPH data, respectively. The other methods considered in this work had comparable but slightly worse error rates. The error rates for trios are similar to the levels of genotyping error and missing data expected. We thus conclude that all the methods considered will provide highly accurate estimates of haplotypes when applied to trio data sets. Running times differ substantially between methods. Although it is one of the slowest methods, PHASE (v2.1) was used to infer haplotypes for the 1 million–SNP HapMap data set. Finally, we evaluated methods of estimating the value of r 2 between a pair of SNPs and concluded that all methods estimated r 2 well when the estimated value was ⩾0.8.
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abstractKnowledge of haplotype phase is valuable for many analysis methods in the study of disease, population, and evolutionary genetics. Considerable research effort has been devoted to the development of statistical and computational methods that infer haplotype phase from genotype data. Although a substantial number of such methods have been developed, they have focused principally on inference from unrelated individuals, and comparisons between methods have been rather limited. Here, we describe the extension of five leading algorithms for phase inference for handling father-mother-child trios. We performed a comprehensive assessment of the methods applied to both trios and to unrelated individuals, with a focus on genomic-scale problems, using both simulated data and data from the HapMap project. The most accurate algorithm was PHASE (v2.1). For this method, the percentages of genotypes whose phase was incorrectly inferred were 0.12%, 0.05%, and 0.16% for trios from simulated data, HapMap Centre d'Etude du Polymorphisme Humain (CEPH) trios, and HapMap Yoruban trios, respectively, and 5.2% and 5.9% for unrelated individuals in simulated data and the HapMap CEPH data, respectively. The other methods considered in this work had comparable but slightly worse error rates. The error rates for trios are similar to the levels of genotyping error and missing data expected. We thus conclude that all the methods considered will provide highly accurate estimates of haplotypes when applied to trio data sets. Running times differ substantially between methods. Although it is one of the slowest methods, PHASE (v2.1) was used to infer haplotypes for the 1 million–SNP HapMap data set. Finally, we evaluated methods of estimating the value of r 2 between a pair of SNPs and concluded that all methods estimated r 2 well when the estimated value was ⩾0.8.
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