Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/8209
Title: A combined long-range phasing and long haplotype imputation method to impute phase for SNP genotypes
Contributor(s): Hickey, John (author); Kinghorn, Brian  (author); Tier, Bruce  (author); Wilson, J F (author); Dunstan, Neil  (author); Van Der Werf, Julius H  (author)orcid 
Publication Date: 2011
Open Access: Yes
DOI: 10.1186/1297-9686-43-12Open Access Link
Handle Link: https://hdl.handle.net/1959.11/8209
Abstract: Background: Knowing the phase of marker genotype data can be useful in genome-wide association studies, because it makes it possible to use analysis frameworks that account for identity by descent or parent of origin of alleles and it can lead to a large increase in data quantities via genotype or sequence imputation. Long-range phasing and haplotype library imputation constitute a fast and accurate method to impute phase for SNP data. Methods: A long-range phasing and haplotype library imputation algorithm was developed. It combines information from surrogate parents and long haplotypes to resolve phase in a manner that is not dependent on the family structure of a dataset or on the presence of pedigree information. Results: The algorithm performed well in both simulated and real livestock and human datasets in terms of both phasing accuracy and computation efficiency. The percentage of alleles that could be phased in both simulated and real datasets of varying size generally exceeded 98% while the percentage of alleles incorrectly phased in simulated data was generally less than 0.5%. The accuracy of phasing was affected by dataset size, with lower accuracy for dataset sizes less than 1000, but was not affected by effective population size, family data structure, presence or absence of pedigree information, and SNP density. The method was computationally fast. In comparison to a commonly used statistical method (fastPHASE), the current method made about 8% less phasing mistakes and ran about 26 times faster for a small dataset. For larger datasets, the differences in computational time are expected to be even greater. A computer program implementing these methods has been made available. Conclusions: The algorithm and software developed in this study make feasible the routine phasing of high-density SNP chips in large datasets.
Publication Type: Journal Article
Source of Publication: Genetics Selection Evolution, 43(1), p. 1-19
Publisher: BioMed Central Ltd
Place of Publication: London, United Kingdom
ISSN: 0999-193X
Field of Research (FOR): 070201 Animal Breeding
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Statistics to Oct 2018: Visitors: 197
Views: 210
Downloads: 1
Appears in Collections:Animal Genetics and Breeding Unit (AGBU)
Journal Article

Files in This Item:
2 files
File Description SizeFormat 
Show full item record

SCOPUSTM   
Citations

66
checked on Nov 26, 2018

Page view(s)

138
checked on Feb 8, 2019
Google Media

Google ScholarTM

Check

Altmetric


Items in Research UNE are protected by copyright, with all rights reserved, unless otherwise indicated.