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A combined long-range phasing and long haplotype imputation method to impute phase for SNP genotypes

John M Hickey1*, Brian P Kinghorn1, Bruce Tier2, James F Wilson3, Neil Dunstan4 and Julius HJ van der Werf15

Author Affiliations

1 School of Environmental and Rural Science, University of New England, Armidale, Australia

2 Animal Genetics and Breeding Unit, University of New England, Armidale, Australia

3 Centre for Population Health Sciences, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG Scotland

4 School of Science and Technology, University of New England, Armidale, Australia

5 Cooperative Research Centre for Sheep Industry Innovation, Armidale, Australia

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Genetics Selection Evolution 2011, 43:12  doi:10.1186/1297-9686-43-12

Published: 10 March 2011

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.