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A phasing and imputation method for pedigreed populations that results in a single-stage genomic evaluation

John M Hickey1*, Brian P Kinghorn1, Bruce Tier2, Julius HJ van der Werf13 and Matthew A Cleveland4

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 (AGBU is a joint unit of NSW DPI and the University of New England), Armidale, Australia

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

4 Genus plc, 100 Bluegrass Commons Blvd., Suite 2200, Hendersonville, TN, 37075, USA

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Genetics Selection Evolution 2012, 44:9  doi:10.1186/1297-9686-44-9

Published: 30 March 2012



Efficient, robust, and accurate genotype imputation algorithms make large-scale application of genomic selection cost effective. An algorithm that imputes alleles or allele probabilities for all animals in the pedigree and for all genotyped single nucleotide polymorphisms (SNP) provides a framework to combine all pedigree, genomic, and phenotypic information into a single-stage genomic evaluation.


An algorithm was developed for imputation of genotypes in pedigreed populations that allows imputation for completely ungenotyped animals and for low-density genotyped animals, accommodates a wide variety of pedigree structures for genotyped animals, imputes unmapped SNP, and works for large datasets. The method involves simple phasing rules, long-range phasing and haplotype library imputation and segregation analysis.


Imputation accuracy was high and computational cost was feasible for datasets with pedigrees of up to 25 000 animals. The resulting single-stage genomic evaluation increased the accuracy of estimated genomic breeding values compared to a scenario in which phenotypes on relatives that were not genotyped were ignored.


The developed imputation algorithm and software and the resulting single-stage genomic evaluation method provide powerful new ways to exploit imputation and to obtain more accurate genetic evaluations.