Assessment of the performance of hidden Markov models for imputation in animal breeding

Andrew Whalen, Gregor Gorjanc, Roger Ros Freixedes, John Hickey

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we review the performance of various hidden Markov model-based imputation methods in animal breeding populations. Traditionally, pedigree and heuristic-based imputation methods have been used for imputation in large animal populations due to their computational efficiency, scalability, and accuracy. Recent advances in the area of human genetics have increased the ability of probabilistic hidden Markov model methods to perform accurate phasing and imputation in large populations. These advances may enable these methods to be useful for routine use in large animal populations, particularly in populations where pedigree information is not readily available
Original languageEnglish
Article number44
JournalGenetics Selection Evolution
Volume50
Issue number1
DOIs
Publication statusPublished - Sep 2018

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