A hybrid method for the imputation of genomic data in livestock populations.

ABSTRACT:

Background

This paper describes a combined heuristic and hidden Markov model (HMM) method to accurately impute missing genotypes in livestock datasets. Genomic selection in breeding programs requires high-density genotyping of many individuals, making algorithms that economically generate this information crucial. There are two common classes of imputation methods, heuristic methods and probabilistic methods, the latter being largely based on hidden Markov models. Heuristic methods are robust, but fail to impute markers in regions where the thresholds of heuristic rules are not met, or the pedigree is inconsistent. Hidden Markov models are probabilistic methods which typically do not require specific family structures or pedigree information, making them very flexible, but they are

SUBMITTER: Antolin R 

PROVIDER: S-EPMC5439152 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature