Enlarging a training set for genomic selection by imputation of un-genotyped animals in populations of varying genetic architecture.

ABSTRACT:

Background

The most common application of imputation is to infer genotypes of a high-density panel of markers on animals that are genotyped for a low-density panel. However, the increase in accuracy of genomic predictions resulting from an increase in the number of markers tends to reach a plateau beyond a certain density. Another application of imputation is to increase the size of the training set with un-genotyped animals. This strategy can be particularly successful when a set of closely related individuals are genotyped.

Methods

Imputation on completely un-genotyped dams was performed using known genotypes from the sire of each dam, one offspring and the offspring's sire. Two methods were applied based on either allele or haplotype frequencies to infer genotypes at ambi

SUBMITTER: Pimentel EC 

PROVIDER: S-EPMC3652763 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature