A comparison of methods for training population optimization in genomic selection.

ABSTRACT:

Key message

Maximizing CDmean and Avg_GRM_self were the best criteria for training set optimization. A training set size of 50-55% (targeted) or 65-85% (untargeted) is needed to obtain 95% of the accuracy.  With the advent of genomic selection (GS) as a widespread breeding tool, mechanisms to efficiently design an optimal training set for GS models became more relevant, since they allow maximizing the accuracy while minimizing the phenotyping costs. The literature described many training set optimization methods, but there is a lack of a comprehensive comparison among them. This work aimed to provide an extensive benchmark among optimization methods and optimal training set size by testing a wide range of them in seven datasets, six different species, different genetic architecture

SUBMITTER: Fernandez-Gonzalez J 

PROVIDER: S-EPMC9998580 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature