Comparing Genomic Prediction Models by Means of Cross Validation.

ABSTRACT: In the two decades of continuous development of genomic selection, a great variety of models have been proposed to make predictions from the information available in dense marker panels. Besides deciding which particular model to use, practitioners also need to make many minor choices for those parameters in the model which are not typically estimated by the data (so called "hyper-parameters"). When the focus is placed on predictions, most of these decisions are made in a direction sought to optimize predictive accuracy. Here we discuss and illustrate using publicly available crop datasets the use of cross validation to make many such decisions. In particular, we emphasize the importance of paired comparisons to achieve high power in the comparison between candidate models, as well as the

SUBMITTER: Schrauf MF 

PROVIDER: S-EPMC8639521 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature