Sample size determination for training set optimization in genomic prediction

Genomic prediction (GP) is a statistical method used to select quantitative traits in animal or plant breeding. For this purpose, a statistical prediction model is first built that uses phenotypic and genotypic data in a training set. The trained model is then used to predict genomic estimated breeding values (GEBVs) for individuals within a breeding population. Setting the sample size of the training set usually takes into account time and space constraints that are inevitable in an agricultural experiment. However, the determination of the sample size remains an unresolved issue for a GP study. By applying the logistic growth curve to identify prediction accuracy for the GEBVs and the training set size, a practical approach was developed to determine a cost-effective optimal training set for a given genome dataset with known genotypic data. Three real genome datasets were used to illustrate the proposed approach. An R function is provided to facilitate widespread application of this approach to sample size determination, which can help breeders to identify a set of genotypes with an economical sample size for selective phenotyping.

Automated summary

Genomic prediction (GP) is a statistical method that predicts genomic estimated breeding values (GEBVs) for individuals in a breeding population using a combination of phenotypic and genotypic data. The determination of the optimal sample size for a training set remains a challenge in GP studies. This study proposes a practical approach using logistic growth curve to determine a cost-effective training set size for a given genome dataset with known genotypic data.