The power of genomic estimated breeding values for selection when using a finite population size in genetic improvement of tetraploid potato

Potato breeding relies heavily on visual phenotypic scoring for clonal selection. Obtaining robust phenotypic data can be labor intensive and expensive, especially in the early cycles of a potato breeding program where the number of genotypes is very large. We have investigated the power of genomic estimated breeding values (GEBVs) for selection from a limited population size in potato breeding. We collected genotypic data from 669 tetraploid potato clones from all cycles of a potato breeding program, as well as phenotypic data for eight important breeding traits. The genotypes were partitioned into a training and a test population distinguished by cycle of selection in the breeding program. GEBVs for seven traits were predicted for individuals from the first stage of the breeding program (T-1) which had not undergone any selection, or individuals selected at least once in the field (T-2). An additional approach in which GEBVs were predicted within and across full-sib families from unselected material (T-1) was tested for four breeding traits. GEBVs were obtained by using a Bayesian Ridge Regression model estimating single marker effects and phenotypic data from individuals at later stages of selection of the breeding program. Our results suggest that, for most traits included in this study, information from individuals from later stages of selection cannot be utilized to make selections based on GEBVs in earlier clonal generations. Predictions of GEBVs across full-sib families yielded similarly low prediction accuracies as across generations. The most promising approach for selection using GEBVs was found to be making predictions within full-sib families.

Automated summary

This study investigated the potential of using genomic estimated breeding values (GEBVs) for selection in potato breeding. The results suggest that information from individuals at later stages of selection cannot be effectively utilized for making selections based on GEBVs in earlier clonal generations. The most promising approach for selection using GEBVs was found to be making predictions within full-sib families.