Potato breeding must improve its efficiency by increasing the reliability of selection as well as identifying a promising germplasm for crossing. This study shows the prediction accuracy of genomic-estimated breeding values for several potato (Solanum tuberosum L.) breeding clones and the released cultivars that were evaluated at three locations in northern and southern Sweden for various traits. Three dosages of marker alleles [pseudo-diploid (A), additive tetrasomic polyploidy (B), and additive-non-additive tetrasomic polyploidy (C)] were considered in the genome-based prediction models, for single environments and multiple environments (accounting for the genotype-by-environment interaction or G × E), and for comparing two kernels, the conventional linear, Genomic Best Linear Unbiased Prediction (GBLUP) (GB), and the non-linear Gaussian kernel (GK), when used with the single-kernel genetic matrices of A, B, C, or when employing two-kernel genetic matrices in the model using the kernels from B and C for a single environment (models 1 and 2, respectively), and for multi-environments (models 3 and 4, respectively). Concerning the single site analyses, the trait with the highest prediction accuracy for all sites under A, B, C for model 1, model 2, and for GB and GK methods was tuber starch percentage. Another trait with relatively high prediction accuracy was the total tuber weight. Results show an increase in prediction accuracy of model 2 over model 1. Non-linear Gaussian kernel (GK) did not show any clear advantage over the linear kernel GBLUP (GB). Results from the multi-environments had prediction accuracy estimates (models 3 and 4) higher than those obtained from the single-environment analyses. Model 4 with GB was the best method in combination with the marker structure B for predicting most of the tuber traits. Most of the traits gave relatively high prediction accuracy under this combination of marker structure (A, B, C, and B-C), and methods GB and GK combined with the multi-environment with G × E model.

Potato breeding aims to improve crop productivity, quality and resilience based on heritable characteristics. Estimating the trait heritability and correlations—both genetic and phenotypic—among characteristics in a target population of environments allows us to define the best breeding method that leads to selection gains. Breeding clones (47) and released cultivars (209) were grown using simple lattice designs at three testing sites in northern and southern Sweden to estimate the best linear unbiased predictors (BLUPs) derived from mixed linear models for characteristics such as tuber weight (total and according to sizes), host plant resistance to late blight (caused by the oomycete Phytophthora infestans) and tuber quality (starch percentage based on specific gravity measurements and reducing sugars). There was significant heritable variation for all the characteristics investigated. Tuber starch percentage and total tuber weight were the traits with the highest broad-sense heritability (H2), while the weight for the smallest size (<40 mm) had the highest H2 among the different tuber categories. These results show the potential for further improving these traits for Scandinavia through recombination and selection in segregating offspring. The genetic and phenotypic correlations among the tuber weight characteristics were significant (p ≤ 0.05) irrespective of their sizes, but none were significant (p > 0.05) with tuber starch percentage. Host plant resistance to late blight was negatively and significantly associated with tuber weight and starch percentage, thereby showing the strong effects of this disease on the productivity and quality of the potatoes.

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 (T1) which had not undergone any selection, or individuals selected at least once in the field (T2). An additional approach in which GEBVs were predicted within and across full-sib families from unselected material (T1) 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.

In this study we extend research on genomic prediction (GP) to polysomic polyploid plant species with the main objective to investigate single trait (ST) versus multi-trait (MT) for multi-environment (ME) models for the combination of three locations in Sweden (Helgegrden [HEL], Mosslunda [MOS], Ume [UM]) over two year-trials (2020, 2021) of 253 potato cultivars and breeding clones for five tuber weight traits and two tuber flesh quality characteristics. This research investigated the GP of four genome-based prediction models with genotype environment interactions (GE): (1) single trait reaction norm model (M1), (2) single trait model considering covariances between environments (M2), (3) single trait M2 extended to include a random vector that utilizes the environmental covariances (M3) and (4) multi-trait model with GE (M4). Several prediction problems were analyzed for each of the GP accuracy of the four models. Results of the prediction of traits in HEL, the high yield potential testing site in 2021, show that the best predicted traits were tuber flesh starch (%), weight of tuber above 60 or below 40 mm in size, and total tuber weight. In terms of GP, accuracy model M4gave the best prediction accuracy in three traits, namely tuber weight of 4050 or above 60 mm in size, and total tuber weight and very similar in the starch trait. For MOS in 2021, the best predictive traits were starch, weight of tuber above 60, 5060, or below 40 mm in size, and total tuber weight. MT model M4 was the best GP model based on its accuracy when some cultivars are observed in some traits. For GP accuracy of traits in UM in 2021, the best predictive traits were weight of tuber above 60, 5060, or below 40 mm in size and the best model was MT M4 followed by models ST M3 and M2.

Genetic gains (ΔG) are determined by the breeders' equation ΔG = [(ck σ2G)/(y σP)], where c, k and y are the parental control, a function of the selection intensity and number of years to complete one selection cycle, respectively, while σ2G and are σP the genetic variance and the square root of the phenotypic variance. Plant breeding programs should deliver above 1% of annual genetic gains after testing and selection. The aim of this research was to estimate genetic gains in potato breeding after testing of cultivars released in western Europe in the last 200 years under high yield potential, and stress-prone environments affected by a pest (late blight) or daylength. The annual genetic gains for tuber yield and flesh's starch content for potato breeding in Europe were about 0.3 and −0.1%, respectively, thus telling that the realized genetic gains of foreign cultivars for both traits are small or negative, respectively, in the Nordic testing sites. The national annual productivity gains in potato grown in Sweden were on average 0.7% in the last 60 years while the genetic gains for tuber yield considering only the table cultivars released after the 2nd World War were about 0.36%, thus showing that breeding contributed just above ½ of it. Furthermore, genetic gains for breeding low reducing sugars in the tuber flesh, and high host plant resistance to late blight were small (<0.2% per year). These results highlight that genetic gains are small when testing bred germplasm outside their target population of environments.