Inbreeding Effects on the Performance and Genomic Prediction for Polysomic Tetraploid Potato Offspring Grown at High Nordic Latitudes.

Inbreeding depression (ID) is caused by increased homozygosity in the offspring after selfing. Although the self-compatible, highly heterozygous, tetrasomic polyploid potato (Solanum tuberosum L.) suffers from ID, some argue that the potential genetic gains from using inbred lines in a sexual propagation system of potato are too large to be ignored. The aim of this research was to assess the effects of inbreeding on potato offspring performance under a high latitude and the accuracy of the genomic prediction of breeding values (GEBVs) for further use in selection. Four inbred (S1) and two hybrid (F1) offspring and their parents (S0) were used in the experiment, with a field layout of an augmented design with the four S0 replicated in nine incomplete blocks comprising 100, four-plant plots at Umeå (63°49'30″ N 20°15'50″ E), Sweden. S0 was significantly (p < 0.01) better than both S1 and F1 offspring for tuber weight (total and according to five grading sizes), tuber shape and size uniformity, tuber eye depth and reducing sugars in the tuber flesh, while F1 was significantly (p < 0.01) better than S1 for all tuber weight and uniformity traits. Some F1 hybrid offspring (15-19%) had better total tuber yield than the best-performing parent. The GEBV accuracy ranged from -0.3928 to 0.4436. Overall, tuber shape uniformity had the highest GEBV accuracy, while tuber weight traits exhibited the lowest accuracy. The F1 full sib's GEBV accuracy was higher, on average, than that of S1. Genomic prediction may facilitate eliminating undesired inbred or hybrid offspring for further use in the genetic betterment of potato.

Partial least squares enhance multi-trait genomic prediction of potato cultivars in new environments.

It is of paramount importance in plant breeding to have methods dealing with large numbers of predictor variables and few sample observations, as well as efficient methods for dealing with high correlation in predictors and measured traits. This paper explores in terms of prediction performance the partial least squares (PLS) method under single-trait (ST) and multi-trait (MT) prediction of potato traits. The first prediction was for tested lines in tested environments under a five-fold cross-validation (5FCV) strategy and the second prediction was for tested lines in untested environments (herein denoted as leave one environment out cross validation, LOEO). There was a good performance in terms of predictions (with accuracy mostly > 0.5 for Pearson's correlation) the accuracy of 5FCV was better than LOEO. Hence, we have empirical evidence that the ST and MT PLS framework is a very valuable tool for prediction in the context of potato breeding data.

Modeling genotype × environment interaction for single and multitrait genomic prediction in potato (Solanum tuberosum L.).

In this study, we extend research on genomic prediction (GP) to polysomic polyploid plant species with the main objective to investigate single-trait (ST) and multitrait (MT) multienvironment (ME) models using field trial data from 3 locations in Sweden [Helgegården (HEL), Mosslunda (MOS), Umeå (UM)] over 2 years (2020, 2021) of 253 potato cultivars and breeding clones for 5 tuber weight traits and 2 tuber flesh quality characteristics. This research investigated the GP of 4 genome-based prediction models with genotype × environment interactions (GEs): (1) ST reaction norm model (M1), (2) ST model considering covariances between environments (M2), (3) ST M2 extended to include a random vector that utilizes the environmental covariances (M3), and (4) MT model with GE (M4). Several prediction problems were analyzed for each of the GP accuracy of the 4 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 the total tuber weight. In terms of GP, accuracy model M4 gave the best prediction accuracy in 3 traits, namely tuber weight of 40-50 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 tubers above 60, 50-60, or below 40 mm in size, and the total tuber weight. MT model M4 was the best GP model based on its accuracy when some cultivars are observed in some traits. For the GP accuracy of traits in UM in 2021, the best predictive traits were the weight of tubers above 60, 50-60, or below 40 mm in size, and the best model was MT M4, followed by models ST M3 and M2.

Genome-Based Genotype × Environment Prediction Enhances Potato (Solanum tuberosum L.) Improvement Using Pseudo-Diploid and Polysomic Tetraploid Modeling.

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.

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 (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.