Modeling genotype environment interaction for single- and multi-trait genomic prediction in potato (Solanum tuberosum L.)

Abstract

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 (Helgeg[a]rden [HEL], Mosslunda [MOS], Ume[a] [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 xenvironment 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 M4 gave the best prediction accuracy in three 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 tuber above 60, 50-60, 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, 50-60, or below 40 mm in size and the best model was MT M4 followed by models ST M3 and M2.