Optimizing genomic prediction–based sparse testing designs for multi-environment trials in tetraploid potato cultivars

Abstract

Genomic prediction (GP)-based sparse testing allows evaluation of more genotypes within a fixed budget in multi-environment trials (METs), thereby reducing phenotyping costs. It assesses untested genotype–environment combinations using varying training sets and allocation schemes. This study implements sparse testing designs in tetraploid potato (Solanum tuberosum L.) breeding trials employing varying compositions of overlapping and nonoverlapping genotypes in different allocation schemes, and utilizing three calibration set sizes (19, 16, and 13). A total of 114 unique genotypes were tested for dry matter content (YDY), tuber length (TL), and tuber count (TC), using three prediction models: E + L (environment + line), E + L + G (environment + line + markers), and E + L + G + GE (environment + line + markers + genotype-by-environment interaction [G×E]). Considering the largest training set and the complete nonoverlapping and zero-overlapping genotypes strategy, no differences were observed between models, yielding predictive ability (PA) values of 0.83, 0.70, and 0.50 for YDY, TL, and TC, respectively. As more overlapping genotypes were included in the designs, PA declined across schemes, with the E + L model showing a more rapid decline than models with genomic data. The similar performance of the E + L + G and E + L + G + GE models indicated minimal G×E interaction in the dataset. Additionally, reducing the training set size affected PA in designs with fewer nonoverlapping genotypes. There was no restoration of PA after adding more overlapping genotypes to the designs. Our findings suggest that evaluating more nonoverlapping and a few overlapping genotypes could reduce phenotyping costs by fivefold and increase testing capacity for tetraploid potato cultivars in METs for maximizing overall genetic gain.