Genomic Selection for Prediction of Fruit-Related Traits in Pepper (Capsicum spp.)
Ju-Pyo Hong, Na‐Young Ro, Hea‐Young Lee, Geon‐Woo Kim, Jin‐Kyung Kwon, Eiji Yamamoto, Byoung‐Cheorl Kang
Abstract
) into further analysis. We used phenotypic and genotypic data to investigate genomic prediction models, marker density, and effects of population structure. Among 10 genomic prediction methods tested, Reproducing Kernel Hilbert Space (RKHS) produced the highest prediction accuracies (measured as correlation between predicted values and observed values) across the traits, with accuracies of 0.75, 0.73, 0.84, 0.83, and 0.82 for fruit length, fruit shape, fruit width, fruit weight, and pericarp thickness, respectively. Overall, prediction accuracies were positively correlated with the number of markers for fruit traits. We tested our genomic selection models in a separate population of recombinant inbred lines derived from two parental lines from the core collection. Despite the large difference in genetic diversity between the training population and the test population, we obtained moderate prediction accuracies of 0.32, 0.34, 0.50, and 0.48 for fruit length, fruit shape, fruit width, and fruit weight, respectively. This use of genomic selection for fruit-related traits demonstrates the potential use of core collections and genomic selection as tools for crop improvement.