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Genomic Selection for Prediction of Fruit-Related Traits in Pepper (Capsicum spp.)

2020; Frontiers Media; Volume: 11; Linguagem: Inglês

10.3389/fpls.2020.570871

1664-462X

Ju-Pyo Hong, Na‐Young Ro, Hea‐Young Lee, Geon‐Woo Kim, Jin‐Kyung Kwon, Eiji Yamamoto, Byoung‐Cheorl Kang,

Genetic and phenotypic traits in livestock

Pepper (Capsicum spp.) fruit-related traits are critical determinants of quality. These traits are controlled by quantitatively inherited genes for which marker-assisted selection (MAS) has proved insufficiently effective. Here, we evaluated the potential of genomic selection, in which genotype and phenotype data for a training population are used to predict phenotypes of a test population with only genotype data, for predicting fruit-related traits in pepper. We measured four fruit traits (fruit length, fruit width, fruit weight, and pericarp thickness) in 350 accessions from the pepper core collection, including 229 Capsicum annuum, 48 Capsicum baccatum, 48 Capsicum chinense, and 25 Capsicum frutescens, in three years at two different locations and genotyped these accessions using genotyping-by-sequencing. We used phenotypic and genotypic data to investigate genomic prediction models, marker density, and effects of population structure. Among ten 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 0.74, 0.84, 0.83, and 0.82 for fruit length, width, 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 0.31, 0.41, and 0.44 for fruit length, fruit weight, and fruit width, 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.