Genetic mapping revealed that the Pun2 gene in Capsicum chacoense encodes a putative aminotransferase.

Several genes regulating capsaicinoid biosynthesis including Pun1 (also known as CS), Pun3, pAMT, and CaKR1 have been studied. However, the gene encoded by Pun2 in the non-pungent Capsicum chacoense is unknown. This study aimed to identify the Pun2 gene by genetic mapping using interspecific (C. chacoense × Capsicum annuum) and intraspecific (C. chacoense × C. chacoense) populations. QTL mapping using the interspecific F2 population revealed two major QTLs on chromosomes 3 and 9. Two bin markers within the QTL regions on two chromosomes were highly correlated with the capsaicinoid content in the interspecific population. The major QTL, Pun2_PJ_Gibbs_3.11 on chromosome 3, contained the pAMT gene, indicating that the non-pungency of C. chacoense may be attributed to a mutation in the pAMT gene. Sequence analysis revealed a 7 bp nucleotide insertion in the 8th exon of pAMT of the non-pungent C. chacoense. This mutation resulted in the generation of an early stop codon, resulting in a truncated mutant lacking the PLP binding site, which is critical for pAMT enzymatic activity. This insertion co-segregated with the pungency phenotype in the intraspecific F2 population. We named this novel pAMT allele pamt11 . Taken together, these data indicate that the non-pungency of C. chacoense is due to the non-functional pAMT allele, and Pun2 encodes the pAMT gene.

Genomic selection with fixed-effect markers improves the prediction accuracy for Capsaicinoid contents in Capsicum annuum.

Capsaicinoids provide chili peppers (Capsicum spp.) with their characteristic pungency. Several structural and transcription factor genes are known to control capsaicinoid contents in pepper. However, many other genes also regulating capsaicinoid contents remain unknown, making it difficult to develop pepper cultivars with different levels of capsaicinoids. Genomic selection (GS) uses genome-wide random markers (including many in undiscovered genes) for a trait to improve selection efficiency. In this study, we predicted the capsaicinoid contents of pepper breeding lines using several GS models trained with genotypic and phenotypic data from a training population. We used a core collection of 351 Capsicum accessions and 96 breeding lines as training and testing populations, respectively. To obtain the optimal number of single nucleotide polymorphism (SNP) markers for GS, we tested various numbers of genome-wide SNP markers based on linkage disequilibrium. We obtained the highest mean prediction accuracy (0.550) for different models using 3294 SNP markers. Using this marker set, we conducted GWAS and selected 25 markers that were associated with capsaicinoid biosynthesis genes and quantitative trait loci for capsaicinoid contents. Finally, to develop more accurate prediction models, we obtained SNP markers from GWAS as fixed-effect markers for GS, where 3294 genome-wide SNPs were employed. When four to five fixed-effect markers from GWAS were used as fixed effects, the RKHS and RR-BLUP models showed accuracies of 0.696 and 0.689, respectively. Our results lay the foundation for developing pepper cultivars with various capsaicinoid levels using GS for capsaicinoid contents.

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

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 proven 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 five fruit traits (fruit length, fruit shape, fruit width, fruit weight, and pericarp thickness) in 351 accessions from the pepper core collection, including 229 Capsicum annuum, 48 Capsicum baccatum, 48 Capsicum chinense, 25 Capsicum frutescens, and 1 Capsicum chacoense in 4 years at two different locations and genotyped these accessions using genotyping-by-sequencing. Among the whole core collection, considering its genetic distance and sexual incompatibility, we only included 302 C. annum complex (229 C. annuum, 48 C. chinense, and 25 C. frutescens) 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.