Articles
Advancing Prunus salicina breeding strategies through genomic data: exploring two genomic prediction methodologies in Japanese plum
Article number
1450_55
Pages
415 – 424
Language
English
Abstract
Fresh Japanese plum exports in Chile have increased from 152 to 188 thousand tons since 2015. In this context, several plum breeding programs have focused on releasing new cultivars to compete in the international market.
However, traditional fruit tree breeding takes time and effort.
Therefore, it is necessary to develop strategies that increase the efficiency of breeding programs to enable the selection of superior genotypes in a shorter time.
Genome sequencing technologies have facilitated the development of methods to assist in agricultural, forestry, and horticultural genetic improvement programs, such as genomic selection/prediction (GS). Consequently, the present study aimed to evaluate single nucleotide polymorphisms (SNP)-based methodologies currently used in crops for identifying promising selections through phenotypic trait prediction.
A population of 192 Japanese plum trees (from ~30 full-sibling families) was genotyped using ~10,000 SNPs.
In parallel, this population was also assessed for three key phenotypic traits: the beginning of harvest date (HVD), the soluble solids content of fruit flesh (SSC), and the weight of the fruit (WEI). Genomic and phenotypic data were employed to develop genomic prediction (GS) models, which exhibited a moderate predictive ability (PA) ranging from 0.34 to 0.67. Notably, an integrated analysis of quantitative trait loci (QTL) detection and GS approaches exhibited a PA up to 8% higher than the traditional GS approach.
The findings provide valuable information for implementing strategies based on GS that facilitate the selection of plum trees and the early development of new high-productivity Japanese plum cultivars.
On the other hand, a larger population of plum individuals could be required to achieve implementation on a larger scale.
However, traditional fruit tree breeding takes time and effort.
Therefore, it is necessary to develop strategies that increase the efficiency of breeding programs to enable the selection of superior genotypes in a shorter time.
Genome sequencing technologies have facilitated the development of methods to assist in agricultural, forestry, and horticultural genetic improvement programs, such as genomic selection/prediction (GS). Consequently, the present study aimed to evaluate single nucleotide polymorphisms (SNP)-based methodologies currently used in crops for identifying promising selections through phenotypic trait prediction.
A population of 192 Japanese plum trees (from ~30 full-sibling families) was genotyped using ~10,000 SNPs.
In parallel, this population was also assessed for three key phenotypic traits: the beginning of harvest date (HVD), the soluble solids content of fruit flesh (SSC), and the weight of the fruit (WEI). Genomic and phenotypic data were employed to develop genomic prediction (GS) models, which exhibited a moderate predictive ability (PA) ranging from 0.34 to 0.67. Notably, an integrated analysis of quantitative trait loci (QTL) detection and GS approaches exhibited a PA up to 8% higher than the traditional GS approach.
The findings provide valuable information for implementing strategies based on GS that facilitate the selection of plum trees and the early development of new high-productivity Japanese plum cultivars.
On the other hand, a larger population of plum individuals could be required to achieve implementation on a larger scale.
Publication
Authors
P. Ballesta, A. Fiol, R. Infante, I. Pacheco
Keywords
fruit quality traits, predictive ability, high-throughput genotyping, stone fruit
Online Articles (59)