Multi-environment genomic prediction for soluble solids content in peach (Prunus persica)

Abstract

Genotype-by-environment interaction (G × E) is a common phenomenon influencing genetic improvement in plants, and a good understanding of this phenomenon is important for breeding and cultivar deployment strategies. However, there is little information on G × E in horticultural tree crops, mostly due to evaluation costs, leading to a focus on the development and deployment of locally adapted germplasm. Using sweetness (measured as soluble solids content, SSC) in peach/nectarine assessed at four trials from three US peach-breeding programs as a case study, we evaluated the hypotheses that (i) complex data from multiple breeding programs can be connected using GBLUP models to improve the knowledge of G × E for breeding and deployment and (ii) accounting for a known large-effect quantitative trait locus (QTL) improves the prediction accuracy. Following a structured strategy using univariate and multivariate models containing additive and dominance genomic effects on SSC, a model that included a previously detected QTL and background genomic effects was a significantly better fit than a genome-wide model with completely anonymous markers. Estimates of an individual’s narrow-sense and broad-sense heritability for SSC were high (0.57–0.73 and 0.66–0.80, respectively), with 19–32% of total genomic variance explained by the QTL. Genome-wide dominance effects and QTL effects were stable across environments. Significant G × E was detected for background genome effects, mostly due to the low correlation of these effects across seasons within a particular trial. The expected prediction accuracy, estimated from the linear model, was higher than the realised prediction accuracy estimated by cross-validation, suggesting that these two parameters measure different qualities of the prediction models. While prediction accuracy was improved in some cases by combining data across trials, particularly when phenotypic data for untested individuals were available from other trials, this improvement was not consistent. This study confirms that complex data can be combined into a single analysis using GBLUP methods to improve understanding of G × E and also incorporate known QTL effects. In addition, the study generated baseline information to account for population structure in genomic prediction models in horticultural crop improvement.

Establishing a Berry Sensory Evaluation Model Based on Machine Learning

“…The determination of soluble solid content (SSC) was as follows [ 26 ]: the blueberries were partially beaten, and the juice was extracted using four layers of gauze. The SSC was measured three times using an Abbemat 500 automatic refractometer.…”

Ppe.RPT/SSC-1: from QTL mapping to a predictive KASP test for ripening time and soluble solids concentration in peach

“…Quantitative Trait Loci (QTLs) and associated signals have been identified using different approaches such as bi-parental QTL mapping 11 – 14 , pedigree-based analysis (PBA) 15 – 17 and genome-wide association studies 18 . In addition, genomic prediction models focusing on SSC were developed 19 , 20 .…”

Genotype-by-environment and QTL-by-environment interactions in sweet cherry (Prunus avium L.) for flowering date

“…In perennial plant species, G×E has been mostly studied in forest tree species for traits related to tree height and trunk diameter (Li et al, 2017;Bird et al, 2022). More recently, horticultural fruit tree species such as apple (Jung et al, 2020), macadamia (Hardner, 2017), sweet cherry (Hardner et al, 2019) and peach (Hardner et al, 2022) have also been studied. For instance, in sweet cherry, low additive G×E and high additive genomic correlations among environments were estimated for maturity date in a germplasm of 597 cultivars, accessions and unselected offspring planted in three locations in Europe and one location in the USA (Hardner et al, 2019).…”