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Abstract Genomic regions associated with ripening time (RPT) and soluble solids concentration (SSC) were mapped using a pedigreed population including multiple F1 and F2 families from the Clemson University peach breeding program (CUPBP). RPT and SSC QTLs were consistently identified in two seasons (2011 and 2012) and the average datasets (average of two seasons). A target region spanning 10,981,971–11,298,736 bp on chromosome 4 of peach reference genome used for haplotype analysis revealed four haplotypes with significant differences in trait values among different diplotype combinations. Favorable alleles at the target region for both RPT and SSC were determined and a DNA test for predicting RPT and SSC was developed. Two Kompetitive Allele Specific PCR (KASP) assays were validated on 84 peach cultivars and 163 seedlings from the CUPBP, with only one assay (Ppe.RPT/SSC-1) needed to predict between early and late-season ripening cultivars and low and high SSC. These results advance our understanding of the genetic basis of RPT and SSC and facilitate selection of new peach cultivars with the desired RPT and SSC.

Rose (Rosa spp.) is one of the most economically important ornamental species worldwide. Flower diameter, flower weight, and the number of petals and petaloids are key flower-size parameters and attractive targets for DNA-informed breeding. Pedigree-based analysis (PBA) using FlexQTL software was conducted using two sets of multi-parental diploid rose populations. Phenotypic data for flower diameter (Diam), flower weight (fresh (FWT)/dry (DWT)), number of petals (NP), and number of petaloids (PD) were collected over six environments (seasons) at two locations in Texas. The objectives of this study were to 1) identify new and/or validate previously reported QTL(s); 2) identify SNP haplotypes associated with QTL alleles (Q-/q-) of a trait and their sources; and 3) determine QTL genotypes for important rose breeding parents. Several new and previously reported QTLs for NP and Diam traits were identified. In addition, QTLs associated with flower weight and PD were identified for the first time. Two major QTLs with large effects were mapped for all traits. The first QTL was at the distal end of LG1 (60.44–60.95 Mbp) and was associated with Diam and DWT in the TX2WOB populations. The second QTL was consistently mapped in the middle region on LG3 (30.15–39.34 Mbp) and associated with NP, PD, and flower weight across two multi-parent populations (TX2WOB and TX2WSE). Haplotype results revealed a series of QTL alleles with differing effects at important loci for most traits. This work is distinct from previous studies by conducting co-factor analysis to account for the DOUBLE FLOWER locus while mapping QTL for NP. Sources of high-value (Q) alleles were identified, namely, ‘Old Blush’ and Rosa wichuraiana from J14-3 for Diam, while ‘Violette’ and PP-J14-3 were sources for other traits. In addition, the source of the low-value (q) alleles for Diam was ‘Little Chief’, and Rosa wichuraiana through J14-3 was the source for the remaining traits. Hence, our results can potentially inform parental/seedling selections as means to improve ornamental quality in roses and a step towards implementing DNA-informed techniques for use in rose breeding programs.

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.

Ten phenological and fruit quality traits were evaluated in seedlings from nine F1 low to medium chill full-sib peach (Prunus persica) families and their parents over 2 years at two locations (Fowler, CA, and College Station, TX) to estimate variance components, genotype by environment interaction (G×E), and phenotypic correlations using restricted maximum likelihood mixed and multivariate models. The removal of nectarine [P. persica var. nucipersica (fruit without fuzz)] and pantao (flat shape fruit) seedlings from the analysis decreased the heritability for the fruit size, blush, tip, and soluble solids concentration (SSC), indicating the importance of taking the effects of the major gene of nectarine/pantao into account when assessing the heritability of traits. A strong correlation coefficient (r = 0.92) found between ripe date (RD) and fruit development period (FDP) and between fruit weight (FW) and fruit diameter (FD), indicates that either measure is equally effective, although the negative correlation between bloom date (BD) and FDP (r = −0.46) implies earlier blooming during cool temperatures tends to extend FDP. FW, FD, blush, and SSC had moderately weak correlations with RD (r = 0.56, 0.53, −0.41, and 0.48) and FDP (r = 0.57, 0.56, −0.50, and 0.39, respectively), which could be explained either by the presence of a strong link between quantitative trait loci of these traits and the ripening date locus or the pleiotropic effect of ripening date on many quantitative fruit characters. The traits RD, FDP, and titratable acidity (TA) had the highest broad-sense heritability (H2) and lowest G×E. FW, tip, and shape showed the lowest H2, the highest of G×E variance to the genetic F (G×E variance/total genotypic variance), and high G×E, whereas the other traits showed moderate G×E. For the traits that had a higher G×E interaction, selection for or against these traits should be done at the production location. A moderate narrow-sense heritability (h2) was estimated for BD, blush, fruit tip, and shape. FW and FD showed low to moderate h2 while H2 was high, whereas RD, FDP, SSC, and TA showed low h2 and high H2 estimates, indicating important nonadditive effects for these traits.

Peach is one of the most important fruit crops in the world, with the global annual production about 24.6 million tons. The United States is the fourth-largest producer after China, Spain, and Italy. Peach consumption has decreased over the last decade, most likely due to inconsistent quality of the fruit on the market. Thus, marker-assisted selection for fruit quality traits is highly desired in fresh market peach breeding programs and one of the major goals of the RosBREED project. The ability to use DNA information to select for desirable traits would enable peach breeders to efficiently plan crosses and select seedlings with desired quality traits early in the selection process before fruiting. Therefore, we assembled a multi-locus genome wide association study (GWAS) of 620 individuals from three public fresh market peach breeding programs (Arkansas, Texas, and South Carolina). The material was genotyped using 9K SNP array and the traits were phenotyped for three phenological (bloom date, ripening date, and days after bloom) and 11 fruit quality-related traits (blush, fruit diameter, fruit weight, adherence, fruit firmness, redness around pit, fruit texture, pit weight, soluble solid concentration, titratable acidity, and pH) over three seasons (2010, 2011, and 2012). Multi-locus association analyses, carried out using mrMLM 4.0 and FarmCPU R packages, revealed a total of 967 and 180 quantitative trait nucleotides (QTNs), respectively. Among the 88 consistently reliable QTNs detected using multiple multi-locus GWAS methods and/or at least two seasons, 44 were detected for the first time. Fruit quality hotspots were identified on chromosomes 1, 3, 4, 5, 6, and 8. Out of 566 candidate genes detected in the genomic regions harboring the QTN clusters, 435 were functionally annotated. Gene enrichment analyses revealed 68 different gene ontology (GO) terms associated with fruit quality traits. Data reported here advance our understanding of genetic mechanisms underlying important fruit quality traits and further support the development of DNA tools for breeding.

Resistance to rose rosette disease (RRD), a fatal disease of roses (Rosa spp.), is a high priority for rose breeding. As RRD resistance is time-consuming to phenotype, the identification of genetic markers for resistance could expedite breeding efforts. However, little is known about the genetics of RRD resistance. Therefore, we performed a quantitative trait locus (QTL) analysis on a set of inter-related diploid rose populations phenotyped for RRD resistance and identified four QTLs. Two QTLs were found in multiple years. The most consistent QTL is qRRV_TX2WSE_ch5, which explains approximately 20% and 40% of the phenotypic variation in virus quantity and severity of RRD symptoms, respectively. The second, a QTL on chromosome 1, qRRD_TX2WSE_ch1, accounts for approximately 16% of the phenotypic variation for severity. Finally, a third QTL on chromosome 3 was identified only in the multiyear analysis, and a fourth on chromosome 6 was identified in data from one year only. In addition, haplotypes associated with significant changes in virus quantity and severity were identified for qRRV_TX2WSE_ch5 and qRRD_TX2WSE_ch1. This research represents the first report of genetic determinants of resistance to RRD. In addition, marker trait associations discovered here will enable better parental selection when breeding for RRD resistance and pave the way for marker-assisted selection for RRD resistance.