Mapping and characterization QTLs for phenological traits in seven pedigree-connected peach families.

BACKGROUND: Environmental adaptation and expanding harvest seasons are primary goals of most peach [Prunus persica (L.) Batsch] breeding programs. Breeding perennial crops is a challenging task due to their long breeding cycles and large tree size. Pedigree-based analysis using pedigreed families followed by haplotype construction creates a platform for QTL and marker identification, validation, and the use of marker-assisted selection in breeding programs. RESULTS: Phenotypic data of seven F1 low to medium chill full-sib families were collected over 2 years at two locations and genotyped using the 9 K SNP Illumina array. Three QTLs were discovered for bloom date (BD) and mapped on linkage group 1 (LG1) (172-182 cM), LG4 (48-54 cM), and LG7 (62-70 cM), explaining 17-54%, 11-55%, and 11-18% of the phenotypic variance, respectively. The QTL for ripening date (RD) and fruit development period (FDP) on LG4 was co-localized at the central part of LG4 (40-46 cM) and explained between 40 and 75% of the phenotypic variance. Haplotype analyses revealed SNP haplotypes and predictive SNP marker(s) associated with desired QTL alleles and the presence of multiple functional alleles with different effects for a single locus for RD and FDP. CONCLUSIONS: A multiple pedigree-linked families approach validated major QTLs for the three key phenological traits which were reported in previous studies across diverse materials, geographical distributions, and QTL mapping methods. Haplotype characterization of these genomic regions differentiates this study from the previous QTL studies. Our results will provide the peach breeder with the haplotypes for three BD QTLs and one RD/FDP QTL to create predictive DNA-based molecular marker tests to select parents and/or seedlings that have desired QTL alleles and cull unwanted genotypes in early seedling stages.

Identification and characterization of QTLs for fruit quality traits in peach through a multi-family approach.

BACKGROUND: Fruit quality traits have a significant effect on consumer acceptance and subsequently on peach (Prunus persica (L.) Batsch) consumption. Determining the genetic bases of key fruit quality traits is essential for the industry to improve fruit quality and increase consumption. Pedigree-based analysis across multiple peach pedigrees can identify the genomic basis of complex traits for direct implementation in marker-assisted selection. This strategy provides breeders with better-informed decisions and improves selection efficiency and, subsequently, saves resources and time. RESULTS: Phenotypic data of seven F1 low to medium chill full-sib families were collected over 2 years at two locations and genotyped using the 9 K SNP Illumina array. One major QTL for fruit blush was found on linkage group 4 (LG4) at 40-46 cM that explained from 20 to 32% of the total phenotypic variance and showed three QTL alleles of different effects. For soluble solids concentration (SSC), one QTL was mapped on LG5 at 60-72 cM and explained from 17 to 39% of the phenotypic variance. A major QTL for titratable acidity (TA) co-localized with the major locus for low-acid fruit (D-locus). It was mapped at the proximal end of LG5 and explained 35 to 80% of the phenotypic variance. The new QTL for TA on the distal end of LG5 explained 14 to 22% of the phenotypic variance. This QTL co-localized with the QTL for SSC and affected TA only when the first QTL is homozygous for high acidity (epistasis). Haplotype analyses revealed SNP haplotypes and predictive SNP marker(s) associated with desired QTL alleles. CONCLUSIONS: A multi-family-based QTL discovery approach enhanced the ability to discover a new TA QTL at the distal end of LG5 and validated other QTLs which were reported in previous studies. Haplotype characterization of the mapped QTLs distinguishes this work from the previous QTL studies. Identified predictive SNPs and their original sources will facilitate the selection of parents and/or seedlings that have desired QTL alleles. Our findings will help peach breeders develop new predictive, DNA-based molecular marker tests for routine use in marker-assisted breeding.

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

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.

Pedigree-based QTL analysis of flower size traits in two multi-parental diploid rose populations.

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.

Corrigendum: Multi-Locus Genome-Wide Association Studies Reveal Fruit Quality Hotspots in Peach Genome.

[This corrects the article DOI: 10.3389/fpls.2021.644799.].

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

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.

Identification of QTLs for Reduced Susceptibility to Rose Rosette Disease in Diploid Roses.

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.

Pedigree-based analysis in multi-parental diploid rose populations reveals QTLs for cercospora leaf spot disease resistance.

Cercospora leaf spot (CLS) (Cercospora rosicola) is a major fungal disease of roses (Rosa sp.) in the southeastern U.S. Developing CLS-resistant cultivars offers a potential solution to reduce pesticide use. Yet, no work has been performed on CLS resistance. This study aimed to identify QTLs and to characterize alleles for resistance to CLS. The study used pedigree-based QTL analysis to dissect the genetic basis of CLS resistance using two multi-parental diploid rose populations (TX2WOB and TX2WSE) evaluated across five years in two Texas locations. A total 38 QTLs were identified across both populations and distributed over all linkage groups. Three QTLs on LG3, LG4, and LG6 were consistently mapped over multiple environments. The LG3 QTL was mapped in a region between 18.9 and 27.8 Mbp on the Rosa chinensis genome assembly. This QTL explained 13 to 25% of phenotypic variance. The LG4 QTL detected in the TX2WOB population spanned a 35.2 to 39.7 Mbp region with phenotypic variance explained (PVE) up to 48%. The LG6 QTL detected in the TX2WSE population was localized to 17.9 to 33.6 Mbp interval with PVE up to 36%. Also, this study found multiple degrees of favorable allele effects (q-allele) associated with decreasing CLS at major loci. Ancestors 'OB', 'Violette', and PP-M4-4 were sources of resistance q-alleles. These results will aid breeders in parental selection to develop CLS-resistant rose cultivars. Ultimately, high throughput DNA tests that target major loci for CLS could be developed for routine use in a DNA-informed breeding program.

QTL mapping and characterization of black spot disease resistance using two multi-parental diploid rose populations.

Black spot disease (BSD) (Diplocarpon rosae) is the most common and damaging fungal disease in garden roses (Rosa sp.). Although qualitative resistance to BSD has been extensively investigated, the research on quantitative resistance lags behind. The goal of this research was to study the genetic basis of BSD resistance in two multi-parental populations (TX2WOB and TX2WSE) through a pedigree-based analysis approach (PBA). Both populations were genotyped and evaluated for BSD incidence over five years in three locations in Texas. A total of 28 QTLs, distributed over all linkage groups (LGs), were detected across both populations. Consistent minor effect QTLs included two on LG1 and LG3 (TX2WOB and TX2WSE), two on LG4 and LG5 (TX2WSE), and one QTL on LG7 (TX2WOB). In addition, one major QTL detected in both populations was consistently mapped on LG3. This QTL was localized to an interval ranging from 18.9 to 27.8 Mbp on the Rosa chinensis genome and explained 20 and 33% of the phenotypic variation. Furthermore, haplotype analysis showed that this QTL had three distinct functional alleles. The parent PP-J14-3 was the common source of the LG3 BSD resistance in both populations. Taken together, this research presents the characterization of new SNP-tagged genetic determinants of BSD resistance, the discovery of marker-trait associations to enable parental choice based on their BSD resistance QTL haplotypes, and substrates for the development of trait-predictive DNA tests for routine use in marker-assisted breeding for BSD resistance.

Multi-Locus Genome-Wide Association Studies Reveal Fruit Quality Hotspots in Peach Genome.

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.