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 F 1 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.

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.

BackgroundEnvironmental 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.ResultsPhenotypic data of seven F1 low to medium chill full-sib families were collected over two years at two locations and genotyped using the 9K 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 40-75% 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.ConclusionsA 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 for the creation of 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.

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.

Two-year-old, field-grown golden kiwifruit (Actinidia chinensis) and fuzzy kiwifruit (Actinidia deliciosa) plants were evaluated for injury following an early freeze event of −4.1 °C on 14 Nov. 2018 in Burleson County, TX. Plant material included seven cultivars: one seed-propagated [Sungold™ (ZESY002)] and three cutting-propagated golden kiwifruit (AU Golden Dragon, AU Golden Sunshine, CK03), and one seed-propagated (Hayward) and two cutting-propagated fuzzy kiwifruit (AU Authur and AU Fitzgerald). Observations were made 5 weeks after the frost event. Base trunk diameter (BD) and maximum trunk diameter damaged (MDD) provided a reference of plant size and crude measurement of damage intensity, as evident by presence of water-soaked necrotic and/or dehydrated tissue following the removal of a thin slice of periderm, vascular cambium, phloem, and xylem. Percent of base diameter damaged (PBDD) was calculated as MDD divided by BD and provided an assessment of damage, unbiased by plant size. Percent of shoot damaged (PSD) was visually evaluated as the percentage of entire shoot system exhibiting damage. In addition, presence of basal damage (DB) and basal cracking (CB) were recorded. A strong cultivar response was observed for BD, MDD, PBDD, and PSD. Mean cultivar values for PSD ranged from 79% and 19% for AU Authur and Sungold™ seedlings, respectively, which represented extremes among cultivars. Fuzzy kiwifruit exhibited greater injury (PBDD, PSD, DB, and CB) as compared with golden kiwifruit cultivars. Basal damage and basal cracking proved unique to fuzzy kiwifruit, as DB ranged from 0% in Sungold™ seedlings to 100% in fuzzy kiwifruit ‘AU Authur’ and ‘AU Fitzgerald’. In spite of having greater vigor, golden kiwifruit plants sustained less injury. Method of propagation had no effect on injury. PBDD and PSD proved to be reliable field assays for documenting injury, based on their strong correlation value (r = 0.92). Greater relative autumn frost tolerance of golden kiwifruit over fuzzy kiwifruit cultivars is previously unreported.

25Background: Fruit quality traits have a significant effect on consumer acceptance and 26 subsequently on peach (Prunus persica (L.) Batsch) consumption. Determining the genetic 27 bases of key fruit quality traits is essential for industry to improve fruit quality and increase 28 consumption. A Bayesian approach embedded in the FlexQTL software increases the 29 accuracy of QTL mapping and the probability of identifying new and validating known QTLs 30 across a wide range of genetic backgrounds. 31Results: Phenotypic data of seven F1 low to medium chill full-sib families were collected over 32 two years at two locations and genotyped using the 9K SNP Illumina array. One major QTL 33 for fruit blush was found on linkage group 4 (LG4) at 40-46 cM that explained from 20 to 32% 34 of the total phenotypic variance and showed three QTL alleles of different effects. For SSC, 35 one QTL was mapped on LG5 at 60-72cM and explained from 17 to 39% of the phenotypic 36 variance. A major QTL for TA that co-localized with the major locus for low-acid fruit (D-locus) 37 was mapped at the proximal end of LG5 and explained 35 to 80% of the phenotypic variance. 38The new QTL for TA on the distal end of LG5 explained 14 to 22% of the phenotypic variance. 39This QTL co-localized with the QTL for SSC and affected TA only when the first QTL is 40 homozygous for high acidity (epistasis). Haplotype analyses revealed SNP haplotypes and 41 predictive SNP marker(s) associated with desired QTL alleles. 42 Conclusions:A multi-family-based QTL discovery approach enhanced the ability to discover 43 a new TA QTL and validated other QTLs which were reported in previous studies. Identified 44 predictive SNPs and their original sources will facilitate the selection of parents and/or 45 seedlings that have desired haplotype alleles. Our findings will help peach breeders develop 46 new predictive, DNA-based molecular marker tests for routine use in marker-assisted 3 Background 51Peach [Prunus persica (L.) Batsch] is the third most important temperate fruit crop globally in terms of 52 production [1]. Peach fruit quality traits such as flesh texture, color, sweetness, acidity, and other 53 organoleptic attributes affect consumer preference and consumption [2]. Most of these traits are 54 quantitatively inherited and their genetic control is still unclear [3]. 55In the last decade, the rate of fresh consumption has decreased from 2.3 to 1.3 kg per capita per 56year in the U.S. [4]. The lack of consistent quality (poor firmness, lack of flavor, low level of 57 sweetness, and non-ripening fruit) is a main reason consumers do not purchase peaches [5]. The 58 primary reason for poor quality is harvesting at immature stages, a lack of good postharvest handling 59 practices, the need for high yields but not necessarily high quality to make production profitable and 60 the relative ease for selecting for external versus internal fruit traits. Consumers are willing to pay more 61 for fruits of better quality [6] which is the reason for developing branded fru...