Scientia Horticulturae 257 (2019) 108734 Contents lists available at ScienceDirect Scientia Horticulturae journal homepage: www.elsevier.com/locate/scihorti High-density genetic map and QTL analysis of soluble solid content, T maturity date, and mealiness in peach using genotyping by sequencing Gerardo Nuñez-Lilloa, Cristóbal Balladaresa, Catalina Paveza, Claudio Urraa, Dayan Sanhuezaa, Elisa Vendraminb, Maria Teresa Dettorib, Pere Arúsc, Ignazio Verdeb, Francisca Blanco-Herreraa,d, ⁎ Reinaldo Campos-Vargasa, Claudio Menesesa,e, a Centro de Biotecnología Vegetal, Facultad de Ciencias de la Vida, Universidad Andrés Bello, Av. República 330, Santiago, Chile b Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria (CREA) – Centro di Olivicoltura, Frutticoltura e Agrumicoltura (CREA-OFA), Via di Fioranello 52 00134, Rome, Italy c IRTA, Centre de Recerca en Agrigenòmica CSIC-IRTA-UAB-UB, Campus UAB, Cerdanyola del Vallès (Bellaterra), 08193 Barcelona, Spain d Millennium Institute for Integrative Biology (iBio), Portugal 49, Santiago, Chile e FONDAP Center for Genome Regulation, Universidad Andrés Bello, Av. República 330, Santiago, Chile A R T I C LE I N FO A B S T R A C T Keywords: Peach (Prunus persica) is one of the most important temperate fruit trees in the world, based on its production Prunus persica and cultivated area. Consumer acceptance is the principal objective of multiple breeding programs and it is Genetic linkage map dependent on many factors. Among these factors, an important role is played by the soluble solids content (SSC) Quality and the postharvest performance represented by mealiness (M) susceptibility as a chilling injury disorder. Traits Additionally, a major maturity date (MD) QTL has been reported to have a pleiotropic effect on both M and SSC. Candidate The aim of this work was QTL identification of SSC, MD, and M and to identify adequate candidate genes that are Genes linked to regulation of these traits. The analysis was performed by evaluation of fruit quality traits during three consecutive seasons in an F1 progeny of 194 siblings, which were obtained from the intraspecific cross between the yellow-flesh peach “O’Henry” and the white-flesh nectarine NR-053. The main result was the construction of a genetic linkage map with 499 markers (486 SNPs, 11 SSRs, and two morphological markers) spanning 717.6 cM, with an average distance between markers of 1.5 cM/cluster. The analysis allowed the identification of consistent QTLs for SSC and M in the linkage group LG5 and for MD in LG1, LG2, LG5, and LG6. A large number of genes were annotated in QTL intervals, which was reduced by selecting the genes with at least one SNP, which caused an amino acid variation. For SSC, the data identified four transcription factors, one gene involved directly with the sugar accumulation process, and one cell wall remodeling-related gene. For MD, 23 cell wall-related genes, three jasmonic acid-linked genes, eight transcription factors, and one ripening-related gene were identified. Finally, only one cell wall gene was identified that was associated with M. In conclusion, these results improve our understanding of the genetic control of fruit quality traits with commercial relevance in P. persica and specifically in the O × N mapping population. 1. Introduction consuming and costly. Thus, the development of genomic tools to en- able marker-assisted selection is a key step to improve fruit tree Peach (Prunus persica) is used as a model species for genetic and breeding efficiency. Consumer acceptance of peaches and nectarines genomic studies in the Rosaceae family (Arús, 2005). Its genome was relies on many traits that are involved in determining the fruit’s quality sequenced in 2013 (Verde et al., 2013) and a second version was re- such as softening, sweetness, and acidity. leased in 2015, which has a genome size of 227 Mbp and 26,873 an- The accumulation of sugars or soluble solids content (SSC) is a notated genes (Verde et al., 2017). Several peach breeding programs crucial trait for consumer acceptance because together with acidity (the are currently focused on the development of new varieties. Fruit quality balance of sugar/acids), it determines the fruit’s sweetness (Abidi, and postharvest performance appear to be the targeted traits (Infante 2016). For example, in cultivars with high acidity (0.7–0.9%), con- et al., 2008). The conventional fruit-breeding process is time- sumer acceptance increases quickly as the SSC increased but it reaches a ⁎ Corresponding author at: Centro de Biotecnología Vegetal, Facultad de Ciencias de la Vida, Universidad Andrés Bello, República 330, Santiago, Chile. E-mail address: claudio.meneses@unab.cl (C. Meneses). https://doi.org/10.1016/j.scienta.2019.108734 Received 18 February 2019; Received in revised form 29 July 2019; Accepted 31 July 2019 Available online 08 August 2019 0304-4238/ © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/). G. Nuñez-Lillo, et al. Scientia Horticulturae 257 (2019) 108734 plateau (11–12°Brix), while in low acidity varieties (0.30–0.5%), the Considering the economic importance and the contribution to acceptance increases at a higher SSC values without reaching a plateau breeding programs, the aim of this research was to identify genomic (Crisosto and Crisosto, 2005; Cantín et al., 2009). As Cantín et al. regions (QTLs) that are involved in the control of SSC, MD, and M traits (2009) indicated, the connection between SSC and consumer accep- and to propose candidate genes for their regulation. tance is specific for each cultivar, and no single SSC value guarantees success with consumers. Thus, an important aspect to be considered 2. Materials and methods when measuring SSC is the relationship with the total sugar content based on the contribution of sucrose, fructose, sorbitol, and glucose. 2.1. Plant material Previous research showed that a direct correlation between SSC and total sugar content in different peach varieties does not necessarily An F1 population with 194 individuals obtained from the cross exist, probably because of the contribution of soluble optically active between the cultivars “O’Henry” and NR-053 from the Chilean peach compounds other than sugars (pectins, salts, and organic acids) that can breeding program (Universidad de Chile-Andes New Varieties interfere and lead to an altered SSC value (Cantín et al., 2009). Even Administration) was used in this study. “O’Henry” produces melting when several genetic studies have reported QTLs for SSC on linkage yellow-fleshed peach fruit with low SSC levels and is a M-susceptible group LG4 of P. persica (Dirlewanger et al., 1999; Nuñez-Lillo et al., cultivar with a late harvest. NR-053 (Maillarmagie cv. Magique®), 2015), only a few candidate genes have been described and validated however, is an early-harvest variety that produces melting white-fle- (Eduardo et al., 2013). An interesting aspect is the existing information shed nectarine fruit with high SSC levels and is a M tolerant cultivar. that relates SSC to some other traits including the co-localization of a The mapping population (O × N) consists of 5-year-old trees grown on QTL in the LG5, which controls both the peach/nectarine character- G × N rootstock in an experimental orchard located at 34°24′S latitude istics and SSC (Quilot et al., 2004, 2005). A similar situation is de- and 70°50′W longitude (INIA-Rayentué, VI Region, Chile). The O × N scribed for LG1 where a QTL that controls the white/yellow flesh color population presented mainly an early-harvest date, showing a dominant and SSC are co-localized (Bliss et al., 2002; Quilot et al., 2005). The genetic effect from the parental NR-053. The population comprises both analysis of the progeny of “Venus” × “Big Top” identified QTLs for SSC nectarines and peaches as well as white and yellow flesh individuals and total sugars located in LG2, LG4, and LG5, as follows: sorbitol and (Table S1). This population segregates for peach fruit quality traits, sucrose in LG4 and LG5; glucose in LG3 and LG4; and fructose in LG1, including SSC and MD, and the physiological disorder M. LG2, LG4, LG5, and LG8 (Zeballos et al., 2016). Etienne et al. (2002) described the unloading of sucrose from the phloem to the cell vacuole 2.2. F1 population phenotyping as a key process and identified several proteins that are involved. The sucrose transporter proteins (STPs), which are located in the cellular One hundred fruits with a green–yellow background color (com- and vacuole membranes, transfer sucrose from the phloem into the fruit mercial maturity determined using a color table) were harvested for flesh cell and take part in sucrose accumulation in the vacuole. More- each F1 individual (one tree per genotype). In the lab, the index of over, Etienne et al. (2002) described two proteins that are associated absorbance difference (IAD) (Ziosi et al., 2008) was measured using a with sugar accumulation called sucrose synthase (SUS) and invertase portable Vis/NIR DA-Meter (Sinteleia, Bologna, Italy). For each fruit, (INV), both of which are involved in sucrose metabolism. two measurements were taken, one per cheek. Only fruits with an For producers, exporters, and consumers, the maturity date (MD) is average IAD between 0.8 and 1.2 were selected (Lurie et al., 2013). Five another key trait that is targeted in breeding programs. In LG4, MD fruits (five biological replicates) were used to calculate the average showed a pleiotropic effect on fruit quality traits such as weight, value for weight at harvest (g), flesh firmness (N), and SSC (ºBrix) over firmness, SSC, and physiological disorders (Cantín et al., 2010; Romeu three consecutive seasons (2015–2017). M was measured using nine et al., 2014; Nuñez-Lillo et al., 2015). Pirona et al. (2013) identified fruits that were stored for 21 days at 0 °C plus a shelf-life period (to NAC072 (Prupe.4G816800) as a candidate gene for MD using a QTL reach consumption firmness) at room temperature using the Paper analysis. This member of the NAC family of transcription factors is lo- Absorbent Method (PAM) strategy (Infante et al., 2009). MD was de- cated on chromosome 4 and has an InDel of 9 bp, which can be used as termined as the number of days starting from September 1 (DASF) until a molecular marker for MD. The same results were obtained by Nuñez- the harvest date (November–March in the southern hemisphere) in Lillo et al. (2015) and Romeu et al. (2014); they also identified other 2015, 2016, and 2017. Flesh firmness was measured using a Fruit genes on chromosome 4 that were related to this trait, such as ERF4 Pressure Tester penetrometer (Effigi, Alfonsine, Italy) on both cheeks of (ethylene responsive factor 4), SPL4 (squamosa promoter binding pro- the fruit. SSC was recorded using a temperature-compensated re- tein-like 4), and DFL1 (indole-3-acetic acid amido synthetase). fractometer (Atago, Tokyo, Japan). The physiological disorder of the fruit flesh called mealiness (M), which is characterized by dry or mealy flesh with a grainy sand-like 2.3. DNA isolation and quantification texture (Crisosto and Labavitch, 2002), is one of the major sources of peach production losses in the Chilean fruit export industry, and is Genomic DNA of 194 F1 O × N progeny individuals and the parents caused by the long transport time at 0 °C to distant consumers (Lurie “O’Henry” and NR-053 was isolated from 50 to 100 mg of young leaves. and Crisosto, 2005). M development has been related to cell wall re- DNA extraction was performed using a DNeasy 96 Plant Mini Kit modeling enzymes, and more specifically to de-esterification of the (QIAGEN, Düsseldorf, Germany), according to the manufacturer’s pro- pectin matrix without its subsequent depolymerization (Manganaris tocol. The DNA integrity was determined by electrophoresis on 1% et al., 2006; Jolie et al., 2010). This trait has been associated in the agarose gels. Quantification was performed using a Qubit® 2.0 peach trees with chromosomic regions of LG4 using QTL strategies Fluorometer (Invitrogen™, Eugene, OR, USA) with a Qubit® dsDNA BR (Cantín et al., 2010; Martínez-García et al., 2013). Moreover, Peace Assay Kit (Invitrogen™), according to the manufacturer’s protocol. et al. (2006) reported a correlation between M and cell wall remodeling enzymes and with low levels of expansins (Pegoraro et al., 2010) or an 2.4. Genotyping using SSR and SNP imbalance in polygalacturonase (PG) or pectin methylesterase (PME) levels (Lurie and Crisosto, 2005). However, other enzymes related to Thirty SSRs distributed over the eight chromosomes in the peach cell wall remodeling can participate in M development, such as PG trees were used to genotype the parents “O’Henry” and NR-053 and the inhibitors (PGIP) (Protsenko et al., 2008), Pme inhibitors (PMEI) 194 individuals in the mapping population. PCR was performed using (Giovane et al., 2004), pectate lyases (PL) (Vicente et al., 2007), and PCR GoTaq® Master Mix (Promega, Madison, WI, USA) in a final vo- xyloglucan endotransglucosidases (XTH) (Eklöf and Brumer, 2010). lume of 10 μL containing 1× reaction buffer, 1.5 mM MgCl2, 0.25 μM of 2 G. Nuñez-Lillo, et al. Scientia Horticulturae 257 (2019) 108734 forward primer, 0.25 μM of reverse primer, and 40 ng of DNA. The Variant identification was determined using the Fixed Ploidy Variant temperature profile was as follows: 94 °C for 4 min, then 35 cycles of Detection function using the base quality filter with the following 94 °C for 40 s, Tm for 30 s and 72 °C for 30 s, and finally 72 °C for 5 min parameters: neighborhood radius, 5.0; minimum central quality, 20.0; in a thermocycler PCR system 9700 (Applied Biosystems, Foster City, and minimum neighborhood quality, 15.0. Using the default para- CA, USA). The PCR products were detected by capillary electrophoresis meters, the Compare Sample Variants Track function was used to identify using the Fragment Analyzer™ Automated CE System (Advanced variants that were present in only one of the parents, and finally, the Analytical Technologies, Ames, IA, USA), and then separated using a functional annotation of the variants was made using the Amino Acid dsDNA Reagent Kit 35–500 bp (Analytical Advanced Technologies), Changes function to filter out the synonymous variants. Variants were according to the manufacturer’s recommendations. To determine the filtered for minimum depth equal to half of the coverage, and a max- number and size of the alleles in the electropherograms the software imum depth equal to double the coverage. ProSize 2.0 (Advanced Analytical Technologies, Ames, IA, USA) was used. 3. Results SNP genotyping was performed using a Genotyping By Sequencing (GBS) strategy in the Sequencing Facilities at Cornell University (New 3.1. Phenotyping York, USA) using 188 siblings (this number of samples was used for technical reasons, and the samples were randomly selected) from the One hundred and ninety-four siblings of an F1 “O’Henry” (O) × NR- mapping population plus the parents “O’Henry” and NR-053 in a 053 (N) population were phenotyped for quality traits during three HiSeq2500. The samples were aligned against the reference genome of evaluation seasons (2015, 2016, and 2017). The average value for flesh Prunus persica v2.0.a1 (Verde et al., 2017) with the BWA alignment firmness considering the three evaluation seasons at the harvest stage software v0.7.8-r455 (Li and Durbin, 2010). The SNP calling pipeline was 45.8 N. The average fruit weight was 169.4 g, ranging from 92.8 g used was Tassel 3.0 (Glaubitz et al., 2014) and the results were ana- (minimum) to 318.5 g (maximum; Table S1). lyzed using the software VCFtools v0.1.11 (Danecek et al., 2011) and The SSC averaged 12.4, 13.3, and 13.8 ºBrix in the 2015, 2016, and Plink v1.07 (Purcell et al., 2007). 2017 seasons, respectively, with a transgressive segregation pattern of distribution. The minimum and maximum observed values were 7.3 2.5. Map construction and QTL analysis and 22.2 ºBrix (Fig. 1A) with a Pearson correlation between seasons of 0.79 (p-value < 0.01). For MD during the three seasons, average values Map construction of the F1 O × N population was performed using of 139, 144, and 130 DASF were recorded, with a minimum and the software JoinMap®4.1 (Van Ooijen and Voorrips, 2001; Van Ooijen, maximum of 105 and 168 DASF. Pearson correlation for this trait was 2011). Marker selection for map construction was performed by marker 0.87 (p-value < 0.01; Fig. 1B). Finally, the average values for M, ex- segregating distortion using a chi-square test with a p-value < 0.05. pressed as percentage of juice in the flesh were 27.9% (2015), 31.2% The O × N map was obtained using the two-way pseudo-testcross (2016), and 44.8% (2017) of juice after cold storage plus the shelf-life strategy (Grattapaglia and Sederoff, 1994). Two maps were constructed period. The minimum and maximum values were 13.4% and 56.1% of separately for each of the two parents, and the map integration tool was juice and the Pearson correlation between seasons was 0.48 (p- used to obtain the consensus O × N map, using the heterozygous loci in value < 0.01; Fig. 1C). both maps as anchors. The normality of the phenotypic data was estimated using a Linkage analysis was performed using a minimum Logarithm of Shapiro–Wilk test (SSC, MD, and M). The results showed a normal Odds (LOD) value of 3.0 and a maximal recombination fraction of 0.45 distribution for SSC (p-values < 0.98) and MD (p-values < 0.81) for all (Ogundiwin et al., 2009). The genetic distance in centimorgans (cM) evaluated seasons. For M, p-values of 0.018, 0.013, and 0.016, were was calculated using the Kosambi’s mapping function (Kosambi, 1943). estimated for the three consecutive seasons. Thus, the data for M did QTL analysis was performed with the MapQTL®6.0 software (Van not have a normal distribution and non-parametric testing was used to Ooijen and Maliepaard, 1996) using the phenotypic data of three eva- detect QTLs for this trait. luation seasons in the consensus O × N-map, as well as each parental In the 2017 season, the segregation distribution of MD showed an map independently. The Interval Mapping method was used for para- earlier ripening for the entire F1 population (105–144 DASF) compared metric traits, considering a significant QTL with a LOD value > 3.0, with the other seasons (2015 and 2016). In 2017, the segregation dis- while the Kruskal–Wallis method was used for non-parametric traits. A tribution showed that fruit had a higher percentage of juice compared QTL with p-value < 0.01 was considered to be significant. to the other seasons, and only two mealy siblings (with less than 30% of juice) were found. The juice percentage exhibited minimum and max- 2.6. Sequencing and variant identification in population parents imum values of 27.2% and 56.1%, respectively (Fig. 1C). However, the Pearson correlation for each trait between the seasons was maintained DNAs from the parents “O’Henry” and NR-053 were sequenced and (SSC, 0.79; MD, 0.87; and M, 0.48). used for variant identification analysis. Construction of DNA libraries was performed using 100 mg of tissue from each parent with the TruSeq 3.2. SNPs detected by GBS in the O×N population and linkage map Nano DNA library prep kit (Illumina, San Diego, CA, USA), according to construction the manufacturer’s protocol. The quality, size, and concentration of the DNA libraries were determined using the Fragment Analyzer™ The F1 O × N population was genotyped using SSR, SNP, and fruit Automated CE System (Advanced Analytical Technologies, Ames, IA, morphological markers (peach/nectarine and white/yellow). Among USA) with the DNF-474 High Sensitivity NGS Fragment Analysis Kit the 30 SSRs tested, 11 were polymorphic and 19 were monomorphic in (Advanced Analytical Technologies, Ames, IA, USA), according to the the mapping population. No off-type siblings were found (data not manufacturer’s protocol. Finally, two libraries were sequenced on shown). One hundred and eighty-eight siblings plus “O’Henry” and NR- HiSeq2500 by the Macrogen Sequencing Service (Seoul, Korea). The 053 parents were genotyped using a Genotyping By Sequencing sequence analysis was undertaken using the CLC Genomic Workbench strategy (GBS) with a coverage of 12.1X, obtaining a total of 29,484 v10.0 software (CLC Bio, Aarhus, Denmark). The Trim Sequences func- high quality SNPs (Table S2). These markers were filtered for SNPs with tion of the software was used to clean the paired sequences using the less than two missing values (1.65%) and chi-square test results with p- default parameters. The Map Reads to Reference function was used to value < 0.05, obtaining 486 SNPs, were useful for map construction. assemble the trimmed reads to the Prunus persica v2.0.a1 (Verde et al., For linkage map construction, 486 SNPs, 11 SSRs and two mor- 2017) reference genome, while the non-specific reads were ignored. phological markers (fruit pubescence (peach/nectarine), and flesh color 3 G. Nuñez-Lillo, et al. Scientia Horticulturae 257 (2019) 108734 Fig. 1. Phenotyping results. Histograms representing the measured data in three evaluation seasons for soluble solids content (SSC), maturity date (MD), and mealiness (M). The dashed lines represent the average, and the dotted lines represent the median value for each trait. (A) SSC phenotype; (B) MD phenotype; (C) M phenotype. (white/yellow)) were used. Of these 499 markers, 143 (denoted as < (Pp05:14,929,750..16,747,980; Fig. 2). Despite the efforts to genotype hkxhk > or < abxcd >) were heterozygous for both parents and other types of markers in LG5 to identify one anchor marker, no useful were used as anchor markers. One hundred and forty-five were het- markers were identified because it was impossible to obtain a consensus erozygous only for the “O’Henry” parent (denoted as < lmxll >) and O × N-LG5. Therefore, the two parental linkage groups were used in- 211 only for the parental line, NR-053 (denoted as < nnxnp >). Each dependently. The final map contains 499 markers (486 SNP, 11 SSR, group of markers, plus the anchor markers, were used to construct the and two morphological markers) in 477 unique positions or clusters “O’Henry” and the NR-053 parental map, independently (O-map and N- distributed in nine LGs spanning a total of 717.6 cM, with an average map, respectively). distance of 1.50 cM between clusters. Finally, the consensus O × N-map For the O-map, 282 markers were mapped spanning 472.04 cM in represents 91.0% of the total Peach v2.0 pseudomolecules, with a eight LGs with an average distance between markers of 1.77 cM/marker minimum LG representativity of 75.9% (LG8). However, some im- (Fig. S1). In the N-map, 347 markers were mapped spanning portant gaps (larger than 5 Mb) were observed in several cases on LG1, 732.998 cM with an average distance between markers of 2.11 cM/ LG2, and LG6. The average physical-to-genetic distance ratio was marker (Fig. S2). Finally, a consensus map was constructed to generate 3.46 cM/Mb. the O × N-map (Fig. 2). For the morphological markers on the con- structed map, the flesh color marker was mapped on the NR-053 par- 3.3. QTL analysis for SSC, MD, and M ental map (N-LG1), and located on Pp01:13,914,855..29,174,501 (flanked by the markers CBV_G1_N13914855 and CBV_G1_N29174501; The QTL analysis was performed using the saturated O × N genetic Fig. 2), which is consistent with the reported flesh color candidate gene linkage map that was described above, and the phenotypic data that PpCCD4 located on Pp01:26,613,248..26,616,233 (Falchi et al., 2013). were obtained from three evaluation seasons (2015, 2016, and 2017) However, Vendramin et al. (2014) reported a peach/nectarine candi- for SSC, MD, and M. One consistent QTL was identified for SSC on date gene that was located on Pp05:15,892,316..15,894,063, which is chromosome 5 (LG5) of the O-map (QTLSSC-LG5) between the 0.0 and described as the transcription factor PpeMYB25 in the same location 29.4 cM markers of O-LG5 (Fig. 3). This region represents approxi- that is described in the “O’Henry” parental map (O-LG5) between the mately 6.1 Mbp containing 1,211 genes (Table 1). markers CBV_G5_O14929750 and CBV_G5_O16747980 For MD, four QTLs were observed in LG1, LG2, LG5, and LG6 4 G. Nuñez-Lillo, et al. Scientia Horticulturae 257 (2019) 108734 Fig. 2. Genetic linkage map of the O×N population. The nine linkage groups (LGs) of the consensus map (O × N-map) that was constructed are represented by vertical bars. Genetic marker names are listed to the right side of each LG. The morphological markers are shown in red, SSR markers in green and SNP markers in black. The anchor SNP markers, SNP markers heterozygous only for “O’Henry” and SNP markers heterozygous only for NR-053 are denoted with an “S”, “O”, or “N”, respectively. The assembly coordinate of each SNP marker in peach reference v2.0 is specified for all SNP marker names. The ruler on the left side refers to genetic distance in centimorgans (cM). (Fig. 3). In O × N-LG1, one QTL was detected (QTLMD-LG1) spanning (SRA experiments SRR8173852 and SRR8173853) were obtained. approximately 7.1 Mbp between the 103.9 and 134.6 cM markers, and Additionally, 83.2% of the reads were mapped against the reference it contained 997 genes. QTLMD-LG2 was located between the 26.3 and genome obtaining a coverage of 38.5X for “O’Henry”, and 81.4% of the 82.3 cM markers on O × N-LG2, which was equivalent to approxi- reads for NR-053, with 46.5X coverage (data not shown). mately 14.7 Mbp spanning 2,282 genes. For O-LG5, a reliable QTL Variant detection was performed for each parent against the re- containing 657 genes was identified between the 12.8 and 26.6 cM ference genome, revealing 648,302 and 614,008 polymorphisms (SNP markers, and this corresponded to approximately 3.3 Mbp (QTLMD-LG5). and InDel) for “O’Henry” and NR-053, respectively. The variants be- Only one QTL (QTLMD-LG6) was identified in O × N-LG6, between the tween the parents were selected, obtaining 519,764 polymorphisms. 0.0 and 81.1 cM markers (23 Mbp) containing 2,311 genes (Table 1) for Finally, identified polymorphisms were filtered by coverage and base this trait. A total of 6,247 genes were associated with at least one QTL calling quality (> Q30), obtaining a final list of 278,984 variants. for MD. Finally, a single QTL for M (Fig. 3) was identified on N-LG5 In QTLSSC-LG5, 11,434 variants were identified, of which 268 were between the 0.0 and 4.9 cM markers (QTLM-LG5), corresponding to non-synonymous amino acid changes in 128 genes. For MD, 22,363 542.7 kbp containing 60 genes (Table 1). variants were detected, of which 202 showed aminoacidic changes in 95 genes on QTLMD-LG1. In QTLMD-LG2, 44,076 variants were found and 234 led to amino acid changes in the CDS region of 88 genes. In QTLMD- 3.4. SNP/InDel identification by whole genome sequencing of population LG5, 5,216 variants were identified, of which 123 resulted in changes in parents the amino acids of 60 different CDS regions. In QTLMD-LG6, 18,734 variants were detected, of which 413 were related to amino acid To identify the DNA variants between the parents of the F1 popu- changes in 190 genes. Finally, for QTLM-LG5, 36 of the 3,026 variants lation (OxN), which could be associated with candidate genes on detected were associated with non-synonymous amino acid changes in chromosomic regions containing QTLs for SSC, MD, and M, whole 18 genes. genome re-sequencing of both parents was performed. A total of 104,835,112 reads for “O’Henry” and 88,346,396 reads for NR-053 5 G. Nuñez-Lillo, et al. Scientia Horticulturae 257 (2019) 108734 Fig. 3. QTL analysis for soluble solids content, maturity date, and mealiness in the O×N population. On the right side, the linkage groups in the consensus O × N-map are represented by vertical bars and the boxes represent the consistent QTL regions that are defined for SSC (green), MD (red), and M (yellow) in the three evaluation seasons that were tested for each trait. On the left side, the genetic distance of each linkage group in centimorgans (cM) is represented, and the significance value for each QTL analysis is detailed in colored heatmaps. Each evaluation season is represented by an independent heatmap bar. The most saturated color represents a major correlation (LOD score and K) between each phenotype and the respectively genetic linkage position for SSC (green), MD (red), and M (yellow). 3.5. Candidate genes components of the plasma membrane and one that is described as having a cell wall remodeling function (Table 2). In summary, 128 annotated genes with at least one variant were identified in a CDS region that produces a non-synonymous amino acid 4. Discussion change for SSC. In the four chromosomal regions for MD, 433 annotated genes were selected. Eighteen genes were associated with at least one 4.1. Fruit quality traits phenotype data non-synonymous SNP or InDel for M. Comparing this list of genes with previously-reported gene in- Three evaluation seasons were used to phenotype the SSC, MD, and formation and putative gene functions, we selected candidate genes for M traits in the O × N mapping population (Fig. 1). Fruit harvested in SSC, MD, and M. 2017 displayed consistent data for SSC compared to previous seasons. Of the 128 genes that were related to SSC, four transcription factors However, a shift in the MD was recorded, with it being earlier in 2017 were identified, along with one cell wall remodeling enzyme, and one than in 2015 or 2016; this was consistent with an improvement in the sucrose-related gene. The latter participated directly in the regulation performance during postharvest. In the 2017 season, the population of the SSC because the differences in the amino acid sequences could almost did not exhibit M development. These differences can probably produce changes in the protein conformation and/or enzymatic activity be explained by changes in the environmental conditions between (Table 2). For MD, 433 genes we redetected, eight of which were seasons (Campos-Vargas et al., 2005). This suggests that a delay in the transcription factors, 23 were associated with cell wall remodeling harvest time of melting flesh cultivars may produce an increase in functions, three were related to the jasmonate biosynthetic pathway, chilling injury susceptibility. Additionally, it is necessary to consider and one was related to ripening (Table 2). Finally, three of 18 genes that the association between the MD and M reported by Lurie and Crisosto were identified for M were selected, two of which encode integral (2005), in which early season varieties are tolerant to chilling injury 6 G. Nuñez-Lillo, et al. Scientia Horticulturae 257 (2019) 108734 Table 1 Summary of the consistently detected QTLs for soluble solids content, maturity date, and mealiness over three evaluated seasons. Quality trait QTL Season LG+ Physical position++ Peak (cM) LOD % Exp Nearest marker Soluble solids content QTLSSC-LG5 2015 O-LG5 Pp05:12,106,999..18,240,259 17.07 14.54 32.70 CBV_G5_O14929750 QTLSSC-LG5 2016 O-LG5 Pp05:12,106,999..18,240,259 22.78 24.84 48.40 CBV_G5_O16747980 QTLSSC-LG5 2017 O-LG5 Pp05:12,106,999..18,240,259 17.07 17.67 38.40 CBV_G5_O14929750 Maturity date QTLMD-LG1 2015 OxN-LG1 Pp01:40,579,435..47,629,089 123.40 4.92 12.10 CBV_G1_S45339388 QTLMD-LG1 2016 OxN-LG1 Pp01:40,579,435..47,629,089 108.10 3.63 9.10 CBV_G1_N41499593 QTLMD-LG1 2017 OxN-LG1 Pp01:40,579,435..47,629,089 123.40 3.12 7.50 CBV_G1_S45351227 QTLMD-LG2 2015 OxN-LG2 Pp02:18,581,711..30,150,063 60.75 4.44 11.00 CBV_G2_S23935049 QTLMD-LG2 2016 OxN-LG2 Pp02:17,459,801..30,150,063 60.75 5.72 14.00 CBV_G2_S23935049 QTLMD-LG2 2017 OxN-LG2 Pp02:19,797,419..30,150,063 60.75 4.88 12.50 CBV_G2_S23935049 QTLMD-LG5 2015 O-LG5 Pp05:14,383,433..17,645,934 17.07 3.34 9.20 CBV_G5_O14929750 QTLMD-LG5 2016 O-LG5 Pp05:12,552,668..17,645,934 25.53 5.07 12.50 CBV_G5_S17396955 QTLMD-LG5 2017 O-LG5 Pp05:14,383,433..17,645,934 17.07 3.17 8.30 CBV_G5_S14929750 QTLMD-LG6 2015 OxN-LG6 Pp06:1,513,504..23,903,548 70.65 16.14 34.50 CBV_G6_N23368757 QTLMD-LG6 2016 OxN-LG6 Pp06:1,513,504..29,955,077 70.65 18.24 38.10 CBV_G6_N23368757 QTLMD-LG6 2017 OxN-LG6 Pp06:1,513,504..23,903,548 14.17 19.68 41.70 CBV_G6_S3366484 Mealiness+++ QTLM-LG5 2015 N-LG5 Pp05:669,877..2,268,907 2.18 8.60 NA CBV_G5_N1105080 QTLM-LG5 2016 N-LG5 Pp05:669,877..11,317,213 23.6 27.48 NA CBV_G5_N5750040 QTLM-LG5 2017 N-LG5 Pp05:669,877..1,212,620 2.72 6.39 NA CBV_G5_N1105080 ()+, O × N linkage groups; ()++, peach genome v2.0.a1; ()+++, phenotype measured by KW method using significance values of K*. Table 2 Candidate genes (CG) for SSC, MD and M on QTL regions. These CGs were selected with at least one single nucleotide variant (amino acid change) between the parents of the O × N population. QTL name Gene ID Gene description Amino acid changes description QTLSSC-LG5 Prupe.5G130400 Transcription factor bHLH14 G419S Prupe.5G172400 Transcription factor MYB98 S101Y Prupe.5G175100 Probable polygalacturonase S31fs; G318D; K466 T; W567R; W567C Prupe.5G208500 MADS-box protein MADS6 N239S Prupe.5G241700 Sucrose synthase 6 K64 T; E67 G; S189 L; F233S; N560S QTLMD-LG1 Prupe.1G519800 UDP-glycosyltransferase 85A2 G443E Prupe.1G520200 UDP-glycosyltransferase 85A2 Q475E Prupe.1G529500 Pectinesterase/pectinesterase inhibitor 7 N544fs Prupe.1G530800 Putative transcription factor bHLH107 H89N Prupe.1G536600 Rhamnogalacturonate lyase G482D;G583D;G599D Prupe.1G548400 12-oxophytodienoate reductase OPR2 Y284fs;M152 T;N34L Prupe.1G548600 12-oxophytodienoate reductase OPR2 T153I Prupe.1G548900 12-oxophytodienoate reductase OPR2 E288K QTLMD-LG2 Prupe.2G100500 Pectinesterase/pectinesterase inhibitor 51 Q43 K;T34P Prupe.2G166800 Putative ripening-related protein 2 K9Q Prupe.2G243600 UDP-glycosyltransferase 76F1 R118 G; Q297 H; F307 L; F314 L; R316D QTLMD-LG5 Prupe.5G172400 Transcription factor MYB98 S101Y Prupe.5G175100 Probable polygalacturonase S31fsI;K466 T;W567R;C41fs Prupe.5G208500 MADS-box protein MADS6 N239S QTLMD-LG6 Prupe.6G019600 Beta-glucosidase 12 P48S Prupe.6G019700 Beta-glucosidase 12 N532S;N533S Prupe.6G020100 Beta-glucosidase 12 P48S Prupe.6G024900 Cellulose synthase-like protein E1 R82fs;Y234 H;T253 P;K259E;L385fs;P485 L;K578E Prupe.6G025000 Cellulose synthase-like protein E1 Y477 H;H412Y;P84 T;F38I;Y682 H;H617Y Prupe.6G036300 Probable WRKY transcription factor 2 G659R;G628R Prupe.6G041400 Transcription factor RF2b A95_E96insAAA;G192del;L391H Prupe.6G050000 UDP-glycosyltransferase 87A1 E254 V;K235T Prupe.6G050100 UDP-glycosyltransferase 87A1 W390*;L283 P;R261H Prupe.6G050200 UDP-glycosyltransferase 87A2 H448Y Prupe.6G128200 Pectinesterase PPME1 Q181H Prupe.6G134900 Beta-glucosidase 24 F47V Prupe.6G136400 Beta-glucosidase 12 L452F Prupe.6G139100 Transcription factor E2FA N67K Prupe.6G150400 Beta-glucosidase BoGH3B R25Q;M23I Prupe.6G150500 Beta-glucosidase BoGH3B F9V Prupe.6G214300 Transcription factor RAX3 E157G Prupe.6G233300 UDP-glycosyltransferase 86A1 F290L Prupe.6G233400 UDP-glycosyltransferase 86A1 L385P Prupe.6G233600 UDP-glycosyltransferase 86A1 H160L Prupe.6G244300 WRKY transcription factor 44 N116 T;Q137 H;T166I;L422P QTLM-LG5 Prupe.5G008200 Glucan endo-1,3-beta-glucosidase 12 I86L 7 G. Nuñez-Lillo, et al. Scientia Horticulturae 257 (2019) 108734 (mealiness) and late season varieties are very susceptible to this injury. observed by Pirona et al. (2013) and Nuñez-Lillo et al. (2015) on LG4 (i.e. the deletions in NAC072), were not detected in our O × N popu- 4.2. Linkage map evaluation and validation lation. The consensus genetic linkage map constructed for the O × N po- 4.4. Candidate genes related to SSC pulation contained a total of 499 markers that were distributed in nine linkage groups. Bielenberg et al. (2015) used the same methodology to In peach, QTLs for SSC have previously been found to be associated construct a genetic linkage map for peach, obtaining a map with similar with chr1 (Quilot et al., 2004), chr2 (Verde et al., 2002; Quilot et al., characteristics but with half the number of markers and a density of 2004; Zeballos et al., 2016; Hernández Mora et al., 2017), chr4 2.85 cM/marker. Linkage maps that were built using other strategies (Dirlewanger et al., 1999; Etienne et al., 2002; Quilot et al., 2004; were obtained in peach with map densities that were similar to the Nuñez-Lillo et al., 2015; Zeballos et al., 2016; Hernández Mora et al., results reported for the O × N-map; for example, Eduardo et al. (2013); 2017), chr5 (Quilot et al., 2004; Zeballos et al., 2016; Hernández Mora Yang et al. (2013); Nuñez-Lillo et al. (2015), and Verde et al. (2017) et al., 2017), and chr6 (Dirlewanger et al., 1999; Etienne et al., 2002; using the IPSC 9 K SNP array v1 (Verde et al., 2012), obtained genetic Verde et al., 2002; Quilot et al., 2004; Hernández Mora et al., 2017),the maps with marker densities of 1.62, 1.15, 1.64, and 1.07 cM/marker, most powerful of which are those on chromosome 4. The genetic results respectively. Two major gaps were found in the LG1 and LG6, and even obtained from the O × N population show a strong correlation between when less exigent parameters were used, no markers were identified. SSC and a 12–18 Mbp region of chromosome 5. Using a multiprogeny The genetic physical ratio (cM/Mb) was 3.46 higher than those re- approach, Quilot et al. (2004); Zeballos et al. (2016) and Hernández- ported by Verde et al. (2017) for seven different linkage maps, high- Mora et al. (2017) identified similar QTLs for SSC on chr5 in popula- lighting the higher recombination rate in the O × N cross. tions containing “Big Top” (yellow nectarine tree) and “Belbinette” The O × N map has a genome coverage of 91%, which is compar- (white peach tree) as parents with transgressive distributions. In the able with other linkage maps that were constructed using different study presented by Font i Forcada et al. (2019) were described two strategies. Da Silva et al. (2015) generated a linkage map in a “NJ SNPs related to the sugar content and sorbitol in the promoter of the Weeping” × “Bounty” population with a genome coverage of 93.6%, candidate gene ppa025636 m located in the LG4. while Verde et al. (2017) used three genetic maps, “Texas” × “Ear- In O × N, a candidate gene was identified for SSC, and it is de- lygold” (T × E), IF7310828 × “Ferganensis” (P × F), and “Contender” scribed as sucrose synthase 6 (SUS6), which is directly-associated with × “Ambra” (CxA) with genome coverage of 97.5%, 96.9%, and 88.1%, the sugar accumulation process (Table 2). Sucrose is the most important respectively. individual sugar that is present in peach fruit at maturity and it is also The positions of microsatellites and morphological markers mapped an energy source and fruit flavor preservative (Cantín et al., 2009). on the O × N-map were evaluated. The physical positions of the mi- Sucrose synthase proteins have been reported to be the main enzymes crosatellites on the O × N-map were consistent with those described in that are responsible in the fruit sugar accumulation process, catalyzing the P. persica genome v2.0 (Verde et al., 2017), which was obtained the conversion of glucose and fructose to sucrose inside the cell from the Rosaceae.org database (data not shown) with few order dis- (Moriguchi et al., 1990). This enzyme has a low activity during the crepancies. initial stages of fruit growth, followed by a rapid increase in activity Considering the high marker density compared with previously- during fruit maturation, concomitant with sucrose accumulation constructed linkage maps, the high percentage of the peach genome (Moriguchi et al., 1992). It, thus, plays a crucial role in the sucrose represented (91%), and the correct positions of the microsatellites and accumulation process, which could directly explain the distribution of morphological markers on the O × N-map, the O × N-map is con- the SSC phenotype. This behavior agrees with previous information sidered to be useful for performing QTL analysis and to identify the where the progeny from “O’Henry” presented higher levels of SSC genes involved in SSC, MD, and M inheritance. compared with the progeny from different parents (Cantín et al., 2009). However, only one gene that is related to the softening process with 4.3. Chromosomic regions related to SSC, MD, and M a cell wall remodeling function was identified. It was described as a probable polygalacturonase (PG) associated with the SSC phenotype. In previous peach studies, the QTLs for fruit quality traits were Etienne et al. (2002) described the relationship between sugar accu- usually linked to chromosome 4 (Dirlewanger et al., 1999; Etienne mulation and softening processes in fruit development. The accumula- et al., 2002; Quilot et al., 2004; Cantín et al., 2010; Eduardo et al., tion of sugars in the vacuole produces an influx of water into the cell by 2013; Socquet-Juglard et al., 2013; Nuñez-Lillo et al., 2015; Font i osmosis, concomitant with cell expansion. Pressey et al. (1971) re- Forcada et al., 2019). However, the latest research has identified can- ported a positive correlation between PG activity and softening in didate genes for different quality traits. Font i Forcada et al. (2019) peach fruit development, as determined by fruit firmness and the levels presented 347 SNPs that were associated with fruit quality in peach of water soluble pectins. (sorbitol and total sugars and MD) that were distributed in all of the Finally, three transcription factors were identified with amino acid eight LGs. A pleiotropic effect has been reported in LG4 for the MD variations related to SSC: bHLH14, MYB98, and MADS6 (Table 2). The phenotype on other traits such as SSC, firmness, fruit weight, and M transcription factor bHLH14 belongs to the subgroup III d + e of the (Eduardo et al., 2011; Pirona et al., 2013; Nuñez-Lillo et al., 2015). Arabidopsis thaliana bHLH family, together with At4g00870, which is a Pirona et al. (2013) identified a NAC transcription factor (NAC072) as a known jasmonic acid (JA)-responsive transcription factor (Zhang et al., candidate gene for MD on chromosome 4, detecting an InDel of 9 bp in 2018). Song et al. (2013) reported that this bHLH subgroup of tran- the last exon of this gene; Nuñez-Lillo et al. (2015) detected a deletion scription factors acts as repressors and function redundantly to nega- of 26 kbp in the same region of NAC072. The deletion was homozygous tively regulate JA responses. The transcription factor MYB98 has a re- (absence of NAC072) and was associated with a slow ripening trait, ported function associated with the regulation of pollen tube guidance suggesting a strong correlation between NAC072 function and MD genes and filiform apparatus formation in A. thaliana (Punwani et al., control. 2008), while the MADS-box protein identified as MADS6 or transcrip- The results obtained with the O × N population showed a correla- tion factor CAULIFLOWER A has been associated with specifying floral tion between SSC, MD, and M in LG1, LG2, LG5, and LG6, but not in organ identities and meristem fates (Li et al., 2011). Although no LG4, suggesting that their control is independent of NAC072. This functions of these transcription factors have been reported in fruit, it is regulation could be provided by a complementary NAC072 pathway or possible that they participate in the regulation of sucrose accumulation by downstream factors in the same pathway. The nucleotide variations genes, thus explaining the SSC phenotype distribution. Expression 8 G. Nuñez-Lillo, et al. Scientia Horticulturae 257 (2019) 108734 analysis is required to complement this information to identify their The transcription factors MYB98 and MADS6 were described-pre- possible role in fruit development. viously in the “Candidate genes for SSC” section, and together with the probable PG, they are candidates for both SSC and MD phenotypes, 4.5. Candidate genes related to MD suggesting a possible partial pleiotropic effect between MD and SSC. Similar situation was described by Font i Forcada et al. (2019), they Twenty-three genes associated with cell wall remodeling enzymes found a SNP very close to the position of SNP_IGA_700469 in the LG6 were related to MD. The cell wall varies in composition and in the close to marker CPPCT030 and the gene ppa006828, associated with enzymes involved in the remodeling process within and between in- sorbitol and harvest date; this gene is related to peroxisome biogenesis. dividual cells, tissues, and species. The basic cell wall structure is a Associated to MD was described the UDP96-001 marker close to the single complex polysaccharide network featuring interactions between SNP_IGA_630302, candidate gene ppa024155 described as the core cellulose, pectin, and hemicellulose (Dick-Pérez et al., 2011). Nine histone H2B. Finally, the same group associated to MD and flavonoids candidate genes involved in cellulose formation were identified the candidate gene ppa021329 on chromosome 6, this gene is similar to (Table 2): two were described as cellulose synthase-like proteins E1 lycopene cyclase. (Richmond and Somerville, 2000), and seven were described as beta- glucosidases (Table 2). These candidate genes were reported as en- 4.6. Candidate genes related to M zymes that are involved in the degradation of cellulose to glucose (Esen, 1993). Fourteen genes related to the pectin structure were also identi- A QTL for M in the LG5 from the O × N population was obtained, fied. Nine UDP-glycosyltransferases, which are proteins that mediate and this phenotype has been reported consistently with a QTL on the the transfer of glycosyl residues regulating properties of the acceptors LG4 of peach in several studies (Peace et al., 2006; Eklöf and Brumer, such as their bioactivity, solubility, and transport within the cell (Ross 2010; Martínez-García et al., 2013; Socquet-Juglard et al., 2013). The et al., 2001); one rhamnogalacturonate lyase, which directly degrades correlation between MD and M reported by Lurie and Crisosto (2005), rhamnogalacturonans (Naran et al., 2007); one pectinesterase (PPME1); together with the QTL for MD and M that was obtained consistently in and two pectinesterase/pectinesterase inhibitors (PEi7 and PEi51) were the LG4 of peach suggests a pleiotropic effect of MD in other pheno- also identified. These enzymes regulate the de-esterification of pectin types, including M. It has been postulated that genetic variations in the and conversion of protopectin to soluble pectin and pectate (Jiang transcription factor NAC072 are responsible for this pleiotropic effect et al., 2002); the role of these proteins was reported by Brummel and (Pirona et al., 2013; Nuñez-Lillo et al., 2015). In the O × N population, Harpster (2001) as being associated with fruit softening during tomato non-nucleotidic variations in this transcription factor (data not shown) fruit ripening. Finally, one polygalacturonase (PG) was found. These in the parents were detected, and no QTL for M in the LG4 of peach was cell wall remodeling enzymes can participate in fruit softening, ex- obtained. This suggests that the absence of genomic variations in the plaining the differences in the harvesting time observed in the O × N NAC072 region in the parents significantly reduces the pleiotropic ef- population. fect of MD in the O × N population. Thus, a QTL for M in LG5 was However, three candidate genes, described as 12-oxophytodienoate obtained, allowing us to study the M phenotype without interference reductases (OPR2), were identified as being associated with MD. All from MD. three participated in the JA biosynthetic pathway (Table 2). The JA For M, one gene was identified, which has been related to cell wall biosynthetic pathway starts by converting linoleic acid to 12-ox- remodeling functions, and is described as a glucan endo-1,3-beta-glu- ophytodienoic acid (OPDA) through the participation of proteins such cosidase 12 (Table 2). as lipoxygenases, allene oxide synthases, and allene oxide cyclases; However, it is possible that the M phenotype is not determined by OPDA is subsequently reduced by OPR proteins to produce JA (Dave variation in the CDS region of a gene. It is necessary to explore this and Graham, 2012). Dave and Graham (2012) described an OPDA possibility by analyzing the promoter region of candidate genes because signaling pathway that is independent of JA signaling in seeds, which is variations may produce differences in expression levels that could ex- associated with the regulation of germination potential. Ribot et al. plain the phenotype variation. Thus, it is necessary to complement or to (2008) demonstrated an OPDA-independent signaling route of JA and integrate the genetic/genome data with transcriptome analysis to ABA in response to root wounding. The results obtained here suggest a identify the genes that are involved in the M trait. possible OPDA- or JA-signaling imbalance, which could explain the distribution of the MD phenotype, but complementary analysis is re- 5. Conclusions quired to determine the real participation of these signals in the reg- ulation of fruit development. The results obtained in this work are the basis for understanding the Finally, eight transcriptional regulators were identified in the MD genetic control of fruit quality traits in the O × N mapping population. analysis (Table 2). The transcription factor bHLH107 was mentioned by The analysis allowed the identification of candidate genes for MD, SSC, Zhang et al. (2018) as a member of the subgroup Vb of the A. thaliana and M, which could be validated by functional strategies. In the future, bHLH family proteins that are associated with genes such as At1g68810 this work will need to be complemented by metabolomic and tran- and At3g25710, which are related to the upwardly curly leaf phenotype scriptomic data using contrasting siblings for each trait to correlate the (An et al., 2014) and ABA-mediated signaling transduction (Kim and genetic variations with gene expression levels to find the regulatory Kim, 2006). The transcription factor WRKY2 has been reported to be gene network that is responsible for the SSC, MD, and M traits. related to seed germination (Jiang and Yu, 2009), xylem development (Guillaumie et al., 2010), and osmotic stress (Wen-Bo and Di-Qiu, Funding 2009). However, WRKY44 is associated with functions such as the drought response (Han et al., 2015) and the defense response against This work was supported by Fondo Nacional de Desarrollo herbivores and microbes (Bonaventure et al., 2011). Moreover, RF2b is Científico y Tecnológico FONDECYT-CHILE1160584, Fondo de a transcription factor that is strongly related to virus resistance in rice Fomento al Desarrollo Científico y Tecnológico FONDEF- (Dai et al., 2004; Liu et al., 2007; Dai et al., 2008). The transcription CHILED13i10005 and Corporación de Fomento de la Producción factor E2FA in A. thaliana has been reported to control proliferation, CORFO-CHILE Biofrutales13CTI21520-SP04. endoreduplication, and differentiation (De Veylder et al., 2002; Boudolf et al., 2004), and the transcription factor RAX3 has been described as a Acknowledgments regulator of axillary meristem formation (Dubos et al., 2010) and plant shoot branching (Guo, 2015). We thank Michael Handford (Universidad de Chile) for language 9 G. Nuñez-Lillo, et al. Scientia Horticulturae 257 (2019) 108734 support. Dubos, C., Stracke, R., Grotewold, E., Weisshaar, B., Martin, C., Lepiniec, L., 2010. MYB transcription factors in Arabidopsis. Trends Plant Sci. 15, 573–581 https://doi.org/ 10.1016/j.tplants.2010.06.005. Appendix A. Supplementary data Eduardo, I., Chietera, G., Pirona, R., Pacheco, I., Troggio, M., Banchi, E., Bassi, D., Rossini, L., Vecchietti, A., Pozzi, C., 2013. Genetic dissection of aroma volatile Supplementary material related to this article can be found, in the compounds from the essential oil of peach fruit: QTL analysis and identification of candidate genes using dense SNP maps. Tree Genet. Genomes 9, 189–204 https:// online version, at doi:10.1016/j.scienta.2019.108734. doi.org/ 10.1007/s11295-012-0546-z. Eduardo, I., Pacheco, I., Chietera, G., Bassi, D., Pozzi, C., Vecchietti, A., Rossini, L., 2011. References QTL analysis of fruit quality traits in two peach intraspecific populations and im- portance of maturity date pleiotropic effect. Tree Genet. Genomes 7, 323–335 http:// doi.org/ 10.1007/s11295-010-0334-6. Abidi, W., 2016. Quality attributes and chilling injury susceptibility of new peach [Prunus Eklöf, E.M., Brumer, H., 2010. The XTH gene family: an update on enzyme structure, persica (L.) Batsch] cultivars grown under Tunisian conditions. JMEST 3, 4223–4227. function, and phylogeny in xyloglucan remodeling. Plant Physiol. 153, 456–466 An, R., Liu, X., Wang, R., Wu, H., Liang, S., Shao, J., Qi, Y., An, L., Yu, F., 2014. The over- https://doi.org/10.1104/pp.110.156844. expression of two transcription factors, ABS5/bHLH30 and ABS7/MYB101, leads to Esen, A., 1993. B-Glucosidases: Overview, Biochemistry and Molecular Biology. pp. 1- upwardly curly leaves. PLoS One 9, e107637 https://doi.org/10.1371/journal.- 14. American Chemical Society, Washington DC, USA. pone.0107637. Etienne, C., Rothan, C., Moing, A., Plomion, C., Bodénes, C., Svanella-Dumas, L., Cosson, Arús, P., 2005. Integrating genomics into rosaceae fruit breeding. Acta Hortic. 738, 29–35 P., Pronier, V., Monet, R., Dirlewanger, E., 2002. Candidate genes and QTL for sugar https://doi.org/10.17660/ActaHortic.2007.738.1. and organic acid content in peach [Prunus persica (L.) Batsch]. Theor. Appl. Genet. Bielenberg, D.G., Rauh, B., Fan, S., Gasic, K., Abbott, A.G., Reighard, G.L., Okie, W.R., 105, 145–159 https://doi.org/ 10.1007/s00122-001-0841-9. Wells, C.E., 2015. Genotyping by sequencing for SNP-based linkage map construction Falchi, R., Vendramin, E., Zanon, L., Scalabrin, S., Cipriani, G., Verde, I., Vizzotto, G., and QTL analysis of chilling requirement and bloom date in peach (Prunus persica (L.) Morgante, M., 2013. Three distinct mutational mechanisms acting on a single gene Batsch). PLoS One 10, e0139406 https://doi.org/10.1371/journal.pone.0139406. underpin the origin of yellow flesh in peach. Plant J. 76, 175–187 https://doi.org/ Bliss, F.A., Arulsekar, S., Foolad, M.R., Becerra, V., Gillen, A.M., Warburton, M.L., 10.1111/tpj.12283. Dandekar, A.M., Kocsisne, G.M., Mydin, K.K., 2002. An expanded genetic linkage Font i Forcada, C., Guajardo, V., Chin-Wo, S.R., Moreno, M.A., 2019. Association mapping map of Prunus based on an interspecific cross between almond and peach. Genome analysis for fruit quality traits in Prunus persica using SNP markers. Front. Plant Sci. 9, 45, 520–529 https://doi.org/10.1139/g02-011. 2005. https://doi.org/10.3389/fpls.2018.02005. Bonaventure, G., VanDoorn, A., Baldwin, I.T., 2011. Herbivore-associated elicitors: FAC Giovane, A., Servillo, L., Balestrieri, C., Raiola, A., D’Avino, R., Tamburrini, M., signaling and metabolism. Trends Plant Sci. 16, 294–299 https://doi.org/10.1016/ Ciardiello, M.A., Camardella, L., 2004. Pectin methylesterase inhibitor. Biochim. j.tplants.2011.01.006. Biophys. Acta 1696, 245–252. https://doi.org/10.1016/j.bbapap.2003.08.011. Boudolf, V., Vlieghe, K., Beemster, G.T., Magyar, Z., Torres Acosta, J.A., Maes, S., Van Der Glaubitz, J.C., Casstevens, T.M., Lu, F., Harriman, J., Elshire, R.J., Sun, Q., Buckler, E.S., Schueren, E., Inzé, D., De Veylder, L., 2004. The plant-specific cyclin-dependent ki- 2014. TASSEL-GBS: a high capacity genotyping by sequencing analysis pipeline. PLoS nase CDKB1;1 and transcription factor E2Fa-DPa control the balance of mitotically One 9. https://doi.org/10.1371/journal.pone.0090346. dividing and endoreduplication cells in Arabidopsis. Plant Cell 16, 2683–2692 Grattapaglia, D., Sederoff, R., 1994. Genetic linkage maps of Eucalyptus grandis and https://doi.org/10.1105/tpc.104.024398. Eucalyptus urophylla using a pseudo-testcross: mapping strategy and RAPD markers. Brummel, D.A., Harpster, M.H., 2001. Cell wall metabolism in fruit softening and quality Genetics 137, 1121–1137. and its manipulation in transgenic plants. Plant Mol. Biol. 47, 311–339 https:// Guillaumie, S., Mzid, R., Méchin, V., Léon, C., Hichri, I., Destrac-Irvine, A., Trossat- doi.org/10.1023/A:1010656104304. Magnin, C., Delrot, S., Lauvergeat, V., 2010. The grapevine transcription factor Campos-Vargas, R., Becerra, O., Baeza-Yates, R., Cambiazo, V., González, M., Meisel, L., WRKY2 influences the lignin pathway and xylem development in tobacco. Plant Mol. Orellana, A., Retamales, J., Silva, H., Defilippi, B.G., 2005. Seasonal variation in the Biol. 72, 215–234. https://doi.org/10.1007/s11103-009-9563-1. development of chilling injury in ‘O`Henry’ peaches. Sci. Hort. 110, 79–83 https:// Guo, D., 2015. The WRKY transcription factor WRKY71/EXB1 controls shoot branching doi.org/10.1016/j.scienta.2006.06.019. by transcriptionally regulating RAX genes in Arabidopsis. Plant Cell 27, 3112–3127. Cantín, C.M., Gogorcena, Y., Moreno, M.A., 2009. Analysis of phenotypic variation of https://doi.org/10.1105/tpc.15.00829. sugar profile in different peach and nectarine Prunus persica (L.) Bastch] breeding Han, Y., Zhang, X., Wang, W., Wang, Y., Ming, F., 2015. The suppression of WRKY44 by progenies. J. Sci. Food Agric. 89, 1909–1917 https://doi.org/10.1002/jsfa. GIGANTEA-miR172 pathway is involved in drought response in Arabidopsis thaliana. Cantín, C.M., Crisosto, C.H., Ogundiwin, E.A., Gradziel, T., Torrents, J., Moreno, M.A., PLoS One 10. https://doi.org/10.1371/journal.pone.0073541. Gogorcena, Y., 2010. Chilling injury susceptibility in an intra-specific peach [Prunus Hernández Mora, J.R., Micheletti, D., Bink, M., Van de Weg, E., Cantín, C., Nazzicari, N., persica (L.) Bastch] progeny. Postharvest Biol. Tec. 58, 79–87 https://doi.org/ Caprera, A., Dettori, M.T., Micali, S., Banchi, E., Campoy, J.A., Dirlewanger, E., 10.1016/j.postharvbio.2010.06.002. Lambert, P., Pascal, T., Troggio, M., Bassi, D., Rossini, L., Verde, I., Quilot-Turion, B., Crisosto, C.H., Labavitch, J.M., 2002. Developing a quantitative method to evaluate peach Laurens, F., Arús, P., Aranzana, M.J., 2017. Integrated QTL detection for key (Prunus persica) flesh mealiness. Postharvest Biol. Tec. 25, 151–158 https://doi.org/ breeding traits in multiple peach progenies. BMC Genomics 18, 404. https://doi.org/ 10.1016/S0925-5214(01)00183-1. 10.1186/s12864-017-3783-6. Crisosto, C.H., Crisosto, G.M., 2005. Relationship between ripe soluble solids con- Infante, R., Meneses, C., Rubio, P., Seibert, E., 2008. Quantitative determination of flesh centration (RSSC) and consumer acceptance of high and low acid melting flesh peach mealiness in peach [Prunus persica L. (Batch.)] through paper absorption of free juice. and nectarine (Prunus persica (L.) Batsch) cultivars. Postharvest Biol. Tec. 38, Postharvest Biol. Tec. 51, 118–121 https://doi.org/ 10.1016/j.post- 239–246 https://doi.org/10.1016/j.postharvbio.2005.07.007. harvbio.2008.06.006. Da Silva, C., Bassi, D., Bianco, L., Pacheco, I., Pirona, R., Rossini, L., 2015. Genetic dis- Infante, R., Meneses, C., Crisosto, C.H., 2009. Preconditioning treatment maintains taste section of fruit weight and size in a F2 peach (Prunus persica (L.) Batsch) progeny. characteristic perception of ripe ‘September Sun’ peach following cold storage. Int. J. Mol. Breeding 35, 71–89 https://doi.org/10.1007/s11032-015-0271-z. Food. Sci. Tech. 44, 1011–1016 https://doi.org/ 10.1111/j.1365- Dai, S., Zhang, Z., Chen, S., Beachy, R.N., 2004. RF2b, a rice bZIP transcription activator, 2621.2008.01864.x. interacts with RF2a and is involved in symptom development of rice tungro disease. Jiang, C.M., Li, C.P., Chang, H.M., 2002. Characterization of pectinesterase inhibitor in PNAS 101, 687–692 https://doi.org/ 10.1073/pnas.0307687100. jelly fig (Ficus awkeotsang Makino) achenes. J. Agr. Food. Chem. 50, 4890–4894 Dai, S., Wei, X., Alfonso, A.A., Pei, L., Duque, U.G., Zhang, Z., Babb, G.M., Beachy, R.N., https://doi.org/ 10.1021/jf011568. 2008. Transgenic rice plants that overexpress transcription factors RF2a and RF2b are Jiang, W., Yu, D., 2009. Arabidopsis WRKY2 transcription factor mediates seed germi- tolerant to rice tungro virus replication and disease. PNAS 105, 21012–21016 nation and postgermination arrest of development by abscisic acid. BMC Plant Biol. 9, https://doi.org/ 10.1073/pnas.0810303105. 96–99. https://doi.org/10.1186/1471-2229-9-96. Danecek, P., Auton, A., Abecasis, G., Albers, C.A., Banks, E., DePristo, M.A., Handsaker, Jolie, R.P., Duvetter, T., Vandevenne, E., Van Vuggenhout, S., Van Loey, A.M., Hendrickx, R.E., Lunter, G., Marth, G.T., Sherry, S.T., McVean, G., Durbin, R., 2011. The variant M.E., 2010. A pectin-methylesterase-inhibitor-based molecular probe for in situ de- call format and VCFtools. Bioinformatics 27, 2156–2158 https://doi.org/ 10.1093/ tection of plant pectin methylesterase activity. J. Agr. Food. Chem. 58, 5449–5456. bioinformatics/btr330. https://doi.org/10.1021/jf100248u. Dave, A., Graham, I.A., 2012. Oxylipin signaling: a distinct role for the jasmonic acid Kim, J., Kim, H.Y., 2006. Molecular characterization of a bHLH transcription factor in- precursor cis-(+)-12-oxo-phytodienoic acid (cis-OPDA). Front. Plant Sci. 3 https:// volved in Arabidopsis abscisic acid-mediated response. Biochim. Biophys. Acta 1759, doi.org/10.3389/fpls.2012.00042. 191–194. https://doi.org/10.1016/j.bbaexp.2006.03.002. De Veylder, L., Beeckman, T., Beemster, G.T., de Almeida Engler, J., Ormenese, S., Maes, Kosambi, D.D., 1943. The estimation of map distances from recombination values. Ann. S., Naudts, M., Van Der Schueren, E., Jacqmard, A., Engler, G., Inzé, D., 2002. Control Hum. Genet. 12, 172–175. of proliferation, endoreduplication and differentiation by the Arabidopsis E2Fa-DPa Li, H., Liang, W., Hu, Y., Zhu, L., Yin, C., Xu, J., Dreni, L., Kater, M.M., Zhang, D., 2011. transcription factor. EMBO J. 21, 1360–1368 https://doi.org/ 10.1093/emboj/ Rice MADS6 interacts with the floral homeotic genes SUPERWOMAN1, MADS3, 21.6.1360. MADS58, MADS13 and DROOPING LEAF in specifying floral organ identities and Dick-Pérez, M., Zhang, Y., Hayes, J., Salazar, A., Zebatina, O.A., Hong, M., 2011. meristem fate. Plant Cell 23, 2536–2552 https://doi.org/ 10.1105/tpc.111.087262. Structure and interactions of plant cell-wall polysaccharides by two- and three-di- Li, H., Durbin, R., 2010. Fast and accurate long-read alignment with burrows-wheeler mensional magic-angle-spinning solid-state NMR. Biochem. 50, 989–1000 https:// transform. Bioinformatics 26, 589–595 http://doi.org/ 10.1093/bioinformatics/ doi.org/ 10.1021/bi101795q. btp698. Dirlewanger, E., Moing, A., Rothan, C., Svanella, L., Pronier, V., Guye, A., Plomion, C., Liu, Y., Dai, S., Beachy, R.N., 2007. Role of the C-terminal domains of rice (Oryza sativa Monet, R., 1999. Mapping QTL controlling fruit quality in peach (Prunus persica (L.) L.) bZIP proteins RF2a and RF2b in regulating transcription. Biochem. J. 205, Batsch). Theor. Appl. Genet. 98, 18–31 https://doi.org/10.1007/s001220051. 243–249. https://doi.org/10.1042/BJ20061375. 10 G. Nuñez-Lillo, et al. Scientia Horticulturae 257 (2019) 108734 Lurie, S., Friedman, H., Weksler, A., Dagar, A., Zerbini, P.E., 2013. Maturity assessment at BMC Plant Biol. 14, 52–67. https://doi.org/10.1186/1471-2229-14-52. harvest and prediction of softening in an early and late season melting peach. Ross, J., Li, Y., Lim, E., Bowles, D.J., 2001. Higher plant glycosyltransferases. Genome Postharvest Biol. Tec. 76, 10–16. https://doi.org/10.1016/j.postharvbio.2012.08. Biol. 2 reviews 3004.1-3004.6. 007. Socquet-Juglard, D., Kamber, T., Pothier, J.F., Christen, D., Gessler, C., Duffy, B., Lurie, S., Crisosto, C.H., 2005. Chilling injury in peach and nectarine. Postharvest Biol. Patocchi, A., 2013. Comparative RNA-Seq analysis of early-infected peach leaves by Tec. 37, 195–208. https://doi.org/10.1016/j.postharvbio.2005.04.012. the invasive phytophatogen Xanthomonas arboricola pv. Pruni. PLoS One 8. https:// Manganaris, G.A., Vasilakakis, M., Diamantidis, G., Mignani, I., 2006. Diverse metabolism doi.org/10.1371/journal.pone.0054196. of cell wall components of melting and non-melting peach genotypes during ripening Song, S., Qi, T., Fan, M., Zhang, X., Gao, H., Huang, H., Wu, D., Guo, H., Xie, D., 2013. after harvest or cold storage. J. Environ. Sci. Health B 86, 243–250. https://doi.org/ The bHLH subgroup IIId factors negatively regulate jasmonate-mediated plant de- 10.1002/jsfa.2310. fense and development. PLoS Genet. 9. https://doi.org/10.1371/journal.pgen. Martínez-García, P.J., Parfitt, D.E., Ogundiwin, E.A., Fass, J., Chan, H.M., Ahmad, R., 1003653. Lurie, S., Dandekar, A., Gradziel, T.M., Crisosto, C.H., 2013. High density SNP Van Ooijen, J.W., 2011. Multipoint maximum likelihood mapping in a full-sib family of mapping and QTL analysis for fruit quality characteristics in peach (Prunus persica L.). an outbreeding species. Genet. Res. 93, 343–349. https://doi.org/10.1017/ Tree Genet. Genomes 9, 19–36. https://doi.org/10.1007/s11295-012-0522-7. S0016672311000279. Moriguchi, T., Abe, K., Sanada, T., Yamaki, S., 1992. Levels and role of sucrose synthase, Van Ooijen, J.W., Maliepaard, C., 1996. MapQTL Version 3.0, Software for the sucrose-phosphate synthase, and acid invertase in sucrose accumulation in fruit of Calculation of QTL Positions on Genetic Map. Plant Res. Int, Wageningen. The Asian pear. J. Am. Soc. Hortic. Sci. 112, 274–278. https://doi.org/10.21273/JASHS. Netherlands. 117.2.274. Van Ooijen, J.W., Voorrips, R.E., 2001. JoinMap Version 3.0: Software for the Calculation Moriguchi, T., Sanada, T., Yamaki, S., 1990. Seasonal fluctuations of some enzymes re- of Genetic Linkage Maps. Plant Res. Int. Wagening, The Netherlands. lating to sucrose and sorbitol metabolism in peach fruit. J. Am. Soc. Hortic. Sci. 115, Vendramin, E., Pea, G., Dondini, L., Pacheco, I., Dettori, M.T., Gazza, L., Scalabrini, S., 278–281. https://doi.org/10.21273/JASHS.115.2.278. Strozzi, F., Tartarini, S., Bassi, D., Verde, I., Rossini, L., 2014. A unique mutation in a Naran, R., Pierce, M.L., Mort, A.J., 2007. Detection and identification of rhamnoga- MYB gene cosegregates with the nectarine phenotype in peach. PLoS One 9, e112032. lacturonan lyase activity in intercellular spaces of expanding cotton cotyledons. Plant https://doi.org/10.1371/journal.pone.0090574. J. 50, 95–107 https://doi.org/ 10.1111/j.1365-313X.2007.03033.x. Verde, I., Jenkins, J., Dondini, L., Micali, S., Pagliarani, G., Vendramin, E., Paris, R., Nuñez-Lillo, G., Cifuentes-Esquivel, A., Troggio, M., Micheletti, D., Infante, R., Campos- Aramini, V., Gazza, L., Rossini, L., Bassi, D., Troggio, M., Shu, S., Grimwood, J., Vargas, R., Orellana, A., Blanco-Herrera, F., Meneses, C., 2015. Identification of Tartarini, S., Dettori, M.T., Schmutz, J., 2017. The peach v2.0 release: high-resolution candidate genes associated with mealiness and maturity date in peach [Prunus persica linkage mapping and deep resequencing improve chromosome-scale assembly and (L.) Batsch] using QTL analysis and deep sequencing. Tree Genet. Genomes 11, 86–98 contiguity. BMC Genomics 18, 225. https://doi.org/10.1186/s12864-017-3606-9. https://doi.org/ 10.1007/s11295-015-0911-9. Verde, I., Abbott, A.G., Scalabrini, S., Jung, S., Shu, S., Marroni, F., Zhebentyayeva, T., Ogundiwin, E.A., Peace, C.P., Gradziel, T.M., Parfitt, D.E., Bliss, F.A., Crisosto, C.H., 2009. Dettori, M.T., Grimwood, J., Cattonaro, F., Zuccolo, A., Rossini, L., Jenkins, J., A fruit quality gene map of Prunus. BMC Genomics 10, 587–599. https://doi.org/10. Vendramin, E., Meisel, L.A., Decroocq, V., Sosinski, B., Prochnik, S., Mitros, T., 1186/1471-2164-10-587. Policriti, A., Cipriani, G., Dondini, L., Ficklin, S., Goodstein, D.M., Xuan, P., Del Peace, C.P., Crisosto, C.H., Garner, D.T., Dandekar, A.M., Gradziel, T.M., Bliss, F.A., 2006. Fabbro, C., Aramini, V., Copetti, D., Gonzalez, S., Horner, D.S., Falchi, R., Lucas, S., Genetic control of internal breakdown in peach. Acta Hortic. 713, 489–496. https:// Mica, E., Maldonado, J., Lazzari, B., Bielenberg, D., Pirona, R., Miculan, M., Barakat, doi.org/10.17660/ActaHortic.2006.713.73. A., Testolin, R., Stella, A., Tartarini, S., Tonutti, P., Arús, P., Orellana, A., Wells, C., Pegoraro, C., Chaves, F.C., Manica-Berto, R., Casaril, J., Schreinert dos Santos, R., Silva, Main, D., Vizzotto, G., Silva, H., Salamini, F., Schmutz, J., Morgante, M., Rokhsar, J.A., Rombaldi, C.V., 2010. Transcript accumulation of cell wall metabolism and D.S., 2013. The high-quality draft genome of peach (Prunus persica) identifies unique endomembrane transport genes in woolly and non-woolly peach. Sci. Hortic. 126, patterns of genetic diversity, domestication and genome evolution. Nat. Genet. 45, 366–370. https://doi.org/10.1016/j.scienta.2010.07.033. 487–494. https://doi.org/10.1038/ng.2586. Pirona, R., Eduardo, I., Pacheco, I., Da Silva Linge, C., Mara, M., Verde, I., Tartarini, S., Verde, I., Bassil, N., Scalabrini, S., Gilmore, B., Lawley, C.T., Gasic, K., Micheletti, D., Dondini, L., Pea, G., Bassi, D., Rossini, L., 2013. Fine mapping and identification of a Rosyara, U.R., Cattonaro, F., Vendramin, E., Main, D., Aramini, V., Blas, A.L., candidate gene for a major locus controlling maturity date in peach. BMC Plant Biol. Mockler, T.C., Bryant, D.W., Wilheim, L., Troggio, M., Sosinki, B., Aranzana, M.J., 13, 166–178. https://doi.org/10.1186/1471-2229-13-166. Arús, P., Lezzoni, A., Morgante, M., Peace, C., 2012. Development and evaluation of a Pressey, R., Hinton, D.M., Avants, J.K., 1971. Development of polygalacturonase activity 9K SNP array for peach by internationally coordinated SNP detection and validation and solubilization of pectin in peaches during ripening. J. Food Sci. 36, 1070–1073. in breeding germplasm. PLoS One 7, e35668. https://doi.org/10.1371/journal.pone. https://doi.org/10.1111/j.1365-2621.1971.tb03348.x. 0035668. Protsenko, M.A., Buza, N.L., Krinitsyna, A.A., Bulantseva, E.A., Korableva, N.P., 2008. Verde, I., Quarta, R., Cedrola, C., Dettori, M.T., 2002. QTL analysis of agronomic traits in Polygalacturonase inhibiting protein is a structural component of plant cell wall. a BC1 peach population. Acta Hort. 592, 291–297 https://doi.org/ 10.17660/ Biochem. Mosc. Suppl. Ser. A Membr. Cell Biol. (Mosc) 73, 1053–1062. https://doi. ActaHortic.2002.592.41. org/10.1134/S000629790. Vicente, A.R., Saladié, M., Rose, J.K.C., Labavitch, J.M., 2007. The linkage between cell Punwani, J.A., Rabiger, D.S., Lloyd, A., Drews, G.N., 2008. The MYB98 subcircuit of the wall metabolism and fruit softening: looking to the future. J. Environ. Sci. Health B synergid gene regulatory network includes genes directly and indirectly regulated by 87, 1435–1448. https://doi.org/10.1002/jsfa.2837. MYB98. Plant J. 55, 406–414 https://doi.org/ 10.1111/j.1365-313X.2008.03514.x. Wen-Bo, J., Di-Qiu, Y., 2009. Arabidopsis WRKY2 transcription factor may be involved in Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M.A., Bender, D., Maller, J., osmotic stress response. Acta Bot. Yunnan. 31, 427–432. https://doi.org/10.3724/SP. Sklar, P., de Bakker, P.I., Daly, M.J., Sham, P.C., 2007. PLINK: a tool set for whole- J.1143.2009.09046. genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, Yang, Y., Foulquié-Moreno, M.R., Clement, L., Erdei, É, Tanghe, A., Schaerlaekens, K., 559–575. https://doi.org/10.1086/519795. Dumortier, F., Thevelein, J.M., 2013. QTL analysis of high thermotolerance with Quilot, B., Wu, B.H., Kervella, J., Génard, M., Foulongne, M., Moreau, K., 2004. QTL superior and downgraded parental yeast strains reveals new minor QTLs and con- analysis of quality traits in an advanced backcross between Prunus persica cultivars verges on novel causative alleles involved in RNA processing. PLoS Genet. 9, and the wild relative species P. Davidiana. Theor. Appl. Genet. 109, 884–897. https:// e1003693. https://doi.org/10.1371/journal.pgen.1003693. doi.org/10.1007/s00122-004-1703-z. Zeballos, J.L., Abidi, W., Giménez, R., Monforte, A.J., Moreno, M.A., Gogorcena, Y., 2016. Quilot, B., Kervella, J., Genard, M., Lescourret, F., 2005. Analysing the genetic control of Mapping QTLs associated with fruit quality traits in peach [Prunus persica (L.) Batsch] peach fruit quality through an ecophysiological model combined with a QTL ap- using SNP maps. Tree Genet. Genomes 12, 37. https://doi.org/10.1007/s11295-016- proach. J. Exp. Bot. 56, 3083–3092. https://doi.org/10.1093/jxb/eri305. 0996-9. Ribot, C., Zimmerli, C., Farmer, E.E., Reymond, P., Poirier, Y., 2008. Induction of the Zhang, C., Feng, R., Ma, R., Shen, Z., Cai, Z., Song, Z., Peng, B., Yu, M., 2018. Genome- Arabidopsis PHO1;H10 gene by 12-oxo-phytodienoic acid but not jasmonic acid via a wide analysis of basic helix-loop-helix superfamily members in peach. PLoS One 13, CORONATINE INSENSITIVE1-dependent pathway. Plant Physiol. 147, 696–706 e0195974. https://doi.org/10.1371/journal.pone.0195974. https://doi.org/ 10.1104/pp.108.119321. Ziosi, V., Noferini, M., Fiori, G., Tadiello, A., Trainotti, L., Casadoro, G., Costa, G., 2008. A Richmond, T.A., Somerville, C.R., 2000. The cellulose synthase superfamily. Plant new index based on vis spectroscopy to characterize the progression of ripening in Physiol. 124, 495–498. https://doi.org/10.1104/pp.124.2.495. peach fruit. Postharvest Biol. Tec. 49, 319–329. https://doi.org/10.1016/j. Romeu, J.F., Monforte, A.J., Sánchez, G., Granell, A., García-Brunton, J., Badenes, M.L., postharvbio.2008.01.017. Ríos, G., 2014. Quantitative trait loci affecting reproductive phenology in peach. 11