This is the accepted version of the article: Cantin, Celia M.; Wang, Xin-Wei; Almira, María; [et al.]. «Inheritance and QTL analysis of chilling and heat requirements for flowering in an interspecific almond x peach (Texas x Earlygold) F2 population». Euphytica, Vol. 216, issue 3 (March 2020), art. 51. DOI 10.1007/s10681-020-02588-9 This version is avaible at https://ddd.uab.cat/record/230980 under the terms of the license This is a post-peer-review, pre-copyedit version of an article published in Euphytica. The final authenticated version is available online at: https://doi.org/10.1007/s10681-020-02588-9 Document downloaded from: 1 Inheritance and QTL analysis of chilling and heat requirements for flowering in an 2 interspecific almond x peach (Texas x Earlygold) F2 population 3 Celia M. Cantin1,2,3, Xin-Wei Wang4, María Almira5, Pere Arús1,6, Iban Eduardo1,6* 4 1 5 Institut de Recerca i Tecnologia Agroalimentàries (IRTA), Barcelona, Spain 2 6 Aragon Agency for Research and Development (ARAID), E-50018 Zaragoza, Spain 3 7 Agrifood Research and Technology Centre of Aragón (CITA)-Agrifood Institute of 8 Aragon (IA2). Avda. Montañana 930. 50059, Zaragoza, Spain. 4 9 Zhengzhou Fruit Research Institute, Chinese Academy of Agricultural Sciences, 10 Zhengzhou 450009, P.R.China 5 11 Universidad Autonoma de Barcelona, Campus UAB, Cerdanyola del Vallès (Bellaterra), 12 08193 Barcelona, Spain 6 13 Centre for Research in Agricultural Genomics (CRAG) CSIC-IRTA-UAB-UB, Campus 14 UAB, Bellaterra, Barcelona, Spain 15 * corresponding author: iban.eduardo@irta.cat Tel. +34 935 636 600 16 17 Abstract 18 Blooming in temperate fruit species is triggered by chilling and heat requirements (CR and 19 HR), with a wide range of requirements within the same species. CR for flower bud dormancy 20 release has become a limiting factor for geographical adaptation of fruit trees in warmer 21 regions. The present study investigated the genetic basis of CR and HR to break dormancy and 22 flowering time (FT) in an almond x peach F2 progeny. FT, HR and CR were evaluated over two 23 consecutive years (2015/2016 and 2016/2017). Seven out of the eight identified quantitative 24 trait loci (QTLs) were found in both periods of analysis. They affected eight traits, and included 1 25 a consistent QTL for breaking dormancy, CR and HR. Two of them, affecting FT and HR for 26 FT (GDHF), colocalized in G1, and the remaining QTLs, affecting chilling and heat 27 requirements, both influenced by dormancy breaking (DB), were located in G6. These results 28 indicate that factors not related to DB affect flowering time in this population. Implications of 29 the results in peach breeding are discussed. 30 31 Keywords: chilling requirements, heat requirements, peach, flowering. 32 33 Introduction 34 Flowering, an essential and complex developmental process in plants (Castède et al., 2015) 35 , is regulated by a number of external signals and internal elements (Hanke et al., 2007). Its 36 correct completion is fundamental for the commercial production of seeds and fruits (Zhang 37 and Taylor, 2011). 38 In fruit tree orchards, there must be synchronization between flowering phenology and 39 climatic conditions (Castède et al., 2015). As flowering is crucial for sexual reproduction, buds 40 of perennial species in temperate regions become dormant (cease growth) during the winter 41 months to survive. Endodormancy requires a certain amount of chilling for the transition to 42 ecodormancy, whereas ecodormancy, requires a certain amount of heat to start the flowering 43 process (Castède et al., 2015). Consequently, dormancy and flowering are linked, and breeders 44 must select cultivars whose CR and flowering time match local climatic conditions (Bielenberg 45 et al., 2015). However, it has been demonstrated that global warming can advance or delay 46 flowering and/or fruiting of temperate fruit trees (Heide, 1993; Ramirez and Kallarackal, 2015; 47 Rivero et al., 2016; Woznicki, et al. 2019), having an unknown and undesired effect on the 48 productivity of fruit crop species. Globally, the temperature has increased by approximately 2 49 0.6ºC over the past 100 years (Walther, 2002). The forecast is for this trend to continue, with 50 studies already having observed the decrease in winter chill and the resulting changes in 51 phenological events (Menzel et al., 2005; Menzel et al., 2006). 52 The Prunus genus, within the Rosaceae family, is characterized by species that grow in 53 areas with well-marked seasons and are adapted to survive cold winters and dry summers 54 (Dirlewanger et al., 2012). Various models have been proposed to measure the accumulation of 55 CR in deciduous fruit-growing areas (Alburquerque et al., 2008). The effect and genetic basis 56 of HR on flowering is not yet well understood and studies on this topic are scarce. Previous 57 studies in Prunus have suggested that CR has a stronger effect on flowering time than HR 58 (Couvillon and Erez, 1985; Campoy et al., 2012; Alburquerque et al., 2008; Okie and 59 Blackburn, 2011; Sanchez-Perez et al., 2012). In this genus, as in most woody perennials, the 60 physiology and biochemistry of the flowering process is poorly understood (Dirlewanger et al., 61 2012; Woznicki et al., 2019). Recent reports suggest that intrinsic and environmental signaling 62 interact and dynamically affect the extent of bud dormancy (Castède et al., 2015; Woznicki et 63 al., 2019). Peach (Prunus persica (L.) Batsch) is an economically important species that 64 provides an excellent system for the genetic analysis of CR and FT due to the ample variation 65 for both traits among peach cultivars (CR between 50 to 1,050 h) (Zhebentyayeva et al., 2014). 66 Both CR and FT are inherited as quantitative traits (Fan et al., 2010; Hauagge and Cummins, 67 1991), but their molecular regulation is not yet fully understood. Some quantitative trait loci 68 (QTLs) associated with FT have been found in Prunus species (reviewed in Salazar et al., 69 2014): almond (Sanchez-Perez et al., 2012; Silva et al., 2005); peach (Dirlewanger et al., 2012; 70 Bielenberg et al., 2015); almond x peach (Donoso et al., 2015); apricot (Campoy et al., 2013; 71 Dirlewanger et al., 2012; Kitamura et al., 2018); sweet cherry (Dirlewanger et al., 2012; Calle 72 et al., 2020) and sour cherry (Wang et al., 2000). The separate effect of CR and HR on FT has 3 73 only been reported in a few studies in peach (Fan et al., 2010), apricot (Olukolu et al., 2009) 74 and cherry (Castède et al. 2014). Since these traits can only be evaluated 2-3 years after seed 75 germination in most Prunus, the identification of genetic markers linked with CR, HR and FT 76 would be a valuable tool to select genotypes at the seedling stage and make the breeding 77 process much more efficient (Bielenberg et al., 2015). 78 In this work, we studied an F2 DOPRQG [ SHDFK SURJHQ\ 7[( 79 µ(DUO\JROG¶  GH veloped at IRTA and used as the Prunus reference map (Dirlewanger et al., 80 2004), for which a high-density linkage map was available (Donoso et al. 2015). The main 81 goals were to study the inheritance of CR, HR and FT, and to test the existing chill unit (CU) 82 models in Gimenells, Lleida (Spain) (latitude 0°23'E /longitude 41°39'N), with a temperate 83 semi-arid climate, to identify which of them fits best with the climatic conditions in one of the 84 major areas of peach and almond production in the world. 85 86 Materials and methods 87 Plant material 88 The Prunus reference interspecific almond x peach F2 progeny (T x E), obtained by selfing 89 DK\EULGLQGLYLGXDO µ0%¶ IURPDFURVV 90 (Donoso et al. 2015), and its parents was used for this study (Tables S1 and S2). From the 91 original progeny of 111 hybrids, we phenotyped the 72 trees that were still alive. Trees of T x E 92 are at the IRTA Experimental Station of Lleida in Gimenells (Spain) grafted on µ*DUQHP¶ 93 (Felipe 2009) rootstocks. Standard agricultural practices were applied. 94 95 Phenotyping 4 96 The parents, hybrid and TxE offspring were evaluated over two seasons (2015/2016 and 97 2016/2017, that we refer to as 2016 and 2017, respectively) following a forcing protocol 98 widely used in temperate fruit trees (Campoy et al. 2011). Traits phenotyped were chilling 99 requirement (CR), flowering time (FT), and heat requirement (HR). Three one-year-old 100 fruiting branches for each individual were randomly collected once a week from November 101 1st until chilling requirements were reached. At least 30 flower buds were collected from 102 the three branches for each sampling date. The bases of the branches were placed in water 103 in a growth chamber at 25°C, under white fluorescent tubes with a 16 h:8 h, light:dark 104 photoperiod to force floral bud break (Ruiz et al., 2007; Sánchez-Pérez et al., 2012). After 7 105 d, the phenological stage of the flower buds was observed. The date of dormancy breaking 106 (DB) was established when 50% of flower buds were at phenological growth stage 53 107 according to the international Biologische Bundesanstalt, Bundessortenamt et CHemische 108 Industrie (BBCH) scale (Meier et al., 1994; Alburquerque et al., 2008). Three chill models 109 were then used to calculate chilling accumulation from October 1st until dormancy release, 110 corresponding to the CR. The flowering time (FT) was scored as the number of Julian days 111 when 50% of flowers were open. Also, the length of the period between DB and FT was 112 calculated as the numbHURIGD\VEHWZHHQERWKHYHQWV JD, increment of Julian Days). For ǻ 113 each genotype, the whole tree was observed by the same person every 1 or 2 days during 114 the flowering period. A scheme of the traits evaluated can be found in Figure 1. 115 116 Weather data 117 Hourly temperatures from October 1st to flowering time for both years were obtained from 118 the Gimenells weather station of the Generalitat de Catalunya, in the same area as the studied 119 population (https://ruralcat.gencat.cat). 5 120 121 Chilling and forcing models 122 For this study, we used the Chill Hours (CH), Utah (CU) and Dynamic (CP) Models. The 123 Chill Hours Model (CH) (Weinberger, 1950) is the oldest and simplest model, which considers 124 all hours with temperatures between 0 and 7q C as effective for chill accumulation. The Utah 125 Model (Richardson et al., 1974), which measures chill in Chill Units (CU), contains a weighted 126 function attributing chilling efficiencies to different temperature ranges, including negative 127 contributions by high temperatures, and it is particularly used in cooler areas of temperate 128 zones (Dennis et al., 2003). The Dynamic Model (Erez and Couvillon, 1987) was developed 129 for warmer areas. It considers that dormancy cessation occurs in two steps, the first being 130 reversible and the second irreversible, and CR are calculated as chill portions (CP). It adopts a 131 process-based concept of chill accumulation: an intermediate chill product is first formed 132 through bud exposure to low temperatures, and once a critical amount of this intermediate has 133 accumulated, it is transformed into a Chill Portion (CP). The CP is then retained until the end 134 of the chilling period (Erez and Fishman, 1998). As with the Utah model, temperatures have 135 different effects on dormancy, but the temperature ranges differ in the two models 136 (Alburquerque et al., 2008; Byrne, 2003). 137 To describe heat accumulation during the later stages of tree dormancy, we used the model 138 proposed by Richardson et al. (1974), which calculates Growing Degree Hours (GDH) between 139 dormancy release and flowering date. According to this model, heat builds up when hourly 140 temperatures are between 4.5ºC and 36ºC (at different rates depending on the maximum 141 temperature), with maximum accumulation at an optimal temperature (25ºC). Additionally, we 142 also calculated GDH between 1st October and flowering date (GDHF). 6 143 144 Chilling and heat requirements 145 Chilling and heat accumulation were calculated for the two consecutive dormancy seasons 146 (2016 and 2017) with the hourly temperatures measured in the field. Chill was computed 147 according to the Chill Hours (CH), Utah (CU) and Dynamic (CP) models and heat according to 148 the GDH Model. Correlations between chilling and heating requirements and blooming were 149 determined using Partial Least Squares (PLS) regression (Luedeling and Gassner, 2012). Since 150 heat cannot have an effect after bloom, the flowering time was considered as the end of the 151 forcing period. CR and HR were estimated as the sum of all daily chill and heat accumulated 152 during the chilling and forcing periods. Heat accumulation was calculated as the number of 153 GDH from DB to FT, and the length of this period of heat aFFXPXODWLRQZDVFDOF 154 and used as a complement to GDH. Annual heat accumulation up to the date of flowering was 155 also recorded (GDHF). 156 157 QTL analysis 158 For QTL analysis, we used the TxE genetic map described by Donoso et al. (2015), which 159 was constructed using 1,948 molecular markers (SNPs and SSRs), covering a total genetic 160 distance of 472.1 cM. The interval mapping method with the MapQTL 6.0 software package 161 (Van Ooijen et al. 2009) was used for QTL analysis of the phenotyped traits. QTLs were 162 considered consistent when the LOD •3.0 in both seasons, or with a LOD •3.0 one year and 163 LOD •2.0 the other year. QTLs were considered as major QTLs when they explained more 164 than 20% of phenotypic variation in both years of study (Tanksley, 1993). QTL positions were 165 drawn using the MapChart 2.1 software (Voorrips 2002). 7 166 Gene action was estimated following the guidelines of Tanksley (1993) with the ratio, d/a, 167 between the additive, where a = (A - B)/2, and dominance d = H - [(A + B)/2] effects, with H, 168 A and B the average phenotypic values of the heterozygous, almond homozygous and peach 169 homozygous genotypes, respectively. Based on the d/a ratio, QTLs were classified as 170 underdominant (d/a ” 1.25; U), dominant for the peach allele (-1.25 ” d/a ” -0.75; DP), 171 partially dominant for the peach allele (-0.75 ”d/a ”-0.25; PD), additive (-0.25 ”d/a ”0.25; 172 A), partially dominant for the almond allele (0.25 ”d/a ”0.75; AD), dominant for the almond 173 allele (0.75 ”d/a ”1.25; DA) and overdominant (d/a > 1.25; O). 174 175 Results 176 Temperature and chilling accumulation 177 Maximum and minimum daily temperatures during the consecutive years studied in 178 Gimenells (Lleida) are shown in Supplementary Fig. S1. Higher maximum and minimum 179 temperatures were registered during the winter in 2016, meaning a warmer winter compared to 180 the following year. However, warmer maximum temperatures were registered at the end of the 181 winter in 2017, reaching 25ºC at the beginning of March. 182 Chilling accumulation was very similar over the winter in both seasons (Fig. 3) using the 183 CU model and the CP model, but the accumulated CH was higher in 2017. Spearman 184 correlations between models were very high for both seasons (Fig. 4). 185 186 Chill requirements to break dormancy 187 The range of CR for dormancy breaking (DB) for the parental lines [µ0% ¶ (H), 188 µ7H[DV¶ (T) and µ(DUO\JROG¶ (E)] was between 42 and 64 CP. Of these, µ EDUO\JROG¶ had the 189 lowest CR (42 and 45 CP for consecutive years), whereas the requirements for H (60 and 59 8 190 CP) and µ TH[DV¶ (54 and 64 CP) were similar. There was transgressive segregation for CR, with 191 values from 33 to 71 CP (579 - 1434 CU; 484 - 1327 CH) averaged for two years (Table 1). CR 192 data for the F2 population showed normal distributions in the two years (Fig. 4), with CR 193 skewed in both yearsto high CR. Data for each individual is given in supplementary material 194 (Table S1 and S2). 195 In most genotypes, CR were similar for both years evaluated, showing high correlations 196 between them (Table 1). The date of DB was, in general, earlier in 2016 than in 2017, with the 197 earliest on 16th Dec and 14th Dec, whereas the latest was on 4th Feb and 2nd Feb (2016 and 198 2017, respectively). 199 200 Heat requirements for flowering 201 The range of HR for flowering among the parents was 3,575 to 6,170 GDH, whereas in the 202 segregating population it was 2,681 to 7,105 GDH (averaged for the two years) (Figure 4). µ0% 203 ¶ had the lowest HR of the parents, and was similar to µ , whereas µ TH[DV¶ EDUO\JROG¶ had 204 the highest. Within the segregating population, most genotypes had similar HR for both 205 consecutive years, showing a high correlation (r2 = 0.78) between years (Table 1). Date of 206 flowering (FT) was, in general, earlier in 2016 than in 2017, with the exception of only three 207 genotypes. The parental lines (µ0%¶µ7H[DV¶DQGµ(DUO\JROG showed very similar FT in 208 both years. Correlation between years for FT was lower than for other traits (r2 = 0.39) (Table 209 1). The earliest FT was on 21st Feb and 3rd March, and the latest 15th and 12th March (2016 and 210 2017, respectively). The number of days between DB to FT in the segregating population 211 (ǻJD), which is the period for heat accumulation, ranged from 27 to 77 days (averaged for the 212 two years), and there was a high correlation between both seasons (r2 = 0.86). HR showed a 9 213 bimodal distribution in 2017, whereas it was a single peak in 2016 (Fig. 4). There were single 214 peaks for FT in both years, although it was slightly skewed to late bloom in 2017. 215 216 Correlations between traits 217 Highly VLJQLILFDQW FRUUHODWLRQV 5•S” three models 218 used to estimate chilling accumulation for both seasons (Supplementary Table S1). Similarly, 219 correlations between DB and these three models were very high 5 •  . S ” 220 Correlations between seasons were very high for most of the traits (r2 = 0.78-0.89), with lower 221 values for FT and GDHF (r2 = 0.39 and r2 = 0.47, respectively) (Table 1). Flowering time (FT) 222 showed a high correlation with GDHF. Correlation between FT and CR and HR traits was low 223 for both years of study. 224 Heat requirements (GDH) were highly and negatively correlated with CR and DB (Table 1 225 and Fig. 5), which means that the lower the CR, the higher the HR. As expected, HR was 226 highly correlated with the number of days bHWZHHQ'%DQG)7 ǻ-' DQG 227 of days for heat accumulation was highly and negatively correlated with CR and DB. 228 229 QTL analysis 230 Data from the eight traits under study (DB, CH, CP, CU, FT, GDH, GDHF, ǻ-' ) were used 231 for QTL analysis. One consistent QTL per trait was identified (Table 2). QTLs were located in 232 G1 for FT and GDHF, and in G6 for DB, the CR models (CH, CU and CP), GDH and ǻ-' 233 (Fig. S2). 234 For dormancy breaking (DB), the LOD for the QTL from G6 was 3.4 in 2016 and 3.4 in 235 2017 and explained 18.4 and 18.1 % of the phenotypic variance, respectively. The homozygote 236 for the peach allele increased the date of DB by 15 days compared to the almond homozygote 10 237 in 2016 and by almost two days in 2017. In 2017, while both homozygous classes had similar 238 values, there was an increase of 13 days for the heterozygous individuals. Very similar results 239 were obtained for ǻ-'DOWKRXJKWKH LOD for the QTL in G6 was less than 2.0 (1.8) in 2017 and 240 therefore was not considered. 241 For CR data similar results were obtained with the three models, although the most 242 significant QTLs were obtained using the dynamic model, while the least was for the Utah 243 model. For the dynamic model, a QTL was identified at the proximal end of G6 with a LOD 244 score of 3.4 in 2016 and 3.2 in 2017, explaining, respectively, 18.4% and 17.4 % of the 245 phenotypic variance. The confidence interval for G6 QTLs spans the genomic region Pp06: 0- 246 4.001.078 where 702 genes have been annotated. In 2016, the individuals with the homozygous 247 peach allele needed 10 CP more to reach DB than those with the homozygous almond allele. In 248 2017, this difference was only four CP, but 10 with heterozygous individuals. 249 In the same region of G6 we DOVR LGHQWLILHG D 47/ IRU *'+ in 2016) 250 explaining between 16.8% and 19.9% of the phenotypic variance respectively. For FT we 251 identified a QTL at the beginning of G1 in 2017 (LOD 3.9; R2 = 21%), but with a LOD score of 252 2.2 in 2016 (R2 = 11.5%). The confidence interval spans the genomic region Pp01:0- 253 10.521.046 bp, where 1584 have been annotated. The peach allele in homozygosis increased 254 the FT by five days in 2016 and three in 2017, compared to the homozygous almond allele. In 255 the same region, we also detected a QTL for GDHF in both years, with LOD values of 3.9 and 256 3.1 respectively. 257 258 Discussion 259 Phenotypic data of chilling and heat requirements 11 260 It is usually assumed that almond flowers before peach. It is interesting to note that in our 261 case, µ7H[DV¶ , a late flowering almond cultivar and µ(DUO\JROG¶ , an early flowering peach, 262 flower at the same time even though they have different CR and HR. The CR of the progeny 263 are in the range of those reported for peach and almond from other Mediterranean areas 264 (Benmoussa et al., 2017; Campoy et al., 2012; Ruiz et al., 2007). However, it must be noted 265 that TxE is an interspecific population, and therefore results are not fully comparable to single 266 species populations studies. The range observed in the TxE population exemplifies the 267 difficulties for growing certain peach and almond cultivars in warm regions where annual chill 268 accumulation is decreasing due to global warming. A large variation in the HR within the 269 studied progeny was also found. 270 The performance of chill accumulation models vary in different climate conditions, as 271 observed in peach (Balandier et al., 1993; Erez et al., 1990; Erez et al., 2000; Perez et al., 2008) 272 and other Prunus species (Alburquerque et al., 2003; Alburquerque et al., 2008; Egea et al., 273 2003; Ruiz et al., 2007). 274 In Lleida (Spain) the CR calculated using the three models were very well correlated and 275 with DB, and therefore could be used for the calculation of CR in this climatic area, as for 276 peach (Fan et al., 2010) and apricot (Campoy et al., 2012) in colder climates. This correlation 277 could be due to the lack of long periods of warm and fluctuating temperatures, so that chilling 278 accumulation based on different models all steadily increased in a similar way through the two 279 seasons. Substantial differences among the many models used have been observed in moderate 280 mild climates (Erez et al., 1990; Erez et al., 2000) since some of them, such as the Utah model, 281 were developed in a cold area and are not appropriate for warmer areas. 282 The variability of chill accumulation between years was lower with the Dynamic model 283 than when the calculations were done with the Utah and the hours-below 7ºC models. This may 12 284 be explained by the homogenizing effect of the Dynamic model, which takes into account the 285 synergistic effect between moderate and low temperatures for breaking dormancy (Fishman et 286 al., 1987). Other authors have previously reported similar results in apricot (Campoy et al., 287 2012; Ruiz et al., 2007), suggesting that the Dynamic model is optimal for the climatic 288 conditions of Lleida and other areas of the Ebro Valley in Northern Spain. 289 290 Correlations among endodormancy and ecodormancy traits 291 7KH KLJK QHJDWLYH FRUUHODWLRQV and GDH vs. CR (CH, IRXQG CP and CU) EHWZH 292 indicate that genotypes with lower CR required a longer period of heat accumulation to bloom. 293 This has been also found in peach (Li et al., 2016) and apricot (Campoy et al., 2012). Li et al. 294 (2016) reported a decrease of 16 days per 200 accumulated CHs, up to a threshold of approx. 295 950 CHs. Overall, these results suggest that in cultivars with low CRs, GDH accumulation just 296 after CR fulfillment is less effectivethan in cultivars with higher CRs, and therefore they need 297 more time to accomplish their HR. 298 A similar result was found for the correlation between DB and CR (CH, CU, and CP) 299 against HR (GDH). Similar high correlations have been found in peach (Fan et al., 2010; Li et 300 al., 2016; Pawasut et al., 2004; Scorza and Okie, 1990) and apricot (Ruiz et al., 2007). We 301 found a high level of variability among the progeny regarding HR, which disagrees with 302 Linsley-Noakes and Allan (1994) who reported no differences in HRs between three nectarine 303 cultivars with different CR. These results suggest the existence of different heat requirements 304 among genotypes and a major genetic contribution in the control of this trait, or that this trait is 305 not being measured accurately because the physiological base is not yet well understood. The 306 first hypothesis is in line with the model for Douglas fir (Harrington et al., 2010), which 307 proposes a variable threshold for the efficiency of chill and heat temperatures. However, other 13 308 authors have reported contradictory results (Couvillon and Erez, 1985; Guerriero et al., 2006; 309 Kotowski et al., 1980), which may be due to the different climate of the cited studies. There is 310 no consensus in the literature about whether there is a clear relationship between CR and HR. 311 No significant correlation was found between HR (GDH) and FT, in agreement with other 312 authors in peach (Fan et al., 2010) and apricot (Campoy et al., 2012). This result indicates that 313 GDH is not as important as CR for determining flowering time. However, a high correlation 314 was found between FT and GDHF which could indicate the importance of warm temperature 315 before dormancy breaking on the flower bud formation and development, as reported 316 previously for plum (Woznicki et al., 2019) The fact that µ EDUO\JROG¶ is a low CR cultivar 317 might also explain this result. Also Li et al. (2016) found that both the days to full bloom date 318 and HR were negatively correlated with CH, which may indicate that less accumulated CHs 319 could lengthen the days to full bloom date and increase the heat requirement. Together, the 320 results indicate that CR is a major factor determining flowering time, although not the only one. 321 Indeed, it is unclear in the literature whether heat accumulation for floral or vegetative bud 322 break starts before or after the release of endodormancy. Recent reports have shown a positive 323 correlation between August-September temperature and the amount and time of flowering in 324 the following spring on plum (Døving 2009; Woznicki et al., 2019) and sweet cherry (Døving 325 et al., 2011).We also found a low correlation between FT and CR. This is contrary to what has 326 been shown by other authors in peach and almond populations (Castède et al., 2014; Sánchez- 327 Pérez et al., 2012; Fan et al., 2010). However, there is no previous literature on CR for 328 interspecific populations. We believe that this distortion to the expected results is due to the 329 existence of two different species in the progeny. Indeed, in the Figure 3 can be observed that 330 WKH DOPRQG µ7H[DV¶ DQG WKH SHDFK µ(DUO\JROG E has 331 significant lower CR than T. 14 332 333 Genetic control of endodormancy and ecodormancy traits 334 We have identified one genomic region controlling endodormancy and ecodormancy traits 335 (CR and GDH) located in G6 and another one controlling FT in G1, that have not been 336 previously identified in other Prunus populations. The interspecific nature of the TxE 337 population might explain some of the differences observed with previous data obtained in 338 single species mapping populations. Data for GDHF, a trait highly correlated to FT, also 339 detected a QTL in G1 co-locating with that of FT, indicating either that GDHF is not related to 340 HR but a different way of measuring FT, or that warm temperatures during the winter 341 dormancy period, and not only after the fulfillment of the CR, are important for determining 342 FT. The latter hypothesis would support the high negative correlation, observed in this and 343 other studies, between CR and HR (Fan et al., 2010; Pawasut et al., 2004; Ruiz et al., 2007; 344 Scorza and Okie, 1990), since the longer the period for chill accumulation, the higher amount 345 of GDHF likely to be accumulated. 346 Consistent QTLs across years for CR, HR and FT have been described in various Prunus 347 progenies. In peach, Fan et al. (2010) identified QTLs for CR, HR and FT at the latter end of 348 G1, where the evergrowing gene (Evg) maps (Bielenberg et al. 2008), and for CR and FT in G4 349 and G7, where QTLs for FT have been found in various Prunus crops (Dirlewanger et al. 350 2012).In similar positions of G1 and G4, QTLs for CR and/or FT have been detected by 351 Bielenberg et al. (2015) in peach, by Sánchez-Pérez et al. (2012) in almond and by Quilot et al. 352 (2004) in an advanced backcross between P. persica cultivars and P. Davidiana. For sweet 353 cherry, a consistent QTL in G4 for CR and FT has also been detected by Castède et al. (2014). 354 In all cases, the QTLs for CR coincided in map position with those of FT and produced effects 355 of similar magnitude and gene action, suggesting CR as a major cause for the FT phenotype. 15 356 Where HR was studied (Fan et al. 2010, Castède et al. 2014, Sánchez Pérez et al 2012), the 357 QTLs were detected at the same positions as those of CR and FT, or were not consistent over 358 the two years and had effects generally opposite to those of the QTLs detected for CR. This 359 suggests that HR is a minor or irrelevant factor in the determination of FT, that its measurement 360 as GDH is inefficient, or both. Our results support these observations as we found that HR and 361 CR detected the same QTL in both years studied. On the other hand, we did not find common 362 QTLs for CR and FT, indicating that FT was mainly determined by factors other than those we 363 measured. A possible explanation for this is that the TxE offspring, from the cross between two 364 cultivars from different species with low CR, had a lower level of variation for CR than other 365 mapping populations studied. In the cold-winter conditions of Lleida, chilling requirements 366 could have been rapidly met, resulting in a narrower distribution of variability that the 367 parameters used measured with low efficiency. In agreement with our results, a QTL for FT 368 was previously found in G1 in the TxE progeny in 2012 and 2013 (Donoso et al. 2016). Here, 369 we also identified a peak with LOD of 2.9 in 2016 for FT in G6 but it was not considered as an 370 stable QTL as it had a LOD<2 in 2017 (results not shown). This does not discount that the CR 371 may be involved in FT variability, with apparently minor effects, although the population size 372 used could have been insufficient to detect them with a significant threshold. 373 374 Conclusions 375 All the models for the estimation of CR (Utah, Dynamic and Hours-below 7º) worked well 376 for the area of study, characterized by short but cold winters, with warm falls and springs. 377 However, the Dynamic model seems the most appropriate as it reduced the year-to-year 378 variation observed in the population. The results indicate that, although CR appears to have a 379 more important role than HR in determining flowering time, neither factor had a major effect 16 380 on this trait under the conditions of this research. For HR, the warm temperatures during 381 endodormacy (not only after endodormancy release) may have also influenced flowering time. 382 In summary, our data supports FT as a quantitatively inherited character with a strong genotype 383 x environment component that is affected by both chilling and heat requirements. The observed 384 variation in the CRs within the population studied highlights the importance and feasibility of 385 breeding for low CRs in a new scenario of low chill accumulation due to global warming. 386 387 Acknowledgements 388 This project was supported in part by funding from the Spanish Ministry of Economy 389 and Competitiveness (MINECO/FEDER projects AGL2015-68329-R and RTA2015- 390 00050-00-00, Severo Ochoa Program for Centres of Excellence in R&D 201-2019 SEV- 391 2015-0533) and from the CERCA Programme-Generalitat de Catalunya. 392 17 393 Tables 394 Table 1. Pearson´s correlation coefficients for seasons 2016 and 2017 between dormancy 395 break (DB), flowering time (FT), chilling requirements for DB [Chill Hours (CH), Chill 396 Units (CU) and Chill Portions (CP)] and HR for blooming [Total Growing Degree hours 397 from 1st October to FT (GDHF), Growing Degree Hours (GDH) and number of days (ǻJD) 398 from DB to FT]. 3HDUVRQ¶V correlation coefficients between seasons 2016 and 2017 are 399 indicated in the diagonal. 2015-2016 DB CH CU CP FT GDHF GDH ɅJD DB 0.89 0.87 0.88 0.89 0.21 0.16 -0.81 -0.86 CH 0.90 0.89 0.89 0.90 0.22 0.16 -0.81 -0.87 2016-2017 CU 0.86 0.84 0.85 0.86 0.21 0.16 -0.78 -0.83 CP 0.87 0.86 0.87 0.88 0.22 0.16 -0.80 -0.84 FT -0.11 -0.07 -0.10 -0.11 0.39 0.54 0.38 0.25 GDHF -0.29 -0.26 -0.28 -0.28 0.32 0.47 0.50 0.40 GDH -0.88 -0.84 -0.87 -0.83 -0.13 -0.05 0.84 0.82 400 ɅJD -0.87 -0.84 -0.86 -0.87 -0.06 -0.02 0.83 0.86 401 9DOXHVLQEROGDUHVLJQLILFDQWDWS” 402 18 403 Table 2. Summary of consistent QTLs identified with the TxE map including trait name, 404 QTL names, LOD score of the maximum peak, position of the maximum peak, closest 405 marker, and parameters of percentage of explained phenotypic variance (R2), additivity (a), 406 dominance/additivity (d/a) and inferred gene action (GA). DB, dormancy break; FT, 407 flowering time; CH, chill hours; CU, chill units; CP, chill portions; GDHF, growing degree 408 hours to floweringǻ-'QXPEHURIGD\VIURP'%WR)7 Trait QTL name LG Position (cM) Closest marker LOD R2 aa d/a b GAc DB-2015 qDB6 G6 2,4 SNP_IGA_613848 3,4 18,4 -7,5 -0,7 PD DB-2016 qDB6 G6 2,4 SNP_IGA_613848 3,3 18,1 -0,9 -14,6 U CH-2015 qCH6 G6 2,4 SNP_IGA_613848 3,2 17,3 -95,9 -0,6 PD CH-2016 qCH6 G6 2,4 SNP_IGA_613848 3,2 17,4 -31,4 -7,0 U CU-2015 qCU6 G6 2,4 SNP_IGA_613848 3,2 17,2 -129,0 -0,7 PD CU-2016 qCU6 G6 2,4 SNP_IGA_613848 2,9 16,1 -2,0 -81,3 U CP-2015 qCP6 G6 2,4 SNP_IGA_613848 3,4 18,4 -5,5 -0,7 PD CP-2016 qCP6 G6 2,4 SNP_IGA_613848 3,2 17,4 -0,3 -26,9 U FT-2015 qFT1 G1 9,8 SNP_IGA_23251 2,2 11,5 -2,5 0,4 AD FT-2016 qFT1 G1 1,4 SNP_IGA_2006 3,9 21 -1,7 0,2 A GDHF-2015 qGDHF1 G1 5 SNP_IGA_10520 3,9 19,9 -324,8 0,1 A GDHF-2016 qGDHF1 G1 1,9 SNP_IGA_2670 3,1 16,8 -144,3 0,1 A GDH-2015 qGDH6 G6 2,4 SNP_IGA_613848 3 16,3 452,4 -1,2 DP GDH-2016 qGDH6 G6 2,4 SNP_IGA_613848 3,5 19,9 82,8 -13,6 U 409 ΔJD-2015 qΔJD6 G6 3 SNP_IGA_612754 3,6 18,9 5,8 -1,1 DP a 410 Additive effects: a = (A - B)/2, where A and B are the average phenotypic values for the 411 homozygotes of the almond and peach alleles, respectively. b 412 Dominance d = H - [(A + B)] c 413 Gene action. U underdominance , DP dominance for peach allele , PD partial dominance for peach 414 allele, A additivity, AD partial dominance for almond allele . 415 19 416 Figures 417 418 Fig. 1. Scheme of the different traits used in this study. CR: Chilling requirement; HR: Heat 419 requirements; CH: Chill hours; CU: Chill units; CP: Chill portions; DB: Dormancy break; 420 FT: Flowering time; GDH: Growing Degree Hours; GDHF: Growing Degree Hours to 421 flowering; ǻ-' number of Julian days between DB and FT. 422 20 2500 2015-2016 2000 2016-2017 1500 Chill Units 1000 500 0 -500 120 100 80 Chill Portions 60 40 20 0 2500 2000 Hours below 7ºC 1500 1000 500 0 423 424 Fig. 2. Progression of chill accumulation in the period October-April in 2016 and 2017 in 425 Gimenells (Lleida). Results are expressed in Chill Units (Utah model) (upper), Chill Portions 426 (Dynamic model) (centre) and Chill Hours (hours below 7ºC) (lower). 427 21 428 429 Fig.3. R-squared regression among CR for breaking dormancy estimated by the Utah, Dynamic 430 and Chill hours models in Gimenells (Lleida, Spain) for 2016 and 2017. All correlations were 431 significant (p<0.01). 22 CR 2015/2016 T E H HR 2015/2016 FT 2015/2016 40 30 40 35 35 25 30 30 20 25 25 20 15 20 15 15 10 10 10 5 5 5 0 0 0 <30 30-40 40-50 50-60 60-70 1-2 2-3 3-4 4-5 5-6 6-7 7-8 65-69 70-74 75-79 80-84 85-89 90-95 CR 2016/2017 HR 2016/2017 FT 2016/2017 35 30 45 40 30 25 35 25 20 30 20 25 15 15 20 10 15 10 10 5 5 5 0 0 0 <30 30-40 40-50 50-60 60-70 1-2 2-3 3-4 4-5 5-6 6-7 7-8 65-69 70-74 75-79 80-84 85-89 90-95 Chill portions 1000 GDH Flowering date (FT) (day of the year) 432 433 Fig. 4. Distribution of phenological traits in the TxE population: chilling requirements (CR) in 434 chill portions, heat requirements (HR) in growing degree hours (GDH), and flowering times 435 (FT) as µGD\ RI WKH the\HDU¶ 'DWD parental lines are IRU indicated by arrows (T, µ Texas¶ ; E, 436 µ ; H, µ Earlygold¶ MB 1.37¶ ). 23 437 438 Fig. 5. Linear regression (R-squared) between chilling requirements (CR) and heat 439 requirements in the TxE population for 2015-2016 (above) and 2016-2017 (below). CR are 440 expressed in chill portions (Dynamic model), chill units (Utah model) and chill hours (hours 441 below 7ºC). Heat requirements (HR) are expressed in growing degree hours (GDH). 442 24 443 SUPLEMENTARY DATA 444 Table S1. Date for dormancy breaking and chilling accumulation (in chill hours, chill units and 445 chill portions) for all the genotypes in the population TxE for the two consecutive years 446 studied. Yearly values, mean values (ave) and coefficient of variation (cv) are shown. 447 25 Genotype BD CH CU CP 2015-2016 2016-2017 2015-2016 2016-2017 ave cv 2015-2016 2016-2017 ave cv 2015-2016 2016-2017 ave cv 1 11-Jan 2-Jan 814 930 872 82.0 1018 936 977 58.3 51 48 50 2.4 3 4-Jan 2-Jan 763 930 847 118.1 914 936 925 15.6 46 48 47 1.1 5 19-Jan 2-Feb 1039 1488 1264 317.5 1319 1345 1332 18.4 65 70 67 3.1 6 24-Dec 24-Dec 582 714 648 93.3 680 806 743 89.1 38 43 40 3.5 10 4-Jan 11-Jan 763 1140 952 266.6 914 970 942 40.0 46 53 50 5.0 11 21-Dec 19-Dec 528 594 561 46.7 618 728 673 77.4 36 39 37 2.4 12 4-Feb 2-Feb 1104 1488 1296 271.5 1429 1345 1387 59.0 69 70 69 0.6 14 21-Jan 9-Jan 972 1098 1035 89.1 1180 958 1069 157.0 59 51 55 5.6 15 11-Jan 9-Jan 814 1098 956 200.8 1018 958 988 42.8 51 51 51 0.3 16 21-Dec 24-Dec 528 714 621 131.5 618 806 712 132.9 36 43 39 5.1 17 21-Jan 11-Jan 972 1140 1056 118.8 1180 970 1075 148.1 59 53 56 3.8 20 11-Jan 2-Jan 814 930 872 82.0 1018 936 977 58.3 51 48 50 2.4 21 24-Dec 2-Jan 582 930 756 246.1 680 936 808 180.7 38 48 43 7.0 22 29-Jan 2-Feb 1039 1488 1264 317.5 1319 1345 1332 18.4 65 70 67 3.1 23 15-Jan 2-Jan 864 930 897 46.7 1084 936 1010 104.7 54 48 51 4.4 25 11-Jan 2-Jan 814 930 872 82.0 1018 936 977 58.3 51 48 50 2.4 30 21-Jan 9-Jan 972 1098 1035 89.1 1180 958 1069 157.0 59 51 55 5.6 31 21-Jan 11-Jan 972 1140 1056 118.8 1180 970 1075 148.1 59 53 56 3.8 34 29-Jan 2-Feb 1039 1488 1264 317.5 1319 1345 1332 18.4 65 70 67 3.1 37 15-Jan 9-Jan 864 1098 981 165.5 1084 958 1021 89.1 54 51 52 2.3 39 4-Feb 2-Feb 1104 1488 1296 271.5 1429 1345 1387 59.0 69 70 69 0.6 40 4-Jan 2-Jan 763 930 847 118.1 914 936 925 15.6 46 48 47 1.1 41 11-Jan 11-Jan 814 1140 977 230.5 1018 970 994 33.9 51 53 52 1.5 43 11-Jan 11-Jan 814 1140 977 230.5 1018 970 994 33.9 51 53 52 1.5 44 29-Jan 26-Jan 1039 1400 1220 255.3 1319 1206 1262 80.3 65 64 64 0.9 46 21-Jan 2-Feb 972 1488 1230 364.9 1180 1345 1262 117.0 59 70 64 7.6 47 29-Jan 2-Feb 1039 1488 1264 317.5 1319 1345 1332 18.4 65 70 67 3.1 49 29-Jan 26-Jan 1039 1400 1220 255.3 1319 1206 1262 80.3 65 64 64 0.9 53 21-Jan 2-Feb 972 1488 1230 364.9 1180 1345 1262 117.0 59 70 64 7.6 55 29-Jan 2-Feb 1039 1488 1264 317.5 1319 1345 1332 18.4 65 70 67 3.1 56 4-Feb 2-Feb 1104 1488 1296 271.5 1429 1345 1387 59.0 69 70 69 0.6 59 24-Dec 24-Dec 582 714 648 93.3 680 806 743 89.1 38 43 40 3.5 61 11-Jan 11-Jan 814 1140 977 230.5 1018 970 994 33.9 51 53 52 1.5 63 29-Jan 26-Jan 1039 1400 1220 255.3 1319 1206 1262 80.3 65 64 64 0.9 69 4-Jan 24-Dec 763 714 739 34.6 914 806 860 76.0 46 43 45 2.5 72 15-Jan 2-Jan 864 930 897 46.7 1084 936 1010 104.7 54 48 51 4.4 73 15-Jan 26-Jan 864 1400 1132 379.0 1084 1206 1145 86.3 54 64 59 6.9 74 4-Jan 11-Jan 763 1140 952 266.6 914 970 942 40.0 46 53 50 5.0 83 16-Dec 24-Dec 452 714 583 185.3 524 806 665 199.8 43 37 7.8 84 21-Jan 2-Feb 972 1488 1230 364.9 1180 1345 1262 117.0 59 70 64 7.6 85 29-Jan 2-Feb 1039 1488 1264 317.5 1319 1345 1332 18.4 65 70 67 3.1 90 24-Dec 2-Jan 582 930 756 246.1 680 936 808 180.7 38 48 43 7.0 91 11-Jan 11-Jan 814 1140 977 230.5 1018 970 994 33.9 51 53 52 1.5 95 2-Nov 2-Feb 1165 1488 1327 228.4 1523 1345 1434 125.5 73 70 71 2.7 97 29-Jan 2-Feb 1039 1488 1264 317.5 1319 1345 1332 18.4 65 70 67 3.1 98 4-Jan 24-Dec 763 714 739 34.6 914 806 860 76.0 46 43 45 2.5 100 16-Dec 16-Dec 452 515 484 44.5 524 634 579 78.1 32 34 33 1.8 105 4-Jan 2-Jan 763 930 847 118.1 914 936 925 15.6 46 48 47 1.1 106 29-Jan 2-Feb 1039 1488 1264 317.5 1319 1345 1332 18.4 65 70 67 3.1 108 16-Dec 16-Dec 452 515 484 44.5 524 634 579 78.1 32 34 33 1.8 117 11-Jan 11-Jan 814 1140 977 230.5 1018 970 994 33.9 51 53 52 1.5 118 21-Jan 11-Jan 972 1140 1056 118.8 1180 970 1075 148.1 59 53 56 3.8 120 21-Jan 11-Jan 972 1140 1056 118.8 1180 970 1075 148.1 59 53 56 3.8 122 16-Dec 19-Dec 452 594 523 100.4 524 728 626 144.2 32 39 35 5.0 125 4-Jan 2-Jan 763 930 847 118.1 914 936 925 15.6 46 48 47 1.1 128 15-Jan 26-Jan 864 1400 1132 379.0 1084 1206 1145 86.3 54 64 59 6.9 133 4-Jan 2-Jan 763 930 847 118.1 914 936 925 15.6 46 48 47 1.1 150 4-Feb 26-Jan 1104 1400 1252 209.3 1429 1206 1317 157.7 69 64 66 3.5 152 15-Jan 11-Jan 864 1140 1002 195.2 1084 970 1027 80.3 54 53 54 0.5 153 21-Jan 9-Jan 972 1098 1035 89.1 1180 958 1069 157.0 59 51 55 5.6 165 29-Jan 2-Feb 1039 1488 1264 317.5 1319 1345 1332 18.4 65 70 67 3.1 166 4-Feb 2-Feb 1104 1488 1296 271.5 1429 1345 1387 59.0 69 70 69 0.6 172 4-Feb 26-Jan 1104 1400 1252 209.3 1429 1206 1317 157.7 69 64 66 3.5 194 21-Jan 9-Jan 972 1098 1035 89.1 1180 958 1069 157.0 59 51 55 5.6 199 11-Jan 11-Jan 814 1140 977 230.5 1018 970 994 33.9 51 53 52 1.5 202 24-Dec 14-Dec 582 515 549 47.4 680 634 657 32.5 38 34 36 2.5 211 11-Jan 2-Jan 814 930 872 82.0 1018 936 977 58.3 51 48 50 2.4 215 24-Dec 2-Jan 582 930 756 246.1 680 936 808 180.7 38 48 43 7.0 218 11-Jan 11-Jan 814 1140 977 230.5 1018 970 994 33.9 51 53 52 1.5 226 21-Jan 9-Jan 972 1098 1035 89.1 1180 958 1069 157.0 59 51 55 5.6 239 4-Jan 24-Dec 763 714 739 34.6 914 806 860 76.0 46 43 45 2.5 448 241 29-Jan 26-Jan 1039 1400 1220 255.3 1319 1206 1262 80.3 65 64 64 0.9 26 449 Table S2. Heat requirements in growing degree hours (GDH), flowering dates (FT) and days 450 from dormancy breaking to FT (ǻJD) for all the genotypes in the population TxE for the two 451 consecutive years studied. Yearly values, mean values (ave) and coefficient of variation (cv) 452 are shown. Only genotypes with two years measurements are shown. 27 Genotype FT GDH ΔJD 2015-2016 2016-2017 2015-2016 2016-2017 Ave cv 2015-2016 2016-2017 Ave cv 1 5-Mar 11-Mar 5034 6641 5837 1137 54 69 62 10.6 3 3-Mar 9-Mar 5855 6508 6181 462 59 67 63 5.7 5 7-Mar 11-Mar 3937 3783 3860 109 38 38 38 0.0 6 1-Mar 11-Mar 6194 7139 6667 668 68 78 73 7.1 10 4-Mar 9-Mar 5987 5444 5716 384 60 58 59 1.4 11 3-Mar 9-Mar 6613 7349 6981 521 73 81 77 5.7 12 25-Feb 3-Mar 2329 3099 2714 544 21 35 28 9.9 14 27-Feb 7-Mar 3829 5658 4743 1293 37 58 48 14.8 15 24-Feb 3-Mar 3991 5253 4622 892 44 54 49 7.1 16 23-Feb 5-Mar 5656 6705 6181 742 64 72 68 5.7 17 3-Mar 8-Mar 4375 5361 4868 697 42 57 50 10.6 20 3-Mar 7-Mar 4816 6362 5589 1093 52 65 59 9.2 21 24-Feb 3-Mar 5663 5956 5810 207 62 61 62 0.7 23 10-Mar 10-Mar 5156 6553 5855 988 55 67 61 8.5 25 24-Feb 7-Mar 3991 6362 5176 1676 44 65 55 14.8 30 8-Mar 10-Mar 4807 5849 5328 737 47 60 54 9.2 31 28-Feb 9-Mar 3885 5444 4664 1103 38 57 48 13.4 34 3-Mar 9-Mar 3588 3650 3619 44 34 35 35 0.7 37 3-Mar 9-Mar 4591 5804 5197 858 48 59 54 7.8 39 3-Mar 3-Mar 2999 3099 3049 71 28 30 29 1.4 40 1-Mar 7-Mar 5561 6362 5961 566 57 65 61 5.7 41 25-Feb 5-Mar 4146 5144 4645 706 45 54 50 6.4 43 25-Feb 6-Mar 4146 5230 4688 766 45 55 50 7.1 44 6-Mar 10-Mar 3874 4253 4063 268 37 43 40 4.2 46 27-Feb 10-Mar 3829 3695 3762 94 37 36 37 0.7 47 3-Mar 6-Mar 3588 3436 3512 108 34 33 34 0.7 49 5-Mar 10-Mar 3806 4512 4159 499 36 43 40 4.9 53 29-Feb 6-Mar 3976 3436 3706 382 39 33 36 4.2 55 5-Mar 8-Mar 3806 3567 3686 169 36 35 36 0.7 56 29-Feb 8-Mar 3347 3567 3457 155 25 35 30 7.1 59 5-Mar 10-Mar 6706 7051 6878 244 72 76 74 2.8 61 25-Feb 7-Mar 4146 5298 4722 814 45 55 50 7.1 63 12-Mar 12-Mar 4338 4703 4520 258 43 45 44 1.4 69 26-Feb 10-Mar 5294 7051 6172 1242 53 76 65 16.3 72 27-Feb 5-Mar 4045 6208 5126 1529 43 62 53 13.4 73 21-Feb 8-Mar 3321 4383 3852 752 37 41 39 2.8 74 1-Mar 10-Mar 5561 5490 5525 51 57 58 58 0.7 84 10-Mar 10-Mar 3588 3695 3642 76 50 36 43 9.9 85 3-Mar 9-Mar 3588 3650 3619 44 34 35 35 0.7 91 6-Mar 10-Mar 5102 5490 5296 274 55 58 57 2.1 95 6-Mar 4-Mar 2144 3218 2681 759 24 31 28 4.9 98 3-Mar 10-Mar 5456 7490 6473 1438 59 76 68 12.0 105 22-Feb 12-Mar 5987 6744 6365 535 49 69 59 14.1 106 1-Mar 4-Mar 2467 3218 2843 531 32 31 32 0.7 108 1-Mar 5-Mar 6719 7490 7105 545 76 80 78 2.8 117 23-Feb 4-Mar 3859 5012 4436 815 43 53 48 7.1 118 6-Mar 10-Mar 4661 5490 5075 586 45 58 52 9.2 120 8-Mar 10-Mar 4807 5490 5148 483 47 58 53 7.8 122 24-Feb 10-Mar 6188 7483 6835 915 70 81 76 7.8 125 7-Mar 11-Mar 6204 6641 6422 309 63 68 66 3.5 128 7-Mar 10-Mar 6204 4512 5358 1196 52 43 48 6.4 133 4-Mar 10-Mar 5987 6553 6270 400 60 67 64 4.9 152 3-Mar 8-Mar 4591 5361 4976 544 48 56 52 5.7 153 3-Mar 12-Mar 4375 5372 4873 705 42 62 52 14.1 165 4-Mar 4-Mar 3720 3218 3469 355 35 31 33 2.8 166 25-Feb 8-Mar 2329 3567 2948 875 21 34 28 9.2 172 11-Mar 4-Mar 2600 3915 3258 930 37 38 38 0.7 194 1-Mar 10-Mar 4081 5504 4793 1006 40 60 50 14.1 199 4-Mar 9-Mar 4948 5444 5196 351 53 57 55 2.8 202 25-Feb 5-Mar 5819 7156 6487 946 63 80 72 12.0 211 1-Mar 7-Mar 4522 6362 5442 1301 50 65 58 10.6 215 29-Feb 10-Mar 6089 6553 6321 328 67 67 67 0.0 218 25-Feb 5-Mar 4146 5144 4645 706 45 54 50 6.4 226 29-Feb 3-Mar 3976 5253 4614 903 39 54 47 10.6 239 15-Mar 11-Mar 6791 7139 6965 246 72 77 75 3.5 453 241 10-Mar 3-Mar 4154 3915 4034 169 42 37 40 3.5 28 454 455 29 456 457 458 Fig. S1. Maximum and minimum daily temperatures registered in the period October-April in 459 2016 and 2017 in Gimenells (Lleida). 30 TxE-1 TxE-6 GDH-2016 SNP_IGA_607240 FT-2016 0,0 AA08B 0,0 CH-2016 1,4 SNP_IGA_2006 0,6 SNP_IGA_607013 GDH-2015 AJD-2015 GDHF-2016 1,2 SNP_IGA_616286 CH-2015 CU-2015 CU-2016 SNP_IGA_2670 DB-2015 DB-2016 CP-2015 CP-2016 1,9 2,4 SNP_IGA_3931 1,8 SNP_IGA_614519 GDHF-2015 3,7 SNP_IGA_4808 2,4 SNP_IGA_613848 4,4 SNP_IGA_7895 3,0 SNP_IGA_612754 5,0 SNP_IGA_10520 4,6 SNP_IGA_611511 FT-2015 5,5 SNP_IGA_11106 5,1 SNP_IGA_610889 6,6 SNP_IGA_14438 5,9 SNP_IGA_610487 7,7 SNP_IGA_435668 6,6 SNP_IGA_609630 8,3 SNP_IGA_17419 7,3 SNP_IGA_604834 8,8 SNP_IGA_18927 9,0 SNP_IGA_621556 9,3 CPPCT010 9,8 SNP_IGA_23251 10,8 SNP_IGA_24481 12,2 pchcms4 12,9 SNP_IGA_28112 14,0 SNP_IGA_28294 14,1 SNP_IGA_624391 14,7 PLG39A 15,8 SNP_IGA_625354 15,4 PC102 16,3 SNP_IGA_625680 16,1 SNP_IGA_29836 16,9 SNP_IGA_625843 17,6 PACITA005 17,6 EPPCU9300 19,0 SNP_IGA_31646 19,4 SNP_IGA_627328 19,4 SNP_IGA_34337 20,0 SNP_IGA_627535 19,9 SNP_IGA_39630 20,7 SNP_IGA_628032 20,5 SNP_IGA_40030 21,9 SNP_IGA_629062 22,5 SNP_IGA_629855 22,9 SNP_IGA_47595 23,1 SNP_IGA_630243 23,4 PCeGA59 23,7 SNP_IGA_631014 23,9 SNP_IGA_63638 24,5 SNP_IGA_631572 24,4 SNP_IGA_67137 26,0 SNP_IGA_632033 24,8 SNP_IGA_76912 26,7 SNP_IGA_632423 26,1 CPSCT036 27,4 SNP_IGA_634798 27,3 SNP_IGA_78954 28,1 snp_6_10042074 29,2 SNP_IGA_635999 30,2 SNP_IGA_80547 29,7 SNP_IGA_637857 30,2 SNP_IGA_639062 33,0 UDP96-005 30,8 SNP_IGA_641339 35,9 SNP_IGA_84324 31,3 SNP_IGA_641637 37,0 PC85 31,9 SNP_IGA_644502 37,1 SNP_IGA_86918 32,9 SNP_IGA_650422 39,1 SNP_IGA_89808 34,0 SNP_IGA_655564 40,6 SNP_IGA_91804 34,6 SNP_IGA_655794 41,1 SNP_IGA_92681 35,1 SNP_IGA_925677 41,8 SNP_IGA_95095 35,6 SNP_IGA_661965 42,4 SNP_IGA_96539 37,8 AG26A 43,7 SNP_IGA_98960 39,1 SNP_IGA_667563 44,2 SNP_IGA_101065 39,6 SNP_IGA_668725 44,7 SNP_IGA_101538 40,8 SNP_IGA_669050 45,4 SNP_IGA_102335 41,3 SNP_IGA_670494 46,1 SNP_IGA_103507 42,3 SNP_IGA_670781 46,8 SNP_IGA_103771 44,2 SNP_IGA_671806 47,5 SNP_IGA_103917 45,2 SNP_IGA_672731 48,0 EPDCU3489 49,2 BPPCT027 49,8 SNP_IGA_104819 47,2 SNP_IGA_675077 50,6 SNP_IGA_106000 51,4 SNP_IGA_107095 49,7 FG4 52,0 SNP_IGA_108053 50,6 SNP_IGA_676571 52,5 SNP_IGA_108299 51,9 SNP_IGA_677147 53,5 SNP_IGA_109223 52,6 SNP_IGA_677625 54,2 SNP_IGA_109353 53,1 SNP_IGA_678681 54,9 SNP_IGA_109619 53,8 SNP_IGA_679038 55,6 SNP_IGA_110227 54,5 CPPCT047 56,3 SNP_IGA_110413 55,6 SNP_IGA_680032 56,9 SNP_IGA_111259 56,2 SNP_IGA_680889 57,3 SNP_IGA_111755 57,3 PC73 57,8 SNP_IGA_112042 57,9 SNP_IGA_682735 58,8 SNP_IGA_112271 58,5 SNP_IGA_683546 59,7 SNP_IGA_112698 59,0 PC105A 60,7 SNP_IGA_112924 60,2 SNP_IGA_686609 61,2 SNP_IGA_113449 60,8 SNP_IGA_687583 61,7 SNP_IGA_113947 61,9 SNP_IGA_688103 62,1 SNP_IGA_114094 63,1 SNP_IGA_688643 63,1 snp_1_39059187 63,6 SNP_IGA_688827 63,3 CPPCT029 64,2 SNP_IGA_689957 63,5 SNP_IGA_118443 65,4 SNP_IGA_690792 64,4 SNP_IGA_120784 66,1 SNP_IGA_691196 65,0 SNP_IGA_121364 66,8 SNP_IGA_691624 66,9 Pr1-12 68,2 SNP_IGA_692396 69,8 snp_1_41535285 68,8 SNP_IGA_692695 70,8 SNP_IGA_123023 70,7 SNP_IGA_693239 71,3 SNP_IGA_123180 71,4 SNP_IGA_693379 71,9 SNP_IGA_135188 72,3 SNP_IGA_694098 72,4 SNP_IGA_133606 73,7 SNP_IGA_694521 72,9 SNP_IGA_132855 74,1 SNP_IGA_694714 74,0 SNP_IGA_131589 74,6 SNP_IGA_695629 74,5 SNP_IGA_129422 75,1 CPPCT030 75,0 SNP_IGA_124466 75,5 SNP_IGA_697617 76,2 SNP_IGA_123766 76,0 SNP_IGA_699432 76,8 SNP_IGA_123719 77,4 CPPCT021 460 461 Fig. S2. Map of the G1 and G6 of the TxE population with the positions of the consistent QTLs 462 mapped in this work. Bars of QTLs indicate the LOD-1 intervals and are named with the trait 463 and the year of evaluation. Abbreviations: FT, flower time; GDHF, total growing degree hours; 464 DB, dormancy breaking; CU, chill units; CP, chill portions; GDH, growing degree hours; ¨JD, 465 days from dormancy breaking to FT. 31 466 REFERENCES 467Alburquerque, N., Burgos, L., Egea, J. 2003. Apricot flower bud development and abscission 468 related to chilling, irrigation and type of shoots. Scientia Horticulturae 98, 265-276. 469Alburquerque, N., Garcia-Montiel, F., Carrillo, A., Burgos, L. 2008. Chilling and heat 470 requirements of sweet cherry cultivars and the relationship between altitude and the 471 probability of satisfying the chill requirements. Environmental and Experimental Botany 472 64, 162-170. 473Balandier, P., Bonhomme, M., Rageau, R., Capitan, F., Parisot, E. 1993. 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