Deciphering the Genetic Determinism of Bud Phenology in Apple Progenies

Deciphering the genetic determinism of bud phenologyin apple progenies: a new insight into chilling and heatrequirement effects on flowering dates and positionalcandidate genes

J-M. Celton1,4, S. Martinez1, M-J. Jammes2, A. Bechti2, S. Salvi3, J-M. Legave1 and E. Costes1

1Montpellier SupAgro, UMR AGAP, Equipe AFEF, Avenue Agropolis, F-34398 Montpellier, France; 2SAS Pepinieres et Roseraies Georges Delbard, 9

Route de Commentry, 03600 Malicorne, France; 3E. Mach Foundation – IASMA Research and Innovation Centre, Via E. Mach 1, I-38010 San Michele

all’Adige (TN), Italy; 4Present address: UMR Genetique et Horticulture (GenHort), INRA ⁄ Agrocampus-ouest ⁄ Universite d’Angers, Centre Angers-Nantes,

42 rue Georges Morel – BP 60057, 49071 Beaucouze cedex, France

Author for correspondence:J-M. Celton

Tel: +33 628437885Email: [email protected]

Received: 27 April 2011

Accepted: 6 June 2011

New Phytologist (2011) 192: 378–392doi: 10.1111/j.1469-8137.2011.03823.x

Key words: apple (Malus · domestica),budbreak, candidate gene, chillingrequirement, heat requirement, phenology,QTL analysis.

Summary

• The present study investigates the genetic determinism of bud phenological

traits using two segregating F1 apple (Malus · domestica) progenies.

• Phenological trait variability was dissected into genetic and climatic components

using mixed linear modeling, and estimated best linear unbiased predictors were

used for quantitative trait locus (QTL) detection. For flowering dates, year effects

were decomposed into chilling and heat requirements based on a previously devel-

oped model.

• QTL analysis permitted the identification of two major and population-specific

genomic regions on LG08 and LG09. Both ‘chilling requirement’ and ‘heat require-

ment’ periods influenced flowering dates, although their relative impact was

dependent on the genetic background. Using the apple genome sequence data,

putative candidate genes underlying one major QTL were investigated. Numerous

key genes involved in cell cycle control were identified in clusters within the confi-

dence interval of the major QTL on LG09.

• Our results contribute towards a better understanding of the interaction

between QTLs and climatic conditions, and provide a basis for the identification of

genes involved in bud growth resumption.

Introduction

In the context of global climate change, vegetative and floralbud phenology of deciduous tree species is crucial as it mayaffect their productivity, adaptability and distribution(Chuine & Beaubien, 2001).

In order to adapt to the naturally changing environ-mental conditions, temperate tree species have developedthe ability to establish a dormant state (endo-dormancy) inthe rest period, during which meristems are unable toundergo ontogenic development towards bud burst(Doorenbos, 1953), followed by an eco-dormancy phasethat occurs at the end of winter and the beginning of spring,during which meristems achieve full growth competence

(Hanninen, 1995; Legave et al., 2008). In order for bud-break to occur promptly and uniformly in spring, treesrequire exposure to cold temperature (chilling requirement,CR), followed by a period of warmth (heat requirement,HR). The time of bloom is an important agronomic traitaffecting seed and fruit development (Fan et al., 2010), andis quantitatively inherited in the majority of fruit tree spe-cies (Anderson & Seeley, 1993).

Apples (Malus · domestica) are classified as high, inter-mediate and low CR. The modeling of flowering time,based on mathematical functions to simulate CR and HR,is commonly based on temperature response functions.Recently, three models have been validated under a range ofclimatic conditions (Legave et al., 2008). Based on

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modeling and phenological data, CR required to breakdormancy can vary from 200 (cv ‘Anna’) to 1500 (cv‘Wright #1’) (Hauagge & Cummins, 1991). However, themajority of commercial apple cultivars are classified as inter-mediate to high CR, that is between 600 and 800 hoursbelow 7�C of CR (Ferree & Warrington, 2003), and arepoorly adapted to mild climates. Changes in apple treeblooming dates have already been observed throughoutEurope (Legave et al., 2009). Results suggest that tempera-ture changes have led to an advance in the mean floweringdates of 7–9 d for cv ‘Golden Delicious’ (GD).

If temperatures were to continue to increase, phenologicaldisorders could be observed, including irregular floral andleaf budbreak and poor fruit set, as is already the case inSouth Africa (Labuschagne et al., 2002), Brazil (Petri, 1987)and Florida (Sherman & Sharpe, 1971). Although the cur-rent strategy involves the application of dormancy-breakingchemicals, increased awareness of the negative environmen-tal effects has resulted in the need to breed cultivars betteradapted to the changing environmental conditions.

In order to understand the genetic determinism of thetime of budbreak in apple, several studies have already beenconducted using a linkage analysis approach. Conner et al.(1998) identified eight genomic regions influencing thetime of budbreak using a population derived from a ‘WijcikMcIntosh’ · NY75441-58 cross. Later, Segura et al. (2007)identified additional quantitative trait loci (QTLs) associatedwith the time of budbreak using a population derived froma ‘Starkrimson’ · ‘Granny Smith’ cross. More recently, astudy using several segregating populations with the lowchilling cv ‘Anna’ as a parent (van Dyk et al., 2010) identi-fied LG09 as having a major QTL effect on the time ofbudbreak.

Despite these efforts, no major QTL has yet been definedagainst a wide genetic background or across variable environ-ments, and no attempt has been made to identify putativecandidate genes (CGs) or regulatory pathways involved in

the determinism of these traits. To do this, we combined twocomplementary approaches: one without a priori assump-tions, screening one major zone of interest; the other basedon an a priori assumption about the physiological mecha-nisms that could be responsible for the date of budbreak.

It is recognized that the break of dormancy in buds isbased on processes that are intrinsic to the bud itself(Metzger, 1996), and that dormancy breaking is unlikely tobe reliant on a linear control pathway (Rinne et al., 2001).Indeed, chilling restores the ability of buds to grow, but doesnot promote growth itself. The capacity to resume growthunder favorable conditions, following CR fulfillment, resultsmainly from the capacity of cells to divide and elongate(Rohde & Bhalerao, 2007). We hypothesized that the key tounraveling the molecular determinism involved in the dateof vegetative or flowering budbreak in apple may resides ingenes involved in the cell cycle and its control. The search forCGs was thus refined on genes involved in cell cycle activa-tion, inhibition and regulation (Meijer & Murray, 2001)(Fig. 1). Phytohormones were also considered, primarilyauxins and cytokinins, as they influence cell proliferation(del Pozo et al., 2005), as well as genes playing a major rolein growth processes, for example, for the organization of theorientation of microtubules during cell expansion (Schwabet al., 2003), or involved in plant cell wall loosening(Cosgrove, 2000). The purpose of our study was first to fur-ther our knowledge on the genetic determinism of appletime of budbreak using a QTL mapping approach based onthe best linear unbiased predictor (BLUP) of the dates ofbudbreak, vegetative budbreak and floral budbreak. Thisstudy was performed on two independent F1 apple popula-tions derived from the crosses ‘Starkrimson’ · ‘GrannySmith’ (STK · GS) and X3263 · ‘Belrene’. Second, weinvestigated the interactions between QTLs, CR and HR.Finally, we proposed putative CGs underlying one majorQTL by in silico mapping using the recently released applegenome sequence (Velasco et al., 2010).

KRPs

ABAAuxin

CYCA2

CYCD2

CYCD3CDK

PRZ1

CYCB1

CDKB1

HBT

APC

AXR3/IAA17 SCFTIR1

BRs

JACK

Mitotic phase

RBR

E2Fa E2Fc

E2F targets

CDKA

AXR1

SCFSKP2

CK

First grow

th phase

econ

d gr

owth

pha

se

M

S

G1G2Growth and

normal metabolic

roles

Growth and preparation for mitosis

Synthesis phase

S

DNA replication

Fig. 1 Genetic and hormonal control of theplant cell cycle (inspired by del Pozo et al.,2005). Specific cyclin-dependent kinase(CDK) ⁄ cyclin complexes control the two majorpathways regulating G1 ⁄ S and G2 ⁄ Mtransitions. Hormones (indicated in boxes)control the level of CDK activators andinhibitors, and proteasome degradationmediates the level of cell cycle regulators byeither SCF or APC complexes. ABA, abscisicacid; APC, anaphase promoting complex; AXR,auxin resistant; BRs, brassinosteroids; CDK,cyclin-dependent kinases; CK, cytokinins;CYC, cyclin; E2F, family of transcriptionfactors; HBT, HOBBIT; IAA, indol acetic acid;JA, jasmonic acid; KRP, p27kip-related protein;PRZ1, PROPORZ1; RBR, etinoblastoma-related protein; SCF, Skp1-Cullin1-F-box.

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Materials and Methods

Plant material

The first F1 progeny is derived from a cross between apple‘Starkrimson’ and ‘Granny Smith’. As described in Seguraet al. (2008), it comprises 123 seedlings replicated twiceand grafted onto ‘Pajam 1’ apple rootstocks. Both replicateswere planted in 2004 at the Melgueil INRA Montpellierexperimental station (co-ordinates: 43�36¢35¢¢N;3�58¢50¢¢E) and grown with minimal training and underirrigated conditions.

The second F1 progeny is derived from a cross betweenthe hybrid X3263 and the cv ‘Belrene’. X3263 is derivedfrom a cross between ‘Red Winter’ and X3177, the latterbeing itself a hybrid derived from a cross between ‘Idared’and ‘Prima’ (F. Laurens, pers. comm.). X3263 was bred atthe INRA station of Angers and is considered to be of type3 (intermediate) growth habit according to the classificationof Lespinasse (1992), whereas ‘Belrene’ is considered to beclass 2 with a more erect growth habit. This population iscomposed of 324 trees, 50 of which were randomly selectedto produce replicates. All 324 trees were phenotyped, and271, including the replicated trees, were used for linkagemap construction.

All trees from the population, including the replicates,were grafted onto ‘Pajam 1’ apple rootstocks and planted in2005 under similar growing conditions.

All parents are classified as moderate to high CR.

Phenotypic assessment

Three phenological stages were defined, based onFleckinger’s apple phenological classification (Fleckinger,1964). These stages comprised: date of green point (GP),corresponding to 50% of all tree buds (either vegetative orflowering) showing some green (C3, D); date of vegetativebudbreak (VB), corresponding to 50% of the vegetativebuds showing the first deployed leaf (D3); and date of flow-ering budbreak (FB), corresponding to 50% of the flowerclusters having the king flower open.

From the beginning of the budbreak period, trees wereobserved at the whole tree scale three times a week.Phenotypic assessments were recorded from 3 to 6 yrdepending on trait and population (Table 1).

Statistical analyses

Statistical analyses were performed using R software v2.8.1(R Development Core Team, 2009). The significance ofthe various effects was estimated using a linear modelPij � l + Gi + Yj + Gi · Yj + eij, where P is the pheno-typic value of tree j of genotype i, l is the total average ofthe population, Gi is the effect of genotype i, Yj is the effectof year j, Gi · Yj is the interaction between the genotypeand year, and eij is the residual error.

Broad-sense heritability of genotypic means (h2b) wasestimated using a balanced subset of the data to allow theestimation of the G · Y effect. This set is defined here ascomprising all the replicated individuals for which pheno-typic data of the character of interest were recorded forevery year studied. Heritability was estimated as:h2b = 1 � 1

F , where F is the Fisher statistic obtained usingANOVA type I (Gallais, 1989). Associated confidenceintervals (CIs) were calculated according to Knapp et al.(1985):

CIinf = 1� 1

F � Fa2Df1;Df2

CIsup = 1� 1

F � F1�a2Df1;Df2

Variables were considered to be heritable if their h2bvalue was > 0.2, and if the lower limit for their CI was > 0(Gallais, 1989).

To further study the environmental effect on the FB vari-able, each year (from 2007 to 2010 for STK · GS, andfrom 2008 to 2010 for X3263 · ‘Belrene’) was character-ized by the length of the periods required to fulfill CR(starting from November 1 of the previous year) and HR(starting the estimated day following CR fulfillment). Bothperiods were estimated using a model built for the cultivar‘GD’ derived from Legave et al. (2008), and thereaftercalled F1Gold1. This model, based on the hypothesis thatchilling and heat temperatures have successive and indepen-dent effects on trees, was built using a triangular function inthe chilling submodel, and an exponential function in theheat submodel. As described previously, chilling and heateffects, and their interaction with genotypes, were estimatedby ANOVA on a balanced dataset.

Finally, mixed linear models were built for all variables,including the year (Y) as a fixed effect, and genotype (G) orgenotype by year (G · Y) interaction as random effects. The

Table 1 Phenotypic assessment of the apple (Malus · domestica) populations derived from the crosses between ‘Starkrimson’ · ‘GrannySmith’ (SG) and X3263 · ‘Belrene’ (XB) between the years 2005 and 2010

2005 2006 2007 2008 2009 2010

GP SG SG SG SG ⁄ XB SG ⁄ XB SG ⁄ XBVB – – SG SG ⁄ XB SG ⁄ XB SG ⁄ XBFB – – SG SG ⁄ XB SG ⁄ XB SG ⁄ XB

FB, floral budbreak stage; GP, green point stage; VB, vegetative budbreak stage; –, absence of phenotypic assessment.

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models were estimated using the residual maximumlikelihood (REML) estimation method and effects to beincluded were selected based on the Akaike information cri-terion (AIC). For each trait, when G and G · Y effects wereincluded, the BLUP was computed for the G effect. This GBLUP was considered to be independent of climatic year. Itwas denoted by the trait name and used for QTL detection.

DNA extraction and molecular marker genotyping

Purification of F1 seedling and parental DNA was per-formed from young leaf tissue using the DNeasy 96 plantkit (Qiagen) following the manufacturer’s instructions.Simple sequence repeat (SSR) marker amplifications wereperformed as described by Gianfranceschi et al. (1998).Single nucleotide polymorphism (SNP) markers were origi-nally identified within the apple genome sequencing projectas heterozygous single nucleotide positions within the culti-var ‘GD’. SNPs were organized and genotyped as five 48-plexed SNP arrays, based on the SNPlex� (AppliedBiosystems Inc., Foster City, CA, USA) technology, asdescribed in Micheletti et al. (2011).

Genetic linkage map construction

For the STK · GS population, in addition to the 107 SSRmarkers from Segura et al. (2009), a further six SSR and 44SNP markers were mapped to the consensus linkage map(B. Guitton et al., unpublished).

For the X3263 · ‘Belrene’ population, a frameworkgenetic map was constructed using 271 individuals with 83SSR markers (Liebhard et al., 2002; Silfverberg-Dilworthet al., 2006), previously tested for their polymorphism onthe parents (144 screened for polymorphism on the parentsin total), and 128 SNP markers (Velasco et al., 2010).

Linkage analysis was performed using JoinMap version3.0 (Van Ooijen & Voorrips, 2001) with a logarithm of theodds (LOD) score of four for grouping. Genetic maps wereconstructed independently for each parent, and a consensuslinkage map was built using all segregating markers. Geneticdistances between markers were calculated using theKosambi mapping function, and linkage groups (LGs) werenumbered in accordance with Maliepaard et al. (1998).

QTL analysis

QTL analysis was performed using MapQTL�4 (vanOoijen et al., 2002) on the extracted G BLUP values andon the mean value per genotype for each year. QTL analysiswas carried out using the interval mapping (step size 1 cM)and multiple QTL mapping (MQM) functions. QTLs weredeclared significant if the maximum LOD, obtained follow-ing multiple rounds of MQM, exceeded the genome-wideLOD threshold calculated (1000 permutations, mean error

rate of 0.05). Each QTL was characterized by its LOD scoreand percentage of phenotypic variation explained. QTLswere graphically displayed as bars next to the LG on whichthey were identified using MapChart version 2.0 (Voorrips,2001), and CIs were estimated in cM and corresponded toa LOD score drop of one or two on either side of the likeli-hood peak.

Allelic effects were estimated for female and male additiv-ity, and for dominance.

A global model including all cofactors and their inter-actions, considered as fixed effects, was built to test epistaticeffects between QTLs when several QTLs were detected fora trait BLUP. The construction of such a model allowed theestimation of the global percentage of phenotypic variation(global R2) explained by all the QTLs.

In silico mapping of CGs

Predicted protein sequences derived from contigs under-lying the major QTL on LG09 were downloaded from theIASMA genome browser (http://genomics.research.iasma.it/gb2/gbrowse/apple).

Gene ontology (GO) annotations were performed usingBLAST2GO. The peptide sequence was loaded into theprogram, and the BLASTP function was run against theGenBank nonredundant protein database. A minimumE-value of < 10)3 was used before mapping and annotationinto GO terms.

A similar analysis was performed on an equivalent num-ber of gene sequences selected randomly throughout thegenome. A v2 test was then performed to compare the genecomposition of the LG09 QTL with this random subset,and to identify classes of genes differentially present withinthe QTL interval.

Further BLAST searches were performed locally with thelatest SWISS-PROT database. To identify putative CGs,we used two distinct strategies. The first involved the identi-fication of genes with potential involvement in phenotypicvariation. For this purpose, in view of the physiology ofvegetative and floral bud development, we chose to focusour attention on genes involved in cell cycle and division,and on its hormonal control. The second strategy involvedthe identification of putative CGs located closest to theLOD peak, as it is statistically the most likely location ofthe gene(s) responsible for the variation.

Results

Construction of genetic maps

The integrated map constructed for the STK · GS popula-tion enabled the positioning of 157 markers, including 113SSR and 44 SNP markers, over 17 LGs and covering1027 cM (data not shown).


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A total of 211 genetic markers was mapped on the inte-grated X3263 · ‘Belrene’ genetic map, which covered1068 cM over 17 LGs (data not shown). Each of the 83SSR markers tested were successfully mapped, and 128SNPs, of the 240 tested (53%), were polymorphic andmapped to the consensus map.

Both consensus linkage maps were aligned to the refer-ence map (Silfverberg-Dilworth et al., 2006) and noinversion in marker order was observed.

Phenotypic trait assessment

Data distribution and significance of CR and HReffects The analysis of the dates of GP, VB and FB revealeda normal distribution for both populations across most ofthe years phenotyped. The distribution of the variable FB isshown as an example (Fig. 2). Both populations showed asimilar pattern of distribution over the years, and the esti-mated dates of FB for ‘GD’ were included in the FB datesrecorded for both populations, except for 2010.

As shown in Fig. 2, the average dates of FB were identicalin 2007 and 2010 for the STK · GS population. However,these two years had different climatic conditions: 2007 wascharacterized by a mild winter (100 d CR), followed by awarm spring (65 d HR), whereas 2010 was characterized bya normal winter (90 d CR) but cold spring conditions(83 d HR). Similar FB dates indicate that the warm springconditions might have compensated for the long CR periodobserved in 2007, whereas the cold conditions of spring2010 resulted in a later than expected FB date based on CRlength period. Furthermore, 2010 showed an atypical

pattern of data distribution (L shape), with most genotypesflowering within a short period. The years 2008 and 2009were representative of average winter and spring conditionsin the south of France.

Significance of the effects and broad sense heritability Forboth populations, results showed highly significant G, Yand G · Y interaction effects (P < 0.01) on all variables(data not shown). To refine these effects, the effects of thelength of CR and HR periods on FB were estimated(Table 2). The results indicated that both periods have aneffect on FB. For the STK · GS population, the CR effecton FB was stronger (F = 1066) than the HR effect(F = 353), and the G · CR effect was close to significant(P £ 0.1). For the X3263 · ‘Belrene’ population, both CRand HR periods had a similar effect on FB, with F = 155and F = 153, respectively. The G · CR interaction had asignificant effect on FB (P £ 0.01). No significant G · HRinteraction effect was detected.

High heritability values were estimated for GP, VB andFB (Table 3) for both populations. Values were between0.83 (FB, STK · GS population) and 0.92 (GP,STK · GS and X3263 · ‘Belrene’ populations).

Correlation analysis Correlation coefficients between phe-notypic traits measured in consecutive years varied from 0.47(GP07–GP08) to 0.06 (VB09–VB10; FB08–FB09; FB09–FB10) for the STK · GS population, and from 0.42 (GP08–GP09) to zero (VB09–VB10) for the X3263 · ‘Belrene’population (Table 4). Correlation coefficients also variedamong phenotypic traits measured in the same year. GP and

2008 2009 20102007

STK x GS

X3263 x Belrène

165 165 161 173

100 91 84 90

Simulated GD FB (d)Expected chilling

140

0.0

0.2

0.4

0.6

0.0

0.2

0.4

0.6

0.00

0.10

0.20

0.00

0.10

0.20

0.00

0.10

0.20

0.0

0.2

0.4

0.6

0.0

0.2

0.4

0.6

145 150 155 160 165 170 140 145 150 155 160 165 170 140 145 150 155 160 165 170 140 145 150 155 160 165 170

140 145 150 155 160 165 170 140 145 150 155 160 165 170 140 145 150 155 160 165 170

65 74 77 83

requirement (d)Expected heat requirement (d)

Fig. 2 Distribution of the flowering budbreak dates (in days) for the apple (Malus · domestica) populations ‘Starkrimson’ · ‘Granny Smith’and X3263 · ‘Belrene’. Average parental floral budbreak (FB) dates (three parental replicates) are represented by arrows. A full line representsthe female parent of each cross, and the male parent is represented by a dashed line. The x-axis represents the date of FB (in days, startingfrom November 1 in the year before); the y-axis represents the proportion of trees that reached the FB stage at their respective dates. Thesimulated FB date for cultivar ‘Golden Delicious’ (GD) (in days, starting from November 1), as well as the estimated number of days necessaryto reach chilling and heat requirements, is given below the graphs corresponding to the four years studied. In 2007, only one replicate of eachparent flowered for ‘Starkrimson’ (STK) and ‘Granny Smith’ (GS).

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VB were correlated in 2007 and 2008 (R2 = 0.32 and 0.28,respectively), whereas the correlation between these two traitswas low in 2009 and 2010 (R2 of 0.07 and zero, respectively),for the STK · GS population. A similar trend was observedfor the X3263 · ‘Belrene’ population, with correlationcoefficients among traits decreasing for the years 2009 and2010 (Table 4).

QTL analysis

STK · GS population GP One QTL was identified usingBLUP of the genotypic effect. This QTL, located on thetop half of LG08 and explaining 23.1% of the variability,displayed mainly female additive effects. For 2005(Fig. 3), two QTLs were identified on LG06 and LG08,and explained 15% and 17% of the variability, respectively(Table 5). Both QTLs were characterized by female addi-tivity and a dominance effect. For GP06, one QTLidentified on LG10 explained 23% of the variation andwas characterized by female additivity and a dominanceeffect. One QTL with female additivity was detected forGP07 on LG08 and explained 27% of the variation. For2008 and 2009, QTLs were also detected on LG08, andexplained 18% and 19% of the variability, respectively.For both years, these two QTLs were characterized byfemale additivity. Finally, two QTLs, located on LG09and LG12, and explaining 16% and 21% of thevariability, respectively, were identified for the variableGP10. The LG09 QTL was characterized by a dominanceeffect, whereas the LG12 QTL was characterized by female

Table 2 Significance (P) of the genotype effect (G), chilling requirement effect, heat requirement effect and the interactions G · chilling andG · heat for the date of flowering budbreak (FB), estimated by ANOVA on balanced data for the apple (Malus · domestica) populations‘Starkrimson’ · ‘Granny Smith’ (STK · GS) and X3263 · ‘Belrene’

STK · GS X3263 · ‘Belrene’

P F value P F value

G *** 5.38 *** 6.12Chilling requirement *** 1066 *** 155Heat requirement *** 353 *** 153G · chilling * 1.26 *** 2.54G · heat ns 0.75 ns 0.88

The significance of each effect is indicated, followed by the F value: *, P £ 0.1; ***, P £ 0.01; ns, not significant.

Table 3 Estimation of the heritability value (h2) of the phenotypictraits green point stage (GP), vegetative budbreak stage (VB) andfloral budbreak stage (FB) calculated for the apple (Malus ·domestica) populations ‘Starkrimson’ · ‘Granny Smith’ (STK · GS)and X3263 · ‘Belrene’

Heritability (h2)

STK · GS X3263 · ‘Belrene’

GP 0.92 (0.90–0.94) 0.92 (0.88–0.96)VB 0.88 (0.84–0.91) 0.91 (0.86–0.95)FB 0.83 (0.76–0.88) 0.84 (0.71–0.92)

Heritability values are presented in bold and are followed by theestimated confidence intervals in parentheses.

Table 4 Correlation coefficients indicating varying phenotypic association between consecutive years and among traits measured in the sameyear (from 2007)

GP08 GP09 GP10 VB07 VB08 VB09 VB10 FB07 FB08 FB09 FB10

GP07 0.47 0.32 0.52GP08 0.31 0.42 0.28 0.47 0.45 0.60GP09 0.20 0.18 0.06 0.07 0.41 0.35GP10 0.01 0 0.36 0.37VB07 0.50 0.33

VB08 0.30 0.11 0.23 0.40VB09 0.06 0 0 0VB10 0.12 0.10FB07 0.47

FB08 0.06 0.18FB09 0.06 0.15

Correlation coefficients for the apple (Malus · domestica) ‘Starkrimson’ · ‘Granny Smith’ (STK · GS) population are indicated in italic, andcorrelation coefficients for the X3263 · ‘Belrene’ population are indicated in bold italic. GP (green point stage), VB (vegetative budbreakstage) and FB (floral budbreak stage) are followed by the year in which the phenotype was recorded (07, 08, 09 and 10: 2007, 2008, 2009and 2010, respectively).


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additivity. Together, both QTLs explained 21.2% ofthe variability.

Because of the strong female additivity displayed by allthe QTLs identified on LG08 for the variable GP, theseQTLs were also detected for all years at the same locationon the female parental genetic map (data not shown).

Vegetative budbreak Four QTLs were identified for thegenotypic effect BLUP. These QTLs were located onLG02, LG03, LG05 and LG06, and explained 21.6%,16.5%, 15.9% and 9.9% of the variability, respectively.The global model build with markers closest to the LODpeak included interactions between markers only andaccounted for 66.2% of the variation (Table 5). For 2008,two QTLs were mapped on LG05 and LG10 (Fig. 3), andexplained 14.4% and 26.4% of the variation, respectively.Both QTLs displayed a female additive effect, and togetherexplained 26.2% of the total variability. No QTL was

detected for VB09. For 2010, one QTL was identified onLG02. It explained 21.6% of the variability and displayed afemale additive effect.

Floral budbreak One QTL explaining 19.5% of the vari-ability and displaying a significant male additive effect wasmapped for G BLUP on LG08. In addition, QTLs weredetected for three of the four years investigated for FB. For2007, three QTLs were identified, two on LG06 and oneon LG08 (Fig. 3), explaining 20.8%, 23.3% and 23.6% ofthe variability, respectively. Both QTLs on LG06 displayeddominance and female additive effects, whereas the LG08QTL was characterized by a male additive effect. For 2008,a single QTL was mapped on LG08 and explained 20.9%of the variability. As for the previous year, this QTL wascharacterized by a male additive effect, as well as a domi-nance effect. For FB09, two QTLs were identified on LG08and LG12, and explained 8.7% and 10.5% of the variability,

CH02f06_SG0.0

RGL3b_G4.0

GD_SNP00243_G10.2

NH033b_SG27.7

AP2_S32.0GD_SNP00979_G35.1

GD_SNP02076_S42.2

CH02c06_S49.9CH05e03_G51.1

CH03d01_S71.0

CH03d01_G75.7

VB

10 LOD

= 5.45 (r2 =

0.21)

VB

_BLU

Ps LO

D =

6.62 (r2 = 0.16)

LG02CH03e03_SG0.0MYB12_S1.6

CH03g07_SG15.0

Hi03d06_SG24.5MdCENa26.8

Hi04c10y_SG35.7

CH05a12_G49.3

Hi07e08y_SG56.0

CH03g12y_SG61.6

VB

_BLU

Ps LO

D =

6.55 (r2 = 0.16)

LG03Hi09b04_G0.0

CH03a09_SG10.6

CH05e06_SG19.2

CH05f06_SG44.6VRN1a_S48.6CH02a08z_S51.9CH03a04_S54.2

CH04e03_SG65.0V

B08 LO

D =

4.58 (r2 = 0.14)

VB

_BLU

Ps LO

D =

4.33 (r2 = 0.1)

LG05HB09TC_S0.0

VRN2_SG9.9CH03d07_SG11.6

NZ23g04_SG22.5

AFL1_G33.3CH03d12_SG35.1

MFT_S50.9CH03c01_G52.9Hi07b06_SG54.9

GP

05 LOD

= 5 (r2 =

0.15)F

B07 LO

D =

5.76 (r2 = 0.21)

FB

07 LOD

= 4.86 (r2 =

0.23)V

B_B

LUP

s LOD

= 4.65 (0.12)

LG06

CLV1a_SG0.0

EFL3a_G12.7CH01c06_G13.5CH01e12_G14.3Hi04b12_S19.4

CH02g09_SG27.6CH05a02y_G30.9

MdPI_SG35.2

MdIAA2_S45.6

CH01h10_S51.4

Hi23g12x_SG58.0

GP

05 LOD

= 4.71 (r2 =

0.17)

GP

07 LOD

= 8.29 (r2 =

0.27)

GP

08 LOD

= 5.14 (r2 =

0.18)

GP

09 LOD

= 4.98 (r2 =

0.19)

GP

_BLU

Ps LO

D =

6.35 (r2 = 0.23)

FB

07 LOD

= 7.74 (r2 =

0.23)

FB

08 LOD

= 6.00 (r2 =

0.21)

FB

09 LOD

= 2.51 (r2 =

0.09)

FB

_BLU

Ps LO

D =

5.1 (r2 = 0.19)

LG08NH029a_G0.0

CH01f03b_SG7.7

Hi05e07_SG17.8

CH01h02_SG33.0RGL2a_S36.3NZ04h11x_SG36.6

GP

10 LOD

= 5.42 (r2 =

0.16)

LG09CH04c06z_G0.0Hi02d04_SG2.7

CH02a08y_SG12.8

VRN1b_S16.9

CH02c11_SG26.6

CH03d11_S31.8

COL_SG52.6

MS06g03_G56.9

GP

06 LOD

= 5.70 (r2 =

0.23)V

B08 LO

D =

6.58 (r2 = 0.26)

LG10

CH05d04_SG0.0

CH05d11_SG13.4CH05g07y_G14.2

CH01g12_G28.7

CH01f02_SG37.7

MdFT_G54.8

GP

10 LOD

= 5.88 (r2 =

0.21)

FB

09 LOD

= 3.11 (r2 =

0.10)

LG12

Fig. 3 Genomic positions of quantitative trait loci (QTLs) detected on the linkage groups (LGs) of the apple (Malus · domestica)‘Starkrimson’ · ‘Granny Smith’ integrated map by multiple QTL mapping (MQM) for the variables green point (GP), vegetative budbreak(VB) and floral budbreak (FB). QTLs are represented on the right side of LGs by boxes extended by lines representing the logarithm of the odds(LOD)-1 and LOD-2 confidence intervals. The numbering of LGs is according to Maliepaard et al. (1998).

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respectively. The global model estimation showed a signifi-cant effect for both QTLs, with no interaction, andexplained 17.9% of the variability. Both QTLs were charac-terized by a female additive effect.

X3263 · ‘Belrene’ population GP Three QTLs weredetected for G BLUP of the GP variable on LG01, LG03 andLG09. These QTLs explained 5.2%, 10.9% and 32.1% of

the variability, respectively. All three QTLs were character-ized by a female additive effect, as well as a dominance effectfor the LG09 QTL. Together, the three QTLs explained 23%of the variability (Table 6).

All three QTLs were recurrently detected over the threeyears investigated (Table 6). The LG01 QTL was identifiedin 2008 and 2009 (Fig. 4), and explained 5.1% and 8.6% ofthe variability, respectively. The LG03 QTL was identified

Table 5 Parameters associated with the quantitative trait loci (QTLs) detected in the apple (Malus · domestica) ‘Starkrimson’ · ‘GrannySmith’ population by multiple QTL mapping (MQM) for the phenological traits described as green point (GP), vegetative budbreak (VB) andfloral budbreak (FB)

Trait LG Position (cM) Locus LODa R2b Allelic effectsc Global R2d Ame Af

f Dg

GP05 6 35.1 CH03d12 5 (4.3) 0.152 )0.206 1.737 4.1738 6.0 CLV1 4.71 (4.3) 0.173 No effect )0.813 )2.330 2.206

GP06 10 6.7 Hi02d04 5.7 (4.6) 0.236 )0.656 )4.301 4.631GP07 8 27.6 CH02g09 8.29 (4.2) 0.271 )1.744 )3.508 2.781GP08 8 27.6 CH02g09 5.14 (4.7) 0.182 )1.102 )2.554 2.288GP09 8 22.4 Hi04b12 4.98 (3.9) 0.188 )0.987 )4.013 )0.312GP10 9 7.7 CH01f03b 5.42 (4.2) 0.159 ad**, bc* 0.212 0.257 0.350 )2.624

12 43.7 CH01f02 5.88 (4.2) 0.213 ad**, bd** )0.660 1.050 )0.105GP_BLUPs 8 25.4 CH02g09 6.35 (4.0) 0.231 )0.420 )0.789 0.514

VB07 No QTLVB08 5 64.2 CH04e03 4.58 (4.0) 0.144 bc***, bd* 0.262 )0.224 )4.146 )0.464

10 35.8 CH03d11 6.58 (4.0) 0.264 lm*** 0.197 )4.822 )2.005VB09 No QTLVB10 2 29.7 AP2 5.45 (4.2) 0.216 )0.435 )6.260 )0.178VB_BLUPs 2 30.7 AP2 6.62 (4.1) 0.165 0.662 0.000 0.000 0.000

3 56.0 Hi07e08y 6.55 (4.1) 0.159 bd 0.000 0.000 0.0005 65.0 CH04e03 4.33 (4.1) 0.099 ad 0.000 0.000 0.0006 9.9 VRN2 4.65 (4.1) 0.120 0.000 0.000 0.000

AP2:CH04e03 lm:bd*Hi07e08:VRN2 ad:eg**, bc:eg*AP2:Hi07e08: VRN2 lm:ad:eg*

lm:bc:eg**lm:bd:eg**

FB07 6 3.0 HB09TC 5.76 (3.8) 0.208 lm* 0.337 )1.041 )3.267 )3.1016 45.1 MFT 4.86 (3.8) 0.233 lm*** 0.410 )3.469 3.0678 0.0 CLV1a 7.74 (3.8) 0.236 eg***, fg* )1.457 )0.886 0.534

FB08 8 0.0 CLV1a 6 (4.0) 0.209 )1.341 )0.211 )1.273FB09 8 19.4 Hi04b12 2.51 (2.3) 0.087 lm** 0.179 )0.955 )6.857 )4.113

12 37.7 CH01f02 3.11 (2.3) 0.105 ad*** )2.002 )5.256 )3.956FB10 No QTLFB_BLUPs 8 13.5 CH01e12 5.1 (4.0) 0.195 )1.796 )0.513 )0.831

Each trait is followed by the year (2005–2010) in which the QTLs were detected. BLUPs (best linear unbiased predictors) indicate QTLs detec-ted for the genetic effect.aMaximum logarithm of the odds (LOD) score value of the QTL with the considered threshold in parentheses.bPercentage of the phenotypic variation explained by the QTL.cSignificance of the allelic combinations estimated by the global model based on genotypic information from the locus used as co-factor.dVariation explained by all QTLs estimated by the global model.eMale additive effect computed as [(lac + lbc) ) (lad + lbd)] ⁄ 4, where lab, lad, lbc and lbd are the estimated phenotypic means associatedwith each of the four possible genotypic classes ab, ac, ad and bd, deriving from an <ab·cd> cross.fFemale additive effect computed as [(lac + lad) ) (lbc + lbd)] ⁄ 4, where lab, lad, lbc and lbd are the estimated phenotypic means associatedwith each of the four possible genotypic classes ab, ac, ad and bd, deriving from an <ab·cd> cross.gDominance effect computed as [(lac + lbd) ) (lad + lbc)] ⁄ 4, where lab, lad, lbc and lbd are the estimated phenotypic means associatedwith each of the four possible genotypic classes ab, ac, ad and bd, deriving from an <ab·cd> cross.*, P = 0.01; **, P = 0.001; ***, P = 0.


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in 2008 and 2010, and explained 10.2% and 9.7% of the var-iability, respectively. Finally, the LG09 QTL was identifiedfor all three years, and explained between 12.1% and 37.5%of the variability. As for G BLUP, QTLs were characterizedby a female additive effect. Together, the QTLs explainedbetween 12.9% (2009) and 24.9% (2008) of the variation.

Vegetative budbreak Four QTLs were detected for GBLUP of the VB variable. These QTLs were identified onLG01, LG09, LG10 and LG15, and explained 14.9%,8.3%, 6.7% and 7.2% of the variability, respectively. Theglobal model built explained 22.3% of the variability.QTLs detected on LG01, LG10 and LG15 resulted from a

Table 6 Parameters associated with the quantitative trait loci (QTLs) detected in the apple (Malus · domestica) population X3263 · ‘Belrene’by multiple QTL mapping (MQM) for the phenological traits described as green point (GP), vegetative budbreak (VB) and floral budbreak (FB)

Trait LG Position Locus LODa R2b Allelic effectsc Global R2d Ame Af

f Dg

GP08 1 14.0 GD_SNP00345 5.17 (4.2) 0.051 lm*** 0.249 )0.694 3.691 0.7493 31.5 GD_SNP00194 8.17 (4.2) 0.102 No effect 1.175 4.381 0.6099 0.4 GD_SNP01189 21.77 (4.2) 0.375 np*** 1.979 6.773 6.604

GP09 1 14.0 GD_SNP00345 4.83 (4.2) 0.086 lm*** 0.129 )0.049 2.153 1.8629 0.4 GD_SNP01189 5.93 (4.2) 0.121 np*** 0.779 2.355 1.571

GP10 3 31.5 GD_SNP00194 4.72 (4.8) 0.097 No effect 0.568 3.617 )0.0109 0.4 GD_SNP01189 7.68 (4.8) 0.169 0.978 2.235 3.504

GP_BLUPs 1 14.0 GD_SNP00345 4.3 (4.3) 0.052 lm*** 0.230 )0.165 1.111 0.0483 31.5 GD_SNP00194 7.75 (4.3) 0.109 kk* 0.280 1.375 )0.0279 0.4 GD_SNP01189 17.6 (4.3) 0.321 np*** 0.517 1.612 1.859

VB08 1 40.3 GD_SNP00066 7.59 (4.9) 0.138 lm*** 0.226 )0.696 8.836 )4.5233 4.1 GD_SNP00588 6.77 (4.9) 0.096 np* 0.460 5.515 2.1579 0.4 GD_SNP01189 6.46 (4.9) 0.112 np*** 1.092 )0.012 5.33115 0.0 GD_SNP00550 6.05 (4.9) 0.065 kk***, hk** )1.131 )2.970 )0.643

VB09 NO QTL 0.000 0.000 0.000VB10 10 84.3 COL_XB 3.25 (4.1) 0.055 1.249 )0.539 0.211VB_BLUPs 1 38.7 GD_SNP00087 5.34 (3.9) 0.149 lm* 0.223 2.108 4.996 )2.957

9 0.4 GD_SNP01189 4.47 (3.9) 0.083 np 0.233 )0.135 1.20210 82.1 GD_SNP00360 5.23 (3.9) 0.067 kk* 0.305 )0.691 0.38415 9.7 NZ02b01_XB 5.95 (3.9) 0.072 kk***, hk** )0.305 )0.783 )0.133

FB08 1 18.0 GD_SNP00074 5.89 (5.5) 0.102 np* 0.210 )0.077 3.500 0.1769 0.4 GD_SNP01189 7.45 (5.5) 0.16 np** 0.859 2.885 2.57512 59.9 GD_SNP00376 4.22 (5.5) 0.069 kk***, hk** 0.389 2.960 0.284

SNP01189:SNP00376 np:hk *SNP01189:SNP00376 np:kk *SNP00074:SNP00376 np:hk *SNP00074:SNP00376 np:kk *SNP01189:SNP00074:SNP00376 np:np:hk *SNP01189:SNP00074:SNP00376 np:np:kk *

FB09 9 0.4 GD_SNP01189 13.18 (4.1) 0.291 0.987 1.207 3.29817 13.4 GD_SNP01584 4.15 (4.1) 0.077 No effect )0.179 2.686 )1.169

FB10 1 18.0 GD_SNP00074 8.47 (4.7) 0.188 )0.105 4.467 )0.833FB_BLUPs 1 7.0 Hi02c07_XB 5.83 (4.3) 0.109 bc***, bd** 0.158 )0.011 0.351 0.059

9 0.4 GD_SNP01189 4.32 (4.3) 0.093 np*** 0.065 0.032 0.266

Each trait is followed by the year (2005–2010) in which the QTLs were detected. BLUPs (best linear unbiased predictors) indicate QTLs detec-ted for the genetic effect.aMaximum logarithm of the odds (LOD) score value of the QTL with the considered threshold in parentheses.bPercentage of the phenotypic variation explained by the QTL.cSignificance of the allelic combinations estimated by the global model based on genotypic information from the locus used as co-factor.dVariation explained by all QTLs estimated by the global model.eMale additive effect computed as [(lac + lbc) ) (lad + lbd)] ⁄ 4, where lab, lad, lbc and lbd are the estimated phenotypic means associatedwith each of the four possible genotypic classes ab, ac, ad and bd, deriving from an <ab·cd> cross.fFemale additive effect computed as [(lac + lad) ) (lbc + lbd)] ⁄ 4, where lab, lad, lbc and lbd are the estimated phenotypic means associatedwith each of the four possible genotypic classes ab, ac, ad and bd, deriving from an <ab·cd> cross.gDominance effect computed as [(lac + lbd) ) (lad + lbc)] ⁄ 4, where lab, lad, lbc and lbd are the estimated phenotypic means associatedwith each of the four possible genotypic classes ab, ac, ad and bd, deriving from an <ab·cd> cross.*, P = 0.01; **, P = 0.001; ***, P = 0.

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female additive effect, whereas the LG09 QTL resulted pri-marily from a dominance effect.

Three of these QTLs were identified for VB08 on LG01,LG09 and LG15 (Fig. 4), and explained 13.8%, 11.2%and 6.5% of the variation, respectively (Table 6). One addi-tional QTL was identified for VB08 on LG03 andexplained 9.6% of the variability. Together, the QTLsexplained 22.6% of the variability. The QTLs mapped onLG01, LG03 and LG15 resulted from a female additiveeffect, whereas the QTL located on LG09 resulted from adominance effect. No QTL was detected for VB09. In2010, one QTL explaining 5.5% of the variability wasdetected on LG10.

Floral budbreak Two QTLs were detected for G BLUP ofthe FB variable on LG01 and LG09, which explained 10.9%and 9.3% of the variability, respectively. The LG01 QTL

resulted from a female additive effect, whereas the LG09 QTLresulted from a dominance effect. The global model built forG BLUP of this variable explained 15.8% of the variability.

Both QTLs were recurrently detected over the 3 yearsinvestigated (Table 6). The LG01 QTL was identified in2008 and 2010, and explained 10.2% and 18.8% of thevariability, respectively. The LG09 QTL was identified in2008 and 2009, and explained 16% and 29.1% of the vari-ability, respectively. Two additional QTLs were detectedfor FB on LG12 (2008) and LG17 (2009), and explained6.9% and 7.7% of the variability, respectively. In 2008, sig-nificant interactions occurred between QTLs located onLG09 and LG12, LG01 and LG12, and among all threeQTLs (Table 6). The global model built for QTLs detectedin 2009 did not permit the confirmation of the effect of theLG17 QTL, which may be a result of the low percentage ofvariability (7.7%) explained.

GD_SNP000140.0GD_SNP018260.3GD_SNP018153.5Hi02c07_XB7.0

GD_SNP0034514.0GD_SNP0059717.4GD_SNP0007418.0

hi04c1032.7GD_SNP0008738.7GD_SNP0078239.9GD_SNP0177240.2GD_SNP0006640.3

hi07d0852.4

CH05g08_XB57.7

GP

08 LOD

= 5.17 (r2 =

0.05)

GP

09 LOD

= 4.83 (r2 =

0.08)

GP

_BLU

Ps LO

D =

4.3 (r2 = 0.05)

VB

08 LOD

= 7.59 (r2 =

0.14)

VB

_BLU

Ps LO

D =

5.34 (r2 = 0.15)

FB

08 LOD

= 5.89 (r2 =

0.10)

FB

10 LOD

= 8.47 (r2 =

0.19)

FB

_BLU

Ps LO

D =

5.83 (r2 = 0.11)

LG01CH03e030.0GD_SNP000852.2GD_SNP000783.1GD_SNP005884.1

CH03g0717.7

GD_SNP0019431.5CONS1434.9hi04c1035.9

AU223657-SSR43.9

GD_SNP0008051.8MS14h0354.4hi07e0855.8

GD_SNP0031962.3GD_SNP0010366.4

GP

08 LOD

= 8.17 (r2 =

0.10)

GP

10 LOD

= 4.72 (r2 =

0.10)

GP

_BLU

Ps LO

D =

7.75 (r2 = 0.11)

VB

08 LOD

= 6.77 (r2 =

0.096)

LG03GD_SNP018910.0GD_SNP011890.4

NZmsCN9439469.1

GD14215.9GD_SNP0051419.0

ch01f03b29.9

Ch05c0740.3

GD_SNP0159651.6ch01h0254.1

GD_SNP0065962.4CN444542_B65.3GD_SNP0045266.3GD_SNP0088666.8

GP

08 LOD

= 21.77 (r2 =

0.37)

GP

09 LOD

= 5.93 (r2 =

0.12)

GP

10 LOD

= 7.68 (r2 =

0.17)

GP

_BLU

Ps LO

D =

17.6 (r2 = 0.32)

VB

08 LOD

= 6.46 (r2 =

0.11)

VB

_BLU

Ps LO

D =

4.47 (r2 = 0.08)

FB

08 LOD

= 7.45 (r2 =

0.16)

FB

09 LOD

= 13.18 (r2 =

0.19)

FB

_BLU

Ps LO

D =

4.32 (r2 = 0.10)

LG09

GD_SNP019470.0CH04c060.5CH02b07_XB2.9GD_SNP018675.4ch02a08_1937.3GD_SNP0131211.2GD_SNP0026012.6GD_SNP0078116.7ch02c11a18.8

ch03d1131.0

GD_SNP0025747.7

GD_SNP0036053.5COL_XB55.7GD_SNP0009957.1

MS06g03_XB66.8

VB

10 LOD

= 3.25 (r2 =

0.05)

VB

_BLU

Ps LO

D =

5.23 (r2 = 0.07)

LG10

GD_SNP010700.0

ch05d044.6

CH05d11_XB19.5

CH01g12_XB26.0

GD_SNP0176938.2GD_SNP0199539.6

GD_SNP0036248.8GD_SNP0071450.5

GD_SNP0037659.9GD_SNP0179365.2GD_SNP0006867.0GD_SNP0029667.8

FB

08 LOD

= 4.22 (r2 =

0.07)

LG12

GD_SNP005500.0GD_SNP001113.6GD_SNP003297.7NZ02b01_XB9.7GD_SNP0019112.3Hi03g06_XB14.5

ch01d0831.5

GD_SNP0027337.2

GD_SNP0126548.0

GD_SNP0061554.6hi21g0554.7

ch02c0969.2

GD_SNP0007973.4

hi23g1280.1

VB

08 LOD

= 6.05 (r2 =

0.06)

VB

_BLU

Ps LO

D =

5.95 (r2 = 0.07)

LG15

GD_SNP001920.0CH04c063.3GD_SNP008646.2

GD_SNP0158413.4

CH01h01-2_XB19.9

Hi03c05-2_XB33.8

GD_SNP0068238.5

GD_SNP0005847.0

GD_SNP00235GD_SNP0208357.8GD_SNP0026558.2ch05d0859.4Hi07h0264.1

FB

09 LOD

= 4.15 (r2 =

0.08)

LG17

Fig. 4 Genomic positions of quantitative trait loci (QTLs) detected on the linkage groups (LGs) of the apple (Malus · domestica)X3263 · ‘Belrene’ integrated map by multiple QTL mapping (MQM) for the variables green point (GP), vegetative budbreak (VB) and floralbudbreak (FB). QTLs are represented on the right side of LGs by boxes extended by lines representing the logarithm of the odds (LOD)-1 andLOD-2 confidence intervals. The numbering of LGs is according to Maliepaard et al. (1998).


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CG selection by in silico mapping

Of the two major QTLs detected on LG08 and LG09, wechose to explore the function of predicted genes underlyingthe LG09 genomic portion based on the importance ofthe percentage of explanation of this QTL and the relativelysmall size of its CI. The region spanned the length ofthe largest QTL interval (15.9 cM) and was locatedbetween the top of chromosome 9 (above GD_SNP01891)and GD142. The interval investigated represented4 039 768 bp and comprised 983 predicted genes.

BLAST results permitted the annotation of 710 genes(72%), whereas 273 (28%) had no match in the databases.A similar proportion was annotated for the set of genesselected randomly. For both sets of genes, the GO termswere distributed into nine main GO categories (Fig. 5),with cellular process and metabolic process being the maincategories of biological process. A v2 test revealed that fiveof these GO categories were over-represented in the LG09QTL: response to stimulus, biological regulation, signaling,death and cell cycle (data not shown).

Throughout our investigation of the genes present withinthe over-represented GO categories, we identified 74 pre-dicted proteins with potential involvement in the cell cycleand its regulation, including 20 located within 900 kb ofthe QTL peak (Table 7).

The closest CG to the LOD peak had sequence similaritywith a cyclin-A3 sequenced in Oryza sativa. This gene waslocated 16 500 bp from GD_SNP01189. Further awayfrom the LOD peak were two genes with high sequencesimilarity to an Arabidopsis thaliana auxin signaling F-BOX3 gene. Other genes located close to the LOD peak includeda phytosulfokine receptor 1 (E-value = 2e-39), a Myb-related protein Pp2 (E-value = 2e-56) and a cytokinin-N-glucosyltransferase 2 (E-value = 3e-122) (Table 7). Finally,14 genes with high sequence similarity to E3 ubiquitin-pro-tein ligase were identified in several clusters, with the maincluster including 10 genes within 400 kb and comprisingthe region of the highest LOD peak.

Additional proteins located further away from the LODpeak and within the QTL CI were also identified. Theseproteins had high functional similarity with cullin-3A (·1),

Metabolic process37.8%

Cellular process35.3%

Biological regulation5.0%

Localization5.1%

Cell cycle1.4%

Response to stimulus5.8%

Signaling4.0%

Death3.5%

Cellular component organization

2.3%

Metabolic process40.6%

Cellular process36.5%

Response to stimulus4.3%

Cell cycle1.1%

Localization7.1%

Biological regulation2.8%

Cellular componentorganization

2.8%

Multicellularorganismal process

0.1%

Developmental process0.1%

Multi-organism process(b)(a)0.4%

Death2.0%

Signaling1.7%

Reproduction0.4%

Fig. 5 Gene ontology (GO) distribution charts by second-level GO terms. (a) The distribution of GO terms of predicted proteins located withinthe LG09 quantitative trait locus (QTL) confidence interval. (b) The distribution of GO terms of an equivalent number of predicted proteinspicked randomly within the apple genome. GO terms indicated in bold represent genes over-represented in the LG09 QTL interval accordingto v2 test results.

Table 7 Predicted proteins located within 900 kb of the marker GD_SNP01189 on LG09 and displaying sequence similarity to genes involvedin the plant cell cycle

Protein number Sequence similarity Number of gene copies E-value In silico position (bp)

MDP0000914555 Cyclin-A3 1 8e-14 chr9:606059..606724MDP0000268652 Auxin signaling F-BOX 3 2 0 chr9: from 41177 to 446464MDP0000375039 Cytokinin-N-glucosyltransferase 2 1 3e-122 chr9:1179208..1182844MDP0000303239 Myb-related protein Pp2 1 2e-56 chr9:1120204..1127872MDP0000241327 E3 ubiquitin-protein ligase 14 2e-36 chr9: from 244643 to 1509459MDP0000167621 Phytosulfokine receptor 1 1 3e-39 chr9:978792..981826

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cyclin-dependent kinases (·1), cytokinin-O-glucosyltrans-ferase (·14, in two clusters), MYB transcription factors(·12), E3 ubiquitin-protein ligase (·10), aquaporin (·4),actin (·3), expansin (·4) and phytosulfokine (·5).

Discussion

Heritability and significance of the effects

The present study highlighted that all three variables inves-tigated were characterized by a significant genotypic effectand a high heritability value. Heritability estimates haveoften been shown to be specific to the population and envi-ronment analyzed (Souza et al., 1998). However, in aprevious study by van Dyk et al. (2010), the heritabilityvalue for the trait initial VB (IVB) was estimated to bebetween 0.62 and 0.92. In our study, similar estimates werefound for VB, which suggest that, despite differences ingenotype and the environment in which trees were grown,the date of budbreak remains a highly heritable trait, assuggested previously by Anderson & Seeley (1993).

Phenotypic correlations, QTL detection andco-localization among traits and populations

Both populations showed similar patterns of data distribu-tion, comparable correlation coefficients betweenconsecutive years and among traits. However, correlationcoefficients between traits decreased notably between consec-utive years (Table 4), which can be attributed to either treeage, climatic conditions or a combination of both. In thepresent state, our experimental design does not permit us todiscriminate between these effects.

Despite the similarities between the two populations,QTL analysis revealed that the genetic determinism regulat-ing GP, VB and FB was different. No common QTL wasidentified between the two populations.

In the STK · GS population, QTL analysis permittedthe identification of one major genomic region influencingtwo of the three variables, with no stable QTL being identi-fied for VB. This major QTL region, located on theproximal part of LG08, influenced GP and FB indepen-dently of the year studied. It is interesting to note that themoderate to high correlation values obtained between GPand FB in 2007–2009 coincided with the years for which theLG08 QTL co-located. For 2010, the correlation betweenthe two traits decreased, and no QTL was detected for FB10.The absence of correlation and common QTL in 2010might be explained by the atypical weather conditionsobserved during spring, and may be the consequence of thecold spring temperatures observed for this year (Fig. 2). Themajor QTL on LG08 confirms the preliminary resultsobtained by Segura et al. (2007). This QTL co-locates withQTLs for the biennial-bearing index, total number of inflo-

rescences (B. Guitton et al., unpublished) and QTLscontrolling leaf ecophysiological traits (Regnard et al., 2009)and hydraulic conductance in the xylem (Lauri et al., 2011).One hypothesis explaining the co-localization among thesevariables could be an increased capacity of the plant to trans-port water, carbohydrates and sugar to the growing organs.

In the X3263 · ‘Belrene’ population, we identified threeQTLs independent of the environmental effect for GP GBLUP. The first, located on the proximal part of LG09, wasconsistently identified over the 3 years investigated. Theother two QTLs identified were located on LG01 and LG03.

The correlation between VB and the other two variablesin the X3263 · ‘Belrene’ population was dependent on theyear studied. The highest correlation was obtained in2008, whereas it was low in 2009 and 2010 (Table 4). Thehigh correlation in 2008 might explain the common QTLidentified on LG09 for all three variables for this year.Furthermore, a QTL on LG09 was also identified for the GBLUP VB variable, indicating that this QTL was indepen-dent of environmental effects. The importance of the LG09QTL was confirmed by the analysis of the FB data. In astudy performed by van Dyk et al. (2010), a major QTLlocated in a similar genomic region was found to influencethe date of IVB. The phenotypic variation was shown to beassociated with alleles inherited from the parent ‘Anna’. Co-localization between QTLs was rendered possible by the useof common markers (GD142 and NZmsCN943946). Thepresence of a common QTL between two unrelated popula-tions suggests that similar sets of genes might be involved inthe control of budbreak dates for both populations.However, the allele sizes amplified by NZmsCN943946were different, which might be caused by the high linkagedisequilibrium expected in tree species (Plomion & Durel,1996). Unlike in van Dyk et al. (2010), the influence of theLG09 QTL did not increase during the consecutive years ofphenological trait assessment in our conditions in whichCR was fulfilled, suggesting that results from our QTL anal-ysis were more dependent on climatic conditions than treeage. In addition, among the seven genomic regions identi-fied by Conner et al. (1998) as influencing the date ofbudbreak, one region later identified on LG09 (Kenis &Keulemans, 2007) was found to influence this trait.

In both populations, the major genomic regions identi-fied by QTL analysis were previously shown to benonhomologous (Celton et al., 2009; Velasco et al., 2010),indicating that different genes or regulatory pathways mightbe involved. Experimental results in tree crops have demon-strated the inconsistency of QTL expression acrosspopulations. The identification of a common QTL amongpopulations derived from an unrelated genetic background,and grown under different environmental conditions, is anunusual finding. Such QTLs, defined against a wide geneticbackground, could be more useful in marker-assisted breeding(MAB) than QTLs defined in a specific background (Plomion


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& Durel, 1996). The identification of a second major and sta-ble QTL on LG08 could allow breeders to engage in astrategy of pyramiding favorable QTLs and beneficial allelesinto a unique hybrid seedling better suited to future warmerclimatic conditions.

Interactions between QTLs and climatic conditions

All models built for GP, VB and FB integrated G, Y andG · Y effects. Decomposition of the Y effect into an esti-mated CR and HR period allowed us to demonstrate astrong effect of both periods on FB.

For almond (Prunus dulcis) growing in cold climatic condi-tions, HR was described as more important than CR for theregulation of blooming time because of the early completionof chilling (Alonso et al., 2005). In warmer areas, almondflowering time was more influenced by chilling than heat(Egea et al., 2003). Similar results were found for ornamentalpeaches (Prunus persica), for which variation in floweringtime over the years was mainly a consequence of CR, withHR contributing a smaller effect (Pawasut et al., 2004).

Our results suggest that, in our climatic conditions, theCR period has a stronger effect than the HR period on thevariation of the time of floral budbreak across genotypes ofa population. However, the relative importance of these twofactors may also be dependent on the genetic background asa higher heat effect was found in X3263 · ‘Belrene’ than inSTK · GS.

The next section of this discussion aims to decipher theinteractions between QTLs, HR and CR for FB over thecourse of the 4 yr studied.

As described earlier, the distribution of FB dates in 2010in the STK · GS population was very narrow comparedwith other years. This reduction in data distribution mightexplain the absence of QTL detected for this year.Furthermore, the results indicate that the effect of theLG08 QTL on FB dates over the years decreased from 23%in 2007 to 9% in 2009, concomitantly with an increase inthe length of the HR period. This suggests that the warmerthe climate (in winter and spring), the more important isthe influence of LG08 on FB.

A similar conclusion can be drawn for the LG01 andLG09 QTLs in the X3263 · ‘Belrene’ population. Whenconsidering the average spring temperature of 2008 and2009 (70–80-d HR period), a decrease in the CR periodled to an increase in the percentage of explanation of LG09QTL for FB. In addition, the longer the HR period (follow-ing moderate winter conditions, i.e. 90-d CR), the moreimportant was the influence of the LG01 QTL on the FBdate. This suggests that the LG01 QTL influences FB datesin cold spring conditions, whereas the LG09 QTL takesover from LG01 in warmer spring conditions.

Finally, as most of the variation was a result of femaleadditive effects, we conclude that alleles derived from

X3263 can regulate FB dates in both cold and warm winterconditions by switching regulation from LG01 to LG09,whereas alleles derived from ‘Starkrimson’ influence FBdates through the LG08 QTL only in mild winter condi-tions. For both populations, more years and climaticconditions should be studied to confirm the effect of therespective QTLs on FB dates.

In silico CG selection

The conservative approach required to determine GOannotations (Dwight et al., 2002) resulted in almost 28%of all the genes falling into the unknown function category.Despite this, we identified five GO categories differentiallypresent in the LG09 region, including one identified as cellcycle. However, the QTL effect could be a result of eitherone or a multitude of genes within the CI of the QTL, fromany of the GO term categories. Thus, the identification ofparticular categories of genes over-represented within thisinterval does not allow us to conclude that the genes respon-sible for the phenotypic variation are present within one ofthese GO categories.

From this first screening, we defined a subset of geneswhose functions are putatively involved in the cell cycle.Most of the 74 genes thus identified were involved in thetwo major pathways regulating G1 ⁄ S and G2 ⁄ M transitions(Fig. 1). The closest CG to the LOD peak had sequencesimilarity with an O. sativa cyclin-A3 (GO: cell cycle),which is part of the CycA plant cyclins (Renaudin et al.,1996). Cyclins are positive regulators that bind to thecyclin-dependent kinases, and are essentially expressed in theDNA replication phase (S phase) (Hemerly et al., 1992). Avariation in the expression of this gene could have conse-quences on the rate of DNA synthesis, and thus cell division.Further results indicated the presence of 12 Myb-related(GO: response to stimulus) proteins within the CI. The clos-est to the LOD peak was identified as Pp2. Leech et al. (1993)demonstrated that the maximum level of transcription of thisgene correlated with the time of maximum mitotic index inmoss. This protein, which probably functions as a transcrip-tional activator, could have a major effect on the cell mitosisrate during bud growth resumption and its regulation, eventhough the GO term may refer to a different category.

Various other genes with homology to known proteinswere also identified within the CI of the QTL, such asMDP0000252231, identified as cullin-3A (GO term: cellcycle), whose loss-of-function mutant in A. thaliana wasshown to induce a late-flowering phenotype (Dieterle et al.,2005). Other genes include multiple copies of actin (GO:cellular process), expansin (GO: cellular component organi-zation) and phytosulfokine (GO: metabolic process), whichare all necessary for plant cell mitosis.

In our study, we also identified genes associated withphytohormones. An auxin signaling F-BOX 3 (AFB3)

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(GO: reproduction ⁄ signaling ⁄ developmental process) genewas identified close to the LOD peak. AFB genes encoderelated F-box proteins that assemble into SCF complexesthat are required for auxin-dependent degradation of auxi-n ⁄ indole-3-acetic acid proteins (Dharmasiri et al., 2005).Mutations in the AFB3 gene could impair theauxin response in the plant and influence the cell cycle dur-ing the mitotic phase (Fig. 1). Underlying the QTL, a totalof 15 genes with homology to cytokinin-O- and cytokinin-N-glucosyltransferase activity were identified (no homologyidentified using BLAST2GO). The closest was located600 kb away from the LOD peak. The remaining 14 werelocated in two clusters along this chromosomal region. InA. thaliana, cyclin-dependent kinases were reported toinduce cyclin D3, necessary for entry into the DNA replica-tion phase (Fig. 1). Variation in the level of cyclin-dependentkinases could impair the capacity of cells to enter this phase.

The ubiquitin-proteasome pathway is also recognized toplay a crucial role in the cell cycle. This pathway targets spe-cific proteins for programmed destruction in response tointernal or external stimuli (Hershko & Ciechanover,1998). In A. thaliana, E3 ubiquitin protein ligase forms acomplex with SCF and plays a major role during the cellmitosis phase (Fig. 1). We identified 24 E3 ubiquitin pro-tein ligase (GO: metabolic process) copies in several clusters.

In recent years, it has become apparent that copy numbervariants (CNVs) are common within the human genome(Stranger et al., 2007), and can positively or negatively corre-late with gene expression levels. The presence of CNV ofgenes involved in the cell cycle within the CI of the QTL couldtherefore point to a possible regulation of cell cycle capacityby these CNVs, although the study of CNV gene expressioncould prove to be challenging. Indeed, our current knowledgeof the apple genome does not permit us to discriminatebetween nonfunctional pseudogenes and functional genes.

In the present study, QTL analysis permitted the identifi-cation of major and population-specific genomic regionsinfluencing the dates of GP, VB and FB. The genetic dissec-tion of phenological characters was performed over 3–4 years for each population, and the results suggested stronginteractions between CR, HR and genetic effects. Variationsin the influence of QTLs over the phenological variablessuggested that trees were capable of adapting to climaticconditions by regulating the expression of genes involved inbud growth and located in different genomic regions.

Acknowledgements

We thank J. J. Kelner and P. E. Lauri for fruitful discus-sions, and F. Laurens (INRA Angers) for providing thevegetative material of the progeny X3263 · ‘Belrene’. Wealso thank B. Guitton, who kindly provided CG markers toimprove the STK · GS map, and G. Droc, who helped

with BLAST2GO. J.M.C. was funded by MontpellierSupAgro Agronomic School.

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Deciphering the Genetic Determinism of Bud Phenology in Apple Progenies