Towards a Strategy for Phenotyping Architectural Traits in Mature F1 Hybrids of an Apple Progeny

1V. Segura , J.J. Kelner2, P.E. Lauri1 and E. Costes1 1INRA, UMR Développement et Amélioration des Plantes, DAP 2Montpellier SupAgro – CIRAD Université Montpellier II, 2 place Pierre Viala, 34060 Montpellier Cedex 1, France Keywords: Malus x domestica (Borkh.), broad sense heritability, primary growth,

secondary growth, branching, form, expert notation Abstract

Over the last decade several genetic studies investigated the genetic determinisms of architectural traits. However, most of these works were carried out in juvenile progenies, and the genetic determinisms of adult tree architecture remained poorly understood. Within this context, the present study is aimed at defining a phenotyping strategy at the beginning of mature stage that should provide both quantitative traits and qualitative criteria for genetic studies and selection schemes respectively. Accurate measurements and visual observations were performed on four-year-old apple hybrids focusing on the main architectural processes: primary and secondary growth, branching and form. Among the set of quantitative traits and qualitative criteria analyzed, a selection of relevant descriptors was carried out: (i) for quantitative traits on the basis of their genetic parameters (i.e. broad sense heritability and genetic correlations); (ii) for qualitative criteria on the basis of the observation robustness, their correlation with quantitative traits, and their genotypic effect. Considering quantitative variables, a set of 8 relevant traits that were related to an architectural process or an axis type was selected to represent the complexity of tree architecture at the beginning of mature stage. Clustering the genotypes from these 8 traits revealed many recombinations. The analysis of qualitative criteria showed that visual observations were efficient for traits that characterized global processes such as tree growth habit or vigor. However, discrepancies were found between quantitative and qualitative clustering, probably because some important traits that recombined in the quantitative clustering were either poorly correlated to qualitative criteria (e.g., percentage of long shoots), or not accounted in qualitative observations (e.g., annual shoot morphology). Further investigations are thus required in particular to define new criteria corresponding to the traits that were found to be relevant in the quantitative study.

INTRODUCTION

Controlling plant architecture is often a desirable goal for perennial crops. In fruit trees, architectural traits are generally controlled through the use of size-controlling rootstocks, pruning, and training (Costes et al., 2006). However, a better knowledge of the genetic determinisms of architectural traits could allow their introduction in selection schemes and reduce the costs of training practices (Laurens et al., 2000). We have recently shown that many traits displayed quite high broad sense heritability in a 1-year-old apple progeny, especially branching traits or primary growth traits (Segura et al., 2006, 2007). QTL mapping studies have also been carried out and succeeded in the identification of genomic zones related to architectural process (Conner et al., 1998; Kenis and Keulemans, 2007; Liebhard et al., 2003; Segura et al., 2007). Most of these works were carried out during the juvenile stage (i.e. before the first flowering occurrence) over which tree architecture establishes and both the expression and variability of many architectural traits are maximal. Indeed the existence of morphogenetic gradients has been shown in apple tree to reduce growth and branching processes when tree ages (Costes et al., 2003; Renton et al., 2006). However, the mature

169Proc. XIIth Eucarpia Symp. on Fruit Breeding and Genetics Eds.: R. Socias i Company et al. Acta Hort. 814, ISHS 2009

stage is also of interest in fruit tree species because fruiting occurs and induces morphological changes that lead to the final tree shape. Until now, few genetic studies investigated architectural traits during mature stage, probably because tree architecture complexity increases when tree ages. The present study aimed at defining a phenotyping strategy at the beginning of mature stage where we assumed that both juvenile and mature developments can be captured. This strategy should be applicable to apple progenies and provide quantitative traits for genetic studies. Nevertheless, in a breeding context, phenotyping should be fast enough to screen a lot of genotypes. In this purpose a phenotyping based on visual observations was also carried and compared to the quantitative approach.

MATERIALS AND METHODS Plant Material

The progeny under study stemmed from the ‘Starkrimson’ x ‘Granny Smith’ cross and initially comprised 125 genotypes. The present research was carried on a sub-sample randomly selected of 50 genotypes replicated three times by grafting onto ‘Pajam 1’ rootstocks. The 150 trees were implanted in 2003 at Melgueil INRA Montpellier Experimental Station. To study their architecture they were not pruned and their trunks were stacked up to 1 m. Further details on the cross, experimental design, and practices performed throughout the study have been given in Segura et al. (2006).

Architectural Descriptions

Both quantitative and qualitative phenotyping were performed on 146 trees (four trees had died) in 2007 after the fourth year of growth. 1. Quantitative Traits. Notations were carried on several types of axis (Fig. 1). On each tree the following number of axes were measured: the trunk; two long sylleptic axillary shoots (LSAS) that developed from the first year of growth on the first annual shoot (AS) of the trunk; two long proleptic axillary shoot (LPAS) that developed from the second year of growth on the first AS of the trunk. Considering an axis, its previous architectural development might be reconstituted by detecting morphological markers that are related to growth stops. These growth stops delimitate growth units (GU) that compose AS which in turn constitute the whole axis. The notations were performed on these imbricate entities also called scales (Godin et al., 1997), and they focused on the following architectural processes: (i) primary growth (GU length, and number of internodes); (ii) secondary growth (AS bottom and top diameter); (iii) branching (number of axillary shoots per AS distinguishing sylleptic from proleptic axillary shoots, and axillary shoot types depending on the length of GUs that composed them); (iv) form (axis bottom and top angles, cord, and projection). From these measured descriptors, others were computed to provide descriptors as close as possible to biological processes. A list of quantitative traits used is given in the Table 1. For further details such as formula of computed traits see Segura et al. (2006, 2007). 2. Qualitative Criteria. Visual observations were defined on the basis of criteria proposed by Lespinasse (1977, 1992) to classify apple cultivars. Observations were performed by three experts according to nine criteria defined in five classes (from 1 to 5 i.e. from low to high). These nine criteria were assumed to represent the main architectural processes previously used for quantitative measurements: (i) primary growth (tree height and internode length); (ii) secondary growth (tree vigor); (iii) branching (percentage of long shoots and functional buds, and branching organization along the trunk, i.e. acrotony vs. basitony); (iv) form (tree surface, trunk bending, and orientation).

Statistical Analysis 1. Quantitative Traits. The strategy described in Segura et al. (2006) was carried out to select relevant quantitative traits (i.e. heritable and non redundant). First, as three trees were observed by genotype, the following linear model was built to test the significance

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of genotypic effect: Pij = µ + Gi + eij, where Pij is the phenotypic value measured on the tree j of genotype i, µ is the overall mean of the progeny, Gi is the random effect of the genotype i, and eij is the random residual error on the tree j of genotype i. Second, broad sense heritability of genotypic means were estimated as: h²b = σ²G / (σ²G + σ²e / n), where h²b is the broad sense heritability, σ²G and σ²e are the genotypic and residual variances respectively, and n is the mean number of trees per genotype. Third, best linear unbiaised predictor (BLUP) of genotypic values were computed from the linear model and used to estimate the genetic correlations between quantitative traits. Fourth, a principal component analysis, and a clustering of traits were carried out from genetic correlations. Finally, a clustering of genotypes was performed on a set of relevant traits using the partitioning around medoids (PAM) algorithm (Kaufman and Rousseeuw, 1990). The methodology described in Segura et al. (2006) was used for selecting the best partition and analysing cluster characteristics. Linear model analysis, PCA, and variable clustering were performed using the MIXED, PRINCOMP, and VARCLUS procedures respectively in the SAS ® v8 software (SAS Institute Inc., 2000). PAM was carried out using the STAT module of VPlant software formally AMAPmod (Godin and Guédon, 2003). 2. Qualitative Criteria. First, observation robustness was evaluated by computing Spearman's rank correlations between experts for each qualitative criterion. Second, Spearman’s rank correlations were computed between the median value per tree of each qualitative criterion and the quantitative traits. Third, the genotypic effect was evaluated for each qualitative criterion through the non-parametric Kruskal-Wallis test, and it was compared to the genotypic effect of the most correlated quantitative trait. Finally a clustering of genotypes was performed on the median values per genotype of the qualitative criteria assumed to be relevant on the basis of their genotypic effect. As previously, the clustering was carried out using the PAM method. Spearman's rank correlation analysis and Kruskal-Wallis test were performed using the CORR, and NPAR1WAY procedures respectively in the SAS ® v8 software respectively (SAS Institute Inc., 2000). RESULTS Quantitative Traits

From the quantitative phenotyping, 43 traits (both measured and computed) were obtained. Among these traits, 35 (about 80 %) were considered heritable since the lower limit of the confidence interval of their broad sense heritability was higher than 0 (data not shown). A representation of these heritable traits on the first two components of the PCA is given in Figure 2. The first two components explained only 30 and 16 percent of the total variability respectively, while the clustering of these 35 heritable traits provided eight clusters that explained 66% percent of the total variability. Clusters at the periphery of PCA were composed of traits related to an architectural process or an axis type: primary growth, branching, LSAS form, Trunk, LSAS, and LPAS morphology (Fig. 2). It was noticeable that these three last clusters, which had the highest weight on the first PCA axis, were mainly composed of secondary growth traits measured on AS. Partitioning of the genotypes was performed on eight traits selected as representative of each cluster. The obtained partition was composed of seven clusters, among which two extremes comprised a low number of genotypes (Table 2). Indeed the cluster A comprised only two genotypes that were mainly characterized by high diameters and many axillary shoots, most of them being spurs. The cluster G comprised only one genotype characterized by long internodes, low diameters, a low number of axillary shoots most of them being long shoots, a high trunk cord bending, and a low LSAS orientation (meaning highly bent LSAS). On the opposite, the cluster B was composed of seven genotypes characterized by a high bottom diameter of last annual shoots, many axillary shoots most of them being spurs, a low trunk cord bending, and an erected LSAS. The analysis of the other intermediary clusters highlighted recombinations between the selected architectural traits. For example, the cluster C was composed of genotypes with an intermediate LSAS

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orientation, and a high proportion of long shoots, while the cluster E was composed of genotypes with bent LSAS, and a high proportion of spurs.

Qualitative Criteria

As an indicator of the observation robustness, the mean rank correlation between experts was computed. High correlations were found for traits that characterized global processes, such as tree height, surface, and vigor, trunk bending, and branch orientation. By contrast the weakest correlations were obtained for criteria that characterized (i) local processes such as internode length, and percentage of functional buds, or (ii) complex processes such as percentage of long shoots, and branching organization along the trunk. Correlations between qualitative criteria and their corresponding quantitative trait are also presented in Table 3. It was noticeable that the highest correlations were found for the criteria that were previously shown to be the most robust. A significant genotypic effect was found for all criteria except tree height (Table 3). Among the most robust criteria, close estimation of genotypic effects between qualitative criteria and their corresponding quantitative traits were found for branch orientation, vigor, and trunk bending. However, the genotypic effect of tree surface and height were underestimated with regard to those computed on their corresponding quantitative trait. Finally, the clustering of genotypes from qualitative criteria highlighted as for quantitative traits extreme genotypes that were mainly characterized by growth habit descriptors such as branch orientation or trunk bending (data not shown). Recombinations between qualitative criteria were also highlighted, however, discrepancies were found especially between intermediary clusters.

DISCUSSION

In the present study a phenotyping strategy that can be applied on trees at the beginning of their mature stage and for genetic purpose emerged from the large screening of architectural processes. PCA showed that tree architecture is complex, and its variability could not be reduced to two sets of variables. The variable clustering showed that architectural variability could be still reduced to eight traits corresponding to ten notations. Genotype partitioning on these selected traits highlighted extreme clusters, among which B and G were respectively close to the characteristics of type II and type IV groups of Lespinasse’s (1977, 1992) classification. In addition, this clustering highlighted many recombinations between the selected traits. We can thus conclude that less notation would lead to spurious results.

The second objective of the present work was to test qualitative criteria that might be used in apple selection schemes. Present results showed that some criteria were efficient, particularly those characterizing global processes such as tree vigor and growth habit (branch orientation and trunk bending). But, visual notations remained difficult especially for local and complex processes. Even though both clustering based on quantitative traits and qualitative criterion revealed extreme genotypes that were mainly separated on the basis of their growth habit, many differences remained especially in the definition of intermediary clusters. Actually, the recombinations differed between the 2 clusterings, probably because some important traits that recombined in the quantitative clustering were either few correlated to qualitative criteria such as percentage of long shoots, or not accounted in qualitative observations such as annual shoot morphology. Further investigations are thus required in particular to define new criteria corresponding to quantitative traits that were found to be relevant in the present study.

ACKNOWLEDGEMENTS

We acknowledge K. Barkaoui, N. Ghikas, A. Huguenin, M. Leroy, and L. Moreaux (Montpellier SupAgro graduate students) for their help in phenotyping. We also acknowledge G. Garcia and S. Feral for the technical assistance in the orchard. This research was partly funded by a grant from the INRA genetic and plant breeding department and Languedoc-Roussillon region, allocated to Vincent Segura.

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Laurens, F., Audergon, J.M., Claverie, J., Duval, H., Germain, E., Kervella, J., Le Lezec, M., Lauri, P.E. and Lespinasse, J.M. 2000. Integration of architectural types in French programmes of ligneous fruit species genetic improvement. Fruits 55:141-152.

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Segura, V., Denancé, C., Durel, C.E. and Costes, E. 2007. Wide range QTL analysis for complex architectural traits in a 1-year-old apple progeny. Genome 50: 159-171.

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Tables Table 1. List of quantitative traits classified by architectural process, type of axis, and

scale considered.

Trait Type of axis Scale1 Abbreviation Primary growth

Length Trunk, LSAS, LPAS AS4 L4 Trunk, LSAS, LPAS Whole Axis L1234 Number of internodes Trunk, LSAS, LPAS AS4 INN4 Trunk, LSAS, LPAS Axis INN1234 Mean internode length Trunk, LSAS, LPAS AS4 INL4 Trunk, LSAS, LPAS Axis INL1234

Secondary growth Bottom diameter Trunk, LSAS, LPAS AS4 BDiaAS4 Trunk, LSAS, LPAS Whole Axis BDia1234 Top diameter Trunk, LSAS, LPAS AS4 TDiaAS4 Volume Trunk, LSAS, LPAS AS4 VolAS4

Branching Number of axillary shoots Trunk, LSAS AS4 Br Number of functional buds Trunk, LSAS AS4 Func Number of latent buds Trunk AS4 Lat Proportion of long shoots Trunk Whole Axis %L Branching organization Trunk AS1 BrOrg

Form Surface LSAS Whole Tree Surf Bottom angle LSAS Whole Axis Bang Top angle LSAS Whole Axis Tang Angle bending LSAS Whole Axis AngBend Cord bending Trunk, LSAS Whole Axis CordBend Orientation LSAS Whole Axis Orient

1 The number after AS refers to the age of the tree during which the AS grew. Table 2. Characteristics of the partition obtained from the selected quantitative traits:

number of genotypes by cluster (N), and mean value by cluster for each selected variable. Letters A to G refer to the cluster identification; letters a, b, and c correspond to a discrimination of the clusters according to the Newman-Keuls test (p<0.05). Due to their low number of genotypes, extreme clusters A and G were removed from the discrimination analysis through Newman-Keuls test. For trait abbreviations see Table 1.

Cluster A B C D E F G N 2 7 5 15 7 13 1 INL1234_tr 0.88 -0.32 a -0.13 a -0.11 a -0.27 a 0.10 a 3.34BDia1234_tr 6.19 -0.56 a,b -1.82 b -0.37 a,b -2.88 b 2.24 a -2.83BDiaAS4_LPAS 0.26 0.07 a 0.00 a,b 0.05 a -0.08 b -0.09 b -0.03TDiaAS4_LPAS 0.39 0.00 b -0.09 b 0.23 a -0.18 b -0.18 b -0.34CordBend_tr -0.10 -0.05 b -0.05 b 0.00 b -0.01 b 0.07 a 0.07Orient_LSAS -0.02 0.20 a 0.03 b -0.01 b,c -0.04 b,c -0.08 c -0.10Br_tr 5.43 1.52 a 1.27 a -1.17 b 0.18 a,b -0.57 a,b -4.17%L -0.01 -0.03 c 0.08 a 0.00 b -0.04 c 0.01 a 0.08

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Table 3. Qualitative criteria analysis: robustness, genotypic effect (G), correlation with the most correlated quantitative trait, and genotypic effect (G) of this quantitative trait. For trait abbreviations see Table 1.

Criterion Robustness1 G effect Trait2 Correlation3 G effect Height 0.75 0.06 L1234_tr 0.62 <.0001 Internode length 0.34 0.007 INL1234_tr 0.47 0.0025 Vigor 0.63 0.002 BDia1234_tr 0.62 <.0001 % Long shoots 0.46 0.0002 %L 0.41 0.0002 % Functional buds 0.48 0.0003 Func 0.47 0.0012 Branching organization 0.47 0.03 BrOrg 0.42 <.0001 Surface 0.72 0.02 Surf 0.57 0.0008 Trunk bending 0.69 0.002 CordBend_tr 0.77 0.0067 Branch orientation 0.60 <.0001 Orient_LSAS -0.74 0.0026 1Mean rank correlation between experts. 2Most correlated quantitative trait to each qualitative criterion. 3Correlations between the qualitative criteria and their most correlated quantitative traits. Figures

Fig. 1. chema

antitative traits directly obtained from measurements and d are indicated and classified by architectural process.

S tic representation of measurements performed on four-year-old apple hybrids. Both qucompute

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Fig. 2. Representation of heritable traits on the first two axes of the principal component analysis. Clusters of traits are surrounded and named according their composition. Relevant traits selected for clustering are indicated in bold. For trait abbreviations see Table 1.

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