Tree Genetics & Genomes DOI 10.1007/s11295-009-0217-x
ORIGINAL PAPER
Estimation of genetic parameters and prediction of breeding values for apple fruit-quality traits using pedigreed plant material in Europe Abou Bakari Kouassi & Charles-Eric Durel & Fabrizio Costa & Stefano Tartarini & Eric van de Weg & Kate Evans & Felicidad Fernandez-Fernandez & Ceri Govan & Anastasia Boudichevskaja & Frank Dunemann & Adriana Antofie & Marc Lateur & Marta Stankiewicz-Kosyl & Andrzej Soska & Kazimierz Tomala & Markus Lewandowski & Krzysztof Rutkovski & Edwards Zurawicz & Walter Guerra & François Laurens
Received: 10 December 2008 / Revised: 11 March 2009 / Accepted: 27 April 2009 # Springer-Verlag 2009
Abstract Genetic parameters for apple (Malus x domestica) fruit external traits (fruit size, ground colour, proportion of over colour and attractiveness) and sensory traits (firmness, crispness, texture, juiciness, flavour, sugar, acidity and global taste) were estimated using 2,207 pedigreed genotypes from breeding programmes in six European countries. Data were scored for 3 years and four periods during storage. Analyses were performed with a
restricted maximum likelihood method using VCE 5.1.2 software. Heritability estimates ranged from medium to high for instrumental traits. Genetic correlations between firmness and sugar were medium and low between firmness and acidity. Sensory traits showed low to high heritability, acidity and flavour being, respectively, the most and the less heritable. Global taste was strongly correlated with texture, juiciness, and flavour and relatively less correlated with crispness and acidity. Sensory
Communicated by E. Dirlewanger A. B. Kouassi : C.-E. Durel : F. Laurens (*) Institut National de la Recherche Agronomique (INRA), Centre de Recherche d’Angers, Unité Mixte de Recherche Génétique et Horticulture (UMR GenHort), BP 60057, 49071 Beaucouzé Cedex, France e-mail:
[email protected] F. Costa : S. Tartarini Department of Fruit Tree and Woody Plant Science, University of Bologna, Viale Fanin 46, 40121 Bologna, Italy E. van de Weg Plant Research International (PRI) B.V., Wageningen University Research Centre (WUR) Genetics and Breeding, Droevendaalsesteeg, 1 6700 AA Wageningen, The Netherlands K. Evans : F. Fernandez-Fernandez : C. Govan East Malling Research, New Road, East Malling, Kent ME19 6BJ, England
A. Boudichevskaja : F. Dunemann Bundesanstalt für Züchtungsforschung an Kulturpflanzen, Institut für Obstzüchtung, Pillnitzer Platz 3a, 01326 Dresden, Germany A. Antofie : M. Lateur Centre Wallon de Recherches Agronomiques, Département Lutte Biologique & Ressources Phytogénétiques, Rue de Liroux 4, 5030 Gembloux, Belgium M. Stankiewicz-Kosyl Laboratory of Basic Research in Horticulture, Faculty of Horticulture and Landscape Architecture, Warsaw Agricultural University (WAU), ul. Nowoursynowska 166, 02–787 Warsaw, Poland A. Soska : K. Tomala Department of Pomology, Faculty of Horticulture and Landscape Architecture, Warsaw Agricultural University (WAU), ul. Nowoursynowska 166, 02–787 Warsaw, Poland
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sugar and acidity showed highly negative correlations whereas their instrumental measurements showed low and increasing positive correlations from harvest to 4 months post-harvest. Sugar exhibited a higher sensory/instrumental divergence. Conversely, instrumental and sensory firmness were highly correlated. Fruit external characteristics had medium heritability. Fruit attractiveness had highest and lowest correlations with fruit size and ground colour, respectively. Best linear unbiased predictors of breeding values were computed for all genotypes with the software PEST. The results were analysed with regard to the dynamic and the reliability of genetic parameters according to the scoring dates. Original issues of the study and the importance of the obtained results for efficient designs of further apple fruit quality breeding programmes were discussed. Keywords Apple . BLUP . Breeding values . Genetic parameters . Pedigree . REML
Introduction Apple is the most important fruit crop in Europe. Good fruit quality is a major requirement of both the marketers and the consumers. Although some local particularities exist in consumers’ preferences, the apple market is becoming more and more international. Generally, traits of interest are fruit external appearance, texture and taste (Daillant-Spinnler et al. 1996; Du Bruille and Baritt 2005). Consequently, all breeding programmes aim to obtain competitive apple varieties satisfying as much as possible the needs of consumers. In Europe, most of apple production is finished at late spring or early summer, and fruit stocks are almost sold out after May. Fruits that are still in storage, after this period, cannot be sold anymore due to an unacceptable decrease in quality. Any increase M. Lewandowski : K. Rutkovski : E. Zurawicz Research Institute of Pomology and Floriculture, Fruit Breeding Department, Pomologiczna 18, 96100 Skierniewice, Poland W. Guerra Research Centre for Agriculture and Forestry Laimburg, Posta Ora, 39040 Vadena, BZ, Italy Present Address: A. B. Kouassi Université de Cocody-Abidjan, Unité de Formation et de Recherche (UFR) ‘BIOSCIENCES’, Laboratoire de Génétique, 22 BP 582 Abidjan 22, Côte d’Ivoire e-mail:
[email protected]
in the shelf life of European apples will greatly increase their competitiveness toward apples from the southern hemisphere and North America. Thus, the current challenge is to obtain apples that will conserve their quality from harvest to retailing (King et al. 2000; Anonymous 2005). The achievement of this challenge requires a good balance of all traits that are of importance consideration to the consumers as well as the growers. Therefore, since many traits have to be improved simultaneously in new cultivars, it is important to know how the traits are related and how heritable they are. Estimates of correlation coefficients allow computation of correlated response in a second trait if selection pressure is applied to the first and establishment of selection strategy (Falconer and Mackay 1996). Estimation of variance components of any trait of interest can provide useful information to enable breeders to determine the most efficient design for genotype evaluation (Yao and Mehlenbacher 2000). Well-organised experimental designs such as factorial and diallel-mating designs are commonly used to analyse variance components and to estimate genetic parameters of interest (heritability, genetic and phenotypic correlations) in plantbreeding programmes. In apple, few quantitative genetic studies of fruit traits have been published (Durel et al. 1998; Currie et al. 2000; Oraguzie et al. 2001; Alspach and Oraguzie 2002). None of these studies used material from well-constructed mating designs. Apple-breeding programmes are directly based on field massal selection without taking into account genetic parameters (Laurens 1999). Controlled crosses are planned by breeders according to their empirical experience on the combining ability of genotypes in previous crosses, thus, giving a network of more or less connected F1 full-sib families. Another peculiarity of apple-breeding populations is the close genetic relationship between the parental genotypes because breeders exploit the same elite varieties such as Golden Delicious, Jonathan, McIntosh, Gala, Fuji and Braeburn worldwide (Noiton and Shelbourne 1992; Kellerhals et al. 2004). The genetic parameters estimated from such plant materials may be biased (Hodge et al. 1996), unless variances components are estimated taking into account the genetic relationship matrix in the whole population (Besbes and Ducrocq 2003). The only known application of this procedure in apple is by Durel et al. (1998) using fruit quality data from the French applebreeding programme. This approach allows more accurate computation of variance components and best linear unbiased predictors (BLUP) for the breeding values of all genotypes in the population (Henderson 1975). By definition, genetic parameters are a function of both the genetic and environmental variances. Up to now, all the reported estimations of genetic parameters in apples dealt
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with plant materials from one research institute and/or one country. However, it is well known that the genetic variance changes as a consequence of selection as well as the conditions of traits assessment (Lynch and Walsh 1998). Hence, the results of each of these previous studies are specific to the plant materials and localities (Oraguzie et al. 2001; Alspach and Oraguzie 2002). The present study was conducted in the framework of the European research project High-Quality DiseaseResistant Apple for a Sustainable Agriculture (HiDRAS; http://users.unimi.it/hidras/; Gianfranceschi and Soglio 2004). The HiDRAS project aimed to develop a genetic study based on both accurate phenotypic assessments of apple fruit-quality traits and new molecular technologies. Eight research institutes from six countries participated in this task. The challenge was to perform standardised assessments and measurements with a very high accuracy in the different institutes participating in the project. The main fruit-quality criteria (external attractiveness and taste components) were assessed both by sensory evaluations and instrumental measurements. A large set of genetically related apple genotypes (cultivars and progenies) from ongoing breeding programmes were analysed. The huge amount of data collected from this very complex design allowed computation of genetic parameters which are practically and directly usable by the breeders since the studied plant materials are part of the current breeding progenies and progenitors. Considering breeders and traders’ preoccupation with fruit storability, estimates were computed with data scored at harvest and after different storage periods. The results are discussed with regard to the dynamic and the reliability of the estimates according to the scoring dates. To our knowledge, this is the first report on the estimation of genetic parameters in a horticultural crop at different scoring dates and at a multinational scale.
Materials and methods Plant material The plant material consisted of mainly 29 control-pollinated full-sib progenies of 25 to 150 individuals (Table 1) plus their parents, grandparents and ancestors from eight research institutes located in six European countries (Belgium, France, Germany, Italy, Poland and UK). The pedigree of these progenies was first traced back as far as possible based on the information available in the literature or provided by breeders. The accuracy of these relationships was then checked by running 82 apple SSR markers through the pedigree (Van de Weg et al. 2004; Patocchi et al. 2008). The whole pedigree consisted of
2,207 genotypes interconnected through up to seven generations from 132 founders to the 29 control-pollinated progenies (Fig. 1). Traits scoring and data preparation Each genotype of the pedigree was evaluated in the institute where it was located. Data were scored by 16 proficient assessors (two assessors per institute). The assessors had three meetings in order to harmonise their way of scoring. Thirty genotypes among the parents, grandparents and ancestors of the 29 control-pollinated full-sib progenies were present in all the institutes participating in the project. Data from these common progenitors were used to control variation among assessors. Sensory evaluation Fruit external appearance and taste were assessed at one to four dates: harvest (date 1), 2 months storage (date 2), 2 months storage plus 15 days at room temperature (date 3) and 4 months storage (date 4; see Table 2 for details about which trait was assessed at which date). Four traits characterising fruit external appearance-fruit size (FSIZE), ground colour (GCOL), proportion of over colour (PCOL) and attractiveness (ATTR) were visually evaluated at harvest on an ordinal scale from 1 (small, weak or bad) to 5 (very big, high or good) with intermediate steps of 0.5. Fruit taste was evaluated at date 1, 2 and 4 on ordinal scores ranking from 1 (very bad or low) to 5 (very good or high), with intermediate steps of 0.5, using a set of eight traits named hereafter ‘’sensory traits’’: firmness (FIRM), crispness (CRISP) = crunchiness related to the sound at the first bite, texture (TEXT) = physical perception of the fruit flesh in mouth from rough (1) to fine (5), juiciness (JUIC) = amount of juice in the mouth at the first bites into the fruit, flavour (FLAV) = evaluation of the amount of flavour when eating the fruit, sugar (SUG) = sweetness, acidity (ACID) = sourness and global taste = a sort of synthetic score given by the assessor to characterise how he likes the overall taste of the fruit. For all external and sensory traits, two fruits per genotype were used at each scoring date. An average score per genotype was obtained from evaluation scores of two assessors. Instrumental measurements Fruit firmness, soluble solids content (sugar) and acidity were also instrumentally measured at the four scoring dates
Tree Genetics & Genomes Table 1 The 29 controlpollinated full-sib families constituting the last generation of the plant material
Family code
Father
Mother
12_F 12_J 12_I 12_K 12_L 12_N 12_O 12_P I_BB I_CC I_J I_M I_W DiPr PiRe
X-3318 X-3318 X-3263 X-6679 X-6679 X-3305 RedWinterX3177 Rubinette X-6417 X-6679 X-3318 X-6683 X-6398 Discovery Pinova
X-6564 Galarina X-3259 X-6808 X-6417 X-3259 Galarina X-3305 X-6564 Dorianne X-3263 X-6681 X-6683 Prima Reanda
51 27 51 50 26 48 48 51 51 50 51 51 49 86 51
RePi FuGa FuPi GaCr GaPi AlPi AlSz FlSz LiAl PiGa MeLi MeU6 Sa10 Sa11
Rewena Fuji Fuji Gala Gala Alwa Alwa Florina Ligol Pinova Melrose Melrose Sawa Sawa
Pirol Gala Pinova PinkLady Pinova Pinova Sampion Sampion Alwa Gala Liberty U633 U1065 U1165
51 149 98 45 48 47 50 41 51 41 50 51 48 51
and hereafter named FIRMinst, SUGinst and ACIDinst, respectively. According to the institute, firmness was assessed either by an automate (model TA.XT.PLUS, Stable Micro System) or a digital penetrometer (Penefel) on ten fruits per tree. It corresponds to the maximal force required for a cylindrical probe of 2 cm long and 1 cm wide to penetrate in the peeled fruit up to a depth of 7 mm. These ten fruits were then separated in three subsets of three to four fruits each and juiced for the measurement of soluble solids content (SUGinst) and titratable acide (ACIDinst). An average value was finally calculated per tree for instrumental traits in order to allow their simultaneous analysis with sensory traits for which only one data per tree was available.
# individuals
Institute
Country
INRA INRA INRA INRA INRA INRA INRA INRA INRA INRA INRA INRA INRA BAZ BAZ
France France France France France France France France France France France France France Germany Germany
BAZ DCA-BO LFW LFW LFW RIPF RIPF RIPF RIPF RIPF SGGW SGGW SGGW SGGW
Germany Italy Italy Italy Italy Poland Poland Poland Poland Poland Poland Poland Poland Poland
during 2 years. A small number of genotypes were evaluated only for 1 year while rare ones were assessed during the 3 years. Within 1 year, all the genotypes were evaluated at the four scoring dates. Thus, for each trait, all the data over the 3 years and the eight institutes were pooled and sorted by scoring date in order to obtain a data file gathering all the genotypes from all the institutes. Furthermore, sorting data by scoring date allowed computation and comparison of estimates of genetic parameters for different dates. Data analysis Variance components and genetic parameters estimation
Data preparation prior to analysis Generally, data were collected for 3 years (2003, 2004 and 2005) but for practical reasons (time and space constrains), most of the genotypes of each institute were evaluated only
Basic statistics were computed, and non-parametric tests for normality (Wilcoxon rank sum test and Kolmogorov–Smirnov test) were carried out on the distribution of all traits using the SAS NPAR1WAY procedure (SAS Institute Inc. 2006).
Tree Genetics & Genomes Idared Jonathan PRI14-126 W agenerap
NJ117637 Prima
PRI14-152
RedW inter
X-3263
NJ123249 GoldenDel
PRI14-510 NJ12
RedW interX3177
Melba
X-3259
F2_26829-2-2 NJ130 McIntosh
Ill_#2
12_I01
X-3177
Coop-17 X-6683
RomBeauty X-3143
F_Melba Chantecler
X-3318
PRI668-100
W ealthy
I_J01 X-2771
X-6681
W inesap
X-6820 Galarina
PRI612-1
Starr F_Ill_#2 Clochard M_PRI668-100 RallsJan
Delicious
X-6564 Gala
Florina
X-6398 X-3305
12_O01
Fuji Crandall
X-4355
Baujade
12_F01 I_BB01
F_Crandall
X-6799
KidsOrRed
I_M01
X-3188 Z185
Cox
Anta34.16
X-6679
12_K01
12_J01
F_X-4355 PRI672-3
GranSmith
X-2599
Jefferies
X-4598
PRI830-101
X-6808
12_N01 I_W 01
X-4638
Rubinette F_X-4598
Dorianne TN_R10A8
12_L01
ReiDuMans
I_CC01
12_P01
X-6417
O53T136 X-6823
Fig. 1 Partial view of the pedigree showing connexions between eight control-pollinated progenies from four institutes (two progenies per institute). Founders are on the left side and the final control-
pollinated progenies are represented each by one individual: INRA– France (12_I01, I_M01), BAZ–Germany (DiPr_01, PiRe_01), LFW– Italy (FuPi_01, GaCr_01), SGGW–Poland (Meli_01, MeU6_01)
Then, for a given scoring date, multi-trait variance components were estimated for external, sensory and instrumental traits, respectively, using the restricted maximum likelihood (REML) algorithm (Patterson and Thompson 1971) implemented in the software REML VCE version 5.1 (Kovac and Groeneveld 2003). As observed by Furlani et al. (2005), REML is considered as the optimum estimation or prediction procedure for unbalanced dataset in perennial plant species since it maximises the likelihood of the genetic variance after correcting for the fixed effects. The same mixed linear model was applied for all the traits. It had the following structure in matrix notation:
and micro-environmental effects). X and Z are known incidence matrices relating the observations in Y to effects in β and α, respectively. The random effects in the model were assumed to follow a multivariate normal distribution with means and variances defined by: 2 3 2 3 y X b 6 7 6 7 E4 a 5 ¼ 4 0 5 e 0 2 3 2 3 y ZGa R 7 6 7 6 V Var4 a 5 ¼ 4 Ga Z 0 Ga 0 5 e R 0 R
Y ¼ X b þ Za þ e; where Y is the column vector of the phenotypic values for a trait measured in the population at one scoring date, β is the vector of fixed effects (i.e. population general mean, institute, year), α is the vector of random additive genetic effects of individual genotypes and e is the vector of random residual terms. Residual effects contain all other genetic effects (i.e. dominance and epistatic effects) and non-genetic effects (i.e. genotype × environment interaction
Where: Ga ¼ As 2a is the variance–co-variance matrix for the vector α of random additive genetic effects, with A = the numerator relationship matrix, which describes the additive genetic relationships among individual genotypes (Henderson 1975) and s 2a ¼the genetic variance between individual genotypes R ¼ Is 2e is the variance–co-variance matrix for the vector of residual effects, with I = an identity matrix of
6.64 22.90
13.68 1.84 13 8.03 22.96 14.36 1.99 14 6.85 22.87 14.34 2.08 14 8.57 23.27 14.13 2.03 14
Minimum Maximum
Mean SD CV (%) Minimum Maximum Mean SD CV (%) Minimum Maximum Mean SD CV (%) Minimum Maximum Mean SD CV (%)
6.41 2.77 43 0.28 34.06 5.37 2.40 45 0.31 31.30 4.67 2.16 46 0.20 32.03 4.38 2.12 48
0.53 35.56 8.15 1.70 21 0.33 15.03 6.43 1.81 28 1.43 13.62 5.58 1.67 30 1.65 15.32 5.67 1.69 30
2.03 17.51
1 5 2.35 0.77 33
3.13 0.76 24 1 5 2.59 0.85 33
1 5
FIRM
FIRMinst
SUGinst
ACIDinst
taste
Instrumental
1 5 2.27 0.78 34
2.99 0.80 27 1 5 2.45 0.85 35
1 5
CRISP
Sensory evaluation
1 5 2.21 0.85 38
2.75 0.82 30 1 5 2.51 0.85 34
1 5
TEXT
1 5 2.30 0.75 32
3.16 0.78 25 1 5 2.58 0.78 30
1 5
JUIC
1 5 2.45 0.91 37
2.60 0.83 32 1 5 2.49 0.85 34
1 5
FLAV
1 5 2.74 0.79 29
2.87 0.76 26 1 5 2.77 0.74 27
1 5
SUG
1 5 2.24 0.86 38
2.87 0.94 33 1 5 2.49 0.91 36
1 5
ACID
1 5 2.17 0.77 36
2.68 0.78 29 1 5 2.37 0.77 33
1 5
GTAST
2.85 0.84 29
1 5
FSIZE
External
2.90 1.39 48
1 5
GCOL
3.56 1.15 32
1 5
PCOL
2.83 0.88 31
1 5
ATTR
FIRM firmness, CRISP crispness, TEXT texture, JUIC juiciness, FLAV flavour, SUG sugar, ACID acidity, GTAST global taste, FSIZE fruit size, GCOL ground colour, PCOL proportion of over colour, ATTR attractiveness, SD standard deviation, CV coefficient of variation
Date 4
Date 3
Date 2
Date 1
Traits
Table 2 Descriptive statistics of the traits
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order equal to n (the number of trees) and s 2e ¼ residual variance. V ¼ R þ ZGa Z 0 is the variance–co-variance matrix for the vector of phenotypic values Y (assuming that the vector of random additive individual genetic effects (α) and the vector of random residual effects (e) are uncorrelated, that is, Cov (α,e)=0). The software REML VCE provides matrices of additive genetic, residual and phenotypic variances/co-variances. Estimates of the narrow-sense heritability (h2), additive genetic (ra) and phenotypic (rp) correlations are also provided from the following formula: h2 ¼
rpxy
s 2a s 2a þ s 2e
s axy raxy ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi s 2ax s 2ay
s pxy ffi; ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 s px s 2py
Where s 2a is the additive genetic variance between individual genotypes, and σ2e is the residual variance; raxy (respectively rpxy ) are genetic (respectively phenotypic) correlations between two given traits x and y; s axy (respectively s pxy ) are genetic (respectively phenotypic) co-variances of traits x and y; s 2ax and s 2ay (respectively s 2px and s 2py ) are genetic (respectively phenotypic) variances of traits x and y. Prediction of breeding values From the previously defined mixed linear model ðY ¼ X b þ Za þ eÞ, best linear unbiased estimator (BLUE) of the vector of fixed effects β (i.e. general mean, institutes, years) and best linear unbiased predictor (BLUP) of the vectors of random effects α (i.e. individual genotypes) were computed with the software PEST (Groeneveld et al. 1990; Groeneveld 1990). The BLUE of β and the BLUP of α are respectively defined by the following structure in matrix notation (Henderson 1975): 1 b b ¼ X 0 V 1 X 0 X 0 V 1 Y b ¼ GZ 0 V 1 Y X b b a The application of these expressions requires the variance components obtained with the software REML VCE and the genetic relationship matrix provided by the pedigree of the plant material.
Results Basic statistical features (mean, standard deviation, coefficient of variation) of each trait are presented in Table 2. The
distributions of all the traits showed no significant deviation from normality (p>0.01). Coefficients of variation (CV) tend to increase from dates 1 to 4 for all sensory and instrumental traits. Instrumental acidity (ACIDinst) and ground colour (GCOL) showed highest values in CV (43≤ CV≤48) while soluble solid (SUGinst) had the lowest variation (13≤CV≤14). As expected, acidity and firmness tend to decrease with increasing storage time irrespective of the assessment method (sensory or instrumental). Instrumental sugar also increases during the first 2 months post-harvest but then remains stable. In contrast, sensory sugar tends to decrease. Genetic parameters External traits Estimates of narrow-sense heritability and correlations (genetic and phenotypic) for external traits are presented in Table 3. Heritability values were moderate and ranged from 0.30 for GCOL to 0.55 for PCOL. ATTR had positive genetic correlations, in decreasing order, with FSIZE (rg = 0.69), proportion of over colour (rg =0.36) and ground colour (rg =0.19). Phenotypic correlations between these traits followed the same trend as genetic correlations but they had significantly lower values. Colour-related traits (GCOL and PCOL) had almost null genetic and phenotypic correlations with fruit size. Sensory and instrumental traits Genetic parameters of taste traits were estimated for three scoring dates: harvest (date 1), 2-month storage (date 2) and 4-month storage (date 4). Heritability estimates ranged from low to high (0.09≤h2 ≤0.63), flavour (0.09≤h2 ≤0.15) and acidity (0.52≤h2 ≤0.63) being respectively the least and the most heritable traits (Table 4). For most of the traits, heritability values were stable over the three scoring dates. However, significant increases were observed between Table 3 Estimates of genetic parameters of fruit external traits: narrow-sense heritabilities (diagonal), phenotypic (lower triangle) and genetic (upper triangle) correlations
FSIZE GCOL PCOL ATTR
FSIZE
GCOL
PCOL
ATTR
0.50±0.01 −0.03±0.01 0.04±0.01 0.49±0.02
−0.14±0.02 0.30±0.01 0.20±0.01 0.17±0.01
0.07±0.02 0.28±0.01 0.55±0.01 0.28±0.01
0.69±0.02 0.19±0.01 0.36±0.02 0.34±0.01
FSIZE fruit size, GCOL ground colour, PCOL proportion of over colour, ATTR attractiveness Bold entries correspond to heritability estimates of the traits
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dates 1 and 2 for firmness (from 0.26 to 0.44) and crispness (from 0.16 to 0.36). On the other hand, values for acidity tend to decrease over the three scoring dates (h2 =0.63, 0.58 and 0.52 respectively). In contrast to heritability values, genetic correlations (rg) and phenotypic correlations (rp) generally showed increasing absolute values over the scoring dates for all sensory traits (Table 4). The main changes were observed from dates 1 to 2 for correlation of firmness (respectively crispness) with other traits. Firmness and crispness were highly and positively correlated both genetically (0.73≤rg ≤ 0.98) and phenotypically (0.53 ≤ rp ≤ 0.82). Sugar and acidity also had high negative genetic correlations (−0.80≤rg ≤−0.65), but their phenotypic correlations were moderate to low over the three scoring dates (−0.40≤r≤ −0.20). Sugar had the lowest genetic correlations with other traits (−0.32≤rg ≤0.45). Global taste showed strong genetic correlations with texture (0.69≤rg ≤0.83), juiciness (0.68≤ rg ≤0.72) and flavour (0.55≤rg ≤0.63) and relatively weak
genetic correlations with crispness (0.26≤rg ≤0.61) and acidity (0.22≤rg ≤0.53). The genetic correlations between global taste and sugar were low and even decreased with increasing storage period (from 0.30 at date 1 to 0.03 at date 4). Phenotypic correlations were generally lower than genetic correlations. They decreased from dates 1 to 4 for global taste and sugar but remained higher than the genetic correlations (from 0.39 to 0.32). When considering separately each sensory trait, high positive genetic correlations and medium phenotypic correlations were observed between different scoring dates (dates 1, 2 and 4; Table 5). Dates 2 and 4 were very highly genetically correlated, date 1 being always less correlated with these two dates. A similar trend was observed for phenotypic correlations despite lower values. Heritability estimates for each instrumental trait ranged from medium to high over scoring dates (0.49≤h2 ≤0.81; Table 6). Except for firmness and sugar which were less heritable at date 1, heritability values were very similar
Table 4 Estimates of genetic parameters of fruit sensory traits at harvest (date 1), 2 months post harvest (date 2), and 4 months post harvest (date 4): narrow-sense heritabilities (diagonal), phenotypic (lower triangle) and genetic (upper triangle) correlations FIRM
CRISP
TEXT
JUIC
FLAV
SUG
ACID
GTAST
FIRM CRISP
0.26±0.01 0.53±0.03
0.73±0.03 0.16±0.01
−0.06±0.04 0.48±0.05
−0.15±0.04 0.32±0.04
−0.24±0.05 −0.03±0.06
−0.19±0.03 −0.32±0.02
0.06±0.02 0.26±0.04
−0.01±0.03 0.26±0.05
TEXT JUIC FLAV SUG ACID GTAST
0.08±0.01 0.05±0.01 0.02±0.03 −0.03±0.02 0.15±0.03 0.12±0.03
0.31±0.05 0.32±0.05 0.13±0.03 −0.02±0.01 0.17±0.02 0.29±0.04
0.14±0.01 0.41±0.04 0.21±0.04 0.14±0.03 0.03±0.01 0.52±0.03
0.82±0.02 0.35±0.01 0.23±0.03 0.09±0.02 0.18±0.03 0.48±0.04
0.55±0.05 0.40±0.04 0.12±0.01 0.35±0.01 0.09±0.02 0.48±0.03
0.26±0.04 0.05±0.03 0.45±0.03 0.29±0.01 −0.40±0.05 0.39±0.01
0.11±0.04 0.29±0.03 0.07±0.03 −0.74±0.02 0.63±0.01 0.12±0.02
0.83±0.03 0.68±0.03 0.60±0.03 0.30±0.03 0.22±0.02 0.31±0.01
Date 2
FIRM CRISP TEXT JUIC FLAV SUG ACID GTAST
0.44±0.01 0.76±0.02 0.27±0.04 0.33±0.05 0.15±0.01 0.15±0.02 0.17±0.03 0.41±0.03
0.93±0.01 0.36±0.01 0.42±0.02 0.48±0.03 0.20±0.04 0.12±0.01 0.20±0.02 0.48±0.02
0.28±0.04 0.57±0.03 0.15±0.01 0.46±0.02 0.22±0.05 0.18±0.03 0.13±0.01 0.51±0.01
0.36±0.03 0.60±0.02 0.80±0.02 0.38±0.01 0.28±0.03 0.11±0.02 0.25±0.04 0.53±0.01
0.08±0.04 0.27±0.04 0.67±0.04 0.50±0.04 0.15±0.01 0.29±0.03 0.18±0.02 0.48±0.04
0.11±0.02 0.04±0.03 0.13±0.05 0.01±0.02 0.22±0.04 0.28±0.01 −0.33±0.06 0.29±0.02
0.14±0.02 0.22±0.03 0.12±0.04 0.33±0.02 0.21±0.03 −0.80±0.02 0.58±0.01 0.33±0.02
0.49±0.02 0.61±0.02 0.69±0.03 0.71±0.02 0.63±0.03 0.08±0.04 0.41±0.02 0.31±0.01
Date 4
FIRM CRISP
0.43±0.01 0.82±0.04
0.98±0.01 0.37±0.02
0.89±0.02 0.92±0.01
0.48±0.03 0.55±0.03
0.24±0.06 0.30±0.02
0.20±0.03 0.20±0.06
0.17±0.03 0.16±0.05
0.61±0.03 0.59±0.02
TEXT JUIC FLAV SUG ACID GTAST
0.55±0.02 0.37±0.02 0.22±0.01 0.24±0.03 0.22±0.02 0.51±0.01
0.59±0.03 0.46±0.03 0.25±0.02 0.24±0.01 0.22±0.01 0.54±0.02
0.20±0.02 0.54±0.03 0.24±0.01 0.26±0.03 0.18±0.02 0.61±0.03
0.79±0.02 0.36±0.01 0.24±0.02 0.13±0.02 0.28±0.03 0.58±0.04
0.50±0.06 0.46±0.06 0.09±0.01 0.28±0.03 0.16±0.02 0.41±0.01
0.23±0.05 −0.08±0.05 0.34±0.06 0.25±0.01 −0.20±0.03 0.32±0.04
0.19±0.04 0.34±0.04 0.23±0.05 −0.65±0.04 0.52±0.01 0.41±0.03
0.77±0.03 0.72±0.03 0.55±0.05 0.03±0.05 0.53±0.03
Date 1
FIRM firmness, CRISP crispness, TEXT texture, JUIC juiciness, FLAV flavour, SUG sugar, ACID acidity, GTAST global taste Bold entries correspond to heritability estimates of the traits
0.30±0.02
Tree Genetics & Genomes Table 5 Genetic (rg) and phenotypic (rp) correlations between evaluation dates for sensory traits
FIRM CRISP TEXT FLAV JUIC SUG ACID GTAST
Dates 1–2
Dates 1–4
Dates 2–4
rg rp rg rp rg rp rg rp rg rp rg rp rg
0.70±0.02 0.40±0.01 0.71±0.01 0.32±0.02 0.68±0.05 0.18±0.02 0.86±0.02 0.26±0.01 0.88±0.06 0.46±0.02 0.96±0.01 0.40±0.04 0.98±0.04
0.71±0.01 0.30±0.02 0.59±0.06 0.23±0.02 0.43±0.07 0.13±0.03 0.78±0.02 0.14±0.03 0.89±0.04 0.42±0.03 0.89±0.01 0.31±0.02 0.98±0.03
0.99±0.03 0.56±0.02 0.98±0.04 0.51±0.03 0.91±0.03 0.31±0.05 0.86±0.01 0.21±0.02 0.99±0.02 0.55±0.02 0.98±0.03 0.41±0.01 0.99±0.04
rp rg rp
0.68±0.02 0.85±0.02 0.37±0.02
0.64±0.01 0.80±0.02 0.31±0.03
0.65±0.03 0.98±0.01 0.49±0.02
Date 1 harvest, Date 2 2 months post-harvest, Date 4 4 months postharvest, FIRM firmness, CRISP crispness, TEXT texture, JUIC juiciness, FLAV flavour, SUG sugar, ACID acidity, GTAST global taste
among all scoring dates for each instrumental trait. Acidity was the most heritable. Genetic and phenotypic correlations between firmness and sugar were moderate and very close (0.34≤rg ≤0.49 and 0.28≤rp ≤0.46). Very low genetic and Table 6 Estimates of genetic parameters of instrumental traits at harvest (date 1), 2 months post-harvest (date 2), 2 months storage plus 15 days at room temperature (date 3) and 4 months post-harvest (date 4): heritabilities (diagonals), phenotypic correlations (lower triangles) and genetic correlations (upper triangles) FIRMinst
SUGinst
ACIDinst
phenotypic correlations were observed between acidity and firmness (0.07≤rg ≤0.15 and 0.12≤rp ≤0.17). Sugar and acidity had almost no genetic correlation (0.02≤rg ≤0.11) and very low phenotypic correlations (0.13≤ rp ≤0.17; Table 6). When considering separately each instrumental trait, very high phenotypic (0.65≤rp ≤0.96) and genetic (0.75≤ rg ≤1.00) correlations were observed between dates (data not detailed). Dates 2, 3 and 4 had higher genetic correlations (0.96≤rg ≤1.00). As for sensorial traits, date 1 had low correlation with other dates for firmness, while acidity and sugar showed very high genetic correlations (0.89≤rg ≤0.99) between dates. Increasing phenotypic and genetic correlations were generally observed between sensorial and instrumental measurements from dates 1 to 4 (Table 7). Sensorial and instrumental sugar showed medium genetic (0.29≤rg ≤0.56) and phenotypic (0.23≤rp ≤0.35) correlations. High genetic and phenotypic correlations were observed between instrumental and sensorial measurements of acidity (0.94≤rg ≤ 0.97 and 0.75≤rp ≤0.78) and firmness (0.73≤rg ≤0.85 and 0.45≤rp ≤0.64) over all scoring dates. Global taste had almost null genetic and phenotypic correlations with SUGinst and null to low values of correlations with ACIDinst (Table 7). Breeding values Multi-trait best linear unbiased predictors of breeding values for all individuals in the pedigree were computed at date 1 for external traits, at dates 1, 2 and 4 for sensorial traits and at dates 1, 2, 3 and 4 for instrumental traits. As observed with estimates of heritability, predictions of breeding values of all individuals for sensorial and instrumental traits were relatively stable over scoring dates.
Date 1
FIRMinst SUGinst ACIDinst
0.57±0.01 0.28±0.02 0.16±0.02
0.34±0.03 0.49±0.02 0.14±0.01
0.15±0.03 0.08±0.03 0.79±0.01
Date 2
FIRMinst SUGinst ACIDinst
0.67±0.01 0.42±0.03 0.12±0.03
0.43±0.03 0.53±0.02 0.13±0.02
0.07±0.02 0.02±0.03 0.80±0.01
FIRMinst SUGinst ACIDinst
0.68±0.01 0.37±0.03 0.15±0.01
0.38±0.03 0.55±0.02 0.15±0.02
0.14±0.02 0.07±0.03 0.80±0.01
ACID-ACIDinst
FIRMinst SUGinst ACIDinst
0.71±0.01 0.46±0.01 0.15±0.02
0.49±0.02 0.55±0.02 0.17±0.03
0.13±0.02 0.11±0.03 0.81±0.01
GTAST-SUGinst
Date 3
Date 4
Bold entries correspond to heritability estimates of the traits
Table 7 Genetic (rg) and phenotypic (rp) correlations between sensory and instrumental measurements at harvest (date 1), after 2 months storage (date 2), and 4 months storage (date 4). Names of instrumental traits are ended with the suffix ‘inst’ Date 1 SUG-SUGinst
FIRM-FIRMinst
GTAST-ACIDinst
Date 2
Date 4
rg rp rg
0.29±0.03 0.23±0.02 0.94±0.01
0.36±0.02 0.26±0.01 0.97±0.03
0.56±0.03 0.35±0.01 0.97±0.02
rp rg rp rg rp rg rp
0.78±0.02 0.73±0.04 0.45±0.01 −0.02±0.01 0.03±0.01 0.06±0.03 0.02±0.01
0.77±0.03 0.88±0.01 0.66±0.02 0.00±0.01 0.05±0.01 0.29±0.02 0.19±0.03
0.75±0.03 0.85±0.03 0.64±0.02 0.05±0.01 0.13±0.01 0.36±0.02 0.25±0.02
Tree Genetics & Genomes Table 8 BLUP of breeding values for some traits of the most represented (in term of number of descendants) genotypes in the pedigree Instrumental (date 2) Genotype
Sensory (date 2)
External (date 1)
# descendants
FIRMinst
ACIDinst
SUGinst
JUIC
GTAST
FSIZE
ATTR
1,413 848 777 746 703
−0.99 −1.10 −0.76 −0.09 −1.51
−1.13 1.33 −2.63 −0.08 1.41
0.04 1.01 0.34 −1.10 −0.33
0.37 −0.50 0.61 −0.30 −0.03
0.31 0.10 0.20 −0.48 0.08
0.46 0.40 0.79 −0.38 0.28
0.48 −0.04 0.43 −0.21 0.13
PRI 14–126 Mac Intosh Winesap BaumanRei MinHammer Doktor Oldenburg Prima PRI668-100 Clivia Reinette Clochard Pinova Kidd's Orange Red Gala PRI 612-1 Florina Wagener Apfel Idared Jersey Black
669 513 439 432 432 431 403 383 361 356 354 350 349 300 299 290 287 274
−0.93 −2.09 −0.33 −0.94 −0.94 −1.87 0.03 −0.38 −0.91 −0.71 0.22 −0.60 0.66 −1.04 −0.46 −1.00 −0.84 −0.19
−0.52 0.48 0.00 0.72 0.72 1.44 1.26 −0.60 −0.71 0.78 −0.11 0.19 −1.83 −1.79 −0.57 0.90 −0.14 −0.84
−0.77 −1.43 1.02 −0.61 −0.61 −1.23 −0.36 −0.62 0.03 0.00 0.12 1.13 0.61 −0.21 0.28 −1.73 −1.46 −0.11
−0.09 −0.12 −0.04 −0.14 −0.14 −0.28 −0.28 −0.05 −0.32 −0.38 0.03 −0.06 −0.11 −0.06 −0.04 0.17 0.46 0.02
−0.21 0.23 0.02 −0.18 −0.18 −0.37 −0.17 −0.14 −0.14 −0.27 −0.11 0.29 −0.06 −0.19 0.17 0.19 0.42 −0.01
−0.07 0.63 −0.10 −0.04 −0.04 −0.08 −0.14 0.02 −0.01 0.07 −0.06 0.74 −0.09 0.39 −0.03 0.88 0.89 −0.19
0.10 0.31 0.11 0.03 0.03 0.05 0.06 0.26 −0.17 −0.25 −0.02 0.25 0.15 0.29 0.19 −0.13 0.63 −0.23
Macoun Ralls’ Janet LongfoBea Linda Fuji Coop-17 Worcester Pearmain PRI 14−152 PRI 672-3 Alwa
273 260 258 257 256 244 227 220 218 208
−1.33 1.36 −0.70 −1.41 1.50 0.68 −1.28 −1.11 −1.12 0.11
−1.01 0.11 2.12 4.25 −1.55 −1.16 0.37 −0.38 −2.05 −0.12
−0.89 0.24 −0.03 −0.06 1.37 −0.73 −0.68 −1.71 −3.47 0.43
−0.03 0.09 0.17 0.35 0.51 0.10 −0.57 0.14 0.31 −0.50
0.10 0.20 0.18 0.36 0.49 0.01 −0.33 −0.17 −0.09
0.03 −0.42 0.25 0.50 −0.04 −0.14 0.35 0.03 −0.18
−0.19 −0.19 0.10 0.20 0.13 −0.13 0.42 0.10 −0.09
Primula Fantazja Red Winter Chantecler Crandall Beauty of Bath Discovery Melrose
188 184 163 160 138 125 124 122
−2.00 −2.06 0.28 −1.08 −0.85 −0.45 −0.06 −0.43
1.29 0.36 1.53 0.92 −1.64 3.22 0.74 −1.09
−2.14 −1.26 −0.99 1.11 −1.19 −0.33 1.68 −0.53
−0.11 0.01 0.69 −0.07 −0.10 −0.80 −0.97 0.23
−0.31 −0.35 0.24 0.28 0.06 −0.05 −0.57 −0.60 −0.12
−0.23 −0.24 0.69 0.15 0.30 −0.38 −0.70 −0.68 0.26
−0.33 −0.34 0.04 0.05 −0.07 −0.25 −0.15 0.07 −0.21
Sawa Antonovka 34.16 Granny Smith Baujade James Grieve
119 112 109 105 102
−1.48 −0.36 0.54 0.35 −2.98
−0.04 2.31 1.82 1.85 1.32
−0.23 −0.93 −0.66 −1.12 −1.67
−0.29 −0.17 0.72 −0.10 −0.34
−0.41 −0.12 0.43 −0.18 −0.24
−0.34 −0.22 0.56 0.46 0.96
−0.94 −0.36 0.39 −0.06 0.44
Golden Delicious Cox's Orange Pippin Red Delicious F2_26829-2-2 Jonathan
Tree Genetics & Genomes
However, values for date 1 were quite different from those of dates 2 and 4. Similar ranks of the genotypes were obtained for traits which had high genetic correlations. Results are illustrated in Table 8 with values of the most represented genotypes (in term of total number of descendants cumulated over generations in the pedigree). Table 8 focus on date 2 for sensorial traits since date 2 was the most correlated with other dates (Table 5). Breeding values of instrumental traits are presented since they were the most heritable among all analysed traits. Breeding values are also presented for sensorial and external traits exhibiting the highest heritability estimates.
Discussion Particular issues of the study Estimation of variance components and all the subsequent genetic parameters (heritability, genetic and phenotypic correlations) are of importance in animal and plant breeding programmes. Indeed these parameters contribute to a better understanding of genetic control of traits of interest. They are also critical for the prediction of breeding values (BLUP and selection index) and for the prediction of expected genetic gain of selection programmes (Lynch and Walsh 1998). These estimates of variance components and genetic parameters depend on many factors: environmental conditions, trait scoring system, population characteristics (i.e. generation numbers, selection intensity, degree of inbreeding). In apple-breeding programmes worldwide, the same elite genotypes are largely used as parents by most of the breeders which may result in an increased degree of inbreeding in breeding populations. Numerous selections steps are also applied within and between generations, and they may modify the genetic relationships within the populations. All these situations should logically motivate relatively frequent estimations of variance components and genetic parameters especially for fruit-quality traits which determine the success of new apple varieties. However, few reports exist on the estimates of heritability and genetic correlations of apple fruit-quality traits (Durel et al. 1998; Currie et al. 2000; Oraguzie et al. 2001; Alspach and Oraguzie 2002) mainly because such genetic parameters are not yet used by breeders on a practical point of view. The reliability of the estimates of genetic parameters is determined by the amount and the quality of data used for computations. For instance, Durel et al. (1998) used pedigreed 213 control-pollinated full-sib families of French scab-resistant apple-breeding programme while Oraguzie et al. (2001) as well as Alspach and Oraguzie (2002) used subsets of 30 to 34 (respectively 50 to 119) open-pollinated families from the
New Zealand Hort Research Institute apple recurrent selection breeding programmes. Each of these studies was performed in a single site, France and New Zealand, respectively. The comparisons of the results from these studies presented some discrepancies since different plant materials, environmental conditions and scoring methods were used. In the current study, we estimated genetic parameters for apple fruit-quality traits using pedigreed plant material from 29 full-sib progenies and their ancestors coming from six representative European breeding programmes located in four countries and assessed over three successive years. Our material is less important as regard to the number of progenies, but it exhibits a wide genetic diversity provided by the diverse origins of the genotypes. Indeed, putative bias in the material of a given country due to selection for particular characteristics may be corrected at the scale of the whole population by the presence of material from the other countries since selections aims are not absolutely similar. Thus, by gathering data from different countries, we are getting more general estimates of genetic parameters which will be more reliable for many research programmes in Europe compared to previous published studies. Estimates of narrow sense heritability are classically obtained from offspring–parent regression methods or ratios of additive genetic variances and phenotypic variances. The first approach requires exclusively data at two successive generations. This approach was used by Currie et al. (2000) and Oraguzie et al. (2001). The second approach requires the estimation of variances components which can be obtained by simple analyse of variance in balanced experimental designs and by REML methods in cases of unbalanced designs. The last method was used by Durel et al. (1998) and Alspach and Oraguzie (2002). The data used in the present study are unbalanced since each of the six subsets of plants was phenotyped in a different institute and for 3 years. As underlined by Durel et al. (1998) and Furlani et al. (2005), the RELM procedure takes into account the genetic relationship in the pedigree and is not affected by the structure of the dataset. Also, the heritability obtained for each trait is characteristic of founders and not of the last generation in the pedigree. The weight of each founder in the estimation of heritability depends on the total number of its descendants and on the pedigree. Assuming that the representation of founders in our pedigree is fairly representative of many breeding programmes in Europe and beyond, our study provides estimates of genetic parameters which can be considered as a reference data set for numerous apple breeders. Genetic parameters In this study, we analysed four apple fruit appearance traits at harvest: FSIZE, GCOL, PCOL and ATTR. From
Tree Genetics & Genomes
absolute numerical point of view, narrow-sense heritability estimates were medium (0.30≤h2 ≤0.55). However, when considering the ordinal scale of these traits and the putative influence of estimator’s subjectivity on the variation of the scores, these estimates are rather high. Such results are indicative of the success of field selection of apple genotypes based on ordinal scores of these fruit appearance traits. Fruit size was the most correlated with fruit attractiveness (r=0.69) followed by the proportion of over colour (r=0.36). Durel et al. (1998) also obtained similar high correlation between fruit size and attractiveness (r=0.59). These consistent results reflect the general and natural trend of consumers’ preference for big fruits with coloured skin. The conservation of fruit quality after storage is an important challenge for apple breeders and traders. However, so far, genetic analysis of fruit sensory evaluations focused on harvest period (Durel et al. 1998; Currie et al. 2000; Oraguzie et al. 2001; Alspach and Oraguzie 2002). In the present study, sensory evaluation was performed based on eight traits (FIRM, CRISP, TEXT, JUIC, FLAV, SUG, ACID and GTAST) at harvest and also after 2 and 4 months of storage. The heritability estimates remain stable for most of the traits throughout the period of the storage. However, some significant changes were observed between harvest and 2 months storage for firmness and crispness and over the three scoring dates for acidity. Such trend reflects the changes in the sensory perception of these traits along the storage period. Indeed, genetic variance for fruit firmness and crispness increased from dates 1 to 2 while residual variances remained relatively stable. These results indicate that the most important change in sensory perception of fruit firmness and crispness occurs during the first 2 months of storage. It is expressed by an increase in genetic differences between genotypes rather than a decrease in residual variability. For acidity, the decreasing heritability was due to a significant decrease of genetic variances while residual variances remained constant. These results illustrate a wider genetic variability between the genotypes for their acidity at harvest while they become more homogenous during storage. Considering the purpose of fruit storability, we may be inclined to consider heritability estimates of the longest storage period, that is, 4 months (date 4). However, we observed that scores of sensory traits after 2 months storage (date 2) had relatively high positive genetic correlations with scores obtained at harvest period (date 1) and after 4 months storage (date 4; Table 5). So, from these observations, it seems more straightforward and accurate to perform analyses of sensory traits after 2 months storage instead of harvest period. Genetic correlations between all sensorial traits increased over the three scoring dates. The biological explanation of
such an evolution is not straightforward. Indeed, a genetic correlation between two given traits is usually explained by either pleiotropic effects of shared genes or genetic linkage between different trait-specific genes leading to linkage disequilibrium. In the current study, each explanation alone cannot be sufficient to account for the changes in correlation estimates with time if gene effects are fixed. Conversely, these changes probably result from differential modifications of gene expressions during storage leading to significant changes in the way their shared actions (pleiotropy) and/or their trait-specific actions (linkage-disequilibrium) affect fruit sensory characteristics. Epistatic interactions with differential modulated expressions of the involved genes according to the trait can also affect genetic correlations with time. However, consistent trends of high positive correlations between firmness and crispness or strong negative correlation between sugar and acidity were observed in agreement with previous published studies (DaillantSpinnler et al. 1996; Durel et al. 1998; Alspach and Oraguzie 2002, Harker et al. 2002a, b). In general, texture, juiciness, flavour and acidity were the most correlated to fruit global taste. This result also supports those of Daillant-Spinnler et al. (1996), and Hampson et al. (2000) who identified these traits as the most important for consumers’ eating preference. Since ordinal scores of sensory traits can be somewhat unstable due to subjective appreciation of assessors, some authors have attempted to relate them to instrumental measurements which are assumed more precise (Hampson et al. 2000; Harker et al. 2002a, b). In this study, we also estimated genetic parameters for instrumentally assessed firmness, sugar content (soluble solids) and acidity. As already reported by Harker et al. (2002a), we obtained a very high correlation between instrumental firmness and sensory firmness and crispness. Considering this, we can conclude in accordance with Harker et al. (2002a) that sensory firmness and crispness are redundant or not enough well-defined criteria. Sensory perception of acidity had strong genetic correlation with instrumental acidity while sensory sweetness was only moderately correlated with instrumental sugar content. These results are in agreement with those of Watada et al. (1981) and Harker et al. (2002b). Nevertheless, we observed an increasing correlation between sensory sweetness and instrumental sugar (soluble solids) during the storage period (from 0.29 to 0.56). Since both sensory and instrumental acidity decrease with time (Table 2), the increasing correlation between both ways of sugar evaluation may be due to the fact that acidity disturbs the sensory perception of sugar and so, when acidity decreases (during storage), sensory sugar is better evaluated. Instrumental sugar and acidity were clearly less correlated to global taste perception than
Tree Genetics & Genomes
sensory sweetness and acidity at harvest period (date 1). Similar results were obtained by Hampson et al. (2000) who found that sensory sweetness and sourness were better predictors of fruit taste than instrumental measurements of soluble solids and titratable acidity. However, in our case, this result remained true only for acidity at dates 2 and 4 while sensory or instrumental sugar became similarly very bad predictors of global taste during storage. Breeding values Sensory and instrumental fruit-quality traits have usually been used by many authors to evaluate preferences and differences among apple cultivars and breeding selections (Hampson et al. 2000 and references therein). In all the different studies, quality of apple genotypes (reference cultivars and new hybrids) was appreciated based on their mean scores for traits of interest. Recently, Oraguzie et al. (2001) selected the best individuals in their recurrent selection population based on approximate estimates of combining ability (standardised difference between family means and general mean) in open-pollinated families. To our knowledge, no other study has been published on the ranking of apple genotypes according to their computed breeding values for sensory and/or instrumental traits. This is mainly due to the fact that apple breeders do not usually examine genetic parameter information from wellconstructed experimental (replication, multiple-site scoring…) and mating designs such as diallel and factorial crosses. Indeed such balanced designs are suitable for the computation of specific combining ability and general combining ability (GCA) useful for the identification of the best parental genotypes and efficient crosses. However, the relatively large size of the progenies from diallel and factorial designs may be problematic in terms of management of space particularly for perennial species such as apple which has a long generation time (4–6 years in average). In the present study, by gathering plant material from different institutes, we avoided that difficulty. Then, by using the individual genetic model and the REML/ BLUP procedure, we estimated breeding values (GCA) of all individuals in the pedigree for all analysed traits. Predicted breeding values for each trait were computed in multi-trait conditions and may be assumed more accurate than mono-trait prediction usually performed from diallel and factorial crosses. We provided, therefore, original information which will be helpful to breeders, not only in the choice of traditional reference cultivars (i.e. Red Delicious, Golden Delicious, Jonathan, Rome beauty, McIntosh, Gala, Fuji, Braeburn etc ...) but also new hybrid genotypes which could be used to design the most efficient future crosses.
Conclusion This study was performed in the framework of the European research project HiDRAS. We estimated genetic parameters for apple fruit external and eating-quality traits, and we computed breeding values for all the genotypes in a pedigreed material located in six European countries. Correlations between different sensory traits were generally in agreement with previous studies which performed sensory evaluation of apple fruits or consumers preference tests. Genetic correlations between instrumental traits are new information not previously published elsewhere. A major contribution of the present study is the estimation of phenotypic and genetic correlations of sensory and instrumental traits at different storage periods. Considering the overall results, it seems that 2 months post harvest is the most appropriate period for obtaining informative and accurate genetic parameters useful for the whole storage period (at least from harvest to 4 months after harvest). Despite the putative influence of the subjectivity of assessors on the variation of sensory scores, we obtained relatively high heritability values which support the success of current field selection of apple genotypes based on ordinal scores of sensory traits. Based on genetic correlations, fruit size appears as the best predictor of fruit attractiveness. On the other hand, texture, juiciness and flavour are the best predictors of fruit global taste perception. These results support those of previous investigations performed in different data scoring conditions with different plant materials. Furthermore, our pedigree-based estimation of genetic parameters and breeding values at a multinational scale is an original approach, and the obtained results are expected to provide reference information to breeders for further efficient designs of apple-breeding programmes.
Acknowledgements This publication was carried out with the financial support from the Commission of the European Communities (contract N° QLK5-CT-2002-01492), Directorate—General Research— Quality of Life and Management of Living Resources Programme. It does not necessarily reflect the Commission's views and in no way anticipates its future policy in this area. Its content is the sole responsibility of the publishers. The authors are very thankful to all the technicians who collected the data in the different institutes. They are also thankful to Dr Joseph ONYEKA for his useful critical advises on the manuscript.
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