Segregation and associations of enological and agronomictraits in Graciano 3 Tempranillo wine grape progeny(Vitis vinifera L.)

Shiren Song • Marıa del Mar Hernandez •

Ignacio Provedo • Cristina M. Menendez

Received: 22 April 2013 / Accepted: 24 August 2013 / Published online: 12 September 2013

� Springer Science+Business Media Dordrecht 2013

Abstract The main objective of this research was

the evaluation of the variability present in a segregat-

ing wine grape population derived from a cross

between Graciano 9 Tempranillo, two Spanish vari-

eties, in order to select improved genotypes with

potential for producing high-quality wines in a climate

change scenario. For that purpose, the phenotypic

segregation of 16 agronomic traits related to produc-

tion and phenology and 11 enological traits related to

technical and phenolic maturity was studied in the

progeny for three consecutive years. All traits pre-

sented transgressive segregation and continuous var-

iation. Year effect was significant for all traits except

total, extractable and skin anthocyanins content.

However, a high level of genotype consistency for

enological traits was revealed by repeatabilities and

correlations between years. Significant correlations

among traits were observed but most associations were

weak. Furthermore, the CAPS (Cleaved Amplified

Polymorphic Sequence) marker for the VvmybA

genotype was tested to determine whether it would

be useful in indirect selection for berry anthocyanins

content. The results showed that the number of

homozygous and heterozygous genotypes for the

functional colour allele adjusted to a 1:1 segregation

ratio, and that homozygous genotypes had signifi-

cantly higher anthocyanins content. Principal compo-

nent analysis found eight variables that contributed up

to 80 % of the phenotypic variability present in the

population. Seven groups of hybrids were distin-

guished based on ripening time, cluster weight, berry

weight and anthocyanins content by cluster analysis;

and fourteen genotypes were pre-selected for further

research.

Keywords Grape breeding � Phenolic maturity �Climate conditions � Berry quality

Introduction

Grapevine (Vitis vinifera L.) is one of the most

important fruit species in the world. The use of

grapevine for fruit, juice and wine production can be

traced back more than 8,000 years (This et al. 2006).

During this time, numerous cultivars have been

selected for their quality and adaptation to different

climatic conditions.

S. Song � M. del Mar Hernandez � C. M. Menendez

Departamento de Agricultura y Alimentacion,

Universidad de La Rioja, C/Madre de Dios, 51,

26006 Logrono, La Rioja, Spain

I. Provedo

Viveros Provedo, 26006 Varea, La Rioja, Spain

C. M. Menendez (&)

Instituto de Ciencias de la Vid y del Vino (Universidad de

La Rioja, CSIC, Gobierno de La Rioja), Complejo

Cientıfico Tecnologico, C/Madre de Dios, 51,

26006 Logrono, La Rioja, Spain

e-mail: cristina.menendez@unirioja.es

123

Euphytica (2014) 195:259–277

DOI 10.1007/s10681-013-0994-z

Many studies have documented a correlation

between climate change and agronomic traits in recent

years (Hall and Jones 2009). Climate affects grapevine

growth and fruit and wine production in many ways.

Temperature is widely accepted as being the primary

climatic factor affecting the quality of viticultural

production (Jackson and Lombard 1993; Gladstones

2004). Besides, the length of the growing season is

considered an important determinant of grape quality

and consequent wine value (Jackson and Lombard

1993; Coombe and Iland 2004) because air temperature

during ripening affects the composition of harvested

grapes (Mullins et al. 1992; Webb et al. 2006, 2007).

During maturation, the total berry phenolic concen-

tration slowly increases until a maximum is reached 1

or 2 weeks before harvest. Phenolic maturity is

attained when the concentration of grape phenolics is

maximal (Ortega-Regules et al. 2006). Grape pheno-

lics are structurally diverse and have variable extrac-

tion potentials. Anthocyanin pigments and tannins are

particularly important in red wine quality. In addition,

the ratio of skins and seeds to berry size, has an

important role in the extraction of phenolics on wine

and therefore in wine quality. Several factors contrib-

ute to berry phenolic maturity, including variety,

climatic conditions and cultural practices (Gonzalez-

San Jose et al. 1990; Cacho et al. 1992; Jordao et al.

1998; Vivas de Gaulejac et al. 2001; Harbertson et al.

2002; Downey et al. 2006). Furthermore, the time at

which ripening takes place can determine potential

wine quality for a particular vintage. The temperature

of the final ripening month is regarded as a particularly

important factor influencing wine styles. Many studies

have demonstrated that temperature influences many

components of grape development, including the

breakdown of acids (Buttrose et al. 1971) and berry

colour development (Bergqvist et al. 2001; Buttrose

et al. 1971; Kliewer 1977). In particular, prolonged

periods with temperatures above 30 �C can induce heat

stress, which may lead to premature veraison, berry

abscission, enzyme inactivation and reduced flavour

development (Mullins et al. 1992).

In traditional wine-growing regions in Europe, the

limiting factor for producing high-quality wines is the

level of ripeness of the grapes at high latitudes. Unripe

grapes give green, acidic wines, with low alcohol

levels, as a result of insufficient sugar accumulation in

the berry. For this reason, early ripening varieties such

as Pinot noir and Chardonnay are grown to optimize

the chances of attaining correct ripeness. At lower

latitudes, where the climate is warmer, grapes might

reach ripeness early in the summer but quick ripening

of the grapes reduces aromatic expression in the wines

(Van Leeuwen and Seguin 2006). Moreover, the

earlier occurrence of phenological events and a

compression of the growth period affect both wine

production and quality. The disruption in flavor and

colour development occurs with significant warming

during maturation and especially at night, finally

upsetting the wine typicity and quality (Webb et al.

2007; Ramos et al. 2008).

Regarding climate change, one of the most impor-

tant concerns for the enologists is the ability of berries

to complete phenolic maturity when the growing

season is shortened by the effect of higher temperatures

and lower and/or erratic rainfall (Nadal 2010) espe-

cially at low latitudes. Besides, the high sugars/acids

ratio derived from higher temperatures results in high

alcoholic wines with low acidity, undesirable to the

consumer. Moreover, increasing temperatures during

ripening could affect dramatically the synthesis of

phenol compounds in the berry. Therefore it is

important to select genotypes that are able to mature

slowly in order to attain complete phenolic maturity at

the date of harvesting. Recent studies have addressed

the selection of novel grape genotypes for a climate

change scenario (Viana et al. 2011a; Bayo-Canha et al.

2012). Nevertheless none has evaluated the ability of

genotypes to complete phenolic maturity, and there-

fore their potential for vinification in such setting.

Although viticulture has a history of over

8,000 years, grape breeding was only started in the

early 1950s (Mullins et al. 1992). So far, the best method

for breeding new varieties of grape is crossbreeding.

Therefore, interspecific hybridization, inter-varietal

hybridization and bud mutation, as well as ploidy

breeding are carried out widely at different institutions

all over the world (Clark 2010; Liang et al. 2011).

However, due to the rigorous regulatory classification

system (Appellation in France and Denominacion de

Origen in Spain) in wine, very few new varieties have

been released in the traditional wine regions of Europe.

The changing climatology, especially during the

growing season, may alter the specific optimum climate

of a traditional variety, hence hindering its ability to

ripen balanced fruit. Producing or preserving current

wine styles will consequently become more challeng-

ing. Thus, it is convenient to breed and select new high

260 Euphytica (2014) 195:259–277

123

quality varieties suited to the new climatic conditions, as

well as satisfactory to consumer demands.

The heterozygous nature of grapevine is a compli-

cating feature for any effective breeding program,

hence the requirement for an investigation on the

inheritance of desirable traits within the species.

Genetic analysis in grapevine is not easy, due to its

long life cycle, large number of chromosomes, partial

sterility of ovules, and low seed germination. More-

over, many genes which belong to a polygenic series

affect anthocyanin formation, chlorophyll formation,

shape and structure of leaves, habit and vigor, etc.

(Alleweldt and Possingham 1988). Quantitative genet-

ics (Falconer 1989) provided us with theoretical base

to obtain useful information on the contribution of

genetic and environmental factors to phenotypic

expression. From a series of quantitative genetics

studies on grapevine, it is known that the year effects

largely account for the environmental variation of

individual fruit traits (Shiraishi et al. 2011).

The main objective of the present study was to

analyze a segregating hybrid population obtained from

two Spanish relevant wine varieties, Tempranillo and

Graciano; in order to evaluate the variability present

and to select improved genotypes, with the greatest

potential for producing high-quality wines in a climate

change scenario.

Tempranillo is the main wine grape variety grown

in Spain so as in Denominacion de Origen Calificada

Rioja (DOCa); and is native to La Rioja and Aragon

(Ibanez et al. 2012). Graciano is a variety native to La

Rioja, usually blended with Tempranillo to strengthen

the wine with colour, acidity, elegant aroma, and aging

potential (Chome et al. 2006; Nunez et al. 2004).

These varieties were selected as parental genotypes in

the present study as they show complementary agro-

nomic and enological characteristics. The breeding

program at Viveros Provedo aims to identify superior

genotypes based on Tempranillo0s genetic background

adapted to La Rioja growing region and/or with wider

adaptation to the new climatic conditions. In that

framework, several F1 progenies with Tempranillo as

parental genotype were developed.

In this work, the segregation of phenology, produc-

tion, berry quality and enological traits present in the F1

population developed from a Graciano 9 Tempranillo

cross was studied. In addition, correlations among

traits were evaluated and a pre-selection of superior

hybrids was performed.

Materials and methods

Plant material

A F1 population of 163 plants obtained from con-

trolled crosses between the wine grape cultivars

Graciano (female parent) and Tempranillo (male

parent) was used for our investigation. The individual

hybrids (one plant of each genotype) have been grown

on their own roots since 2004, on a sandy-loam soil, in

East–West orientation with 3 m spacing between rows

and 1 m between plants in double Royat cordon.

Standard irrigation, fertilization and plant protection

practices for La Rioja region were performed. The

plants first flowered and fruited in 2007.

The population was genotyped for 5 SSRs markers:

VVS2 (Thomas and Scott 1993), VrZAG62, VrZAG79

(Sefc et al. 1999), VVMD6, VVMD34 (Bowers et al.

1996, 1999), in order to discard individuals resulting

from self-pollinations and foreign pollen sources,

resulting in a final population of 151 plants. DNA was

extracted from 200 mg frozen leaves using a DNAeasy

Plant Mini kit (Quiagen, Germany) following the

manufacturer’s protocol. The microsatellite analysis

was conducted following the methods described by

Martın et al. (2003). The fragments were separated on a

LICOR 4200 DNA Analyzer (LI-COR, Inc., USA) on

8 % denaturing polyacrylamide gels and sized with

SAGA software (LI-COR, Inc., USA).

Phenotypic evaluation

Twenty-seven agronomic and enological traits, overall,

were evaluated in the hybrid population during three

growing seasons (2008–2010). The number of geno-

types that bore fruit varied each year due to hail damage

during flowering, and bird damage during veraison–

ripening stages. Thus, 116, 123, and 132 genotypes were

harvested in 2008, 2009, and 2010 respectively.

Agronomic traits

Regarding phenology, the dates of sprouting (S,

considered as when 50 % of the buds were in

Baggiolini stage C), flowering time (F, 50 % flowering

in Baggiolini stage I, Baggiolini 1952), veraison time

(V, 50 % berry veraison), and ripening time (R) were

scored. R was established as the date when random

grapes picked from the top, medium and bottom of the

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123

clusters reached 13� Baume and consequently harvest

started on September the 23rd, 9th and 10th in 2008,

2009 and 2010 respectively.

Flowering period (FP, time between the opening of

first flower and that of all flowers), veraison period

(VP, time between the veraison of first berry and that

of all the berries), interval from sprouting to flowering

(S–F), interval from flowering to veraison (F–V), and

interval from veraison to ripening (V–R) were calcu-

lated as described by Duchene and Schneider (2005),

Costantini et al. (2008).

For each genotype, yield per vine and number of

clusters per vine (CN), were measured at harvest, and

the average weight of clusters (CW) was calculated.

The fertility index (FI) was scored as the number of

inflorescences per young shoot. Mean berry weight

(BW) was estimated from a sample of 100 berries

randomly taken from each genotype. Seed traits,

including mean seed number per berry (SN) and mean

fresh seed weight (SW), were evaluated by triplicate

from samples of 20 berries randomly selected.

Enological quality traits

Two sets of data were generated for the enological

analysis, one related to the technological parameters and

another related to the phenolic maturity indices. For the

first set, 200 whole berries from each genotype were

sampled at random, from different positions within the

cluster, to avoid effects due to sun exposition. Grapes were

squeezed and parameters of the resulting musts were

evaluated by triplicate. Sugar content (TSS, expressed as

degree Baume) was measured with an Atago Master-

Baume refractometer (Atago, Tokyo, Japan); pH and total

acidity (TA, expressed as g/L tartaric acid) were measured

with a TitroMatic 1S-1B (Crison, Barcelona). In addition,

60 randomly selected berries were frozen to measure berry

skin anthocyanin content (BSAn, mg/g) by triplicate as

described in Nadal and Lampreave (2007).

The phenolic maturity indexes were determined

following Saint-Criq de Gaujelac et al. (1998) as

described in Nadal (2010). Two hundred whole berries

were sampled and crushed to obtain a homogeneous

mixture, and then macerated for 4 h at two different

pH values (pH = 1.0 (A) and pH = 3.6 (B)). The total

anthocyanins content (TAn) was determined with

extract A; the extractable anthocyanins (EAn), colour

intensity (CI), total polyphenols index (TPI) and

tannin contents (TC) were determined with extract B.

The extractability index (%EI) and seed maturity

(%SM) were calculated as follows (Nadal 2010):

%EI ¼AntpH1:0 � AntpH3:6

� �� 100

AntpH1:0

%SM ¼Abs280pH3:6 � AntpH3:6 � 40

� �� �� 100

Abs280pH3:6

Genotypic evaluation of the VvmybA allele with CAPS

To establish whether the total anthocyanins content was

correlated with the VvmybA genotype, the CAPS

(Cleaved Amplified Polymorphic Sequence) marker

20D18CB9 (Walker et al. 2007) was tested against the

progeny and parental plants, using a PCR assay. The

amplification product obtained using the primers

20D18CB9f (50-GATGACCAAACTGCCACTGA-30)and 20D18CB9r (50-ATGACCTTGTCCCACCAA

AA-30) was then restricted with DdeI and separated by

gel electrophoresis on 2.5 % agarose gels, using 19

TBE buffer (Bayo-Canha et al. 2012). Gels were stained

with Midori green Advanced DNA stain (Nippon

Genetics EUROPE GmbH, Germany), the DNA frag-

ments were photographed under UV light with CHEMI

GENIUS Bio Imaging System (Syngene, Cambridge,

UK), and documented with GeneSnap from SynGene

software (Syngene, Cambridge, UK).

Statistical analysis

Descriptive statistics for all traits were conducted. A

t test was carried out to detect differences between both

parents. Analysis of variance with LSD test was used to

evaluate mean value differences among the 3 years.

The normality of each trait distribution was evaluated

by the Kolmogorov–Smirnov test. Data that signifi-

cantly deviated from normality were transformed

(square-root or logarithm) to fit a normal distribution.

Phenotypic correlations between traits were deter-

mined in each year with the Spearman rank-correla-

tion coefficient. Correlation analysis between years

was used to evaluate the genotype stability across

years for each trait. Year effect was tested with

analysis of variance and non-parametric Kruskal–

Wallis test (Fanizza et al. 2005). Based on the multiple

measurements, the repeated-measures analysis of

variance (years, genotypes within year) was applied

to estimate the repeatability over years for each trait

(Falconer 1989; Fanizza et al. 2005). The repeatability

262 Euphytica (2014) 195:259–277

123

was calculated as: r ¼ VGþVEg

VP, VP ¼ VG þ VEs þ VEg,

where r is repeatability; VG is genotypic variance; VP

is phenotypic variance; VEs is special environment

variance (within individual variance component); VEg

is general environment variance (environmental var-

iance contributing to the between-individual

component).

A factor analysis of the phenotypic data was carried

out using the principal components extraction method

(PCA) to identify the main variance components

contributing to the variability among hybrids. Then the

gained matrix was analyzed with hierarchical cluster

methods to pre-select the best suitable hybrids. A

hierarchical cluster analysis was carried out using the

squared Euclidian distance combined with the average

linkage clustering methods.

All the statistical analyses were performed with

software SPSS 14.0 and STATGRAPHICS 16.0.

Results

For this research, wine grape varieties that differ in

several agronomic (phenology, fertility, berry weight,

seed number, seed weight) and enological attributes

(total acidity, anthocyanins content) were chosen as

parents. A t test detected significant differences

between both progenitors in mean berry weight, pH,

total acidity, fertility index, mean seed number and

mean seed weight (p \ 0.01) as expected.

Phenotypic evaluation of agronomic traits

Segregating agronomic traits relating to phenology,

production and fruit characteristics were scored in the

population over 3 years (2008–2010) (Table 1). The

number (N) of plants producing enough berries for

evaluation each year varied from 116 to 132, as a result

of hail damage during flower bud formation in 2008,

and bird damage during veraison–ripening.

Phenotypic data distributions were similar in the

3 years of study. Figure 1 shows the distributions

for year 2010. Continuous variation and transgres-

sive segregation were observed for all traits in the

three growing seasons evaluated, indicating that

genetic variability is present in the population and

confirming the quantitative nature of the traits

evaluated.

Table 1 Mean values of 16 agronomic traits evaluated in the Graciano 9 Tempranillo population

Agronomic traits Year Total

N 2008 N 2009 N 2010 Mean Min Max

Yield per vine (Kg) 116 0.8a 123 2.1b 132 2.5c 1.8 0.1 10.8

Number of clusters per vine 116 9.5a 123 11.5a 132 20.4b 14.0 1 57

Mean cluster weight (g) 116 84.7a 123 173.7b 132 121.8c 127.3 15.0 487.0

Fertility index 116 0.8a 123 0.9a 132 1.2b 0.9 0.1 2.4

Mean berry weight (g) 116 1.5a 123 1.7b 132 1.5a 1.6 0.7 2.8

Mean seed number per berry 114 2.0a 123 2.4b 132 2.0a 2.2 1.1 3.5

Mean seed weight (mg) 114 33.1a 123 32.1a 132 30.1b 31.8 9.9 51.8

Sprouting (days from March 1st) – – 151 35a 150 49b 42 20 60

Flowering (days from March 1st) 140 108a 132 93b 136 99c 97 80 113

Veraison (days from March 1st) 137 171a 131 158b 134 174c 167 142 185

Ripening (days from March 1st) 137 216a 129 201b 132 223c 214 189 240

Flowering period (days) 140 15a 131 13b 134 13b 14 4 28

Veraison period (days) 137 9a 131 11b 134 15c 12 2 27

Sprouting–flowering interval (days) – – 131 58a 136 49b 53 38 70

Flowering–veraison interval (days) 137 63a 131 65b 134 75c 68 51 86

Veraison–ripening interval (days) 137 45a 129 43a 132 50b 46 13 72

Values with different letters show significant differences between years (p \ 0.05) according to the LSD test. N number of genotypes

evaluated each year. Mean values (Mean), minimum (Min) values and maximum (Max) values for the 3 years

Euphytica (2014) 195:259–277 263

123

The Kolmogorov–Smirnov test indicated depar-

tures from normality for yield per vine, cluster

number, mean seed number and all phenology-related

traits (S, F, V, R, FP, VP, S–F, F–V, and V–R)

(p \ 0.05 in all years).

Yield mean value and mean cluster weight showed

significant differences in the 3 years (Table 1). The

distribution of production traits in the progeny in 2010

growing season is shown in Fig. 1a. 44 % of the

genotypes (59/132) showed low production (below

2.0 kg), 71 % of the genotypes (94/132) displayed low

cluster weight (below 150 g) and 38 % (50/132)

exhibited low fertility (below 1.0). The standard

values were set according to DOCa Rioja Regulation

for grafted vines in commercial fields. Mean cluster

number (14) and mean fresh seed weight (31.8 mg)

showed intermediate values between the parents.

Mean seed number per berry (2.2) was the only trait

with mean values higher than the parental varieties,

and mean berry weight (1.6 g) showed lower values

than the progenitors. Yield, cluster weight and fertility

index increased gradually from 2008 to 2010, as

grapevines became older and production became more

stable.

The growing season (from sprouting to ripening)

lasted on average 172 days, the same as Graciano, but

8 days longer than Tempranillo. Dates for sprouting,

flowering, veraison and ripening (days from March 1st),

as well as veraison period, sprouting–flowering interval,

and flowering–veraison interval showed significant

(a)

Fig. 1 Distribution of agronomic traits in 2010. Parental values are indicated by GR (Graciano) and TE (Tempranillo). a Distribution

of production traits; b Distribution of phenology-related traits

264 Euphytica (2014) 195:259–277

123

differences among the 3 years (Table 1). Phenology-

related traits were the most influenced by the environ-

ment, and significant differences among years were

attributed to the temperature variation in the 3 years of

study. Mean temperatures during the three growing

seasons (April–October) obtained from the closest

weather station, varied significantly between 17.9 �C

in 2008 and 19.3 �C in 2009, with a 18.1 �C mean

temperature in 2010. The 2008 growing season was cool

and rainy (425.8 mm) for La Rioja conditions and total

sunshine hours were lower (1743.8 h) compared to 2009

(163.9 mm and 1911.4 h) and 2010 (193.8 mm and

(b)

Fig. 1 continued

Euphytica (2014) 195:259–277 265

123

1778.2 h) seasons. The early occurrence of rainfall in

2008 had a strong effect on yield likely due to flower

drop and lower fertility but did not influence ripeness

(Tables 1 and 2). Accordingly, sugar content was the

greatest in the 2009 growing season as temperatures and

sunshine hours were also the highest (Table 2).

Tempranillo was earlier than Graciano for all

phenology-related traits, and transgressive segrega-

tion was observed in the progeny for all traits

(Fig. 1b). Sprouting ranged between 20 and 60 days,

15 % of the population being earlier than Tempranillo

and only 5 % of the genotypes later than Graciano in

2010. For flowering date, 60 % of the genotypes were

earlier than Tempranillo and 5 % of them later than

Graciano. The veraison–ripening interval varied

between 13 and 72 days and approx. 30 % of the

progeny showed a longer V–R than Graciano

(Fig. 1b).

Phenotypic evaluation of enological traits

Traits related to both technical and phenolic maturity

were evaluated in the population over 3 years and

mean values and ranges are shown in Table 2. Only

pH and colour intensity showed significant differences

among the 3 years studied (Table 2).

Phenotypic data of enological traits showed similar

distributions in the 3 years of study and therefore

distributions are reported only for 2010 (Fig. 2). Total

acidity ranged between 2.9 and 12.4 g/L, with an

average value in the progeny intermediate between the

parental values. Total polyphenol index and total

tannin content showed also intermediate values in the

progeny compared to both progenitors (Fig. 2).

However, mean skin anthocyanins (1.3 mg/g),

colour intensity (19.5) and both total and extractable

anthocyanins (709.0 and 442.9 mg/L) were lower in

the progeny than in both parental genotypes. In all

cases, there were genotypes showing higher values

than Graciano, the parent with the highest polyphenols

content.

The mean extractability index (33.8 %) in the

progeny showed intermediate values between the

parents and 32.5 % of the progeny showed values

between 30 and 40 % which are optimal values for

wine grape. Seed maturity was higher in the progeny

than in both parental genotypes.

Phenotypic correlations

In order to evaluate the effect of genetic and environ-

mental factors on agronomic traits; the year effect, the

correlation coefficient of the same traits between years

and the repeatability were estimated. Analysis of

variance and Kruskal–Wallis test revealed a highly

significant year effect (p \ 0.01) for all traits studied

except berry skin anthocyanins content, total antho-

cyanins and extractable anthocyanins.

Table 2 Mean values of 11 enological traits evaluated in the Graciano 9 Tempranillo population

Enological traits Year Total

N 2008 N 2009 N 2010 Mean Min Max

Total soluble solids (8Baume) 116 12.4a 123 12.8b 132 12.2a 12.4 8.4 15.6

pH 116 3.5a 123 3.6b 132 3.5c 3.5 3.1 4.2

Total acidity (g/L tartaric acid) 116 7.4a 123 5.9b 132 6.1b 6.5 2.9 12.4

Berry skin anthocyanins (mg/g) 95 1.3ab 123 1.4a 132 1.2b 1.3 0.3 3.5

Colour intensity 68 16.2a 112 19.1b 123 21.7c 19.5 6.6 56.3

Total polyphenol index 68 51.6a 112 61.7b 123 58.7b 58.2 18.7 107.6

Tannin content (mg/L) 68 295.8a 112 355.1b 123 333.9b 333.4 77.3 665.9

Total anthocyanins (mg/L) 68 587.8a 110 763.0b 123 727.7b 709.0 142.6 1903.1

Extractable anthocyanins (mg/L) 68 398.1a 110 434.7ab 123 475.0bc 442.9 108.5 1114.7

Seed maturity (%) 68 68.9a 110 72.2b 123 68.0a 69.8 35.2 90.6

Extractability index (%) 68 29.9a 110 39.1b 123 31.2a 33.8 0.8 70.4

Values with different letters show significant differences between years (p \ 0.05) according to the LSD test. N number of genotypes

evaluated each year. Mean values (Mean), minimum (Min) values and maximum (Max) values for the 3 years

266 Euphytica (2014) 195:259–277

123

The differential response of genotypes to year-

environment variation was estimated by phenotypic

correlations of the same trait between years (Table 3).

Correlations were highly significant (p \ 0.01) for all

traits, except for flowering time and veraison period.

The highest coefficient was observed for total

anthocyanins content between year 2009 and year

2010 (r = 0.90, p \ 0.01). This result confirms the

year effect analysis previously reported.

Production traits (yield, cluster number, mean

cluster weight, fertility index and mean berry weight)

showed correlations ranging from 0.41 to 0.67

Fig. 2 Distribution of enological traits in 2010. Parental values are indicated by GR (Graciano) and TE (Tempranillo)

Euphytica (2014) 195:259–277 267

123

(p \ 0.01). Enological traits (sugar content, pH, total

acidity, skin anthocyanins content and phenolic

maturity indexes) were significantly (p \ 0.01) cor-

related in the 3 years with coefficients ranging from

0.37 to 0.88. Seed traits among years were also related

with values ranging from 0.38 to 0.80 (p \ 0.01). The

phenology-related traits showed the lowest correlation

coefficients (0.25–0.63) (p \ 0.01).

The repeatability, that sets the upper limit to the

broad-sense heritability (Falconer 1989; Fanizza et al.

2005), was estimated for all traits over 3 years

(Table 3). Agronomic traits showed low

repeatabilities (0.17–0.31) and those were even lower

for phenology-related traits indicating a strong envi-

ronmental effect, as expected. On the other hand, traits

related to enological potential, berry and seed param-

eters showed the highest repeatabilities (up to 0.82).

Several associations between traits were revealed

within each year, using Spearman rank correlation

coefficient (Table 4). Overall, coefficients observed in

the 3 years (2008, 2009 and 2010) were similar and the

values reported in Table 4 correspond to the correlations

averaged over 3 years, indicating if the correlation was

found only in 2 years or if the results were contradictory.

Table 3 Phenotypic correlations (Spearman rank coefficient) between years and repeatability for each trait

Traits Coefficients between years r

2008 and 2009 2008 and 2010 2009 and 2010

Yield per vine (Kg) 0.55** 0.44** 0.57** 0.29

Number of clusters per vine 0.58** 0.55** 0.63** 0.21

Mean cluster weight (g) 0.52** 0.41** 0.54** 0.17

Fertility index 0.62** 0.49** 0.67** 0.31

Mean berry weight (g) 0.51** 0.38** 0.70** 0.52

Mean seed number per berry 0.56** 0.57** 0.80** 0.47

Mean seed weight (mg) 0.38** 0.39** 0.75** 0.29

Sprouting (days from March 1st) – – 0.47** 0.0

Flowering (days from March 1st) 0.23* 0.30** 0.34** 0.0

Veraison (days from March 1st) 0.65** 0.48** 0.58** 0.0

Ripening (days from March 1st) 0.57** 0.51** 0.52** 0.0

Flowering period (days) 0.32** 0.25** 0.38** 0.26

Veraison period (days) 0.39** 0.19 0.36** 0.02

Sprouting–flowering interval (days) – – 0.26** 0.0

Flowering–Veraison interval (days) 0.63** 0.35** 0.57** 0.0

Veraison–ripening interval (days) 0.50** 0.36** 0.45** 0.28

Total soluble solids (8Baume) 0.70** 0.40** 0.50** 0.43

pH 0.68** 0.59** 0.64** 0.55

Total acidity (g/L tartaric acid) 0.64** 0.63** 0.71** 0.39

Berry skin anthocyanins (mg/g) 0.43** 0.54** 0.88** 0.54

Colour intensity 0.80** 0.72** 0.78** 0.72

Total polyphenol index 0.48** 0.34** 0.55** 0.36

Tannin content (mg/L) 0.42** 0.45** 0.49** 0.45

Total anthocyanins (mg/L) 0.81** 0.84** 0.90** 0.82

Extractable anthocyanins (mg/L) 0.82** 0.79** 0.83** 0.81

Seed maturity (%) 0.79** 0.83** 0.79** 0.33

Extractability index (%) 0.47** 0.37** 0.46** 0.74

r repeatability, * and ** mean correlations significant at the 0.05 and 0.01 level, respectively

‘‘–’’ means missing data

268 Euphytica (2014) 195:259–277

123

Ta

ble

4P

hen

oty

pic

corr

elat

ion

sb

etw

een

trai

tsav

erag

edo

ver

3y

ears

CN

CW

FIB

WSN

SWS

FV

RFP

VP

S-F

F-V

V-R

TSS

pH

Yie

ld

-0.2

8b

0.29

b -0

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0.89

-0

.26b

-0

.23b

-0

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0.

29b

-0.2

7b

0.35

0.

38

-0.3

2b

ns

-0.3

5b

-0.3

0b

ns

-0.3

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0.34

0.33

b

nsns

0.33

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b

ns0.

23b

0.30

0.28

b-0

.26b

-0.3

1b

0.77

0.65

0.73

0.36

0.32

nsns

nsns

nsns

0.25

0.24

-0.4

8

nsns

nsns

ns

nsns

0.19

b

nsns

nsns

nsns

nsns

nsns

nsns

nsns

ns

nsns

nsns

nsns

nsns

nsns

-0.2

4bns

-0.3

2b-0

.2

nsns

nsns

nsns

nsns

nsns

nsns

ns

ns

-0.2

6b

-0.2

5b

0.30

b

0.46

b-0

.85

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3b

nsns

cns ns

ns-0

.30b

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b ns

0.

85

-0.4

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0.78

-0

.49

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2

nsns

ns

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.31b

-0.3

0b

nsns

nsns

ns

-0.2

5b-0

.27b

nsns

nsns

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0

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5

-0.5

9 -0

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0.31

0.

39

TA

BSA

nC

IT

PI

TC

TA

nE

An

SME

I

nsns

nsns

nsns

nsns

nsns

nsns

4b-0

.35b

ns-0

.26b

-0.2

4bns

ns

0.29

ns

nsns

nsns

nsns

nsns

nsns

nsns

nsns

-0.2

0bns

ns-0

.21b

nsns

ns

nsns

nsns

nsns

nsns

nsns

nsns

nsns

nsns

nsns

nsns

nsns

ns

nsns

nsns

nsns

ns

nsns

nsns

ns

nsns

nsns

nsns

nsns

nsns

ns

nsns

nsns

nsns

nsns

-0.5

4 -0

.30b

nsns

nsns

ns

nsns

nsns

ns-0

.25b

nsns

-0.2

3bns

ns

0.81

0.53

ns

0.68

ns

0.84

0.83

-0.7

10.

59b

0.91

0.94

-0.6

50.

49b

0.62

0.60

0.62

-0.2

4bns

0.95

-0.8

40.

61

-0.8

50.

47b

CN

1ns

CW

1ns

FI1

nsns

nsns

BW

1

SN1

SW1

nsns

nsns

ns-0

.24b

nsns

nsns

ns

S1

F1

nsns

V1

ns-0

.19b

R1

nsns

nsns

FP1

nsns

nsns

nsns

nsns

nsns

nsns

VP

1ns

nsns

nsns

nsns

nsns

nsns

ns

S-F

1ns

nsns

nsns

nsns

nsns

0.22

b

F-V

1

V-R

1

TSS

1

pH1

TA

1ns

0.23

bns

nsns

nsns

ns

BSA

n1

CI

1

TP

I1

TC

1ns

ns0.

25b

ns

TA

n1

EA

n1

SM1

-0.4

1

Bol

d an

d no

rmal

fon

t ind

icat

e co

rrel

atio

ns s

igni

fica

nt a

t the

0.0

1 an

d 0.

05 s

igni

fica

nce

leve

l, re

spec

tive

ly; n

s =

not

sig

nifi

cant

; b =

cor

rela

tion

sig

nifi

cant

in tw

o ye

ars;

c =

con

trad

icto

ry r

esul

t. C

N: C

lust

er n

umbe

r; C

W: M

ean

clus

ter

wei

ght;

FI: f

erti

lity

inde

x; B

W: M

ean

berr

y w

eigh

t (g)

; SN

: Mea

n se

ed n

umbe

r; S

W: M

ean

seed

wei

ght,

S: s

prou

ting,

F: F

low

erin

g, V

: Ver

aiso

n, R

: Rip

enin

g, F

P: F

low

erin

g pe

riod

, VP:

Ver

aiso

n pe

riod

, S-F

: spr

outin

g-fl

ower

ing

inte

rval

, F-V

:

Flow

erin

g –

vera

ison

inte

rval

, V-R

: Ver

aiso

n-ri

peni

ng in

terv

al, T

SS: t

otal

sol

uble

sol

ids;

TA

: Tot

al a

cidi

ty; B

SAn:

Ber

ry s

kin

anth

ocya

nins

con

tent

; CI:

Col

our

Inde

x, T

PI:

Tot

al p

olyp

heno

l ind

ex; T

C: T

anni

ns c

onte

nt; T

An:

Tot

al

anth

ocya

nins

; EA

n: E

xtra

ctab

le A

ntho

cyan

ins;

SM

: see

d m

atur

ity, E

I: E

xtra

ctab

ility

inde

x

Euphytica (2014) 195:259–277 269

123

As expected, many of the observed associations

concerned the component variables of the same trait.

Among those; yield related traits, harvesting related

factors and colour derived parameters. High signifi-

cant (p \ 0.01) correlations were detected between

vine yield and cluster number (0.77), cluster weight

(0.65) and fertility index (0.73).

Positive significant correlations were found between

colour intensity and berry skin anthocyanins (0.81), and

total and extractable anthocyanins (0.91 and 0.94,

respectively). In addition, significant (p \ 0.01) high

correlations were revealed between veraison–ripening

interval and ripening date (0.78) and between flower-

ing–veraison interval and veraison date (0.85).

However, correlations between different groups of

traits were also detected in at least 2 years (Table 4).

Production traits (yield, cluster weight) showed neg-

ative correlations with enological quality traits (skin,

total and extractable anthocyanins content, total

polyphenol index) as expected, but coefficients were

low, ranging from 0.30 to 0.35.

Berry weight is one of the most relevant quality

parameters in grape. A positive significant correlation

was found between berry weight and seed number

(0.25) but lower than the value (0.41) reported by

Costantini et al. (2008). Berry weight also showed

significant negative correlations with colour parame-

ters as expected, but associations were weak (-0.24 to

-0.32). The number of seeds per berry correlated

positively with productivity traits such as yield (0.25)

and cluster weight (0.38) and negatively with seed

fresh weight (-0.48) and anthocyanins content, both

skin and total (-0.25 and -0.26) indicating that the

larger the seed number, and consequently the berry

weight, the lower the anthocyanin content. The

negative correlation between seed number and seed

fresh weight contradicts the results of Costantini et al.

(2008) that reported a positive correlation between SN

and SW (0.36).

Regarding phenology traits, few relevant correla-

tions were observed. Low significant (p \ 0.01)

correlations were revealed between flowering period

and veraison period with yield (0.34 and 0.33 respec-

tively), mean cluster number (0.28 and 0.31), and

fertility index (0.31 and 0.29). This result is expected

as genotypes with higher productivity would take

longer to complete reproductive developmental

stages. Sprouting showed a high negative correlation

with the sprouting–flowering interval (-0.85) and a

moderate correlation with flowering time (0.45),

indicating that early sprouting genotypes will take

longer to reach flowering. Ripening and veraison–

ripening interval exhibited high negative correlations

with total soluble solids (-0.76 and -0.59) and lower

with tanins content (-0.37 and -0.30 respectively).

Wei et al. (2002) and Bayo-Canha et al. (2012)

reported a negative correlation (-0.62 and -0.50)

between ripening and acidity in contrast with the

results of Jones and Davis (2000). However, in our

work, only a low negative correlation between

(a) (b)

400bp

200bp

100bp

M TE GR 008 011 015 020 023 026 Berry skin anthocyanins content (mg/g)

0

5

10

15

20

25

30

0.25

0.50

0.75

1.00

1.25

1.50

1.75

2.00

2.25

2.50

>2.

50

% H

ybri

ds

homozygous hybrids

heterozygous hybrids

Fig. 3 PCR analysis of VvmybA allele in the F1 population

Graciano (GR) 9 Tempranillo (TE) and the distribution of 123

hybrids (year 2010) evaluated for skin anthocyanins content.

a Functional VvmybA allele (248 bp) indicated homozygous red

genotypes (GR, TG008, TG020 with 248/248/329 bp) and

heterozygous genotypes (TE, TG011, TG015, TG023 and

TG026 with 213/248/329 bp). b Distribution of 123 genotypes

for berry skin anthocyanins content

270 Euphytica (2014) 195:259–277

123

ripening time and pH (-0.24) was found in 2 years.

These results indicate that late-ripening genotypes in

our growing conditions cannot reach maturity.

Correlations observed in only 1 year, as well as

discordant correlations over different years, were not

considered.

Association of anthocyanins content with allelic

composition for VvmybA

The 151 F1 plants together with the Graciano and

Tempranillo progenitors were tested also for colour

genotype with the CAPS marker 20D18CB9, which

flanks the VvmybA genes (Walker et al. 2007). These

genes co-segregate with the berry colour locus

mapped in linkage group 2 (Riaz et al. 2004). An

example of a gel used to score the marker is presented

in Fig. 3a. Tempranillo and 74 genotypes (49 %)

carried one copy of the functional allele (heterozygous

red); Graciano and 77 genotypes (51 %) carried two

copies of the functional allele (homozygous red).

Thus, the PCR-established VvmybA genotype adjusts

to a 1:1 segregation ratio (v2 = 0.062, p [ 0.5).

The LSD test for the average berry skin anthocy-

anin content, as well as total and extractable antho-

cyanins of the corresponding genotypic classes

showed significant differences (p \ 0.01) between

homozygous and heterozygous plants in the 3 years.

In 2010, 132 of the 151 F1 plants were analyzed for

berry skin anthocyanins content. Although skin antho-

cyanins differed even within the same genotypic

classes (Fig. 3b), hybrids with two copies of the

functional allele (homozygous) had significantly

higher anthocyanin contents (average value of

1.74 mg/g) than hybrids with only one copy (hetero-

zygous)(average of 0.86 mg/g). Eighty six percent of

hybrids homozygous for the functional allele were

distributed in the high anthocyanin content range

(above 1.25 mg/g), in comparison with 9 % of the

heterozygous hybrids.

Principal component and cluster analysis and pre-

selection of improved genotypes

A principal component analysis was conducted with the

aim of elucidating which variables accounted for the

phenotypic variability present in the F1 population.

Eight components were extracted in all 3 years with

PCA, and 78.3, 79.5 and 80.9 % of the variance was

explained in 2008, 2009 and 2010 respectively. The first

principal component (explaining 22.4 % of the vari-

ance) was strongly associated with the group of

enological traits (CI, TPI, BSAn, EAn, TAn) and

negatively correlated with a group of productivity traits

(yield, CW, BW). The second component (explaining

14.4 % of the variance) was negatively correlated with a

group of phenology traits (V, R, VP and F–V) (Fig. 4).

The F1 population was classified based on all traits

in each year using only hybrids that had no missing

values. The genotypes were grouped based on the most

relevant criteria for the wine grape breeding program

in all analyzed years: production, anthocyanin content,

and ripening time.

The groups obtained based on the 2010 evaluation,

are shown in Fig. 5. The 132 hybrids were grouped

into two main clusters and seven subgroups, in

agreement with the above mentioned criteria. Our

aim was to preselect hybrids which not only showed

high quality (low berry weight, low seed number, high

berry skin anthocyanin content), but also relatively

high production as well as an earlier maturation date

for La Rioja, a Mediterranean climate region with

pronounced continental influence in northern Spain.

Cluster 1 includes 77 genotypes with a longer

harvesting time (average of 234 days after March 1st)

compared to cluster 2 (average of 207 days). In this

last cluster, three subgroups are present (Table 5;

Fig. 5). The first one, subgroup V (25 genotypes; 136

to 13), shows the lowest anthocyanins content (aver-

age of 0.8 mg/g) and the second one (subgroup VI

including genotypes 108 to 83) presents the highest

Fig. 4 Principal component analysis. Distribution of variables

on the score plot

Euphytica (2014) 195:259–277 271

123

Cluster 1

Cluster 2

Fig. 5 Cluster analysis of

132 progeny from a

Graciano 9 Tempranillo

wine grape population

272 Euphytica (2014) 195:259–277

123

values (average of 2.3 mg/g) but displays the lowest

cluster weight. Most of the selected hybrids fall in the

third subgroup, subgroup VII including genotype

numbers 126 to 17, which exhibit both moderate

anthocyanins content and cluster weight. Late-matur-

ing genotypes were classified in cluster 1, and among

them genotypes 131 to 21 could be interesting for

warmer climate regions based on their higher antho-

cyanins content and their optimal cluster and berry

weight values. Moreover, those genotypes would be

useful in a climate change scenario, where both

average and maximum temperatures will increase.

On the basis of those criteria, we selected 14

hybrids from the population: genotypes 136, 115 and

13, belonging to subgroup V; and 8, 122, 124, 119, 57,

58, 40, 39, 29, 30, and 17 from subgroup VII

(Table 5). All 14 pre-selected genotypes were grafted

on rootstock (R-110) and planted in different exper-

imental fields to confirm their aptitude for wine grape

breeding. Further research is focused on evaluating the

quality of wines produced and their potential for

aging.

Discussion

Grapevine is a perennial crop from which a high-value

product, wine is obtained. For that reason, it is also one

of the most studied in the context of climate change

(Fraga et al. 2013; Hall and Jones 2009). In this study

we conducted an analysis of a progeny between two

Spanish relevant wine varieties in order to evaluate the

phenotypic variability present in the population and

the possibility of selecting improved hybrids, which

combine features of both parents, for a climate change

scenario.

Significant differences between parental genotypes

were found only for six traits. Although differences are

essential when using inbred lines, in allogamous

species such as grapevine, the presence of enough

variability in the progeny is not conditioned by the

differences in the parental values due to the hetero-

zygous nature of grape varieties. All traits studied

showed transgressive segregation indicating that

recombinant allelic combinations were generated in

the population.

The 27 traits evaluated in the present study

exhibited continuos variation in the segregating prog-

eny, indicating a polygenic quantitative nature as

previously reported (Fanizza et al. 2005; Liang et al.

2011, 2012; Costantini et al. 2008; Bayo-Canha et al.

2012; Liu et al. 2007). Distributions of several

parameters evaluated in the progeny fitted an additive

model of inheritance, including mean seed number per

berry, seed weight, veraison and ripening date,

flowering–veraison interval and veraison–ripening

interval. Quality characteristics such as berry weight,

ripening date, acidity, total soluble solids, and antho-

cyanins content were reported to be under strong

additive genetic control (Wei et al. 2002; Liang et al.

Table 5 Groupings of genotypes obtained from squared Euclidian distance combined with the average linkage clustering methods

based on the evaluation of agronomic and enological traits in a Graciano 9 Tempranillo population

Cluster Subgroup Genotypes N R

(days)

BSAn

(mg/g)

CW

(g)

BW

(g)

1 I 149, 151, 1, 138, 145, 130, 135, 120, 121, 91, 94, 80, 84, 41, 72, 19, 25 17 234.5 1.6 67.1 1.3

II 141, 150, 20, 109, 110, 61, 67, 48, 51, 35, 45, 26 12 235.1 1.0 75.9 1.6

III 131, 146, 2, 125, 129, 114, 116, 104, 106, 99, 103, 96, 98, 92, 93, 85,

87, 63, 65, 44, 62, 33, 43, 22, 24, 21

26 233.8 1.4 137.6 1.5

IV 140, 148, 3, 132, 137, 127, 128, 113, 123, 107, 112, 101, 102, 68, 89,

47, 66, 34, 46, 10, 12, 4

22 235.0 0.7 160.8 1.6

2 V 136, 143, 5, 118, 133, 111, 115, 100, 105, 81, 82, 73, 79, 64, 69, 50, 56,

36, 37, 23, 31, 15, 18, 7, 1325 210.2 0.8 129.2 1.6

VI 108, 144, 71, 78, 83 5 205.0 2.3 44.8 1.2

VII 126, 139, 8, 122, 124, 117, 119, 90, 95, 75, 88, 59, 60, 57, 58 52, 55, 40,

42, 32, 39, 29, 30, 9, 1725 206.6 1.5 135.7 1.5

Bold font numbers correspond to pre-selected genotypes. R ripening (days from March 1st), BSAn berry skin anthocyanins, CW

cluster weight, BW berry weight

Euphytica (2014) 195:259–277 273

123

2009). In our population, the total acidity and total

polyphenols and tannins content were intermediate

between the parental values but the mean total soluble

solids and anthocyanins content (skin, total and

extractable) were lower than the values of both

progenitors. This fact is due to the late-ripening

genotypes being unable to reach maturity. However,

20–30 % of the progeny showed high total and

extractable anthocyanins content (above 1000 and

600 mg/L, respectively). Production-related variables

that affect wine quality such as cluster weight and

fertility index, showed a dominant model of inheri-

tance towards low values, in agreement with the

results of Bayo-Canha et al. 2012. The phenotypic

variability observed in our population confirms that it

is feasible to select advanced breeding materials in an

intra-specific cross with the final aim of obtaining new

improved varieties.

Another issue relevant for selection is the consis-

tency of genotype performance over years. All traits

showed a significant year effect except those related to

anthocyanins content. Despite the year effect

observed, phenotypic correlations between years were

moderate for fruit yield components (yield per vine,

cluster weight, number of clusters per vine, and berry

weight (0.38–0.70) (Table 4) and higher than those

reported by Fanizza et al. 2005. Higher values

(0.43–0.90) were observed for enological parameters

such as total acidity and both total and skin anthocy-

anins contents (Table 4).

Therefore, the genotype 9 year interaction affects

moderately the yield components and much less the

anthocyanins content. This was confirmed by the QTL

analysis carried out by Fanizza et al. 2005, that

reported no stable QTLs across years for each of the

components of fruit yield (yield, cluster number,

cluster weight, number of berries per cluster and berry

weight). Poor fruit-set caused by environmental fac-

tors such as hail, has been reported by other authors as

the main cause of yield variability among years

(Mullins et al. 1992) and may be, besides juvenility,

in the basis of the much lower correlations found in our

study between years 2008 and 2010.

Phenology-related traits and indices such as flow-

ering time, flowering period and veraison period

showed low phenotypic correlations between years

and very low repeatabilities confirming the results of

Costantini et al. (2008) that reported a highly signif-

icant year effect for all phenological traits and no

correlations between years for flowering time. How-

ever, moderate correlations were observed in our study

for flowering–veraison interval and veraison–ripening

interval (0.45–0.63, respectively). The same authors

identified QTLs for phenology-related traits in three

chromosomic regions and a clear association, both at

the correlation coefficient and QTL localization level,

between flowering–veraison interval and veraison time

and a less clear between veraison–ripening interval and

flowering or veraison time. The onset of the different

developmental stages is strongly influenced by envi-

ronmental factors and particularly by temperature

(Cleland et al. 2007) and selection for both veraison

and ripening time would be more effective.

In order to obtain a balanced wine, it is essential to

have a relatively long veraison–ripening interval,

which allows sugars to accumulate to optimal levels,

maintaining acid structure, and producing an optimum

profile for polyphenols and flavor and aroma com-

pounds (Jackson and Lombard 1993; Jones et al.

2005). The results in this work indicate that long

veraison–ripening intervals reduce the sugar and

tannin content (r = -0.59 and -0.30, respectively),

meaning that a subset of the progeny is unable to reach

maturity, neither technical, nor phenolic, in agreement

with the seed maturity values observed. Therefore in

our conditions, a continental Mediterranean climate

region, early genotypes with a long veraison–ripening

interval would be now the most promising. However

in the context of climate change with higher temper-

atures, late-ripening genotypes such as those useful in

Rioja Baja and Southern Spain would be more

desirable, as they would show better quality attributes.

In our population 19 genotypes (14 %) showed earlier

ripening time than Tempranillo and 57 % were later

than Graciano. Besides, 30 % of the progeny showed a

V–R interval longer than Graciano, indicating that it is

possible to obtain offspring with better adaptation to

La Rioja climatic conditions in the future.

An estimate of the correlation between traits is of

fundamental importance in breeding programs, espe-

cially if selected traits have negative correlations, low

heritability or are difficult to quantify (Viana et al.

2011b). Moreover, indirect selection for low herita-

bility traits based on other correlated and highly

heritable is particularly relevant in woody species with

long generation times.

Significant correlations among different groups of

traits were observed in our population, but most

274 Euphytica (2014) 195:259–277

123

coefficients were low and similar to those reported for

table and wine-grape segregating populations (Wei

et al. 2002; Fanizza et al. 2005; Costantini et al. 2008;

Viana et al. 2011a, b; Bayo-Canha et al. 2012).

Low seed number is associated with fruit quality in

apple and kiwi, by reducing the growth of the fruits and

the fruit development time (Lai et al. 1989). This

consideration arises from the role of seeds as both sinks

and sources of hormones, and therefore inductors of

increased cell division and expansion, resulting in larger

fruits. In grape, seed number has been related to fertility

and maturation rate, and productive clones are usually

associated with a larger number of seeds per berry. A

positive correlation between berry weight and seed

number (Doligez et al. 2002) and a negative correlation

with seedlessness were reported (Wei et al. 2002). In this

work, the association between mean berry weight and

mean seed number per berry was weak (r = 0.25), in

consonance with the findings reported by Coombe and

Hale (1973) in table grape and Viana et al. (2011a) in

wine grape but contrary to Costantini et al. (2008) in

table grape. These contradictory results could be

explained by the existence of at least 3 minor indepen-

dent QTLs for seed number and for berry weight

reported by Doligez et al. 2002. Furthermore, seed

number was negatively correlated with anthocyanins

content (skin, total and extractable) as well as colour

intensity, which are considered maturity parameters for

wine grapes. However no correlation was found

between veraison–ripening interval and seed number.

Given the correlations detected in this research, seed

number could be a better parameter than seed weight for

indirect selection of berry quality.

Climate, soil, cultivation and biology are some of

the most relevant factors affecting synthesis and con-

centration of phenols in berries (Downey et al. 2006).

Nadal (2010) reported that when analyzing the phenolic

maturity on the whole berry, the total polyphenol

content in grapes gave a better approach than anthocy-

anins for predicting the polyphenol extraction and

content in wines. In our study, total polyphenols

exhibited moderate correlations (0.53–0.62) with antho-

cyanins contents, colour intensity showing the highest

coefficient (0.68).

One way to adapt grapevine to climate change is to

breed new varieties. This study found that crossing

Graciano 9 Tempranillo generated a large phenotypic

variability which may be useful for the selection of new

improved genotypes. Hybrids could be selected from

the segregating population with a ripening date earlier

than Tempranillo or later than Graciano. The selection

of prime hybrids should be based also on low cluster

weight, high sugar content, moderate acid content and

high anthocyanins content and extractability. The

CAPS VvmybA marker genotype would be a useful

marker in indirect selection for anthocyanins content as

reported also by Bayo-Canha et al. (2012). The value of

the 14 pre-selected hybrids as grafted genotypes should

be further evaluated for berry quality and production in

different wine regions. In addition, micro-vinifications

should be made to assess the organoleptic features of

the produced wines before considerating these geno-

types for high quality production systems.

Our study confirmed that it is feasible to select

advanced breeding materials in an intra-specific cross

with the final aim of obtaining new improved varieties

better adapted to future climate conditions.

Acknowledgments This research was funded by the

Government of La Rioja through the FOMENTA 04/2008

research project. Shiren Song was initially supported by a

fellowship from the Chinese Research Council and for the last

3 years by a MAE-AECID fellowship from the Spanish

Government. We thank the collaboration of Aleceia Bermejo,

Elena Lopez-Ocon and Ana Mangado for laboratory work.

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