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Molecular BreedingNew Strategies in Plant Improvement ISSN 1380-3743Volume 30Number 1 Mol Breeding (2012) 30:79-92DOI 10.1007/s11032-011-9600-z

Relationships between grain proteincontent and grain yield componentsthrough quantitative trait locus analysesin a recombinant inbred line populationderived from two elite durum wheatcultivarsA. Blanco, G. Mangini, A. Giancaspro,S. Giove, P. Colasuonno, R. Simeone,A. Signorile, et al.

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Relationships between grain protein content and grain yieldcomponents through quantitative trait locus analysesin a recombinant inbred line population derived from twoelite durum wheat cultivars

A. Blanco • G. Mangini • A. Giancaspro • S. Giove • P. Colasuonno •

R. Simeone • A. Signorile • P. De Vita • A. M. Mastrangelo • L. Cattivelli •

A. Gadaleta

Received: 25 January 2011 / Accepted: 2 June 2011 / Published online: 18 June 2011

� Springer Science+Business Media B.V. 2011

Abstract Grain protein content (GPC) in durum

wheat (Triticum turgidum var. durum) is negatively

correlated with grain yield. To evaluate possible

genetic interrelationships between GPC and grain

yield per spike, thousand-kernel weight and kernel

number per spike, quantitative trait loci (QTL) for

GPC were mapped using GPC-adjusted data in a

covariance analysis on yield components. Phenotypic

data were evaluated in a segregating population of

120 recombinant inbred lines derived from crossing

the elite cultivars Svevo and Ciccio. The material was

tested at five environments in southern Italy. QTL

were determined by composite interval mapping

based on the Svevo 9 Ciccio linkage map described

in Gadaleta et al. (2009) and integrated with DArT

markers. The close relationship between GPC and

yield components was reflected in the negative

correlation between the traits and in the reduction

of variance when GPC values were adjusted to yield

components. Ten independent genomic regions

involved in the expression of GPC were detected,

six of which were associated with QTL for one or

more grain yield components. QTL alleles with

increased GPC effects were associated with QTL

alleles with decreased effects on one or more yield

component traits, or vice versa (i.e. the allelic effects

were in opposite direction). Four QTL for GPC

showed always significant effects, and these QTL

should represent genes that influence GPC indepen-

dently from variation in the yield components. Such

genes are of special interest in wheat breeding since

they would allow an increase in GPC without a

concomitant decrease in grain yield.

Keywords Wheat � Grain protein content � Yield

components � Molecular markers � QTL mapping

Introduction

Grain protein content (GPC) partially determines the

nutritional value and the baking properties of

common wheat (Triticum aestivum L.) as well as

Electronic supplementary material The online version ofthis article (doi:10.1007/s11032-011-9600-z) containssupplementary material, which is available to authorized users.

A. Blanco (&) � G. Mangini � A. Giancaspro �S. Giove � P. Colasuonno � R. Simeone �A. Signorile � A. Gadaleta

Department of Environmental and Agro-Forestry Biology

and Chemistry, Sect. Genetic and Plant Breeding,

University of Bari, Via Amendola 165/A, 70126 Bari,

Italy

e-mail: blanco@agr.uniba.it

P. De Vita � A. M. Mastrangelo � L. Cattivelli

CRA—Cereal Research Centre, S.S. 16 km 675, 71122

Foggia, Italy

L. Cattivelli

CRA—Genomic Research Centre, Via S. Protaso, 302,

29017 Fiorenzuola d’Arda, Italy

123

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DOI 10.1007/s11032-011-9600-z

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the pasta-making technology characteristics of

durum wheat (Triticum turgidum L. var. durum).

GPC is a typical quantitative trait controlled by a

complex genetic system and influenced by environ-

mental factors and management practices (nitrogen

and water availability, temperature and light inten-

sity). Heritability estimates for GPC range from 0.41

(Kramer 1979) to 0.70 (Suprayogi et al. 2009),

depending upon the genetic material, environment

and the method of computation. During the last

decades, a rise in GPC has been mainly achieved

through increased nitrogen fertilization. Simulta-

neous increases of GPC and grain yield are difficult

to achieve in practical breeding programs, as both

traits are the products of complex interdependencies

between plant developmental traits and yield com-

ponents. Any genetic improvement in GPC has been

restricted by the negative correlation between pro-

ductivity and GPC found in segregating populations,

germplasm collections and recurrent selection mate-

rial in all cereals, with reported correlations ranging

from -0.20 to -0.80 (for reviews, see Simmonds

1995; Feil 1997; Oury et al. 2003; Oury and Godin

2007). A study over 14 years of multi-site trials

conducted between 1977 and 1999 showed that the

negative yield–GPC relationship is often masked by

environmental effects, and that when environmental

effects were reduced in comparison to genotype

effects, a strong negative relationship was always

revealed (Oury et al. 2003). The negative yield–

protein correlation has been attributed to environ-

mental factors, genetic components (McNeal et al.

1972), dilution of grain nitrogen with a much larger

grain biomass accumulation (Terman 1979; Grant

et al. 1991), or to bio-energetic requirements for

synthesis of carbohydrates and proteins (Bathia and

Rabson 1987). As a result of this generally inverse

relationship, high-yielding modern cultivars have

lower GPC compared to older cultivars (Simmonds

1995). Nonetheless, some genotypes were selected

in bread wheat (Sears 1998; Oury et al. 2003) and

durum wheat (De Ambrogio and Ranieri 2002;

Clarke et al. 2005) that did not fit the general

relationship, showing increases in both grain yield

and GPC. According to Sears (1998) it is possible to

improve both GPC and grain yield simultaneously

when an adequate source of genes increasing GPC is

used in wheat breeding.

The extensive review by Konzak (1977) and more

recent investigations (Levy and Feldman 1989; Stein

et al. 1992; Snape et al. 1995; Sourdille et al. 1996;

Joppa et al. 1997; Prasad et al. 1999; Khan et al.

2000; Perretant et al. 2000; Dholakia et al. 2001;

Zanetti et al. 2001; Campbell et al. 2001; Borner et al.

2002; Blanco et al. 2002, 2006; Olmos et al. 2003;

Groos et al. 2003; Prasad et al. 2003; Gonzalez-

Hernandez et al. 2004; Turner et al. 2004; Huang

et al. 2006; Nelson et al. 2006; Zhang et al. 2008;

Mann et al. 2009; Raman et al. 2009; Suprayogi et al.

2009; Sun et al. 2010) have indicated that factors

influencing protein concentration in cultivated and

wild wheats are located on all chromosomes. The

lack of sufficient genetic variation for useful traits

within the cultivated wheats has limited the ability of

plant breeders to improve grain yield and grain

quality. Several accessions with higher GPC (as

compared with durum and bread wheat), have been

identified within the Triticeae, among both close and

more distant relatives. The wild tetraploid wheat

T. turgidum L. var. dicoccoides is particularly

promising as a donor of useful genetic variation for

several traits including GPC and some major quan-

titative trait loci (QTL) for GPC have been intro-

gressed in cultivated genetic background (Levy and

Feldman 1989; Joppa et al. 1997; Blanco et al. 2002,

2006; Olmos et al. 2003; Gonzalez-Hernandez et al.

2004). The high grain protein gene Gpc-B1 from var.

dicoccoides, also associated with increased grain zinc

and iron content and earlier leaf senescence, has

recently been cloned (Uauy et al. 2006). However,

although introgression of traits from closer relatives

to wheat is relatively easy, the use of wild relatives in

breeding programmes for high GPC has not been a

popular approach for wheat breeders and new vari-

eties have yet to reach farmers’ fields. The major

limitation has been the low yield-potential of the

progeny and the introduction of deleterious charac-

teristics from the wild parent due to linkage drag (see

summary by Islam and Shepherd 1991). The cloned

high-GPC Gpc-B1 from var. dicoccoides (Uauy et al.

2006) was found to be associated with reduced grain

weight and yield penalties and a dedicated breeding

effort is suggested to ameliorate its potential nega-

tive effect (Brevis and Dubcovsky 2010). Thus the

identification of genetic sources of elevated protein

content without negative pleiotropic effects would

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be useful for improving GPC and grain yield

simultaneously.

Our objective was to evaluate the genetic influence

of variation in grain yield components (grain yield

per spike, thousand-kernel weight and kernel number

per spike) on protein content in a recombinant inbred

line (RIL) population derived from a cross between

two elite durum wheat cultivars. To investigate the

genetic relationship between QTL for protein content

and other traits, a statistical procedure for estimating

of adjusted values in a covariance analysis was

combined with the QTL mapping (Kearsey and Pooni

1996). If protein content is genetically correlated with

a secondary trait like thousand-kernel weight (TKW),

the adjusted values of protein content to TKW allows

the analysis of protein content independently of

variation in TKW. The protein content adjusted to the

other yield component traits can be analyzed by QTL

mapping in the same way as the original protein

content. By comparing unadjusted and adjusted QTL

for protein content, the genetic interdependencies

between GPC and yield components can be identified

at the level of individual QTL.

This study was initiated to (a) determine the

genetic basis of the relationship between grain

protein content and grain yield components through

QTL analyses, and (b) identify molecular markers

associated to high protein content QTL without

decreasing grain yield.

Materials and methods

Genetic materials and field experiments

A set of 120 RILs was developed from a cross

between two elite durum wheat cultivars (Svevo and

Ciccio) by advancing random individual F2 plants to

the F7 generation by the single-seed descent proce-

dure. After the last generation of selfing, each line

was bulk-harvested to provide seed for replicated

trials and for DNA extraction. The parental lines are

two commercial cultivars widely grown in Italy

contrasting in yield potential and protein content:

Ciccio has higher yield potential and TKW while

Svevo has a higher GPC. The parents and the RILs

were evaluated for GPC and grain yield components

in replicated trials in southern Italy at three locations

(Valenzano, Gaudiano and Foggia) in 2006 and two

locations (Valenzano and Foggia) in 2007. A ran-

domized complete block design with twelve replica-

tions and plots consisting of 1-m rows, 30 cm apart,

with 80 germinating seeds per plot, was used in all

the field experiments. During the growing season,

10 g of nitrogen per m2 and standard cultivation

practices were adopted. Plots were hand-harvested at

maturity and grain yield per spike (GYS) was

determined dividing grain yield per row by the

number of spikes per row (about 70–80 spikes). GPC,

expressed as a percentage of protein on a dry weight

basis, was determined on a 2-g sample of whole-meal

flour using near-infrared reflectance spectroscopy. A

15-g seed sample per plot was used to determine

TKW.

Linkage map

The Svevo 9 Ciccio RI lines were characterised with

259 simple sequence repeat (SSR) and expressed

sequence tag (EST)-SSR markers (Gadaleta et al.

2009) and subsequently with 638 Diversity Arrays

Technology� (DArT) markers. DArT markers (Wenzl

et al. 2004) were generated by Triticarte Pty Ltd

(http://www.triticarte.com.au/). DNA samples from

each RIL were subjected to PstI/TaqI digestion and size

purification, and probed against the durum DArT array.

Individual genotypes were scored for the presence or

absence of hybridization signal based on fluorescence

signal intensity. Each DArT marker was designated with

the Triticarte name (wPt-) or with a preliminary name

(D_clone number). JoinMap 4.0 (Van Ooijen and

Voorrips 2001) was used to integrate DArT markers into

the established Svevo 9 Ciccio map (Gadaleta et al.

2009), and to evaluate the quality of the DArT data. Data

were screened for missing data points, segregation dis-

tortion, and similarity between markers or individuals,

and those markers with a high level of segregation dis-

tortion or missing values were removed. DArT inte-

gration into chromosome maps was carried out at a LOD

threshold of 3.0. Final mapping was performed by

combining two or more linkage groups belonging to the

same chromosome if the distance was less than 40 cM.

Statistical analysis and QTL detection

Standard procedures for analysis of variance for each

trait were carried out with MSTAT-C software.

Genetic variance (rG2 ) and broad-sense heritability

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(hB2 ) were obtained using the variance component

estimates. Pearson phenotypic correlation coefficients

were calculated between GPC, GYS, TKW and

kernel number per spike (KNS). The estimation of

adjusted values of protein content to grain yield per

spike (GPC/GYS), thousand-kernel weight (GPC/

TKW) and on kernel number per spike (GPC/KNS)

were calculated by covariance analysis and expressed

with the following relationship: Ym = Yj ? b(Xm

- Xj), where Ym and Yj are the mean protein content

and the value of individual genotypes, respectively;

Xm and Xj are the mean GYS (or TKW, or KNS) and

the value of individual genotypes, respectively, and

b is the regression coefficient between the traits. The

Inclusive Composite Interval Mapping (ICIM)

method (Li et al. 2007) was employed for QTL

mapping using QGene 4.0 software, an updated

version for PowerPC hardware (Joehanes and

Nelson 2008). A scanning interval of 2 cM between

markers and putative QTL with a window size of

10 cM was used to detect QTL. The number of

marker cofactors for background control was set by

forward regression with a maximum of five con-

trolling markers. Putative QTL were defined as two

or more linked markers associated with a trait at

LOD C 3. Suggestive QTL at the sub-threshold

2.0 \ LOD \ 3.0 values were reported for further

investigation. For main QTL effects, positive and

negative signs of the estimates indicate the contri-

bution of Svevo and Ciccio respectively toward

higher trait value. The proportion of phenotypic

variance explained by a single QTL was determined

by the square of the partial correlation coefficient

(R2). Graphical representation of linkage groups and

QTL was carried out using MapChart 2.2 software

(Voorrips 2002).

Results

Field trait analysis

GPC and yield components (GYS, TKW and KNS)

were evaluated in southern Italy at three locations

(Valenzano, Gaudiano and Foggia) in 2006 and two

locations (Valenzano and Foggia) in 2007. The

environments, genotypes and environment 9 geno-

type interaction items were all significant (P \ 0.01)

for each trait in the combined analysis across

environments (data not shown), therefore data were

interpreted separately for each environment. The

means and ranges of parentals (Svevo and Ciccio)

and RILs, variance components and broad-sense

heritability estimates for GPC and grain yield

components in each environment are presented in

Table 1. The parental lines had significantly differ-

ent GPC values in each environment, with Svevo

always higher than Ciccio. Correlations of GPC

means among environments were significant and

ranged from 0.35 to 0.74 (Supp. Table 2), consistent

with the strong environmental influence on pheno-

typic expression of GPC. Large segregation was

observed in each of the five trials, with phenotypic

means of RILs being normally distributed without

significant skewness or kurtosis. The RIL popula-

tion means were near the mid-parental values.

Differences in mean values and variances of paren-

tal lines and RIL population were observed among

the trials conducted in different years and locations,

very likely due to the different environmental

factors. Estimates of broad-sense heritability (geno-

type mean basis) of GPC ranged from 0.47 to 0.71

in the five environments.

The phenotypic data for yield components high-

lighted a considerable difference between the parents

and a significant genetic variation in the RIL

population for GYS and TKW in all environments.

Except at Foggia 2007, Ciccio had 0.15–0.25 g

higher GYS, and 2.8–7.3 g heavier TKW than Svevo,

while KNS was similar. For the first two traits, the

mean RIL value was close to the value of the poorest

parental line. For all traits, the range of the RIL

population was much larger than the range of the

parental lines, suggesting that favourable alleles are

present in both parents. For example, the difference

between the lines with the lowest and the highest

GYS was more than twice the difference between

Svevo and Ciccio in all environments. Estimates of

heritability across environments were low for GYS

(0.21–0.40) and KNS (0.27–0.50) and low to mod-

erately high for TKW (0.25–0.76).

GPC was correlated with yield component traits

scored in the same environment (Table 2). As

expected, GPC was negatively correlated with GYS

(r values ranging from -0.27 to -0.58) and with

KNS (r = -0.21 to -0.54) in all environments, and

with TKW only at Valenzano 2006 (r = -0.20) and

Foggia 2007 (r = -0.68).

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Table 1 Means, ranges,

coefficients of variation (CV),

genetic variance (rG2 ) and

heritability (hB2 ) of grain

protein content and grain yield

components in the

Svevo 9 Ciccio population

and parents evaluated in five

environments

Trait Environments

Valenzano 2006 Gaudiano 2006 Foggia 2006 Valenzano 2007 Foggia 2007

Grain protein content (%)

Svevo 13.9 16.3 16.4 12.3 16.4

Ciccio 12.9 15.6 15.6 11.6 15.9

Mean RIL 13.4 16.2 16.1 11.9 16.5

Range (11.1–15.4) (13.8–18.8) (13.9–18.9) (10.5–14.4) (14.3–19.9)

CV 4.17 3.42 3.34 4.12 3.41

rG2 0.377 0.774 0.546 0.366 1.210

hB2 0.47 0.64 0.59 0.58 0.71

Grain yield per spike (g)

Svevo 1.46 1.12 1.16 1.53 0.73

Ciccio 1.61 1.31 1.35 1.78 0.72

Mean RIL 1.48 1.19 1.13 1.54 0.65

Range (1.14–1.85) (0.91–1.47) (0.79–1.42) (1.13–1.94) (0.37–0.98)

CV 8.48 12.44 11.82 8.37 18.22

rG2 0.015 0.014 0.010 0.018 0.014

hB2 0.39 0.23 0.26 0.21 0.40

Thousand-kernel weight (g)

Svevo 46.5 44.5 46.4 51.6 32.2

Ciccio 49.7 49.2 53.7 54.4 36.3

Mean RIL 46.9 44.9 48.7 51.7 32.7

Range (42.9–53.0) (35.4–53.0) (42.0–54.4) (43.8–60.1) (24.6–40.4)

CV 4.55 4.02 5.20 3.28 6.14

rG2 2.574 8.010 4.377 12.864 13.392

hB2 0.25 0.65 0.31 0.76 0.70

Kernels per spike (n)

Svevo 30.7 26.0 20.7 25.3 22.3

Ciccio 32.0 26.0 28.0 31.0 19.7

Mean RIL 31.8 26.4 23.7 29.9 20.5

Range (23–38) (18–33) (17–31) (21–38) (14–19)

CV 9.02 10.05 11.35 8.17 14.28

rG2 6.398 4.326 4.740 9.600 6.070

hB2 0.34 0.29 0.27 0.50 0.36

Table 2 Correlation

coefficients between grain

protein content and grain yield

components in five

environments in the

Svevo 9 Ciccio RIL mapping

population

*, ** and ***: significant

differences at 0.05P, 0.01P and

0.001P, respectively

Trait Grain protein content

Valenzano

2006

Gaudiano

2006

Foggia

2006

Valenzano

2007

Foggia

2007

Grain yield per spike -0.30** -0.26** -0.48*** -0.57*** -0.58***

Thousand-kernel

weight

-0.20* -0.01 -0.01 -0.04 -0.68***

No. of kernels per

spike

-0.21* -0.25** -0.54*** -0.49*** -0.29**

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The Svevo 9 Ciccio map

QTL were determined based on the genetic map

described in Gadaleta et al. (2009) integrated with

DArT markers. The whole map comprises 132

genomic SSRs, 110 EST-SSRs, 587 DarT markers,

two morphological markers (Glume colour, Bla1, and

spike glaucousness, Ws) and two seed storage protein

loci (Gli-A2 and Gli-B2). Out of 913 loci, 833 loci

assembled in 34 linkage groups assigned to the

chromosomes of the A and B genome and 80

remained unlinked or were removed due to highly

significant segregation distortion. Linkage groups

were assigned to chromosomes by comparing the

markers of the new map to previously published

durum maps (Blanco et al. 2004; Gadaleta et al.

2009) and the hexaploid wheat SSR consensus map

(Somers et al. 2004). Four chromosomes (1B, 2A, 6A

and 6B) were assembled in a single linkage group.

The majority of chromosomes was assembled in two

or three linkage groups, while chromosomes 3A, 3B

and 5B were represented by four linkage groups. A

total of 516 (62%) markers were localized on the B

genome, while 317 (38%) mapped to the A genome.

The whole map covered 1,716 cM with an average

chromosome length of 122.6 cM. The B genome

accounted for 1,033 cM of genetic distance, with an

average chromosome length of 143.3 cM (from

86.8 cM for chromosome 4B to 164.7 cM for chro-

mosome 2B). The A genome basic map spanned

713 cM with an average chromosome length of

101.8 cM (from 75.2 cM for chromosome 4A to

139.9 cM for chromosome 7A). The number of

markers per chromosome ranged from 17 (5A) to

195 (3B) with an average of 60 markers per

chromosome. Seventy-two DArT markers were

merged into other markers due to highly similar

scoring patterns. For the QTL analysis, cosegregating

DArT markers were removed from the final map and

only one marker was kept for each 1-cM interval.

QTL analysis

The Inclusive Composite Interval Mapping (ICIM)

method as proposed by Li et al. (2007) was employed

for QTL analysis. Putative QTL for GPC and yield

components in individual environments are listed in

Supp. Table 2 and map positions are reported in

Fig. 1. Only QTL with LOD values C 3.0 were

considered; suggestive QTL at the sub-threshold

2.0 \ LOD \ 3.0 values were reported for further

investigation.

ICIM identified ten QTL for unadjusted GPC

values on chromosome arms 1AS, 1AL, 2AS (two

loci), 2BL, 3BS, 4AL, 4BL, 5AL and 6BS, account-

ing for a large proportion of the total phenotypic

variation of GPC scored in the five environments. Six

alleles increasing GPC were contributed by the high-

GPC parent Svevo, and four alleles increasing GPC

by the low-GPC parent Ciccio. The allelic effect

changed greatly in the different environments, rang-

ing from non-significant values to 0.65 protein

content unit (QTL on 3BS at Foggia 2007). The

amount of phenotypic variation (R2) explained by

individual QTL in each environment ranged from

13.2 to 40.2%. One QTL was significant in all but one

environment, two were significant in two or three

environments, and six were significant in one out of

five environments. QTL on 3BS and 4AL had the

greatest overall effects, explaining up to 40.2 and

17.6% of phenotypic variation, respectively.

When protein content data were adjusted to grain

yield components (GPC/GYS, GPC/TKW and GPC/

KNS), QTL analysis confirmed five QTL on chro-

mosome arms 1AL, 2AS (marker interval TC82001-

Xgwm372c), 2BL, 4AL and 5AL to be independent

from GYS, TKW and KNS. On the other hand, three

of the ten QTL, located on chromosome arms 1AS,

2AS (marker interval Xwmc630b-Xwmc453) and

6BS, failed to show significant effects, and the QTL

on 3BS showed a reduced effect, indicating a genetic

association between GPC and yield component traits.

The GPC QTL on 4BL was significant in the GPC/

GYS and GPC/KNS mapping but failed to show

Fig. 1 Genetic map and QTL for grain protein content (GPC),

grain yield per spike (GYS), thousand-kernel weight (TKW),

kernel number per spike (KNS) and for GPC adjusted values on

GYS (GPC/GYS), on TKW (GPC/TKW) and on KNS (GPC/

KNS), detected in 120 RILs derived from crossing the cvs.

Svevo and Ciccio of durum wheat. Map positions are given in

cM. Dotted lines in the chromosome indicate distantly linked

marker. QTL are represented by bars (1 LOD interval). Solidbars represent QTL significant at LOD C 3.0 and diagonalhatch bars represent suggestive QTL at the sub-threshold

2.0 \ LOD \ 3.0 value. The up and down arrows indicate

additive effects associated with an increased or decreased

effect from Svevo alleles. QTL names also indicate the

environment (V06 Valenzano 2006, G06 Gaudiano 2006, FG06Foggia 2006, V07 Valenzano 2007, FG07 Foggia 2007)

c

84 Mol Breeding (2012) 30:79–92

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D_378846D_304810TC69046aXcfd15D_347559wPt-8627TC87195bTC85294bXwmc597bwPt-1684Xbarc8Xwmc406Xwmc329Xwmc500cD_306051Xwmc326aXbarc83aD_305821Xcfa2129aCA679329awPt-0202CA651264BJ318987Xgwm153CA679329bBE419757wPt-9032Xgwm268Xgwm124Xgwm544bD_306155wPt-5281TC81688D_521783Xwmc44CA677684bXgwm659wPt-1313D_312188TC88378Xwmc728

1,35,02,31,62,04,32,83,02,31,11,40,61,91,73,11,44,28,84,60,63,71,81,76,44,82,41,31,90,57,31,03,4

15,8

5,1

11,0

2,52,56,22,11,2

TK

W_F

06

GY

S_F

07

TK

W_F

07

TK

W_V

06

TK

W_G

06

TK

W_V

07

KN

S_V

07

1B

wPt-4533Xwmc326cD_304730D_117417D_306000D_348404D_344011Xwmc382bTC90641TC90640Xgwm636Xwmc630bwPt-9624D_378243wPt-7026CA658758

Xwmc453D_373205D_346386D_344422

TC82001

Xgwm339

Xgwm95

Xgwm372cXcfa2164lXgwm895

Xgwm304

Xgwm328TC81096

3,33,31,01,31,21,62,20,81,51,83,05,90,9

16,7

3,1

14,3

3,24,51,2

8,7

10,2

9,5

8,1

3,43,05,9

4,84,4

GP

C_F

07

GY

S_V

06

GY

S_V

07

TK

W_V

07

KN

S_V

06

KN

S_V

07

GP

C_G

06

GP

C_F

06 GP

C_V

07

GP

C/G

YS

_G06

GP

C/G

YS

_F06

GP

C/T

KW

_G06

GP

C/T

KW

_V07

GP

C/T

KW

_F07

GP

C/K

NS

_G06

GP

C/K

NS

_V07

2A

TC69046bD_374903BJ237020aBQ607256Xgwm835cTC85294aXgwm136TC87195awPt-2527wPt-3870D_378503Ws-1AD_378601TC95235Bla1-1A

3,32,00,91,22,80,60,71,61,20,45,00,84,22,6

GP

C_F

06G

YS

_F06

GY

S_V

07

KN

S_F

06

CA741577aCA703897D_343731Xbarc28bwPt-9474D_305107wPt-8773D_306680TC70788

BQ838884

D_303843D_521050

TC84551

3,51,62,01,70,82,54,55,5

9,2

12,0

2,06,3

Xgwm633D_346395D_376404wPt-2847Xwmc254Xgwm99TC91645Xgwm497CA594434a

3,34,10,60,90,82,21,14,3

GP

C_G

06

GP

C_F

06

GP

C/G

YS

_G06

GP

C/T

KW

_G06

GP

C/T

KW

_F06

GP

C/K

NS

_G06

GP

C/K

NS

_F06

KN

S_G

06

KN

S_V

07

1A

CA677684awPt-6204D_379652D_311272D_117472TC89014bXwmc206cD_344745Xwmc206d

TC77302wPt-1888wPt-4545wPt-0398Xgwm162

5,81,41,30,60,60,74,1

12,2

12,5

1,30,18,11,6

TK

W_G

06

Xwmc527b

D_408393Xwmc428TC74823

D_313026D_408114D_304884D_344396

11,2

3,21,4

14,3

0,90,51,5

GP

C/K

NS

_F07

TK

W_F

07

Xgwm155

Xwmc1538,6

TC80528aD_380225D_345146D_344554TC91009TC88560

3,61,60,82,43,2

3A

D_521287D_519751D_304576wPt-0302wPt-3536Xgwm493wPt-1081Xwmc430aD_520704D_520391wPt-7984D_521656TC89014aXgwm389

2,51,65,30,11,22,03,73,00,61,21,02,33,0

GP

C_V

06

GP

C_F

06

GP

C_V

07

GP

C_F

07

GP

C/G

YS

_V06

GP

C/G

YS

_F06

GP

C/G

YS

_F07

GP

C/T

KW

_V06

GP

C/T

KW

_F06

GP

C/T

KW

_V07

GP

C/K

NS

_V06

GP

C/K

NS

_F06

GP

C/K

NS

_F07

GY

S_V

06G

YS

_G06

GY

S_F

06

GY

S_V

07

GY

S_F

07

TK

W_G

06T

KW

_F06

TK

W_F

07

KN

S_V

06

KN

S_F

06

Xgwm566D_344962D_520079wPt-6604D_348559CA499601BJ274952BJ253815a

6,41,42,70,31,76,50,4

wPt-7502

D_521613

Xwmc527a

15,5

7,0

D_519841Xgwm108aD_520854TC87011D_520293wPt-8480D_346697D_520512D_519748D_520335

D_521896wPt-5947D_520520Xgwm299D_519868D_348421D_306332D_379865wPt-2416D_520241wPt-0405wPt-4412Xgwm340

5,80,81,70,92,10,92,83,47,2

14,83,00,64,83,43,31,24,71,10,90,81,11,8

3B

KN

S_V

07

15,0

10,9

13,1

Xwmc489aXwmc764BQ170801Xwmc382awPt-0100Xgwm5aXwmc661Xgwm210wPt-5738wPt-2106BJ227727CA695634D_520964Xwmc597cwPt-7695wPt-3561D_345364

1,81,60,90,31,61,01,52,24,51,17,41,12,03,7

3,2

TC82742

Xgwm132c

TC71236

Xgwm55Xbarc18

CA724675CA594434b

Xwmc441Xwmc363

wPt-8569Xwmc175TC72953wPt-9350D_521221D_304657BJ253815bCA681959bXwmc332D_522006TC89976b

D_373432

CA662535BU099658D_305569wPt-4917D_380269wPt-3378D_305231

5,2

6,8

4,3

6,62,56,80,9

9,00,82,92,31,93,45,13,03,82,85,4

8,5

1,92,11,00,62,71,4

GP

C_G

06

GP

C/T

KW

_G06

GP

C/K

NS

_G06

GY

S_F

07

GP

C/G

YS

_G06

2B

Mol Breeding (2012) 30:79–92 85

123

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BQ805704

D_379339Xgwm334wPt-9679D_346010CA681959awPt-3965wPt-7486wPt-2573Gli-A2TC85303bTC84464BE427655CA716967TC85125NP234852BQ246417TC84481aXwmc630aXgwm1009BJ261821BF483631Xgwm132bXwmc335

13,32,32,60,74,82,70,90,53,61,40,50,20,40,30,20,70,80,61,12,3

18,24,9

10,0

GY

S_V

06

GY

S_F

06

KN

S_F

066A

Xwmc430bD_311069wPt-3774D_304934wPt-9532D_349417Xgwm613D_379317wPt-2095D_346335D_304383D_381689

wPt-8814Gli-B2Xgwm508CA741546TC85303aXgwm132aBJ236800Xwmc597dTC85035D_117419TC85037TC65966TC84481bTC80528cD_379092Xbarc68wPt-8721D_306023BJ213673bXwmc398Xgwm58TC101037CA594434dwPt-5037Xgwm88

wPt-1730

wPt-3581

Xbarc178

5,91,44,73,12,14,11,6

8,32,12,51,1

16,5

11,62,51,41,50,23,20,71,34,83,61,52,43,01,81,48,81,34,14,15,10,43,13,34,6

13,6

6,5

15,3

GP

C_F

06G

PC

/GY

S_F

07

GP

C/T

KW

_F07

GP

C/K

NS

_G06

GP

C/K

NS

_F06

TK

W_F

06

6B

D_376852

BJ262177d

Xgwm601

10,3

6,6G

PC

_V06

Xwmc161

Xgwm637wPt-4660D_408305Xgwm937Xgwm894CA499463

8,44,21,71,80,23,6

D_377680D_380782wPt-2542wPt-5249wPt-7926Xgwm274aXwmc232wPt-0105wPt-4596TC85050D_346402Xgwm160wPt-9196Xwmc219D_305944D_373792wPt-2151wPt-5489Xwmc500aXgwm350BQ169752

0,62,50,42,33,61,61,41,64,61,40,61,41,01,12,40,61,02,61,26,4

KN

S_V

06

KN

S_V

07

4A

GP

C_G

06G

PC

_F06

GP

C_V

07

GP

C/G

YS

_V06

GP

C/G

YS

_G06

GP

C/G

YS

_F06

GP

C/G

YS

_V07

GP

C/T

KW

_V06

GP

C/T

KW

_G06

GP

C/T

KW

_V07

GP

C/K

NS

_V06

GP

C/K

NS

_G06

GP

C/K

NS

_V07

GY

S_G

06

KN

S_F

07

TC77481Xwmc430c4,1

D_520255

Xwmc617D_348732TC80528bXwmc710TC67416

Xgwm368CA694714wPt-3991D_305166Xwmc48D_345668Xwmc206bD_346351TC69937CA663888

D_310555wPt-6209Xgwm251

15,8

1,81,33,91,3

16,1

2,11,70,62,81,01,37,94,83,5

11,7

2,02,7

KN

S_G

06G

PC

_V06

GP

C/G

YS

_V06

GP

C/G

YS

_V07

GP

C/K

NS

_V06

GP

C/K

NS

_V07

GP

C/K

NS

_F07

TK

W_V

06

TK

W_V

07

4B

Xwmc489b

TC91851

Xwmc705

BJ262177a

D_304896

D_345862D_373063Xbarc141Xgwm156

10,0

6,7

8,6

7,2

8,3

3,72,54,4

Xgwm330

D_348667D_379033D_305101D_343880D_521528

Xgwm126

21,0

6,62,50,10,18,2

GP

C_V

06

GP

C_F

06

GP

C/G

YS

_V06

GP

C/K

NS

_V06

GP

C/K

NS

_V07

5A

Xgwm234

wPt-1261wPt-9666

17,1

0,9

wPt-6263

D_408111

Xgwm499

Xwmc206a

Xwmc415a

wPt-3661D_344317

Xgwm408D_304736

Xwmc235

D_379898D_373098D_349207

Xcfd86

12,0

11,1

9,8

8,8

21,6

1,8

10,2

1,5

8,7

9,01,80,3

10,2

BJ306922TC86533wPt-7708wPt-4723D_377383D_305419

4,91,91,00,31,4

5B

CA707573

Xgwm544a

11,5

D_345958Xwmc479D_304566

wPt-7188D_311342Xgwm60TC67645wPt-7785D_373816D_346237D_379889D_345315wPt-4345TC77994

TC77993

4,40,4

17,7

2,33,24,9

4,3

7,04,01,41,41,32,98,2

GY

S_G

06

CA668775CA668788bwPt-4877

D_345804wPt-8399

1,21,1

13,0

0,9

D_377804

Xgwm332

Xcfd6Xgwm63wPt-0961D_344975BJ262177e

Xcfa2040wPt-3439D_305120BJ262177bD_522372D_310811D_344116Xgwm1061aTC92445

7,6

11,5

2,62,60,91,6

11,6

3,01,24,83,51,01,42,54,5

7A

D_379773wPt-8920wPt-7975wPt-3147wPt-0276Xwmc606Xwmc597a

CA594434c

Xgwm400

BJ239878

1,92,60,82,32,46,8

8,7

9,1

8,2

Xbarc83b

Xgwm333AL825137

wPt-6498D_520428TC69177TC69176wPt-2305wPt-3730wPt-4025CA668788aTC95791D_408422wPt-1817D_344276Xwmc540BJ213673aD_304875Xwmc517

5,0

4,1

8,5

6,77,61,41,74,40,42,72,60,71,60,61,71,25,66,9

GP

C_F

07

GY

S_V

06

GY

S_F

07T

KW

_F07

GP

C/K

NS

_F07

D_345322wPt-5892Xwmc311D_379105Xgwm611wPt-2356Xwmc581D_378041TC88833Xgwm783Xgwm577D_372949wPt-5138wPt-0884D_408137TC70722Xwmc500bXgwm1061b

6,3

9,63,91,32,34,90,41,02,35,54,43,11,01,01,21,80,9

7B

Fig. 1 continued

86 Mol Breeding (2012) 30:79–92

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significant effects when GPC was adjusted to TKW.

Two additional QTL were detected at Foggia 2007 on

chromosome arms 6BL and 3AL in the GPC/TKW

and GPC/KNS mapping, respectively.

Eight chromosomal regions were detected for GYS

(Supp. Table 2); one of them (3BS) was significant in all

environments. This stable and strong QTL explained up

to 31.7% of the variation for GYS and the positive allele

was from Ciccio, the parental line with the highest GYS.

The GYS QTL on 2AS and 7BL were significant in two

environments, and the positive alleles were from Ciccio

and Svevo, respectively. The other five QTL for GYS

were significant in one environment and the proportion

of phenotypic variation explained by these QTL ranged

from 11.7 to 30.0%.

Of the eight QTL detected for TKW, the QTL located

on 1BL and 3BS were significant in three and two

environments, respectively, and the other six QTL

located on chromosome arms 2AS, 3AL (two loci),

4BL, 6BL and 7BL were significant only in a single

environment. Each QTL had an additive effect of

0.93–1.72 g and explained 11.3–20.8% of the variation

of the trait. Nine QTL, located on chromosome arms

1AS, 1AL, 1BL, 2AS, 2BS, 3BS, 4AL (two loci) and

4BS, had an effect on KNS, three out of which were

significant in two environments, while the remaining six

were significant only in one environment. These nine

QTL accounted for 11.7–43.4% of the phenotypic

variation with the positive effect attributed to five Svevo

alleles and four Ciccio alleles.

Some co-locations occurred between QTL for

grain yield component traits and QTL for GPC on

chromosome arms 1AS, 1AL, 2AS, 3BS, 4AL and

4BL (Fig. 1). In most cases, the QTL was ‘stable’

only for one of the traits. On chromosome arm 3BS,

QTL were detected for the four traits.

Discussion

One of the major objectives of wheat breeding

programs has been to increase GPC while maintain-

ing grain yield of lines to be released for commercial

production. The improvement of both traits has been

hampered by the generally negative relationship

between GPC and grain yield components in wheats,

as well as in barley, maize, oat and sorghum (see

review by Simmonds 1995; Feil 1997; Oury et al.

2003; Oury and Godin 2007). The present study was

designed to explain the genetic basis of the interde-

pendence of GPC and yield components in adapted

Italian durum wheat cultivars. The primary compo-

nents of grain yield per area are grain yield per spike

(GYS) and number of spikes per unit of area. The

latter depends on sowing density and is strongly

affected by environmental factors and agro-technique

practices. The basic components of GYS are the

number of kernels per spike and the kernel weight

which are generally negatively correlated each other

(Kuchel et al. 2007; McIntyre et al. 2010). In this

study we evaluated grain yield components and GPC

in five field trials with twelve replicates each because

of the expected variability in phenotypic data due to

the remarkable environmental influences on the

examined traits. Yield components in the Svevo 9

Ciccio RIL population were largely influenced by

QTL distributed among all chromosomes excluding

5A and 5B. GYS QTL were mapped in the same

positions as those for TKW on chromosome arms

1BL, 2AS (marker interval Xwmc630b-Xwmc453),

3BS and 7BL, and for KNS on chromosome arms

1AS, 2AS (marker interval Xwmc630b-Xwmc453)

and 3BS, suggesting that kernel size and kernel

number may directly contribute to yield in those

genomic regions. Considering all three traits, a QTL

for GYS, TKW and KNS mapped to the same marker

intervals on 1BL, 2AS and 3BS. A QTL for increased

TKW and decreased KNS mapped in the marker

intervals on 1BL and 2AS, indicating a partial

compensating effect on GYS. In previous studies,

grain yield QTL were reported on almost all wheat

chromosomes (Borner et al. 2002; Huang et al. 2003,

2004, 2006; Marza et al. 2005; McCartney et al.

2005; Quarrie et al. 2005; Kuchel et al. 2007; Kumar

et al. 2007; Maccaferri et al. 2008; McIntyre et al.

2010; Zheng et al. 2010). Most of these studies

identified several grain yield QTL; however, the

majority of these QTL were detected only in a single

environment. When a QTL was detected in more than

one environment, variation in the magnitude of its

effects was typically observed (Huang et al. 2003,

2004; Kumar et al. 2007; Kuchel et al. 2007,

Maccaferri et al. 2008; McIntyre et al. 2010).

In the present study, the methodology of estimat-

ing adjusted values in a covariance analysis was used

to assess the interrelationships between GPC and

grain yield components in elite durum wheat culti-

vars. The close relationship between GPC and yield

Mol Breeding (2012) 30:79–92 87

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component traits was reflected by the negative

correlation between the examined traits (Supp.

Table 1) and by the reduction of variance when

GPC values were adjusted to yield component traits

(GPC/GYS, GPC/TKW and GPC/KNS) (Supp.

Table 3). Accordingly, three of the initially ten

mapped QTL for GPC located on 1AS, 2AS (marker

interval Xwmc630b-Xwmc453) and 6BS failed to

show significant effects in the GPC adjusted data.

These QTL could represent genes involved in carbo-

hydrate synthesis with indirect effects on grain

protein content. As protein and carbohydrate contents

are usually expressed in relation to total grain mass,

the increase of one compound implies a decrease of

the other one. Two additional QTL for GPC detected

on chromosome arms 4BL and 5AL failed to show

significant effects in the adjusted GPC/TKW. The

GPC QTL on 4BL was associated with a QTL for

TKW. The allele for low GPC and the one for high

kernel weight were both contributed by Svevo,

therefore this QTL is likely associated with the

concentration of the protein in a reduced carbohy-

drate content of smaller kernels. However, four

additive QTL on 1AL, 2AS (marker interval

TC82001-Xgwm372c), 2BL and 4AL still showed

significant effects in the mapping of adjusted GPC

values to all grain yield components traits. These

QTL should represent genes that influence GPC

independently from variation in the grain yield

components. The QTL on 3BS showed reduced but

still significant effects in the adjusted GPC mapping,

indicating that the effects of this QTL observed in the

unadjusted GPC mapping was partially due to genetic

effects on yield components. These five QTL with

direct genetic effects on GPC are of special interest in

wheat breeding since they would allow an increase in

GPC without a concomitant decrease in grain yield.

Two additional QTL for GPC located on 3AL and

6BL that were not apparent in the unadjusted GPC

mapping could be detected in the adjusted GPC/KNS

and GPC/TKW mapping, respectively. The likeli-

hood of detecting a QTL in the QTL mapping is

dependent on the ratio between the variance caused

by the QTL’s effect and the total variance of the trait

(Landeer and Botstein 1989). The reduction in

variance observed after adjusting GPC to yield

component traits (Supp. Table 3) could allow the

mapping of QTL with reduced effects, indicating that, in

the case of correlated traits like GPC and yield

components, adjusted GPC mapping could be used to

detect additional QTL that would remain below the

detection threshold in unadjusted GPC mapping.

We compared the genomic regions involved in the

quantitative expression of GPC found in the

Svevo 9 Ciccio RIL population with the map position

of QTL found in different genetic materials. The

influence of group-2 chromosomes on GPC control

was first reported by Joppa and Cantrell (1990) using

durum wheat var. dicoccoides chromosome substitu-

tion lines. The GPC QTL detected on the proximal

region of chromosome arm 2AS is located in a similar

position to the QTL found by Blanco et al. (2006) in the

durum backcross line 3BIL-85 (Latino 9 dicocco-

ides) and to the QTL detected by Suprayogi et al.

(2009) in the Canadian durum line DT695. These

QTLs are closely linked to the SSR marker Xgwm339

common in the three different mapping populations.

The GPC locus was not associated to grain yield

components in the Svevo 9 Ciccio population, while

it was associated to low grain yield in the

DT695 9 Strongfield (Suprayogi et al. 2009) and

Latino 9 3BIL85 (Blanco et al. 2006) populations.

Groos et al. (2003) and Prasad et al. (2003) also

reported a protein content QTL on chromosome arm

2AS; although based on some common markers, the

QTL found in the our study should be considered

different as it is localized on 2AS near the centromere,

whereas Groos et al. (2003) and Prasad et al. (2003)

mapped the QTL in the distal region of 2AS more than

45 cM distant from the common markers Xgwm122–

2A and Xgwm515–2A.

The GPC QTL detected on chromosome arm 2BL

was also found by Suprayogi et al. (2009) in the

DT695 9 Strongfield population flanked by the same

SSR marker Xwmc332. This QTL was also not

associated with grain yield components in the

Svevo 9 Ciccio population, whereas it was associ-

ated with low grain yield in the DT695 9 Strongfield

population (Suprayogi et al. 2009). The QTL detected

on chromosome arm 4AL, which explained

7.9–17.6% of the phenotypic variance, flanked by

the marker Xgwm601, is in a similar position to a

QTL found by Groos et al. (2003), Prasad et al.

(2003) and Raman et al. (2009), associated with

Xgwm397. The markers Xgwm601 and Xgwm397 are

very close on the wheat consensus map (http://

wheat.pw.usda.gov/GG2/index.shtml; Somers et al.

2004). Interestingly the three QTL were not

88 Mol Breeding (2012) 30:79–92

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associated with QTL for reduced grain yield com-

ponent in any of the four different mapping popula-

tions. A QTL for GPC located on the long arm of

chromosome 5A was repeatedly detected by Snape

et al. (1995), Zanetti et al. (2001), Singh et al. (2001)

and Blanco et al. (2002), but the different markers

used in the above-mentioned studies do not allow the

alignment of 5AL chromosome maps and the QTL

comparison.

As far as we know, no QTL for GPC has been

reported on the long arm of chromosome 1A and on

the short arm of chromosome 3BS where two QTL

for GPC were detected in the present study.

Conclusions

Grain protein content, grain yield and many other

agronomically important traits are complex traits

with low to intermediate heritability and strong

genotype 9 environment interaction; early genera-

tion selection for such traits based on phenotypic

evaluation of single plants and/or in single environ-

ments has generally not been effective. Molecular

markers and genetic maps are useful tools both for

understanding the genetic control of GPC and the

relationships between GPC and yield components by

the QTL dissection of the GPC and yield-related

traits, and for developing improved cultivars by

marker-based selection. Combining QTL for GPC

with those for high yield and other agronomically

important traits (e.g. plant height and heading date)

will however provide information on the genetic

bases of these traits in the breeding material. In

particular, QTL analysis aiming to detect useful QTL

alleles for a trait to be transferred in commercial lines

by marker-assisted selection should consider whether

those regions contain QTL for other traits that will

affect the total performance of the genotypes.

In the current study GPC was negatively correlated

with grain yield components. Ten independent geno-

mic regions involved in the expression of GPC were

detected, six of which were associated with QTL for

one or more grain yield components. QTL alleles

with increased GPC effects were associated with

QTL alleles with decreased effects on one or more

yield component traits, or vice versa (i.e. the allelic

effects were in opposite direction). A survey of the

literature reporting results on QTL for GPC and grain

yield components simultaneously assessed on the

same population (Zanetti et al. 2001; Borner et al.

2002; Blanco et al. 2002, 2006; Groos et al. 2003;

Zhang et al. 2008; Raman et al. 2009; Suprayogi et al.

2009; Brevis and Dubcovsky 2010; Sun et al. 2010)

revealed a rather similar trend and explain the

negative correlation between GPC and grain yield

in both wild and cultivated germplasm. Since a QTL

usually spans over 20–25 cM length, it is hard to

ascertain if a single gene with pleiotropic effects or

different loci within the linkage group are responsible

for the different traits. More detailed genetic analyses

on much larger populations are needed to distinguish

between clusters of linked genes and single genes

with pleiotropic effects. On the other hand, physio-

logical and biochemical analyses on appropriate

genetic materials, such as near-isogenic lines for

GPC, are needed to ascertain if the high protein

concentration of wild and cultivated genotypes is

primarily due to genes for high GPC instead of loci

for low grain yield. In any case, the present study, as

well as the works cited above, also reported several

QTL for GPC without effects on grain yield and/or

grain yield components. Most of these QTL were

detected only in a single environment and/or in

individual population and they can hardly be

employed in wheat breeding programs with success.

Some major QTL for GPC were detected in a

number of environments and populations, such as the

cloned gene Gpc-B1 on 6BS (Joppa et al. 1997;

Mesfin et al. 1999; Chee et al. 2001; Blanco et al.

2002; Olmos et al. 2003; Brevis and Dubcovsky

2010), the QTL on 2AS (present study; Blanco et al.

2006; Suprayogi et al. 2009), 2BL (present study;

Suprayogi et al. 2009), 3AS (Groos et al. 2003; Sun

et al. 2010), 4AL (present study; Prasad et al. 2003;

Groos et al. 2003; Raman et al. 2009), 7AS (Borner

et al. 2002; Blanco et al. 2002; Turner et al. 2004;

Suprayogi et al. 2009) and 7BL (Blanco et al. 2006;

Zhang et al. 2008; Suprayogi et al. 2009). These QTL

were not associated with grain yield components in

some environments while in others the same QTL

showed an association with reduced yield, thus

indicating the influence of genotypes and environ-

mental factors on the effect of the GPC QTL.

Therefore, the utilization of these QTL in breeding

programs always needs additional studies to identify

appropriate genetic backgrounds where the GPC loci

are expressed without penalties on grain yield, and

Mol Breeding (2012) 30:79–92 89

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the evaluation of the breeding lines in multi-environ-

mental trials. Alternatively, the breeder should

choose the right balance between grain protein

content and grain yield according to economic factors

and the demand of the wheat market.

Acknowledgments The research project was supported by

grants from Ministero dell’Istruzione, dell’Universita e della

Ricerca, projects ‘FISR’ and ‘AGROGEN’.

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