Research
Can genetic variability for nitrogen metabolism in the developing
ear of maize be exploited to improve yield?
Rafael A. Cañas1, Isabelle Quilleré1, André Gallais2 and Bertrand Hirel1
1
Adaptation des Plantes à leur Environnement, Unité Mixte de Recherche 1318, Institut Jean-Pierre Bourgin, Institut National de la Recherche Agronomique (INRA), Centre de Versailles-
Grignon, RD 10, F-78026 Versailles, France; 2Station de Génétique Végétale du Moulon, Institut National de la Recherche Agronomique, Université de Paris Sud, Institut National Agronomique
Paris Grignon, Ferme du Moulon, F-91190 Gif ⁄ Yvette, France
Summary
Author for correspondence:
Bertrand Hirel
Tel: +33 1 30 83 30 89
Email: [email protected]
Received: 13 September 2011
Accepted: 5 January 2012
New Phytologist (2012) 194: 440–452
doi: 10.1111/j.1469-8137.2012.04067.x
Key words: ear, genetics, maize, nitrogen
(N), quantitative trait loci (QTLs), variability.
• Quantitative trait loci (QTLs) for the main steps of nitrogen (N) metabolism in the developing ear of maize (Zea mays L.) and their co-localization with QTLs for kernel yield and putative
candidate genes were searched in order to identify chromosomal regions putatively involved
in the determination of yield.
• During the grain-filling period, the changes in physiological traits were monitored in the cob
and in the developing kernels, representative of carbon and N metabolism in the developing
ear. The correlations between these physiological traits and traits related to yield were examined and localized with the corresponding QTLs on a genetic map.
• Glycine and serine metabolism in developing kernels and the cognate genes appeared to be
of major importance for kernel production. The importance of kernel glutamine synthesis in
the determination of yield was also confirmed.
• The genetic and physiological bases of N metabolism in the developing ear can be studied
in an integrated manner by means of a quantitative genetic approach using molecular markers
and genomics, and combining agronomic, physiological and correlation studies. Such an
approach leads to the identification of possible new regulatory metabolic and developmental
networks specific to the ear that may be of major importance for maize productivity.
Introduction
Nitrogen (N) fertilization and the development of new plant
breeding strategies, such as the production of hybrids, have been
the two most powerful tools for increasing kernel yield (KY), particularly in maize (Moose & Mumm, 2008). Nowadays, a combination of both agricultural and economic constraints means
that farmers must optimize the application of N fertilizers to prevent pollution by nitrates and the release of nitric oxides to the
atmosphere, whilst, at the same time, preserving their economic
margin (Hirel et al., 2007a). Cereal kernels provide 60% of the
world’s nutrition, either directly in the human diet or indirectly
as animal feed. Worldwide, maize is the most important single
crop, comprising 35% of overall cereal production. Recent
improvements in maize yield of c. 1% each year from 1955
onwards have been estimated to be a result of improvements in
agronomic practices (40%) and genetic gains (60%) (Hallauer &
Carena, 2009). Maize is not only recognized as a major crop, but
also as a model species that is well adapted for fundamental
research into the understanding of the genetic basis of yield performance to improve kernel productivity and quality in terms of
nutritional value to feed the world’s population (Hirel et al.,
2007b).
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Therefore, it has become of major importance to select for
maize genotypes that take up and utilize N in the most efficient
way for silage and kernel production. To reach such an objective,
various complementary approaches, including conventional
breeding, molecular genetics, whole-plant physiology and the use
of improved or alternative farming techniques, have been developed (Hirel et al., 2007b, 2011; Moose & Below, 2009). Whatever the mode of N fertilization, an increased knowledge of the
mechanisms controlling plant N economy is essential to improve
nitrogen use efficiency (NUE) and to reduce excessive input of
fertilizers, whilst maintaining an acceptable yield. Using plants
grown under agronomic conditions at low and high N mineral
fertilizer input, whole-plant and physiological studies have been
combined with gene, protein and metabolite profiling. This has
allowed the development of a comprehensive picture depicting
the different steps of N uptake, assimilation and recycling in
maize to produce either biomass in vegetative organs or proteins
in storage organs (Hirel & Lea, 2011).
Moreover, the development of quantitative genetic studies
associated with the use of molecular markers has become a
powerful tool to identify putative candidate genes involved in the
genetic variation of complex physiological traits, such as NUE
(Hirel et al., 2007a). Furthermore, the availability of the maize
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genome sequence (Schnable et al., 2009) and of more detailed
genetic maps allows the precise location of chromosomal regions
and, ultimately, the key genes influencing the expression of
desired traits. In turn, this strategy will be of great potential for
plant breeders to carry out marker-assisted selection for improved
NUE in relation to yield, particularly under low fertilization
input (Ribaud & Hoisington, 1998; Moose & Below, 2009).
Recently, the main steps of N metabolism in the developing
ear of the two maize lines F2 and Io have been characterized
(Cañas et al., 2009). During the kernel-filling period, the changes
in metabolite concentration, enzyme activities and transcript
abundance for marker genes of amino acid synthesis and
interconversion in both the cob and kernels are strongly dependent on the genetic background (Cañas et al., 2009). This has
given rise to the conclusion that, in maize, there is genetic and
environmental control of N metabolism not only in vegetative
source organs, but also in reproductive sink organs, which could
cooperatively contribute to plant productivity.
This preliminary study prompted us to develop a quantitative
genetic approach, similar to that already performed on vegetative
organs (Hirel et al., 2001; Gallais & Hirel, 2004) and on germinating kernels (Limami et al., 2002), to obtain more information
on the genetic basis of N metabolism in the developing ear and
its possible relationship to yield. The aim of such a study was to
identify coincidences between QTLs for agronomic traits and
QTLs for physiological traits related to N metabolism, in both
the cob and developing kernel, during the kernel-filling period.
In addition, co-mapping of agronomic and physiological QTLs
with genes encoding enzymes involved in N and carbon (C)
metabolism, and other metabolic and developmental processes,
was also investigated in order to provide a genetic meaning for
the QTLs.
To further explore the possible relationship between N metabolism in the developing ear, whole-plant and organ-specific NUErelated traits and KY traits, correlation studies were carried out
using the entire available dataset for these traits, measured either in
the present studies or gathered from previously published work by
Coque et al. (2008). These correlation studies were performed on
datasets obtained from different years of experimentation in order
to overcome potential environmental effects and to strengthen the
significance of the correlations between the different agronomic
and physiological traits.
Materials and Methods
Plant material for agronomic and physiological studies
Data obtained by Hirel et al. (2001) and Coque et al. (2008)
served as an agronomic reference for the studies performed on
the developing ear of maize (Zea mays L.). A total of 100 recombinant inbred lines (RILs) and the two parental lines Io and F2
were grown in the field over two consecutive years, 2008 and
2009, as described by Bertin & Gallais (2000, 2001) at the Institut National de la Recherche Agronomique, Versailles, France
(4848.133¢N, 204.942¢E). The phenotypes of the two parental
lines and a number of RILs exhibiting a large genetic diversity in
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Table 1 Nitrogen use efficiency (NUE)-related and agronomic traits listed
in alphabetical order and used to perform correlations with the developing
ear physiological traits
Trait
Abbreviation
% N from N uptake within kernel
Anthesis date
Anthesis–silking interval (SD–AD)
Whole-plant dry matter per plant at silking
Kernel dry matter per plant
Kernel moisture
Kernel N concentration
Kernel N yield
Kernel yield m)2
Harvest index
Kernel number per plant
Nitrogen concentration at silking
N harvest index
N nutrition index
N remobilized (15N method)
N remobilized (balance method)
N from N uptake within kernel
N utilization efficiency
% of 15N remaining at silking ⁄ whole plant 15N
Silking date
Visual notation of leaf senescence at silking + 10 d
Visual notation of leaf senescence at silking + 45 d
Visual notation of leaf senescence at maturity
Silking N uptake per plant
Stover dry matter yield per plant at maturity
% of sterile plants
Stover N per plant at maturity
Stover N concentration at maturity
Proportion of post-silking N uptake allocated to kernels
Thousand kernel weight
Proportion of remobilized N (balance method)
Proportion of N remobilized corrected by residual postsilking 15N uptake
Whole-plant dry matter per plant at maturity
Whole-plant N yield at maturity
%NupG
AD
ASI
DMsilk ⁄ pl
GDM ⁄ pl
GMoist
GNC
GNY
KY
HI
KN
Ncsilk
NHI
NNI
Nrem
NremB
NupG
Nute
RE15NF ⁄ M
SD
SEN
SEN1
SEN2
SilkNup ⁄ pl
StDM ⁄ pl
Sterile
StN ⁄ pl
StNC
tG
TKW
tremB
tremC
WpDM ⁄ pl
WpNY
The agronomic and NUE traits correspond to those previously described by
Coque et al. (2008).
terms of vegetative and ear biomass and structure are shown in
Supporting Information Fig. S1(a,b). The soil was a deep silt
loam without any stone. The level of N fertilization was 175 kg
N ha)1 and N provided by the soil was estimated at c. 60 kg
ha)1. Both phosphorus (P205) and potassium (K20) were also
applied at 100 kg ha)1. The RILs were grown side by side in
separate lines of 25 plants, in two separate blocks of
25 m · 25 m, with an outside border area of 3 m (line UH002)
included in each block. Plants were sown on 6 May in both 2008
and 2009. Two ears from two individual plants for each RIL were
harvested per block for the physiological studies, making four
replicates per sample point and year. For all the RILs, the ears
were harvested 14 d after silking (14 DAS), a time corresponding
to the beginning of the kernel-filling period (Méchin et al.,
2007), when genetic variability for the different measured traits
in the two parental lines, Io and F2, was optimal (Cañas et al.,
2009). Moreover, this date corresponds to major physiological
changes in terms of amino acid biosynthesis and interconversion
(Seebauer et al., 2004; Cañas et al., 2009). For the ears, the husk
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and shank were discarded and the remainder was separated into
cobs and kernels (including pedicels) (Fig. S1c). Cobs and
kernels were immediately frozen in liquid N2. All frozen tissues
were reduced to a homogeneous powder and stored at ) 80C
until required for the metabolite and enzyme activity measurements. All the harvesting of fresh material was carried out
concomitantly between 10:00 h and 13:00 h. At maturity, the
total ear number per plant was determined on 10 plants of each
line and block. Ten ears of each line and block were harvested
and their lengths measured.
Several traits were measured in the cob and in developing kernels at 14 DAS. Physiological traits were as follows: total free
amino acid concentration (AAK for kernels and AAC for the
cob), dry weight ⁄ fresh weight (DW : FW) ratio (DWFWK for
kernels and DWFWC for the cob), glutamate dehydrogenase
activity (GDHK for kernels and GDHC for the cob), glutamine
synthetase activity (GSK for kernels and GSC for the cob), soluble protein concentration (PROTK for kernels and PROTC for
the cob), starch concentration (STARK for kernels and STARC
for the cob), sugar concentration (SUGK for kernels and SUGC
for the cob) and the concentration of specific amino acids,
including alanine, aspartate, asparagine, glutamate, glutamine,
glycine, proline, serine and threonine (ALAK, ASPK, ASNK,
GLUK, GLNK, GLYK, PROK, SERK and THRK, respectively,
for kernels and ALAC, ASPC, ASNC, GLUC, GLNC, GLYC,
PROC, SERC and THRC, respectively, for the cob). Two additional phenotypic traits were measured: the total number of ears
per plant (EARN) and the ear length (EARL).
The QTLs for the agronomic trait KY and its components
(kernel number per plant (KN) and thousand kernel weight
(TKW)), used previously to identify co-localization with ear
physiological traits (already listed), were those originally described
by Hirel et al. (2001) using the same RIL population. The
additional agronomic and NUE traits measured on line per se or in
test cross Io · F2 derived RIL populations used for the correlation
studies were obtained as described in Coque et al. (2008). Only
the traits showing significant correlation with physiological traits
measured in the present study are listed in Table 1.
Protein extraction, enzyme assays, metabolite extraction
and analyses
Protein extraction was carried out on 100 mg of frozen cob and
kernel material, as described previously (Cañas et al., 2009). Soluble protein concentration was determined using a commercially
available kit (Coomassie Protein assay reagent; Biorad, München,
Germany), with bovine serum albumin as a standard (Bradford,
1976). Enzymes were extracted from frozen material stored at
) 80C. All extractions were performed at 4C. GS activity was
measured according to the method of O’Neal & Joy (1973).
GDH (NADH-GDH) was measured in the direction of glutamate synthesis as described by Turano et al. (1996), except that
the extraction buffer was the same as for GS. Amino acids were
extracted from frozen tissue with 2% 5-sulfosalicylic acid
(100 mg FW ml)1) (Ferrario-Méry et al., 1998). Total free
amino acids were determined by the Rosen colorimetric method
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using glutamine as a standard (Rosen, 1957). The composition of
individual amino acids was performed by ion exchange
chromatography, followed by detection with ninhydrin using an
AminoTac JLC-500 ⁄ V amino acid analyzer, according to the
instructions of the manufacturer (JEOL, Tokyo, Japan). Sucrose,
glucose, fructose and starch were extracted from aliquots (100 mg
FW ml)1) of fresh plant material using a three-step ethanol–water
procedure, as described by Lemaı̂tre et al. (2008). Soluble sugars
(glucose, fructose and sucrose) were measured enzymatically using
a commercially available kit assay (Roche, Boehringer Mannheim,
Mannheim, Germany). Starch concentration was determined as
described by Ferrario-Méry et al. (1998).
Statistical analysis
SAS software was used for the ANOVA, to calculate the heritability and Pearson correlation (SAS, 1990, SAS procedures guide,
version 6, 3rd edn). The statistical model for the ANOVA was a
mixed model in which we used fixed effects for the year and random effects for the lines or the line · year interaction. For each
trait considered, the ANOVA allowed the estimation of the
genetic variance among lines (VG), the line · year interaction
variance (VGY) and residual variance (VE), from which the heritability at the level of means is derived: h2 = VG ⁄ (VG +
VGY ⁄ 2 + VE ⁄ 4) (Table 2). In order to determine the importance of the genotypic and environmental interactions (G · E), a
Fisher’s test was performed (Table S3). As the G · E effect was
much lower than the genotypic effect over the 2 yr of experimentation, both the data presented and the detection of QTLs correspond to the mean of the 2-yr experimentation. The phenotypic
correlations, and not the genotypic correlations, between the
Table 2 Heritabilities for the ear physiological and phenotypic traits
Kernels
h2
Cob
h2
Alanine
Asparagine
Aspartate
DW : FW
GDH activity
Glutamate
Glutamine
Glycine
GS activity
Proline
Serine
Sol. proteins
Sol. sugars
Starch
Threonine
Total AA
0.85
0.90
0.62
0.80
0.75
0.67
0.76
0.89
0.84
0.83
0.91
0.43
0.86
0.79
0.89
0.78
Alanine
Asparagine
Aspartate
DW : FW
GDH activity
Glutamate
Glutamine
Glycine
GS activity
Proline
Serine
Sol. proteins
Sol. sugars
Starch
Threonine
Total AA
0.74
0.67
0.76
0.81
0.83
0.65
0.65
0.75
0.76
0.47
0.85
0.77
0.82
0.83
0.69
0.71
Ear
h2
Mean kernel
0.74
Ear length
Ear number
0.88
0.77
Mean cob
Mean ear
0.79
0.82
AA, amino acid; DW, dry weight; FW, fresh weight; GDH, glutamate
dehydrogenase; GS, glutamine synthetase.
Results are the mean of a 2-yr field experiment.
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agronomic experiment and the physiological experiment were
considered, because the accuracy is greater on the phenotypic
than on the genotypic correlations. Moreover, as the two experiments are statistically independent, the phenotypic covariances
between means are also the genotypic covariances. For the
physiological traits, a heat map of the Pearson correlation matrix
was obtained using Excel software. Network diagrams of the
Pearson correlation matrix were obtained using Cytoscape 2.8
software (Smoot et al., 2011) with the network analyzer plug-in
(Assenov et al., 2008).
Genetic map
In the present study, the genetic map originally constructed using
the RILs derived from the crossing between Io · F2 (Causse
et al., 1996), and further updated by Coque et al. (2008), was
used. This reference map contains 410 loci covering 2147 cM. A
subset of 203 markers, well distributed along the chromosomes,
was used for QTL detection. The mean interval between two
markers, depending on the chromosome, varies from 8 to
18 cM. As a result of the choice of the subset of markers, it was
not possible to reduce the maximum values of such an interval by
adding more markers.
QTL detection
QTLs were detected using the Plab-QTL software (Utz &
Melchinger, 1995) following simple interval mapping. Only
QTLs with a logarithm of the odds ratio (LOD) score > 2 were
considered (Lander & Botstein, 1989). To represent a QTL on
the map taking into account the error in the location, chromosomal regions corresponding to a LOD greater than the maximum
LOD – 1 are shown, which is not a true confidence interval, and
is called a LOD ) 1 interval. Two QTLs for different traits are
declared as coincident when their LOD ) 1 intervals overlap. A
coincidence is said to be positive when there is coincidence of
favorable (or unfavorable) alleles for both traits. A coincidence is
said to be negative when there is coincidence of a favorable allele
for one trait with an unfavorable allele for the other trait. For each
trait, the percentages of phenotypic (R2p) and genotypic (R2g)
variation identified by the markers were calculated. R2g was equal
to R2p divided by the heritabilities (h2). In addition, for each QTL
detected, the estimated additive effect (half of the difference
between the estimated values of the two homozygous genotypes at
the QTL) is presented.
Results
QTL detection for ear physiological traits and coincidence
with yield traits and candidate genes
QTLs (with LOD ‡ 2) for the main physiological traits representative of C and N metabolism, in both the cob and developing
kernels, were detected using the dataset obtained from two consecutive year experiments. The QTLs detected over the 2-yr
experiment are presented for the kernels in Table 3, and for the
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cob and the ear physiological and phenotypic traits in Table 4.
The position of the different QTLs on the maize restriction fragment length polymorphism (RFLP) map is shown in Fig. 1 for
the developing kernel traits, and in Fig. 2 for the cob and ear
phenotypic traits.
The heritabilities for the physiological traits measured in the
developing kernels ranged from 0.91 for serine concentration to
0.43 for soluble protein concentration (Table 2). For the cob,
the highest heritability was 0.85 for serine concentration and the
lowest was 0.47 for proline concentration. The heritabilities for
the number of ears and ear length were 0.77 and 0.88, respectively (Table 2).
For the developing kernels, the following QTLs were identified: two for total free amino acid concentration on chromosome
1, two for GDH activity on chromosome 1, one for GS activity
on chromosome 5, one for soluble sugar concentration on chromosome 10, two for serine concentration on chromosome 1,
three for glycine concentration (two on chromosome 2 and one
on chromosome 3), two for proline concentration (on chromosome 5 and on chromosome 10), and three for alanine concentration (one on chromosome 3 and two on chromosome 5). The
lowest percentages of phenotypic and genetic variation identified
by the QTLs for physiological traits were 9% and 10%, respectively, for serine concentration. The highest were 31% and 36%,
respectively, for alanine concentration (Table 3).
For the cob, the following QTLs were identified: one for the
DW : FW ratio on chromosome 6, one for total free amino acid
concentration on chromosome 5, one for GDH activity on chromosome 9, one for GS activity on chromosome 4, one for soluble
protein concentration on chromosome 4, two for soluble sugar
concentration on chromosome 1, one for aspartate concentration
on chromosome 5, three for serine concentration on chromosomes 1 and 5, one for asparagine concentration on chromosome
9, one for glutamine concentration on chromosome 5, and two
for glycine concentration on chromosome 2 and chromosome
10. For these QTLs, the lowest percentages of phenotypic and
genetic variation were 8% and 10%, respectively, for soluble sugars. The highest were 28% and 33%, respectively, for serine concentration (Table 4). Interestingly, a QTL for serine
concentration located on chromosome 1 and a QTL for glycine
concentration located on chromosome 2 were detected in both
the cob and developing kernels (Figs 1, 2). For the number of
ears, a QTL was identified on chromosome 3, and, for ear length,
a QTL was located on chromosome 9.
A number of co-localizations between QTLs for the physiological traits of developing kernels and QTLs for KY and its
components (Hirel et al., 2001) and putative candidate genes
were identified, as shown in Fig. 1.
On chromosome 1, two QTLs for serine, a QTL for total free
amino acid concentration and a QTL for the GDH activity in
the kernels co-localized with QTLs for yield (KN and KY). The
parental line Io provided the favorable allele for the four physiological QTLs and for the two yield QTLs. On this chromosome,
an interesting co-localization with a candidate gene encoding one
of the two GDH isoenzymes (GDH1) was found between the
corresponding enzyme activity and two QTLs for yield.
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Table 3 Quantitative trait loci (QTLs) detected for the different physiological traits measured in developing kernels 14 d after silking
Location
2 a
2 b
c
Trait
R p
R g
Chr
Marker
+ cM
Distance (cM)
Confidence intervald
LOD
Additive effecte
Total AA
0.15
0.19
GDH activity
0.20
0.27
GS activity
Sol. sugars
Serine
0.16
0.10
0.09
0.19
0.12
0.10
Glycine
0.14
0.16
Proline
0.24
0.29
Alanine
0.31
0.36
1
1
1
1
5
10
1
1
2
2
3
5
10
3
5
5
SC143B
SC282A
SC143B
UMC39C
UMC39B
SC348A
SC61_B
UMC83A
SC108
UMC16A
SC224A
PGM2-I
SC170B
SC224A
PGM2_I
SC154
9
22
8
9
3
1
5
1
15
7
8
11
3
8
15
10
17
175
16
219
153
91
113
139
63
81
65
50
73
65
54
78
3–35
167–187
8–23
207–230
138–164
81–104
103–121
134–150
50–74
74–97
49–80
38–64
53–83
52–77
46–64
69–93
2.68
3.23
3.51
3.03
3.96
2.36
4.22
3.52
3.01
2.63
2.97
2.72
3.1
4.95
3.51
3.18
26.11
33.421
0.151
0.145
)0.211
126.237
4.509
4.170
)0.839
)0.771
0.943
3.573
3.533
10.692
7.551
7.586
AA, amino acid; GDH, glutamate dehydrogenase; GS, glutamine synthetase; LOD, logarithm of the odds ratio.
Results are the mean of a 2-yr field experiment.
a
Percentage of phenotypic variance explained by the markers.
b
Percentage of genotypic variance explained by the markers. R2g = R2p ⁄ h2.
c
Chromosome number.
d
Approximate confidence interval (LOD ) 1).
e
Additive effect with positive value for the parental line Io.
Table 4 Quantitative trait loci (QTLs) detected for the different physiological and phenotypic traits measured in the cob 14 d after silking
Location
2 a
2 b
c
Trait
R p
R g
Chr
Marker
+ cM
Distance (cM)
Confidence intervald
LOD
Additive effecte
DW : FW
Total AA
GDH activity
GS activity
Sol. proteins
Sol. sugars
0.09
0.09
0.09
0.13
0.12
0.08
0.12
0.12
0.11
0.17
0.16
0.10
Aspartate
Serine
0.10
0.28
0.13
0.33
Asparagine
Glutamine
Glycine
0.10
0.11
0.17
0.15
0.17
0.22
Ear number
Ear length
0.15
0.10
0.19
0.11
6
5
9
4
4
1
1
5
1
2
5
9
5
2
10
3
9
SC224
SC154
BNL510
SC292
SC431
UMC67
UMC83A
SC258A
SC61
SC108
SC168
SC143A
SC168
SC108
SC412A
UMC60
BNL142
1
3
2
3
10
8
4
26
4
4
12
4
11
3
5
4
2
14
71
47
86
69
88
142
194
112
52
66
113
65
51
81
85
126
4–23
57–82
39–61
65–96
56–84
72–108
128–161
183–209
100–122
46–64
50–81
84–130
57–76
43–71
73–88
57–95
98–143
2.14
2.12
2.81
2.97
2.98
2.57
2.44
2.34
2.28
2.93
2.43
2.43
3.25
2.78
2.47
3.42
2.6
0.014
10.897
0.091
)0.179
)2.13
)166.452
)159.774
2.459
1.354
)1.679
1.549
)3.304
4.005
)0.269
0.253
0.308
)7.305
AA, amino acid; DW, dry weight; FW, fresh weight; GDH, glutamate dehydrogenase; GS, glutamine synthetase; LOD, logarithm of the odds ratio.
Results are the mean of a 2-yr field experiment.
a
Percentage of phenotypic variance explained by the markers.
b
Percentage of genotypic variance explained by the markers. R2g = R2p ⁄ h2.
c
Chromosome number.
d
Approximate confidence interval (LOD )1).
e
Additive effect with positive value for the parental line Io.
On chromosome 2, a QTL for glycine concentration partially
overlapped with a QTL for yield (TKW). For both QTLs, the
favorable allele originated from line F2. On chromosome 3, a
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co-localization between two physiological QTLs (one for glycine
concentration and one for alanine concentration) and a QTL for
KY was identified. For these three QTLs, the positive allele was
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Fig. 1 Coincidences between quantitative trait loci (QTLs) for developing maize kernel physiological traits and traits related to kernel yield and its components. The locations of the QTLs for physiological traits on the maize genetic map are indicated by vertical bars with a dot at both ends: black, amino acid
concentration; dark blue, glutamate dehydrogenase activity; green, glutamine synthetase activity; yellow, soluble sugar concentration. The locations of the
QTLs for specific amino acids are indicated by vertical bars: black, alanine concentration; yellow, glycine concentration; red, serine concentration; brown,
proline concentration. The locations of QTLs for yield and its components are indicated by dotted vertical bars: brown bars, kernel yield (KY); green bars,
kernel number (KN per plant); blue bars, thousand kernel weight (TKW). A favorable allele from the parental line Io is indicated by (+) and an unfavorable
allele from the parental line F2 is indicated by ()). Coincident QTLs between the cob and developing kernels are shown with an open oval symbol (see also
Fig. 2). The positions of the loci for genes encoding enzymes involved in nitrogen (N) or carbon (C) assimilation are indicated in bold italics: AlaAT1–4
(alanine aminotransferase 1–4); AspAT1.1, AspAT1.2, AspAT1.3, AspAT2.1 and AspAT2.2 (aspartate aminotransferase 1.1–2.1); AS1–4 (asparagine
synthetase 1–4); Fd-GOGAT (ferredoxin-dependent glutamate synthase); GDH1 and GDH2 (glutamate dehydrogenase 1 and 2); GS1.1–1.5 (cytosolic
glutamine synthetase 1–5); Inv (invertase); NADH-GOGAT 1–3 (NADH glutamate synthase 1–3); NR (nitrate reductase); PEPC (phosphoenolpyruvate
carboxylase); P5CS1–3 (pyrroline-5-carboxylate synthetase 1–3); SHMT1–5 (serine hydroxymethyltransferase 1–5).
from the parental line Io. A putative candidate gene was found in
the chromosomal region containing these QTLs, namely that
encoding serine hydroxymethyltransferase (SHMT3). On
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chromosome 5, a QTL for GS activity partially overlapped with
QTLs for KY and TKW. On this occasion, the favorable allele
for yield was provided by line Io, whereas, for GS activity, it was
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Fig. 2 Coincidences between quantitative trait loci (QTLs) for the maize cob and phenotypic ear traits and traits related to kernel yield and its components.
The locations of the QTLs for physiological traits on the maize restriction fragment length polymorphism (RFLP) genetic map are indicated by vertical bars
with a dot at both ends: black, amino acid concentration; red, dry weight (DW) : fresh weight (FW) ratio; blue, ear number; brown, ear length; dark blue,
glutamate dehydrogenase activity; green, glutamine synthetase activity; pink, protein concentration; yellow, soluble sugar concentration. The locations of
the QTLs for specific amino acids are indicated by vertical bars: pink, aspartate concentration; green, asparagine concentration; blue, glutamine concentration; yellow, glycine concentration; red, serine concentration; brown, proline concentration. The locations of ear phenotypic QTLs are indicated by oval
symbols: blue, ear number; brown, ear length. The locations of the QTLs for yield and its components are indicated by dotted vertical bars: brown bars, KY
(kernel yield); green bars, KN (kernel number per plant); blue bars, TKW (thousand kernel weight). A favorable allele from the parental line Io is indicated
by (+) and an unfavorable allele from the parental line F2 is indicated by ()). Coincident QTLs between cob and kernels are shown with an open oval symbol (see also Fig. 1). The positions of the loci for genes encoding enzymes involved in nitrogen (N) or carbon (C) assimilation are indicated in bold italics:
AlaAT1–4 (alanine aminotransferase 1–4); AspAT1.1, AspAT1.2, AspAT1.3, AspAT2.1 and AspAT2.2 (aspartate aminotransferase 1.1–2.1); AS1–4 (asparagine synthetase 1–4); Fd-GOGAT (ferredoxin-dependent glutamate synthase); GDH1 and GDH2 (glutamate dehydrogenase 1 and 2); GS1.1–1.5 (cytosolic
glutamine synthetase 1–5); Inv (invertase); NADH-GOGAT 1–3 (NADH glutamate synthase 1–3); NR (nitrate reductase); PEPC (phosphoenolpyruvate
carboxylase); P5CS1–3 (pyrroline-5-carboxylate synthetase 1–3); SHMT1–5 (serine hydroxymethyltransferase 1–5).
provided by line F2, thus having a negative effect. Although no
QTLs were found to co-localize with yield on chromosome 5,
there was a co-localization between the alanine concentration of
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the kernels and the gene encoding an alanine aminotransferase
(AlaAT1), the enzyme catalyzing the interconversion of this
amino acid.
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Several QTLs detected for the physiological traits of the cob
also co-localized with QTLs for yield (Fig. 2). On chromosome
1, two QTLs for soluble sugar concentration and a QTL for
serine concentration were coincident with QTLs for KY, KN and
TKW. On chromosome 2, two QTLs for glycine and serine
concentrations co-localized with a QTL for TKW. On chromosome 5, the QTL for aspartate concentration partially overlapped
with a QTL for TKW and KY. Finally, a QTL for soluble protein
concentration was coincident with a QTL for GS activity on
chromosome 4.
In the cob, the positive or negative additive effects of the various yield and physiological QTLs showed a more complex distribution pattern compared with that of the developing kernels. For
example, on chromososme 2, the favorable allele was provided by
line F2 for the coincident QTL, whereas, on chromosome 5, the
favorable allele originated from Io. On chromosome 1, the QTL
for serine concentration and the QTL for KN had positive additive effects, whereas the QTL for soluble sugar concentration and
the QTLs for KN and KY had an opposite negative additive
effect. The favorable allele was from line F2 for the two coincident
QTLs represented by the soluble sugar concentration and TKW.
In the cob, co-localizations with candidate genes were less
obvious when compared with those found between kernels and
yield traits. On chromosome 5, a gene encoding an aspartate
aminotranferase (AspAT1.2) was close to a QTL for the aspartate
concentration in the cob. On chromosome 9, there was a colocalization of a QTL for the cob asparagine concentration, a
QTL for the ear length and a gene encoding the enzyme asparagine synthetase (AS4) catalyzing asparagine synthesis. Interestingly, on chromosome 3, a co-localization was detected between
a group of QTLs involved in yield determination and a QTL for
ear number, all of which were positively controlled by alleles
from Io. In this chromosomal region, several QTLs for the
glycine and alanine concentrations of the kernel were also found.
Correlations between physiological traits in the developing
ear, agronomic traits for yield and traits related to NUE
In order to identify possible functional relationships between the
physiological traits measured in the cob or kernel, or between
these two parts of the developing ear, their Pearson correlation
coefficients were calculated. The coefficients are shown in a
graphical manner in Fig. 3(a–c). Their values are presented in
Table S1 and visualized in the heat map presented in Fig. 3(d).
Together, a total of 561 correlations were detected, only 295 of
which were significant (P £ 0.05). There were 78 ⁄ 120 significant
correlations between traits measured in the developing kernel
(Fig. 3a) and 98 ⁄ 120 significant correlations between traits measured in the cob (Fig. 3b). Of 256 correlations, only 111 were
significant when only the interaction between the traits of the cob
and the developing kernel were considered (Fig. 3c).
For the length of the ear, no correlations were found with the
physiological traits of either the cob or developing kernels. For
this phenotypic trait, only a negative correlation () 0.211) was
found with the number of ears (Table S1, Fig. 3d). By contrast,
for the number of ears, only 8 ⁄ 33 significant correlations were
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found with the physiological traits measured in the developing
kernels. Of these, the highest significant correlation (0.422) was
with the threonine concentration (Table S1 and Fig. 3a,d).
When the physiological traits of the developing kernels were
considered separately, the highest positive significant correlation
was found between the serine and glycine concentrations (0.762).
For the cob, the highest correlation was found between the total
free amino acid and threonine concentrations (0.765). When the
interactions between the ear and kernel traits were considered, it
was found that the most strongly correlated physiological trait
was their serine concentration (0.762), as shown in Fig. 3(c).
The highest negative correlation was found between the soluble sugar concentration and the DW : FW ratio when the kernel
and cob traits were analyzed separately () 0.854 for the kernels
and ) 0.895 for the cob). The highest negative correlation was
found between the soluble protein concentration and the DW :
FW ratio when the interaction between the developing kernel
and cob traits was analyzed () 0.750) (Table S1, Fig. 3d).
In order to identify any relationship between the physiological
traits measured in the two parts of the developing ear and the
agronomic traits related to whole-plant NUE and yield, their
Pearson correlation coefficients were calculated. This was carried
out using the set of physiological data obtained in the present
study and the set of NUE and agronomic traits gathered in the
database used previously by Coque et al. (2008). As, in the present investigation, plants were grown under high N fertilization,
only the previous agronomic data corresponding to plants grown
under the same N regime were used. The 34 yield or NUE traits
exhibiting significant correlations with the physiological traits of
the ear are presented in Table 1. The correlations obtained for
the entire set of traits are shown in Table S2.
Of 1156 correlations, only 263 were significant (P £ 0.05).
For clarity, the investigation focused on the main correlations
with a high level of significance (‡ 0.4 or £ ) 0.4, P < 0.0002)
(Table 5 and Fig. 4). Twenty-five correlations with a Pearson
coefficient higher than 0.4 were obtained, which were mostly
related to the physiological traits measured in the kernels, except
for the cob glutamine concentration and the ear number.
In the developing kernels, the alanine and glycine concentrations were the two traits that showed the highest number of
significant correlations with the agronomic and NUE-related
traits. Among these correlations, that between the alanine concentration and the whole-plant dry matter accumulated at maturity (WpDM ⁄ pl) was the highest (Pearson coefficient, 0.585).
Several correlations with a Pearson coefficient ranging from 0.42
to 0.57 were also found for a number of yield and NUE traits
and the alanine concentration of the developing kernels. The
glycine concentration of the developing kernels also showed a
number of significant correlations with traits more specifically
related to NUE and a trait corresponding to dry matter accumulation at silking (DMsilk ⁄ pl). Interestingly, both the glycine and
alanine concentrations of the kernels were strongly correlated
with the nutrition harvest index (NHI) and the nitrogen nutrition index (NNI).
The highest negative correlation () 0.437) was obtained
between the serine concentration of the developing kernels and
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(a)
(b)
(c)
(d)
Fig. 3 Correlation matrix and network diagrams for maize ear, developing kernel and cob physiological traits. (a–c) Network diagrams showing significant
correlations (P < 0.05) between traits based on the calculation of Pearson coefficients. Traits with a larger number of correlations are represented by the
largest and darkest red dots. Traits with a smaller number of correlations are represented by smaller and darker green dots. The lines (edges) represent a significant correlation between two traits. Thicker and darker red lines represent the highest positive correlations. Thinner and darker green lines represent the
highest negative correlations. (a) Network diagram of the correlations between physiological kernel traits. (b) Network diagram of the correlations between
cob physiological traits. (c) Network diagram of the correlations between developing kernels and cob physiological traits. (d) Heat map of the correlation
matrix for kernel and cob traits based on the calculation of Pearson coefficients. Darkest red squares, coefficients closest to 1; darkest green squares,
coefficients closest to ) 1; yellow squares, coefficients closest to 0 (see scale). The group of negative correlations between cob and kernel DW : FW and
other cob physiological traits is outlined with a black rectangle, whereas the group of positive correlations between physiological cob traits is outlined with
a black triangle. See Materials and Methods section for definitions of abbreviations.
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Discussion
Fig. 4 Network diagram showing the main correlations found between
physiological traits and agronomic traits related to nitrogen use efficiency
(NUE) and yield in maize. Network diagrams show significant correlations
(P £ 0.05) between traits based on positive and negative Pearson coefficients > 0.4 (P < 0.0002). Traits with a larger number of correlations are
represented by larger and darker red dots. Traits with a smaller number of
correlations are represented by smaller and darker green dots. Lines represent a significant correlation between two traits. Thicker and darker red
lines represent the highest positive correlations. Thinner and darker green
lines represent the highest negative correlations. See text and Table 1 for
definitions of abbreviations.
Table 5 Main correlations observed between physiological ear traits,
nitrogen use efficiency (NUE)-related traits and agronomic traits
corresponding to those previously described by Coque et al. (2008)
AD
DMsilk ⁄ pl
GMoist
GNC
GNY
KY
KN
NHI
NNI
Nrem
Nute
RE15NF ⁄ M
SEN2
SilkNup ⁄ pl
StDM ⁄ pl
Sterile
StN ⁄ pl
StNC
WpDM ⁄ pl
ASPK
SERK
GLNK
GLYK
ALAK
EARN
GLNC
0.422
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
0.404
) 0.437
–
–
–
–
–
–
0.474
–
–
–
–
–
–
–
–
–
–
–
–
0.402
–
–
–
–
–
–
0.417
0.442
–
–
–
–
0.519
0.526
–
–
0.401
–
0.449
0.554
–
0.556
0.426
–
–
–
–
0.414
0.539
0.576
0.504
0.437
0.447
0.433
0.424
–
–
–
–
–
–
–
0.585
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
) 0.405
–
–
–
–
–
0.443
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
See Table 1 for definitions of abbreviations.
the visual annotation of leaf senescence at maturity (SEN2). All
the traits exhibiting a positive or negative correlation were interconnected directly or indirectly, except for the percentage of sterile plants (Sterile) and the number of ears, which exhibited an
independent negative correlation coefficient of ) 0.405.
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Previous studies have demonstrated that N metabolism in maize
ears is an important component controlling N allocation during
the grain-filling period (Seebauer et al., 2004; Cañas et al., 2009,
2010). Moreover, it is well established that N translocation and,
presumably, N assimilation in the kernels facilitate the utilization
of carbohydrates, thus being a major component in the determination of yield (Below et al., 2000). However, there is a paucity
of data on both the physiological and molecular control of this
process, although it has been suggested that a strong relationships
exists between source and sink organs during kernel filling
(Seebauer et al., 2004; Cañas et al., 2010).
Therefore, the present study focused on the identification of
key metabolic reactions and candidate genes involved in the control of N assimilation in maize ears by exploiting the genetic variability in a population of maize RILs. In previous investigations,
this RIL population allowed the identification of important components of NUE in both vegetative and reproductive organs in
relation to yield (Hirel et al., 2001; Limami et al., 2002; Coque
et al., 2008).
A number of physiological traits representative of C and N
assimilation in both the cob and developing kernels (Cañas et al.,
2009) were first measured in the RIL population grown over two
consecutive years. As the heritability over the 2-yr experiment
was high (0.7–0.8) for most of the physiological traits of both
cob and developing kernels, it can be concluded that there is a
highly significant genotypic effect for all measured physiological
traits. This finding strengthens the power of the QTLs detected
for these traits, and indicates that they may represent good putative biological markers to be used in breeding programs (Moose
& Mumm, 2008).
Among the various QTLs or groups of QTLs detected for
these sets of ear physiological traits, some showed interesting colocalization with putative candidate genes. One of the most interesting groups of QTLs identified concerned those controlling the
concentrations of glycine and serine in the two parts of the developing ear. On chromosome 1, a QTL for serine concentration
and, on chromosome 2, a QTL for glycine concentration were
found in both the developing kernels and cob. These results are
in agreement with correlation studies showing that there is a
strong positive correlation between the serine and glycine
concentrations of the cob and ear (Fig. 3, Table S1). It was also
found that QTLs for both serine and glycine concentrations
co-localized with QTLs for yield. This suggests that there is a
genetic mechanism shared by the two parts of the developing ear
that controls the synthesis and use of these two amino acids in an
interactive manner, and that this control is important for the
determination of yield. Moreover, it was shown that the glycine
concentration in the developing kernels is highly correlated with
several traits related to the plant N metabolic status, plant growth
and development, such as NHI and NNI, and dry matter accumulation (Table 5, Fig. 4). In addition, it was observed that the
glycine concentration of developing kernels is highly correlated
with that of alanine, an amino acid shown to be of major importance in plant NUE (Good et al., 2007; Shrawat et al., 2008;
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Good & Beatty, 2011). In line with this finding, strong relationships were also found between the alanine concentration of the
kernels, NHI, NNI, most of the yield traits (KY, KN) and plant
dry matter accumulation (Fig. 4). It is therefore probable that
alanine and glycine metabolism are strongly interrelated, as
revealed by the high level of correlation obtained between these
two traits in both the cob and developing kernels. This interaction between glycine and alanine metabolism may occur
through the activity of the enzyme alanine:glyoxyate aminotransferase (AGT) which catalyzes the conversion of alanine to glycine.
This enzyme has been shown to be mainly involved in the pathway
of photorespiration in the leaves of C3 plants (Igarashi et al.,
2006). The importance of glycine and alanine metabolism and
accumulation during kernel filling is further strengthened by the
presence, on chromosome 3, of a group of QTLs for the concentrations of glycine and alanine in the kernels, for ear number and
for yield. In this chromosomal region, a gene encoding serine
hydroxymethyltransferase (SHMT3), an enzyme that plays an
important role in cellular one-carbon pathways by catalyzing the
conversion of serine to glycine (which can be reversible depending
on the metabolic pathway involved), was also detected. As the
reaction catalyzed by the enzyme SHMT provides the largest part
of the one-carbon units available to the cell (Douce et al., 2001;
Maurino & Peterhansel, 2010), it is probable that this metabolic
pathway is of major importance for the determination of yield
during kernel development.
On chromosome 5, a QTL for kernel GS activity co-localized
with QTLs for KY and TKW, previously found to be coincident
with the Gln1.3 gene locus and a QTL for leaf GS activity (Hirel
et al., 2001). Such findings reinforce the validity of previous
quantitative genetic approaches, as the same QTL for GS activity
was found in developing kernels (present study), in leaves (Hirel
et al., 2001) and in germinating kernels (Limami et al., 2002).
Surprisingly, the parental line F2 provided the favorable allele for
kernel GS activity, whereas, for leaf GS activity, it originated
from the parental line Io. In agreement with this finding, a low
but significant negative correlation between TKW and GS activity in developing kernels was determined (Table S2), whereas it
was found that there was a positive correlation with leaf GS activity (Hirel et al., 2001). It is probable therefore that GS activity at
the Gln1.3 locus may be controlled by alleles of opposite effects
according to the organ examined, which could represent an
example of advantageous additive gene expression relative to one
or both parents, which can increase the yield in hybrids (Springer
& Stupar, 2007).
In developing kernels, there was a positive coincidence of a
QTL for GDH activity and QTLs for yield and its components
(TKW and KN) at the end of chromosome 1. Moreover, this
group of QTLs also showed a coincidence with the Gdh1 locus,
which therefore appears to be a good candidate gene, influencing
yield. Previously, two QTLs for leaf GDH activity were found to
be positively coincident with two QTLs for KY (Dubois et al.,
2003; Gallais & Hirel, 2004), which confirms the hypothesis that
GDH activity may be an important factor controlling plant productivity (Ameziane et al., 2000; Dubois et al., 2003; Loulakakis
et al., 2009).
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On chromosome 9, it was found that a QTL for asparagine
concentration in the cob co-localized with a gene encoding asparagine synthetase (AS4). Interestingly, it has been shown previously that this gene is strongly induced during the process of N
remobilization and recycling within the developing ear (Cañas
et al., 2010). Moreover, the finding that the asparagine concentration of the cob is highly correlated with that of the kernels
(Fig. 3c, Table S1) and, within the cob, with the other physiological traits related to amino acid interconversion (Fig. 3b,
Table S1), further supports the hypothesis that asparagine is of
major importance for C and N translocation between the cob
and the developing kernels (Seebauer et al., 2004; Lea et al.,
2007; Cañas et al., 2010). By contrast, asparagine may not be
directly involved in grain yield if the negative correlations
observed between yield components and the asparagine concentration of the developing kernels are considered. These negative
correlations are in line with a previous investigation, in which a
large accumulation of asparagine was observed during kernel
abortion, thus being, in turn, detrimental to the final yield
(Cañas et al., 2010). By contrast, the importance of asparagine
during the process of ear elongation is an attractive hypothesis, as
co-localization between the ear length and asparagine concentration was found on the same region of chromosome 9.
Although a QTL for the DW : FW ratio was only found for
the cob on chromosome 6, with no co-localization with other
QTLs or any particular candidate gene, this trait may be a good
predictor of the water status of the whole plant. This hypothesis
is supported by the fact that a significant negative correlation was
found between kernel moisture (GMoist) and DW : FW of the
kernels (Table S2). Furthermore, it was shown that a high DW :
FW in the cob negatively affects KY (Table S2). A negative relationship was also found between DW : FW of both the cob and
the kernels and most of the physiological traits measured in the
cob (Fig. 3, Table S1). This finding indicates that, when there is
a deficit of water in the developing ear, independent of dry matter
production, most of the metabolites are not actively synthesized
and transported, which may lead to kernel abortion (Zinsemeier
et al., 1995; Ribaud et al., 2009). In line with these conclusions,
DW : FW ratios of both the cob and developing kernels were
highly correlated (Table S1, Fig. 3d), indicating that water deficit occurs in both parts of the developing ear.
In addition to studying QTL detection and the interpretation
of their physiological meaning in terms of plant performance,
interesting correlations between several physiological and agronomic traits were identified. If we consider that the genotypic
effect predominates for most of the traits, in comparison with
the genotype · year interaction, these correlation studies can be
very informative for identifying important relationships between
physiological and agronomic traits. Similarly, in the agronomic
study of Coque et al. (2008), the genotype · year interaction
was much smaller than the genotypic effect alone. Therefore,
when measuring different sets of traits, the presence of genotype · year interaction experiments can only reduce the value of
the correlation coefficients, which, in turn, does not bias the
interpretation of the results based on the highest correlation coefficients. These correlations were calculated using a large dataset
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of agronomic and physiological traits obtained over several years
of experimentation in order to circumvent potential environmental effects caused by climatic changes and variable N nutrition
under field growth conditions. Moreover, stable relationships
between traits will be essential if used by breeders to improve
plant performance, both in terms of yield and N use. Together,
they were consistent with well-known relationships existing
between these traits, thus reinforcing the validity of this quantitative genetic approach and correlation studies performed with
maize. For example, there is a strong negative correlation
between the percentage of sterile plants and the total number of
ears per plant (Tables 5, S2), simply because sterile plants have
less ears or empty ears. By contrast, the number of ears is positively correlated with yield and its components (Table S2), as a
plant with several ears produces generally more kernels than a
plant with only one ear (Pan et al., 1986). The total or individual
amino acid concentration of the developing ear and visual leaf
senescence at 14 DAS were positively correlated, whereas this
correlation was negative with visual leaf senescence at 45 DAS
and at maturity. Thus, if the level of leaf senescence is high
between 10 and 14 DAS, there will be an accumulation of amino
acids in the ear because N remobilization to this organ is already
occurring. By contrast, if there is a shortage of amino acids at the
end of the grain-filling period, premature leaf senescence will
occur to provide more amino acids to the developing ear through
the N remobilization process.
It is also worth noting that the majority of the physiological
traits of the cob are positively correlated with each other
(Fig. 3d), suggesting that the C and N metabolic pathways in this
organ are interconnected and that, when there is active metabolism, all metabolites are rapidly synthesized and transported.
Conclusions and perspectives
From both the study of correlations among traits and the detection of QTLs for various agronomic and physiological traits, one
can conclude that genetic variability for N metabolism may be an
important determinant for the yield and its components, not
only in vegetative organs, but also in the developing ear of maize.
This genetic variability mainly concerns amino acid metabolism
and interconversion, mostly in developing kernels and, to a lesser
extent, in the cob. One of the major breakthroughs from these
studies concerns the metabolism of glycine and serine, and, presumably, the interconversion of glycine to alanine in the kernels,
and the putative role of the cognate genes encoding the enzymes
involved in these pathways. It is well established that glycine and
serine play a major role during photorespiration (Maurino &
Peterhansel, 2010), although, in C4 plants, this process is limited,
but necessary, for proper functioning of photosynthesis (Lacuesta
et al., 1997; Zelitch et al., 2009). Further work is thus needed to
investigate the regulation of this metabolic pathway in the kernels, an organ in which photorespiration is normally absent.
Finally, this work has confirmed that both the GS enzyme
(Martin et al., 2006) and, possibly, GDH are important in the
determination of yield. Experiments are now in progress to overexpress these genes encoding the two enzymes, either constitutively
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or in an organ-specific manner in both source and sink organs, in
order to verify whether grain filling and grain yield are improved.
Acknowledgements
We thank Professor P. J. Lea for helpful comments on the manuscript. The determination of the composition of individual
amino acids and the N and C concentrations was carried out at
the Plant Chemistry Platform of the Institute Jean-Pierre
Bourgin, INRA, Versailles-Grignon, France.
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Supporting Information
Additional supporting information may be found in the online
version of this article.
Fig. S1 Phenotype of the two parental lines and of representative
members of their recombinant inbred line (RIL) progeny.
Table S1 Pearson correlation matrix between the different ear
traits
Table S2 Pearson correlation matrix between the different ear
and agronomic traits and nitrogen use efficiency (NUE) traits
Table S3 Fisher’s tests of the line effect and the line · year effect
for different ear traits
Please note: Wiley-Blackwell is not responsible for the content or
functionality of any supporting information supplied by the
authors. Any queries (other than missing material) should be
directed to the New Phytologist Central Office.
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