Download Quantitative trait loci affecting amylose, amylopectin and starch

Plant Physiol. Biochem., 2000, 38 (6), 459−472 / © 2000 Éditions scientifiques et médicales Elsevier SAS. All rights reserved
S0981942800007622/FLA
Quantitative trait loci affecting amylose, amylopectin and starch
content in maize recombinant inbred lines
Mouhamadou Sénea, Mathilde Causseb§, Catherine Damervalb, Claudine Thévenota, Jean-Louis Prioula*
a
Laboratoire structure et métabolisme des plantes, Institut de biotechnologie des plantes, bât. 630, université Paris-Sud (CNRS,
UMR 8618), 91405 Orsay cedex, France
b
Station de génétique végétale (Inra, CNRS, UPS), La ferme du Moulon, 91190 Gif-Sur-Yvette, France
* Author to whom correspondence should be addressed (fax +33 1 69 33 64 24; e-mail [email protected])
(Received 15 December 1999; accepted 2 March 2000)
Abstract – The loci explaining the variability of quantitative traits related to starch content and composition (amylose,
amylopectin and water soluble fraction) were searched for in maize kernels. Multifactorial genetic methods were used to detect
and locate QTLs (quantitative trait loci) on a genetic map consisting mainly of RFLP markers for genes with known function.
The genetic material was recombinant inbred lines originating from parents differing in starch structure (dent vs. flint). Kernels
were harvested from field grown plants for two successive years and under two pollination systems. Main effect and epistasis
QTLs were detected using two methods, composite interval mapping (MQTL) and ANOVA. Despite large year-to-year
differences, physiologically meaningful co-locations were observed between trait QTLs. Moreover, the number of expressed
sequences on our map allowed the search for co-locations between QTLs and genes involved in carbohydrate metabolism. The
main co-location was between an amylose QTL and Shrunken 2 (SH2) locus, on chromosome 3 (SH2 encoding for the large
subunit of ADPglucose pyrophosphorylase). The importance of this locus as a candidate gene for a starch QTL is in agreement
with previous studies based either on QTL co-locations or on revertant analysis. Other co-locations were observed between
amylose and amylopectin QTLs and the two loci of IVR1 invertase genes on chromosomes 2 and 10. Further comparison with
previously detected QTLs for carbohydrate metabolism in maize leaves showed consistent co-location in map regions devoid of
candidate genes, such as near chromosome 1S telomere. The possible contribution of regulatory genes in this region is discussed.
© 2000 Éditions scientifiques et médicales Elsevier SAS
Candidate gene / invertases / kernel / maize / QTL / Shrunken 2
AGPase, ADPglucose-pyrophosphorylase / AM, amylose / AP, amylopectin / cM, centiMorgan / DW, dry weight / INCW1,
INCW2, cell wall invertase genes / IVR1, IVR2, soluble invertase genes / QTL, quantitative trait locus / RIL, recombinant
inbred line / SH2, Shrunken 2 / SPS, sucrose phosphate synthase / Starch.T, total starch / Starch.I, insoluble starch / WSF,
water soluble fraction / WX, waxy
1. INTRODUCTION
Most traits of agricultural and biochemical importance, such as kernel starch content, display complex
inheritance because they are governed by several
genes, each of them contributing to a fraction of the
genetic variance. Environmental factors also modulate
§
Present address: Station de génétique et d’amélioration des
fruits et légumes, Domaine St-Maurice, BP 94, 84143 Monfavet, France.
gene expression, the final result being a continuous
distribution of the phenotypic values. The use of DNA
based markers (RFLP, RAPD) has allowed the construction of dense linkage maps in several crops,
making possible the localisation of loci controlling
quantitative trait variation (QTL) as in the pioneering
work of Paterson et al. [23]. Mapping of QTLs has
been documented for numerous traits: yield and yield
components [2, 10, 35], abiotic stresses [17, 29, 32],
insect [16] or pathogen resistance [11]. Crop biochemical composition has also been studied e.g. starch,
Plant Physiol. Biochem., 0981-9428/00/6/© 2000 Éditions scientifiques et médicales Elsevier SAS. All rights reserved
460
M. Séne et al.
oil, or protein content, in maize kernels [1, 13]. As
these traits are more directly related to the biosynthetic
pathways, one may expect to identify more easily the
genes corresponding to the QTLs [27]. Accordingly, a
co-location was pinpointed between one starch content
QTL and one Shrunken 2 (SH2) locus on chromosome 3 [13]; furthermore Giroux et al. [12] demonstrated later on, that point mutations in the 3’-end of
the Sh2 gene were correlated with changes in kernel
starch content.
Starch is the major component of cereal kernels; its
composition and structure are well documented in
maize endosperm [21, 25]. Starch is separable into
several components, all of them glucose polymers:
amylose (AM) an essentially linear unit, amylopectin
(AP) a branched molecule of very high molecular
mass (5·106) and phytoglycogen a highly branched and
soluble polymer. The AM:AP ratio is rather constant
and close to 20/80, in the wild type but it may be
strongly affected by mutations. Amylose-free kernels
are produced by the waxy mutation. Conversely, the
amylose extender mutation leads to a higher amylose
proportion than in the wild type. Sugary mutation
results in a large starch deficiency and a large increase
in water soluble fraction.
Progress in mutant characterisation and in biochemical analysis now allow a good understanding of
the starch biosynthetic pathway (figure 1). The sucrose
unloaded from phloem is cleaved by invertase (EC
3.2.1.26) in the basal endosperm, then re-synthesised
and further cleaved by sucrose synthase (EC 2.4.1.13).
The reaction products are used by ADPglucosepyrophosphorylase (EC 2.7.7.27, AGPase) synthesising ADP-glucose, the glycosyl donor for starch synthases (EC 2.4.1.21) forming alpha-1,4 glycosyl chains.
Then, branching enzymes (EC 2.4.1.18) form ramifications at alpha-1,6 positions on amylopectin and
phytoglycogen. The debranching enzymes simplify the
phytoglycogen, leading to amylopectin. The pathway
was ascertained by mutations in structural genes affecting each step. For example, the shrunken phenotypes
in maize grains are impaired in sucrose synthetase,
shrunken 1, in AGPase small and large subunits, brittle
2 and shrunken 2, respectively. The waxy and amylose
extender mutations are due to deficient granule bound
starch synthase (GBSS) and starch branching enzyme
(SBE IIa), respectively, whereas the sugary mutation
originates from an impairment of a starch debranching
enzyme (see Neuffer et al. [22] for review on mutants).
Now all the corresponding genes have been mapped
[5].
Plant Physiol. Biochem.
Figure 1. Present knowledge of the metabolic pathway for starch
synthesis in the developing maize kernel. A mutation on any of the
presented enzymes induces abnormal starch phenotypes which proved
their involvement in the pathway, at some period of kernel filling. Cell
wall invertase (1, in figure) seems to be required in the basal
endosperm, only in the first 10–15 d. The majority of ADPglucose
pyrophosphorylase is now considered to be located in the cytosol of
maize endosperm [8]. Enzyme numbering and corresponding mutations: 1, cell wall invertase/miniature 1 (mn1); 2, sucrose-synthase/
shrunken1 (sh1); 3, ADPglucose pyrophosphorylase/shrunken2 (sh2)
and brittle2 (bt2); 4, ADPglucose translocator/brittle1 (bt1); 5, GBSS
(granule bound starch synthases)/waxy; 6, SSS (soluble starch synthases); 7, branching enzymes/amylose-extender1 (ae1); 8, debranching enzymes/sugary1 (su1).
The purpose of this study was to combine the
physiological and biochemical knowledge on starch
metabolism with a QTL mapping approach in order to
get insights into the genetic determinism of starch
content and composition (i.e. amylose (AM), amylopectin (AP) and phytoglycogen estimated as water
soluble fraction (WSF)) in mature maize kernels, using
a recombinant inbred line (RIL) population derived
from a cross between a French flint and an American
dent genotype. The observed co-locations of QTLs and
major genes affecting starch biosynthesis were considered in order to identify potential candidate genes
involved in the genetic variation of starch accumulation and composition.
QTLs for starch in maize kernel
461
Figure 2. Reproducibility of amylopectin (AP)
and amylose (AM) measurements in 100 identical RILs in 1994. The two independent series
were measured after several months interval.
2. RESULTS
2.1. Reproducibility of amylose and amylopectin
measurements
The presently used colorimetric method [31] used
here for amylose and amylopectin determination was
adapted to maize for this experiment because it provided the possibility of measuring a great number of
samples in a reasonable amount of time. However, it is
potentially less precise than methods based on size
exclusion chromatography, for example. In order to
test its reliability, a preliminary analysis was done by
measuring independently two kernel samples from
each of the 100 identical RILs in the 1994 harvest. The
results in figure 2 show strong correlation (r = 0.81
and 0.85 for AP and AM, respectively) between the
two independent assays, which validate the method.
2.2. Correlation among traits and year effect
The two parental lines had previously been shown
to differ significantly for the measured traits [31]
(table I): starch and AM contents were lower in F-2
than in Io for both 1994 and 1995, while AP presented
large year-to-year differences, since the 1994 content
was lower in F-2 than in Io, and the reverse was true
in 1995. Water soluble fraction (WSF) was measured
only in 1994 and was higher in F-2.
The correlations between traits in the RILs are
presented in table II for the 2 years. The kernel starch
content was correlated to AP (r = 0.81 and 0.92) which
is not surprising as starch consists mainly of AP. By
contrast, the AM content was poorly but positively
correlated to starch in 1994 and not significantly in
1995. AM and AP were negatively correlated both
years. A negative correlation was observed between
amylopectin and water soluble fraction. Correlations
between values for the 2 years were examined for each
trait (figure 3). The 1994 and 1995 contents were
significantly correlated only for amylose and AM:AP
ratio. Comparison of the mean AM and AP values
showed that contents were several percents lower in
1995 than in 1994. Trait analysis and QTL detection
were thus performed independently for each year.
Table I. Total starch, amylose, amylopectin and water soluble fraction (WSF) contents in the two parental lines (Io and F-2) and the recombinant
inbred lines. RILs, Recombinant inbred lines; M, mean; SD, standard deviation; CV, coefficient of variation. Values are expressed as % kernel dry
weight. The coefficients of variation for amylose were 0.03 in Io and F-2. The corresponding figures were 0.02 and 0.05 for amylopectin in Io and
F-2, respectively (from [31]).
Years
1994
1995
Traits
Amylose
Amylopectin
Total starch
WSF
Amylose
Amylopectin
Total starch
Io
15.7
58.1
73.6
9.1
15.1
49.2
68.4
F-2
12.5
55.2
71.7
12.2
10.2
52.3
63.6
RILs
M
SD
CV
Range
12.1
50.6
68.3
7.7
13.4
44.0
57.2
2.33
3.84
4.11
3.40
2.66
6.40
5.36
0.19
0.08
0.06
0.45
0.20
0.15
0.09
8–19.2
42.3–62
59–80.2
0–15.1
6.9–19.4
31.0–61.3
45.6–70.2
vol. 38 (6) 2000
462
M. Séne et al.
Table II. Correlation coefficients between trait pairs in the RIL
population for 1994 and 1995 samples. ** Very significant = P < 1 %;
*** highly significant = P < 1 %; ns = non-significant.
WSF94
AM94
AP94
WSF94
AM94
AP94
Starch94
1
–0.06
–0.36**
0.37**
1
–0.31**
0.22**
1
0.81***
AM95
AP95
Starch95
AM95
1
–0.52***
–0.151ns
AP95
1
0.92***
For all traits, the frequency distribution fitted the
normal distribution and the phenotypic range was
larger than in the parents, since the highest RIL values
were larger than the best parent value and the lowest
lines had lower values than the lowest parent value.
Such an observation is common with segregant lines
and is indicative of transgressive variation which has
been well documented for various traits in many crops.
It is interpreted as the result of the recombination of
favourable or unfavourable alleles in the RILs [9].
2.3. Quantitative trait loci involved in starch
composition and content
QTLs detected by the MQTL composite interval
method (CIM) with five to nine co-factors depending
on the trait are presented in figure 4 for the two
successive years. The two measurements from 1994
were averaged. Water soluble fraction was not measured in 1995, thus the traits WSF95 and Starch.T95
were not available. Thirteen QTLs were detected for
seven of the ten traits evaluated. No QTL were
detected either for total or insoluble starch content, and
one (AM94, AP94 and AM95) to three (AP95, WSF94)
QTLs were found for the other traits (table III).
In 1994, a QTL for AP was detected on the short
arm telomere of chromosome 2, an AM QTL was
located at the other telomeric end of chromosome 3,
near the SH2 locus and two QTLs for AM:AP ratio
were detected: a) on the first quarter of chromosome 9,
at 8 cM from the WAXY locus (gsy200 WX); b) in the
second-third of chromosome 10 close to the soluble
invertase locus IVR1(gsy348a). Water soluble fraction
content showed three QTLs: a) and b) middle of
chromosomes 4 and 5 (gsy419, ATPsynthase locus,
and gsy403_PAL, phenylalanyl-ammonialyase locus,
Figure 3. Correlation between years 1994 and
1995 for amylose (A), AM/AP ratio (B), amylopectin (C), and starch (D) content in grains from
100 recombinant inbred lines originating from Io ×
F-2 cross.
Plant Physiol. Biochem.
QTLs for starch in maize kernel
463
Figure 4. Mapping of QTLs by MQTL for amylose (AM), amylopectin (AP), amylose/amylopectin ratio (AM:AP), insoluble starch (Starch.I), total
starch (Starch.T) and water soluble fraction (WSF) in maize grains grown in 1994 and 1995 (94 and 95 suffix, respectively). All values are expressed
as % kernel dry weight. The recombinant inbred lines originated from the Io and F-2 parent lines (8th generation). Continued on following page.
vol. 38 (6) 2000
464
M. Séne et al.
Figure 4. Cont’d. Main effect QTLs are displayed by black bars when effect is from the Io parent, and by open bars for F-2 parent. Interaction
QTLs are represented by stippled bars, the position of the other pair member is given in italics by chromosome # and distance in cM from the
telomere top. Whatever the QTL type, the bar length is proportional to the R2 value. The full description of marker names placed along each
chromosome is reported in Causse et al. [5].
Plant Physiol. Biochem.
QTLs for starch in maize kernel
465
Table III. Influence of the detection method on the number and position of QTLs for starch components in a population of 100 RILs for two
successive years (1994 and 1995). Marker by marker detection (ANOVA, P = 0.01), compared to composite interval mapping (MQTL). The allele
providing the higher value for the trait is coded by a negative sign for Io and a positive one for F-2.
Traits
AP95
AM95
AP95
AM:AP95
AP95
AP94
AP94
AM95
AP95
AM:AP95
AM:AP95
AP95
AM95
AM94
AM:AP94
WSF94
WSF94
AP95
AM:AP95
AP94
AM:AP94
AM:AP94
AM94
WSF94
ANOVA (P = 0.01)
Allele effect
R2 (%)
–0.76
9.3
–0.03
9.0
1.16
–0.70
1.94
–0.03
–0.04
1.99
–1.01
0.78
0.02
–1.23
9.0
7.9
8.7
10.1
14.1
8.4
15.4
12.2
11.4
10.9
1.77
–0.02
–1.17
7.7
7.7
9.6
0.02
0.70
1.09
11.8
10.1
8.8
Chromosome and loci
chr. 1
chr. 1
chr. 1
chr. 2
chr. 3
chr. 3
chr. 3
chr. 3
chr. 4
chr. 5
chr. 6
chr. 9
chr. 10
respectively); c) long arm of chromosome 10 (gsy348a,
soluble invertase IVR1 locus).
In 1995, a similar number of QTLs were detected
but, at the chosen threshold, no QTL was detected at
the same position as in 1994 for the same trait. Such a
result could be anticipated in view of the low correlation coefficients obtained between years for most of
the traits. Two AP95 QTLs were detected in close
proximity at the telomeric end of chromosome 1
(gsy297a_EMB and gsy271_P), with opposite allelic
effects. The detection curve showed two clear peaks
above 1, and the test values were highly significant:
P < 0.0007 for the Io allele QTL, and P < 0.0001 for
the F-2 allele QTL. Such detection appears consistent
with the reported power of CIM methods to detect
close QTLs with opposite effects. Another AP95 QTL
was found at 175 cM further along the same chromosome 1, at ‘adh1_iso’ locus. The only AM95 QTL
mapped near the middle of chromosome 3
(gsy406a_6PGDH) at the same place as an AM:AP95
QTL. A possible co-location of QTLs for related traits
between the 2 years was another AM:AP95 QTL in the
gsy297a_EMB
gsy271_P
idem
idem
ADH1_iso
umc6
umc92
umc10
idem
idem
gsy406a_6PGDH
idem
idem
gsy58_SH2
idem
gsy419_AT
gsy403_PAL
idem
idem
umc65
gsy89
gsy348a_IVR1
idem
idem
MQTL (co-factors < 10)
R2 (%)
Allele effect
8.7
–6.8
12.3
8.8
2.9
5.2
5.6
4.3
8.2
–2.0
8.5
–0.07
10.1
1.5
11.4
7.8
–3.0
–2.9
7.1
–0.06
6.6
7.8
0.03
0.04
6.5
3.3
central part of chromosome 5 (gsy403_PAL) close to
the WSF94 QTL.
The low number of QTLs detected for each trait
(0–3) compared with results frequently reported in the
literature may be explained by two factors: a low trait
heritability, and/or the choice of a detection method
which is more stringent than conventional analyses
based on MAPMAKER/QTL with a LOD threshold of
2 or ANOVA with P = 0.01. In order to try to clarify
this point, we compared the MQTL results with those
from ANOVA using a 1 % threshold (table III). Under
these conditions, ANOVA results are theoretically
more subject to type I error than those from MQTL.
This argument may be challenged by two facts: a) the
permutation test may not be robust when co-factors are
used; and b) the estimate of a global type I risk is
difficult for ANOVA as the markers are not independent [14]. For the seven traits showing main effect
QTLs, eighteen QTLs were detected by ANOVA vs.
thirteen by MQTL, seven being detected by both
methods. However, when examining our data (table III),
it is doubtful that all the QTLs detected by ANOVA
vol. 38 (6) 2000
466
M. Séne et al.
and not by MQTL are false, as some of them were
frequently located close to QTLs for similar traits or
where likely candidate genes are located. All the
genetic distances, derived from recombination rates,
are measured in centiMorgan (cM) from the top of
each chromosome. The confidence intervals for QTL
position cannot be easily determined [18]; however,
simulation studies (Agnès Leonardi, pers. comm.)
have shown that 10 to 20 cM is a likely range. Taking
this range into account, QTL clusters are apparent on
chromosomes 1 (gsy297a_EMB – gsy271_P), 3 (umc10
– gsy406a_6PGDH and gsy58_SH2), 5 (gsy403_PAL)
and 10 (gsy348a_IVR1) when comparing the two
columns of table III. Close examination of the graphical display of the test statistics (TS) output from
MQTL showed, in fact, that these additional QTLs
detected by ANOVA were frequently just below the
threshold value (not shown). These co-locations
revealed by lowering the threshold might be meaningful, since the probability of co-location by chance of
several QTLs for two traits is very low, except if they
rely on common genetic determinants.
Examination of the parental allele effects showed
that favourable effects over the 2 years are well balanced between Io and F-2, eleven vs. thirteen, respectively, when pooling all traits and both methods
(table III). However, large differences appeared when
looking at trait by trait. In 1995 for AP, most of the
beneficial alleles were from F-2 (5/1) while in 1994
F-2 brought favourable alleles in two cases compared
with one in Io. The fact that a greater number of
favourable F-2 alleles is present in 1995 may be
consistent with a higher AP content in F-2 than in Io in
1995 but not in 1994. Similarly the three AM-favourable
alleles originated from Io in 1995, compared with two
F-2 favourable alleles in 1994, which is also consistent
with the relative contents (table III). For the water
soluble fraction (measured only in 1994), the favourable alleles came from both parents (two from Io and
one from F-2).
2.4. Epistasis
Genetic interaction between loci may play an important role in the determinism of some traits [7, 9]. This
can be investigated by testing interactions between all
pairs of markers by two-way ANOVAs. Twelve interactions were detected at a P < 0.0005 threshold, compared to thirteen main effect QTLs (figure 4). Either
total or insoluble starch content, that had no main
effect QTLs, showed four significant interacting QTLs
in 1994. The highest number of interaction QTLs (6)
was found for AM in both years. Whatever the trait, a
Plant Physiol. Biochem.
large increase of the variation explained was observed
when including interactions in the model, with R2
values ranging from ca. 12 % (Starch) to 38 % (WSF).
Part of the interaction QTLs mapped in the same
region as main effect QTLs for similar or related traits
are: on chromosome 1, AP95 and AM:AP95-inter; on
chromosome 2, AP94, Starch.T94-inter, and Starch.I94inter; on chromosome 3, AP95-inter, AM95 and
AM:AP95; on chromosome 10, AM94-inter, AM95inter, WSF94 and AM:AP94. However, a large part of
the interaction QTLs mapped away from main effect
QTLs. They formed clusters on chromosomes 3 and 4,
and only interaction QTLs were detected on chromosomes 6, 7 and 8. On chromosome 4, the interaction
QTLs for 1995 and 1994 are co-located.
2.5. Relationships between starch components
The contents of amylose, amylopectin, the two
components of starch, and phytoglycogen (water soluble
fraction), a likely intermediate of amylopectin, are not
well correlated (table II). This is confirmed by the
QTL distribution since AM and AP QTLs were not
co-located. However, among the four amylose/
amylopectin QTLs detected by MQTL over the 2 years,
three of them were co-located with other starch component QTLs: AM:AP95 with AM95 on chromosome 3, AM:AP95 with WSF94 on chromosome 5 and
AM:AP94 with WSF94 on chromosome 10. These
co-locations between AM:AP and AM or AP were
confirmed by ANOVA (table III) which provided some
more associations with AM95 and AP95 on chromosome 1 (gsy271_P at 19–23 cM) and chromosome 3
(umc10 at 36 cM). Taking into account the epistasis
QTLs and the main effect QTLs, two clusters appeared
on chromosomes 1 (at 23 cM) and 10 (at 90 cM)
(figure 4). On chromosome 10, the AM:AP94 QTL
was close to AM94-inter, AM95-inter and WSF94.
Although no main effect QTL was detected for total
starch content either by MQTL or by ANOVA (table III),
two epistasis QTLs mapped close to the AP94 QTL at
14 cM on chromosome 2.
2.6. QTL candidate genes from starch
biosynthetic pathway
Taking advantage of the location of numerous
known function genes on our map, we looked for
co-locations between starch component QTLs and
genes involved in carbohydrate metabolism, in order
to detect candidate genes explaining trait variations.
The main genes involved in kernel starch biosynthesis
are located on chromosome 3 (for AGPase large sub-
QTLs for starch in maize kernel
unit, gsy58_SH2, Shrunken 2), on chromosome 4 (for
AGPase small subunit, gsy59c_BT2, Brittle2) and (for
starch debranching enzyme, gsy315_SU1, Sugary 1),
on chromosomes 2 and 10 (for the two loci of vacuolar
invertase IVR1, gsy348c and gsy348a, respectively),
on chromosome 5 (for cell wall invertases,
gsy343b_INCW1), and on chromosome 9 (for sucrosesynthase, gsy66_SH1, Shrunken 1 and for granule
bound starch synthase, gsy200_WX, waxy mutation)
(figure 4). Assuming a 10–20-cM confidence interval
for QTL position, we considered that the co-locations
between candidate gene and QTL may be significant
when distances were lower than 10 cM.
One QTL (AM:AP94) detected on chromosome 9
was located between the SH1 and WX loci but closer
to WX (9 cM). Thus we considered that this co-location
may be significant. Mutations of those two genes are
known to affect starch (SH1) and amylose content
(WX), respectively. QTLs located in three other regions
are also close to genes of interest: a) on chromosome 2, the AP94 QTL and two Starch-inter QTLs at
15 cM from the vacuolar invertase locus IVR1
(gsy348c); b) on chromosome 10, at another locus of
the same probe (gsy348a) and QTLs for two similar
traits (AM94, AM:AP94 and AM95); c) on chromosome 3, an AM94 QTL located exactly at the SH2
locus (gsy58). In addition, more distant co-locations
were noted: a) on chromosome 4, a WSF QTL was
rather far (40 cM) from the Sugary1 locus
(gsy315_SU1) but closer (26 cM) to the Brittle2 locus
(gsy59c_BT2); b) on chromosome 5, another WSF
QTL and an AM:AP95 QTL were 18 cM from the cell
wall invertase locus INCW1 (gsy343b_INCW1). Less
obvious candidates appeared on chromosome 1 since it
is not clear whether the SH2 locus located 20 cM
downstream of the AP95, AM:AP95 QTLs corresponds to an expressed gene. Similarly, QTLs associated with AM95 in the middle of chromosome 3 are
located near 6-phosphogluconate dehydrogenase and
SPS loci, but a direct involvement of these enzyme
loci in kernel starch biosynthesis has not been established.
Examination of the interaction QTLs suggested
further candidates. The AM:AP95 QTL-inter on chromosome 1 interacts with the SPS locus on chromosome 8; Starch.I94 QTL-inter on chromosome 2 interacts with a BT2 locus on chromosome 8; AM95
QTL-inter on chromosome 10 interacts with another
SPS locus on chromosome 6, although AM94 QTLinter from the same chromosome 10 region interacts
with another region of chromosome 6 close to two loci
related to starch synthesis: AGP1 and BT1 (figure 4).
467
3. DISCUSSION
In maize kernels, the control of genetic variation in
starch synthesis can occur at various levels of that
complex biosynthesis pathway, making valuable a
QTL approach, as previously shown by Goldman et al.
[13] using lines divergently selected for low or high
kernel protein, for 76 generations. The RIL population
used in this study is more likely representative of the
natural variability occurring in lines commonly used
for breeding. QTLs were sought not only for total
starch but also for its components (AM, AP, WSF),
from data collected at the same place, in two successive years, and under two pollination systems.
Large year-to-year differences in QTL location for a
particular trait were observed, which addresses the
question of inbreeding effect and of QTL-environment
interactions. The comparison of self-pollination vs.
allo-pollen fertilisation, by Bulant and Gallais [3],
showed that the yield was consistently, ca. 12 %, lower
in self-pollinated plants over 3 years, at the same
experimental site as in this experiment. Thus, the
lower starch content observed in 1995 might be, at
least partly explained by a self-pollination effect.
However a well known problem, when dealing with
yield QTLs, is the great influence of environment, and
especially drought which is critical during the grain
filling period [28, 35, 36]. In the case of water stress,
the evidence of stress specific QTLs is now established
[24, 29, 32]. In terms of physiology, this means that a
different set of genes was probably expressed under
stress conditions. Examination of weather records
from July 1st to mid-August showed that the number
of days with maximum temperature higher than 30 °C
was 13 in 1994 and 17 in 1995. During August, which
corresponded to the first phase of grain filling in most
RILs, the average temperature was 2 °C higher in 1995
than in 1994 (18.7 vs. 20.7 °C). Thus, the high
temperature coupled to a low humidity during the
1995 grain filling period could also explain the difference with 1994. Under this hypothesis, the 1995-QTLs
could be associated with stress whereas those from
1994 would represent more normal conditions. The
lower starch yield in 1995 is also consistent with this
hypothesis. In other cereals, similar problems associated with high temperature have been described: in
wheat, above 30 °C, a slower starch deposition rate
was associated with reduction of the soluble starch
synthase activity [15].
One to three main effect QTLs were detected for all
traits except for total starch (% DW). It is worth noting
that traits for starch and for its components resulted in
vol. 38 (6) 2000
468
M. Séne et al.
this study from independent measurements since starch
was measured enzymatically whereas AM and AP
determinations were colorimetric. As the first method
is much more precise than the second, the experimental variance is likely to have been lower. Thus the poor
starch QTL detection may be due to a low genetic
variability as suggested in figure 2 when comparing
starch and AP scattering. Furthermore, in the present
study, the parent lines were not very different in starch
content, as compared with the Goldman et al. [13]
material. Another point to consider is that starch
represents the major part of kernel DW (72–75 %),
thus introducing a physical constraint for upward
variation. The existence of such a constraint could
explain why genetic alteration of the SH2 gene in the
starch pathway did not increase kernel starch content
but rather the starch amount per kernel [12]. However,
looking for epistasis in the control of starch content
allowed the detection of two pairs of interaction QTLs
in 1994. In one pair, the QTLs co-located with an
AP94 QTL on chromosome 2 and the Bt2 locus on
chromosome 8, respectively. In the other pair, one
QTL also mapped close to the AP94 QTL on chromosome 2. Both co-locations may be physiologically
meaningful since the common precursor of AM and
AP is ADPglucose through the action of the
ADPglucose-pyrophosphorylase encoded by the BT2
and SH2 genes.
Amylose, amylopectin and water soluble fraction
QTLs were not frequently co-located (figure 4) which
could be anticipated from the fact that once ADPglucose is synthesised, their synthetic pathways are separated (figure 1). The low correlation between these
traits (table II) is consistent with the low frequency of
QTL co-locations (figure 4). For instance, the only
case of co-location between AM and AP QTLs was for
an interaction QTL in the cluster on chromosome 4.
However ANOVA results (table III) showed further
associations on chromosome 1 (gsy271_P) and chromosome 3 (umc10) for AM95 and AP95. WSF mapped
twice on chromosomes 4 and 5 and co-located once on
chromosome 5 with AM:AP95. Most of the other
co-locations appeared with AM:AP, as expected from a
composite trait. Over the eight AM:AP QTLs detected
on chromosomes 1, 3, 4, 5, 8, 9, 10 (figure 4), three
main effect and three interaction QTLs co-located with
other starch component QTLs; association with AM
(on chromosome 3 at two locations, on chromosomes 4
and 10) was more frequent than with AP (on chromosomes 1, 3 and 4), suggesting that the AM:AP ratio in
mature grain might be controlled more strongly by the
Plant Physiol. Biochem.
amount of AM rather than by that of AP in the progeny
studied here.
The identification of candidate genes is based on the
widely accepted idea that a QTL corresponds to a
slightly altered gene allele and that a major QTL in one
genetic background could look like a mutation in
another. But, in general, mutations are likely to be
more extreme allelic (or null) forms of QTLs. Most
genes for starch synthesis were first known from large
mutations leading to altered kernel phenotype
(shrunken, brittle, waxy, miniature, etc.). Then, they
were mapped and their functions uncovered. Our map
consists mainly of loci derived from known function
probes, and many of them encode for enzymes of
carbohydrate metabolism and especially of the starch
biosynthetic pathway [5]. Thus, co-locations between
starch component QTLs and these probe loci may be
fruitful in detecting possible candidate genes explaining the trait variations.
The most documented candidate in this experiment
is the SH2 gene located on chromosome 3 at the gsy58
locus, at the same position as an AM94 QTL. A similar
co-location has been previously described by Goldman
et al. [13] for starch QTLs. In a separate experiment,
we tried to test the validity of SH2 as a candidate by
examining the gene polymorphism in 46 genetically
diverse lines, in relation to kernel starch. The significant correlation obtained between one restriction pattern and starch content represents a first step in the
candidate validation [26]. The role of SH2 as a
candidate gene for starch content variation was further
supported by a totally different strategy [12] through
the analysis of series of revertants after Ac/Ds
transposition/excision at the SH2 locus. The imperfect
excision of Ds element produced micro-alterations in
the 3’-region of the gene, leading to larger kernels in
one revertant without alteration of the starch content
relative to dry weight. However, this observation does
not fully account for our results since the observed
QTL at the SH2 locus was for amylose and not for
amylopectin. Actually, data from various starch deficient mutants in Chlamydomonas may provide some
explanations since a mutation in the large sub-unit of
ADPglucose-pyrophosphorylase reduced ADPglucose
supply, and altered AM synthesis more than AP,
because the granule bound starch synthase might have
less affinity for ADPglucose than the other starch
synthases involved in AP synthesis [34]. Another
related candidate may be inferred from the close
location between the AM:AP94 QTL and the WX
locus on chromosome 9, as the corresponding gene
QTLs for starch in maize kernel
encodes for the granule bound starch synthase whose
deficiency leads to an amylose-free phenotype.
Other co-locations of interest occurred between
QTLs and enzyme structural genes. In the case of
soluble acid invertase, two loci of the same gene
(IVR1) on chromosomes 2 (gsy348c) and 10 (gsy348a)
showed co-location with starch, AM and/or AP QTLs.
The fact that similar QTLs were associated with
homologous invertase loci is likely to indicate that
such associations may be pertinent since IVR1 is
expressed in reproductive organs [37]. Also, a cell wall
invertase gene (INCW2) is located on chromosome 2
26 cM downstream from the IVR1 locus and close to
a AM95-inter QTL. The mutation of INCW2 produces
a miniature-1 grain phenotype [20] due to a depression
of both soluble and insoluble invertase activities. The
mutation was also mapped on chromosome 2 at the
INCW2 locus.
A large proportion of the interaction QTLs mapped
in regions where no main effect QTLs were found,
namely on chromosomes 6, 7 and 8. However
co-locations with some copies of structural gene may
be significant: for example, AM:AP95-inter with a
SPS locus on chromosome 8, Starch.I94 QTL-inter
with a BT2 locus on chromosome 8, AM95-inter with
another SPS locus on chromosome 6, and AM94 with
another region of chromosome 6 close to two loci
related to starch synthesis: AGP1 and BT1 (figure 4).
All these co-locations may be physiologically pertinent since: a) SPS encodes for a sucrose-phosphate
synthase which is likely to be involved in the
re-synthesis of sucrose in the grain after its initial
hydrolysis in the basal endosperm cell layer; b) BT2
and AGP1 correspond to AGPase subunits, expressed
in endosperm and embryo respectively; c) BT1 encodes
for the ADPglucose transporter in the amyloplast
envelope. It remains to be proven however that these
loci correspond to genes expressed in kernels.
Comparison of the present QTL locations with those
for carbohydrate metabolism QTLs in maize mature
leaves [4] shows striking similarities on chromosomes 1 and 10. Thus, on chromosome 1 (umc11 to
gsy59b_SH2), QTLs for leaf sucrose-synthase activity,
hexose and sucrose contents were found in the same
region as two main effect QTLs for grain AP content
and one interaction QTL for grain AM:AP level. In
addition, on chromosome 10 (gsy15_POL to umc44b),
QTLs for leaf AGPase and invertase activities, and
sucrose content were found in the same region as two
main effect QTLs for grain WSF and AM:AP level and
two interaction QTLs for grain AM content. One
possible explanation for these QTL clusters for related
469
traits could be that the candidate genes would correspond to common regulatory genes for the pathway, as
shown by McMullen et al. [19] in the case of anthocyanin biosynthesis, or that they correspond to the
expression of the same structural genes transcribed in
different organs.
4. METHODS
4.1. Genetic material and sampling
The maize (Zea mays L.) recombinant inbred lines,
used for mapping, were derived from a cross between
an American dent line (Io) and a French early flint line
(F-2) followed by eight generations of self-pollination.
The two parents and a sample of 100 RILs, taken
among the 145 lines from the mapping population,
were grown in the field at Gif-sur-Yvette, France
(20 km SW Paris) in 1994 and 1995. Plants were
planted in a complete randomised two-block design. In
each block, each line plot consisted of ten plants in a
row. Open-pollinated (1994) or self-pollinated (1995)
ears from three plants in each block were collected at
maturity. Six grains picked in the central zone of the
six ears were mixed and, among these 36 grains, eight
grains were sampled for further biochemical measurements.
4.2. Biochemical and chemical analysis of starch
Starch samples were prepared from eight kernels,
air-dried overnight at 50 °C and ball-milled to a fine
powder. A sub-sample (70 mg) was defatted by twice
shaking in 2 mL 70 % acetone. After centrifugation the
pellet was suspended in 1 mL water and solubilized in
4 mL 5 M NaOH under constant stirring at ambient
temperature during 1 h, then neutralised with HCl.
Starch amount was determined by an enzymatic coupled
assay by hydrolysis with amyloglucosidase (Boehringer 150 units·mL–1) in 0.3 M citrate buffer pH 4.6
for 1 h at 50 °C. The generated glucose was spectrophotometrically determined after hexokinase and glucose 6-phosphate dehydrogenase treatment, in the
presence of NAD and ATP, as described in Séne et al.
[31]. Water soluble fraction was extracted from a 1-g
subsample of maize kernel powder, by deionized water
under constant stirring during 1 h at ambient temperature. The iodine spectrum of the supernatant containing soluble starch corresponds to that of phytoglycogen (brown colour), but the presence of some
oligosaccharides cannot be excluded. This supernatant
was submitted to amyloglucosidase and then to
vol. 38 (6) 2000
470
M. Séne et al.
hexokinase/glucose 6-phosphate dehydrogenase in the
presence of NAD and ATP. Two variables were derived
from these measurements: Starch.I94, insoluble starch
and Starch.T94, soluble + insoluble starch, so-called
total starch. The insoluble starch corresponds to starch
granules, it represents at least 90 % of total starch.
So-called soluble starch corresponds to the water
soluble fraction. Amylose and amylopectin contents
were measured simultaneously using the iodine colorimetric method fully described in Séne et al. [31] and
which is based on the difference in colour of iodine
complex with amylose (blue) and with amylopectin
(red). The starch neutral solution was diluted four
times and a 30-µL aliquot was added to a spectrophotometric cuvette containing 0.92 mL 0.05 M phosphate
buffer (pH 7). Then, 50 µL iodine solution (0.2 g
iodine in 100 mL, 85 % v/v DMSO) was added. The
colour was allowed to develop for 10 min and absorbance was determined at six wavelengths (530, 545,
560, 600, 630, 680 nm). Standard solutions containing
AM (15 µg·mL–1) and AP (75 µg·mL–1) and mixture
AM + AP (15 + 75 µg.mL–1) were used. An Excel 4
(Microsoft) spreadsheet allowed the resolution of the
six equations derived from the Lambert-Beer’s relation:
A = eo共 AM 兲 × C共 AM 兲 + eo共 AP 兲 × C共 AP 兲
The extinction coefficients eo(AM) and eo(AP) were
experimentally determined for each wavelength using
AM and AP standards [31].
4.3. Genetic map
The genetic map was constructed using 145 RILs
and 148 loci were revealed using 130 RFLP probes
corresponding to expressed sequences (gsy codes) and
anchor markers already mapped in other populations
(University of Missouri - Columbia (umc) and
Brookhaven National Laboratory (bnl)). In addition,
isozyme marker loci were mapped. They were based
on four enzymes involved in carbon metabolism:
malate dehydrogenase (MDH1 and 2), isocitrate dehydrogenase (IDH2) and alcohol dehydrogenase (ADH1).
Twenty-seven other mapped loci were related to carbohydrate metabolism, 26 corresponding to enzyme
structural genes of the starch biosynthetic pathway and
one to a putative sugar transporter. The enzyme
structural genes code for AGPase (SH2, BT2, BTL2,
AGP1 loci), GBSS (WX locus), starch branching
enzyme (SBE I, SBE II loci), starch debranching
enzyme (SU1 locus), sucrose synthase (SH1, SUS1
loci), vacuolar invertase (IVR1, IVR2 loci), cell wall
Plant Physiol. Biochem.
invertase (INCW1, INCW2 loci), and sucrose phosphate synthase (SPS locus) (see [4] and [25] for
complete references on these probes).
4.4. Quantitative trait locus detection
Both single-marker analysis (ANOVA) and composite interval mapping (MQTL-CIM) [33] were used for
QTL detection. In the case of MQTL, special care was
taken for the choice of co-factors by stepwise regression. A cut-off was placed just where a drop in the
partial R2 values was evident in the ordered list of
co-factors (the minimum R2 was usually more than
0.03), so that the final number of co-factors varied
from 5 to 9 according to the trait. Increasing the
number of co-factors in MQTL up to 20, which is the
limit (2*SQUARE_ROOT(N), N being the number of
RILs) proposed by Sakamoto et al. [30] increased the
number of detected QTLs by a factor of 2, as 27 QTLs
were obtained (data not shown) instead of thirteen.
This shows the great sensitivity of the method to the
number of co-factors used. Simulation calculations
with known QTLs have shown that reducing the
number of co-factors strongly reduced the false positive (type I error) without affecting true positive
detection (type II error) (A. Leonardi, pers. comm.).
For each trait, the TS (test statistics) was calculated as
a function of the ratio of the residual sums of squares
in the full model to the residual sums of squares of a
model without the effect being tested. A permutation
test was used for determining a threshold test value
corresponding to a 5 % type I error for the whole
genome [6]. For each trait, the TS values were then
divided by the threshold value obtained from the
permutation test, and the ratio was plotted along the
chromosomes. QTLs were retained at each peak above
the TS ratio value of 1. Confidence intervals for QTL
position, evaluated by simulation, were estimated as
10–20 cM (Leonardi, pers. comm.). The actual value is
dependent on a great number of parameter: detection
method, marker density, etc. [18]. Pearson correlation
coefficients were calculated using the PROC CORR
procedure. The SAS package (SAS Institute, Cary,
NC) was also used for ANOVA and all the other
calculations before and after the MQTL procedure.
Epistasis QTLs were searched using analyses of
variance, checking for significant interaction between
all pairs of markers after removal of the part of
variation explained by the main effect QTLs. A
P < 0.0005 threshold was used, which would yield by
chance five false QTLs if the more than 10 000 tests
were independent, which is not the case because of the
genetic linkage between markers. Although difficult to
QTLs for starch in maize kernel
estimate, the expected number of false positives may
be lower. As a supplementary criterion, interactions
having a probability three times that of the less
probable main effect QTL were discarded. When
several pairs of markers yielded significant interaction
but involved genetically linked markers, only the most
significant pair was kept. Then, the combination of
interactions and main effect QTLs yielding the highest
R2 value was chosen as the final model to account for
trait variation.
Acknowledgments
We gratefully acknowledge Dr Agnès Leonardi
(Station de génétique végétale) for fruitful discussions
on the statistical bases and the genetic meaning of the
QTL detection methods, and for sharing results of
simulations and providing numerous program/software
utilities for using MQTL.
REFERENCES
[1] Alrefai R., Berke T.G., Rocheford T.R., Quantitative
trait locus analysis of fatty acid concentrations in
maize, Genome 38 (1995) 894–901.
[2] Beavis W.D., Grant D., Albertsen M., Fincher R.,
Quantitative trait loci for plant height in four maize
populations and their associations with quantitative
genetic loci, Theor. Appl. Genet. 83 (1991) 141–145.
[3] Bulant C., Gallais A., Xenia effects in maize with
normal endosperm: I. Importance and stability, Crop
Sci. 38 (1998) 1517–1525.
[4] Causse M., Rocher J.P., Henry A.M., Charcosset A.,
Prioul J.L., de Vienne D., Genetic dissection of the
relationship between carbon metabolism and early
growth in maize, with emphasis on key-enzyme
loci, Mol. Breeding 1 (1995) 259–272.
[5] Causse M., Santoni S., Damerval C., Maurice A.,
Charcosset A., Deatrick J., de Vienne D., A composite
map of expressed sequences in maize, Genome 39
(1996) 418–432.
[6] Churchill G.A., Doerge R.W., Empirical threshold
values for quantitative trait mapping, Genetics 138
(1994) 963–971.
[7] Damerval C., Maurice A., Josse J.M., de Vienne D.,
Quantitative trait loci underlying gene product variations - a novel perspective for analyzing regulation of
genome expression, Genetics 137 (1994) 289–310.
[8] Denyer K., Dunlap F., Thorbjornsen T., Keeling P.,
Smith A.M., The major form of ADP-glucose pyrophosphorylase in maize endosperm is extraplastidial, Plant Physiol. 112 (1996) 779–785.
471
[9] De Vicente M.C., Tanksley S.D., QTL analysis of
transgressive segregation in an interspecific tomato
cross, Genetics 134 (1993) 585–596.
[10] Edwards M.D., Helentjaris T., Wright S., Stuber C.W.,
Molecular-marker-facilitated
investigations
of
quantitative-trait loci in maize. 4 Analysis based on
genome saturation with isozyme and restriction fragment length polymorphism markers, Theor. Appl. Genet.
83 (1992) 765–774.
[11] Geffroy V., Sévignac M., Fouilloux G., Thoquet P.,
Gepts P., Langin T., Dron M., Inheritance of partial
resistance against Colletotrichum lindemuthianum in
Phaseolus vulgaris and co-localisaton of QTL with
genes involved in specific resistance, Mol. PlantMicrobe Interact. 13 (2000) 287–296.
[12] Giroux M.J., Shaw J., Barry G., Cobb B.G., Greene T.,
Okita T., Hannah L.C., A single gene mutation that
increases maize seed weight, Proc. Natl. Acad. Sci.
USA 93 (1996) 5824–5829.
[13] Goldman I.L., Rocheford T.R., Dudley J.W., Quantitative trait loci influencing protein and starch concentration in the Illinois long term selection maize
strains, Theor. Appl. Genet. 87 (1993) 217–224.
[14] Hyne V., Kearsey M.J., Pike D.J., Snape J.W., QTL
analysis: unreliability and bias in estimation procedures, Mol. Breeding 1 (1995) 273–282.
[15] Jenner C.J., Starch synthesis in the kernel of wheat
under high temperature conditions, Austr. J. Plant
Physiol. 21 (1994) 791–806.
[16] Knipple D.C., Doyle K.E., Marsellaherrick P.A., Soderlund D.M., Tight genetic linkage between the kdr
insecticide resistance trait and a voltage-sensitive
sodium channel gene in the house fly, Proc. Natl. Acad.
Sci. USA 91 (1994) 2483–2487.
[17] Lebreton C., Lazic Jancic V., Steed A., Pekic S.,
Quarrie S.A., Identification of QTL for drought
responses in maize and their use in testing causal
relationships between traits, J. Exp. Bot. 46 (1995)
853–865.
[18] Mangin B., Goffinet B., Comparison of several confidence intervals for QTL location, Heredity 78 (1997)
345–353.
[19] McMullen M.D., Byrne P.F., Snook M.E., Wiseman
B.R., Lee E.A., Widstrom N.W., Coe E.H., Quantitative trait loci and metabolic pathways, Proc. Natl.
Acad. Sci. USA 95 (1998) 1996–2000.
[20] Miller M.E., Chourey P.S., The maize invertasedeficient miniature-1 seed mutation is associated with
aberrant pedicel and endosperm development, Plant
Cell 4 (1992) 297–305.
[21] Nelson O., Pan D., Starch synthesis in maize
endosperm, Annu. Rev. Plant Physiol. Plant Mol. Biol.
46 (1995) 475–496.
[22] Neuffer M.G., Coe E.H., Wesseler S.R., Mutants of
maize, in: Neuffer M.G., Coe E.H., Wesseler S.R.
(Eds.), Cloned Genes, Cold Spring Harbor Laboratory
Press, 1997, pp. 325–388.
vol. 38 (6) 2000
472
M. Séne et al.
[23] Paterson A.H., Lander E.S., Hewitt J.D., Paterson S.,
Lincoln S.E., Tanksley S.D., Resolution of quantitative
traits into mendelian factors by using a complete
linkage map of restriction fragment length polymorphisms, Nature 335 (1988) 721–726.
[24] Pelleschi S., Recherche de locus à effet quantitatif, liés
au métabolisme glucidique au cours d’une contrainte
hydrique, chez le maïs (Zea mays L.), Ph.D. thesis,
université Paris-Sud, France, 1997.
[25] Prioul J.L., Corn, , in: Zamski E., Shaffer A.A. (Eds.),
Photoassimilate Distribution in Plants and Crops, Marcel Dekker Publisher, New York, 1996, pp. 549–593.
[26] Prioul J.L., Pelleschi S., Séne M., Thévenot C., Causse
M., de Vienne D., Leonardi A., From QTLs for enzyme
activity to candidate genes in maize, J. Exp. Bot. 50
(1999) 1281–1288.
[27] Prioul J.L., Quarrie S., Causse M., de Vienne D.,
Dissecting complex physiological functions through
the use of molecular quantitative genetics, J. Exp. Bot.
48 (1997) 1151–1163.
[28] Ragot M., Sisco P.H., Hoisington D.A., Stuber C.W.,
Molecular-marker mediated characterization of favorable exotic alleles at quantitative trait loci in
maize, Crop Sci. 35 (1995) 1306–1315.
[29] Ribaut J.M., Jiang C., Gonzales-de-Leon D., Edmeades
G.O., Hoisington D.A., Identification of quantitative
trait loci under drought conditions in tropical maize. 2.
Yield components and marker-assisted selection strategies, Theor. Appl. Genet. 94 (1997) 887–896.
[30] Sakamoto Y., Ishiguro M., Kitagawa G., Akaike Information Criterion Statistics, KTK Scientific Publishers,
Tokyo, 1986.
Plant Physiol. Biochem.
[31] Séne M., Thévenot C., Prioul J.L., Simultaneous spectrophotometric determination of amylose and amylopectin in starch from maize kernel by multiwavelength analysis, J. Cereal Sci. 26 (1997) 211–221.
[32] Teulat B., This D., Khairallah M., Borries C., Ragot C.,
Sourdille P., Leroy P., Monneveux P., Charrier A.,
Several QTLs involved in osmotic adjustment trait
variation in barley (Hordeum vulgare L.), Theor. Appl.
Genet. 96 (1998) 688–698.
[33] Tinker N.A., Mather D.E., MQTL: software for simplified composite interval mapping of QTL in multiple
environmentsJQTL
1:2
(1995)
(http://
probe.nalusda.gov:8000/otherdocs/jqtl/).
[34] Van den Koornhuyse N., Libessart N., Delrue B.,
Zabawinski C., Decq A., Iglesias A., Preiss J., Ball S.,
Control of starch composition and structure through
substrate supply in the monocellular alga Chlamydomonas reinhardtii, J. Biol. Chem. 271 (1996)
16281–16288.
[35] Veldboom L.R., Lee M., Molecular-marker-facilitated
studies of morphological traits in maize. 2. Determination of QTLs for grain yield and yield components, Theor. Appl. Genet. 89 (1994) 451–458.
[36] Westgate M.E., Boyer J.S., Reproduction at low silk
and pollen water potentials, Crop Sci. 26 (1986)
951–956.
[37] Xu J., Avigne W.T., McCarty D.R., Koch K.E., A
similar dichotomy of sugar modulation and developmental expression affects both paths of sucrose metabolism: evidence for a maize invertase gene family, Plant
Cell 8 (1996) 1209–1220.