Download Dissecting complex physiological functions

Journal of Experimental Botany, Vol. 48, No. 311, pp. 1151-1163, June 1997
Journal of
Experimental
Botany
REVIEW ARTICLE
Dissecting complex physiological functions through the
use of molecular quantitative genetics
Jean-Louis Prioul1'5, Steve Quarrie2, Mathilde Causse3 and Dominique de Vienne4
1
1nstitut de Biotechnologie des Plantes, Bat. 630, (CNRS URA 1128), Structure et Metabolisme des Plantes,
University de Pahs-Sud, F-91405 Orsay Cedex, France
2
John Innes Centre, Norwich Research Park, Colney, Norwich NR4 7UH, UK
3
Station d'Ame'lioration des Plantes Marafcheres, INRA—Domaine Saint Maurice, BP 94, F-84143 Montfavet
Cedex, France
4
Station de G^mStique Veg&ale (UPS/CNRS 2154/INRA/INA PG), Ferme du Moulon, F-91190 Gif-sur-Yvette, France
Received 6 February 1997; Accepted 20 March 1997
Abstract
Testing possible associations between physiological
and biochemical traits by comparing plant phenotypes
and looking for correlations between them is unreliable. The development of molecular marker technologies offers powerful alternative methods to examine
the relationships between traits. This review describes
the genetical methods required to analyse possible
associations between traits that are inherited in a
quantitative manner using quantitative trait locus (QTL)
analysis. The regulation of carbohydrate metabolism
is chosen as an example of how QTL analysis can be
used to identify key control factors in a series of processes, by identifying possible candidate genes for
QTL effects on sucrose and starch metabolism.
Methods are also described to study the association
between physiological traits such as abscisic acid concentrations and stomatal conductance. Advantages
and some limitations of QTL analysis over other
methods currently in use by physiologists to test associations between traits are discussed.
Key words: Candidate genes, genetic maps, molecular
markers, quantitative trait locus (QTL) analysis, physiological traits.
Introduction
The problem of crop physiology
The study of plant physiology is the study of how plants
work, to understand how complex processes are regulated
8
and integrated to achieve the observed results. For a crop
physiologist this is a means to an end, namely to facilitate
the improvement of plant growth and agronomic performance in various environments. The problem is that to
improve plant growth the physiologist needs to know
what the important control processes are and how they
can be manipulated for plant improvement. Although
complex processes such as nutrient uptake, assimilate
partitioning or morphogenesis can be broken down into
a number of elementary processes, these are highly interrelated and each has complex and versatile regulatory
systems allowing plants to adapt to and to withstand
wide variations encountered in the environment. For
example, Dudley and Lambert (1992) calculated that as
many as 173 genetic factors were effective in the inheritance of protein and starch concentrations in maize kernels. A similar number of factors could easily be obtained
by summing the biochemical and biophysical steps
involved in the nitrogen or carbon assimilation pathways,
from uptake in the root or shoot to accumulation of
protein and starch in the seeds. However, it is likely that
not all steps are equally effective in controlling the output,
as their relative importance may vary depending upon
their position in the pathway and upon external
conditions.
Since the introduction of the limiting factors theory,
first applied to photosynthesis by Blackman (1905), it
has been a tenet of plant physiology that only a few steps
may limit a complex function. This theory and its possible
pitfalls may be illustrated by the question: does Rubisco
activity limit photosynthetic rate in most physiological
To wtiom the correspondence should be addressed: Fax: 33 1 69 33 64 23/4.
C Oxford University Press 1997
1152
Prioul et al.
situations? The enzyme has a low specific activity which
necessitates a high Rubisco—protein amount, usually
reaching up to 50% of total leaf protein in most plants.
This led to the idea that the enzyme could be rate-limiting
for photosynthesis. The observation that maximum
photosynthetic rate was highly correlated to Rubisco
activity in many physiological conditions (Bjorkman,
1968; Wareing et al., 1968) provided support for that
assumption. Antisense technology is usually regarded as
giving a definitive answer to the importance of a specific
step in a process. The genetical modification of Rubisco
content by plant transformation with an antisense gene
to the Rubisco small-subunit was reported by Rodermel
et al., 1988. Nevertheless, even these results were not
clear-cut as the control coefficient of Rubisco on photosynthesis varied from 0% (no Rubisco control) to 90%
(Stitt and Schulze, 1994).
However, the molecular biology strategy used for
Rubisco can hardly be applied to most crops, especially
cereals, most of which are not easy to transform. Another
general limitation is that only one (or exceptionally two)
gene(s) can be studied at a time. Modern quantitative
genetics, which relies on the use of molecular markers,
may provide a more general alternative.
The emergence of quantitative genetics as an aid for plant
physiology
For a long time the heredity of quantitative traits (traits
showing continuous variation in a segregating population)
was supposed to be ascribed to the additive effects of a
large number of genes with small and similar actions,
modulated by environment. In fact, this assumption had
been derived from considerations that were practical (to
simplify the theoretical models) rather than biological. It
is well known that a small number of segregating loci can
account for the usual Gaussian distribution observed for
most quantitative traits. Careful analyses of experimental
data showed that this oligogenic view was consistent with
the variation of quantitative traits in Drosophila (Thoday,
1961). With the development of molecular markers to
construct detailed genetic maps, initially based on isozymes (Edwards et al., 1987) or on DNA restriction
fragment length polymorphism (RFLP) (Helentjaris,
1987), it became clear that even for highly complex traits
like tomato fruit size and composition (Paterson et al.,
1988), or crop yield (Edwards et al., 1987, 1992), a small
number of quantitative trait loci (QTL) explained a large
part of the genetic variability. The rest of the genetic
variation was due to a large and variable number of genes
of smaller effect.
Physiological traits tend to be quantitative in nature.
Even though their expression may result from the action
of numerous factors, the major-effect loci whose allelic
forms (polymorphisms) account significantly for the
traits' genetic variation (i.e. the traits' QTLs) are expected
to be quite few, and to be detectable in an appropriate
population of plants using molecular markers. The aim
of this review is to present a plant physiologist's viewpoint
of the use of QTL methodology and is not intended to
be comprehensive. Thus, a few physiological examples
will be given to illustrate that it can be a valuable and
powerful tool for answering some of the major questions
physiologists have to address for plant improvement.
The QTL methodology
The principle of QTL analysis
Although there is no fundamental difference between the
type of genes regulating quantitative and those regulating
qualitative traits, the genetical mapping method used for
qualitative (Mendelian) factors cannot be applied directly
to quantitative traits because the individuals of the progeny cannot be classified into discrete phenotypic classes.
The prerequisites for QTL detection are: (i) to have a
mapping population of plants displaying genetic variability for the trait of interest, (ii) to establish genetic linkage
groups for that population by analysing the recombination ratios amongst qualitative molecular markers, and
(iii) to be able to score the trait of interest on every
individual of the population. The simplest QTL detection
method relies on one-way ANOVA. For every marker,
the phenotype means are compared according to the
genotype classes. A significant difference indicates that
the marker is likely to be linked to a segregating QTL
having an effect on the trait. The earliest attempt to
establish linkage of an individual QTL to morphological
mutations was done by Sax (1923) but it was severely
limited by the lack of numerous and neutral markers.
These conditions were only fulfilled 60 years later when
saturated genetic linkage maps of DNA markers became
available through the use of restriction fragment length
polymorphism (RFLP). Many other molecular marker
systems are now available, such as RAPD, AFLP or
microsatellites (see Quarrie, 1996, for a review).
Genetic material
Fj-derived populations are the most easily accessible
genotypes for QTL analysis, but other segregating populations can be used. An F! generation between two inbred
lines may be used to derive: backcrosses to one of the
parental lines (BC); homozygous doubled haploid (DH)
lines; or F 2 plants. Further single seed descent from an
F 2 leads to recombinant inbred lines (RIL) which are
close to homozygosity after at least 5-6 generations of
selfing. From the practical and genetic points of view,
these populations are not equivalent. DHs and RILs,
which can be genetically reproduced by selfing, are
'immortalized' populations and hence very useful for
QTL analysis
accumulating markers and data over time and space.
Additional plants can be grown any time to give more
DNA to complement molecular maps and/or to measure
novel phenotypic quantitative traits. In addition, the
plants can be replicated across sites to analyse
genotype x environment interactions. DHs can rapidly be
produced (one generation) when the genotype is capable
of regeneration from tissue or anther culture, but segregation distortions and other in vitro genomic anomalies
may be common in some species, and with some methods
of production. Carefully self-pollinated RIL populations
are probably the most convenient and reliable material,
but they take time to obtain (at least 5-6 generations).
However, neither DH nor RIL populations can be used
to estimate dominance effects. F 2 and BC populations are
rapidly obtained (two generations) and they allow dominance to be analysed (even though reciprocal progenies
are required for BC), but they do not allow replication
of the genotypes (except by using vegetative propagation,
when applicable). This problem may be partly circumvented by working on the pools of the descendants
obtained by selfing from each F 2 or BC individual
(Gardiner et al., 1993). The dominance can be estimated
in the F2, or in the BC if reciprocal backcrosses are
available.
1153
marker-by-marker ANOVA and multiple marker
methods. The principle of the ANOVA is to test whether
there are significant differences between the phenotypic
means for the genotype classes at a particular marker
locus. If the parents I and II of the progeny have the
genotypes A,A, and A,,An, respectively, at a given
marker, the genotypic classes will be A,Ah A,An and
A,,AH in the F2, A,Aj and A,,An in the DH and RIL,
and A,A, and AjA,, in the BC. A significant F-value
indicates that at least one segregating locus acting on the
expression of the trait (a QTL) is linked to the marker.
The principle of the marker-by-marker ANOVA for QTL
detection is shown in Fig. 1 using a hypothetical F 2
population of eight plants and two mapped markers on
one chromosome.
The effect of a QTL may be quantified by the difference
between the homozygous classes, but it should be noted
Genotype I Genotype II
Low allele
Trait: High allele
Marker 1
Marker 2
QTL mapping methods
Single segregating markers can be used to search for
linked QTLs, and a saturated genetic linkage map gives
the opportunity to scan the whole genome for the presence
of QTLs with significant effects. Readily available software packages are used to compute such maps from the
estimates of the recombination rates (r) between marker
loci. The most popular software used in plant genetics is
MAPMAKER (Lander et al., 1987). Another, which has
some advantages over MAPMAKER, is JoinMap (Stam,
1993). This software package will also produce a composite map from maps for two or more crosses which have
markers common to two or more populations. For most
genomes, 100-150 marker loci evenly spaced would be
sufficient to obtain sufficient coverage of the genome, i.e.
a genetic map where any point of the genome is genetically
linked to at least one marker (thus there are as many
linkage groups as chromosomes). In Arabidopsis, with a
genetic length of about 630 cM, such a number results in
a marker density of about 5 cM, and in wheat (about
3500 cM) of about 25-30 cM. For QTL detection,
increasing the number of markers does not necessarily
improve the power of the tests and the accuracy of QTL
location. As shown by Darvasi et al. (1993), once a
density around 20 cM is achieved, increasing the number
of individuals of the progeny is more profitable than
increasing the number of markers.
Two classical approaches used for QTL detection are
Marker 1
Marker 2
• •
•O
- - OO
Genotype Mean value
high
Marker 1
• o
intermediate
oo
low
Marker 2
• •
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intermediate
intermediate
intermediate
F- ratio
significant
nonsignificant
Fig. 1. A schematic representation of plants used with a one-way
ANOVA to detect markers associated with a QTL in a population of
F 2 plants derived from a cross between parents differing in the trait of
interest and having two mapped polymorphic markers.
1154
Prioul et al.
that this underestimates the actual effect in the case of
recombination between the marker locus and the QTL.
Another useful measurement of the QTL effect is the
fraction of the variation explained by the QTL in the
progeny (R2 = ratio of the sum of squares for the marker
factor to the total sum of squares). A difficult decision is
the choice of a significance threshold, due to the high
number of ANOVA (one for each marker) which increases
the risk of detecting false positive QTLs. Several corrections have been proposed. A rule of thumb may be that
a 0.5% level corresponds to an overall Type I error of 5%
(but see Rebai et al., 1995, for discussion). However,
lowering the Type I error increases conversely the Type
II error (i.e. no detection of a real QTL). In practice, the
choice of the threshold may depend on the goal of
the work.
A particular problem with ANOVA is that it is not
possible to distinguish between a strong-effect QTL distant from the marker and a weak-effect QTL located
close to the marker. Apart from adding more markers to
the regions of interest, this problem can be solved by
multiple marker methods, which can be divided into two
types: interval mapping and marker regression. The principle of interval mapping has been described in detail by
Luo and Kearsey (1992). An example of the interval
mapping method is the software package MAPMAKERQTL, developed by Lander and Botstein (1989). In this,
the most likely phenotypic effect of a putative QTL
affecting a trait is computed every few centimorgans
between two markers. A LOD score (Logarithm of the
ODds ratio: the chance that the data would arise from a
QTL with this effect divided by the chance that it would
arise given no linked QTL) is calculated and the maximum
LOD score indicates the most likely position of the QTL.
The software can give a graphical display of the LOD
score and the confidence interval along the chromosomes,
thus visualizing clearly the range in possible position of
the QTL. However, the method has limitations: it does
not work properly when the residuals of the regression
for the trait are not normally distributed, and artefactual
QTLs may be detected if there is more than one QTL in
the linkage group. The threshold choice with this method
is as important as for ANOVA, but a correspondence
may be established between the two methods (Darvasi
et al., 1993). The precision of the methods is similar when
the markers are close, but multiple marker methods are
more advantageous when distance between markers
increases.
The alternative multiple marker strategy is multiple
regression, as illustrated by the work of Martinez and
Curnow (1992) and Wu and Li (1994). This method uses
the coefficients of regression of the phenotype on the
genotype of the different markers. Software packages for
this approach are not readily available, but programmes
for carrying out the method can readily be written in the
statistical languages GENSTAT or SAS.
Other methods have also been developed, such as the
composite interval mapping method and software,
QTLCartographer, developed by the group of Zeng in
the USA (Zeng, 1993, 1994). This allows markers to be
chosen as cofactors to reduce the background genetic
noise and increase the resolution of QTL detection.
QTLCartographer produces a profile of the QTL likelihood ratio (LR) along the chromosome linkage group.
Figure 2 compares the output from ANOVA, interval
mapping and composite interval mapping methods using
QTLCartographer. A similar composite interval mapping
method has been developed by Jansen and co-workers
(Jansen, 1993; Jansen and Stam, 1994) called Multiple
QTL Modelling (MQM), released as the software package MapQTL. The precision of these QTL mapping
methods in locating a QTL is determined largely by the
population size and not the frequency of the markers.
Thus, for a population size of 150 DH lines, the 95%
confidence intervals for a major QTL would be about
30-40 cM (Hyne et al., 1995). Each method for detecting
QTLs has its advantages and disadvantages, though these
need not be major considerations in the context of this
review, which focuses only on major QTL effects and
candidate genes for those QTLs.
The detection of a QTL is a first step towards the
ultimate goal of identifying the actual gene which participates in controlling the quantitative trait. The precision of
the location depends on the size of the QTL effect, the
size of the population and on the level of saturation of
the map but, even with densely-distributed markers, it is
often several centimorgans. The physical distance for one
centimorgan varies in relation to genome size, from
140 kb in Arabidopsis to a few thousand kb in maize or
wheat. Thus, except for Arabidopsis and rice, which has
a genome size about three times that of Arabidopsis,
chromosome walking is very difficult for QTL identification in most plant genomes. Alternative approaches to
characterizing a QTL are based on the use of candidate
genes (see below).
Application to physiological traits
A locus for any quantitative trait may be mapped as long
as polymorphism is observed in the segregating population under analysis and phenotypic information is available for the lines in the population. This will be the case
when there is a phenotypic difference between the parental
lines. Even if the difference between the parents is small
or null, the theory of inheritance of complex traits predicts
that the offspring may express a much larger variability.
This phenomenon, called transgression, is due to the
dispersion between the parents of genes increasing or
decreasing the trait which results in extreme values in
QTL analysis
20
160
40
Xpsr326
Xpsr128 Xpsb85 Xpsr911 Xpsr967
1155
180
Xpsr145
Distance from first marker (cM)
Fig. 2. A comparison of methods for locating QTL effects using different options of the QTLCartographer software package. The analyses were
carried out on data for leaf ABA content in a population of wheat doubled haploid lines growing in saline hydroponics. QTL effects are shown for
chromosome 5A. Shaded bars show the difference between genotype means at each marker analysed by one-way ANOVA, with significance levels
(*/><0.05, **P<0.01, ***P <0.001). Option 3 is the simple interval mapping method (MAPMAKER-QTL) and option 6 is the composite interval
mapping method. Note the differences in QTL peaks using option 6 and option 3. The dashed lines and dotted lmes indicate significance at the 5%
and 1% levels, respectively. Selected marker loci are named.
some individuals where all the alleles increasing or
decreasing the trait have combined.
Although until now QTL analysis has mainly been
used for agronomical and morphological traits (Beavis
et al., 1991; Edwards et al, 1992; Faktokum et al, 1992;
Kamatsuda et al, 1993), the first QTL analysis of physiological traits in plants used an RFLP map of tomato
(Paterson et al, 1988). This study analysed parameters
associated with fruit growth (mass, soluble solids content
and pH) in a back-cross population between Lycopersicon
esculentum and L. chmielewski, a wild South American
green-fruited tomato. Seventy genetic markers were
scored for each of the 237 back-cross progeny, resulting
in a genetic map covering the 12 chromosomes, with an
average spacing of 14.3 cM. Five QTLs controlling fruit
mass, four QTLs for the concentration of soluble solids
and six QTLs for fruit pH were mapped. These QTLs
accounted for 58, 44 and 48% of the phenotypic variance
for mass, soluble solids and pH, respectively. The six
QTLs for pH did not produce effects in the same direction:
in relation to L. esculentum, pH was increased by four
QTLs and decreased by two, thus providing a demonstration of transgression and explaining how many backcross progenies exhibit more extreme phenotypes than
the parental lines. On chromosome 6, the same QTL was
apparently observed for fruit mass, pH and concentration
of solids. Similarly, pH and soluble solids apparently
shared the same QTL on chromosomes 3 and 7.
This report led to three main conclusions which are
applicable to other QTL analyses: (i) a small number of
Mendelian factors can explain a large part of the genetic
variance, (ii) traits for different levels of organization,
but for related processes (e.g. growth and some of its
physiological components) frequently seem to share
common QTLs, and (iii) as a consequence, a complex
trait is dissected into discrete factors increasing or decreasing the trait. Although not discussed by Paterson et al.
(1988), who concentrated on genetical aspects, the apparent co-location of QTLs for tomato fruit pH and sugars
may also be given a physiological interpretation: the role
of proton-driven translocation of carbohydrate from the
cytosol into the vacuole is well established, and a QTL
for such a translocator or a proton pump is likely to
affect both pH and soluble sugars.
Owing to the low precision of the initial mapping (15
cM), identification of QTLs for several traits at the same
locus could not distinguish between either the pleiotropic
effect of a single gene or close genetic linkage of two or
more QTLs. To circumvent this problem, Paterson et al.
(1990) used a method called substitution mapping or fine
mapping, based on tomato genotypes carrying short
chromosome segments from L. chmielewski. This allowed
them to identify intervals less than 3 cM separating QTLs,
which opened the way for controlled transgression from
the wild to the domestic species of genes for the desirable
traits, thereby avoiding incorporation of undesirable
traits.
From the QTL analyses published so far, two complementary uses of the methodology have emerged: the
applied and the fundamental. The first deals with the
1156
Prioul et al.
possibility of performing marker-assisted selection aimed
at gathering the favourable alleles and breaking their
possible linkage with undesirable alleles. In this case, the
markers may be anonymous (unknown DNA sequence)
as long as they are closely linked to the gene(s) of interest.
In contrast, the second approach targets the identification
of the QTL by determining the contribution of its alleles
to physiological components of macroscopic traits. The
present review concentrates on the latter aspect as this is
of greater interest for the physiologist. In this context, an
obvious question is the possible linkage between the
polymorphism of genes for known functions (coding
sequences for an enzyme, a membrane protein, a transcription factor, etc.) and the variation of a related
quantitative trait. In this case, the method enables us to
evaluate the genetic control introduced by that gene,
which is a way of considering the question of the limiting
factor concept at the genetic level.
QTL identification using candidate genes
The first attempt to match a plant QTL with a candidate
gene was a search by Ottaviano et al. (1991) for
co-location between resistance of maize seedling roots to
heat stress as measured by electrolyte leakage, and the
production of a segregating heat shock protein (HSP).
Although six QTLs explaining 53% of the genetic variability were detected, no correlation was obtained with the
presence-absence of HSP-17, a low molecular weight
peptide segregating among the 44 RILs of maize analysed.
A more encouraging observation was made by Helentjaris
(1987), Beavis et al. (1991) and Edwards et al. (1992)
who found that several QTLs for plant height in maize
mapped in the same chromosomal regions as loci previously reported for major (qualitative) genes for height.
For example, a major QTL on chromosome 9 maps to a
dwarfing locus (d3), involved in gibbereUin (GA) biosynthesis. The likelihood of d3 being a candidate gene for
this height QTL was recently confirmed by fine mapping
of the d3 region in near-isogenic lines differing at that
locus and by the concurrent response of the lines to GA
treatment (Touzet et al., 1995).
The search for QTLs associated with enzymes of primary metabolism was first attempted by Goldman et al.
(1993) who compared the location of QTLs influencing
protein and starch concentration in maize kernels with
the location of Shrunken-2 (Sh2), a structural gene encoding the large subunit of ADP-glucose pyrophosphorylase,
a key-step enzyme in the starch synthesis pathway. A
population of 100 F 3 families was derived from a cross
between two strains which had been divergently selected
for protein concentration for 76 generations, resulting in
large differences in protein content, which were inversely
related to starch concentration. Twenty and 19 QTLs
were detected for protein and starch, respectively; 16 of
these loci being significant for both traits. One of these
loci was found to be linked with the Sh2 locus on
chromosome 3L and associated with several protein and
starch traits.
A more comprehensive approach to the relationships
between QTLs and their candidate genes has recently
been established by the groups of D de Vienne and J-L
Prioul. The original idea was to consider a biosynthetic
pathway where significant knowledge of the physiological
and biochemical factors controlling or limiting the overall
process was available and to use these factors as quantitative traits in a QTL analysis. A search for QTLs controlling
biochemical or metabolic parameters may also be used
as a strategy for analysing the physiological basis of
macroscopic trait variation. Wherever QTLs common to
two levels of organization are detected, a causal relationship between them may be hypothesized which can be
tested by further experimentation. In higher plants,
growth and dry matter accumulation are more dependent
upon the distribution of photosynthates than upon carbon
uptake (Gifford and Evans, 1981). Carbon partitioning
between sucrose, the main translocated carbohydrate, and
its storage form starch, is the primary level of regulation.
A preliminary study showed great intervarietal differences in maize sucrose and starch metabolism which were
related to plant growth (Rocher et al., 1989, and references therein). The existence of numerous mutants for
carbon accumulation, especially in maize kernels (Nelson
and Pan, 1995) and the heritability of enzyme activities
such as sucrose phosphate synthase (SPS) (Causse et al.,
1995a) provide tools for tackling the problem. Another
advantage of carbohydrate metabolism is that several
major genes of the pathway have been cloned, thus
providing the possibility of comparing the QTL for
enzyme activity and the location of the corresponding
structural genes. In a first set of experiments, 65 RILs
resulting from a cross between two unrelated inbred lines,
an early French flint and a late American semi-dent, were
analysed. Initially, differences between the parental lines
were established for SPS, sucrose synthase (Susy),
invertase (INV) and ADP-glucose pyrophosphorylase
(AGPase) activities in 2-3-week-old seedlings sampled at
the adult third leaf stage. SPS and AGPase were measured
in the exporting source leaf (leaf 3), and Susy and INV
in the growing undifferentiated zone at the base of leaf
4. In parallel, plant growth rate, sucrose, hexoses and
starch contents in the source and sink were determined.
DNA probes for the two AGPase subunits, Susy and SPS
were included in the 145 RFLP markers used to construct
a linkage map (Causse et al., 1994).
This study (Causse et al., 1995ft; Fig. 3) gave three
main results: co-location of traits at different levels of
organisation (growth, leaf morphology, enzyme activity),
biochemical QTLs associating the enzymes with their
substrates or products, but few co-locations apparent
QTL analysis 1157
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Fig. 3. RFLP and QTL map for a population of 65 recombinant inbred lines (adapted from Causse et al. 1995ft). Chromosomal regions where
QTL were detected are indicated by vertical lines, and the traits involved (LW, leaf width; LH, plant height; LL, leaf length; DMW, dry matter
weight; DAY, day number; other abbreviations defined in the text) in bold type when significant associations were detected at the P = 0.01 threshold.
between structural genes and the corresponding QTLs for
enzyme activity. Thirteen groups of QTLs were detected,
all of them linking from two to seven traits. Eight QTLs
were associated with both growth traits and biochemical
traits. For example, QTLs at the same location were
observed for invertase activity in sink leaves and growth
rate. QTLs apparently common to Susy activity and plant
dry matter and earliness were found on chromosomes IS
and 9S. Two QTLs had opposite effects on SPS activity
and growth on chromosomes 1L and 8L, and on chromosome 2, one parent allele contributing to longer leaves
had higher SPS activity.
In a number of instances, the QTLs for sucrose and/or
hexose content mapped at similar locations to those for
the enzyme activities. A closer examination of the locations showed that QTLs for sucrose synthase and
invertase were never common except for one location (ch.
6) where there was also a QTL for sucrose concentration.
This observation is instructive as the two activities are
not physiologically related, but they depend at least partly
on their common substrate, sucrose. It is also noteworthy
that expression of the genes for sucrose synthase (Koch
et al., 1992) and invertase (Xu et al., 1996) are regulated
by tissue sucrose content. SPS and AGPase have only
one QTL apparently in common (on ch. 5L), which
reflects the fact that they are located in different compartments (cytosol and chloroplast, respectively) and are
largely independently regulated.
Finally, among the 15 putative candidate genes represented by the multiple loci of the structural genes for the
three enzymes, only three seemed to be aligned with QTLs
for the corresponding activity: one of the SPS loci on
1158
Prioul et al.
chromosome 8L, Shi on 9S for Susy and a possible
Genetic analysis of relationships between traits
AGPase locus on chromosome 10L. The most likely
Traditionally, physiological relationships between traits
candidate gene for a QTL is probably Shi because the
are detected by looking for correlated changes in the two
regulation of Susy is simpler than that of SPS and
traits, either with time or in response to treatments. Any
AGPase, as the activity, at least in growing leaves, is
inconsistencies in those associations then need explaining
mainly controlled by the enzyme protein quantity which
in terms of compartmentation or interactions with other
in turn is controlled by transcript level (Nguyen-Quoc
factors not previously considered. Whether such correlaet al., 1990; Nguyen-Quoc, 1991). The only problem is
tions are due to causal relationships between traits, or to
that the large majority of the enzyme consists of isoforms
other factors, such as closely-linked genes having separate
encoded by another gene Susl located distally on the
actions,
cannot easily be determined (see Lebreton et al.,
same chromosome. However, Nguyen-Quoc (1991) has
1995, for a discussion of linked genes versus pleiotropy
demonstrated that the Shi gene is expressed in leaves at
in relation to correlated traits). Studying the consequences
a low level which always parallels that of Susl. Moreover,
of isogenic variation in expression of a trait, achieved
Chourey and Taliercio (1994) showed an epistatic intereither by traditional crossing and backcrossing, mutation
action between the two genes in mutants. The recent
or transformation is a more reliable approach. However,
mapping of newly-available sequences for a vacuolar
neither of these methods is particularly easy to use as the
invertase gene (Xu et al., 1996) provided a fourth candidgenotypes need to be well-characterized genetically to
ate for invertase activity on chromosome 10L.
avoid confounding effects of unidentified genetic variation
The validation of those candidate genes is now the next
elsewhere in the genome.
step. Among the various available strategies, the most
straightforward is probably the analysis of sequence polyQTL methodology cannot give an unequivocal answer
morphisms of the candidate loci in a range of unrelated
to this problem. However, by testing coincidence between
genotypes. If the correlation between the trait variation
QTLs for correlated traits, QTL analysis is a powerful
and the gene polymorphism still holds, it may become
tool to analyse the genetic basis of the association between
possible to determine the part of the sequence responsible
traits. Thus, if trait X has a regulatory role on trait Y,
for the variation. For instance, this approach was successsuch as xylem abscisic acid (ABA) content and stomatal
fully used to identify the angiotensinogen locus as a QTL
conductance, respectively, for most significant QTLs for
for blood hypertension in man (Jeunemaitre et al., 1992).
trait X there should be a measurable effect at that locus
At present relatively few of the genes isolated and
on trait Y. If the genetic control of trait Y (e.g. stomatal
studied by plant molecular biologists have been mapped.
conductance) is more complex than that of trait X (e.g.
The routine integration of newly characterized genes on
xylem ABA content), then trait Y will have some QTLs
to a molecular map of the species from which the gene
not coincident with those for trait X, under the assumpwas isolated will facilitate the candidate gene approach
tion that both traits have similar heritabilities (h 2 ).
as the functions of more genes are elucidated (Chao et al.,
Therefore, physiologists can benefit from aspects of the
1994; Kurata et al., 19946).
complex genetic control of traits that are a hindrance to
Furthermore, advantage can be taken of extensive
breeders, namely having several QTLs in the genome to
colinearity of blocks of genes (i.e. genes occurring in the
test simultaneously for coincidence between traits and
same order) along chromosomes of related species. Thus
having significant QTLs for the traits varying from envirthe location of a structural gene for a particular enzyme
onment to environment. Thus, if two traits share only
in one species can be predicted on a related species, by
one chromosomal region, this could be due to chance
comparative genetic mapping. For example, extensive
given the large confidence interval associated with QTL
colinearity has been identified amongst the cereal and
location. However, if such a coincidence is found several
grass genomes (Ahn and Tanksley, 1993; Kurata et al.,
times, the physiological relationship between the traits
1994a; Moore et al., 1995). Thus, the gibberellinbecomes more likely to be causal. If, in addition, the
insensitive dwarfing genes of wheat (Rht) have homoeoldirection of the additive effects and the dominance levels,
ogous genes mapped in maize (Devos and Gale, 1997).
and possible epistatic interactions, are consistent over the
This knowledge of the location of genes with similar
common
QTLs, the physiological link becomes highly
function across related species is a powerful concept. For
probable.
Thus, for the physiologist, QTL analysis is a
example, two QTLs influencingfloweringtime were identipowerful
tool
to dissect the genetic bases of traits which
fied on linkage groups 1 and 8 of Brassica nigra. These
are
likely
to
be
a priori related (for example, xylem ABA
two regions are duplicated homologues of one region of
content
and
stomatal
conductance).
the Arabidopsis genome containing a mutant gene called
For
example,
Lilley
et al. (1996) located several QTLs
CO, constant—which reduces greatly the flowering time
for
osmotic
adjustment
in rice using both ANOVA and
(Lagercrantz et al., 1996). Thus this approach adds
MAPMAKER-QTL.
Coincidence
of these QTLs was
greatly to the value of biochemical and molecular
compared with those for root traits and leaf rolling scores
information on a particular enzyme or gene product.
QTL analysis
(a measure of drought avoidance) measured on a subset
of the same RIL population. A major QTL for osmotic
adjustment (explaining about a third of the variation)
was located on chromosome 8. This locus was also found
to be associated with significant effects on root morphology and dehydration tolerance, and the authors discussed
possible physiological explanations for the coincidence of
QTLs for these traits. Furthermore, comparative genetic
analysis shows that this region of rice chromosome 8 is
homoeologous (i.e. contains similar genes in the same
relative order) with part of wheat chromosome 7A which
was shown by Morgan (1991) to carry a major gene for
osmotic adjustment.
The association between QTLs for physiological traits
involved in drought response was examined in maize by
Quarrie et al. (1994) and Lebreton et al. (1995). Using
an F 2 population of 81 plants derived from the cross
between two inbred maize lines differing in their ABA
contents and drought resistance, plants given a mild
drought stress were sampled for leaf and xylem ABA
contents, stomatal conductance, water status, pulling
force for root extraction from the soil and the number of
nodal roots. QTLs were detected for all the traits scored.
Although QTLs for ABA content were found on every
chromosome except ch. 8, unexpectedly, the ABA content
of leaves and xylem sap appeared to be regulated by
different QTLs. The lack of coincidence between QTLs
for leaf and xylem sap ABA contents was confirmed in
the F 3 generation (Quarrie and Steed, unpublished
results). QTLs for root characteristics were also widely
dispersed, being present on all chromosomes except 9
and 10.
The highly significant QTLs for leaf ABA content on
chromosomes 2 and 3 (Lebreton et al., 1995) were also
present in the F 3 generation tested in each of four
contrasting environments (Quarrie et al., unpublished
results). However, QTLs for xylem ABA contents measured on two occasions in 45 F 3 families differed not only
between occasions, but also between generations. Yield
was also measured in the F 3 trials, and the strongest
QTLs for yield were consistently on chromosome 8, with
other significant ones on chromosomes 2, 3, 4, 5, and 6
varying with the trial.
Associations between traits were tested by Lebreton
et al. (1995) using correlation analysis to test for the
coincidence of QTLs. This analysis may be carried out
only if the trait data are normally distributed and the
population size is large enough for the effect of any
outliers not to bias the analysis. For the two traits to be
compared (designated traits I and II), pooled mean data
for one of them (trait I) were used for each genotype
class at every marker locus giving an ANOVA variance
ratio significant at P < 0.1 (a significance level selected to
give about eight marker loci per trait). Where several
markers were thought likely (on the basis of output from
1159
MAPMAKER-QTL) to be associated significantly with
a single QTL, only the marker having the most significant
variance ratio was included in the analysis. For the marker
most closely associated with each significant QTL for
trait I, the phenotypic means for each genotype (parental
type A or B, or heterozygote) at the marker were determined for both trait I and trait II and means for one
trait plotted against corresponding means for the other
trait at the same marker.
If a causal relationship exists for those two traits, the
regression of means (each, ideally, weighted according to
the number of individuals) of one trait on the other is
likely to be significant. However, a significant regression
does not imply necessarily a causal relationship. The
method is illustrated in Fig. 4 using QTL data for leaf
ABA content and stomatal conductance of F 3 maize
plants and, in this example, the analysis shows no obvious
association between leaf ABA content and stomatal conductance in these plants. For traits that should be physiologically related, this type of analysis can test not only
whether the directions of the allele effects are consistent
for both traits at major QTLs, but, with an F 2 or bulked
F 3 population, will also take account of dominance, i.e.
if traits are related, dominance effects at a particular QTL
are likely to be present for both traits.
This method was used to test the relationships between
stomatal conductance and ABA content (Lebreton et al.,
1995; Quarrie et al, 1995), and between ABA content
and root pulling force (Lebreton et al., 1995). Although
the regressions of phenotype means for each QTL genotype for stomatal conductance on those for leaf and
xylem sap ABA contents were significant for both F 2 and
F 3 plants, the correlation coefficients were higher for
relationships between xylem ABA content and stomatal
conductance in both generations. This rinding was consistent with the results of Tardieu et al. (1992) who found a
tight relationship between stomatal conductance of maize
leaves and xylem ABA contents but not with leaf ABA
contents as drought developed. The QTL analysis also
showed that leaf ABA content and root pulling force
were likely to be related (Lebreton et al., 1995).
It may also be possible to exploit the presence of
epistatic interactions (the size of an allelic difference at
one locus being determined by the genotype present at
another locus) to test associations between traits. In these
cases, where two traits are related, it is likely that simple
phenotypic correlations between them would be nonsignificant, though QTL analysis may identify an association between them.
Conclusion and prospects for the future
The QTL approach opens a new and exciting perspective
for dissecting and understanding the complex regulation
1160 Prioul etal.
0.65
Difference in means between:
0.6 8
C=D Heterozygote and F-2
« • Heterozygote and Polj 17
T3
Heterozygote mean on
8 o 55
C
to
the chromosome.
I
(5
o
C/5
Increasing ABA concentration
0.5
300 , - ' 400
500 , - 600
700
Leaf ABA concentration (ng gDW 1 )
(•)-
Significance levels of ANOVA:
(*)
P<0.10
*
P<0.05
*** P< 0.001
Wi
4
10
6
8
Fig. 4. An example of the method to test causal relationships between traits by regressing pooled phenotype means for stomatal conductance on
leaf ABA content for each genotype at markers associated with significant QTL effects for leaf ABA content in F 3 maize plants derived from the
cross Polj 17 x F-2. Each significant QTL for leaf ABA content is indicated by a different symbol. Open symbols indicate the F-2 means, filled
symbols the Polj 17 means and half-filled symbols the heterozygotes. The 10 maize chromosomes are shown with horizontal bars indicating the
location of mapped markers. Open and filled bars (with lengths proportional to the ABA content) are posiuoned at the markers closest to the
significant QTL effects.
of processes taking place at the whole plant level.
Molecular marker technologies bring together the traditional specializations of physiology, genetics, molecular
biology, and breeding and can be used to answer both
basic and applied problems. QTL analysis will inevitably
lead to better relationships between the physiologists and
breeders, who have often in the past been sceptical of
claims made by physiologists for the significance of a
particular trait for plant improvement. In this connection,
two important points must be underlined. Firstly, as usual
in genetics, but not in physiology, the traits of interest
must be measured in segregating populations of large
numbers of plants (ideally at least 100-150 genotypes,
depending on the effect of the QTL and the goal of the
study). Whilst this may be relatively easy with a biochemical trait where tissue can be harvested quickly and frozen
for later analysis, for many physiological traits such as
stomatal conductance and plant water status, problems
QTL analysis
of sampling time and diurnal drift need to be addressed.
A method of adjusting for diurnal trends is described in
Lebreton et al. (1995). In some cases, such variation can
be overcome by growing plants in controlled environment
cabinets, testing genotypes in batches under the same
conditions (Lilley et al., 1996). Secondly, only the polymorphic QTLs can be detected in a given genetic background. Thus, in some instances it may prove to be
necessary to screen not only other varieties or accessions,
but also wild relatives with crossability to the species of
interest, to find polymorphism (Paterson et al., 1988). In
any case, it is worth noting that a gene 'important' for
the molecular biologist or for the physiologist may be
useless for the geneticist or the plant breeder: if the gene
is physiologically crucial, its variation over generations
of breeding will have been strongly counterselected, so
no QTL will ever be detected.
Adapting the QTL techniques for use with bulk segregant populations can also help the molecular biologists in
testing the likely value of allelic variation in genes of
interest, such as stress-induced genes. These genes are
currently the subject of intense activity by plant molecular
biologists, yet very little is known of the value to a plant
of any of these stress-induced genes. The bulk segregant
methodology is explained by Michelmore et al. (1991)
and its application to testing the consequences of allelic
variation in drought-induced genes is illustrated by
Quarried a/. (1996).
Although mapping with molecular markers requires
specialized facilities and expertise, physiologists will not
necessarily need to embark upon large-scale molecular
mapping of their own to make use of QTL analysis. As
more and more high-density molecular maps become
available in the public domain for the species most
commonly in use, physiologists need only to screen the
parents of the available mapping populations for variation
in expression of the trait(s) of interest and then to score
the appropriate mapping population for the trait. Of
course, to exploit QTL analysis for testing 'candidate
genes', it will be necessary to have the gene mapped in
the population being studied. However, mapping techniques are becoming easier and quicker and should not
present a major obstacle for laboratories having good
facilities for molecular biology.
It is time for physiologists to start studying markercharacterized segregating populations and marker-specific
near-isogenic lines instead of varieties. QTL analysis
should be the physiologist's tool of the future, enabling
him/her to understand how to improve plant growth and
behaviour in a range of environments.
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