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Review
Identifying the molecular basis of
QTLs: eQTLs add a new dimension
Bjarne G. Hansen1, Barbara A. Halkier1 and Daniel J. Kliebenstein2
1
Plant Biochemistry Laboratory, Department of Plant Biology and Center for Molecular Plant Physiology,
Faculty of Life Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg C, Copenhagen, Denmark
2
Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
Natural genetic variation within plant species is at the
core of plant science ranging from agriculture to evolution. Whereas much progress has been made in mapping quantitative trait loci (QTLs) controlling this natural
variation, the elucidation of the underlying molecular
mechanisms has remained a bottleneck. Recent systems
biology tools have significantly shortened the time
required to proceed from a mapped locus to testing of
candidate genes. These tools enable research on natural
variation to move from simple reductionistic studies
focused on individual genes to integrative studies connecting molecular variation at multiple loci with physiological consequences. This review focuses on recent
examples that demonstrate how expression QTL data
can be used for gene discovery and exploited to untangle
complex regulatory networks.
eQTL analysis: expression as a trait
Most plant species contain significant levels of natural
genetic and phenotypic variation between individuals
within the species for traits ranging from development
to metabolism to pathogen resistance. This intraspecific
variation is a foundation of research for evolutionary and
ecological biologists interested in understanding plant fitness, as well as for plant biologists focused on increasing
the fitness or yield of agricultural plants. The latter has
been a foundation of plant breeding research for decades
and is a rich source of both quantitative genetics theory
and innumerous, detailed phenotypic quantitative trait
locus (QTL) studies that have been extensively described
in other reviews [1–6]. More recently, natural variation has
begun to be intensively studied within the model plant
Arabidopsis thaliana as a means to improve our understanding of, for example, gene function, biosynthetic
capacity and evolution [7–10].
Dissecting natural variation can be done by a QTL
analysis, which is a statistical search for regions of the
genome where genetic variation associates with phenotypic variation, for instance, plant height. The ultimate goal
of QTL mapping is to determine which genes are responsible for variation in the trait [11]. In Arabidopsis, most
work has focused on structured, segregating populations as
exemplified by recombinant inbred lines (RILs) [7]. In crop
species, a wider range of structured mapping populations
have been utilized for QTL analysis, including doubled
haploids, F2 and backcross populations, among others.
Corresponding author: Kliebenstein, D.J. ([email protected]).
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This review will focus on immortal populations, such as
RILs. RILs are developed by several generations of singleseed descent from individual plants of an F2 population
derived from a cross of two different accessions, that is, two
parental genotypes. Thereby, RILs have fixed genotypes at
all markers, with each individual RIL containing a random
mixture of genotypes from the original parents. This
greatly simplifies replicated experiments and assures that
the RILs can be stored, disseminated and used by different
laboratories to analyze any desired trait in any environment. QTL mapping begins with the collection of phenotypic data from the RILs, followed by statistical analysis to
reveal genomic regions where allelic variation correlates
with the phenotype. Depending on the number of RILs and,
thereby, recombination events, these genomic regions can
span a broad genetic interval, which can include several
hundred genes, any of which might contain a polymorphism affecting the phenotype [12]. Thus, although it is fairly
simple to detect loci controlling the observed variation, it
has required significant effort to identify the molecular
basis of the QTL.
Recent work has begun to show how the application of
systems biology approaches to natural variation, such as
transcriptomics or genomic re-sequencing, can greatly
benefit QTL cloning by reducing the number of candidate
genes in a QTL interval. A major step forward in QTL
cloning has occurred via the application of microarray
technology to obtain genome-wide expression profiling
from the individuals in a RIL population. This enables
the mapping of QTLs controlling the transcript level for
each gene (expression QTLs, eQTLs) and, thereby, the
study of the relationship between genome and transcriptome. These eQTLs can then be utilized to search for
associations between gene expression polymorphisms
and a phenotypic QTL to identify candidate genes controlling phenotypic variation for a given trait, for example,
plant height or metabolite content. Another benefit of
eQTL analysis is that the same arrays can be simultaneously used for genetic mapping and phenotyping
[13–15]. This review article aims to introduce recent
advances in eQTL analysis, including new approaches to
the use of gene expression analysis to improve our ability to
understand the molecular basis of QTLs.
Global analysis of cis and trans eQTLs
eQTLs for the transcript level of a given gene define regions
in the genome that potentially control the expression of the
given gene. eQTLs are categorized as cis or trans, where cis
1360-1385/$ – see front matter ß 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.tplants.2007.11.008 Available online 11 February 2008
Review
Trends in Plant Science
Vol.13 No.2
Figure 1. Example of cis and trans eQTLs. In this example, transcription factor A (TF A) has gene B as regulatory target. The Y axis represents the LOD score, which is the
logarithm of odds. The horizontal dashed lines indicate the significance threshold for the LOD score for TF A and gene B. Roman numerals represent the chromosome
number (Chr.I–V). (a) Expression of TF A and gene B in parent accessions z and q for the RIL population used for the analyses in (b) and (c). The protein level or functionality
of TF A is indicated by the number of blue ovals. The expression levels of all genes are measured for all RILs in the population using microarray, and the expression level of
the gene of interest is now the trait that is analyzed. (b) eQTL for TF A. An expression polymorphism is observed for TF A. The genomic location of the polymorphic locus is
identical to the genomic position of TF A. Therefore, this is a cis eQTL. Explanation: the polymorphism is located within the promoter of TF A [marked in green in (a)]
causing a difference between the parent lines z and q. This expression polymorphism results in an eQTL for TF A at the same position as the genomic position of TF A. (c)
eQTL for gene B. An expression polymorphism is observed for gene B. However, the genomic location of the responsible locus is different to the genomic position of gene
B. Therefore, this is a trans eQTL for gene B. The location of the eQTL for gene B coincides with the location of the cis eQTL for TFA. Explanation: The polymorphism in the
promoter of TF A [marked in green in (a)] results in an expression polymorphism of TF A. This results in expression polymorphism of gene B, which generates an eQTL for
gene B at the genomic position of TF A on Chr.V and not at the genomic position of gene B on Chr.I.
eQTLs represent a polymorphism physically located near
the gene itself, for example, a promoter polymorphism that
gives rise to differential expression of the gene (Figure 1).
Many QTLs cloned before the existence of genome-wide
eQTL analyses are in fact cis eQTLs, that is, based on
variation in transcript level. This includes genes in glucosinolate biosynthesis and activation [16–19], genes in
phosphate sensing [20] and genes controlling flowering
time and development [21–25].
By contrast, trans eQTLs are the result of polymorphisms at a location in the genome other than the actual
physical position of the gene whose transcript level is being
measured (Figure 1). This region could, for example, contain a polymorphism in the expression of a transcription
factor that correspondingly modulates the transcript level
for the target genes. Thereby, the target genes have trans
eQTLs at the physical position of the transcription factor
due to this transcription factor having a cis eQTL. This sets
up a potential network where cis variation in regulatory
factors controls changes in transcript level for numerous
genes in trans, potentially giving rise to phenotypic variation.
eQTL analysis has been performed in mice, yeast and
humans, identifying numerous cis- and trans-acting regulatory regions [26–30]. This review article describes the
ability to utilize eQTLs for phenotypic association; we do
not differentiate between cis eQTLs caused by promoter
polymorphisms and those generated via indels, splicing
variants or differential RNA degradation because all of the
above polymorphisms will generate differential transcript
presence. Two large-scale microarray studies have recently
been published on 160 and 211 lines, respectively, in the
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two Arabidopsis RIL populations, Ler Cvi [31] and Bay0 Sha [32]. Additional research has identified large numbers of eQTLs in a doubled haploid barley population and
structured populations of eucalyptus [33,34]. These datasets revealed that gene expression traits are very variable
in plants, as was also seen in yeast, humans and rats [26–
30]. Furthermore, the gene expression traits are highly
genetically controlled and can have a complex underlying
genetic architecture [31,32,34].
The global transcript profiling studies showed that cis
eQTLs were mainly larger-effect QTLs, whereas trans
eQTLs were mainly smaller-effect QTLs. A possible explanation for the difference in effect size between cis and trans
eQTLs could be that cis sequence polymorphisms (e.g. in a
promoter) have a direct influence on expression of the gene
giving rise to cis eQTLs. By contrast, trans eQTLs are
caused by a polymorphism in, for example, a regulatory
factor located elsewhere in the genome. Because transcript
abundance of most genes is regulated by multiple factors, a
polymorphism in one regulatory factor might only result in
a small change in the transcript accumulation of genes
controlled in trans by that polymorphism. Furthermore,
the polymorphism underlying a trans eQTL typically
affects numerous other genes and is therefore pleiotropic.
Large-effect mutations in pleiotropic genes are likely to be
deleterious and, as such, there might be a constraint on the
effect size of trans eQTL loci [32,35–38].
Interestingly, the two global eQTL investigations in
Arabidopsis had significant differences in the number of
eQTLs identified and in the observed ratio between cis and
trans eQTLs. A replicated study on the Bay Sha RIL
population found >36 000 eQTLs (the sum of cis and trans
QTLs) impacting 75% of the transcripts measured [32]; a
different study on the Ler Cvi RIL population found a
total of around 4000 eQTLs [31]. Furthermore, the Bay Sha study found that 86% were trans eQTLs and 14% cis
eQTLs whereas the Ler Cvi found 50% trans and 50%
cis [31,32]. A study in Barley estimated the level of cis
eQTLs to be between 28 and 39% [34]. These discrepancies
are most likely to be the result of differences in the numbers of lines used and replications performed (Box 1) but
could also be partly due to different genetic architectures
between the populations. 160 RILs and one replicate per
RIL were measured in the Ler Cvi study, 139 lines with a
single replicate were used in barley; the Bay Sha study
analyzed 211 RILs with two independent replicates per
RIL [31,32,34]. An increase in both parameters results in
more statistical power for the identification of small-effect
QTLs [11]. Because most small-effect eQTLs appear to be
in trans, the increase in experimental power would simultaneously lead to more eQTLs being identified and to a
shift in the ratio between cis and trans eQTLs (Box 1).
These observations suggest that small-effect trans
eQTLs are the predominant eQTLs and that numerous
small-effect eQTLs remain undetected in both populations.
By contrast, it is likely that most large-effect cis eQTLs
present in the tissues measured within each population
have been identified. The cis eQTLs provide a library of
candidate genes ready for moderate-to-large-effect QTL
analyses in both populations. Additionally, the association
of trans eQTLs with QTLs for a given phenotype provides a
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Box 1. eQTL analyses: optimizing sample size
Two separate factors need to be taken into account when
determining the level of replication for eQTL and QTL mapping:
the number of independent measurements per line and the number
of lines to utilize. Increasing both factors is critical to increase an
experiment’s statistical power but they do not have the same effect.
Because eQTL mapping will be most useful in immortal populations, such as RILs or fertile doubled haploid populations, we will
focus on factors impacting these populations. Numerous additional
factors determine QTL detection power, for instance, heritability,
epistasis and environmental interactions. However these factors are
dependent upon the specific gene and, as such, we cannot make
generalizations and use these as specific determinants in designing
a global eQTL analysis. Other factors such as QTL mapping models
come into play after data generation and, therefore, are not dealt
with in this discussion.
Replication
Increasing the number of replicated measurements per line leads to
a statistically more accurate estimate of the mean of that line for the
given trait and hence an increase in statistical power leading to an
improved ability to detect a QTL at a given location. This also
provides the benefit of sampling across micro-environmental
variation and dampening any effect that this might have on the
resulting data.
Line number
Increasing the number of lines per experiment has two potential
benefits for eQTL- or QTL-mapping experiments. The first and most
direct is similar to the benefit from increasing the number of
replicates. An increase in RIL population size results in more
measurements per allelic class at any given genomic position,
which increases the ability to separate the means between the two
parental alleles at a genomic position. A second benefit of
increasing the number of lines is that there will be more
recombination events within the population. This provides greater
genetic resolution in refining the position of a given QTL, leading to
shorter QTL intervals; furthermore, this allows the separation of
QTLs in close proximity.
Combined decisions
Given that most populations have a large number of ‘small-effect’
QTLs that can combine to cause dramatic phenotypic differences, it
is critical to have replicated measures on a large collection of lines.
Time and money are in most cases the limiting factors when
determining the number of samples that it will be feasible to
analyze. In this situation, it will be preferable in most cases to
increase the number of RILs at the cost of increasing the number of
replicates; however, having independent replication per line is the
preferred scenario for structured immortal populations.
list of genes that are candidates for association with the
phenotype but not necessarily candidates for causing the
phenotypic QTL, which is more probably caused be a gene
containing a cis eQTL. Therefore, such a list is very valuable for gene discovery and for all researchers working on
traits for which QTL analyses have been performed using
these RIL populations.
Using eQTL analysis to identify candidate genes for
phenotypic QTLs
At present, QTL mapping within Arabidopsis and numerous crop species is readily applicable because several
structured populations with high-density marker maps
are publicly available. Hitherto, the identification of the
gene or genes responsible for the QTL was hampered by
the large regions encompassed by the QTL owing to the low
density of markers and the small number of recombinants
Review
utilized. This generated difficulties in identifying candidate genes among the hundreds of genes in the QTL region.
The availability of a full-genome sequence is a helpful tool
in filtering through genes in the QTL interval, because the
examination of the annotation can often suggest which of
the genes in the QTL interval might be likely candidates.
However, even after this filtering process, the number of
candidate genes will in most cases be overwhelming.
Recent work has shown that, when a phenotypic QTL
has been identified in a RIL population where genomewide eQTL analysis has been conducted, it becomes
possible to investigate whether differential variation in
gene expression can be the cause of the phenotypic variation [16,39,40]. Promising results have been obtained by
combining QTL analysis of physiological traits and gene
expression traits based on colocalization of the respective
phenotypic QTLs and eQTLs in multiple species [33,41–
47]. In wheat, differential gene expression in coordination
with rough eQTL mapping was used to identify numerous
candidate genes for involvement in seed development [41].
In corn, phenotype QTL to eQTL linkage was used to
identify genes of potential interest in cell wall digestion
[48]. However, these genes have yet to be shown to play a
role in the respective trait.
Expression variation has previously been used to
identify the genes actually controlling phenotypic QTLs.
One example is the identification of EPITHIOSPECIFIER
MODIFIER 1 (ESM1) as the gene responsible for a QTL
controlling glucosinolate breakdown in Arabidopsis [16].
It was found that the expression of ESM1 was greatly
diminished in Col-0 Ler RILs that were Col-0 at a
specific marker, and high when Col-0 Ler RILs were
Ler at this marker. This pointed to ESM1 as the most
likely candidate gene, which was validated in ensuing
experiments.
One pitfall in the approach of using eQTLs to identify
the polymorphism responsible for a physiological QTL
occurs when the polymorphism does not change the expression level of a gene. For example, the molecular polymorphism causing the physiological QTL could be in the coding
region, leading to variations in protein stability, enzymatic
activity or post-translation modification, or possibly even a
polymorphism in the methylation level of the DNA. This
should be kept in mind when using eQTLs to search for
candidate genes, and it is probable that, in processes
where, for example, post-translational modifications are
the predominant regulatory mechanisms, this approach is
not useful.
Network eQTLs: QTLs controlling gene expression
networks
For many biological processes, the genes contributing to a
certain process, for example the synthesis of specific compounds or the control of flowering time, are often well
known. However, in most cases, little is known about
the regulation of, and interaction between, these genes.
To gain knowledge on this, one could ask if the genes with
trans eQTLs at the same specific location on the genome
are involved in the same genetic network, biological process or metabolic pathway. Addressing such questions can
identify genetic variation influencing entire processes and
Trends in Plant Science
Vol.13 No.2
thereby reveal polymorphisms upstream in the network,
process or pathway.
A way to develop hypotheses about regulatory regions in
the genome controlling the expression level of a network of
genes, is by ‘network eQTL’ analyses. There are two major
approaches to conduct network eQTL analysis. These can
roughly be classified as either ‘a priori’ or ‘a posteriori’.
‘A priori’ network analysis
In an a priori analysis, the network being tested (e.g. a
biosynthetic or a signal transduction pathway) must be
known or at least predicted. Different approaches, such as
z scaling or mean shifting, can be used to convert the
expression level of single genes into a common measure
that can be used as a measure for the expression level of the
entire gene network. This common value is then used as
the trait for the QTL analysis. This approach has been used
to search for network eQTLs for several known biosynthetic pathways and processes. A first attempt at network
eQTLs analyzed 18 networks mainly involved in plant
defense, for example glucosinolate and flavonol biosynthesis [49]. The gene encoding the known transcription
factor PAP1, which regulates, for example, flavonol biosynthesis [50,51], was found to be located at the same
position as a network eQTL for the pathway, and PAP1
was shown to have a cis eQTL at this same position [49].
This suggests that the variation in the expression of this
transcription factor is responsible for the flavonol network
eQTL. Further support was provided by the observation
that PAP1 was the likely basis of a QTL for anthocyanin
accumulation within a different Arabidopsis RIL population [51]. However, not all flavonol transcription factors
with a cis eQTL resulted in a network eQTL [49]. This
might be because the natural variation was not large
enough to have a statistically detectable effect upon the
network. Another explanation as to why a network eQTL
was not detected for all flavonol transcription factors with
expression variation could be limiting factors in the
analyses, such as the size of the RIL population and
replication number analyzed (Box 1). That a change in
expression of one regulator does not always give rise to a
change in transcript level in the target network shows that
the approach is not a bullet-proof method to identify regulators.
The use of a priori-defined networks enables the
researcher to measure simultaneously the biological phenotype used to define the network. For instance, if the
network is defined as a metabolic pathway the researcher
can measure the metabolite and directly compare this to
the network eQTL, in this case consisting of biosynthetic
genes in the pathway. This enables us to begin addressing
an important question in the genetics of complex traits: to
what extent is variation in gene expression associated with
variation in complex traits at the phenotypic level [52]? In
Arabidopsis, a recent comparison of network eQTLs for two
secondary metabolite biosynthetic pathways (indolic and
aliphatic glucosinolates) with QTLs controlling the
accumulation of these secondary metabolites showed that
all network eQTLs altered accumulation of the metabolites
[52]. However, metabolite-specific QTLs did exist. An interesting observation was that a network eQTL for the
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aliphatic glucosinolate pathway on chromosome IV colocalized with a cis eQTL in the biosynthetic gene AOP2
[49,52]. Transgenic analysis confirmed that differential
expression of the biosynthetic gene, AOP2, regulates transcript accumulation for the entire aliphatic glucosinolate
gene network, which suggests a potential feedback regulatory mechanism between the metabolome and transcriptome [52]. This shows that loci controlling network eQTLs
encompass a wide range of genetic functions.
‘A posteriori’ network analysis
The a priori approach to network analysis requires that the
network in question be already known or at least hypothesized. However, recent work is showing that the reliance
on a few RIL populations and pre-selected pathways do not
fully sample the available gene networks present within a
plant species [53,54]. The identification of novel networks
and their underlying regulators requires an a posteriori
approach, where the eQTL data are utilized to generate
networks. This approach typically uses either correlation
of expression patterns or colocalization of eQTL positions
to identify clusters or networks of genes [55]. A similar
approach generates a hierarchical relationship akin to a
regulatory network [56]. After the eQTL data are utilized
to identify novel networks, trans eQTLs can be identified
for these novel networks, and subsequently these genetic
loci can be searched for regulatory genes containing cis
eQTLs that can be tested for the ability to regulate the
novel gene networks [57].
A hybrid a posteriori approach has been tested within
Arabidopsis using a predefined genomic sample of genes
involved in flowering time. A predefined set of 175 genes,
involved in the well-known process of transition to flowering but with unknown connections, was tested for network
relationships using eQTL information [31]. This study
confirmed many of the flowering-time regulatory interactions identified previously, and also predicted numerous
unknown interactions. Thus, this approach is useful when
searching for unidentified regulators and to gain knowledge about previously known regulators. It will be interesting to see this approach extended to the full genome to
better understand the complex interactions between gene
networks. Additionally, it will be important to understand
if natural genetic variation merely tweaks genetic networks present in all members of the species or if there
are in fact new networks and different connections in play
within the other individuals within a species, for example
Arabidopsis accessions other than the standard model
accessions Col-0 or Ler.
Available and desired genomic tools
A whole-genome eQTL analysis provides a genomic database of genetic variation as well as the local (cis) and distal
(trans) effects of this genetic variation. Such datasets provide a ready library of candidate genes for phenotypic QTLs.
This is especially true when the same RILs used for the
eQTL analyses are tested for QTLs controlling a researcher’s trait of choice, because they would be able to use the
available eQTL data to search for candidate genes. This is
particularly true if plants have been grown under similar
conditions. Unfortunately, at present there are no databases
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that allow for the querying of eQTLs for the gene of interest
in the Bay Sha and Ler Cvi RILs, where intensive eQTL
analyses have been performed. The development of such an
easy-to-use database through which community-generated
eQTL data could be rapidly queried would greatly aid the
use of the published eQTL data.
Concluding remarks and future perspectives
The combination of phenotypic QTLs and eQTL data is a
powerful tool for gene discovery. This systems biology
approach to natural variation has enhanced our ability
to identify the underlying molecular basis of QTLs within
model and crop plant systems. eQTL analysis is currently
underway in numerous organisms, and when genomic tools
such as dense marker maps, mapping populations, massively parallel genomic re-sequencing and microarray platforms become available for more organisms, this promising
approach is likely to aid QTL cloning in these organisms.
Recently, QTL mapping of physiological and metabolic
traits has also moved from mapping one or a few traits [58–
60] to mapping all mass peaks detected in a mass-spectrometric analyses [61,62]. Combining metabolomics data
with eQTL data from the same lines has great potential
in investigating hundreds of metabolites and linking them
to eQTLs. Furthermore, this makes it possible to link
metabolites in a biosynthetic network with network eQTLs
for the biosynthetic genes. This provides tremendous
potential for the expansion of our understanding of regulatory interactions between the transcriptome and metabolome. Finally, the generation of eQTL datasets in crop
species containing long-term mapping populations with
extensive phenotypic information will allow for rapid
growth in the understanding of the molecular basis of
phenotypic QTLs, which will greatly benefit plant breeding.
Conflicts of interest
The authors have no conflicts of interest to report with
regards to this review.
Acknowledgement
The Danish National Research Foundation is acknowledged for its
support to PlaCe (Center for Molecular Plant Physiology). BGH
acknowledges FOBI (Graduate School in Biotechnology, University of
Copenhagen) for providing a PhD stipend. Funding for this manuscript
was provided in part by NSF grant DBI#0642481 to DJK. We also thank
three anonymous reviewers for helping to improve the manuscript.
References
1 Collard, B.C.Y. et al. (2005) An introduction to markers, quantitative
trait loci (QTL) mapping and marker-assisted selection for crop
improvement: The basic concepts. Euphytica 142, 169–196
2 Asins, M.J. (2002) Present and future of quantitative trait locus
analysis in plant breeding. Plant Breed. 121, 281–291
3 Kearsey, M.J. and Farquhar, A.G.L. (1998) QTL analysis in plants;
where are we now? Heredity 80, 137–142
4 Yano, M. and Sasaki, T. (1997) Genetic and molecular dissection of
quantitative traits in rice. Plant Mol. Biol. 35, 145–153
5 Dudley, J.W. (1993) Molecular markers in plant improvement –
manipulation of genes affecting quantitative traits. Crop Sci. 33,
660–668
6 Holland, J.B. (2007) Genetic architecture of complex traits in plants.
Curr. Opin. Plant Biol. 10, 156–161
7 Koornneef, M. et al. (2004) Naturally occurring genetic variation in
Arabidopsis thaliana. Annu. Rev. Plant Biol. 55, 141–172
Review
8 Borevitz, J.O. and Chory, J. (2004) Genomics tools for QTL analysis and
gene discovery. Curr. Opin. Plant Biol. 7, 132–136
9 Mitchell-Olds, T. and Schmitt, J. (2006) Genetic mechanisms and
evolutionary significance of natural variation in Arabidopsis. Nature
441, 947–952
10 Maloof, J.N. (2003) Genomic approaches to analyzing natural variation
in Arabidopsis thaliana. Curr. Opin. Genet. Dev. 13, 576–582
11 Mackay, T.F.C. (2001) The genetic architecture of quantitative traits.
Annu. Rev. Genet. 35, 303–339
12 Salvi, S. and Tuberosa, R. (2005) To clone or not to clone plant QTLs:
present and future challenges. Trends Plant Sci. 10, 297–304
13 Luo, Z.W. et al. (2007) SFP genotyping from Affymetrix arrays is robust
but largely detects cis-acting expression regulators. Genetics 176, 789–
800
14 West, M.A.L. et al. (2006) High-density haplotyping with microarraybased expression and single feature polymorphism markers in
Arabidopsis. Genome Res. 16, 787–795
15 Rostoks, N. et al. (2005) Single-feature polymorphism discovery in the
barley transcriptome. Genome Biol. 6, R54
16 Zhang, Z. et al. (2006) The gene controlling the Quantitative Trait
Locus EPITHIOSPECIFIER MODIFIER1 alters glucosinolate
hydrolysis and insect resistance in Arabidopsis. Plant Cell 18, 1524–
1536
17 Kroymann, J. et al. (2003) Evolutionary dynamics of an Arabidopsis
insect resistance quantitative trait locus. Proc. Natl. Acad. Sci. U. S. A.
100, 14587–14592
18 Lambrix, V. et al. (2001) The Arabidopsis epithiospecifier protein
promotes the hydrolysis of glucosinolates to nitriles and influences
Trichoplusia ni herbivory. Plant Cell 13, 2793–2807
19 Kliebenstein, D.J. et al. (2001) Gene duplication and the diversification
of secondary metabolism: side chain modification of glucosinolates in
Arabidopsis thaliana. Plant Cell 13, 681–693
20 Svistoonoff, S. et al. (2007) Root tip contact with low-phosphate media
reprograms plant root architecture. Nat Genet. 39, 792–796
21 Johanson, U. et al. (2000) Molecular analysis of FRIGIDA, a major
determinant of natural variation in Arabidopsis flowering time.
Science 290, 344–347
22 Werner, J.D. et al. (2005) Quantitative trait locus mapping and DNA
array hybridization identify an FLM deletion as a cause for natural
flowering-time variation. Proc. Natl. Acad. Sci. U. S. A. 102, 2460–2465
23 Caicedo, A.L. et al. (2004) Epistatic interaction between Arabidopsis
FRI and FLC flowering time genes generates a latitudinal cline in a life
history trait. Proc. Natl. Acad. Sci. U. S. A. 101, 15670–15675
24 Salvi, S. et al. (2007) Conserved noncoding genomic sequences
associated with a flowering-time quantitative trait locus in maize.
Proc. Natl. Acad. Sci. U. S. A. 104, 11376–11381
25 Clark, R.M. et al. (2006) A distant upstream enhancer at the maize
domestication gene tb1 has pleiotropic effects on plant and inflorescent
architecture. Nat. Genet. 38, 594–597
26 Morley, M. et al. (2004) Genetic analysis of genome-wide variation in
human gene expression. Nature 430, 743–747
27 Brem, R.B. and Kruglyak, L. (2005) The landscape of genetic
complexity across 5,700 gene expression traits in yeast. Proc. Natl.
Acad. Sci. U. S. A. 102, 1572–1577
28 Brem, R.B. et al. (2002) Genetic dissection of transcriptional regulation
in budding yeast. Science 296, 752–755
29 Schadt, E.E. et al. (2003) Genetics of gene expression surveyed in
maize, mouse and man. Nature 422, 297–302
30 Yvert, G. et al. (2003) trans-acting regulatory variation in
Saccharomyces cerevisiae and the role of transcription factors. Nat.
Genet. 35, 57–64
31 Keurentjes, J.J.B. et al. (2007) Regulatory network construction in
Arabidopsis by using genome-wide gene expression quantitative trait
loci. Proc. Natl. Acad. Sci. U. S. A. 104, 1708–1713
32 West, M.A.L. et al. (2007) Global eQTL mapping reveals the complex
genetic architecture of transcript level variation in Arabidopsis.
Genetics 175, 1441–1450
33 Kirst, M. et al. (2005) Genetic architecture of transcript-level variation
in differentiating xylem of a eucalyptus hybrid. Genetics 169, 2295–
2303
34 Potokina, E. et al. (2008) Gene expression quantitative trait locus
analysis of 16 000 barley genes reveals a complex pattern of
genome-wide transcriptional regulation. Plant J. 53, 90–101
Trends in Plant Science
Vol.13 No.2
35 Wagner, A. (2000) The role of population size, pleiotropy and fitness
effects of mutations in the evolution of overlapping gene functions.
Genetics 154, 1389–1401
36 Jeong, H. et al. (2001) Lethality and centrality in protein networks.
Nature 411, 41–42
37 Fraser, H.B. et al. (2002) Evolutionary rate in the protein interaction
network. Science 296, 750–752
38 Yu, H. et al. (2004) Genomic analysis of essentiality within protein
networks. Trends Genet. 20, 227–231
39 Meng, H. et al. (2007) Identification of Abcc6 as the major causal gene
for dystrophic cardiac calcification in mice through integrative
genomics. Proc. Natl. Acad. Sci. U. S. A. 104, 4530–4535
40 Brem, R.B. et al. (2005) Genetic interactions between polymorphisms
that affect gene expression in yeast. Nature 436, 701–703
41 Jordan, M.C. et al. (2007) Identifying regions of the wheat genome
controlling seed development by mapping expression quantitative trait
loci. Plant Biotechnol. J. 5, 442–453
42 DeCook, R. et al. (2006) Genetic regulation of gene expression during
shoot development in Arabidopsis. Genetics 172, 1155–1164
43 Juenger, T.E. et al. (2006) Natural genetic variation in whole-genome
expression in Arabidopsis thaliana: the impact of physiological QTL
introgression. Mol. Ecol. 15, 1351–1365
44 Street, N.R. et al. (2006) The genetics and genomics of the drought
response in Populus. Plant J. 48, 321–341
45 An, C. et al. (2007) Transcriptome profiling, sequence characterization,
and SNP-based chromosomal assignment of the EXPANSIN genes in
cotton. Mol. Genet. Genomics 278, 539–553
46 Venu, R.C. et al. (2007) RL-SAGE and microarray analysis of the rice
transcriptome after Rhizoctonia solani infection. Mol. Genet. Genomics
278, 421–431
47 Poormohammad Kiani, S. et al. (2007) Genetic variability for
physiological traits under drought conditions and differential
expression of water stress-associated genes in sunflower (Helianthus
annuus L.). Theor. Appl. Genet. 114, 193–207
48 Shi, C. et al. (2007) Identification of candidate genes associated with
cell wall digestibility and eQTL (expression quantitative trait loci)
analysis in a Flint x Flint maize recombinant inbred line population.
BMC Genomics 8, 22
49 Kliebenstein, D.J. et al. (2006) Identification of QTLs controlling gene
expression networks defined a priori. BMC Bioinformatics 7, 308
50 Borevitz, J.O. et al. (2000) Activation tagging identifies a conserved
MYB regulator of phenylpropanoid biosynthesis. Plant Cell 12, 2383–
2393
51 Teng, S. et al. (2005) Sucrose-specific induction of anthocyanin
biosynthesis in Arabidopsis requires the MYB75/PAP1 gene. Plant
Physiol. 139, 1840–1852
52 Wentzell, A.M. et al. (2007) Linking metabolic QTL with network and
cis-eQTL controlling biosynthetic pathways. PLOS Genet. 3, e162
53 Van Leeuwen, H. et al. (2007) Natural variation among Arabidopsis
thaliana accessions for transcriptome response to exogenous salicylic
acid. Plant Cell 19, 2099–2110
54 Kliebenstein, D.J. et al. (2006) Genomic survey of gene expression
diversity in Arabidopsis thaliana. Genetics 172, 1179–1189
55 Lan, H. et al. (2006) Combined expression trait correlations and
expression quantitative trait locus mapping. Plos Genet. 2, 51–61
56 Lee, S.I. et al. (2006) Identifying regulatory mechanisms using
individual variation reveals key role for chromatin modification.
Proc. Natl. Acad. Sci. U. S. A. 103, 14062–14067
57 Sun, W. et al. (2007) Detection of eQTL modules mediated by activity
levels of transcription factors. Bioinformatics 23, 2290–2297
58 Kliebenstein, D.J. et al. (2001) Comparative quantitative trait loci
mapping of aliphatic, indolic and benzylic glucosinolate production
in Arabidopsis thaliana leaves and seeds. Genetics 159, 359–370
59 McMullen, M.D. et al. (1998) Quantitative trait loci and metabolic
pathways. Proc. Natl. Acad. Sci. U. S. A. 95, 1996–2000
60 Thormann, C.E. et al. (1996) Mapping loci controlling the
concentrations of erucic and linolenic acids in seed oil of Brassica
napus L. Theor. Appl. Genet. 93, 282–286
61 Keurentjes, J.J.B. et al. (2006) The genetics of plant metabolism. Nat.
Genet. 38, 842–849
62 Schauer, N. et al. (2006) Comprehensive metabolic profiling
and phenotyping of interspecific introgression lines for tomato
improvement. Nat. Biotechnol. 24, 447–454
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