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Hierarchical Nuclear and Cytoplasmic Genetic Architectures
for Plant Growth and Defense within Arabidopsis
CW
Bindu Joseph,a Jason A. Corwin,a Tobias Züst,b,1 Baohua Li,a Majid Iravani,c Gabriela Schaepman-Strub,b
Lindsay A. Turnbull,b,2 and Daniel J. Kliebensteina,3,4
a Department
of Plant Sciences, University of California at Davis, Davis, California 95616
of Evolutionary Biology and Environmental Studies, University of Zürich, Zurich CH-8057, Switzerland
c Department of Natural Resources, Isfahan University of Technology, 83111-84156 Isfahan, Iran
b Institute
To understand how genetic architecture translates between phenotypic levels, we mapped the genetic architecture of growth
and defense within the Arabidopsis thaliana Kas 3 Tsu recombinant inbred line population. We measured plant growth using
traditional size measurements and size-corrected growth rates. This population contains genetic variation in both the nuclear
and cytoplasmic genomes, allowing us to separate their contributions. The cytoplasmic genome regulated a significant
variance in growth but not defense, which was due to cytonuclear epistasis. Furthermore, growth adhered to an infinitesimal
model of genetic architecture, while defense metabolism was more of a moderate-effect model. We found a lack of
concordance between quantitative trait loci (QTL) regulating defense and those regulating growth. Given the published
evidence proving the link between glucosinolates and growth, this is likely a false negative result caused by the limited
population size. This size limitation creates an inability to test the entire potential genetic landscape possible between these
two parents. We uncovered a significant effect of glucosinolates on growth once we accounted for allelic differences in
growth QTLs. Therefore, other growth QTLs can mask the effects of defense upon growth. Investigating direct links across
phenotypic hierarchies is fraught with difficulty; we identify issues complicating this analysis.
INTRODUCTION
A key interest of quantitative genetics and systems biology is to
causally link genetic polymorphisms to systems of interacting
phenotypes at different levels of biological organization, from
individual molecules to the whole organism (Mackay, 2001). One
such hierarchical system of particular interest to ecologists and
biochemists is the network of links between genetic polymorphisms and changes in chemical defense compounds,
a low-order phenotype, with that of growth and fitness, higher
order phenotypes (Karban and Baldwin, 1997; Strauss and
Agrawal, 1999; Agrawal, 2007; Lankau, 2007). Similar hierarchical systems exist in agronomics and crop breeding, where
there is an interest in linking enzyme polymorphisms to nitrogen
use efficiency and plant yield (Loudet et al., 2003, 2005). However, the interpretation of such hierarchical systems is usually
confounded by their inherent complexity, as the number of
1 Current
address: Department of Ecology and Evolutionary Biology,
Cornell University, Ithaca, NY 14853.
2 Current address: Department of Plant Sciences, University of Oxford,
South Parks Road, Oxford OX1 3RB, United Kingdom.
3 Current address: DynaMo Center of Excellence, University of Copenhagen, Thorvaldsensvej 40, DK-1871 Frederiksberg C, Denmark.
4 Address correspondence to [email protected].
The author responsible for distribution of materials integral to the findings
presented in this article in accordance with the policy described in the
Instructions for Authors (www.plantcell.org) is: Daniel J. Kliebenstein
([email protected]).
C
Some figures in this article are displayed in color online but in black and
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W
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www.plantcell.org/cgi/doi/10.1105/tpc.113.112615
components that causally affect a phenotype increases strongly
with hierarchical level (Fu et al., 2009). For example, linking
a promoter polymorphism to variation in the resulting transcript
is relatively straightforward because there are a limited number
of molecular factors that will influence the abundance of that
transcript. By contrast, linking the same promoter polymorphism
to a change in yield is dramatically more difficult since it is
possible that nearly every aspect of a genome and environment
may affect the yield of a plant. This potential for increasing
complexity accompanying a shift from molecular to wholeorganism phenotypes can lead to a change in genetic architecture, from the molecular phenotypes having a small number
of large-effect loci with moderate epistasis, to the wholeorganism phenotypes having a large number of loci with moderate to small effects and apparent additivity (West et al., 2007;
Rowe et al., 2008; Buckler et al., 2009).
The idea of different genetic architectures across the phenotypic hierarchy is long established but also linked to significant
disagreement that we propose can be resolved by partitioning
causality across the phenotypic hierarchy (Fisher, 1930; Wright,
1931). We demonstrate this using the two causally linked phenotypes of growth and chemical defense (Paul-Victor et al.,
2010; Kerwin et al., 2011; Züst et al., 2011). Since these phenotypes are at different levels in the same hierarchical genetic
system with chemical defense subordinate to growth, they are
ideal candidates to empirically test the relation between hierarchical level and genetic architecture. We then explore if these
differences in genetic architecture could actually mask the
causal link between the two phenotypes. To accomplish this, we
chose to compare the architecture of growth and chemical defense in an Arabidopsis thaliana recombinant inbred line (RIL)
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population by measuring the same plants for both phenotypes,
thereby minimizing any confounding effects from environmental
variation.
Key components of the defense chemistry of Arabidopsis are
the glucosinolates, which provide both effective antiherbivore
and antimicrobial properties (Lambrix et al., 2001; Kliebenstein
et al., 2002; Zhang et al., 2006; Hansen et al., 2008; Clay et al.,
2009). The indole glucosinolates play a predominant role in
providing defense against aphids, nonhost pathogens, and
oviposition by specialized lepidopteran moths (Cui et al., 2002;
de Vos et al., 2008; Bednarek et al., 2009; Pfalz et al., 2009;
Müller et al., 2010). The aliphatic glucosinolates are the dominant mechanism providing resistance to chewing insects and
some adapted pathogens (Lambrix et al., 2001; Kliebenstein
et al., 2002; Beekwilder et al., 2008; Hansen et al., 2008; Fan
et al., 2011). Importantly for our study, there is significant genetic variation in glucosinolates regulated by natural knockout
polymorphisms in enzymatic or regulatory loci (Kliebenstein
et al., 2001a; Hansen et al., 2007; Wentzell et al., 2007; Hansen
et al., 2008; Chan et al., 2011). This variation in chemical defense
has been linked to fitness in the presence of insect herbivores at
both the local and continental scale (Mauricio, 1998; BidartBouzat and Kliebenstein, 2008; Züst et al., 2012). In addition, the
recreation of some of the natural knockout polymorphisms responsible for this variation and directly testing their influence on
higher whole organism phenotypes has shown that natural
glucosinolate variation is responsible for at least part of the
changes in growth and yield (Mauricio, 1998; Lankau and
Strauss, 2008; Paul-Victor et al., 2010; Kerwin et al., 2011; Züst
et al., 2011), which is partly due to changes in the circadian
clock (Kerwin et al., 2011). Thus, there is ample evidence for the
existence of molecular links between chemical defense and
growth phenotypes.
For this study, we treated growth as whole-organism phenotype as it is considered to be a highly integrative phenotype
with minimal epistasis (Meyer et al., 2007; Lisec et al., 2008;
Paul-Victor et al., 2010; Züst et al., 2011; Zhang et al., 2012).
There are multiple approaches to estimating growth rates, such
as size at a given time, the relative growth rate over a given
period, or size-corrected growth rates (Paul-Victor et al., 2010;
Züst et al., 2011; Paine et al., 2012). In Arabidopsis, rosette size
can be readily measured using real-time digital image analysis,
and repeated measures of size at different time intervals can be
used to calculate growth rates. By measuring all growth descriptors and chemical defense phenotypes in the same experiment on the same individuals, we are able to reduce the
amount of environmental variation that may differentially influence both phenotypes and thus be able to directly compare
the variation in genetic architecture of growth and defense.
When considering the comparative genetic architecture of
growth and defense, nuclear genomic variation is only one part
of the genetic variation present in any possible cross between
two genotypes. Recently, it has been shown that there is considerable genetic diversity in the cytosolic genomes of Arabidopsis, both plastidic and mitochondrial, that could influence
growth or glucosinolate production (Moison et al., 2010; Davila
et al., 2011). This is especially true since a fraction of glucosinolate synthesis occurs in the cytosolic organelles and these are
also the main powerhouses to provide energy for growth
(Sønderby et al., 2010a). Studies in other species have shown
that genetic variation in the cytosolic genome can influence the
structure of genetic diversity in the nuclear genome and may
alter the genetic architecture underpinning fitness-related phenotypes (Wade and Goodnight, 2006; Wolf, 2009). In addition to
this potential influence on genetic architecture, cytosolic genetic
variation could also affect our ability to identify genetic loci in
mapping populations (Moison et al., 2010; Tang et al., 2013). To
allow us to measure both the nuclear and cytoplasmic genetic
architecture of growth and defense in Arabidopsis, we used the
Kas 3 Tsu RIL population, which is one of the largest available
Arabidopsis RIL populations to date (McKay et al., 2008;
Juenger et al., 2010). The Kas 3 Tsu RIL population was generated from a reciprocal cross with approximately half of the
resulting lines carrying Kas organelles, while the other half carry
Tsu organelles, which allows for the explicit analysis of the influence of cytosolic genetic variation on the genetic architecture
of growth and defense (McKay et al., 2008; Juenger et al., 2010).
This simple reciprocal RIL structure provides an increase in
statistical power for genetic architecture studies over unstructured or more complex structured populations (Mackay,
2001).
In this study, we used high-throughput HPLC analysis to
measure glucosinolates (Kliebenstein et al., 2001b; Wentzell
et al., 2007) and high-throughput digital image analysis to
measure the developmental progression in rosette size of 341
lines in the Kas 3 Tsu RIL population of Arabidopsis. Using
multiple measures of growth, biomass, and different defense
compounds, we were able to demonstrate that growth and
defense do have different quantitative genetic architectures with
growth displaying a structure more related to an infinitesimal
model, which has a large number of genes of small to moderate
effect underlying the phenotype, and the glucosinolate defenses
containing more large-effect loci (Paul-Victor et al., 2010; Kerwin
et al., 2011; Züst et al., 2011). Both the growth and chemical
defense phenotypes showed epistatic networks with similar
levels of variance partitioned to the epistatic terms, but this
variation was partitioned across the loci such that the chemical
defenses had more highly connected networks of genetic loci,
while growth had fewer detected connections between genetic
loci. Significant cytoplasmic effects were present for all traits,
but this was almost entirely due to epistatic interactions with
nuclear genetic loci. Interestingly, traditional quantitative trait
locus (QTL) overlap or genetic correlation approaches could not
identify our mechanistically proven link between glucosinolates
and growth in this population. We were able to recover this link
using analysis of covariance (ANCOVA) where we accounted for
the identified growth-associated loci and showed that, as previously identified, this link was strongest when using the sizecorrected growth rate estimate. Thus, there appear to be genetic
linkages present within standard RIL populations that are uncoverable using standard statistical methodologies. While the
two phenotypic classes display different genetic architectures, it
is more appropriate to consider the chemical defense architecture
as a subset of a broader genetic architecture governing growth.
Future work will be required to understand how this integration
from one phenotype to another occurs at a molecular level.
Hierarchical Genetic Architecture
RESULTS
Population and Phenotype Choice and
Phenotypic Heritability
To investigate the genetic architecture of plant growth and defense, we measured three distinct phenotypic classes on the
same individual plants to minimize any influence of environment.
We used high-throughput HPLC to measure two related but
biochemically distinct classes of metabolic defense in the same
plants, the indolic and aliphatic glucosinolates. In this population, we were able to measure 15 aliphatic glucosinolaterelated phenotypes: 10 individual aliphatic glucosinolates and
five composite phenotypes (see Supplemental Data Set 1 and
Supplemental Figures 1 and 2 online) (Wentzell et al., 2007;
Hageman et al., 2012). The Kas-1 parent for this population has
a different glucosinolate profile than the Kas that was previously
analyzed for glucosinolate content and is most likely a different
genotype, which illustrates the importance of measuring the
exact parents of any population (Kliebenstein et al., 2001c;
Lambrix et al., 2001). We could measure all four known indole
glucosinolates and two composite phenotypes known to specifically identify previously cloned indolic loci, providing six indolic glucosinolate phenotypes (see Supplemental Data Set 1
and Supplemental Figures 1 and 2 online) (Reichelt et al., 2002;
Pfalz et al., 2009). Additionally, there were three previously unidentified compounds that copurified with the glucosinolates
that were measured but were at levels too low to chemically
identify (see Supplemental Data Set 1 online). We measured
rosette size throughout plant development six times at regular
intervals between days 7 and 23 postgermination and used
these data to estimate size-standardized growth rate (SGR),
giving us seven growth-related traits (see Supplemental Data
Set 1 and Supplemental Figures 1 and 2 online) (Paul-Victor
et al., 2010; Züst et al., 2011; Paine et al., 2012; Zhang et al.,
2012).
For all phenotype classes, we estimated both the nuclear and
cytoplasmic heritability using all of the per-line measurements
(see Supplemental Data Set 1 online). Cytoplasmic heritability is
the amount of variation regulated by the cytoplasmic difference
between the two reciprocal crosses where the cytoplasmic genome of specific lines is derived from the Kas-1 or Tsu-1
mother. Nuclear heritability is the phenotypic variation ascribable to differences in nuclear genotypes as defined by each
inbred line nested within the cytoplasmic background. Using
analysis of variance (ANOVA), nuclear genotypic variation significantly altered all phenotypes, except one indolic phenotype
(4OH-I3M, which was removed from further analysis). Nuclear
genetic variation also had the strongest influence upon phenotypic variation (Figure 1; see Supplemental Data Set 1 online).
Interestingly, cytoplasmic genotypic variation significantly regulated phenotypic variation for SGR, two indolic glucosinolates,
and two ratios derived from these indolic glucosinolates using
the same ANOVA (see Supplemental Data Set 1 online). However, even for these phenotypes, cytoplasmic heritability was
at least two log10 orders less than nuclear heritability (see
Supplemental Data Set 1 online). At a preliminary level, this indicates that the nuclear genotypic variation is the primary
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genetic player in regulating phenotypic variance within this
population for defense metabolism and growth, as has been
previously noted when investigating populations containing
reciprocal crosses (Lisec et al., 2008). The average nuclear
heritability for the phenotypic classes was statistically indistinguishable: aliphatic glucosinolates (38.5% 6 3.8%),
indolic glucosinolate (40.2% 6 4.1%), and growth phenotypes (40.0% 6 2.1%) (average heritability 6 SE). However,
both glucosinolate classes showed a multimodal distribution
of nuclear heritabilities, while growth phenotypes displayed
a more monomodal distribution (Figure 1; see Supplemental
Data Set 2 online). The multimodal distribution of glucosinolates
was largely caused by a presence/absence polymorphism at the
alkenyl hydroxyl–producing (AOP) structural locus. In the same
model, we also tested the effect of block and experiment that,
while significant in all cases, were typically one-sixth the
variance of the genotype term (0.39 median heritability versus
0.06 median variance explained by the combination of experiment and block). More critically, the genotype 3 experiment term was rarely significant and never explained more
than the direct genotype term; thus, we further combined
data across experiments.
Restriction Enzyme Sequence Comparative Analysis
Mapping of the Kas 3 Tsu RIL Population
The available map for the Kas 3 Tsu RILs had a number of map
gaps larger than 10 centimorgans (cM) that could diminish our
ability to detect QTLs in these regions (Mackay, 2001; McKay
et al., 2008). To close these gaps and provide more markers
throughout the genome, we used restriction enzyme sequence
comparative analysis (RESCAN) to generate a high-density map
for the 341 Kas 3 Tsu RILs (Monson-Miller et al., 2012; Seymour
et al., 2012). From this analysis, we identified a total of 908 good
Figure 1. Partitioning of Heritability.
Shown are frequency plots of different aspects of heritability ascribable
to nuclear genomic variation for the different phenotypic classes: aliphatic glucosinolates (solid; red online), indole glucosinolates (dotted;
blue online), and growth (dashed; green online). For each phenotypic
class, the bin size is 5% for the frequency plots.
[See online article for color version of this figure.]
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quality single nucleotide polymorphisms that were called in at
least 59% of the lines. These markers were combined with the
161 markers from the previously published map to create a total
set of 1069 markers (see Supplemental Data Set 3 online). Any
missing genotypes were imputed using the fill.geno function in
the R/qtl package using the single simulation replicate method
(Broman et al., 2003). This generated a total map of 519 cM with
an average gap size of 0.5 cM and a maximal gap size of 4.5 cM.
Several of the gaps are in apparent recombination hotspots on
chromosomes two and four (see Supplemental Figures 3 and 4
online). Thus, this map provides a high genetic resolution to
accurately map QTLs.
QTL Mapping
To query the underlying genetic architecture of plant growth
and chemical defense, we next mapped the QTLs regulating
phenotypic variation in aliphatic glucosinolates, indolic glucosinolates, and growth-related traits within the Kas 3 Tsu
population (Figure 2; see Supplemental Data Set 4 online).
Aliphatic glucosinolates had the fewest detectable loci (4.1 6
0.5, range 6 to 1), indolic glucosinolate phenotypes had the
highest number of QTLs per phenotype (6.5 6 1.5, range 2 to
12), and growth-related traits were intermediate (5.6 6 0.4,
range 7 to 4). The number of QTLs per aliphatic glucosinolate
phenotype was significantly lower than that observed for
growth-related traits or indolic glucosinolates (t test, P < 0.05),
while growth-related traits and indolic glucosinolates were
statistically similar.
The distribution of allelic effects across the QTLs for each
phenotypic class also showed that there were differences in
the genetic architecture underpinning growth-related traits
and defense. Both defense phenotypic classes identified
QTLs of moderate to large effect (here defined as >25% additive effect) with aliphatic QTLs being over one-third of the
moderate to large effect class and indolic QTLs being approximately one-fifth moderate to large effect QTLs (Figure 3;
see Supplemental Data Set 4 online). In distinct contrast,
growth-related traits had no loci with effects over 16%, and as
such, all growth loci are small-effect loci (Figure 3). Furthermore, there was a difference in the distribution of allelic effects
with growth and indolic glucosinolates showing an equal
fraction of positive and negative effects from the Kas-1 alleles,
whereas aliphatic glucosinolates showed a bias toward negative Kas-1 alleles. The bias toward negative Kas-1 QTL alleles for aliphatic glucosinolates was statistically significant in
comparison to both growth traits and indolic glucosinolates
(x2, P < 0.05).
Using QTL Localization to Partition Phenotypic Classes
The distribution of QTLs across the genome was distinct for
all three phenotypic classes with surprisingly little overlap
(Figure 2; see Supplemental Data Set 4 online). This lack of
overlap was readily apparent when clustering the phenotypes
using the position and effect of the detected QTLs as input for
the algorithm (Figure 2D). The clustering algorithm showed
that aliphatic glucosinolate-related phenotypes formed two
distinct phenotypic clusters. These two clusters were largely
separated by the presence or absence of a QTL at the position
of the GSL-Elong (glucosinolate elongation) locus (Figures 2A
and 2D). The short-chain glucosinolates (i.e., allyl and butenyl)
partitioned into a single cluster with no significant QTLs at
GSL-Elong. Both the Kas and Tsu parents produce predominantly 3C glucosinolates, thus limiting variation in the 3C
versus 4C glucosinolate allelic state that is the hallmark of
the major effect alleles at GSL-Elong (Magrath et al., 1994;
Kliebenstein et al., 2001b, 2001c; Kroymann et al., 2003). By
contrast, variations in a cluster of long-chain aliphatic glucosinolate phenotypes (i.e., MSOO and MSOH) were associated with a QTL at the position of GSL-Elong. This suggests
that despite both parents having a 3C allele at GSL-Elong,
there is likely subtle variation between these two alleles and
their effect on the production of long-chain glucosinolates.
Therefore, the major-effect locus GSL-Elong appears to encode for additional minor-effect allelic variation, as has previously been suggested (Kroymann et al., 2003). It remains to
be seen how often major-effect QTLs, such as those for other
defense compounds or flowering time, have minor effect allelic classes in what were thought to be functionally equivalent alleles.
The individual indolic glucosinolate phenotypes clustered together, while the summation of total indolic glucosinolate accumulation acted as an outlier for this class (Figure 2D).
Interestingly, two of the unknown sulfated metabolites that were
detected in the HPLC clustered with the major indole glucosinolate cluster, suggesting that they may actually be previously
unidentified indolic glucosinolates. Agreeing with this hypothesis, both Unk2 and Unk3 had retention times that were similar to
I3M and NMOI3M, and their absorption spectra were highly
similar to I3M and NMOI3M, which indicates that they may be
modified forms of these compounds. Additionally, the unknown
metabolites shared a QTL on chromosome V with opposing
allelic effects, suggesting that they may be precursor and
product. However, their abundance was much lower than either
I3M or NMOI3M, preventing us from conducting any analysis of
their chemical structure. However, this does suggest that
coassociating phenotypes based on QTL identification might be
a useful method to link unknown metabolites to known pathways.
This clustering of all the phenotypes showed that growthrelated loci formed a distinct cluster that shared almost no
QTLs with the defense-related phenotypes, suggesting that
there is no strong link between growth and metabolic defense
within this population. Within the growth-related traits, there
was a clustering by day such that rosette size at days 15, 19,
and 23 shared almost all of the same QTLs within the cluster
but had fewer shared QTLs with the early rosette size measures at days 9 and 11. This suggests that growth-related
traits QTLs may have some modularity where there is a transition between groups of loci (modules) regulating size at early
and late stages. Interestingly, both SGR and rosette size at
day 7 had the fewest shared QTLs with the early and late
growth modules. While SGR did share several loci with rosette
size, there were unique growth QTLs detected by this more
direct measure of growth.
Hierarchical Genetic Architecture
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Figure 2. Clustering of Glucosinolate and Growth QTLs across the Arabidopsis Genome.
The distribution of QTLs across the genome for each phenotypic class is presented using a 5-cM sliding window. The genetic distances in graphs from
(A) to (C) are scaled to match the genetic plot in part (D). The chromosomes are labeled with Roman numerals at the top of the figure.
(A) The distribution of QTLs for 15 aliphatic glucosinolate phenotypes.
(B) The distribution of QTLs for six indolic glucosinolate phenotypes.
(C) The distribution of QTLs for seven growth phenotypes.
(D) Heat map showing the location and effect of loci with LOD scores above the permuted LOD threshold 2, across the five chromosomes. Red
indicates a positive effect of the Kas allele, while green (light hue) indicates a positive effect of the Tsu allele. Vertical white lines separate the
chromosomes (1 to 5 from left to right). Clustering on the left is based on the absolute Pearson correlation of QTL effects across all significant loci for
each phenotype. Phenotypes are colored as to their phenotypic class, with blue for indolic glucosinolate, red (dark) for aliphatic glucosinolate, and
green (light) for growth. Three unknown metabolites detected in the glucosinolate HPLC are included.
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Figure 3. Differential QTL Effects per Phenotypic Class.
Frequency plots of allelic effect sizes of aliphatic glucosinolate QTLs (solid; red online), indole glucosinolate QTLs (dotted; blue online), and growth QTLs
Tsu 2 X
Kas (dashed; green online). Allelic effects for each significant QTL are presented as percent effect, by estimating ½X
for each significant main
=
XRIL
effect marker. For each phenotypic class, the bin size for the frequency plots is 10% effect size.
[See online article for color version of this figure.]
Epistatic Architecture of Growth and Defense
To compare the epistatic architecture regulating variation in
growth and chemical defense within the Kas 3 Tsu population,
we first developed statistical models for the QTL regulating each
phenotypic class. To do this, we took all of the QTL positions
linked to variation in a particular phenotypic class (Figures 2A to
2C; see Supplemental Data Set 4 online), identified the genetic
marker closest to the peak, and used them within an ANOVA to
test the effects of all main effect loci upon the phenotypes within
the phenotypic class. Any markers that showed no significance
with any phenotype within a phenotypic class was dropped from
the model in a stepwise fashion until the final model contained
only statistically significant markers (ANOVA). Individual nonsignificant loci were added back to test for significance, but
none were able to improve the model. This generated a main
effect model for each phenotypic class that had 11, 12, and 15
QTLs for aliphatic glucosinolate, growth-related traits, and indolic glucosinolates, respectively (see Supplemental Data Set 5
online). To test whether there were detectable effects of the
cytoplasmic genomic variation upon the phenotypes after controlling for the main-effect QTLs, we also included cytoplasmic
genotype as a factor in the model. Interestingly, none of the
phenotypes in any phenotypic class had a detectable main effect of cytoplasmic genetic variation (see Supplemental Data Set
5 online). These phenotypic class-specific models were then
used to test all possible pairwise epistatic interactions between
significant main effect loci by ANOVA with a false discovery rate
(FDR) of 0.2 per model (see Supplemental Data Set 6 online).
The largest of our models for the indolic glucosinolates only
used 136 of the 314 available degrees of freedom (43%) leaving
the majority of the degrees of freedom for the residual variance,
which suggests that we are not over fitting the model. Similarly
for the aliphatic model, we only used 78 of the 314 available
degrees of freedom (25%). All significant interactions were
confirmed by rerunning the model containing only the significant terms from the full interaction model.
The phenotypic class ANOVA models showed that all three
phenotypic classes had significant pairwise epistasis in which all
loci could be connected to all other loci through different epistatic networks within the phenotypic class (ANOVA; Figure 4;
see Supplemental Data Set 6 online). As expected, the AOP
(located at 17 cM on chromosome IV) and methylthioalkylmalate
(located at 27 cM on chromosome V) loci that have been previously shown to epistatically regulate aliphatic glucosinolates
with numerous other glucosinolates loci were highly interconnected
within the aliphatic glucosinolate network (Kliebenstein et al.,
2001a; Hansen et al., 2007, 2008; Wentzell et al., 2007; Chan
et al., 2011). This supports the validity of these epistatic networks. For all three phenotypic classes, the frequency with
which a main-effect QTL altered the phenotypes did not inherently predict the frequency with which it participated in
epistatic interactions. In all instances, there were minor loci
(i.e., GR.III.56, AL.II.4, and IN.III.63) that participated in epistatic interactions altering the majority of phenotypes in a
phenotypic class; conversely, there were examples of dominant main-effect loci (i.e., GR.IV.2, AL.I.80, and IN.III.10) that
rarely participated in a pairwise epistatic interaction (Figure 4).
While all of the growth-related QTLs could be connected in an
epistatic network, there was a distinct dichotomy where the
epistatic links identified using SGR were completely distinct
from those found for rosette diameter (Figure 4C). This is despite the fact that there is overlap in the main effect loci for
both phenotypes (Figure 2D; see Supplemental Data Sets 4
and 5 online). Given that SGR and rosette size were measured
on the same plants and all phenotypes showed a similar normal distribution, this is likely not a statistical artifact. Plotting
the line means for epistatic pairs that center on the GR.V.82
Hierarchical Genetic Architecture
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locus showed that rosette size and SGR show differential responses across the RILs (Figure 5). This supports the concept
that SGR, while being estimated directly from the rosette size
measurements, is a separate measure of growth in comparison
to plant size at any single time.
The epistatic networks for the three different phenotypic
classes also had different connectivities (Figure 4). Aliphatic
glucosinolate phenotypes had 3.5 (60.5) interactions, indolic
glucosinolate phenotypes had the most interconnected epistatic
network with each main effect QTL participating in 4.3 (60.4)
interactions, and growth-related phenotypes had the fewest
pairwise interactions, with 2.5 (60.4) interactions when SGR is
included as part of the network. Both glucosinolate networks
were statistically more connected than the growth-related trait
network (t test, P = 0.003 indolic versus growth-related traits and
P = 0.045 aliphatic versus growth-related traits) but similarly
connected to each other. The mechanistic or statistical basis for
this difference in connectivity for growth networks versus metabolic networks remains to be elucidated.
Cytonuclear Epistasis
Our previous analysis of heritability and main effects suggested
that the genetic variation present within the cytosolic (plastidic
and mitochondrial) genomes had little to no effect upon phenotypic variation for any of the phenotypes (Figure 1B; see
Supplemental Data Sets 1 and 5 online). However, previous
research had shown that the variation in cytosolic genome can
have an influence on seed germination (Moison et al., 2010) and
that it is possible to map epistatic interactions between the
cytosolic and nuclear genomes (Tang et al., 2007). Thus, we
included the cytosolic genomic variation present in this population as a main-effect term and tested for pairwise epistatic
interactions between the cytosolic genome and the nuclear
QTLs for each of the phenotypic classes (see Supplemental
Data Set 6 online). As previously stated, all P values were adjusted to FDR of 0.2, and all significant interactions were confirmed within a simplified model containing these terms for
each phenotype (ANOVA; see Supplemental Data Set 6 online).
This showed that the cytosolic genome variation has significant epistasis with nuclear QTLs, especially for the indolic
glucosinolates and growth-related traits. In fact, nearly every
Figure 4. Epistatic Networks Regulating Glucosinolates and Growth.
Epistatic networks were plotted with the nodes representing the loci and
the edges showing statistically significant epistatic interactions for the
three different phenotypic classes. Loci are named by the phenotypic
class and the respective chromosome location in cM, for example, AL.
V.8 indicates the aliphatic glucosinolate QTL on chromosome V at 8 cM.
The previously cloned AOP and Elong loci are named as such for ease of
reference. Cytonuclear interactions are represented by lines connecting
nuclear loci to the cytoplasmic node. The sizes of the nodes are proportional to the fraction of phenotypes showing significant main effects
at each locus. The color intensity of the edges indicate the percentage of
phenotypes per phenotypic class that statistically identify each interaction with solid black being 50 to 75%, dark gray (dark blue online)
being 25 to 50% of phenotypes, and light gray (light blue online) being
<25% of the phenotypes within that phenotypic class.
(A) Aliphatic glucosinolate epistatic network.
(B) Indolic glucosinolate epistatic network.
(C) Growth epistatic network. SGR epistatic interactions are indicated by
dotted lines, while solid lines show epistatic interactions for rosette size.
[See online article for color version of this figure.]
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growth-related trait and indolic glucosinolate phenotype identified
at least two interactions between cytosolic and nuclear variation.
The most common cytonuclear interaction for growth was with
the GR.I.96 QTL, while the indolic glucosinolate interactions were
spread over more markers but did not overlap with the growth
interaction (Figures 4A and 4B). By contrast, there were only two
aliphatic glucosinolate phenotypes that identified a single cytonuclear interaction, specifically with AL.III.74 (Figure 4A). Thus,
there is a significant fraction of phenotypic variance that depends
upon the cytosolic genome variation, but this variance in growth
and defense is only detectable after identifying specific nuclear
QTLs with which the cytosolic genome epistatically interacts.
Plotting the line means for cytonuclear epistatic interactions
showed that there were a variety of directionalities for this epistasis and the direction of allelic effects were specific for discrete
phenotypes (Figure 6).
Differential Genetic Architectures between
Phenotypic Classes
The three phenotypic classes differed in their number of QTLs
and frequency of epistatic and cytonuclear interactions. To further
examine these differences, we estimated the total variance
explained by the significant terms within each class of terms
(ANOVA, main effect, cytoplasmic effect, cytonuclear interaction,
and epistatic interaction) across all the phenotypes for each
phenotypic class using the simplest model for each phenotype as
previously determined. Among the phenotypic classes, the fraction of genetic variance partitioned into the main effects and
cytoplasmic effects were similar with main effects being about
two-thirds of total variance and cytoplasmic being negligible. Interestingly, there were significant differences between the three
phenotypic classes in the fraction of variance that was attributed
to two epistatic classes. Aliphatic and indolic glucosinolate phenotypes had about one-third of their variance in the nuclear epistatic component, while growth was only 20% (Figure 7; t test,
P < 0.05). Conversely, growth had the highest level of cytonuclear
interactions, while aliphatic had negligible cytonuclear interactions, which agrees well with the epistatic network results
(Figure 7). This agrees with the different number of identified
cytonuclear interactions with aliphatic glucosinolate phenotypes having only one interaction to the five found for indolic
glucosinolate phenotypes and three for growth phenotypes
(Figure 4). The lower level of epistasis within the growth phenotypes also agrees with the lower connectivity of this network
(Figures 4 and 7). This suggests that the genetic architecture
for is fundamentally different, and the molecular and evolutionary basis for this remains to be empirically tested.
Growth and Defense Relationship
In several previous reports, we have mechanistically proven that
natural polymorphisms in many glucosinolates genes, including
the AOP and Elong loci polymorphic in this population, have
causal effects on growth (Paul-Victor et al., 2010; Kerwin et al.,
Figure 5. Average Phenotypic Values for Loci Showing Epistatic Interactions Affecting Growth.
Average phenotypic values for the four allelic classes of genotypes at pairwise QTL found to significantly interact for growth variation. Error bars indicate
SE, and each genotype is represented by minimally 60 individuals. Asterisks in each individual graph identify phenotypes showing significant epistasis
with P value < 0.05, while a cross sign indicates the phenotypes showing epistasis with P value < 0.1. The bottom letter on the x axis indicates the allelic
state at the first QTL in the interaction, while the top letter indicates the allelic state of the second QTL listed for the interaction. The phenotype being
plotted is listed in each graph including SGR. All daily growth phenotypes follow a similar pattern; therefore, only representative evenly spaced days are
shown.
(A) GR.III.56 3 GR.V.82 interaction specific to rosette daily growth but not SGR.
(B) GR.IV.39 3 GR.V.82 specific for SGR but not rosette daily growth.
[See online article for color version of this figure.]
Hierarchical Genetic Architecture
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Figure 6. Average Phenotypic Values for Loci Showing Cytonuclear Epistatic Interactions Affecting Growth.
Example plots showing the average phenotypic values for the four allelic classes of genotypes at cytonuclear epistatic interactions are shown. Error
bars indicate SE, and each genotype is represented by minimally 60 individuals. Asterisks in each individual graph identify phenotypes showing
significant epistasis with P value < 0.05, while a cross sign indicates the phenotypes showing epistasis with P value < 0.1. The bottom letter on the x
axis indicates the allelic state at the nuclear QTL in the interaction, while the top letter indicates the allelic state of the cytosolic genome for the
interaction. The phenotype being plotted is listed in each graph. GLS stands for glucosinolate.
(A) Effect of GR.I.96 3 cytoplasmic interaction on growth phenotypes.
(B) Effect of GR.V.82 3 cytoplasmic interaction on growth phenotypes.
(C) Effect of IN.I. 53 3 cytoplasmic interaction on I3M glucosinolate.
(D) Effect of IN.II.59 3 cytoplasmic interaction on I3M glucosinolate.
(E) Effect of IN.III.63 3 cytoplasmic interaction on I3M glucosinolate.
(F) Effect of In.V.75 3 cytoplasmic interaction on I3M glucosinolate.
[See online article for color version of this figure.]
2011; Züst et al., 2011). The AOP locus encodes two genes,
AOP2 and AOP3, which contain natural knockout polymorphisms between the Kas and Tsu genotypes due to a local
inversion that swaps the functional promoter. This leads to Kas
expressing only the AOP2 gene in the rosette tissue and Tsu
only expressing the AOP3 gene (Chan et al., 2011). Similarly, the
methylthioalkylmalate locus contains functional knockout level
polymorphisms within Arabidopsis (Magrath et al., 1994;
Kliebenstein et al., 2001a, 2001b; Kroymann et al., 2003). Given
that we have a validated mechanistic relationship between
glucosinolates variation and growth variation that entails largeeffect polymorphisms in both the natural variation and induced
variation, we tested if we could identify this relationship using
this standard RIL data set. Using both QTL overlap and genetic
correlation tests showed that we could not identify this mechanistic linkage in this standard sized RIL population (Figure 2).
Combining this with our previous observation that even large RIL
populations have a significant level of false negative error in
detecting QTLs led us to hypothesize that the complexity of the
growth architecture may hide the effect of glucosinolate variation at the whole organism level (Chan et al., 2011). To directly
test this possibility, we conducted an ANCOVA to compare
growth and glucosinolate variation where we included genotypic
variation at all significant growth QTLs and their epistatic interactions directly in the ANCOVA model. This ANCOVA allows us
to emphasize any potential links that may exist between growthrelated phenotypes and glucosinolate phenotypes but that
may be hidden by the detected growth QTLs. This ANCOVA
showed that we could identify a significant negative relationship
between growth and long-chain aliphatic glucosinolates (see
Supplemental Data Set 7 online). There was no identifiable relationship between indolic glucosinolates and any growthrelated trait. This was exactly as we had previously found in our
mechanistic tests of glucosinolates genes where long-chain
glucosinolates had the strongest effect on SGR and there was
no significant link to daily growth estimates (Paul-Victor et al.,
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infinitesimal model of genetic architecture than the chemical
defenses (Fisher, 1918, 1930; Wright, 1931, 1968). These data
suggest that chemical defenses will have fewer causative loci
than growth, yet it does not prescribe a baseline for the number
of causative loci regulating variation in chemical defenses. Recent work has suggested that there are 100s to 1000s of causal
polymorphisms regulating variation in these chemical defenses
within Arabidopsis (Chan et al., 2010, 2011). Since growth is not
a product of glucosinolate metabolism alone, it is not surprising
that there are likely many more causal polymorphisms that
regulate natural variation in growth within Arabidopsis given that
it is a higher order phenotype.
Figure 7. Differential Partitioning of QTL Variances across Phenotypic
Classes.
The proportion of genetic variance explainable by various genetic factors
in this population, as measured using type III sums of squares, for aliphatic glucosinolates (red online, left), indole glucosinolates (blue online,
center), and growth (green online, right). Error bars show SE, and there are
15 aliphatic glucosinolate traits, six indolic glucosinolate traits, and
seven growth traits. Main is the total variance linked to all of the nuclear
QTL main effects, cytoplasmic is the proportion variance affiliated with
variation in the cytoplasmic genome, cytonuclear epistasis is the proportion variance partitioned to epistatic interactions between nuclear loci
and the cytoplasmic genomes, while nuclear epistasis is the fraction of
variance explained by interactions between nuclear QTLs. Error variance
was removed from this calculation to allow a comparison among the
genetic effects. Letters show those phenotypic classes with a statistical
difference in the variance fractioned into a given component.
[See online article for color version of this figure.]
2010; Kerwin et al., 2011; Züst et al., 2011). Thus, by incorporating allelic variation at detected growth QTLs, we could
uncover a previously hidden link between SGR and glucosinolate variation within this population. This indicates that there are
likely other mechanistic links between phenotypes that cannot
be identified using standard quantitative genetics statistics and
population sizes. To identify these links requires more detailed
investigations or much larger populations to better estimate the
true genetic architecture of growth and defense.
DISCUSSION
Quantitative Genetics of Growth and Defense
Within this population, the genetic architecture of quantitative
variation in growth and chemical defense phenotypes showed
significant differences. Even though all three phenotypic classes
had similar average heritabilities, growth-related phenotypes
displayed an approximately normal distribution of heritabilities,
while the defense phenotypes displayed a bimodal distribution
(Figure 1). The three phenotypic classes detected similar numbers of main effect QTLs, but the estimated allelic effects for
each growth locus were significantly smaller than those for the
chemical defenses (see Supplemental Data Set 4 online) (t test,
P < 0.05). This suggests that growth-related traits follow a more
Epistatic Networks and Causality versus Detection Power
A key use of modern quantitative genetics is to begin testing
and identifying mechanistic links between phenotypes, such as
growth and chemical defense, that have had a long theoretical
connection and a more recent causal link (Karban and Baldwin,
1997; Mauricio, 1998; Strauss and Agrawal, 1999; Thaler et al.,
1999; Paul-Victor et al., 2010; Agrawal, 2011; Kerwin et al.,
2011; Züst et al., 2011). We had previously proven that there
was a link between glucosinolate genes and growth, but at a first
approximation, our standard quantitative genetics data did not
identify any common main effect QTLs between these phenotypic classes (Figures 2 to 4) (Kerwin et al., 2011; Züst et al.,
2011). A key difference between this work and our previous
studies is statistical power: Our studies identifying a mechanistic
link focused on single gene manipulations mimicking natural
glucosinolate polymorphisms, whereas this experiment used an
RIL population with an unknown number of segregating loci. By
accounting for the detected growth QTL directly in the ANCOVA
statistical test, we could identify the previously known link between growth and glucosinolate variation (see Supplemental
Data Set 7 online) (Mauricio, 1998; Lankau and Strauss, 2007,
2008; Lankau and Kliebenstein, 2009; Züst et al., 2011). This link
was as predicted to be strongest between long-chain aliphatic
glucosinolates and SGR. However, the fact that we were unable
to identify a common main effect QTL despite this link between
growth and glucosinolates suggests that even this relatively
large RIL population is susceptible to an unrecognized rate of
false negative QTL detection. Thus, we suggest that the glucosinolate epistatic network is causal with regards to growth but
the link is simply hidden by the vast number of other detected
and undetected growth QTLs. It is important to point out this
observation, since the failure to detect causal links even in large
RIL population would suggest that we currently have no way of
accurately estimating how many loci are regulating growth and
defense metabolism within any given Arabidopsis population.
Cytonuclear Interactions and Natural Variation Detection
Epistatic interactions between genetic variation in the nuclear
and cytoplasmic genomes, cytonuclear interactions, have
been found in most species where they have been empirically
tested, including plants, insects, fungi, and animals (Burke
et al., 1998; Roubertoux et al., 2003; Tao et al., 2004; Zeyl
et al., 2005; Dowling et al., 2007, 2010; Etterson et al., 2007;
Hierarchical Genetic Architecture
Dowling Tang et al., 2013). In this report, we show that there
are significant cytonuclear interactions for three phenotypic
classes (Figures 4, 6, and 7). The fraction of phenotypic variance regulated by cytonuclear interactions differed among
the three phenotypic classes, with growth showing the most
variance regulated by cytonuclear interactions and aliphatic
glucosinolates the least (Figure 7). Thus, natural variation in
cytonuclear interactions can influence a significant fraction of
phenotypic variation in traits that determine field fitness within
Arabidopsis (Mauricio, 1998; Bidart-Bouzat and Kliebenstein,
2008).
Interestingly, the presence of cytonuclear interactions did
not depend on the identification of a main effect of the cytoplasmic genome, suggesting that the effects are not due to
general changes in the cytosolic genome but instead are due
to how the cytosolic and nuclear genomes interact. This
could occur because a large fraction of the plants’ nuclear
genome encodes proteins that function within either the mitochondria or plastids (Ajjawi et al., 2010). Thus, the fact that
the effect of cytosolic genetic variation is largely detectable
by interactions with the nuclear genetic variation suggests
that variation in cytosolic genomes alters only a subset of
the plastid’s or mitochondria’s function and not the operation
of the entire organelle. In agreement with this model, the
cytonuclear interactions are spread throughout the genome
and do not act as master regulatory factors influencing all
phenotypes, but instead are specific to distinct phenotypic
classes (Figure 4). Thus, trying to identify cytoplasmic effects
by solely measuring reciprocal F1 progeny will only identify
a small subset of the real phenotypic effects of the cytosolic
genomic variation (Moison et al., 2010). Instead, cytoplasmic
effects need to be investigated and studied in a more structured population where cytonuclear interactions are directly
measured.
A major complication to the need to conduct a broader
survey of cytonuclear interactions is that the vast majority of
mapping populations are not designed to directly test cytosolic genetic variation because they are made using only
one cytoplasmic genome. For instance, only two of the
myriad of Arabidopsis RIL populations are made with reciprocal crosses necessary to allow variation in the cytosolic
genome to be testable (Alonso-Blanco et al., 1998; Loudet
et al., 2002; Meyer et al., 2007; McKay et al., 2008; Zhang
et al., 2012). In other populations that are currently favored,
such as mixed-parent populations (MAGIC, AMPRIL, etc.)
(Kover et al., 2009; Huang et al., 2011) or association mapping populations (Atwell et al., 2010; Platt et al., 2010), there
is variation in the cytosolic genomes present, but this variation has not been structured and would thus be confounded
with the nuclear genomic variation even if it was measured.
This confounding structure greatly hinders our ability to tease
apart the cytosolic effects from the nuclear genetic background. Based on our observations, it could also greatly inhibit the ability to see either cytosolic or nuclear effects on
a phenotype especially in situations of strong cytonuclear
epistasis. Thus, there is a need to incorporate cytosolic genetic variation directly into the development of future populations.
11 of 17
Quantitative Genetics of SGR versus Growth
A major interest in plant quantitative genetics for ecology, evolution, and agriculture is the study of higher order phenotypes,
such as fitness, growth, and yield, which are likely highly integrative measures of numerous underlying networks. An interesting debate that occurs in each field for each integrative
phenotype is how to measure fitness, growth, or yield when
there are multiple descriptions and mathematical layers to these
integrative phenotypes. Our results on the quantitative genetics
of rosette size versus the SGR measure suggest that the argument on how to measure a phenotype is in fact flawed and that
the true answer is that each measure of a highly integrative
phenotype will provide a different view. In our analysis, rosette
size and SGR identified only a subset of QTLs and epistatic
interactions in common (Figures 2 and 4). If we had analyzed
only one or the other description of growth, we would have
obtained a picture of the quantitative genetics of growth that
would have been severely lacking in its breadth. For instance,
the costs of glucosinolate production on growth is most visible
on the SGR measure with only hints of an effect on rosette size
at later time points (see Supplemental Data Set 7 online) as has
been previously found (Paul-Victor et al., 2010; Züst et al., 2011).
Thus, measuring multiple aspects of integrative phenotypes, like
growth, fitness, and yield, is more desirable than any individual
number that can be placed on these phenotypes.
In this analysis, we show that growth and chemical defenses
in Arabidopsis have different quantitative genetic architecture
with growth displaying a more infinitesimal model of variation.
We were able to link chemical variation with imposing a variation
in growth, suggesting that the identified chemical defense loci
are potentially also undetected growth loci. We also revealed the
significant effect of genetic variation in the cytosolic genomes
upon the different phenotypic classes that was identified as
predominantly interacting with nuclear genomic variation. Taken
in its entirety, these data show that growth and chemical defenses are genetically linked but that future analysis will be required to develop a more focused image of how the quantitative
genetics of these phenotypes are related. Finally, any future
experiment attempting to measure the genetic architecture of
defense, much less growth quantitative variation in Arabidopsis,
requires significantly larger genetic populations of a different
structure than are currently available or even being considered.
METHODS
Growth of the Kas 3 Tsu RIL Population
Seeds of the 341 lines of the Kas 3 Tsu recombinant inbred population of
Arabidopsis thaliana were obtained from the ABRC (Columbus, OH)
(Juenger et al., 2006; McKay et al., 2008; Juenger et al., 2010). We grew
a total of four per line, split into two randomized complete blocks per
experiment with two independent experiments separated by approximately 3 months. We grew plants in large planting trays with 156 individual
wells (bxwxh: 30 3 25 3 100 mm), filled with standard potting soil
(Sunshine Mix #1; Sun Gro Horticulture). Prior to sowing, we imbibed
seeds in distilled water and cold stratified them at 4°C for 4 d. We placed
approximately three to five seeds of a single genotype in the center of
a well and covered the trays with a transparent plastic hood to retain
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The Plant Cell
humidity during germination. Plants from both reciprocal subpopulations
where intermixed in the block design to allow direct statistical testing of
cytoplasmic effects. We recorded the germination day for all plants. After
1 week, we removed the transparent hoods and surplus plants to leave
one seedling per well. We watered plants twice a week with nutrientenriched water (0.5% N-P-K fertilizer in a 2-1-2 ratio; Grow More 4-18-38)
and kept them in a climate-controlled chamber at 22°C and a day/night
cycle of 10 h/14 h.
Genotyping the Kas 3 Tsu RIL Population
DNA was extracted from each of the 341 Kas 3 Tsu RILs and rapidly
genotyped with next-generation sequencing using the RESCAN method
for Arabidopsis as previously described (Monson-Miller et al., 2012). Due
to failed samples at the extraction or inappropriate genotypes from the
sequencing, 25 of the lines had to be dropped, leaving 316 lines for QTL
mapping with 136 RILs with Kas cytoplasm and 180 with Tsu cytoplasm.
Libraries for each line were constructed with a unique 5-bp barcode at the
59-end of the adapters, and libraries were pooled in groups of 96 lines.
These pools were sequenced on a single lane of an Illumina GAII or HiSeq
high-throughput sequencer as well as a single lane of pooled Kas and Tsu
genomic libraries. Single nucleotide polymorphisms were called using an
in-laboratory pipeline using the BWA and SAM tools (Li and Durbin, 2009;
Li et al., 2009). The final set of markers used for mapping at minimum 59%
observed genotypic data across the individual RILs. The missing genotypes were imputed using the fill.geno function in the R/qtl package using
the single simulation replicate method. Using the data, we tested for
segregation distortion at individual loci and pairs of loci but did not detect
any significant segregation distortion as previously reported for this
population (McKay et al., 2008).
Glucosinolate Analysis
Thirty-one days after sowing, we harvested plants for glucosinolate
analysis. One leaf from the first fully mature leaf pair of each plant was
removed and stored in 90% methanol to inhibit enzymatic breakdown of
chemical compounds and begin the extraction. We extracted glucosinolates and analyzed them by HPLC according to previously described
methods (Kliebenstein et al., 2001b, 2001c). A number of lines failed to
grow and were discarded from the analysis. Ten lines with aberrant or
impossible glucosinolate profiles based on the known parental genotypes
at the glucosinolates loci were removed from the analysis, leaving a total
of 316 lines. The removal of these 10 lines was supported by the RESCAN
analysis showing that these lines were genetically contaminant lines that
had genotypes that were nearly identical to the homozygous Columbia0 accession and are impossible to obtain from the Kas or Tsu germplasm.
Growth Recording
To record plant growth, we photographed each individual planting tray
every second day, starting from day 7 after sowing. We photographed the
first block of the experiment in TIFF image format using a Nikon Coolpix
E995 digital camera (3.1 megapixel) and the second block in JPEG format
using a Fujifilm FinePix S1500 digital camera (10.0 megapixel). Plant size
was measured relative to a 1-cm marker present in each tray. Flash was
disabled, and we took the pictures under ambient (artificial) light with
automatic camera settings for shutter speed, aperture, and focus.
Image Processing and Extraction of Individual Leaf Areas
We extracted the rosette area of plants from photographs using the image
analysis software ENVI (version 4.6; ITT Vis) and supervised maximum
likelihood classification, which uses digital number values of the images to
separate plant area from the soil background. Pictures were taken so that
plants only occupied the central quarter of each picture to minimize any
consequences of lens distortion, while the random distribution of plants
ensured that any remaining distortion was partitioned randomly. Because
the distance of the camera to the trays differed slightly between photographs, the pixel/mm relation differed among images, making direct
comparisons of leaf area impossible. In a first step, we thus registered all
images to the scale of one reference image, using the image-to-image
registration method as implemented in ENVI. This method requires the
user to mark a minimum of four points or nodes that are present in both the
reference and the sample image. The sample image was then transformed
to match the reference based on those nodes. We registered all images to
one reference image, placing up to eight nodes at the corners of corresponding wells within the planting trays. In the next step, we classified
the images into three categories, namely, leaf, soil, and planting tray,
using a maximum likelihood classifier in ENVI. We then selected training
areas for the three classes in each image to calculate training class
statistics based on the digital number values of the red, green, and blue
layers of the image. Based on this training sample, every pixel within an
image was assigned to one of the three classes by the maximum likelihood algorithm. As the final step, we marked the area of each well of
a planting tray assigning leaf area within a well to a unique plant or rosette
identity. The individual plant areas were then defined as the number of leaf
pixels within each of the 104 masked wells. Pixel numbers were transformed into rosette areas (mm2) using the 1-cm reference marker present
in each image. Growth and size measurements were based on the first 23
d of vegetative growth since plant area measurements were impossible at
late stages due to leaf overlap.
Size-Standardized Growth Rates
To calculate SGR, we first need to fit an appropriate growth function. In
this case, we found that a power-law function (Enquist et al., 1998, 1999)
provided a good fit to the vegetative growth phase. Power-law growth
assumes that the absolute growth rate is given by:
dM
¼ aMb
dt
where M is a measure of size, a is the growth coefficient, and b is the
scaling exponent. We modeled untransformed rosette area as a function
of days after germination using the closed form solution of the equation
above:
Areat ¼
8
>
<
>
:
ðArea0 1 2 b þ að1 2 bÞtÞ
Area0 expat
1=
12b
if b1
9
>
=
>
if b ¼ 1 ;
The rosette area of a plant at time t is thus a function of the three
parameters: Area0 (estimated rosette area at time = 0), the growth coefficient a, and the scaling exponent b. This equation for area at time t
can be fitted to data in the nlme package for the statistical software R (R
Development Core Team, 2012), and all three parameters can be estimated (Hautier et al., 2010; Paine et al., 2012). We started with a model
that only contained random effects of plant identity on all three parameters, essentially fitting unique growth curves to each individual
plant. We then modified this model by adding fixed effects of RIL and
experimental block to the model parameters and compared alternative
models with different fixed effects using likelihood ratio tests. We extracted RIL-specific parameters from the best model and calculated
SGR for each RIL. SGR is given by:
SGR ¼ aArearef b 2 1 , where Arearef is a common size at which growth
rate is calculated (Paul-Victor et al., 2010; Züst et al., 2011; Paine et al.,
2012). The final best model supported a RIL effect on alpha but not on
beta, therefore all RILs shared a common beta and choice of reference
size did not affect relative rankings of SGR.
Hierarchical Genetic Architecture
Phenotype Abbreviations
For phenotypes within the aliphatic glucosinolates, the abbreviations are
as follows: 3-hydroxypropyl glucosinolate (OH3), 3-methylsulfinylpropyl
glucosinolate (MSO3), 4-hydroxybutyl glucosinolate (OH4), allyl glucosinolate (Allyl), But-3-enyl glucosinolate (butenyl), 3-methylthiopropyl
glucosinolate (MT3), 7-methylsulfinylheptyl glucosinolate (MSOH), 4methylthiobutyl glucosinolate (MT4), 8-methylsulfinyloctyl glucosinolate
(MSOO), 8-methylthiooctyl glucosinolate (MT8), sum of three and four
carbon glucosinolates (SC), sum of seven and eight carbon glucosinolates (LC), total aliphatic glucosinolate (TotAli), ratio of SC to TotAli
(SCpTAli), and ratio of MTO to total 8C glucosinolate (MTOpMTMSOO).
For indolic glucosinolates, the abbreviations are as follows: 4-hydroxyindol-3ylmethyl glucosinolate (OHI3M4), indol-3ylmethyl glucosinolate
(I3M), 4-methoxy-indol-3ylmethyl glucosinolate (MOI3M4), N-methoxyindol-3ylmethyl glucosinolate (NMOI3M), sum of all indolic glucosinolates
(indole), ratio of MOI3M4 to indole (MO4pInd), ratio of TotAli to indole
(AlipInd), ratio of SC to indole (SCpInd), and Ratio of LC to indole (LCpInd).
There were three putatively sulfated unknown compounds that were
detected, but we did not determine their structure and these are abbreviated Unk1, Unk2, and Unk3. The growth phenotypes are abbreviated
as follows: rosette size at 7 d after germination (Day7), rosette size at 9
d after germination (Day9), rosette size at 11 d after germination (Day11),
rosette size at 15 d after germination (Day15), rosette size at 19 d after
germination (Day19), and rosette size at 23 d after germination (Day23).
Further information about these abbreviations is shown in Supplemental
Data Set 1 online.
Estimation of Heritability
All 316 lines were represented in every experiment and every block
in both experiments creating a perfectly balanced randomized complete block design. All phenotypic data were used to calculate estimates of broad-sense heritability (H) for each glucosinolate and daily
growth estimate as H = s2g/s2p, where s2g was estimated for both
the RIL nuclear and cytoplasmic genotypes and s2p was the total
phenotypic variance for a trait (Liu, 1998). The ANOVA model for
each metabolite or growth phenotype in each line (ygmeb) was:
ygceb ¼mþCc þGg ðCc Þ þ Ee þBb ðEe Þ þ Cc 3Ee þGg ðCc Þ3Ee þ«gceb , where
c = the Kas or Tsu cytoplasm; g = the 1.316 for the 316 RILs, e =
experiment 1 or experiment 2, and b = block 1..4 nested within experiment. This allowed cytoplasmic effects to be directly tested in the C
term and each RIL genotype (G) nested within the appropriate cytoplasmic class, either Kas or Tsu. Experiment and block nested within
experiment were treated as random terms within the model, while
genotype and cytoplasm are considered fixed (see Supplemental Data
Set 1 online). s2g for the nuclear variation was pulled from the Gg(Cc)
term, while s2g for cytoplasmic variation was pulled directly from the Cc
term. We were not able to estimate heritability for SGR because of the
nonlinear nature of the function. Thus heritability for this term would not
be comparable to the heritabilities of other traits. We were able to directly use mean values for the RILs for further analysis as we had
a perfect randomized complete block design with no missing values
which means that LSmeans are identical to raw means in this instance.
All statistical analyses in this article were run within R (R Development
Core Team, 2012).
13 of 17
mapping was implemented using the cim function in the R/qtl package
with a 10-cM window. Forward regression was used to identify three
cofactors per trait. We estimated permutation-derived empirical
thresholds using 1000 permutations for each mapped trait. This provided a range of 1.92 to 1.98 as being statistically significant so we set
a uniform threshold for all traits and QTLs where a LOD score above 2
was considered significant for further analysis to begin the analysis
somewhat conservatively (Churchill and Doerge, 1994; Doerge and
Churchill, 1996). Composite interval mapping to assign significance
based on the underlying trait distribution is robust at handling normal or
near normal trait distributions (Rebai, 1997), as found for most of our
phenotypes. The define.peak function implemented in the R/eqtl
package was used to identify the location and one-LOD interval of each
significant QTL for each trait (Wang et al., 2006). The effectscan function
in the R/qtl package was used to estimate the QTL additive effect (R
Development Core Team, 2012).
QTL clusters were identified using a QTL summation approach where
the position of each QTL for each trait was plotted on the chromosome by
placing a 1 at the peak of the QTL. This was then used to sum the number
of traits that had a detected QTL at a given position using a 5-cM sliding
window across the genome (Kliebenstein et al., 2006). The traits were
divided into three phenotypic classes: growth, aliphatic glucosinolates,
and indolic glucosinolates. Glucosinolates were split into two classes
based on previous data showing that aliphatic and indolic glucosinolates
are synthesized by different enzymes and regulated by different transcription factors and can therefore be considered distinct pathways
(Burow et al., 2010; Sønderby et al., 2010a, 2010b). The summation was
conducted within a 5-cM sliding window for the three trait classes. The
QTL clusters identified defined genetic positions that were named respective to their phenotypic class and genetic positions with a prefix
indicating the phenotypic class followed by the chromosome number and
the cM position. For example, AL. I. 80 indicates an aliphatic glucosinolate
QTL on chromosome I at 80 cM. The QTLs detected at the previously
characterized and cloned GSL-AOP and GSL-Elong loci were named as
such (Magrath et al., 1994; Kliebenstein et al., 2001a, 2001b; Kroymann
et al., 2003).
Phenotype Marker Model ANOVA
To directly test the effect of each identified QTL cluster for each phenotypic class, we used an ANOVA model containing the markers most
closely associated with each of the significant QTL clusters as individual
main effect terms. For each phenotypic class (growth, aliphatic glucosinolate, and indolic glucosinolate), the average phenotype in lines of
genotype g at marker m was shown as ygm. The metabolite or growth
phenotype for each metabolite in each line (ygm) was as follows:
ygm ¼mþ∑2g¼1 ∑m
m¼1 Mmg þ«gm , where g = Kas(1) or Tsu(2); m = 1, .,m. The
main effect of the markers was denoted as M with growth having a model
involving 12 markers (m), aliphatic glucosinolates having 11 markers, and
indolic glucosinolate having 15 markers. For each phenotypic class, the
cytoplasmic genome was included as an additional marker to test for
cytosolic genome effects. We tested all phenotypes per phenotypic class
with the appropriate model implemented in the R/car package, which
returned all P values, Type III sums of squares for the complete model and
each main effect, and QTL main-effect estimates (in terms of allelic
substitution values) (Fox and Weisberg, 2011; R Development Core Team,
2012).
QTL Analysis
We used the above genotyping analysis on the Kas 3 Tsu RIL population to generate a genetic map for the population. To detect glucosinolate and growth QTLs, we used the average phenotype per RIL
across all experiments (see Supplemental Data Set 2 online) (Basten
et al., 1999; Zeng et al., 1999; Wang et al., 2006). Composite interval
QTL Epistasis Analysis
To test directly for epistatic interactions between the detected QTLs, we
conducted an ANOVA using the marker model generated for each of
the three phenotypic classes whereby each marker in the model had
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a significant main effect within the phenotype class. We used this full
model per phenotypic class because we had previous evidence that RIL
populations have a significant false negative QTL detection issue and
wanted to be inclusive of all possible significant loci (Chan et al., 2011).
Within the model, we tested all possible pairwise interactions between the
markers for each of the three phenotypic classes. For each phenotype, the
average value in the RILs of genotype g at marker m was shown as ygm.
The model for each metabolite or growth phenotype (ygm) was
2
m
m
ygm ¼mþ∑2g¼1 ∑m
m¼1 Mgm þ∑g¼1 ∑m¼1 ∑n¼mþ1 Mgm Mgn þ«gmn , where g =
Kas or Tsu; m = 1, .,m; and n was the identity of the second marker for an
interaction. The main effect of the markers was denoted as M with growth
having a model involving 12 markers, aliphatic glucosinolates having 11
markers, and indolic glucosinolates having 15 markers, as above. For
each phenotypic class, the cytoplasmic genome was included as an
additional single-locus marker to test for interactions between the cytosolic and nuclear genomes. Q values, Type III sums of squares for the
complete model and each individual term, and QTL pairwise-effect estimates in terms of allelic substitution values were obtained as described
for marker model ANOVA (Fox and Weisberg, 2011; R Development Core
Team, 2012). All presented significance values were corrected for multiple
testing within a model using FDR (<0.20) in the automated script (Storey,
2002; Storey and Tibshirani, 2003). We chose this FDR because we have
previously shown that we can readily validate loci even significant at even
a nominal P value (Chan et al., 2011). Thus, we feel that this FDR is a good
compromise between false positives and false negatives that can both
dramatically influence the results. Going to a more stringent FDR would
increase our false negatives and remove significant insight (Chan et al.,
2011). The main effect and epistatic interactions of the loci in each
phenotypic class were visualized using cytoscape.v2.8.3. with interactions significant for <10% of the phenotypes in each phenotypic class
excluded from the network analysis (Smoot et al., 2011). For the estimation of epistasis, each phenotype was reanalyzed only with the significant model components to ensure that they remained significant in the
simplified model. From this simplified model, variance attributable to each
term was obtained.
Growth and Glucosinolates ANCOVA
To test for a link between growth and glucosinolates that may be conditioned upon the existing genetic architecture regulating growth, we
conducted an ANCOVA. The ANCOVA specifically tested for a link between growth and glucosinolates after controlling for the above genetic
model found to regulate growth. The model was specifically as follows:
2
m
m
ygm ¼mþ∑2g¼1 ∑m
m¼1 Mgm þ∑g¼1 ∑m¼1 ∑n¼mþ1 Mgm Mgn þxgm þ«gmn , where
ygm is the growth of all lines and xgm is the specific glucosinolate content of
the same lines. In this model, all markers and interactions from the growth
QTL model are included as above where g = Kas or Tsu; m = 1, .,m; and n
was the identity of the second marker for an interaction. This was conducted independently for the different growth phenotypes to test if there
was a difference among the different estimates of growth. Additionally, we
focused on just three glucosinolate phenotypes, total indolic, total longchain aliphatic glucosinolates, and total short-chain glucosinolates, as the
QTL mapping data suggested that these could be considered key descriptors of the different glucosinolate phenotypes within this population.
The ANCOVA was generated using the R package (R Development Core
Team, 2012).
Accession Numbers
Sequence data from this article can be found in the GenBank/EMBL
data libraries under the following accession numbers: AOP2,
At4g03060; AOP3, At4g03050; MAM1, At5g20310; and MAM3,
At5g203020.
Supplemental Data
The following materials are available in the online version of this article.
Supplemental Figure 1. Distributions of Phenotypes Showing Statistically Normal Distributions.
Supplemental Figure 2. Distributions of Non-Normal Phenotypes.
Supplemental Figure 3. High-Density Genetic Map.
Supplemental Figure 4. Physical to Genetic Distance in Kas 3 Tsu.
Supplemental Data Set 1. Phenotypic Heritability.
Supplemental Data Set 2. Phenotypic Means for Glucosinolate and
Growth Traits.
Supplemental Data Set 3. Genotypic Data for 316 RILs Used for
Genetic Mapping after Imputation.
Supplemental Data Set 4. QTL Information.
Supplemental Data Set 5. Single Marker QTL Analysis by ANOVA.
Supplemental Data Set 6. Pairwise Epistatic ANOVA q Values and
Type III Sums of Squares.
Supplemental Data Set 7. Analysis of Covariance of Glucosinolates
and Growth.
ACKNOWLEDGMENTS
This work was supported by the Forschungskredit of the University of
Zürich (T.Z.), by Swiss National Science Foundation Grant 31-107531
(L.A.T.) and by U.S. National Science Foundation Grant DBI0820580
(D.J.K.).
AUTHOR CONTRIBUTIONS
J.A.C., B.L., T.Z., L.A.T., and D.J.K. contributed to the design of the
experiment. B.J., J.A.C., T.Z., B.L., G.S.S, and M.I. contributed to the
execution of the experiments and collection of the data. All authors
contributed to interpretation of the results. B.J., J.A.C., T.Z., L.A.T., and
D.J.K. contributed to the writing of the article.
Received April 11, 2013; revised May 9, 2013; accepted May 16, 2016;
published June 7, 2013.
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Bindu Joseph, Jason A. Corwin, Tobias Züst, Baohua Li, Majid Iravani, Gabriela Schaepman-Strub,
Lindsay A. Turnbull and Daniel J. Kliebenstein
Plant Cell; originally published online June 7, 2013;
DOI 10.1105/tpc.113.112615
This information is current as of June 17, 2017
Supplemental Data
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