QTL analysis of root morphology, flowering time, and yield reveals trade-offs in response to drought in Brassica napus
Department of Soil and Crop Sciences, Colorado State University
Fort Collins, CO 80523
Cargill Specialty Seeds and Oils
Fort Collins, CO 80525
Department of Bioagricultural Sciences and Pest Management, Colorado State University
Fort Collins, CO 80523
Plant Genomics LLC
Fort Collins, CO 80524
Drought escape and dehydration avoidance represent alternative strategies for drought adaptation in annual crops. The mechanisms underlying these two strategies are reported to have a negative correlation, suggesting a trade-off. We conducted a quantitative trait locus (QTL) analysis of flowering time and root mass, traits representing each strategy, in Brassica napus to understand if a trade-off exists and what the genetic basis might be. Our field experiment used a genotyped population of doubled haploid lines and included both irrigated and rainfed treatments, allowing analysis of plasticity in each trait. We found strong genetic correlations among all traits, suggesting a trade-off among traits may exist. Summing across traits and treatments we found 20 QTLs, but many of these co-localized to two major QTLs, providing evidence that the trade-off is genetically constrained. To understand the mechanistic relationship between root mass, flowering time, and QTLs, we analysed the data by conditioning upon correlated traits. Our results suggest a causal model where such QTLs affect root mass directly as well as through their impacts on flowering time. Additionally, we used draft Brassica genomes to identify orthologues of well characterized Arabidopsis thaliana flowering time genes as candidate genes. This research provides valuable clues to breeding for drought adaptation as it is the first to analyse the inheritance of the root system in B. napus in relation to drought.
Nearly all aspects of terrestrial plant form and function
depend upon adequate water availability. As a result, drought
is the most common cause for reductions in crop yields,
frequently causing reductions well below half of the crops
theoretical yield potential (Boyer, 1982). Avariety of mechanisms
have been associated with drought acclimation (plasticity)
and adaptation (heritable differences in traits) leading to the
proposal of three distinct coping strategies (Ludlow, 1989):
drought escape, dehydration avoidance, and dehydration
tolerance. This report focuses on drought escape and
dehydration avoidance, as dehydration tolerance is not prevalent in
vascular plants, especially crops (Oliver etal., 2010).
Acommon strategy exploited in crop breeding is drought escape,
which refers to plants that complete their life cycle prior to
the onset of drought, thus avoiding moisture limitations. The
alternative strategy, dehydration avoidance, is the sustaining
of internal water status during dry external conditions by
minimizing water loss and/or maximizing water uptake.
The Author 2014. Published by Oxford University Press on behalf of the Society for Experimental Biology.
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Resource limitation creates a necessity for organisms to
allocate energy to processes in a competitive manner such
that relationships among processes are constrained (Levins,
1968; Obeso, 2002). In plants there is a major energetic
trade-off between investments in vegetative growth and
reproduction, which can also be thought of as a life history
trade-off (Reznick, 1985). Many studies have reported a
trade-off between drought escape and mechanisms of
dehydration avoidance, such as water-use efficiency and root size
(Mitchell-Olds, 1996; McKay et al., 2003; Wu et al., 2010;
Franks, 2011). However, results reporting the absence of
such a trade-off (Sherrard and Maherali, 2006) suggest that
more research is needed to understand the generality of this
Trade-offs can be quantified as genetic correlation
coefficients, which measure the degree to which genetic variation
in one trait predicts variation in the other (Robertson, 1959).
Genetic correlations among traits can impose significant
constraints on the efficacy and response to selection (both
natural and artificial). This is because the adaptive optimum of
trait values may be orthogonal to the vector of trait
covariation. Genetically correlated traits are mechanistically the
result of either genetic linkage or pleiotropy (Wagner and
Zhang, 2011). In the case of genetic linkage (Fig.1A),
polymorphisms underlying variation at each trait are at different
loci but are nearby physically, limiting recombination so that
the trait value caused by the allele at one locus covaries with
the trait value of the allele at the linked locus. Pleiotropy, on
the other hand, refers to the effect an allele has on two or
more phenotypes (Fig.1B). Finally, genetic correlations may
be due to physiological interactions among traits where one
trait acts upstream of another (Fig.1C and D). Ultimately,
genetic correlations due to pleiotropy constrain the response
to selection far more than those due to genetic linkage
In crops, drought escape is often achieved through
breeding by optimizing flowering time. Flowering time marks the
transition from vegetative to reproductive growth, and its
influence on fitness and yield can be dramatic, making it
perhaps the most important of all life history traits and the focus
of extensive research in both crops and natural plant
populations (reviewed in Michaels, 2009; Pose et al., 2012). The
impact of flowering time on fitness may be due in large part
to its many correlations with other diverse and potentially
adaptive traits such as vegetative biomass (Shi et al., 2009;
Edwards et al., 2012), vascular system development (Sibout
etal., 2008), oxidative stress (Kurepa etal., 1998), water-use
efficiency (McKay etal., 2003; Franks, 2011) and a variety of
root characteristics (Bolaos and Edmeades, 1993;
MitchellOlds, 1996; Lou et al., 2007). A study comparing isolines
carrying mutant alleles in five loci annotated as flowering
time genes showed significant differences in morphological
traits such as leaf length, leaf number, and auxillary shoot
number, providing further evidence of pervasive pleiotropy
at loci involved in flowering (van Tienderen etal., 1996). The
recurring association between flowering time and other traits
is perhaps not surprising, since variation at any genes related
to environmental sensing, resource acquisition, or resource
Fig.1. Diagram of putative mechanistic relationships; (A) genetic linkage;
(B) pleiotropy; (C) physiological interaction; (D) combination of pleiotropy
and physiological interaction.
allocation are also likely to lead to variation in flowering time
(McKay etal., 2008).
Dehydration avoidance is less well characterized, but
mechanisms include reduced stomatal conductance and increased
water uptake by roots. The root system has been long
recognized as a central component of crop productivity (Sharp and
Davies, 1979). This is due to the role of roots in water and
nutrient acquisition, anchorage, mechanical support and,
perhaps most importantly, sensing and responding to the
complex and often heterogeneous soil environment.
Adehydration avoidance strategy through maximization of water
uptake clearly involves the root system, making it a focal
subject of breeding for low rainfall environments (Ludlow
and Muchow, 1990). Associations between drought
adaptation and increased root system size and/or rooting depth have
been drawn across many species (Cortes and Sinclair, 1986;
Ekanayake, 1986; White and Castillo, 1989; Johnson et al.,
2000; Price et al., 2001; Kirkegaard and Lilley, 2007; Lopes
and Reynolds, 2010). However, the adaptive value of large
or deep root systems varies by geography so that an applied
breeding strategy must consider the climatic trends of the
target production zone (Araus, 2002; Cativelli, 2008; Palta
et al., 2011). Selection for root traits is hindered by
generally low heritabilities and the difficulty of phenotyping large
populations (Wasson et al., 2012; Topp et al., 2013). Root
traits remain a relatively unexploited breeding target, but
additional insight into the genetic architecture of root system
variation will be necessary for engineering designer root
systems to meet the worlds growing demand for food, fuel and
fibre (Gregory etal., 2013).
In this study, we investigated variation and covariation in
drought escape and avoidance traits in Brassica napus. Of the
Brassica oilseed crops, B. napus is the most important and
trails only soybean and oil palm in terms of global
pdf; 2014). Changing climate conditions and expansion into
new production geographies are increasing the exposure of
B. napus crop production to drought stress. However, little
research has focused on its root system, especially regarding
inheritance, genetics, or relationship to drought.
We utilized a quantitative trait locus (QTL) mapping
approach to better understand the genetic basis of root traits,
flowering time, and their impacts on grain yield in B.napus.
The QTL method is ideal for elucidating loci underlying trait
correlations and, by including drought as an
experimental treatment, loci associated with drought strategy
tradeoffs and yield sensitivity. We focused on a doubled haploid
(DH) population of 225 lines derived from a cross between
IMC106RR, an annual cultivar, and Wichita, a biennial
cultivar, to maximize genetic and phenotypic diversity. The
vernalization requirement differentiating annual (spring)
and biennial (winter) lines also defines genetic and
morphologically distinct pools (Diers and Osborn, 1994; Lhs etal.,
2003). We measured root pulling force (RPF, the vertical force
required to remove a plant from the soil; Hayes and Johnson,
1939) as an index of root system size. Specifically, we
identified a trade-off between flowering time and RPF, and revealed
the genomic regions involved. In a more detailed follow-up
study, we determined that variation in RPF is largely due to
variation in taproot mass.
Materials and methods
This study utilized a DH population of 225 lines named SE-1
that was produced from an F1 generation microspore donor plant
derived from a cross made between the annual variety IMC106RR
(Cargill Inc., National Registration No. 5118), and the biennial
variety Wichita (Rife et al., 2001; Reg. no. CV-19, PI 612846) at
Cargill (Fort Collins, CO, USA) using the method of Palmer etal.
(1996). The resulting population segregated for the requirement of
vernalization to initiate flowering and consisted of approximately
1200 lines. From this, about 900 lines flowered in the greenhouse
and, thus, were deemed to have an annual growth habit. Of these
900 DH lines, we randomly selected 225 for use in this experiment.
Genotyping and mapping
Genotyping was done using the Illumina (San Diego, CA, USA)
Brassica 60K Infinium array at DNA Landmarks (Quebec, Canada).
The final list of 1179 markers used in linkage map construction was
selected based on GenTrain genotype scores above 0.75 as
suggested by Illumina followed by selection for those which lack an
inter-homeologous polymorphism (Trick et al., 2009). The genetic
linkage map was constructed in JoinMap3 (Van Ooijen etal., 2001)
using a threshold recombination frequency of <0.25 and a minimum
logarithm of the odds ratio (LOD) score of 6 for grouping loci into
linkage groups. The Kosambi function (Kosambi, 1944) was used
to calculate genetic distances. Each linkage group was named based
on the nomenclature recommended by the Multinational Brassica
Genome Project steering committee (http://www.brassica.info/
resource/maps/lg-assignments.php). The map was analysed further
in the R/qtl program of the R statistical package (Broman et al.,
2003; Broman and Sen, 2009) to confirm marker orders and assess
general map quality.
The DH lines and parents were planted at Colorado State
Universitys Agricultural Research Education and Demonstration
Center (40.66N/105W) near Fort Collins, CO, USA on 19 April
2010. The study was arranged in a Row-Column design (created
with CycDesigN 3.0, www.cycdesign.co.nz) with three replicates
per treatment. Plots comprised two rows separated by 0.23 m and
were 1 m in length. Plots were separated by a distance of 0.33 m
and thinned to ~10 plants per plot. Irrigation was applied using a
linear-move system at approximately 2.5 cm per week for the first
month of development at which point it was discontinued in the
rainfed (dry) treatment. Irrigation was maintained at the rate of
2.5 cm per week for the duration of the experiment in the irrigated
Days to flower (DTF) was recorded for each plot as the interval
from sowing date to the date on which 50% of the plants in the plot
had initiated flowering. To measure yield, plots were swathed by
hand, allowed to dry, threshed using a Wintersteiger (Wintersteiger
AG, Austria) combine harvester and weighed immediately
thereafter. Yield sensitivity was calculated as the difference between the
yield of a DH line in the wet environment and its yield in the dry
Yield (wet) Yield (dry)
Relative yield was calculated as a Z score to account for the large
differences in mean yield and standard deviation between the wet
and dry treatments. The RPF method designed and used extensively
in maize (Hayes and Johnson, 1939; Spencer, 1940; Rogers et al.,
1976; Lebreton etal., 1995) was modified in a manner suitable for
B.napus. In short, a lasso was formed from a nylon rope and
harnessed around the base of a single B.napus plant within a field plot.
Aloop was formed at the other end of the rope and used to attach it
to a hand-held Imada DS2 dynamometer (Imada Inc., Northbrook,
IL, USA). The dynamometer was then slowly pulled vertically until
the root system came completely out of the soil, and the maximum
force (Kgf) generated during the root system removal was recorded.
RPF was measured within 12 days after grain was harvested. To
optimize the phenotype, the field was watered 24 hours before
Quantitative genetic analyses
A linear mixed model was used to analyse the data using the PROC
MIXED procedure in the SAS software package (SAS Institute)
with the DH line treated as a fixed effect and row and/or column
treated as random effects. The broad-sense heritability (H2) for each
trait was estimated using variance components computed in the
PROC VARCOMP procedure in SAS as theratio:
VG : (VG +VE )
where VG is the variance among DH lines and VE is the residual
variance. Among-trait phenotypic correlations were computed as
Pearson correlation coefficients using data points collected from
individual plots and genetic correlations were computed from least
square means estimated for each DH line within each environment
(Falconer and Mackay, 1996).
QTL mapping was performed using Haley-Knott Regression
(Haley and Knott, 1992) in R/qtl using 1 cM steps. QTLs were
selected using a step-wise model selection approach (Manichaikul
etal., 2009) based on significance thresholds made from 1000
permutations (Churchill and Doerge, 1994). Genome-wide scans for
QTL by environment interactions were conducted by comparing a
model including the environment (moisture treatment) as a covariate
along with a QTLenvironment interaction to a model lacking the
interaction. LOD 1.5 confidence intervals were determined in the R/
qtl software package (Broman etal., 2003; Broman and Sen, 2009).
QTLs were named using the trait and treatment with which they
were associated with ascending numbers based on linkage group
QTL confirmation in thefield
In an effort to validate the effect of a QTL discovered in this study
(RPF.dry1) and to gain a better understanding of the mechanisms
underlying RPF, a second study was performed during the
summer of 2012. It focused on a total of 40 lines of which each
parental haplotype at RPF.dry1 was represented by 20 lines. Haplotypes
were defined by DH lines which carried marker alleles spanning the
LOD 1.5 confidence interval. The physical position of this
interval was determined by comparing SNP sequence information with
the B. rapa reference (Wang et al., 2011; Cheng et al., 2011) using
BLAST (Altschul etal., 1990). The experiment was conducted at the
same experimental farm in which each experimental unit consisted of
a single plant per DH line watered by a precision drip irrigation
emitter. Three randomized blocks were planted and thinned to a single
plant 2 weeks after germination. All other factors of the experiment
were conducted as they were in 2010. In addition to collecting data
on RPF and DTF, aboveground biomass was weighed for each plant
in the field as they were extracted and shoot fresh weight (SFW) was
also recorded. Root mass extracted during RPF measurement was
oven dried at 80C for 3days prior to measurement of the taproot
dry mass, lateral root dry mass, total root dry mass, tap root diameter
(diameter of the basal portion of the dried taproot), tap root length
(length of the extracted taproot), branching zone length (length of
the taproot with primary laterals), and the number of coarse
secondary roots (total number of secondary roots >1 mm in diameter).
The 19 chromosomes of B.napus are recovered in the
The genetic map recovered 19 linkage groups which represent
the 10 chromosomes of the Agenome (B.rapa; 2n=20) and
the nine chromosomes of the C genome (B.oleracea; 2n=18)
which comprise the allopolyploid genome of B. napus. The
map was constructed using 1179 markers and resulted in a
total length of 2041 cM, had an average intermarker distance
of 1.8 cM, and carried large gaps of 46 cM and 33 cM on A01
and A08, respectively (Supplementary Figure S1). On average,
segregation in the population met the expected 1:1 ratio (48.4%
Wichita allele, 51.6% IMC106RR allele). Several regions
showed segregation distortion in favour of the alleles from the
Wichita parent, including most of linkage groups A01 and
A08 with maximum biases of 66.2% (chi-square=24.4) and
66.1% (chi-square = 24.1), respectively. This segregation in
favour of alleles from the winter parent occurred despite
selection for lines lacking a vernalization requirement and is
probably the product of gametic selection during the microspore
culture process (Ferrie and Mllers, 2011). Other regions of
lower segregation distortion were also observed on A06 and
C03 in favour of the IMC106RR allele and on C01 in favour
of the Wichita allele. This minor amount of segregation
distortion did not have a noticeable impact on map construction
or QTL analysis (Hackett and Broadfoot, 2003).
RPF, DTF, and yield demonstrate strong genetic
A significant treatment effect (Supplementary Table S1) and
heritable variation for RPF, DTF, and yield was observed.
Much of this variation was attributable to genetics with
estimates of heritability ranging from 0.16 for RPF in the dry
treatment to 0.83 for DTF in the wet (Table1). In agreement
with previous research (Udall et al., 2006; Shi et al., 2009),
flowering time had the highest heritability estimates in both
treatments. Despite selecting initially against the
vernalization requirement, some lines didnt flower in the dry treatment
resulting in a right-censored distribution (Leung etal., 1997).
Those DH lines that flowered did so in a minimum of 59days
in both treatments and a maximum of 101days in the dry
treatment and 108days in the wet treatment. RPF and yield
demonstrated transgressive inheritance in the DH population in both
treatments, a parameter that could only be measured relative to
IMC106RR for DTF and yield since Wichita, a winter
growthhabit line, neither flowered nor set seed during the field season.
Correlations were highly significant (P < 0.0001) among
all traits (Fig.2). Yield had a strong negative genetic
correlation with both DTF and RPF under both treatments. Positive
genetic and phenotypic correlations were observed between
RPF and DTF so that late-flowering lines required a larger
force for removal (Supplementary Table S2).
QTL analysis identifies two major pleiotropic factors
underlying the trade-off between drought escape and
We scanned for QTLs associated with DTF, RPF, and yield
along with the sensitivity of yield to drought. Seven QTLs
Table1. Descriptive statistics for DTF, RPF, and yield measured in
the SE-1 population in Fort Collins, CO, USA in 2011
for DTF were mapped; four in the wet and three in the dry
treatments. For RPF, three QTLs were identified in the wet
environment and two in the dry. Analyses of yield found three
QTLs for each environment. Summing across all four traits,
a total of 20 additive QTLs were discovered (Fig. 3). QTLs
co-localized to regions on linkage groups A03, A10, and
C02 (Fig. 3), thus implicating tight linkage or pleiotropy as
the cause of the strong genetic correlations observed among
traits. In particular, two regions on A10 and C02 (bracketed
in red in Fig. 3) explained a large proportion of the
variation for each trait and their estimated effects were always
larger than other QTLs discovered for any particular trait.
All of the QTLs discovered for yield co-localized with QTLs
Fig.2. Genetic correlations among traits in the wet (A) and dry (B)
treatments in the SE-1 population (n=195225; P<0.0001).
Fig.3. Localization and relative effect sizes of QTLs for the six traits
analysed. Box widths indicate LOD 1.5 confidence intervals for the
QTLs. The box height represents the percentage variance explained.
Colour indicates the directional effect of the Wichita allele (blue, positive;
grey, negative). The pleiotropic QTLs on chromosomes A10 and C02
are bracketed in red. Numbers next to boxes indicate the QTL naming
for DTF, further supporting the strong relationship between
these traits where a flowering time of ~68days increases the
probability of higher yield (Fig. 2). These results also show
that, unlike yield, the genetics underlying DTF and RPF did
not overlap entirely. For example, DTF.wet1 and DTF.wet2
had no relationship to RPF where RPF.wet3, located on C07,
had no relationship to DTF. This also suggests that the QTLs
on A10 and C02 may be responsible for most, or all, of the
genetic correlation (r=0.45) of RPF measured in the wet and
dry treatment (Supplementary Table S2).
Analysis of variance showed the genotype by
environment interaction to be significant for flowering time and yield
(Supplementary Table S1), but significant QTL by
environment interactions were only found for yield on chromosomes
A10 and C02. The impact of the QTLs on A10 and C02 in
response to treatment was further supported by QTLs which
mapped to these two chromosomal locations for yield
sensitivity (Fig.3; a detailed summary of all QTL results is
provided in Supplementary Table S3). A closer examination of
the allele effects at each locus shows that late-flowering QTL
alleles have a larger impact in the dry treatment than the wet
Examination of the relationship between RPF and DTF
using conditional QTL models shows evidence that
RPF.dry1 may be acting directly on bothtraits
To better understand the genetic architecture between the
traits, the data were analysed with QTL models which
conditioned upon flowering time, the hypothetically upstream
trait. The goal of this additional analysis was to infer the
causal relationships among traits which share QTLs (Li etal.,
2006; Broman and Sen, 2009). More specifically, the objective
was to elucidate whether a particular QTL is affecting a trait
directly (Fig.1B), as a downstream effect of delayed
flowering (Fig.1C), or a combination of both (Fig.1D).
All of the QTLs for yield under both treatments
disappeared when DTF was included as a covariate in the QTL
scan (data not shown). This supports the intuitive notion
that DTF is an upstream determinant of yield and the
colocalizing QTLs act indirectly on yield via their effects on
Conditional genome-wide scans for RPF in the wet
treatment identified a new QTL on A08 (RPF.wet4). Interestingly,
the high RPF allele at RPF.wet4 which did not affect
flowering time originated from the spring parent, IMC106RR.
Another QTL was mapped to the same location on C07 as
RPF.wet3, identified previously in the unconditional scan
(Fig.5A). These findings further support a genetic basis to
RPF that does not entirely overlap with that of DTF. The
stepwise model selection used included the DTF term (1.09
Kg 0.08) which resulted in the disappearance of RPF.
wet1 on A10 and RPF.wet2 on C02. This suggests that the
QTLs at these two loci may have been affecting RPF as a
downstream result of their effect on DTF (Fig.1C) in this
The conditional QTL scans of the dry treatment yielded
a model with a single QTL on A10 (Fig. 5B) co-localizing
in the same location on A10 as RPF.dry1 (0.50 Kg 0.09),
along with a DTF effect. The significant impact of this QTL
and the DTF covariate term in the model support a mode of
causality similar to that of Fig. 1D where the QTL affects
RPF directly as well as indirectly through its impact on
Fig.4. Difference between alleles (IMC106RR-Wichita) for relative yield (Z
score) at QTLs on A10 and C02 under wet and dry treatments.
Fig.5. LOD profiles comparing conditional (incorporating DTF as a
covariate; red) and unconditional (no covariate; blue) QTL scans in the
wet (A) and dry (B) environments. The horizontal line indicates the LOD
threshold based on 1000 permutations.
Single marker analysis of RPF using models conditional
on DTF strata account for a right-censored distribution
and further support the direct role of RPF.dry1 on
In the dry treatment, 22 DH lines were censored (omitted)
from the flowering time distribution because they did not
flower and, therefore, had no observed flowering time to use
as a covariate in the conditional QTL analysis. To account
for these missing data, the population was stratified into
five classes of approximately 45 lines based on their
flowering times (Supplementary Table S4). Single marker analyses
of RPF, conditional on DTF strata, were then performed at
each of the QTLs identified previously.
The stratification factor was highly significant (P<0.0001)
in all analyses, further supporting the strong effect of
flowering time on RPF. In the dry treatment, only RPF.dry1 (A10)
remained significant as the estimated difference in the mean
allele value changed only slightly between the conditional
and unconditional analyses (Table2). In contrast, RPF.dry2
(C02) became insignificant in the conditional analysis despite
the major difference in mean allele values estimated during
the unconditional examination (Table2). Analyses of the wet
treatment provided further support for the presence of RPF.
wet3 (C07) and RPF.wet4 (A08) and produced an
insignificant result for RPF.wet2. RPF.wet1 remained significant in
the conditional model suggesting its effect may be
constitutive across treatments. To further illustrate that the alleles at
RPF.dry1 affect roots independently of flowering time, mean
RPF values were plotted as a function of DTF strata, where
it is demonstrated that RPF values are higher for the Wichita
allele across any of the five DTF strata than they are for the
IMC106RR allele (Fig.6).
The effect of RPF.dry1 is validated in a second
field experiment and determined to be acting on
To validate the effects of RPF.dry1, 20 lines representing each
parental haplotype at the QTL were selected and the
experiment was repeated. The haplotype was defined by the interval
Table2. Mean RPF difference between parental alleles (Wichita
IMC106RR) estimated in unconditional and conditional (using DTF
as a covariate) single marker analysesa,b
Unconditional Conditional Unconditional Conditional
a Differences significant at P<0.05.
b Differences significant at P<0.01.
spanning the length of the LOD 1.5 confidence interval, a region
encompassing a minimum of 1.0Mb (physical positions 13 498
846 to 14 558 300)as estimated by the physical locations of the
flanking markers (Cheng etal., 2012) relative to the B.rapa
reference genome V1.5 (Cheng etal., 2011; Wang etal., 2011).
The pleiotropic effect of RPF.dry1 was confirmed, as
lines carrying the Wichita haplotype flowered an average of
12days later (P<0.0001) and required nearly 17 Kgf more
force to remove the roots (P < 0.0001) than lines carrying
the IMC106RR haplotype. Conditional analyses accounting
for DTF estimated a significant haplotype effect (P<0.05),
confirming that the effect of genotype on RPF at this locus is
significant even after accounting forDTF.
In this experiment, the root system was harvested after
RPF measurement and analysed in an effort to gain a
better understanding of the root qualities measured by RPF.
RPF was most highly correlated with total root dry mass
but had significant correlations with DTF, SFW, taproot dry
mass, lateral dry mass, taproot diameter, taproot length and
branching zone length (Table 3). No significant correlation
Fig.6. Dependence of RPF in the dry treatment on flowering time
strata (1, earliest; 5, latest) for each allele at RPF.dry1 (mean SE). See
Supplementary Table4 for further description of the strata.
Table3. Genetic correlation coefficients of traits measured in the
2012 field experiment (n=39)ad
SFW DMT DML DM
a Correlations significant at P<0.05.
b Correlations significant at P<0.01.
c Correlations significant at P<0.0001.
d RPF, root pulling force; DTF, days to flower; SFW, shoot fresh weight;
DMT, dry mass taproot; DML, dry mass laterals; DM, dry mass; TRD,
tap root diameter; TRL, tap root length; BZL, branching zone length;
NSR, number of coarse secondary laterals.
was found between RPF and the number of coarse
secondary roots. Analyses of the specific root components found the
effect of haplotype was significant for all traits except lateral
root dry mass, branching zone length, and the number of
Examination of the correlation matrix reveals significant
and generally strong correlations between SFW and all
measured root traits except the number of coarse
secondary roots. Since aboveground biomass is expected to have a
significant association with belowground biomass, the data
were re-analysed using models incorporating SFW and DTF
as covariates to further investigate the relationship between
genotype and the measured root traits while accounting for
these correlated and potentially confounding factors. We
found that only taproot dry mass was significant in models
conditioning on SFW as well as those incorporating both
SFW and DTF as covariates (Table4). It is remarkable that
any trait remained significant after conditioning on two
correlated traits; this suggests that the specific root trait which
this locus is acting upon may be taproot size, as lines with
the IMC106RR allele had an average taproot mass 74% as
large as those with the Wichita allele. Thus, evidence for the
direct effect of this QTL on taproot size provides a more
detailed understanding of the genetics and specific root
characteristics underlying the observed DTF:RPF
correlation and the inferred trade-offs between adaptive drought
Strong genetic correlations and conditional QTL
models indicate that the trade-off between drought
escape and avoidance may be due to pleiotropy
The strong correlations observed among root traits,
flowering time, and yield in this study are concordant with previous
research (Bolaos and Edmeades, 1993; Mitchell-Olds, 1996;
Lou et al., 2007; Shi et al., 2009). Our QTL results provide
first steps toward understanding the common and
independent genomic regions contributing to variation in each of these
traits, thus providing a better understanding of their
inheritance and the genetic architecture of their covariance. Further,
these results suggest a trade-off between drought escape and
avoidance strategies as there was a significant difference in
yield between early-flowering lines with small root systems
and late-flowering lines with larger root systems (Fig.2).
Co-localization of QTLs discovered through mapping
approaches can be considered circumstantial evidence for
pleiotropy (Lebreton et al., 1995; Tuberosa et al., 2003;
Lanceras etal., 2004). Our results show that RPF and DTF
are not invariably linked as four of 12 QTLs show
independent effects. Most QTL results in our study support a model
of broad-sense pleiotropy (i.e. an allele affecting more than
one trait) underlying the correlations we observed between
RPF, DTF, and yield. The overall prevalence of genome-wide
pleiotropy is expected to be rare, but those genes
demonstrating higher levels of pleiotropy (i.e. affecting a larger number
of traits) are also expected to have larger effects on a per-trait
Haplotype + DTF
Haplotype + SFW
Haplotype + DTF + SFW
basis (Wang etal., 2010). Therefore, we should expect that the
effect sizes of pleiotropic genes should be larger than those
due to genetic linkage. This is consistent with our QTLs on
A10 and C02 which showed larger effects, explained more
variation on a per trait basis, and had higher LOD support
than the other QTLs we discovered. However, further work
to create and phenotype near-isogenic lines (NILs), mutants,
and transgenics will be necessary to conclusively rule out
genetic linkage (Huang etal., 2013; Lovell etal., 2013; Uga
et al., 2013). For breeding, this information would enable
the design of breeding schemes to dissociate trait covariance
should an increase or decrease in root investment be of value
to the target production geography. For natural selection, it
would facilitate our understanding of the genetics of
adaptation in natural populations since pleiotropic genes have
been shown to have both adaptive (Le Corre et al., 2002;
Toomajian etal., 2006; Lovell etal., 2013) and maladaptive
(Rose, 1982; Scarcelli etal., 2007) consequences.
In an effort to elucidate the functional pathway, we utilized
the highly correlated, and putatively upstream, flowering time
trait as a covariate in conditional analyses, essentially scanning
for the significance of genetic effects using residual variation
that is not explained by the correlated trait (Broman and Sen,
2009). These results provide support for a model where RPF.
dry1 impacts RPF directly. The clear difference between the
effect of the parent alleles at RPF.dry1 (Fig.6) demonstrate that
the Wichita allele increases RPF regardless of flowering date. In
contrast, RPF.dry2 appears to work indirectly through
flowering time since RPF does not differ between alleles when
flowering time is used as a covariate. The mean difference in RPF
between alleles at the C02 locus may therefore be simply due
to the fact that the majority of lines carrying the Wichita allele
also flower later. The results of the 2012 QTL validation study
suggest that the morphological characteristic underlying RPF
may be taproot size as lines carrying the Wichita allele were
consistently larger when analyzing the data using conditional
models accounting for the correlated traits DTF andSFW.
The proposed mechanism of direct pleiotropy suggests
that targeting root-specific promoters might be an avenue for
increasing root biomass without major effects on flowering
time. However, root-specific reductions in cytokinin, a
negative regulator of root system size, were shown to increase
root biomass with minimal impacts on shoot growth except
that bolting and flowering were delayed (Werner etal., 2010).
These results may be indicative of inherent root-to-shoot
feedback that would override the efficacy of such a strategy.
Discovery of root QTLs independent of flowering time
QTLs suggest that root system size can be increased
without impacts on floweringtime
Despite the strong correlation between DTF and RPF across
the population, trait values in some DH lines were
contrary (i.e. high RPF and early flowering) to this expectation.
Accordingly, we mapped two QTLs in the wet environment
located on linkage groups A08 and C07, loci which do not
co-localize with flowering time QTLs. The IMC106RR allele
at the A08 QTL increases RPF, a result opposite to the rest of
the QTLs for RPF in which the Wichita allele increases RPF.
This result partially explains the transgressive segregation we
observed for RPF where some lines required more than 1.5
times more force than Wichita for root removal. Associations
between loci on C07 and root traits such as root length and
root mass have been identified in other experiments
conducted to understand the genetics of nutrient use efficiency
(Hammond etal., 2009; Yang etal., 2010; Yang etal., 2011;
Shi et al., 2012). Because the markers used in those studies
do not overlap with ours, it is difficult to draw strong
conclusions about specific locational overlap but it suggests that this
chromosome is a source of interesting variation in root
biology across Brassica species. These QTLs and their associated
markers could be valuable resources for breeding larger root
systems without correlated responses in maturity.
Many mutants and QTLs associated with root
development have been identified in research using the model plant
Arabidopsis thaliana (Benfey etal., 2010). In particular,
several QTLs related to root growth have been mapped to the top
of Arabidopsis chromosome 1 (Kobayashi and Koyama, 2002;
Reymond etal., 2006; Sergeeva etal., 2006; Kellermeier etal.,
2013) and the bottom of chromosome 4 (Loudet etal., 2005;
Fitz Gerald et al., 2006; Reymond et al., 2006; Kellermeier
et al., 2013). The QTLs we identified for RPF on A08 and
C07 appear to be in regions of the B.napus genome that are
homologous to these segments of chromosomes 1 and 4,
respectively (Cheng etal., 2011; Zhao etal., 2013). This may
be suggestive of root-specific genetic mechanisms that have
been conserved within the Brassicaceae, but more research is
clearly necessary to confirm this.
Many candidate genes exist across the five identified
Flowering time QTLs have been identified previously on
A02, A03, A10, C02, and C03 in other B.napus and B.rapa
populations (Osborn, 1997; Schranz etal., 2002; Osborn and
Lukens, 2003; Udall etal., 2006; Long etal., 2007; Shi etal.,
2009) and, with the exception of the locus on C03, entirely
agree with our results. All of these chromosomal regions are
syntenic to the top of Arabidopsis chromosome 5 (Parkin
et al., 2005), a region that contains the well characterized
flowering time genes CO (Putterill etal., 1995), FY (Simpson
etal., 2003), and FLC (Michaels and Amasino, 1999), among
others. Previous research (Schranz et al., 2002; Razi et al.,
2008) as well as the draft genomes of B. rapa and B.
oleracea (Cheng etal., 2011; Cheng etal., 2012; Zhao etal., 2013)
indicates that FLC has been retained after two rounds of
whole-genome duplication (Tang etal., 2012) and is present
on all of the aforementioned chromosomes. In addition, CO
has been maintained on A02, A10, and C02; and FY on A02
and A03. Beyond the three relatively well characterized genes,
discussed above, examination of the draft Aand C genomes
suggests that ~22 additional genes with gene ontology (GO)
annotations to flowering are predicted to reside within these
Several studies in species of Brassica have mapped QTLs
for flowering time which overlap with those for primary root
(taproot) traits such as fresh weight, length, and width (Lou
et al., 2007; Lu et al., 2008; Kubo et al., 2010; Yang et al.,
2010). In agreement with the results of our study, at least one
of the QTLs identified in each of those experiments was in a
location orthologous to the top of Arabidopsis chromosome
5. Approximately 20 genes with GO annotations to roots
are predicted to lie within this region. We hesitate to suggest
any of them as primary candidates since the genetics of root
development are poorly understood and their suggestion
would be entirely speculative. The results of our conditional
analyses also suggest that we should consider the possibility
that a flowering time gene may be acting pleiotropically. For
instance, it was recently shown that the protein product of
the transcription factor FLC has over 500 potential binding
sites in the Arabidopsis genome, sites which were enriched in
several GO categories including response to stress and abiotic
stimulus (Deng etal., 2011). This may be considered
circumstantial support for the results of our conditional
examinations of the QTL on A10, and its putatively direct role in root
development, since it seems possible that FLC could be
regulating genes involved in root biosynthesis in trans. Similar to
our results, a recent analysis of FLC in Arabidopsis found that
it impacted leaf shape and trichome number independently
of its impact on flowering time (Willmann and Poethig, 2011).
The results of this research support a body of evidence in
which traits relevant to differential drought coping strategies
may be genetically constrained, thereby creating an inherent
trade-off (Mitchell-Olds, 1996; McKay etal., 2003; Heschel
and Riginos, 2005; Wu et al., 2010; Franks, 2011). These
results must be considered in the context of the Brassica
species in which little work has been conducted on drought
coping mechanisms and none has focused on the roots. We are
currently developing NILs so that the many alleles residing
within RPF.dry1 and RPF.dry2 can be separated and their
impacts on root mass and flowering time may be
unequivocally estimated. Additionally, these NILs should be grown
under diverse growing conditions and different geographies
to understand the role of the environment on these traits
and its interaction with the underlying genetics. Results from
these experiments will inform fine-mapping activities aimed
at cloning the causal variant(s), a process that will require
identification of many more molecular markers within the
candidate QTL regions. This task will be greatly enabled by
the recent release of draft Brassica genomes (Cheng et al.,
2011; Wang etal., 2011; Zhao etal., 2013; Chalhoub et al.,
2014). These activities will show whether the QTL
co-localization observed in this study is the result of pleiotropy or
genetic linkage, ultimately improving our understanding of
the genetics of drought physiology and enabling breeding for
Supplementary data can be found at JXB online.
Supplementary Table S1. ANOVA results for DTF, RPF,
Supplementary Table S2. Genetic and phenotypic
correlations among traits and environments.
Supplementary Table S3. Detailed summary of all QTLs
Supplementary Table S4. Flowering time strata used in
single marker analyses.
Supplementary Figure S1. Visual representation of the
genetic map of the SE-1 doubled haploid population.
The authors would like to thank Cargill Inc. for funding and Jan Leach,
Patrick Byrne, William Bauerle, and Katy Navabi for many helpful
comments on the manuscript. This work was also supported by National Science
Foundation grant IOS 1025837 to JKM.
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