Quantitative circadian variation across 191 Swedish
Arabidopsis accessions
We used delayed fluorescence imaging to characterize circadian rhythms
in groups of 14 day old seedlings entrained in 12h L 12h D cycles at
22°C and assayed under free-running conditions of constant light (L:L)
at 22°C. Accession means and standard errors (SE) adjusted for
experimental effects were obtained using linear mixed models for period,
amplitude and RAE and a circular regression model for phase (see
Extended Methods). We found a 4.42h difference between the mean period
of the fastest (21.28h, SE=0.329) and slowest (25.70h, SE=0.355)
accessions tested (Figure 1A). The mean phase of peak DF intensity for
each accession occurred over the dark half of the cycle (12-24/0h) with
a huge difference (10.32h) from the earliest peaking (12.21h, SE=0.58)
to the latest (22.54h, SE=0.20) (Figure 1B). This variation in period
and phase is consistent with data previously reported from a global
collection of Arabidopsis accessions(Michael et al., 2003). We
also measured the robustness of these rhythms by looking at their RAE
values. The most rhythmic (lowest RAE) mean was 0.31 (SE=0.023) and the
least rhythmic was 0.44 (SE=0.024) representing approximately 20% of
the possible range of this trait in this study (Figure 1C). Accession
means for circadian phenotypes can be viewed in Supplementary File 1.
To assess the effect of genotype on each trait we performed a likelihood
ratio test against a model which omitted genotype as a variance
component. Including genotype in the mixed model had highly significant
effects on period (χ2 (1 df)=455.57,p< 0.0001) and RAE (χ2 (1)= 65.97,p< 0.0001). Including genotype in the circular
regression model also had a strongly significant effect on phase
(χ2 (191)=407, p< 0.0001). Amplitude
was analysed as a log10 transformation (see Extended
Methods) and there was no significant effect of genotype on
Log10Amplitude (χ2 (1)= 0.19, p= 0.6). With this
in mind, we dropped Amplitude as a trait of interest for the temperature
and mutant screening experiments. REML output tables (Supplementary
Tables 1-3), model checking graphs (Supplementary Figures 1-3) and
log-likelihood results (Supplementary Tables 4-7) are available to view.
We observed a strong correlation between the period, RAE and phase of
each accession (Supplementary Figure 4), especially between period and
phase (Adjusted R2 =0.43, p< 0.0001).
We are unaware of any previously published correlation between natural
variation in period and RAE. In this study, we found a highly
significant negative relationship with longer periods having lower RAE
scores (Adjusted R2 =0.1, p< 0.0001).
We chose 10 accessions from the tails of the distributions of mean
period, phase and RAE to create “phenotypic tail” groups representing
the extremes of each trait. Some accessions represented two or more tail
groups and could be split into two master groups of: long period, dusk
phased, low RAE accessions and short period, dawn phased, high RAE
accessions as explained in Figure 1D. These phenotypic tail groups were
used for the temperature experiments described later in this paper
(accessions in each tail are listed in Supplementary Tables 8-10).
Our next question was whether other quantified traits co-varied with our
circadian data. We used several previously published datasets to examine
possible phenotypic relationships between our traits and flowering time
(Y. Li et al., 2010; Sasaki et al., 2015), seed dormancy (Kerdaffrec et
al., 2016) and freezing tolerance (Horton et al., 2016) (Supplementary
Table 11). Only flowering time
data from Li et al. (2010) showed a moderate correlation with period
(p<0.01, R2 =0.28) and phase (p<0.01, R2 =0.26).
This flowering time data is based on flowering times recorded in
conditions of variable day-length reflecting annual conditions of a
natural Swedish or Spanish climate (Y. Li et al., 2010). However, no
significant correlations were found between flowering times recorded
under long-days under either 10°C or 16°C by Horton et al. (2016),
indicating that the relationship is highly dependent on the exact
experimental conditions used.
We then investigated whether circadian traits varied according to the
original location of the accessions. Period was found to be
significantly correlated with longitude and latitude
(p< 0.001), however a considerable amount of variation
remained unexplained (Adjusted R2 <0.1)
(Supplementary Figure 5). Despite this weak overall association with
latitude, we observed an obvious segregation of accessions from the
phenotypic tails of the period distribution, with the shortest period
accessions in Northern and mid-latitude regions and the longest period
accessions in the south (Figure 2A). No significant correlation with
altitude was observed for these accessions.
Following on from this, we asked the question whether population
sub-structure could better explain the distribution of period
phenotypes. We re-classified the population into three groups split
across the axes of the first two principal components (which together
explain over 15% of the total genetic variation) (Figure 2D and E).
PC.B broadly contained Northern accessions (N=54, red points in Figure
2D). The Southern accessions split into two sub-groups existing within
the same geographic region; PC.A (N=111) and PC.C (N=26). We found that
period is significantly longer in group PC.C (Figure 2F) (F(
2,188)=8.39, TukeyHSD for PC.A and PC.C p< 0.01, One-way
ANOVA). PC.A had mean periods similar to those obtained from Northern
accessions (PC.B, Figure 2F). PC.C accessions also have significantly
earlier phase peaks than those in PC.A (One-way ANOVA, F(2,188)=6.9149,
TukeyHSD for PC.A and PC.C p< 0.001).
We removed the effects of PC groups from period length variation and
found that the effect of latitude on the remaining period variation was
much less significant (REML Model: Period ~ (1
| PC.blocks) + Latitude, applying Satterthwaite’s method.
F(1,7.23) = 8.87, p >0.01). This indicates that period
variation with latitude is due to the different locations of
sub-populations, rather than due to adaption following a latitudinal
cline. Accessions in the long period tail group described in Figure 1A
belonged to both PC.A (N=6) and PC.C (N=4), suggesting that long-period
traits may have been preserved in both sub-populations in this area.