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\begin{document}

\title{Association analysis reveals genetic control underlying natural
variation in circadian rhythms in Arabidopsis}



\author[1]{Hannah Rees}%
\author[1]{Ryan Joynson}%
\author[2]{James Brown}%
\author[1]{Antony  Hall}%
\affil[1]{Earlham Institute}%
\affil[2]{John Innes Centre}%


\vspace{-1em}



  
  \date{May 26, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Circadian clocks have evolved to resonate with external day and night
cycles. However, these entrainment signals are not consistent everywhere
and vary with latitude, climate and seasonality. This leads to divergent
selection for clocks which are locally adapted. To investigate the
genetic basis for this circadian variation, we used a Delayed
Fluorescence (DF) imaging assay to screen 191 naturally occurring
Swedish Arabidopsis accessions for their circadian phenotypes. We
demonstrate that period length co-varies with both geography and
population sub-structure. Several candidate loci linked to period, phase
and Relative Amplitude Error (RAE) were revealed by genome-wide
association mapping and candidate genes were investigated using TDNA
mutants. We show that natural variation in a single non-synonymous
substitution within COR28 is associated with a long-period and
late-flowering phenotype similar to that seen in TDNA knock-out mutants.
COR28 is a known coordinator of flowering time, freezing tolerance and
the circadian clock; all of which may form selective pressure gradients
across Sweden. We demonstrate the effect of the COR28-58S SNP in
increasing period length through a co-segregation analysis. Finally, we
show that period phenotypic tails remain diverged under lower
temperatures and follow a distinctive `arrow-shaped' trend indicative of
selection for a cold-biased temperature compensation response.%
\end{abstract}%



\sloppy


Title: Association analysis reveals genes underlying natural variation
in circadian rhythms in \emph{Arabidopsis}

Authors: Hannah Rees\textsuperscript{1,2}, Ryan
Joynson\textsuperscript{1}, James K.M. Brown\textsuperscript{3}, Anthony
Hall\textsuperscript{1}

Corresponding author: Anthony Hall anthony.hall@earlham.ac.uk

Contact information: \textsuperscript{1}Earlham Institute, Norwich
Research Park, Norwich, NR4 7UG, UK; \textsuperscript{2}Institute of
Integrative Biology, University of Liverpool, Crown Street, Liverpool,
L69 7ZB, UK; \textsuperscript{3}John Innes Centre, Norwich Research
Park, Norwich, NR4 7UH, UK

Funding: This project was supported by the BBSRC via the Earlham
institute CSP (BB/P016774/1, AH, HR) and BBSRC Design Future Wheat
(BB/P016855/1, AH, RJ).

Abstract: Circadian clocks have evolved to resonate with external day
and night cycles. However, these entrainment signals are not consistent
everywhere and vary with latitude, climate and seasonality. This leads
to divergent selection for clocks which are locally adapted. To
investigate the genetic basis for this circadian variation, we used a
Delayed Fluorescence (DF) imaging assay to screen 191 naturally
occurring Swedish \emph{Arabidopsis} accessions for their circadian
phenotypes. We demonstrate that period length co-varies with both
geography and population sub-structure. Several candidate loci linked to
period, phase and Relative Amplitude Error (RAE) were revealed by
genome-wide association mapping and candidate genes were investigated
using TDNA mutants. We show that natural variation in a single
non-synonymous substitution within \emph{COR28} is associated with a
long-period and late-flowering phenotype similar to that seen in TDNA
knock-out mutants. \emph{COR28} is a known coordinator of flowering
time, freezing tolerance and the circadian clock; all of which may form
selective pressure gradients across Sweden. We demonstrate the effect of
the \emph{COR28} -58S SNP in increasing period length through a
co-segregation analysis. Finally, we show that period phenotypic tails
remain diverged under lower temperatures and follow a distinctive
`arrow-shaped' trend indicative of selection for a cold-biased
temperature compensation response.

Keywords: Circadian clock, temperature compensation, \emph{Arabidopsis}
, natural variation, Sweden, 1001 genomes project,
GWA-mapping,\emph{COR28} , \emph{ELF3}

Acknowledgements: We thank Paul W. Goedhart (Wageningen University) for
advice about circular phase statistics using the RCIRCULAR procedure and
Grant Calder (John Innes Centre) for his assistance developing the
96-well ROI selection using FIJI. We thank Susan Duncan for help and
advice with the flowering time investigation.

We also thank the Dean Research group (JIC) for providing the 191
Swedish accessions and the Liu Research group (Shanghai Institutes for
Biological Sciences) for providing the \emph{cor28-2} ,
\emph{cor27-1}and \emph{cor27-1/28-2} mutants. We also thank Ewan Holmes
and Georgia Scutter for help with seed harvesting.

Main text:

Introduction

Plants are highly adapted to survive and exploit the daily fluctuations
in light and temperature experienced as the earth spins on its axis. The
circadian clock plays an intrinsic role in this; integrating temporal
cues from the environment to inform photosynthetic, metabolic and
developmental processes (Covington et al., 2008; Harmer et al., 2000). A
robustly oscillating circadian clock which is highly synchronised to
external day-length contributes to the overall fitness of the plant,
giving it an edge over competitors, predators and pathogens (Dodd et
al., 2005; Green et al., 2002; Ingle et al., 2015; Michael et al.,
2003). Circadian rhythms can be quantified by their period (the time
taken to complete one cycle), their phase (the time of day in which the
rhythm peaks), their amplitude (the change in intensity from a baseline)
and their Relative Amplitude Error (RAE) which is the amplitude error
divided by the overall amplitude of the rhythm and can be equated to
rhythm robustness. In our study, circadian phenotyping was achieved
using delayed fluorescence (DF) imaging. Delayed fluorescence levels are
circadian regulated and reflect the changes in the photosynthetic state
of photosystem II (Gould et al., 2009). DF imaging has been validated as
a reliable and flexible tool to measure circadian rhythms in a range of
plant models but has not previously been used for phenotyping of a large
population of individuals on the scale required for genome-wide
association mapping.

\emph{Arabidopsis thaliana} is a model plant system which has been
extensively studied in circadian biology. The \emph{Arabidopsis} clock
is comprised of a series of interlocking negative transcriptional
feedback loops connected by key activators that control the oscillation
of clock gene expression(Hernando et al., 2017). Key genes within this
network include \emph{CIRCADIAN CLOCK ASSOCIATED 1 (CCA1)} and\emph{LATE
ELONGATED HYPOCOTYL (LHY)} which transcriptionally repress\emph{TIMING
OF CAB EXPRESSION (TOC1)} (Alabadi et al., 2001). Downstream genes
interact with the clock to communicate circadian rhythmicity to
physiological outputs. Examples include photoperiodic regulation of
flowering time and the temporal gating of cold acclimation responses
(Kinmonth-Schultz et al., 2013). Flowering under long days is instigated
through accumulation of the floral promoter CONSTANS (CO) controlled by
the circadian clock component GIGANTEA (Su\selectlanguage{ngerman}árez-López et al., 2001). In
winter-annuals, flowering is also dependent on the vernalization
response of \emph{FLOWERING LOCUS C (FLC)} and\emph{FRIGIDA}
\emph{(FRI)} (Bastow et al., 2004; Johanson et al., 2000). Cold
tolerance is diurnally activated through alternative splicing of
\emph{CCA1} which co-regulates the expression of \emph{COLD REGULATED
(COR)} genes alongside light and temperature stress pathways (James et
al., 2012; Kinmonth-Schultz et al., 2013).

The endogenous core circadian network is entrained by external stimuli;
most notably light and temperature. Day-length, light composition and
light intensity have all been shown to affect circadian rhythms
(Aschoff, 1979; Más et al., 2003; Pittendrigh \& Minis, 1964; Yanovsky
\& Kay, 2002). Temperature also has a well-documented effect on
entrainment of circadian rhythms (Edwards et al., 2005; Gould et al.,
2006; Salome \& McClung, 2005) with a degree of period shortening
expected under higher temperatures. Circadian rhythms are said to be
`temperature compensated'; they resist large changes in period-length in
response to temperature (Pittendrigh \& Harvey, 1954). The extent of
temperature compensation has been shown to vary
between\emph{Arabidopsis} accessions (Gould et al., 2006; Kusakina et
al., 2014). Rhythm robustness is also strongly affected by temperature,
although the temperature which produces the most rhythmic oscillations
appears to be dependent on the species and circadian assay used
(Kusakina et al., 2014; Rees et al., 2019). Plants with clocks which
resonate with environmental conditions are typically fittest, however it
has been suggested that in climates with large seasonality there may be
a compromise for clocks which are adaptable to rapidly changing
day-lengths (Michael et al., 2003).

Natural populations of several important model organisms that exhibit
extensive diversity in their circadian behavior have been documented.
Pupal eclosion rhythms of Drosophila are latitude dependent; with
shorter rhythms, earlier phases and less robust rhythms observed in
Northern latitudes (Allemand \& David, 1974; Lankinen, 1986).
In\emph{Arabidopsis} , Michel et al. conducted a global study of leaf
movement rhythms in 150 accessions and found that day-length of origin
country correlated positively with period, but not phase or amplitude.
They also identified several loci in the \emph{TOC1/PRR} family which
determined natural variation in period, phase and amplitude
independently (Michael et al., 2003). Other investigations have shown
allelic diversity of several clock related genes including
\emph{FLC}(Swarup et al., 1999) \emph{GI} (de Montaigu et al., 2015)
and\emph{EARLY FLOWERING 3 (ELF3)} (Anwer et al., 2014) which contribute
to natural circadian phenotypes without fully disrupting the clock
mechanism. The positive relationship with period and latitude has also
been observed in natural populations of \emph{Mimulus guttatus}(Greenham
et al., 2017) and in cultivated varieties of soybean and tomato
(Greenham et al., 2017; Müller et al., 2016), potentially due to
unintentional selection for circadian rhythms which function best at
particular latitudes.

In this study, we focused on a collection of
\emph{Arabidopsis}accessions from across a large latitudinal range in
Sweden; a country with variations in climatic, anthropogenic and
day-length factors all possibly influencing clock adaption. Northern
latitudes around 63°N have permanent snow cover during winter months and
the growing season is much cooler and shorter than in the South. At the
solstice, there is almost 3 hours difference in day-length between the
North and South. This divergence of climate has led to selection for
ecotypes with adapted growth and flowering strategies (Bloomer \& Dean,
2017; Shindo et al., 2005). Analysis of the global population structure
of \emph{Arabidopsis}accessions has previously identified Swedish
accessions as being genetically distinct from the wider population, with
further differentiation within the country between North and South
(Horton et al., 2012; Huber et al., 2014; Long et al., 2013; Nordborg et
al., 2005). Accessions from South Sweden have high genetic diversity
within a relatively small area, perhaps suggesting a historic emigration
from central Europe following glacial retreat (Alonso-Blanco et al.,
2016). Northern Swedish accessions have lower genetic diversity but
larger genome sizes (Long et al., 2013) and also carry a surprising
reservoir of drought tolerance genes (Exposito-Alonso et al., 2018).
Completion of the 1001 genomes project in 2006 has facilitated a recent
expansion in\emph{Arabidopsis} genome wide association (GWA) studies
made possible through the provision of high-quality re-sequenced
genotype data. These accessions are publicly available, geo-referenced
and genetically inbred making it easy for researchers to perform
experiments over several generations under a variety of conditions
(Weigel \& Mott, 2009). Many of the accessions in this study have also
been characterized for other phenotypic traits such as seed dormancy
(Kerdaffrec et al., 2016), flowering time (Sasaki et al., 2015), and
freezing tolerance (Horton et al., 2016).

The objectives for this research were firstly; to investigate whether
circadian diversity exists in Swedish \emph{Arabidopsis} and to
understand to what extent this variation can be explained by either
latitude or the underlying population structure. We next wanted to
identify and validate genetic variation in candidate genes which might
explain the observed circadian variation. To our knowledge, this work
provides the first example of a GWA study using circadian phenotypes in
plants. Our final objective was to measure circadian rhythms under 10°C
and 16°C to test whether divergence of circadian traits persists across
a range of temperatures.

Methods

\emph{Plant material, plate set-up and growth conditions}

Seed for the 192 \emph{Arabidopsis} accessions were gas sterilised using
3ml hydrochloric acid in 100ml sodium hypochlorite. We then stratified
seed in 1ml of sterile water for 2 days at 4°C before plating. Clear 96
well plates with flat bottomed wells (Thermo Scientific, cat no:
10287631) were filled with 250ul of Murashige and Skoog agar media
(1.5\% Agar, pH5.8, no sucrose). Approximately 20 seeds were added to
each well of the plates following a randomized Alpha-lattice design (see
Extended Methods) and left to dry until all residual water had
evaporated. Each accession was replicated 18 times across the 3 imaging
runs. Clear microplate lids (Thermo Scientific, cat no: 10334311) were
secured to each plate with micropore tape ensuring that the lid was
raised slightly from the top of the wells to allow condensation to
circulate freely. Plates were returned to the fridge for 2 more days
before transfer to the growth cabinet set at 12:12 L:D cycle at 22°C
under approximately 200 \selectlanguage{greek}µ\selectlanguage{english}mol
m\textsuperscript{-2}~s\textsuperscript{-1}white light for 14 days.
Prior to the temperature response experiments, accessions from the 191
phenotyping screen were bulked for seed (see Extended Methods). Plates
were all grown under 12:12 L:D at 22\selectlanguage{ngerman}°C light for 10 days and were then
transferred to the temperatures in which they would be imaged for 4
days.

Note: For the temperature and mutant screening experiments, the corner
wells of the 96-well plates were not used in the designs as we found
that corners had significant effects on circadian rhythms in the 191
dataset; potentially due to these wells drying out more quickly
(Supplementary Figure 8).

\emph{Imaging conditions}

Delayed fluorescence imaging was carried out using Lumo Reteiga CCD
cameras (QImaging, Canada) housed in two temperature-controlled
cabinets. Six 96-well plates can be imaged simultaneously in each
cabinet. Imaging conditions were exactly those described for constant
light (L:L) imaging of \emph{Brassica} leaves in Rees et al. 2019 (Rees
et al., 2019). Images were captured every hour with a 1-minute exposure
time.

\emph{FIJI ROI selection and circadian analysis}

Image stacks were imported into FIJI (RRID:SCR\_002285) (Schindelin et
al., 2012) and regions of interest (ROI) were selected using a
semi-automated approach (full details in Extended methods; macro and
user guide in Supplementary files 4 and 5). Measurements for integrated
density were taken for these regions across the stack using the
Multi-measure plugin. The time-series was converted to decimalized time
relative to hours elapsed from entrained dawn (T0) and was cropped to
150h to standardize between experiments. Period estimation was done
using the online platform Biodare2 (https://biodare2.ed.ac.uk) applying
the FFT-NLLS algorithm on a data window of 24-120h with expected periods
set to 18-34h. Raw data was baseline and amplitude de-trended prior to
analysis. Manual inspection of resulting periods ensured that all
arrhythmic traces were excluded from further analysis.

\emph{Data analysis and REML}

We filtered period estimates from Biodare2 to exclude rhythms with
RAE\textgreater{}0.65 and period \textgreater{}28h as we found that
rhythms beneath these cut-offs gave the most reliable period estimates.
We next used restricted maximum likelihood (REML) to fit a linear mixed
model to the 191 accession dataset and thus obtain accession means for
period and RAE which were adjusted for the effects of cabinet and
experimental run (see details in Extended Methods). An additional step
was required to calculate phase means for each accession as the raw
phase data is circular and relative to dawn (0) with a full circle
representing 24 hours. Phase data was analysed with a circular
regression model using the Genstat RCIRCULAR procedure to obtain
accession mean phases adjusted for cabinet and run effects (Fisher \&
Lee, 1992).

For temperature experiments, no period or RAE cut-offs were imposed as
we predicted an increase in both these variables with lower temperatures
(Dodd et al., 2014; Rees et al., 2019). We fitted linear models to the
period and RAE data and identified the contribution of each component by
analysis of variance (Supplementary Tables 12-14). The above data
analysis was done using Genstat 18\textsuperscript{th} edition
(RRID:SCR\_014595).

Map figures (Figure 2 and 4) were created using the ggmaps package in R
using Google maps (2018) (Kahle \& Wickham, 2013).

\emph{Principal component analysis}

The PCA for genomic variation in the 191 Swedish accessions in Figure 2E
was performed using a compressed mixed linear model run in R
implementing the GAPIT package (Lipka et al., 2012; Zhang et al., 2010).
Genotype data was downloaded from the 1001 genomes dataset filtered for
bialleleic SNPs with \textgreater{}5\% MAF (minor allele frequency). The
first two PCs were used to split the accessions into the categories in
Figure 2D and F.

\emph{GWAS}

Genome wide association mapping was conducted using an Accelerated Mixed
Model (AMM) via the online web application GWA-Portal (Seren, 2018).
Accession means estimated from REML were used for period, phase and RAE
with no further transformation necessary. Genotype data used was from
the full-sequence 1001 genomes dataset. Results were filtered for the
top 10,000 -log10(P) values and then for hits with \textgreater{}5\%
MAF. Comparison to results from other models confirmed the major peaks
and can be viewed in Supplementary Figure 6.

\emph{Mutant screening}

T-DNA and EMS mutants were obtained from the
Nottingham\emph{Arabidopsis} Stock centre (RRID:SCR\_004576) and primers
designed using the iSect tool from the SIGnAL website. In addition, seed
for\emph{cor28-2, cor27-1} and \emph{cor28-2/cor27-1} was kindly donated
from Hongtao Liu's laboratory (Shanghai, China) where these mutants have
previously been identified as long-period using leaf-movement and
ProCCR2:LUC constructs (X. Li et al., 2016). \emph{Cor28-2} is a null
mutant and \emph{cor28-1, cor27-1} and \emph{cor27-2} are all knockdown
mutants. Gene candidates, mutant IDs and primers used can be viewed in
Supplementary File 2. All lines were genotyped to confirm homozygosity
by PCR and gel electrophoresis prior to seed bulking other
than\emph{sco2} which was genotyped by sanger sequencing to reveal the
premature stop. We were unable to obtain or confirm homozygous mutants
for \emph{dnf} and \emph{fab1c} and therefore these lines were not DF
screened (Supplementary File 2). SALK lines for \emph{elf3} were not
obtained as the \emph{elf3-1} mutant has been previously confirmed in
Box et al. 2015 as having a phenotype of reduced robustness using
delayed fluorescence imaging (Box et al., 2015). WT control lines; Col-0
and Ler were grown for seed in parallel to standardize seed quality.
For\emph{cor28-1} , a homozygous WT control was derived by segregation
of the original heterozygous TDNA line was used as a control. Five
imaging experiments using a single camera and cabinet were carried out
with each line present in at least three experiments. We did not apply a
mixed model to the mutant screening data because only one camera was
used.

\emph{Co-segregation analysis}

The co-segregation analysis was conducted using a Cleaved Amplified
Polymorphic Sequences (CAPS) assay to genotype and DF to phenotype
crossed and selfed individual plants.

Primers were designed to amplify a 252bp fragment within
\emph{COR28}exon 2 (\emph{COR28-F} : 5'-ACAGTAAGTAACCCCGAACC-3',
\emph{COR28-R:}5'-TTCTTGACAAAAGTCCCACTC-3'). These fragments were then
digested using TaqI (Thermo Fisher Scientific, \#ER0671, recognition
site: T\^{}CGA). TaqI was added to 10ul of PCR reaction mixture and
incubated at 65C for 1.5h according to manufacturer's instructions.
Individuals homozygous for the \emph{COR28} SNP 58S (found in the parent
St-0) are not recognised by TaqI and so presented as a single 252bp band
on an agarose gel (3\% Agarose, run for 1.5h at 50V). Individuals
homozygous for the\emph{COR28} SNP 58W (found in the parent T800) were
fully cleaved resulting in two bands 92bp and 160bp in length.
Heterozygous individuals therefore had three bands at 252bp, 160bp and
92bp clearly separated by electrophoresis. Restriction digest gels for
identifying F2 plants can be viewed in Supplementary file 6. All plants
in generations (F0-F1) were vernalised for 12 weeks at 4°C prior to
growth at 22°C under long days (16:8h). Parent accessions St-0 and T800
were genotyped to check for homozygosity prior to crossing. All three
heterozygous F1 individuals were (\selectlanguage{english}T800 x \selectlanguage{english}St-0) and were also confirmed
by the CAPS assay. These F1 plants were then selfed and the circadian
rhythms of the F2 progeny were analysed using DF on detached leaves from
24 day old plants grown under 12:12h light:dark conditions at 22degC.
Leaves were placed on 0.5\% agar plates and were exposed to 24h constant
L:L prior to imaging as described for the 96 well plates.

Results

\emph{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
22degC and assayed under free-running conditions of constant light (L:L)
at 22degC. 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 \emph{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 (\selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2} (1
df)=455.57,\emph{p\textless{}} 0.0001) and RAE (\selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2}
(1)= 65.97,\emph{p\textless{}} 0.0001). Including genotype in the
circular regression model also had a strongly significant effect on
phase (\selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2} (191)=407, \emph{p\textless{}} 0.0001).
Amplitude was analysed as a log\textsubscript{10} transformation (see
Extended Methods) and there was no significant effect of genotype on
Log10Amplitude (\selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2} (1)= 0.19, \emph{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 R\textsuperscript{2} =0.43, \emph{p\textless{}} 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 R\textsuperscript{2} =0.1, \emph{p\textless{}} 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\textless{}0.01, R2 =0.28) and phase
(p\textless{}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\selectlanguage{ngerman}°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
(\emph{p\textless{}} 0.001), however a considerable amount of variation
remained unexplained (Adjusted R\textsuperscript{2} \textless{}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 \emph{p\textless{}} 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 \emph{p\textless{}} 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 \textasciitilde{} (1
\textbar{} PC.blocks) + Latitude, applying Satterthwaite's method.
F(1,7.23)~= 8.87,~p \textgreater{}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.

\emph{GWA mapping reveals loci associated with natural circadian
variation}

We used the online GWA-portal to perform association mapping on the
three circadian traits found to be significantly affected by genetic
variation (Seren, 2018). We used an Accelerated Mixed Model (AMM) to
account for population structure, although the other association models
(linear and non-parametric) gave similar results (See Supplementary
Figure 6). Pseudo-heritability estimates for each trait were given as an
output of the GWA analysis: period=71\%, RAE=37\% and phase=13\%.

GWA identified multiple genomic regions associated with period, phase
and RAE as shown in Figure 3. We used --log10(\emph{p} ) 6.5 as an
arbitrary p-score cut-off to select SNPs for further investigation. This
threshold is conservative compared to several other previously published
studies in \emph{Arabidopsis} (Kooke et al., 2016; Proietti et al.,
2018; van Rooijen et al., 2015). We investigated known core circadian
and flowering time genes to see whether these were significantly
associated with any of the traits measured. Other than \emph{ELF3} (see
below), none of these genes fell within the window of association for
significant SNPs. Genes in regions 30kb upstream or downstream of the
most significant SNPs were considered as potential gene candidates and
were selected for further analysis based on their previously attributed
functionality and designated GO term. Genes involved in circadian
rhythms, flowering time or chloroplast regulation were given prevalence
(details in Supplementary File 2). The most significant associations had
three SNPs with a --log10(\emph{p} ) score of 10.4-11.5, found on
chromosome 4 associated with period variation. Within this interval we
identified a non-synonymous SNP in the gene \emph{COLD-REGULATED GENE
28,} a gene that has previously been identified as a negative regulator
of several core clock genes (\emph{PRR7, TOC1, PRR5} and \emph{ELF4} )
and is also implicated in the trade-off between flowering-time and
freezing tolerance (X. Li et al., 2016; Wang et al., 2017). The
substitution resulted in a tryptophan (W) to serine (S) amino acid
change at position 58 within the second exon of \emph{COR28} (Figure
4A). This had a SIFT score of 0 indicating a highly probable deleterious
effect on protein function. 16 accessions in this study had the minor
allele, all found in the South of Sweden. The 58S accessions had a
period 1.29h longer than the 58W accessions
(t(17.4)=-7.46,\emph{p\textless{}} 0.001, Welch Two Sample t-test) (see
Figure 4B) and mostly belonged to the genomic sub-group PC.A. The
long-period of cluster PC.C therefore cannot be explained by variation
in this SNP. We used the online tool Polymorph 1001 to look for other
variants with the serine substitution and found only 5 other variants
not assayed in this study (making 21 in total), all of which were also
from the South of Sweden (Figure 4C).

We used flowering time data by Li et al. 2010 (Y. Li et al., 2010) and
Sasaki et al. 2016 (Sasaki et al., 2015) to look for flowering time
differences between the accessions carrying the \emph{COR28} -58W major
allele and the -58S minor allele (see Table 1). We found that the
accessions with \emph{COR28} -58S had significantly extended flowering
times under simulated seasons for Sweden and Spain (Li) and under a 10°C
and 16°C long-day temperature regime (Sasaki) (see Figure 4D). This
complements previous findings that \emph{COR28} is a flowering promoter
and that modifications to this gene increase flowering time as well as
lengthening period.

{[}Table 1{]}

Other significant SNPs associated with period (chromosome 3) and phase
(chromosome 1) identified gene candidates involved in chloroplast
function (\emph{PARC6, SCO2, DNF, AT1G71015} and \emph{FAB1C} ). The DF
circadian output we used to assay these traits is based on oscillating
activation phases of PSII (Goltsev V, Zaharieva I, Chernev P, 2009;
Jursinic, 1986) and therefore natural variation in genes regulating
chloroplast functionally could also affect the DF output in these
candidates.

A RAE associated SNP on chromosome 2 was found 5726bp upstream from a
SNP in \emph{ELF3} previously characterized as the
\emph{ELF3-Sha}allele. Our associated SNP was found to be under strong
linkage disequilibrium with the \emph{ELF3-Sha} allele
(R\textsuperscript{2}=0.86). The alanine-to-valine transition in amino
acid position 362 has been associated with naturally occurring
alterations to periodicity and robustness in accessions from Central
Asia, specifically Tajikistan (Anwer et al., 2014). Here, 13 Swedish
accessions were shown to carry the \emph{ELF-Sha} allele and had mean
RAE ratios 0.032 higher on average than for the other accessions. We
substantiate evidence that \emph{ELF3-Sha} accessions have lower
rhythmicity and extend the global range of this allele into Northern
Sweden. A surprising kinship between \emph{Arabidopsis} accessions from
Northern Sweden and Central Asia has been previously demonstrated
through analysis of global population structure and indicates that the
presence of this allele has not evolved convergently between the two
populations (Nordborg et al., 2005).

\emph{Validation of COR28 effects using knockout lines and
co-segregation analysis}

11 mutant lines representing eight gene candidates were genotyped to
confirm homozygous mutations before being bulked for seed. Confirmed
mutants were then assayed for their circadian rhythms using DF under the
same conditions as for the 191 accession screen. Mutant details and
genotyping results can be viewed in Supplementary File 2.
\emph{Cor27}mutants and the double mutant \emph{cor27-1/28-2} were also
assayed as\emph{cor27} is known to be partially redundant with
\emph{cor28} . Analysis of the DF rhythms confirmed that periods in
\emph{cor28}mutants (\emph{cor28-1} =25.3h, SD=1.73; \emph{cor28-2}
=25.3h, SD=1.78) were significantly longer than their respective
controls (23.9h, SD=1.62 and 24.0h SD=2.02) (Welch Two Sample t-test
\emph{p\textless{}} 0.05) as shown in Table 2. This confirms results
previously reported using leaf movement, qPCR and ProCCR2:LUC
bioluminescence rhythms (X. Li et al., 2016; Wang et al., 2017).
\emph{Cor27} mutants showed no significant difference in period length,
however the double knock-out\emph{cor27-1/28-2} had an exaggerated long
period phenotype (26.4h, SD=2.37). The peak phases of \emph{cor27} ,
\emph{cor28} and especially\emph{cor27-1/28-2} were also earlier than
for Col-0, with\emph{cor27-1/28-2} having a mean phase peak 2.6h earlier
than the control. Other mutants characterized were not found to have
significantly altered periods or phases compared to their WT controls
(Supplementary Tables 12-14).

{[}Table 2{]}

Finally, we carried out a co-segregation analysis for the
\emph{COR28}58S allele to demonstrate that this SNP was causal for the
long period phenotype. Crosses were made between the line T800 (Watkins
6133) and St-0 (Watkins 8387), with T800 carrying the 58S minor allele
and St-0 carrying the 58W major allele. Parent individuals were
genotyped using a CAPs assay prior to crossing and were found to be
homozygous for their respective SNPs. We selfed three F1 individuals
from this cross and analyzed the genotypes and circadian period
phenotypes in the F2 offspring using DF on dissected leaves (Table 3.)
Restriction digest gels from F2 genotyping can be viewed in
Supplementary File 6. F2 individuals carrying the 58S SNP had a mean
period length of 23.7h (SD=1.95) compared to a mean of 21.3h (SD=1.65)
for those carrying the 58W SNP (a difference of 2.4h) which was shown to
be highly significant (F( 2,62)=12.27, p\textless{}0.0001, One-way
ANOVA. TukeyHSD for SNP 58S and 58W: p\textless{}0.001). Heterozygous
individuals had period lengths which were on average closer to the long
period phenotypes of the 58S homozygote individuals (23.1h (SD=1.95))
suggesting a semi-dominant effect for the 58S allele.

{[}Table 3{]}

\emph{The phenotypic tails of circadian traits remain largely diverged
under lower temperatures}

The variation characterized in the first part of this paper was observed
at 22°C which was chosen to make the data comparable to several
previously published studies of interest. We wanted to test whether the
circadian phenotypic diversity we observed at 22°C continued at
temperatures of 16°C and 10°C; closer to those found in the natural
Swedish environment. To simplify the dataset, we selected 10 accessions
to represent each of the six phenotypic tails as highlighted in Figure
1. We wished to investigate whether reduced temperatures would affect
the phenotypic tails equally, drive them further apart or lead to a
convergence of their phenotypes. Our null hypothesis was that the
phenotypic diversity seen at 22\selectlanguage{english} would exist consistently at lower
temperatures with no differential effect on the phenotypic tail groups.

The results show that decreasing temperature had a massive overall
effect on all three circadian outputs, particularly RAE and phase
(accession means can be viewed in Supplementary File 3). Overall, the
divergence between the tail groups was largely maintained, although the
gap reduced at 10degC (Figure 5a-c). For each trait, a general linear
model was fitted to the data in order to test the significance of
explanatory factors (Supplementary Tables 15-17).

For period, membership to the short or long tail groups was the largest
explanatory variable and the group means remained clearly distinct
across all temperatures. Temperature also had a large overall effect on
period, with rhythms at 16degC running much slower than at 22degC. At 10degC
periods were again shorter, accompanied by higher RAE (reduced rhythm
robustness) (Supplementary Figure 7). The difference in the period
temperature responses of the two groups can be seen by the gradients of
the thick colored lines in Figure 5A. There was also significant
variation between the temperature response of individual accessions
within each tail group, especially in the long period group (see thin
grey lines in Figure 5A). Interestingly, Col-0 reacted very differently
to the Swedish accessions tested, showing an almost linear decrease in
period with increasing temperature (dashed line in 5A).

For RAE, which we equate to rhythm robustness, we found that temperature
had an even greater effect than for period, with rhythms at 10\selectlanguage{english}?C showing
a marked decrease in robustness (Figure 5B). The tails converge as the
temperature decreases, with lower temperatures having an especially
large effect on the low RAE group.

For phase, decreasing temperature to 10degC caused a large shift of
approximately 7.4 hours towards dawn accompanied by increased
variability for each accession (Figure 5C). The means of the two
phase-tail groups remained distinct across the three temperatures and
there was no significant difference in their relative change of phase
with temperature.

We also conducted an independent experiment with the phase tail
accessions to verify the phase estimates from the two seed batches
measured at 22degC (Supplementary Figure 9).

Across all traits we observed an increase in the within-accession
variability at lower temperatures indicated by larger standard
deviations in period, higher RAE scores and a greater number of rhythms
being rejected from Biodare2 analysis as arrhythmic.

Discussion

We measured DF rhythms in 191 naturally occurring
Swedish\emph{Arabidopsis} accessions and show that circadian phenotypes
display considerable variation. This variation does not conform to
previously described latitudinal clines in \emph{Arabidopsis} (Michael
et al., 2003), as we find the longest periods in accessions from the
South of the country and the shortest periods in the North. In our
study, circadian phenotypes are also clustered into geographical groups
rather than following a distinct latitudinal cline. We suggest that the
period variation we observe in the Swedish population is due largely to
founder effects from ancestral migrations of individuals adapted to
different selection pressures (Flohr et al., 2013), and may be
influenced by serendipitous fixing of alleles through genetic drift
(Song et al., 2006).

We found a high level of correlation between circadian period, phase and
RAE in these accessions; period was negatively correlated with both RAE
and phase. Interestingly, in the study by Michael et al, period, phase
and amplitude were reported to vary independently for leaf movement
rhythms in a global panel (Michael et al., 2003).

Periods were found to be significantly different between two
sub-populations co-existing in the same geographical area in the South.
In spite of this, accessions with very long periods (belonging to the
long-period phenotypic tail) were found in both of these populations and
the minor \emph{COR28} SNP was also found in both the PC.A and PC.C
sub-groups.

We show that circadian variation assayed by DF is genetically heritable
and is associated with several highly significant polymorphisms, two of
which (\emph{ELF3} and \emph{COR28} ) had previously acknowledged
circadian functions. \emph{COR28} is partially redundant with its
partner \emph{COR27} and acts both upstream and downstream of the
circadian clock (X. Li et al., 2016; Wang et al., 2017). TDNA insertions
in these genes have been shown to lengthen periods, extend flowering
time and increase freezing tolerance (X. Li et al., 2016; Wang et al.,
2017). Natural allelic diversity in \emph{COR28} has not previously been
described. Here, we show that a set of 16 naturally occurring accessions
from southern Sweden have a W58S amino acid substitution which results
in a long period comparable to that seen in \emph{cor28} TDNA insert
mutants. \emph{COR28} and \emph{COR27} are expressed in a blue-light and
temperature dependent manner, plausibly suggesting why this gene could
be under selection in the Swedish environment. The mechanism through
which W58S affects the function of COR28 remains unclear. COR28 is a
small peptide of \textasciitilde{}26kDa and so may not require active
transport for nuclear localization (Hicks GR, 2013). No DNA binding
domains have been identified in the COR28 sequence, however it has been
suggested to regulate \emph{TOC1} and \emph{PRR5} transcription through
the formation of protein complexes (X. Li et al., 2016). It is possible
that this modification affects the ability of COR28 to form these
protein interactions.

We also identified ELF-sha alleles in Northern accessions which were
associated with higher RAE ratios. In barley, a mutant ortholog
of\emph{ELF3} ; \emph{eam8} was shown to have been positively selected
for growth in high-latitude environments, particularly in Scandinavia
(Zakhrabekova et al., 2012). \emph{eam8} cultivars are rapid flowering
enabling survival under short growing seasons, but also have severally
attenuated circadian function (Faure et al., 2012).

Finally, we investigated the effect of temperature on natural circadian
variation between accessions with divergent circadian phenotypes.
Temperature had a large effect on period and an even greater one on
phase and RAE means in both tail groups, indicating a low level of
temperature compensation for these outputs. This also shows that the
forces governing compensation for period do not act equally on
maintaining constant RAE or peak phase. Interestingly, rhythms appeared
to be most robust at 22degC, which might not be expected given that the
warmest months in Sweden average around 15-17degC. A similar loss of
rhythm robustness at lower temperatures has been observed in wheat (Rees
et al., 2019).

Period had a non-linear relationship with temperature in these
accessions, lengthening from 22degC to 16degC, before shortening again at
10C. This arrow shaped profile likely reflects two interacting forces at
work; 1) between 22degC and 27degC the acceleration of rhythms due to
increased rate kinetics and 2) between 22degC and 10degC the balancing
forces of circadian temperature compensation. Gould et al. found a
similar effect of temperature on period using leaf-movement rhythms at
22degC, 17degC and 12degC, and showed that the cold temperature compensation
response works through an independent mechanism to the hot temperature
compensation response (Gould et al., 2006). The profile suggests that
temperature compensation is biased towards correction at colder
temperatures in these accessions. It is possible that adaptation to a
cold climate has selected for a cold compensation response to overcome
excessive deceleration of the clock, although we are unable to explain
why the rhythms should be even shorter at 10degC than at 16degC or why the
shortening of periods are accompanied by a loss of overall rhythmicity.
Although divergence between the phenotypic tails decreased at lower
temperatures, the groups remained largely separate, reconfirming that
these tail phenotypes are due to heritable genotypic differences. This
work demonstrates the utility of using DF imaging to analyse natural
variation across genetically diverse populations.

Authors' contributions:

This project was conceptualized by AH and HR . HR designed and conducted
imaging experiments and mutant screening. JKMB and HR carried out
statistical analysis and data processing. The GWA analysis was done by
RJ and HR. All authors contributed to interpretation of results. The
paper was written by HR with assistance from RJ, JKMB and AH. All
authors approved the final manuscript.

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\begin{longtable}[]{@{}lllllllll@{}}
\toprule
\begin{minipage}[b]{0.08\columnwidth}\raggedright\strut
Datasets\strut
\end{minipage} & \begin{minipage}[b]{0.08\columnwidth}\raggedright\strut
Datasets\strut
\end{minipage} & \begin{minipage}[b]{0.08\columnwidth}\raggedright\strut
Datasets\strut
\end{minipage} & \begin{minipage}[b]{0.08\columnwidth}\raggedright\strut
Datasets\strut
\end{minipage} & \begin{minipage}[b]{0.08\columnwidth}\raggedright\strut
Days to flower\strut
\end{minipage} & \begin{minipage}[b]{0.08\columnwidth}\raggedright\strut
Days to flower\strut
\end{minipage} & \begin{minipage}[b]{0.08\columnwidth}\raggedright\strut
Welch Two sample t-test\strut
\end{minipage} & \begin{minipage}[b]{0.08\columnwidth}\raggedright\strut
Welch Two sample t-test\strut
\end{minipage} & \begin{minipage}[b]{0.08\columnwidth}\raggedright\strut
Welch Two sample t-test\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
N for 58W\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
N for 58S\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Mean 58W\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Mean 58S\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
df\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
t\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{p}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
Li (49)\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
Sweden Average\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
470\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
10\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
54.43\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
143.66\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
10.8\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
16.46\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
\textless{}0.001\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Spain Average\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
64.78\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
122.67\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
9.5\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
9.70\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\textless{}0.001\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
Sasaki (47)\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
10\selectlanguage{ngerman}°C\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
1145\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
18\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
83.24\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
100.92\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
17.8\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
4.93\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
\textless{}0.001\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
16\selectlanguage{ngerman}°C\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
73.97\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
94.91\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
17.6\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
5.75\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\textless{}0.001\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 1. Longer flowering time correlation with \emph{COR28}W58S
Allele.}

N=Number of accessions used for each \emph{COR28} allele from a global
population. Flowering data means reflect an average of all replicates
within each study by Li et al. 2010 or Sasaki et al. 2016.

\emph{}\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
\textbf{Plant ID} & \textbf{N} & \textbf{Period mean (hours)} &
\textbf{Period SD} & \textbf{RAE mean} & \textbf{RAE SD} & \textbf{Phase
circular mean (hours)} & \textbf{Phase SD}\tabularnewline
\midrule
\endhead
col-0 & 180 & 24 & 2.02 & 0.42 & 0.18 & 15.35 & 1.04\tabularnewline
cor27-1 & 108 & 24 & 1.75 & 0.34 & 0.18 & 13.39 & 0.77\tabularnewline
cor27-2 & 107 & 24.2 & 2.21 & 0.29 & 0.14 & 14.02 & 0.83\tabularnewline
cor28-2 & 158 & 25.3 & 1.78 & 0.28 & 0.13 & 12.93 & 0.91\tabularnewline
cor28-2/27-1 & 101 & 26.5 & 2.33 & 0.28 & 0.14 & 12.67 &
0.98\tabularnewline
mybl2 & 103 & 24 & 1.97 & 0.37 & 0.17 & 14.27 & 0.93\tabularnewline
parc6 & 97 & 23.9 & 2.08 & 0.37 & 0.16 & 13.91 & 0.97\tabularnewline
parc6-1 & 104 & 23.4 & 1.77 & 0.35 & 0.17 & 14.95 & 0.87\tabularnewline
atg1g71015 & 102 & 23.7 & 2.11 & 0.38 & 0.16 & 14.45 &
0.92\tabularnewline
WT for cor28-1 & 72 & 23.9 & 1.62 & 0.33 & 0.17 & 14.54 &
0.79\tabularnewline
cor28-1 & 70 & 25.3 & 1.73 & 0.28 & 0.16 & 12.95 & 0.86\tabularnewline
ler & 108 & 23.2 & 2.55 & 0.43 & 0.17 & 16.37 & 1.04\tabularnewline
sco2 & 101 & 23.7 & 1.95 & 0.51 & 0.16 & 15.33 & 1.00\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2. Mean results from DF screening of GWA candidate mutant
lines.}

Mutants are listed under their respective WT controls highlighted in
grey. Circular means for phase were calculated using the `circular'
package in R.\selectlanguage{english}
\begin{longtable}[]{@{}llllllllllll@{}}
\toprule
\begin{minipage}[b]{0.06\columnwidth}\raggedright\strut
\emph{}\strut
\end{minipage} & \begin{minipage}[b]{0.06\columnwidth}\raggedright\strut
Genotyping\strut
\end{minipage} & \begin{minipage}[b]{0.06\columnwidth}\raggedright\strut
Genotyping\strut
\end{minipage} & \begin{minipage}[b]{0.06\columnwidth}\raggedright\strut
Genotyping\strut
\end{minipage} & \begin{minipage}[b]{0.06\columnwidth}\raggedright\strut
Genotyping\strut
\end{minipage} & \begin{minipage}[b]{0.06\columnwidth}\raggedright\strut
Genotyping\strut
\end{minipage} & \begin{minipage}[b]{0.06\columnwidth}\raggedright\strut
Genotyping\strut
\end{minipage} & \begin{minipage}[b]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.06\columnwidth}\raggedright\strut
Phenotyping\strut
\end{minipage} & \begin{minipage}[b]{0.06\columnwidth}\raggedright\strut
Phenotyping\strut
\end{minipage} & \begin{minipage}[b]{0.06\columnwidth}\raggedright\strut
Phenotyping\strut
\end{minipage} & \begin{minipage}[b]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
F1 ID\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{Genotype}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{N plants genotyped}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{Expected Ratio of genotypes}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{df}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
X\textsuperscript{2}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\emph{p}\textbf{-value}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{Mean Period (h)}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{SD Period}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
F-value\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\emph{p}\textbf{-value}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\emph{p}\textbf{-value}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{T1A1}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{HZ}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
9\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
10.5\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.43\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.81\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
22.09\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1.139\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
26.55\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\textless{}0.00001 ***\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\textless{}0.00001 ***\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\emph{HM 58S}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
6\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
5.25\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
24.38\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.902\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\emph{HM 58W}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
6\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
5.25\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
20.50\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.469\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\emph{T1A2}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\emph{HZ}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
17\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
13\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
3.15\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.20\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
23.75\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
1.92\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
1.826\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.184\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.184\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\emph{HM 58S}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
3\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
6.5\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
22.51\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
3.33\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\emph{HM 58W}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
6\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
6.5\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
21.96\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
1.92\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{T1A3}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\emph{HZ}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
14\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
13.5\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
3.67\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.16\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
23.05\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1.043\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
4.08\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.0299 *\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.0299 *\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\emph{HM 58S}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
3\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
6.75\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
23.58\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
2.050\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\emph{HM 58W}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
10\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
6.75\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
21.47\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
1.856\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 3. Results from co-segregation analysis of the\emph{COR28}
SNP 58S with long period.}

Three heterozygous F1 plants were selfed to produce F2 offspring broadly
following a Mendelain segregation ratio of 2:1:1 tested using a
Chi-Square test. F2 individuals were phenotyped using DF imaging on
detached leaves and were analysed within each F1 group using a One-way
ANOVA (analysis across all F1 individuals presented in main text).



\textbf{Figure 1- Circadian variation in 191 naturally occurring
accessions from Sweden}

Delayed fluorescence rhythms were characterised by period, phase and RAE
and show significant variation (A-C). Colours represent the 10 most
extreme accessions for each phenotype which we use as tail groups for
further analysis. Some accessions belong to multiple tail groups as is
shown in D. This reflects the strong correlation between circadian
characteristics (See Supplementary Figure 4). Number of individual wells
contributing to the mean of each accession ranged from 4 to 18, with
each well representing the mean rhythms of approximately 15 seedlings.

\textbf{Figure 2- Period phenotypes are linked by both the geography and
the genetic relatedness} \textbf{of accessions across Sweden}

Figure A shows the location of origin of the 10 accessions with the
shortest (red triangles) and longest (blue triangles) periods. The
longest periods are found near the city of Malmo where day length and
temperature fluctuates less throughout the year than in Ornskoldsvik
where periods tend to be shorter (B,C). Day-length and temperature
averages were downloaded from timeanddate.com and are based on
predictions for 2019 (Thorsen, 1995).

PC analysis revealed a sub-clade within the Southern Swedish accessions;
PC.C coloured light blue in plots D-F. This was used to distinguish
accessions with significantly longer period phenotype than in the other
PC groups; PC.A which represents the remaining southern accessions in
yellow and PC.B which represents the northern accessions in red. Map
figures were created using the ggmaps package in R using Google maps
(accessed 2018) (Kahle \& Wickham, 2013).

\textbf{Figure 3- GWA analysis reveals candidate genes underlying
natural variation in DF circadian phenotypes}

Manhattan plots show AMM derived SNP associations with period (top
panel), phase (middle panel) and RAE (bottom panel). Data shown has
\textgreater{}5\% MAF and reflects the 10,000 highest --log10(p) values.
Gene candidates selected for further analysis are labelled in red with
arrows. The red dotted line indicates the 6.5 --log10(p) arbitrary
threshold used for this study. Vertical grey lines are labelled under
the x axis and represent positions of known circadian and flowering time
genes with altered circadian phenotypes previously reported in mutants.

\textbf{Figure 4- Natural Variation in \emph{COR28} is associated with
covariation in period and flowering time}

The SNP in \emph{COR28} associated with period variation causes a
tryptophan (W) to serine (S) amino acid change within the second exon
(A). Accessions carrying the 58S allele had periods 1.29h longer on
average than those with the 58W allele based on the 191-accession data
from this study (B). Out of the 191 accessions originally phenotyped, 16
had the 58S allele (red triangular points). Globally, 5 further 58S
accessions were identified (orange triangles) and all were found in the
south of Sweden (C). Accessions carrying the 58S allele also had
flowering times delayed by 89.23 days compared to those carrying the 58W
allele (data shown is based on flowering times from plants grown under
Swedish conditions previously published in Li et al. 2010 (D).

\textbf{Figure 5- Effects of temperature on circadian rhythms in
phenotypic tails}

We selected 10 accessions to represent the phenotypic tails of each
trait as highlighted in Figure 1. Period, phase and RAE in these
accessions were then re-quantified at 10\selectlanguage{ngerman}°C, 16°C and 22°C to see if the
tails remained diverged at lower temperatures. In Figure A, light grey
lines show the mean periods of accessions in the long period tail group
with the thick blue line showing the mean period for the whole group.
Black lines show the mean periods for accessions in the short period
group with the thick red line reflecting the mean for the whole short
period group. In Figure B, light grey lines show the mean periods of
accessions with high RAE (low robustness) with the thick maroon line
indicating the overall high RAE tail mean. The black lines show the mean
periods for accessions with low RAE (high robustness). The green line
shows the overall low RAE tail mean. In both Figure A and B the dotted
line is the mean for Col-0 at each temperature.

Figure C shows individual phase estimates from accessions belonging to
the dusk-phased (dark filled point) or dawn-phased (open point) tail
groups plotted as clock plots relative to 24/0 representing dawn. Tail
group phase means are indicated by coloured arrows (dusk tail in orange
and dawn tail in purple). Sample size contributing to accession mean at
each temperature ranged from 14 to 24.)\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-1/Figure-1}

\end{center}
\end{figure}\selectlanguage{english}
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\includegraphics[width=0.70\columnwidth]{figures/Figure-2/Figure-2}

\end{center}
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\includegraphics[width=0.70\columnwidth]{figures/Figure-3/Figure-3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-4-new/Figure-4-new}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-5/Figure-5}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

