Although multiple environmental cues regulate the transition to flowering inArabidopsis thaliana, previous studies have suggested that wildA. thaliana accessions fall primarily into two classes, distinguished by their requirement for vernalization (extended winter-like temperatures), which enables rapid flowering under long days. Much of the difference in vernalization response is apparently due to variation at two epistatically acting loci,FRI andFLC. We present the response of over 150 wild accessions to three different environmental variables. In long days,FLC is among those genes whose expression is most highly correlated with flowering. In short days,FRI andFLC are less important, although their contribution is still significant. In addition, there is considerable variation not only in vernalization response, but also in the response to differences in day length or ambient growth temperature. The identification of accessions that flower relatively early or late in specific environments suggests that many of the flowering-time pathways identified by mutagenesis, such as those that respond to day length, contribute to flowering-time variation in the wild. In contrast to differences in vernalization requirement, which are mainly mediated byFRI andFLC, it seems that variation in these other pathways is due to allelic effects at several different loci.

Extensive Variation in Flowering Responses ofA. thaliana Accessions

We analyzed flowering time in a random set of accessions of single-seed descent, reflecting much of the geographic diversity represented in the European Arabidopsis Stock Centre (http://www.arabidopsis.info) at the time (Dataset S1;Table S1;Figure S1). We measured several traits, including days to flowering (DTF) and total leaf number (TLN), which were highly correlated under different conditions (r² = 0.6 to 0.9), with environment-dependent variation in growth rate found in only a few accessions. Heritability of TLN was higher than that of DTF (Table S2). We studied the effects of vernalization, ambient growth temperature, and photoperiod, using four different environments: 23 °C long days (23LD), 16 °C long days (16LD), 16 °C long days after 5 wk vernalization (16LDV), and 23 °C short days (23SD). DTF and TLN data from 155 wild strains, along with the common laboratory strains Col-0 and Ler and 32 flowering time mutants in Col-0 or Ler backgrounds, passed our quality controls in at least two conditions. A complete dataset across all environments was obtained from 177 strains.

In general, the distribution of flowering times reflected the action of genetic pathways known from the analysis of laboratory strains (Figure 1). In these, both long days and elevated temperatures accelerate flowering, and the wild accessions flowered on average earlier in 23LD compared to 23SD and 16LD. Similarly, a large tail of late-flowering accessions seen in 16LD largely disappeared upon vernalization. In contrast to the prominent peak of early-flowering accessions observed in 23LD (Figure 1A), flowering time in 23SD was more evenly distributed (Figure 1D).

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Figure 1. Distribution of Flowering Times among Wild Accessions

(A–D) Distribution of flowering times expressed as TLN in23LD, 16LD, 16LDV, and 23SD. White and black bars represent accessions with and without Col- or Ler-type deletions inFRI, respectively. Gray bars indicate strains subsequently identified to have defects inFRI orFLC.

(E) Mean flowering times of putativelyFRI/FLC functional (black bars) and nonfunctional (white bars) accessions in four environments.

https://doi.org/10.1371/journal.pgen.0010006.g001

Having found substantial variation in the flowering time of accessions, we asked how similar the accessions were in their responses to environmental cues. The degree of genetic correlation between different environments indicated that the largest differences were in the response to vernalization, and the smallest in the response to ambient temperature (Table 1). Genotype-by-environment interactions accounted for 27% of flowering-time variation in response to vernalization (16LDV vs. 16LD); 9% in response to photoperiod (23LD vs. 23SD); and 3% in response to ambient temperature (23LD vs. 16LD), all of which were significant atp < 0.0001. The variation in sensitivity to different environmental factors was obvious when flowering times were regressed on the environmental mean (Figure 2) [17]. Accessions also differed extensively in other traits related to flowering, such as juvenile, adult, and cauline leaf number, indicating further differences in developmental physiology (seeTable S1). Taken together, these results demonstrate that there is extensive variation in the responses of accessions to several environmental cues.

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Table 1.

Genetic Correlation across Environments

https://doi.org/10.1371/journal.pgen.0010006.t001

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Figure 2. Environmental Responses Plotted against TLN

Environmental responses were calculated from regression of TLN onto environmental means. (A) 23LD vs. 16LD; (B) 16LD vs. 16LDV; (C) 23LD vs. 23 SD; 95% of all values are between the gray dotted lines. The means are not centered because the responses are not normally distributed.

https://doi.org/10.1371/journal.pgen.0010006.g002

Co-variation between environmental factors and a particular trait can be evidence for selection. Environmental factors such as light and temperature, in turn, are strongly correlated with latitude, and latitudinal clines for different phenotypes are not uncommon [18]. Previously, a strong correlation between flowering time and latitude was demonstrated for accessions that were sown in fall and overwintered in a common garden in Rhode Island. Among strains with apparently functionalFRI/FLC (see below), this correlation was particularly high (r² = 0.38,n = 21) [10].

In our set of accessions, we found the strongest correlation between latitude and flowering (TLN) in 16LDV (Figure 3A;r² = 0.18,p < 0.0001), suggesting that the latitude correlation in the aforementioned common garden experiment is dependent on vernalization during natural winter conditions. This correlation is even higher in the group of accessions that was shared between our study and that of Stinchcombe and colleagues [10], namelyr² = 0.38,n = 9. Thus, the differences in the strength of the latitudinal cline that we and Stinchcombe and colleagues [10] observed might be due to different sampling biases.

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Figure 3. A Latitudinal Cline in Flowering after Vernalization

Correlation of latitude with (A) flowering time, (B) and vernalization sensitivity (expressed as the regression coefficient of TLN on 16LD and 16LDV grand means). Both accessions with putatively functional alleles (black dots) and nonfunctional alleles (grey dots) atFRI/FLC are shown. Regression line with onlyFRI/FLC functional accessions (black line) and with all accessions (grey line) is shown separately.FRI/FLC functional accessions are indicated by black dots,FRI/FLC nonfunctional accessions by hollow circles. The correlation of flowering inFRI/FLC functional accessions with latitude (black line;r² = 0.18) is higher than of all accessions (grey line;r² = 0.13).

https://doi.org/10.1371/journal.pgen.0010006.g003

Consistent with flowering time being correlated with the magnitude of the vernalization response, there is also a significant latitudinal cline in the vernalization sensitivity of accessions (Figure 3B;r² = 0.12,p < 0.0001). Because there was very little correlation between latitude and longitude for the origin of strains (r² = 0.035), we did not include longitude as a covariant in our analyses. Although population structure may be a confounding factor in evaluating latitudinal clines, this concern is somewhat mitigated by the relatively small amount of population structure inA. thaliana [19,20].

The Contribution ofFLC Activity to Flowering-Time Variation

In both laboratory-induced mutant strains and natural accessions, there is broad correlation ofFLC expression levels, as determined by RNA blots, and flowering time, such that plants flower late whenFLC levels are high and early whenFLC levels are low [5,6,8,11,21]. However, a recent study of 17 accessions revealed also considerable variation inFLC expression among late-flowering accessions [22]. To evaluate more precisely the relationship between absoluteFLC levels and quantitative variation in flowering time, we analyzedFLC expression by qRT-PCR in 149 accessions grown at 23LD (Figure 4A). Across all strains, variation inFLC levels accounted for 42% and 40% of TLN and DTF variation (r²), respectively (p < 0.0001). As expected, this is largely due toFLC mediating the vernalization response, which can be calculated as the relative reduction in TLN or DTF when 16LDV and 16LD are compared. The correlation ofFLC expression level with vernalization response using TLN and DTF is 47% and 52%, respectively.

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Figure 4. Correlation ofFLC Expression with Flowering Time

(A) Correlation ofFLC RNA levels and DTF in 149 accessions, as measured by qRT-PCR. Hollow circles indicate accessions with lesions inFRI.

(B) Correlation of expression levels of 68 flowering regulators with flowering time. Arrow indicatesFLC.

(C) Correlation of expression estimates (expressed as log2) of 22,500 genes represented on Affymetrix ATH1 array with flowering times across 34 accessions. Arrow indicatesFLC, and line indicates distribution from 1,000 permutations.

https://doi.org/10.1371/journal.pgen.0010006.g004

In other members of the Brassicaceae family, loci controlling flowering time co-localize withFLC orthologs [23], but the relationship between expression of differentFLC paralogs and flowering time appears to be more complex than inA. thaliana [24,25]. Furthermore,FLC expression inBrassica oleracea var.capitata shows differential responses to vernalization, depending on the tissue examined [26].

To determine whether the substantial correlation betweenFLC expression levels and flowering time across 149A. thaliana accessions was unusual compared to other genes known to control flowering, or even to the remainder of the transcriptome in general, we examined Affymetrix ATH1 array data for 34 accessions, which had been generated as part of the AtGenExpress project (Table S3). The correlation ofFLC with TLN and DTF at 23LD that we obtained using array analyses was 37% and 42%, respectively, which is quite similar to the 42% and 40% estimated from qRT-PCR analyses. We found that, among a set of 68 floral regulators,FLC was the one most highly associated with either TLN or DTF (Figure 4B;Table S4). In addition, when examining other conditions, we found none of the other floral regulators were as highly correlated with flowering time asFLC was for 23LD.

When we considered all genes represented on the ATH1 array, we foundFLC to be among the ten most highly correlated genes at 23LD (both positively and negatively correlated) (Figure 4C). This was true regardless of whether expression was regressed linearly or logarithmically onto flowering time, and whether Pearson or Spearman rank correlation was calculated. With respect to DTF,FLC levels were considerably more correlated with this trait than expression levels of any other gene. We determined significance by permutation analysis, and found thatFLC was the only gene significantly correlated with DTF at ap < 0.05 level under both Pearson and Spearman rank correlation. The prominence ofFLC, whose levels are highly correlated with vernalization response, may reflect the fact that this response is the most important variable affecting flowering time inA. thaliana accessions. We note, however, that the analysis was biased, because we analyzed young seedlings, a stage in whichFLC levels are particularly high.

There was a general association of lowFLC levels with the Col- and Ler-type deletions inFRI and early flowering, but this relationship was not absolute. Several exceptions to this rule are explained by other nonfunctionalFRI alleles or variation inFLC itself, as shown by a combination of genetic and molecular analyses (Table 2;Figure 5;Figure S2). Several of the early accessions have missense mutations inFRI that are different from those described before [8,9], whereas one nonfunctionalFRI allele has extensive polymorphisms (Figure 5A). Because the effects ofFRI on flowering time are entirely dependent onFLC [7], we were also curious whether there are any molecular effects ofFRI in the absence ofFLC. To this end, we compared the global expression profile at the shoot apex in response to floral induction by long days betweenFRI flc-3 andfri-Colflc-3 plants, similar to a design used before [27]. We detected no significant differences between the two genotypes (data not shown), confirming thatFRI function absolutely requiresFLC activity.

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Table 2.

Accessions with CompromisedFRI orFLC Activity

https://doi.org/10.1371/journal.pgen.0010006.t002

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Figure 5. Newly Identified Alleles ofFRI andFLC

(A) Diagrams ofFRI alleles. Exons are represented by boxes, introns by lines. Black lines indicate nonsynonymous changes, gray lines synonymous changes as well as polymorphisms in the non-coding region compared to the reference sequence of H51 [4]. Insertions and deletions (indels) are designated by triangles, numbers indicate size in base pairs (bp). Premature stop codons are caused by a 1-bp insertion in Ang-1, single bp polymorphisms in BG1 and Ri-0, or a deletion and inversion (gray box) in Pog-0. Note extensive polymorphisms in Jl-1. BG4, BG6, BG9, and HR5 were only partially sequenced.

(B) Changes inFLC transcripts in three accessions. Dotted lines indicate exons that are missing in part or completely. These deletions cause frame shifts and thus altered amino acid sequences (grey boxes). Premature stop codons are indicated. The reasons for the aberrant transcript processing in Ll-2 are not known. In Cen-0, an alternative splice acceptor site in the last exon is used, leading to a deletion of exon sequences and a frame shift, whereas in Cal-0 an alternative splice acceptor site in the last intron is used, which adds additional sequences and also causes a frame shift of sequences of the last exon.

(C) Large insertions in the first intron ofFLC in several accessions.

https://doi.org/10.1371/journal.pgen.0010006.g005

Although null alleles ofFRI are easily found in natural accessions [4,9], only transposon insertions with reduced expression level of a wild-type transcript have been described forFLC [6,8,11,16], which has led to the proposal that null alleles ofFLC have reduced fitness in the wild [3]. However, we discovered three naturalFLC alleles with severely affected protein function (Figure 5B). At least one of the very early accessions, Ll-2, likely carries a null allele, because transgenic overexpression of the Ll-2FLC cDNA, which lacks exons 2 to 6, in anflc-3 background has no effect on flowering (not shown). These findings demonstrate thatFLC alleles with severely compromised protein function can be recovered from wild populations. We also found several new transposon insertions in the first intron ofFLC (Figure 5C).

Variation in Responses to Photoperiod and Ambient Temperature

In total, our collection of 155 strains included 67 strains that carry either the common Col- or Ler-type lesions inFRI, and at least 23 accessions with alternativeFRI/FLC lesions. A two-way analysis of variance (ANOVA) model showed thatFRI andFLC can account for 63% of variation in 23LD TLN. In contrast,FRI andFLC accounted for only 23% variation in 23SD, consistent with this pathway being most important when other floral inductive stimuli are strong, as in long days. Nevertheless, our finding thatFRI andFLC significantly affect flowering in short days in a large set of wild strains is at variance with previous surveys that did not find a significant effect ofFRI [10,16]. Twenty-four wild strains are in common between the previous studies and our study. A comparison of flowering times indicates limited correlation in these 24 strains (r² = 0.55 for long days, 0.35 for short days). Consistent with the differences in observed flowering time,FRI andFLC are significant factors in our long day and short day conditions in this set of 24 strains, indicating that these discrepancies are not due to sampling bias, but different experimental conditions. Our results are consistent with earlier studies usingFRI/FLC introgressed laboratory strains, which also displayed late flowering in short days [28,29]. In addition, we did not limit our assessment ofFRI andFLC activity to genotyping of major, previously known alleles.

It has been suggested that a disadvantage of growth chamber experiments compared to common garden experiments is an increased contribution of random environmental variation, which may obscure ecologically relevant responses [10,30]. We believe that we have been able to limit random environmental variation with very accurate growth facilities along with a completely randomized experimental design (seeMaterials and Methods).

Consistent withFRI andFLC mediating most of the vernalization response, there is no significant difference between groups with and withoutFRI/FLC activity in 16LDV, suggesting that our vernalization treatment is largely saturating (seeFigure 1E). However, although vernalization strongly reduces late flowering, it does not abolish variation in flowering time, which still varies more than 4-fold (seeFigure 1C).

One of our long-term goals is to identify genes and pathways that contribute to vernalization-independent variation in flowering time of natural accessions. To identify candidate accessions with interesting variation in flowering responses, we applied hierarchical clustering, which has been used before to group accessions according to light sensitivity during seedling development [31] (Figures 6A–6C,S3–S5). Potentially interesting accessions were also identified as those having large residuals when comparing contrasting conditions, such as 23LD versus 16LD, 23LD versus 23SD, and 16LD versus 16LDV. These specific comparisons yielded similar results to hierarchical clustering (Figure 6D–6G).

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Figure 6. Variation in Responses to Environmental Cues Other than Vernalization

(A–C) Hierarchical clustering identifies groups with different flowering behaviors (seeFigure S3 for entire cluster diagram). Green and red indicate earlier and later flowering than the mean, respectively. Gray indicates missing data. Conditions are, from left to right: 16LD, 23LD, 16LDV, 23SD; with six columns each (DTF, juvenile rosette leaf number, adult rosette leaf number, rosette leaf number, cauline leaf number, TLN).

(A) Accessions that cluster with photoperiodic mutants.

(B) Accessions that cluster with mutants of the autonomous pathway.

(C) Accessions that flower late in 16LD.

(D–G) Comparison of specific accessions with laboratory strains Col-0 and Ler.

(D) Accessions that are temperature-insensitive and not delayed in 16LD (black) compared to 23LD (white bars).

(E) Accessions that do not respond to vernalization and flower similarly in 16LD (black) and 16LDV (white).

(F) Accessions that flower late in long days (23LD, black), but not short days (23SD, white). For comparison, the photoperiodicco-1 mutant is included.

(G) Accessions that flower early in short days (23SD, white) compared to long days (23LD, black).

https://doi.org/10.1371/journal.pgen.0010006.g006

To associate accessions with variation in specific genetic pathways, the cluster analysis included a large number of known flowering-time mutants. The first major distinction was between accessions that flowered either relatively early or late in long days, with the first group comprising accessions that carry lesions atFRI orFLC. The second cluster consisted of late-flowering, vernalization-responsive accessions along with strains that behaved similarly to very late-flowering photoperiodic mutants such asconstans (co) orgigantea (gi) [32,33]. Because all the mutants we examined had been isolated from backgrounds that lackedFRI function, we independently clustered rapid-cycling accessions with laboratory-induced mutants (seeFigure S4), which resulted in several major groups, two of which were similar to photoperiodic mutants in that there was relatively little difference in flowering under long and short days. One was relatively late in long days (Figure 6F), whereas the other was relatively early in short days (Figure 6G). Another group behaved similarly to autonomous pathway mutants and included accessions that were rather late in 16LD and 23LD, but not in 23SD (Figure 6B).

We looked specifically for variation among putativelyFRI/FLC functional accessions, by clustering them together with some of the accessions that we had identified as lackingFRI/FLC activity. This resulted in three major groups, which differ in their vernalization sensitivity (Figure S5). Several accessions from lower latitudes were found in a class that becomes particularly early after vernalization (Figure S5). This observation, although consistent both with our observation of a latitudinal cline in vernalization sensitivity (Figure 5B) and with earlier findings [10], does not imply thatFRI promotes flowering after vernalization, but rather may reflect that other mechanisms, which otherwise delay flowering, are dispensable in this cluster of accessions.

As a first step toward identifying the genetic mechanisms underlying the newly identified flowering behaviors, we examined F₂ populations from crosses of Ei-6 and Fr-2, two strains that flower relatively early in 23SD, to the reference strain Col-0. In both cases, the earliness appeared to be recessive compared to Col-0 (Figure 7A). Consistent with the recessive nature of earliness, neither strain carries the naturally occurring, dominantly actingEDI allele ofCRY2, which can cause early flowering in short days accession [14]. In addition, when we crossed Ei-6 and Fr-2 to each other, the F₁ hybrids were later than either parent, indicating that earliness in short days has a different genetic basis in these strains (Figure 7A). The F₂ progeny from a cross of Fr-2 to Col-0 included a distinct early class, suggesting that early flowering is due to variation at a major locus (Figure 7B). In contrast, in the Ei-6 × Col-0 cross, continuous variation can be seen, indicating the involvement of multiple loci (Figure 7C).

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Figure 7. Genetic Analysis of Two Accessions that Flower Early in 23SD

(A) Flowering time of accessions and F₁ progeny.

(B) A distinct class of early-flowering plants in the Fr 2 × Col-0 F₂ population.

(C) Continuous segregation of flowering times in Ei-6 × Col-0 F₂ population.

https://doi.org/10.1371/journal.pgen.0010006.g007

Conclusions

We provide an extensive dataset on flowering-time associated traits in a large number ofA. thaliana accessions across multiple environments, which forms a valuable resource for both experimental and population genetic studies. We have tested the response to three major environmental variables affecting flowering time: photoperiod, vernalization, as well as ambient growth temperature, whose effects had not been studied before in natural strains. Although our data confirm thatFRI andFLC are indeed major factors regulating the rapid-cycling or winter-annual habit, loss ofFRI andFLC function cannot explain all of the variation observed. Some of the remaining variation may be due to more subtle differences in the activity of the vernalization pathway.

Although there is less variation in the ambient temperature response compared to vernalization and photoperiod, we could identify accessions that responded more strongly or more weakly to temperature than the majority of accessions (seeFigure 6C and6D). The identification of accessions that flower relatively early or late in specific environments suggests that many of the flowering-time pathways identified by mutagenesis, such as those that respond to photoperiod or temperature, participate in generating flowering-time variation in the wild. In contrast to variation in vernalization requirement, which appears to be largely due toFRI andFLC, it seems that variation in these other pathways is due to allelic effects at several different loci, a conclusion that is supported by a growing number of other studies [13,14,22]. The hypothesis of a complex genetic basis of non-vernalization variation is further supported by the finding that, in contrast to vernalization response, none of the known flowering-time regulators are particularly highly correlated with photoperiod or temperature response. It will be interesting to learn which genes control variation in these other pathways.

Dataset S1.Means of Flowering Time Traits in Accessions and Mutant Strains

Data file of flowering-time-related traits in accessions and mutants. Also available as a PDF file (Table S1).

https://doi.org/10.1371/journal.pgen.0010006.sd001

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Figure S1.Geographic Distribution of Wild Strains Used in the Survey

Open circles indicate knownFRI/FLC defects.

https://doi.org/10.1371/journal.pgen.0010006.sg001

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Figure S2.Genetic Analysis of Rapid-Cycling Accessions

Flowering times of F₁ progeny from crosses of accessions that do not carry Ler- or Col-type deletions inFRI tofri-Colflc-3 (white),FRI-Sf2flc-3 (gray),fri-ColFLC (black), andFRI-Sf2FLC (striped).

(A) F₁ progeny in whichFRI causes late flowering.

(B) F₁ progeny in whichFLC causes late flowering.

(C) F₁ progeny in which only simultaneous introduction ofFRI andFLC causes late flowering.

https://doi.org/10.1371/journal.pgen.0010006.sg002

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Figure S3.Hierarchical Cluster Analysis of All Genotypes

Columns for each condition are DTF, JLN, ALN, RLN, CLN, and TLN. Hierarchical clustering identifies groups with different flowering behaviors. Green indicates earlier flowering than the mean, red later flowering than the mean, and gray indicates missing data.

https://doi.org/10.1371/journal.pgen.0010006.sg003

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Figure S4.Hierarchical Clustering of Accessions Lacking FunctionalFRI orFLC

(A) Accessions that are similar to Col.

(B) Accessions that are similar to Ler, with a reduced adult phase at 16LD.

(C) Accessions that are severely delayed in 16LD compared to 23LD.

(D) Accessions that cluster together with mutants of the photoperiodic pathway. This group includes several accessions that flower early in 23SD.

(E) Accessions that cluster with mutants of the autonomous pathway.

https://doi.org/10.1371/journal.pgen.0010006.sg004

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Figure S5.Hierarchical Clustering of Accessions with FunctionalFRI/FLC Pathway

(Top) Accessions with moderate vernalization response.

(Middle) Accessions with extreme vernalization response. Several accessions in this cluster originate from lower latitudes (given in parentheses).

(Bottom) Accessions that are delayed after vernalization, possibly indicatingFRI-independent variation. Note that BG6, HR5, and Wil-1, which lack functionalFRI, cluster with this group.

https://doi.org/10.1371/journal.pgen.0010006.sg005

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Table S1.Means of Flowering Time Traits in Accessions and Mutant Strains

A, B, C, etc., denote multiple stocks and repeated assays. Latitude information was obtained from Geographic Names Information System (US Geological Survey).

ALN, adult rosette leaf number; CLN, cauline leaf number; DTF, days to flowering; JLN, juvenile rosette leaf number; TLN, total leaf number.

The data are also available as a CSV file (Dataset S1).

https://doi.org/10.1371/journal.pgen.0010006.st001

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Table S2.Summary Statistics Based on Individual Plants

Asterisk indicates ± 95% confidence intervals (2 × SEM).

https://doi.org/10.1371/journal.pgen.0010006.st002

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Table S3.Key to ATH1 Array Data

https://doi.org/10.1371/journal.pgen.0010006.st003

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Table S4.Pearson Correlation of Known Flowering Regulators with DTF and TLN in 23LD

https://doi.org/10.1371/journal.pgen.0010006.st004

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Table S5.Oligonucleotide Primers Used

Uses: 1/3, amplification of 3.3-kbFRI genomic region; 1–13,FRI sequence analysis; 12/13, PCR genotyping of Col-type deletion inFRI; 3/4, PCR genotyping of Ler-type deletion inFRI; 6/7,FRI qRT-PCR; 30/31 and 28/34, amplification of 4.2-kbFLC genomic region (divided into two fragments); 14–35,FLC sequence analysis; 34/35,FLC cDNA amplification; 36–38, PCR genotyping of Ler-type insertion inFLC; 39/40,UBQ10 qRT-PCR; 41/42,FLC qRT-PCR.

https://doi.org/10.1371/journal.pgen.0010006.st005

(36 KB PDF)

Accession Numbers

GenBank (http://www.ncbi.nlm.nih.gov/Genbank/) accession numbers of the new sequences discussed in this paper are AY964090–AY964098 and AY0528–AY970557.