pmc.ncbi.nlm.nih.gov

The PHYTOCHROME C photoreceptor gene mediates natural variation in flowering and growth responses of Arabidopsis thaliana

. Author manuscript; available in PMC: 2006 Oct 5.

Published in final edited form as: Nat Genet. 2006 May 28;38(6):711–715. doi: 10.1038/ng1818

Abstract

Light plays an important role in modulating seedling growth and flowering time1. We show that allelic variation at the PHYTOCHROME C (PHYC) photoreceptor locus affects both traits in natural populations of A. thaliana. Two functionally distinct PHYC haplotype groups are distributed in a FRIGIDA-dependent latitudinal cline that is stronger than the one reported for FLOWERING LOCUS C, which together with FRIGIDA explains a large portion of the variation in A. thaliana flowering time2. In a genome-wide scan for association of 65 loci with latitude, there was an excess of significant p-values, indicative of population structure. Nevertheless, PHYC was the most strongly associated locus across 163 strains, suggesting that PHYC alleles are under diversifying selection in A. thaliana. Our work, together with previous findings3–6, suggests that photoreceptor genes are major agents of natural variation in plant flowering and growth response.

Arabidopsis thaliana occurs across a wide range of latitudes in the northern hemisphere, and wild strains display vary extensively in light sensitivity and flowering responses. FRI and FLC, which mediate the effects of exposure to winter-like temperatures (vernalization), account for about 23% of the phenotypic variation in flowering time of A. thaliana in short days7–9. In contrast to FRI, alleles of other loci that modulate variant flowering responses tend to be rare3–5,10. In addition, most of the characterized natural alleles affect either seedling growth or flowering time, even though both traits are light dependent. Linkage disequilibrium mapping studies suggest that common allelic variation at the CRYPTOCHROME2 (CRY2) photoreceptor locus is associated with flowering time variation, but this has not yet been experimentally verified6.

We have recently identified an accession from Frankfurt, Germany, Fr-2, which responds only weakly to a change in photoperiods. Compared to standard laboratory strains such as Col-0, Fr-2 flowers early in short days. This phenotype shows simple segregation in an F2 population derived from a cross of Fr-2 to Col-08. By performing whole-genome scans of 140 early flowering individuals selected from 900 F2 plants, we mapped the earliness to a 1 Mb region on chromosome 5 (Supplementary Fig. 1 online). Within this interval, we found 11 genes that were differentially expressed between Col and Fr-2 (Fig. 1a), as deduced from AtGenExpress microarray data of 34 wild strains8. Because none of the other strains showed an Fr-2 like flowering behavior, and because the early flowering phenotype was recessive, we asked whether any of the 11 genes was specifically underexpressed in Fr-2. Only one gene, which encodes the red-light receptor PHYC, fulfilled this criterion (Fig. 1b).

Supplementary Figure 1.

Supplementary Figure 1

Markers used for mapping of the early flowering phenotype in Fr- 2. About 80 early plants were genotyped for all markers and linkage of the phenotype to MSat 5.22 and SO191 was confirmed with 60 additional early plants.

Figure 1.

Figure 1

Identification of a defective PHYC allele in Fr-2. (a) Expression profiles of genes that are differentially expressed between Fr-2 and Col across 34 wild strains. The genomic interval that co-segregates with early flowering of Fr-2 were analyzed for differentially expressed genes using the AtGenExpress dataset of expression data from 34 strains8. Of 112 genes in this interval represented on the ATH1 microarray, 11 were differentially expressed between Col-0 and Fr-2, and are shown here. (b) Expression profile of PHYC (At5g38540) in the 34 strains shown in (a). (c) Red light response of Fr-2, Col-0 and phyB-9 in Col-0. The response of Fr-2 is similar to that of phyB-9, indicating reduced red light sensitivity. (d) Flowering under short days of different parental lines and their F1 progeny.

PHYC knockout alleles not only cause early flowering in short days, but also confer reduced sensitivity to red light during seedling growth11,12. Consistent with a defect in PHYC, Fr-2 seedlings responded to red light across a range of fluences less strongly than Col-0 seedlings, similar to plants with a mutation in the canonical red-light receptor PHYB (Fig.1c). In an F1, Fr-2 alleles complemented the flowering phenotypes of phyA and phyB mutants induced in the Ler background, indicating that PHYA and PHYB alleles of Fr-2 are functional (not shown). In contrast, Fr-2 failed to complement the flowering phenotype of a phyC knockout allele in Col-0, confirming that the early flowering of Fr-2 is largely due to a defect in PHYC (Fig. 1d).

Sequencing of a PHYC cDNA from Fr-2 revealed a nonsense change in the first exon, which converts the K299 codon to a stop codon. The predicted Fr-2 PHYC protein therefore lacks half of the GAF domain, and the entire PHY, PAS and histidine kinase domains, all of which are typically required for phytochrome function. Premature stop codons can trigger non-sense mediated mRNA decay13, which may explain the reduced levels of PHYC mRNA detected on microarrays14.

In addition to the stop codon, the Fr-2 PHYC allele is highly polymorphic with 12 nonsynonymous changes compared to Col-0. Ten of these substitutions are present in another common laboratory strain, Ler15. Many screens for seedlings with reduced light responses have been carried out in the Ler background, but phyC alleles were never recovered, suggesting that the Ler PHYC allele may have limited activity. Earlier overexpression studies of PHYC are in agreement with this hypothesis. Overexpression of Col-0 PHYC in Arabidopsis resulted in reduced hypocotyl length under red light. In contrast, while overexpression of Ler PHYB in transgenic tobacco plants also lead to shorter hypocotyls, Ler PHYC had no effect on hypocotyl length in red light16,17.

If the Ler PHYC allele is less active than that of Col-0, PHYC should at least partially explain the earlier flowering of Ler in short days. Indeed, a quantitative trait locus (QTL) that maps to the PHYC region has been detected in Ler x Col recombinant inbred lines (RILs) grown under natural conditions in fall, when days are short18. Consistent with a similar genetic basis of early flowering under short days in Fr-2 and Ler, a Ler x Fr-2 F2 population showed a near normal distribution of flowering time in short days, which contrasts with the behavior of a Col-0 x Fr-2 F2 population (Supplementary Fig. 2a online). Genotyping of F2 populations derived from Fr-2 x Col or Fr-2 x Ler revealed that the average flowering time of plants that carry Col-0 PHYC was much higher than that of plants with Fr-2 or Ler alleles (Supplementary Fig. 2b online). Furthermore, the PHYC allele of Ler failed to fully complement the phyC knockout in the Col-0 background. Fr-2 x Ler F1 plants flowered early in short days, in agreement with the limited activity of Ler PHYC (Supplementary Fig. 2c online). Finally, quantitative complementation analyses showed that phenotypic differences due to allelic variation between Fr-2 and Ler PHYC were less than those observed between Col-0 and either Fr-2 or Ler (Fig. 2).

Supplementary Figure 2.

Supplementary Figure 2

Flowering behavior of populations segregating for different PHYC alleles. (a) Distribution of flowering time in an F2 population derived from Ler x Fr-2 cross showing a continuous distribution, in contrast to a bimodal distribution observed in an F2 population derived from the Col x Fr-2 cross (see Lempe, J. et al. Diversity of flowering responses in wild Arabidopsis thaliana strains. PLoS Genet. 1, e6 [2005]). (b) Average flowering times of plants with different allelic combinations at PHYC in F2 populations derived from Ler x Fr-2 and Col x Fr-2. A single copy of the Col-0 allele delays flowering much more than a single Ler allele. (c) Genetic complementation analysis with Ler.Flowering times of F1 progeny in short days along with parental lines are shown.

Figure 2.

Figure 2

Quantitative complementation analysis with different parental lines. Col-0 was used as the wild type and the null allele phyC-2 in the Col-0 background as a tester for the crosses. An ANOVA was performed with the following model: TLN~ Line + Cross + Line x Cross. The Line x Cross interaction was significant in all three combinations (p < 0.0001). However, the proportion of total variance accounted for by the Line x Cross interaction varies (shown as R2), which is consistent with the Ler allele being intermediate in activity between the Col-0 and Fr-2 alleles.

Since both the sequence of PHYC in Col, Ler, and Fr-2 and the genetic data suggested functionally distinct PHYC alleles, we studied the haplotype structure at the PHYC locus in more detail. We sequenced PHYC from a randomly chosen sample of 29 wild strains. Phylogenetic analysis, using the sister species A. lyrata as outgroup, indicated two major haplotype classes, with Ler in one class and Col-0 in the other (Fig. 3). A total of 40 SNPs that result in eight informative amino acid changes were in complete linkage disequilibrium among the two haplotype groups (Fig 3). In addition, the two haplotype groups were distinguished by an indel 500 bp upstream of the start codon.

Figure 3.

Figure 3

PHYC haplotypes. A representative unrooted phylogenetic tree generated from PHYC coding sequences is shown on top. Numbers indicate bootstrap values above 60. The amino acid changes that delineate the two haplotype groups are given below. Unique changes compared to the outgroup A. lyrata are found in both haplotypes. Amino acids that are conserved in other phytochromes (Q or E at position 735, and E at position 822), but changed in either the Col-0- or Ler-type haplotype, are boxed. See Supplementary Table 6 online for provenance of strains.

Mapping experiments have detected QTL for flowering time and hypocotyl length in many RIL populations across multiple environments. Haplotype analysis on multiple crosses has been previously used to identify candidate genes for a QTL19. In a reverse approach, we predicted that if the two PHYC haplotypes are distinguished by their activity, variation in flowering time and hypocotyl length should map to PHYC in crosses derived from parental lines with contrasting haplotypes. Using the promoter indel as a diagnostic marker for the two haplotype groups, we assessed the PHYC haplotype of more than 220 Eurasian strains (Supplementary Table 1 online), and then examined published QTL maps from six different crosses for flowering time and hypocotyl length10,18,20–24. As predicted, only populations in which the parents carried different haplotypes at PHYC showed a QTL near PHYC (Table 1). The direction of the QTL effect in all crosses was consistent with the Ler-like haplotype being less active than the Col-like haplotype. In agreement with the known function of PHYC, the QTL depend on the environment, with the light sensitivity QTL being detected under red light and the flowering time QTL in short days.

Supplementary Table 1.

PHYC haplotypes of A. thaliana strains.

No Accessions Stock# Country Latitude PHYC FRI*† FLC†
1 Aa-0 CS900 Germany 51 Ler Del B
2 Ag-0 CS901 France 45 Ler Wt A
3 Ak-1 N939 Germany 48.6 Ler Del A
4 An-1 CS6603 Belgium 51.5 Ler Del A
5 Ang-1 N951 Belgium 50.62 Ler Del A
6 Bay-0 N955 Germany 49.95 Col Del B
7 Bch-1 N957 Germany 53.37 Col Del A
8 Bch3 N959 Germany 53.37 Ler Del B
9 Bch4 N961 Germany 53.37 Col Del A
10 Be-1 N967 Germany 49.5 Ler Del B
11 Ber-0 CS8068 Denmark 55 Ler
12 Bl-1 CS6615 Italy 44.5 Col Wt B
13 Bla-1 N971 Spain 41.68 Ler Wt A
14 Bla-11 N985 Spain 41.68 Ler Wt A
15 Bla-12 N987 Spain 41.68 Col Del A
16 Bla-14 N989 Spain 41.68 Col Del
17 Bla-2 N973 Spain 41.68 Ler Wt A
18 Bla-3 N975 Spain 41.68 Ler Wt A
19 Bla-5 N6620 Spain 41.68 Col Wt
20 Bla-6 N6621 Spain 41.68 Ler Wt A
21 Blh-1 CS6645 Czech 48.83 Ler Wt
22 Bor-4 CS22591 Czech 49.5 Ler Wt
23 Br-0 N995 Czech 49.2 Ler Wt A
24 Bs-1 N997 Switzerland 47.55 Ler Del B
25 C24 CS906 Portugal 40.2 Ler Wt A
26 Ca-0 CS6658 Germany 50.5 Ler Del B
27 Can-0 N1065 Spain 28 Ler Wt
28 Cha-0 N1069 Switzerland 46.03 Col Wt
29 Chi-0 N1073 Russia 54 Col Wt
30 Co-1 N1085 Portugal 40.2 Ler Wt A
31 Co-2 N1087 Portugal 40.2 Col Wt
32 Co-3 N1089 Portugal 40.2 Col Wt A
33 Col Lab Stock Col Del A
34 Ct-1 CS6674 Italy 37.5 Ler Del
35 Da(1-12) CS917 Czech 49.45 Ler Wt A
36 Da-0 CS6676 Germany 50 Ler Del B
37 Db-0 N1101 Germany 50 Col Del B
38 Db-1 CS6678 Germany 50 Col Del B
39 Db-2 N6679 Germany 50 Col Del B
40 Di-1 N1109 France 47.42 Col Del B
41 Di-2 N1111 France 47.42 Col Del A
42 Di-g CS910 France 47.2 Ler Del A
43 Dr-0 N1115 Germany 51.05 Ler Del B
44 Dra-0 CS6685 Czech 49.42 Ler Wt A
45 Dra-1 N1119 Czech 49.42 Ler Del A
46 Dra-2 N1121 Czech 49.42 Col Del A
47 Ei-2 CS6689 Germany 50.25 Ler Wt A
48 Ei-5 N6691 Germany 50.25 Ler Wt
49 Ei-6 N1131 Germany 50.25 Ler Del B
50 Eil-0 N1133 Germany 51.5 Col Del A
51 El-0 N1135 Germany 51.5 Ler Del B
52 En-1 N1137 Germany 50.15 Ler Del B
53 En-2 N1139 Germany 50.15 Col Del
54 Ep-0 N1141 Germany 50.5 Ler Del
55 Er-0 N1143 Germany 49.58 Col Del A
56 Est-1 N1151 Estonia 59 Ler Del A
57 Et-0 N1153 France 44.63 Ler Wt
58 Fei-0 CS22645 Portugal 39 Ler Del
59 Fi-0 N1157 Germany 50.5 Col Del A
60 Flo-0 CS6044 Italy 43.8 Ler Wt A
61 Fr-2 N1169 Germany 50.2 Ler Del B
62 Fr-3 N1171 Germany 50.12 Ler Del B
63 Fr-4 N1173 Germany 50.12 Ler Del B
64 Fr-5 N1175 Germany 50.12 Col Del
65 Fr-6 N1177 Germany 50.12 Ler Del A
66 Fr-7 N1179 Germany 50.12 Ler Del B
67 Ga-0 N1181 Germany 50.3 Col Del A
68 Gd-1 N1185 Germany 53.55 Ler Del
69 Gie-0 N1193 Germany 50.5 Col Del B
70 Go-0 N1195 Germany 51.5 Ler Del
71 Go-2 N1197 Germany 51.5 Ler Del B
72 GOT1 N22277 Germany 51.5 Ler Wt
73 GOT10 N22286 Germany 51.5 Ler Wt
74 GOT7 CS22608 Germany 51.5 Ler Wt
75 Gr-1 N1199 Austria 47.06 Ler Del A
76 Gr-3 N1203 Austria 47.06 Col Wt A
77 Gr-5 N1207 Austria 47.06 Col Wt B
78 Gr-6 N6728 Austria 47.06 Ler Wt B
79 Gu-0 N1213 Germany 50.4 Ler Del B
80 Gu-1 N1215 Germany 50.4 Ler Del B
81 Gy-0 N1217 France 49 Col Del A
82 H55 CS932 Czech 50 Col Del A
83 Ha-0 N1219 Germany 52.5 Col Del A
84 Hau-0 N1221 Denmark 55.67 Ler Del
85 Hi-0 CS6736 Netherlands 52.5 Ler Del B
86 Hl-2 N1231 Germany 52.38 Ler Del B
87 Hl-3 N1233 Germany 52.38 Ler Del A
88 Hn-0 N1235 Germany 51.5 Ler Del B
89 Is-0 N1241 Germany 50.48 Ler Del B
90 Jl-3 CS6745 Czech 50.1 Col Del A
91 Jm-0 CS6748 Czech 49.04 Ler Del B
92 Jm-1 N1261 Czech 49.04 Col Del A
93 Ka-0 CS6752 Austria 46.5 Col Del A
94 Kas-2 CS6751 India 34 Ler Wt B
95 Kb-0 CS6753 Germany 50.5 Col Del A
96 Kl-0 N1275 Germany 50.93 Ler Del B
97 Kl-1 N1277 Germany 50.93 Ler Del B
98 Kl-5 CS1284 Germany 50.93 Ler Del A
99 Kn-0 N1287 Lithuania 54.9 Col Wt B
100 Ko-2 N1289 Denmark 55.5 Ler Wt
101 Kondara N6175 Tajikistan 38.81 Ler Wt B
102 Kro-0 N1301 Germany 50.2 Col Del B
103 KZ10 N22442 Kazakhstan 50.42 Col Wt
104 KZ11 N22443 Kazakhstan 50.42 Col Wt
105 La-1 N1303 Poland 52.73 Ler Wt
106 Ler Lab Stock Ler Del
107 Ler-K Lab Stock Ler Del
108 Li-10 N6911 Germany 50.38 Col Wt B
109 Li-2 N6908 Germany 50.38 Col Wt
110 Li2:1 N1315 Germany 50.38 Col Wt A
111 Li-7 CS6878 Germany 50.38 Ler Del B
112 Lip-0 N1337 Poland 53.47 Col Wt A
113 Ll-0 N1339 Spain 41.82 Ler Wt A
114 Ll-1 N1341 Spain 41.82 Ler Wt A
115 Ll-11 N1341 Spain 41.82 Col Wt
116 Ll-2 N1343 Spain 41.82 Ler Del A
117 Lm-2 CS6784 France 48 Ler Del A
118 Lo-1 N1347 Germany 47.62 Col Del B
119 Lo-2 N1349 Germany 47.62 Ler Del B
120 Lov-5 CS22575 Sweden 62 Ler Wt
121 Lz-0 CS6788 France 46 Ler Del A
122 Ma-0 N1357 Germany 50.5 Ler Del A
123 Mh-0 N1367 Poland 53.78 Ler Del B
124 Mh-1 N1369 Poland 53.78 Ler Del B
125 Mir-0 N1379 Italy 45.65 Ler Wt
126 Mnz-0 N1371 Germany 50 Ler Del
127 Mr-0 N1373 Italy 44.5 Col Wt A
128 Mrk-0 N1375 Germany 49 Ler Del B
129 Ms-0 N1377 Russia 55.75 Col Wt B
130 Mz-0 N1383 Germany 50.85 Ler Del A
131 Na-1 CS6801 France 47.5 Ler Del B
132 Nc-1 N1389 France 48.6 Ler Del A
133 Nd-0 N1391 Germany 50.47 Col Del
134 Nd-1 N1680 Germany 50.47 Col Del A
135 No-0 N1395 Germany 51 Col Del A
136 Nok-0 N1399 Netherlands 52.23 Col Wt B
137 Nok-3 N1405 Netherlands 52.23 Ler Wt B
138 Np-0 N1397 Germany 52.68 Col Del
139 Nw-0 N1409 Germany 50.32 Ler Del B
140 Nw-2 N1413 Germany 50.32 Ler Del B
141 Nw-3 N1415 Germany 50.32 Col Del B
142 Ob-0 N1419 Germany 50.2 Ler Del B
143 Ob-1 N1421 Germany 50.2 Col Del
144 Ob-2 N1423 Germany 50.2 Ler Del B
145 Ob-3 N1425 Germany 50.2 Col Del
146 Old-1 N1427 Germany 53.03 Ler Del B
147 Old-2 N1429 Germany 53.03 Ler Del B
148 Or-0 N1433 Germany 50.5 Col Wt B
149 Ost-0 N1431 Sweden 60.5 Col Wt
150 Ove-0 N1435 Germany 53.33 Col Del B
151 Oy-0 N1437 Norway 60.5 Ler Del B
152 Oy-1 N6929 Norway 60.5 Col Del A
153 Pa-2 N1441 Italy 37.83 Ler Wt A
154 Pa-3 N1443 Italy 37.83 Ler Del B
155 Per-1 N1445 Russia 58.02 Col Wt B
156 Per-2 N1449 Russia 58.02 Col Wt
157 Per-3 N1451 Russia 58.02 Col Wt
158 Pf-0 N1453 Germany 48.5 Ler Del B
159 Pi-0 N1455 Austria 47.12 Ler Del B
160 Pi-2 N1457 Austria 47.12 Ler Del B
161 Pla-1 N1459 Spain 41.82 Ler Wt A
162 Pla-2 N1463 Spain 41.82 Ler Wt
163 Pn-0 N1469 France 48 Ler Wt A
164 Po-1 N1473 Germany 50.5 Col Wt A
165 Pr-0 N1475 Germany 50.5 Ler Del B
166 PUZ16 N22451 Czech 49.12 Ler Wt
167 Ra-0 N1481 France 46.02 Ler Del
168 Rak-2 CS6846 Czech 49.03 Col Del A
169 Rd-0 N1483 Germany 50.5 Ler Del B
170 REN1 N22253 France 46.02 Ler Del
171 REN11 N22263 France 46.02 Ler Del
172 RLD1 N913 Russia 56.5 Col Del B
173 Rou-0 N1489 France 49.5 Ler Wt A
174 Rsch-0 N1491 Russia 56.86 Col Wt B
175 Rsch-4 N1495 Russia 56.86 Col Del B
176 Sav-0 N1515 Czech 50 Col Del
177 Se-0 N1503 Spain 42.5 Ler Wt A
178 Sei-0 N6853 Italy 46.5 Ler Del B
179 Sf-1 N1513 Spain 42.05 Ler Wt A
180 Sf-2 N1517 Spain 42.05 Ler Wt
181 Sf-2e N1675 Spain 42.05 Ler Wt
182 Sg-1 N1519 Germany 48.12 Ler Del A
183 Sg-2 N1521 Germany 48.12 Ler Del A
184 Shakdara N6180 Tajikistan 37.48 Ler Wt B
185 Sn(5)-1 N6181 Czech 50 Col Del A
186 Sorbo CS931 Tajikistan 39 Ler Wt B
187 Sp-0 N1531 Germany 52.5 Col Wt A
188 St-0 N1535 Sweden 59 Ler Wt A
189 Stw-0 N1539 Russia 52.96 Ler Wt A
190 Ta-0 N1549 Czech 49.42 Col Wt A
191 Tamm-2 CS22604 Finland 60 Col Wt
192 Te-0 N1551 Finland 60 Ler Wt A
193 Ts-1 N1553 Spain 41.72 Ler Wt
194 Ts-5 N6871 Spain 41.72 Ler Wt A
195 Ts-6 N1561 Spain 41.72 Ler Wt A
196 Ts-7 N1563 Spain 41.72 Ler Wt B
197 Tu-1 N1569 Italy 45 Ler Del B
198 Uk-1 N1575 Germany 48.03 Ler Del A
199 Uk-2 N1579 Germany 48.03 Ler Del A
200 Uk-3 N1577 Germany 48.03 Ler Del A
201 Uk-4 N1581 Germany 48.03 Ler Del A
202 ULL2-5 CS22586 Sweden 56.09 Ler Wt
203 Var2-6 CS22581 Sweden 55.33 Col Wt
204 Wa-1 N1587 Poland 52.5 Col Wt B
205 Wc-1 N1589 Germany 52.6 Col Del A
206 Wc-2 N1591 Germany 52.6 Col Del A
207 Wei-0 N6182 Switzerland 47.42 Ler Del A
208 Wei-1 N1683 Switzerland 47.42 Ler Del B
209 Wil-0 CS6888 Lithuania 54.68 Col
210 Wil-1 N1595 Lithuania 54.68 Col Wt A
211 Wil-3 N1599 Lithuania 54.68 Col Wt A
212 Wl-0 N1631 Germany 48.75 Ler Del B
213 Ws N6924 Ukraine 52.5 Col Del
214 Ws-0 CS6891 Ukraine 52.5 Ler Wt A
215 Ws-2 CS2360 Ukraine 52.5 Col Del B
216 Wt-1 N1605 Germany 52.67 Ler Del A
217 Wt-4 N1611 Germany 52.67 Ler Del A
218 Wt-5 N1613 Germany 52.67 Ler Del B
219 Wu-0 N1615 Germany 49.78 Ler Del B
220 Zu-0 N1627 Switzerland 47.42 Ler Wt A
221 Zu-1 N1629 Switzerland 47.42 Ler Del A

Table 1.

Summary of QTL in existing RIL sets.

RIL set Trait PHYC haplotype contrast QTL at PHYC Reference
Ler x Col-0 Flowering time in fall cohorts Yes Yes 18
Bay-0 x Shahdara Flowering time in short days Yes Yes 22
Kas-1 x Col-0 Hypocotyl length in red light Yes Yes 24
Ler x Cvi-0 Flowering time in short days; hypocotyl length in red light No No 20; 21
Nd-1 x Col-0 Flowering time in short days No No 10
Ler x Shahdara Flowering time in short days No No 23

We also asked whether PHYC haplotype groups could account for variation in hypocotyl lengths of 115 wild strains, which we had previously analyzed in several different light conditions5. We found that PHYC could explain 8% of the variation in white light, and that PHYC was significantly associated with hypocotyl lengths across several conditions (Table 2). All these results support the hypothesis that PHYC is responsible for the flowering time and hypocotyl length QTL that have been reported in this genomic region. We note, however, that a tightly linked locus could contribute to these results.

Table 2.

Associations of PHYC with hypocotyl lengths across range of conditions.

Average hypocotyl length (mm)*
Environment Ler haplotype group Col-0 haplotype group p-value
White 7.11 6.09 0.001
Blue 6.42 5.78 0.02
Red 10.42 9.43 0.02
Far red 5.40 4.77 0.02
Gibberellin (GA) 8.42 7.03 0.0003
Brassinozole (BRZ) 5.62 5.08 0.05
Dark 12.45 12.00 0.21

That the less active PHYC haplotype group is quite common suggests that variation at this locus may be adaptive in the wild. We therefore asked whether the frequency of PHYC haplotypes varies with latitude, since co-variation with environmental factors can be evidence of adaptation25. Latitudinal clines in light sensitivity and flowering time are known in A. thaliana5,8,26. Indeed, we found that the more active Col-0 PHYC haplotype group was more frequent at northern latitudes (p = 0.0001, n = 221). This is particularly striking among strains that do not carry obvious lesions in FRI (Fig. 4a). In addition, the contribution of PHYC to flowering time is latitude-dependent. A PHYC x latitude interaction can explain 10% of the residual variation in short day flowering, after accounting for effects of FRI, suggesting that PHYC partially contributes to the late flowering of FRI positive strains at northern latitudes (Supplementary Table 2 online). Since latitudinal associations could also be due to population structure, we asked how often significant interaction terms were observed in 163 strains for a set of 65 SNPs that have comparable allele frequencies and that are spaced throughout the genome27. The same analysis was carried out for FLC, which has been reported to show a FRI-dependent latitudinal cline independent of population structure28. The p-values for the association of PHYC ranked first for latitude (Fig. 4b) and fourth for the FRI-dependent interaction with latitude (Fig. 4c). In addition, it always ranked ahead of the corresponding p-values for FLC. PHYC was also the highest ranking marker for association with hypocotyl length after gibberellin treatment5 (Supplementary Table 3 online). While there is an excess of significant p-values among the random loci, which is consistent with the effect of population structure, the relative ranks of the PHYC p-values are suggestive of adaptive selection29 (Supplementary Table 3 online). A similar analysis with an independent set of 69 SNP markers, even with a much smaller sample size of 56 strains, yielded comparable results (Supplementary Table 4 online).

Figure 4.

Figure 4

Latitudinal cline of PHYC alleles. (a) Proportion of Ler-type (black) and Col-0-type (white) PHYC alleles at different latitudes among apparently FRI functional strains. The absolute numbers for each of the classes is given on top of the histograms. (b) Distribution of p-values of a nominal logistic regression model with latitude as a factor and genotypes as response. Allele information of 65 random SNP markers with similar allele frequency as that of PHYC was available in a set of 163 strains. This information was used as a response. Note genome-wide skew towards small p-values. (c) Distribution of p-values for interaction of a given random marker with FRI in an interaction model with latitude as the response and FRI and marker genotypes as factors with interaction.

Supplementary Table 2.

ANOVA of latitude by PHYC haplotype group interaction on residual variation in flowering time at 23°C in short days after accounting for FRI functionality. The significance for each of the factors is given.

Factor DF Sum of Squares FValue Probability (>F)
Latitude 1 2144.61 6.374 0.013
PHYC haplotype group 1 484.79 1.44 0.23
PHYC haplotype group x 1 5344.66 15.88 0.0001
latitude interaction

Supplementary Table 3.

p-values obtained from for different models testing for association with latitude (Latitude), FRI dependent interaction with latitude (Interaction), hypocotyl length under GA treatment (GAHypo) and total leaf number in short days (TLNSD) for 67 markers across 163 strains. The rankings for each of the markers in each of the association is given (A) Model: Logistic regression model of SNP ~ Latitude. (B) Model: Latitude ~ FRI functionality + SNP + SNP * FRI functionality. The p-values for the whole model, FRI functionality, SNP, and for interaction with FRI are tabulated. Ranks are given for the interaction with FRI. (C) Model: Hypocotyl Length ~ SNP (D). Model: TLNSD ~ SNP. The table is sorted according to the rankings obtained for the logistic regression model for association with latitude. While the p-value ranking for flowering time (D) does not appear to be significant after background correction, the relative ranking is still consistent with a true association.

S.No SNP_ID Chr Latitude Interaction GAHypo TLNSD A B C D
1 PHYC 5 0.0008 0.0053 0.0007 0.0464 1 4 1 12
2 MASC05657 2 0.0015 0.003 0.5509 0.0107 2 1 45 5
3 MASC09206 1 0.0018 0.008 0.0048 0.1806 3 5 4 22
4 MASC04123 4 0.002 0.6459 0.9914 0.0015 4 41 65 2
5 MASC01545 5 0.004 0.1401 0.3519 0.4806 5 17 27 43
6 MASC03470 5 0.0043 0.6222 0.0015 0.2799 6 40 2 28
7 MASC04925 3 0.0085 0.0542 0.0232 0.6168 7 12 9 49
8 MASC02841 3 0.0157 0.9957 0.0413 0.5713 8 60 11 47
9 MASC02668 4 0.0158 0.0425 0.2932 0.0898 9 10 26 15
10 MASC04608 3 0.0208 0.0839 0.7705 0.4357 10 14 55 40
11 MASC04199 4 0.0322 0.003 0.2083 0.012 11 2 20 6
12 FLC 5 0.065 0.6914 0.165 0.4469 12 43 17 42
13 MASC04642 4 0.0691 0.7149 0.4218 0.026 13 45 34 8
14 MASC09219 3 0.0763 0.2447 0.8056 0.5349 14 25 58 46
15 MASC03128 5 0.0982 0.0138 0.0098 0.6111 15 6 6 48
16 MASC04262 3 0.1074 0.9856 0.9817 0.2048 16 59 64 24
17 MASC09216 4 0.1219 0.0405 0.5819 0.7262 17 9 47 55
18 MASC04523 3 0.1243 0.216 0.2516 0.1393 18 22 23 19
19 MASC03898 3 0.1271 0.2063 0.285 0.0656 19 19 25 14
20 MASC09214 4 0.1306 0.9575 20 65 62 65
21 MASC03344 3 0.1359 0.0484 0.216 0.4093 21 11 21 38
22 MASC03263 4 0.1501 0.039 0.4177 0.6615 22 8 33 50
23 MASC03336 4 0.1545 0.3065 0.7493 0.0025 23 31 53 3
24 MASC09224 3 0.1908 24 64 66 64
25 MASC04275 5 0.1928 0.0326 0.7308 0.9608 25 7 51 61
26 MASC03218 3 0.1978 0.3064 0.9651 0.0075 26 30 63 4
27 MASC05434 2 0.2061 0.0031 0.1286 0.1418 27 3 16 20
28 MASC05803 2 0.223 0.4262 0.7569 0.1165 28 33 54 18
29 MASC02820 4 0.2553 0.9957 0.0758 0.1925 29 61 15 23
30 MASC02675 5 0.265 0.4309 0.3951 30 34 29 67
31 MASC04350 5 0.3068 0.3032 0.5487 0.4376 31 29 44 41
32 MASC03340 1 0.3377 0.9327 0.0016 0.9389 32 57 3 59
33 MASC01582 5 0.3984 0.1158 0.4567 0.0564 33 16 38 13
34 MASC09208 5 0.4068 0.0915 0.5423 0.2976 34 15 43 29
35 MASC09222 2 0.4078 0.2145 0.6152 0.8375 35 21 48 57
36 MASC09225 4 0.4341 0.189 0.3974 0.6978 36 18 30 52
37 MASC09204 1 0.4425 0.4427 0.3189 37 62 37 32
38 MASC05029 1 0.446 0.2473 0.9011 38 26 61 63
39 MASC09209 5 0.4692 0.2837 0.4333 0.018 39 28 35 7
40 MASC04170 1 0.4773 0.4648 0.3656 0.9573 40 35 28 60
41 MASC04516 3 0.4804 0.2292 0.5042 0.0014 41 23 40 1
42 MASC04531 5 0.507 0.7203 0.0459 0.1567 42 46 12 21
43 MASC01361 5 0.5156 0.9625 0.0065 0.2725 43 58 5 27
44 MASC04983 5 0.5687 44 67 67 66
45 MASC05258 4 0.5879 0.2078 0.4156 0.0929 45 20 32 16
46 MASC05360 2 0.6034 0.8711 0.8991 0.5288 46 51 60 45
47 MASC09223 1 0.6925 0.8852 0.6706 0.3146 47 52 50 31
48 MASC03911 1 0.7062 0.6533 0.6247 0.6995 48 42 49 53
49 MASC03631 1 0.708 0.7279 0.5422 0.3653 49 47 42 34
50 MASC03754 1 0.7213 0.9171 0.199 0.4061 50 55 19 37
51 MASC09203 1 0.7375 0.5523 0.7385 0.2307 51 38 52 25
52 MASC06808 2 0.749 0.0572 0.0107 0.8759 52 13 7 58
53 MASC05386 2 0.7646 0.4901 0.0741 0.705 53 36 14 54
54 MASC03001 3 0.7705 0.2707 0.178 0.418 54 27 18 39
55 MASC03447 1 0.7952 0.927 0.0237 0.3217 55 56 10 33
56 MASC01171 3 0.7967 0.9082 0.4587 0.7354 56 54 39 56
57 MASC05962 2 0.798 0.255 0.4043 57 63 24 36
58 MASC03612 5 0.8153 0.2268 0.3117 58 66 22 30
59 MASC07090 3 0.8166 0.8059 0.5729 0.4927 59 49 46 44
60 MASC05857 2 0.8183 0.2335 0.7916 0.0456 60 24 56 11
61 MASC04591 5 0.831 0.8886 0.0133 0.9837 61 53 8 62
62 MASC02577 1 0.8586 0.7039 0.886 0.2659 62 44 59 26
63 MASC03658 1 0.8815 0.5312 0.0733 0.0407 63 37 13 10
64 MASC04209 1 0.9083 0.559 0.4395 0.3838 64 39 36 35
65 MASC09210 5 0.9267 0.852 0.7992 0.0972 65 50 57 17
66 MASC03952 5 0.9722 0.3312 0.5352 0.6953 66 32 41 51
67 MASC04819 3 0.975 0.7392 0.4083 0.0403 67 48 31 9

Supplementary Table 4.

p-values obtained from for two different models testing for latitudinal association with a completely independent set of markers across 56 strains. (A) Model: Latitude ~ FRI functionality + SNP + SNP * FRI functionality. The p-values for the entire model, FRI functionality, SNP, and for interaction with FRI are tabulated. Ranks are given for the interaction with FRI. (B) Model: Logistic regression model of SNP ~ Latitude. SNP_ID refers to unique identifier for SNP assays (see http://naturalvariation.org). The table is sorted according to the rankings obtained for FRI-dependent interaction with latitude.

S.No SNP_ ID Chr Model FRI SNP Interaction(A) Latitude(B) Rank A Rank B
1 44607503 2 9E-05 9E-05 8E-04 0.00009 0.0669 1 15
2 44607971 1 9E-05 9E-05 3E-04 0.00009 0.0756 2 19
3 44607332 2 9E-05 9E-05 0.448 0.0021 0.8365 3 64
4 44606338 5 9E-05 0.0011 0.005 0.0027 0.1083 4 22
5 44606550 4 9E-05 0.0061 0.002 0.0032 0.0097 5 6
6 44606183 1 0.0001 0.0039 0.256 0.0036 0.7846 6 59
7 44607627 2 9E-05 9E-05 7E-04 0.0048 0.0675 7 16
8 44607727 2 9E-05 0.0853 0.031 0.0049 0.1595 8 28
9 44607751 4 0.0002 0.0001 0.355 0.0099 0.6177 9 48
10 PHYC 5 9E-05 0.0048 0.004 0.0137 0.0213 10 10
11 21607148 5 0.0002 0.5391 0.003 0.0255 0.0041 11 3
12 44607470 2 0.0007 0.0883 0.025 0.0336 0.0487 12 14
13 21607640 1 0.0001 0.008 0.003 0.0342 0.0221 13 11
14 44607250 5 9E-05 0.0021 0.009 0.0433 0.1002 14 21
15 44606460 5 0.0002 9E-05 0.018 0.0457 0.1407 15 26
16 44607372 1 0.0006 0.0063 0.135 0.048 0.3208 16 35
17 44606631 3 9E-05 0.0146 0.003 0.057 0.0044 17 4
18 44607685 1 9E-05 9E-05 0.001 0.0598 0.0126 18 8
19 44607364 1 0.001 0.0002 0.507 0.0639 0.1609 19 29
20 44606867 1 0.0028 0.0002 0.308 0.0902 0.762 20 58
21 44607792 4 0.0006 0.0002 0.277 0.1278 0.1403 21 25
22 21607556 2 0.0023 0.2412 0.05 0.1356 0.0403 22 12
23 44607389 3 0.0008 0.0005 0.575 0.1359 0.7544 23 57
24 44607528 1 0.0067 0.0084 0.402 0.1421 0.7198 24 56
25 44606134 1 0.0005 0.0086 0.036 0.1612 0.0713 25 18
26 44607545 4 0.0029 0.0002 0.321 0.1794 0.8851 26 66
27 21607250 3 0.0019 0.0001 0.599 0.1898 0.8422 27 65
28 44607014 3 9E-05 0.0009 6E-04 0.1948 0.0004 28 1
29 44607841 4 0.0003 0.0405 0.009 0.2167 0.0066 29 5
30 21607496 3 0.0044 0.1019 0.477 0.2335 0.3426 30 37
31 44608020 3 0.0134 0.0314 0.351 0.2398 0.2445 31 31
32 44606484 5 0.0007 0.0119 0.439 0.2566 0.0024 32 2
33 FLC 5 0.0022 0.0002 0.939 0.2993 0.4111 33 41
34 44606273 3 0.0077 0.0228 0.543 0.3061 0.6601 34 51
35 44607307 1 0.0127 0.0017 0.095 0.3287 0.6678 35 52
36 44606354 1 0.0007 0.0409 0.024 0.3377 0.0142 36 9
37 44606989 2 0.0045 0.0041 0.983 0.3435 0.8179 37 60
38 44606843 3 0.0018 0.0002 0.824 0.344 0.5148 38 45
39 44606199 2 0.0028 0.0002 0.82 0.4136 0.9311 39 68
40 44607561 4 0.001 0.0028 0.036 0.4194 0.0108 40 7
41 44607701 2 0.0015 0.0008 0.299 0.4318 0.0404 41 13
42 44606208 3 0.0087 0.0014 0.912 0.4388 0.9917 42 71
43 44607446 5 0.0037 0.0004 0.362 0.5047 0.9009 43 67
44 21607463 1 0.0087 0.0065 0.924 0.5338 0.9351 44 69
45 44607718 3 0.0035 0.0003 0.595 0.5496 0.8255 45 61
46 44607775 1 0.0043 0.0009 0.436 0.5526 0.3139 46 34
47 44607898 2 0.0035 0.0016 0.359 0.5668 0.4164 47 42
48 44606118 4 0.0106 0.0014 0.87 0.5742 0.6848 48 55
49 44607397 3 0.0021 0.0003 0.59 0.5852 0.3223 49 36
50 44608060 1 0.0039 0.0008 0.4 0.5973 0.2513 50 32
51 44607759 4 0.0041 0.001 0.565 0.618 0.6765 51 53
52 44606313 1 0.0049 0.0006 0.525 0.6253 0.56 52 46
53 44606940 2 0.0007 0.0002 0.233 0.6292 0.2321 53 30
54 44606981 1 0.0068 0.0012 0.421 0.6471 0.3441 54 38
55 21697327 4 0.0176 0.0555 0.681 0.6748 0.4299 55 44
56 44606753 1 0.0018 0.0004 0.095 0.6838 0.1227 56 23
57 44607553 4 0.0476 0.0652 0.765 0.6963 0.4169 57 43
58 44607479 2 0.0054 0.0007 0.791 0.7222 0.3566 58 39
59 44607193 5 0.0055 0.0026 0.926 0.7245 0.8339 59 63
60 44606387 3 0.0068 0.0007 0.885 0.7247 0.8307 60 62
61 44607873 1 0.004 0.0003 0.287 0.7387 0.5846 61 47
62 44606794 1 0.0054 0.0017 0.761 0.8111 0.1506 62 27
63 44606607 3 0.0014 0.0005 0.065 0.8348 0.0775 63 20
64 44606916 4 0.0055 0.0007 0.774 0.8535 0.6765 64 54
65 44607160 5 0.0037 0.0005 0.722 0.8843 0.6312 65 50
66 44607955 4 0.0047 0.0022 0.188 0.9104 0.0688 66 17
67 44606216 4 0.0072 0.0495 0.873 0.9307 0.276 67 33
68 44607299 1 0.0032 0.0064 0.355 0.9356 0.9446 68 70
69 21607631 4 0.0028 0.0003 0.348 0.9685 0.6289 69 49
70 44607405 4 0.0036 0.0047 0.214 0.9749 0.1342 70 24
71 44606102 3 0.0059 0.0587 0.98 0.9833 0.3713 71 40

Of the informative SNPs that distinguish the PHYC haplotype groups, 20 Col-0 SNPs are shared with A. lyrata, as are 20 Ler-SNPs. Of the 8 amino acid changes that characterize the Col-0 and Ler haplotypes, four are clustered in a small region that seems to have regulatory activity in both PHYA and PHYB30,31. These changes could in principle be responsible for the functional difference between the haplotypes, although both specify unique amino acids at positions that are conserved in other phytochromes, not only in Arabidopsis, but also in land plants in general.

PHYC appears to have arisen by duplication of PHYA, which encodes a far-red light receptor. phyC knock out alleles in Col-0 and Ws backgrounds have phenotypes mostly in red and blue light11,12, but we found phenotypic associations even in far-red light. Our results could indicate differential cross talk among photoreceptors, dependent on PHYC haplotype. Alternatively, our understanding of phyC mediated light perception may have been limited by having only knock out alleles in Col-0 and Ws, which share the same haplotype.

While PHYC is mostly far-red sensing in rice, in Arabidopsis it is functionally more similar to the red-light sensing PHYB11,12,32,33. Nevertheless, loss of PHYC has similar consequences in Arabidopsis and rice, namely early flowering under non-inductive conditions12,33. This suggests that the functional divergence of PHYC from PHYA varies in different taxa. Consistent with this idea, PHYA evolves faster than PHYC in some species, but PHYC sequences are more divergent than those of other phytochrome genes in Arabidopsis, tomato and sorghum34–36. PHYC has even been suggested as a target for adaptive evolution in sorghum35. In Arabidopsis, allelic variation with demonstrated functional effects appears to be more pervasive at PHYC than at other photoreceptor loci3–5. The functional role of allelic variation at PHYC in other species is therefore an important question for future research.

Materials and Methods

Plant material and phenotyping

Seed stocks and growth conditions have been described8. phyC insertion lines were obtained from the Nottingham Arabidopsis Stock Centre. Typically, 120 F2 plants were used to analyze flowering time, as measured by counting total leaf number on the main shoot. For complementation studies, 30–50 F1 plants each were examined. For hypocotyl length measurements, about 20 seeds were stratified on Murashige-Skoog medium for four days at 4°C in dark, and then transferred to growth chambers at 23°C. A week later, plants were flattened and imaged on a flatbed scanner. Hypocotyls lengths were measured using NIH Image.

DNA Analyses

Genomic DNA was amplified with Pfu polymerase (Fermentas), and products pooled from two to four independent reactions were directly sequenced. See Supplementary Table 5 online for oligonucleotide primers. A 4.9 kb region covering the promoter and almost the entire coding region was obtained from 26 A. thaliana strains, the same fragment without the 1.5 kb promoter from three A. thaliana and one A. lyrata strain (GenBank accession numbers XXXX-XXXXX).

Supplementary Table 5.

Oligonucleotide primers.

Lab designation Sequence
G-4861 CTC AGC TTC TCT CCC ACC AC
G-4862 CCC CAT AAG TGT CTG CCA GT
G-4863 CAA GTA TGG AGC AGC GTG AA
G-4864 GCA TAC CCC ATT TTC ATT GG
G-4865 TAC CGC AAG CTT CGA GAT TT
G-4866 TCG AGA GCC AAG GCT AAC AT
G-4867 CTG TGG TTT CTG GCT CCA AT
G-4868 TCC CTT TCT CAA AGG CTG AA
G-4873 GAT TGG CAG TTG AAC AAG CA
G-4874 GCA TAC CCC ATT TTC ATT GG
G-5349 CAA ATC GCA TAA ATGCAT GG
G-5350 AGT GGT GGG AGA GAA GCT GA
G-5351 CTT GTG CTC ATG AAC GGC TA
G-5352 CGT GAT GAC AAA CCA CCA AG
G-5353 CCA ATG AAA ATG GGG TAT GC
G-5354 CCT GAT GCG TCT TCT TCT CC
G-5355 GGA GAA GAA GAC GCA TCA GG
G-5356 TTC TTT CGG GAA TTT CAT CG
G-5357 GTT TGT GGC TCC CAT TTT GT
G-5358 GGA AAA GAC CGA AAC ACC AA
G-5359 GTG TCG TGA GTC GTG ACC AG
G-5360 TGG AAT CAA ACC CAA CAT CTC
G-5361 ACG CAA AGC TAC ACG GAA AC
G-5362 GAC GCC ACT GAT CCC ATA TT
G-5363 GGC TTC AGC AAA TCC TTT CA
G-5364 TCG AAC CCA GAT GAC ACA AA
G-5365 TTT TGT GTC ATC TGG GTT CG
G-5366 TGC CCG TTT AAT ACC TGC AT
G-5367 TCT CCA TCG ACG TTA AAC CA

Phylogenetic Analysis

Sequences were aligned using Seqman (DNA Lasergene Inc, USA) and alignment was verified manually. Diversity measurements were obtained with DnaSP v4.10 (http://www.ub.es/dnasp)37. Sequence alignments were imported into PAUP38 and a heuristic search with maximum likelihood was performed using the settings for HKY model specified through Modeltest39. A maximum parsimony search was performed with ACCTRAN character state optimization. In both methods, initial trees were generated through step-wise addition. TBR branch swapping option was used. 1000 bootstrap permutations were performed using the same search settings used for parsimony search with the full heuristic search option in PAUP. Parsimony and maximum likelihood resulted in similar trees. The maximum parsimony tree is shown in Fig. 3. The same split in the tree was obtained when using coding or non-coding regions.

Statistical Analyses

Data were analyzed using JMP (version 5.1, SAS Institute), Excel (Microsoft) or R (http://www.r-project.org). For the latitudinal association studies, only Eurasian strains were used. The larger set made use of SNP genotyping information published in ref. 27. Information on the independent SNPs used in the second, smaller analysis is available at http://naturalvariation.org. A nominal logistic regression model with genotype as the response and latitude as a factor was used to compute p-values for latitudinal cline. For testing the interaction with FRI, a simple linear regression model with latitude as the response, and FRI and the genotype of PHYC or a random SNP as interaction term was used. The intrinsic multiple testing problem associated when comparing PHYC with random SNPs is somewhat mitigated by the fact that of 43 SNPs in PHYC, 40 are fixed in the two haplotype groups. For association with flowering time, residuals on regression with flowering time as the response and FRI functionality as a factor, were regressed using PHYC haplotype group and latitude as factors with interaction. PHYC x latitude interaction accounted for about 10% of the variation in the residuals. For association with hypocotyl length, a single factor ANOVA was performed with PHYC haplotype group as the factor and hypocotyl lengths as response. Previously published flowering time and hypocotyls length measurements were used for association studies5,8.

Supplementary Figure 3.

Supplementary Figure 3

Latitudinal cline of PHYC alleles. (a) Distribution of p-values of a nominal logistic regression model with latitude as a factor and genotype as response. Allele information of 69 random SNP markers with similar allele frequency as that of PHYC was available in a set of 56 strains. This information was used as a response. Note genome-wide skew towards small p-values. (c) Distribution of p-values for interaction of a given random marker with FRI in an interaction model with latitude as the response and FRI and marker genotypes as factors with interaction. PHYC falls in the top bin for both associations and the p-values for PHYC are smaller than those for FLC.

Supplementary Table 6.

Strains sequenced for PHYC.

Strain Stock Number
Bay-0 N955
Bur-0 N1029
C24 Lab Stock
Col-0 N1093
Est-1 N1151
Fr-2 N1169
Ga-0 N1181
Gu-0 N1213
Gy-0 N1217
HR5 N22205
Jm-1 N1261
Kondara N6175
Ler Lab Stock
Ll-2 N1343
Ms-0 N1377
Mz-0 N1383
Nd-1 N1680
Nok-3 N1405
Ra-0 N1481
REN1 N22253
Ri-0 N1493
RLD1 N913
Se-0 N1503
Sf-2 N1517
Te-0 N1551
Ts-1 N1553
Tsu-1 N6926
Van-0 CS6884
Wei-0 N6182
A. lyrata Lab stock

Acknowledgments

We thank the Nottingham Arabidopsis Stock Center for seed stocks of SALK T-DNA lines donated by Joe Ecker and colleagues. We thank Christa Lanz for help with sequencing, and Kirsten Bomblies and Ya Long Guo for help with phylogenetic analysis. We thank Kirsten Bomblies, Vava Grbic, Yasushi Kobayashi, Janne Lempe and Stephen Russell for discussion and critical reading of the manuscript, and the anonymous reviewers for insightful comments that led to improvements of the manuscript. Supported by an EMBO Long-Term Fellowship to S.B., an NIH Postdoctoral Fellowship to T.P.M., an NIH grant (GM62932) to J.C. and D.W., and by the Max Planck Society, of which D.W. is a director. J.C. is an HHMI Investigator.

S.B., J.C and D.W. conceived the experiments; S.B., S.S., M.A., T.P.M., C.W, and J.N.M performed the experiments; S.B., S.S., J.N.M., R.C., N.W., J.C and D.W. analyzed the data and wrote the manuscript.

References

  • 1.Chen M, Chory J, Fankhauser C. Light signal transduction in higher plants. Annu Rev Genet. 2004;38:87–117. doi: 10.1146/annurev.genet.38.072902.092259. [DOI] [PubMed] [Google Scholar]
  • 2.Johanson U, et al. Molecular analysis of FRIGIDA, a major determinant of natural variation in Arabidopsis flowering time. Science. 2000;290:344–347. doi: 10.1126/science.290.5490.344. [DOI] [PubMed] [Google Scholar]
  • 3.Aukerman MJ, Amasino RM. Molecular genetic analysis of flowering time in Arabidopsis. Sem Cell Dev Biol. 1996;7:427–433. [Google Scholar]
  • 4.El-Assal SED, Alonso-Blanco C, Peeters AJ, Raz V, Koornneef M. A QTL for flowering time in Arabidopsis reveals a novel allele of CRY2. Nat Genet. 2001;29:435–40. doi: 10.1038/ng767. [DOI] [PubMed] [Google Scholar]
  • 5.Maloof JN, et al. Natural variation in light sensitivity of Arabidopsis. Nat Genet. 2001;29:441–6. doi: 10.1038/ng777. [DOI] [PubMed] [Google Scholar]
  • 6.Olsen KM, et al. Linkage disequilibrium mapping of Arabidopsis CRY2 flowering time alleles. Genetics. 2004;167:1361–9. doi: 10.1534/genetics.103.024950. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Shindo C, et al. Role of FRIGIDA and FLOWERING LOCUS C in determining variation in flowering time of Arabidopsis. Plant Physiol. 2005;138:1163–73. doi: 10.1104/pp.105.061309. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Lempe J, et al. Diversity of flowering responses in wild Arabidopsis thaliana strains. PLoS Genet. 2005;1:e6. doi: 10.1371/journal.pgen.0010006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Werner JD, et al. FRIGIDA-independent variation in flowering time of natural A. thaliana accessions. Genetics. 2005 doi: 10.1534/genetics.104.036533. in press. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Werner JD, et al. Quantitative trait locus mapping and DNA array hybridization identify an FLM deletion as a cause for natural flowering-time variation. Proc. Natl. Acad. Sci. USA. 2005;102:2460–5. doi: 10.1073/pnas.0409474102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Franklin KA, Davis SJ, Stoddart WM, Vierstra RD, Whitelam GC. Mutant analyses define multiple roles for phytochrome C in Arabidopsis photomorphogenesis. Plant Cell. 2003;15:1981–9. doi: 10.1105/tpc.015164. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Monte E, et al. Isolation and characterization of phyC mutants in Arabidopsis reveals complex crosstalk between phytochrome signaling pathways. Plant Cell. 2003;15:1962–80. doi: 10.1105/tpc.012971. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Conti E, Izaurralde E. Nonsense-mediated mRNA decay: molecular insights and mechanistic variations across species. Curr Opin Cell Biol. 2005;17:316–25. doi: 10.1016/j.ceb.2005.04.005. [DOI] [PubMed] [Google Scholar]
  • 14.Dybbs M, Ngai J, Kaplan JM. Using microarrays to facilitate positional cloning: identification of tomosyn as an inhibitor of neurosecretion. PLoS Genet. 2005;1:e2. doi: 10.1371/journal.pgen.0010002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Cowl JS, et al. The PHYC gene of Arabidopsis. Absence of the third intron found in PHYA and PHYB. Plant Physiol. 1994;106:813–4. doi: 10.1104/pp.106.2.813. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Qin M, Kuhn R, Moran S, Quail PH. Overexpressed phytochrome C has similar photosensory specificity to phytochrome B but a distinctive capacity to enhance primary leaf expansion. Plant J. 1997;12:1163–72. doi: 10.1046/j.1365-313x.1997.12051163.x. [DOI] [PubMed] [Google Scholar]
  • 17.Halliday KJ, Thomas B, Whitelam GC. Expression of heterologous phytochromes A, B or C in transgenic tobacco plants alters vegetative development and flowering time. Plant J. 1997;12:1079–90. doi: 10.1046/j.1365-313x.1997.12051079.x. [DOI] [PubMed] [Google Scholar]
  • 18.Weinig C, et al. Novel loci control variation in reproductive timing in Arabidopsis thaliana in natural environments. Genetics. 2002;162:1875–84. doi: 10.1093/genetics/162.4.1875. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Wang X, Korstanje R, Higgins D, Paigen B. Haplotype analysis in multiple crosses to identify a QTL gene. Genome Res. 2004;14:1767–72. doi: 10.1101/gr.2668204. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Alonso-Blanco C, El-Assal SE, Coupland G, Koornneef M. Analysis of natural allelic variation at flowering time loci in the Landsberg erecta and Cape Verde Islands ecotypes of Arabidopsis thaliana. Genetics. 1998;149:749–764. doi: 10.1093/genetics/149.2.749. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Borevitz JO, et al. Quantitative trait loci controlling light and hormone response in two accessions of Arabidopsis thaliana. Genetics. 2002;160:683–96. doi: 10.1093/genetics/160.2.683. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Loudet O, Chaillou S, Camilleri C, Bouchez D, Daniel-Vedele F. Bay-0 x Shahdara recombinant inbred line population: a powerful tool for the genetic dissection of complex traits in Arabidopsis. Theor Appl Genet. 2002;104:1173–1184. doi: 10.1007/s00122-001-0825-9. [DOI] [PubMed] [Google Scholar]
  • 23.El-Lithy ME, Clerkx EJ, Ruys GJ, Koornneef M, Vreugdenhil D. Quantitative trait locus analysis of growth-related traits in a new Arabidopsis recombinant inbred population. Plant Physiol. 2004;135:444–58. doi: 10.1104/pp.103.036822. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Wolyn DJ, et al. Light-response quantitative trait loci identified with composite interval and eXtreme array mapping in Arabidopsis thaliana. Genetics. 2004;167:907–17. doi: 10.1534/genetics.103.024810. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Endler JA. Princeton University Press; Princeton, NJ: 1977. Geographic Variation, Speciation, and the Clines; p. 262. [PubMed] [Google Scholar]
  • 26.Stinchcombe JR, et al. A latitudinal cline in flowering time in Arabidopsis thaliana modulated by the flowering time gene FRIGIDA. Proc. Natl. Acad. Sci. USA. 2004;101:4712–7. doi: 10.1073/pnas.0306401101. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Schmid KJ, et al. Evidence for a large-scale population structure of Arabidopsis thaliana from genome-wide single nucleotide polymorphism markers. Theor Appl Genet. 1:11. doi: 10.1007/s00122-006-0212-7. 2006. [DOI] [PubMed] [Google Scholar]
  • 28.Caicedo AL, Stinchcombe JR, Olsen KM, Schmitt J, Purugganan MD. Epistatic interaction between Arabidopsis FRI and FLC flowering time genes generates a latitudinal cline in a life history trait. Proc. Natl. Acad. Sci. USA. 2004;101:15670–15675. doi: 10.1073/pnas.0406232101. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Aranzana MJ, et al. Genome-wide association mapping in Arabidopsis identifies previously known flowering time and pathogen resistance genes. PLoS Genet. 2005;1:e60. doi: 10.1371/journal.pgen.0010060. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Wagner D, Quail PH. Mutational analysis of phytochrome B identifies a small COOH-terminal-domain region critical for regulatory activity. Proc. Natl. Acad. Sci. USA. 1995;92:8596–8600. doi: 10.1073/pnas.92.19.8596. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Xu Y, Parks BM, Short TW, Quail PH. Missense mutations define a restricted segment in the C-terminal domain of phytochrome A critical to its regulatory activity. Plant Cell. 1995;7:1433–43. doi: 10.1105/tpc.7.9.1433. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Reed JW, Nagpal P, Poole DS, Furuya M, Chory J. Mutations in the gene for the red/far-red light receptor phytochrome B alter cell elongation and physiological responses throughout Arabidopsis development. Plant Cell. 1993;5:147–157. doi: 10.1105/tpc.5.2.147. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Takano M, et al. Distinct and cooperative functions of phytochromes A, B, and C in the control of deetiolation and flowering in rice. Plant Cell. 2005;17:3311–25. doi: 10.1105/tpc.105.035899. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Mathews S, Donoghue MJ. The root of angiosperm phylogeny inferred from duplicate phytochrome genes. Science. 1999;286:947–50. doi: 10.1126/science.286.5441.947. [DOI] [PubMed] [Google Scholar]
  • 35.White GM, Hamblin MT, Kresovich S. Molecular evolution of the phytochrome gene family in sorghum: changing rates of synonymous and replacement evolution. Mol Biol Evol. 2004;21:716–23. doi: 10.1093/molbev/msh067. [DOI] [PubMed] [Google Scholar]
  • 36.Devos KM, Beales J, Ogihara Y, Doust AN. Comparative sequence analysis of the Phytochrome C gene and its upstream region in allohexaploid wheat reveals new data on the evolution of its three constituent genomes. Plant Mol Biol. 2005;58:625–41. doi: 10.1007/s11103-005-6801-z. [DOI] [PubMed] [Google Scholar]
  • 37.Rozas J, Sanchez-DelBarrio JC, Messeguer X, Rozas R. DnaSP, DNA polymorphism analyses by the coalescent and other methods. Bioinformatics. 2003;19:2496–7. doi: 10.1093/bioinformatics/btg359. [DOI] [PubMed] [Google Scholar]
  • 38.Swofford DL. PAUP: A computer program for phylogenetic inference using maximum parsimony. J Gen Physiol. 1993;102:9A. [Google Scholar]
  • 39.Posada D, Crandall KA. MODELTEST: testing the model of DNA substitution. Bioinformatics. 1998;14:817–8. doi: 10.1093/bioinformatics/14.9.817. [DOI] [PubMed] [Google Scholar]