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Genetics. Dec 2007; 177(4): 2433–2444.
PMCID: PMC2219480

Flowering Time Quantitative Trait Loci Analysis of Oilseed Brassica in Multiple Environments and Genomewide Alignment with Arabidopsis

Abstract

Most agronomical traits exhibit quantitative variation, which is controlled by multiple genes and are environmentally dependent. To study the genetic variation of flowering time in Brassica napus, a DH population and its derived reconstructed F2 population were planted in 11 field environments. The flowering time varied greatly with environments; 60% of the phenotypic variation was attributed to genetic effects. Five to 18 QTL at a statistically significant level (SL-QTL) were detected in each environment and, on average, two new SL-QTL were discovered with each added environment. Another type of QTL, micro-real QTL (MR-QTL), was detected repeatedly from at least 2 of the 11 environments; resulting in a total of 36 SL-QTL and 6 MR-QTL. Sixty-three interacting pairs of loci were found; 50% of them were involved in QTL. Hundreds of floral transition genes in Arabidopsis were aligned with the linkage map of B. napus by in silico mapping; 28% of them aligned with QTL regions and 9% were consistent with interacting loci. One locus, BnFLC10, in N10 and a QTL cluster in N16 were specific to spring- and winter-cropped environments respectively. The number of QTL, interacting loci, and aligned functional genes revealed a complex genetic network controlling flowering time in B. napus.

FLOWERING is a primary requirement for plant reproduction and one of the most important agronomic traits for crop production (Tasma et al. 2001). Climate changes and global warming bring greater challenges to farmers in getting their crops to flower on time and therefore maintain high seed yield. A key factor in plant breeding programs is to understand the genetic mechanisms that control crop flowering time under different climatic conditions. Plants of the same species that grow in different ecological conditions have developed various mechanisms to respond to environmental conditions, such as high temperature, vernalization, and day length. This may involve changes in the time of flowering to avoid unfavorable weather, such as harsh winters or hot summers (David and Griffiths 2002; Shindo et al. 2005). Flowering marks the transition from the vegetative to the reproductive stage. Plant flowering is the consequence of a series of developmental events, such as the transition from stem meristem to inflorescence meristem, floral formation, stem elongation, bud development, and blooming. These processes are described in biennial rapeseed as budding, bolting, and flowering. Budding and bolting processes are basic mechanisms for controlling flowering time in plants. However, plants can bolt before budding in spring-cropped environments, which suggests that the two events are independent.

Several pathways controlling floral transition have been revealed in Arabidopsis: photoperiod, vernalization, gibberellic acid (GA), autonomous pathway, and thermal clock (Poethig 2003; Putterill et al. 2004). More than 100 genes control floral transition, and increasing numbers of genes have been shown to be related to flowering time in Arabidopsis. Two type of annuals, biennial (winter annuals) and summer annuals, were classified in Arabidopsis according to whether or not they require vernalization to flower (Amasino 2004). The same two types of crop, winter and spring type, have been bred in crops such as wheat and rapeseed to facilitate crop seasonality. Initial insight into the control of flowering time in crops has been provided by the isolation of a gene (VRNI) in wheat that responds to vernalization (Yan et al. 2003) and two genes in rice, Hd1 and Hd6, which respond to photoperiod (Yano et al. 2000; Takahashi et al. 2001). However, compared with the model plant Arabidopsis, little is known of the mechanisms that control flowering time in crops.

Mapping quantitative trait loci (QTL) onto linkage maps, with different segregating populations, is a powerful genetic approach to dissecting complex agronomical characteristics (Mauricio 2001). Flowering time QTL with large phenotypic effects that have been mapped in Brassica crops include 4 to 9 in B. napus (AACC), 4 to 11 in B. oleracea (CC), and 2 to 7 in B. rapa (AA) (Teutonico and Osborn 1994; Ferreira et al. 1995; Osborn et al. 1997; Bohuon et al. 1998; Butruille et al. 1999; Rae et al. 1999; Lan and Paterson 2000; Kole et al. 2001). Brassica crops, including tetraploid rapeseeds and diploid vegetables, share a common ancestor with Arabidopsis that existed 14–20 million years ago (Yang et al. 1999). The existence of a common ancestor for Brassica and Arabidopsis increases the efficiency of QTL analysis by allowing comparison of Brassica genomic information with that of Arabidopsis (Schmidt et al. 2001). Lagercrantz et al. (1996) presented evidence that flowering time genes in B. nigra were orthologous to CO, a key factor for promoting flowering in the photoperiod pathway in Arabidopsis. Robert et al. (1998) isolated four genes in B. napus orthologous to CO, and a gene designated BnCOa1 was shown to complement the co mutant of Arabidopsis. Bohuon et al. (1998) identified flowering time QTL distributed across four linkage groups in B. oleracea and found that all the QTL regions were syntenic with the region located on chromosome 5 of Arabidopsis, which harbors a number of flowering time genes. It has been reported that most of the difference in flowering time between annual and biennial B. rapa (AA) is controlled by replicated loci of FLC, a gene that is crucial to the vernalization pathway of Arabidopsis (Sheldon et al. 2000). One of the identified flowering time loci, VFR2, was mapped as a single Mendelian locus that overlaps precisely with BrFLC1 (Kole et al. 2001). In B. oleracea, the gene associated with the largest genetic effect (36.8%) among flowering time QTL has been designated BoFLC2 (Okazaki et al. 2007). The diploid Brassica genomes are three to five times larger than the Arabidopsis genome, which suggests that many flowering time QTL remain undiscovered. The availability of genomic resources, such as the high-density comparative map of B. napus and Arabidopsis (Parkin et al. 2005) and the BAC sequence of B. rapa (http://www.brassica-rapa.org), allows the identification of novel flowering time candidate genes, which can be located in the QTL regions of Brassicaea by in silico mapping.

In the study reported here, we evaluated changes in flowering time in B. napus TN DH (Qiu et al. 2006) and its derived F2 population in nine winter-cropped and two spring-cropped environments. Forty-two flowering time QTL (including QTL detected at a statistically significant level and QTL repeatedly detected but nonsignificant) and 63 digenic interacting pairs were detected. The QTL, the epistatic loci, and the underlying genes comprise a genetic architecture that controls flowering time in response to different natural environments in B. napus.

MATERIALS AND METHODS

Plant materials and growing environments:

A doubled haploid (DH) segregating population of 202 lines (designated as TN DH), was derived from a cross between a European winter cultivar Tapidor and a Chinese semi-winter cultivar Ningyou7 (Qiu et al. 2006). The DH lines were also crossed with each other to develop a reconstructed F2 population (RC-F2 population, J. Shi, unpublished data) consisting of 404 lines. The DH population and its parents were grown in 11 natural environments at 3 different locations over a period of 4 years. They were planted in a winter rapeseed crop area near Wuhan in central China (at the field station of Huazhong Agricultural University, in Daye county and in Jingzhou county, coded W1, W2, and W3, respectively) and a winter rapeseed area, Dali, in northwest China (coded D) for 4 years (2002–2003, 2003–2004, 2004–2005, and 2005–2006) and in a spring-rapeseed crop area, Hezheng, a plateau farther northwest in China (coded as H) for 2 years (2005 and 2006). Four hundred and four F2 lines were also grown in Daye (W2) and Dali (D) for 1 year (2005–2006) (see supplemental Table 1 at http://www.genetics.org/supplemental/). Populations planted in winter-cropped environments were sown in October and harvested the following May. Populations planted in spring-cropped environments were sown in April and harvested in August of the same year.

For the DH population, each field trial consisted of 205 plots, including two parents and their F1 hybrids. Each plot contained one genotype with 30 plants. Three replications were held in winter-cropped environments (Qiu et al. 2006) and one (2005) or two (2006) replications in spring-cropped environments. For the RC-F2 population, each field trial consisted of 407 plots with three replications.

Phenotypic measurements:

Flowering time data were recorded from the sowing day to the day when the first flower had opened on half of the plants in the plot. DH lines that did not bloom at all in the spring-cropped environments were given a score of 150 days. Data for bolting time (the time when the stem elongated to 10 cm) and budding time (when visible buds appeared) were also collected from the S05H, S06H, and W06W1 environments for the DH population to determine whether genes located in the QTL respond to floral transition or stem/flower development.

Linkage map construction:

A total of 344 markers, including SSR, RFLP, SNP, MS-AFLP (methylation sensitive-AFLP), and STS markers, were added to the basic linkage map generated with the TN DH population (Qiu et al. 2006) using the software program Joinmap3.0 (http://www.kyazma.nl/index.php/mc.JoinMap). The new linkage map contained 621 markers, spanning 2060 cM (Kosambi function) with an average interval of 3.3 cM between markers (see supplemental Table 2a at http://www.genetics.org/supplemental/). The sequences of primers amplifying the FLC orthologs of Arabidopsis, BnFLC-13a, and BnFLC-13b were obtained directly from Pires et al. (2004), and the PCR products were converted to cleaved amplified polymorphic sequence (CAPS) markers. The primer for BnFLC-10 was designed according to the cDNA sequence of B. napus in the database (AY036888). The primers for CO, FT, and AP1 were designed from the homologous sequences in Arabidopsis using the single strand configuration polymorphism (SSCP) method for mapping (see supplemental Table 2b at http://www.genetics.org/supplemental/). The nomenclature of the orthologs of flowering time genes was prefixed with Bn, followed by the name of the gene in Arabidopsis and suffixed with the number of the linkage group of B. napus where the locus was mapped. When multiple loci were detected for an ortholog in one linkage group, an Arabic letter was added to the end of the name and arranged in order.

Map alignment between B. napus and Arabidopsis:

Two hundred and twenty-nine of 621 linked markers with known sequence information were employed as anchored markers to carry out map alignment between B. napus and Arabidopsis. The physical positions of the 51 RFLP markers in the pseudochromosomes of Arabidopsis were identical to the results of Parkin et al. (2005); the physical positions of the 47 SSR markers (prefixed with CNU and NIAB) were obtained according to the physical positions of the corresponding seed BACs of B. rapa, which are described in the Brassica rapa Genome Project (BrGP, http://www.brassica-rapa.org/). The physical positions of the other 30 SSR and 101 SNP markers are found on the websites of BrassicaDB (http://brassica.bbsrc.ac.uk/cgi-bin/ace/searches/browser/BrassicaDB) and the Brassica Genome Project (http://brassica.bbsrc.ac.uk/IGF/) and of the GEBOC project of the National Basic Research Program of China (http://www.geboc.org/compare/compare.html). The alignment result was used to construct the synteny block or insertion fragment (island) between Arabidopsis chromosomes and TN linkage groups. A synteny block was considered to exist if a region from the TN genetic map had at least three closely linked homologous loci within one specific segment of Arabidopsis as described by Parkin et al. (2005). The genetic position of a boundary (Pb) between two closely linked blocks in the TN linkage group corresponding to the different blocks in Arabidopsis was determined by

equation M1

where equation M2 and equation M3 are the genetic position of two closely linked markers, and equation M4 and equation M5 denote the physical distance from the position of the marker to the corresponding boundary of the block in Arabidopsis.

An island (insertion segment) was considered to exist if there was only one or two closest marker(s) laid independently or inserted in the determinate blocks. An island identified by a single marker expands to 3.4 cM on the linkage group of B. napus equivalent to a relative segment of 2 Mbp in Arabidopsis pseudochromosome. If two markers with a genetic distance >3.4 cM but their physical positions located to the same synteny block, the two islands were joined to form one island.

The flowering time genes of Arabidopsis on each synteny block were located according to their physical positions in the genome of Arabidopsis. The positions of putative genes were aligned to the TN linkage map according to their closest anchored marker(s) in the same synteny block. The genetic distance between each putative gene and its closest anchored marker(s) in the TN linkage map was proportional to the physical distance between the position of the corresponding flowering time gene and the closest anchored marker(s) in the Arabidopsis pseudochromosome. If the position of aligned gene(s) was located in the confidence interval (C.I.) of a QTL or marker interval of an IP (interacting pair of loci), the gene(s) was associated with the QTL or the IP.

Data analysis:

Statistical analyses were conducted using the GLM procedure of SAS 8.0 (Sas Institute 1999). In each experiment, genotypes (of DH lines or RC-F2 lines), environments, genotype by environment (GE) interactions, and replicates within environment were treated as random effects. The four components of variance were used to estimate the narrow-sense heritability (h2) on a mean basis for each trait as described by Hallauer and Miranda (1988).

QTL detection and genetic analysis:

Both single-locus QTL and epistatic QTL were analyzed in the two populations in all the environments. The QTL mapping was analyzed using the composite interval method (CIM) with WinQTL cartographer 2.5 software (Zeng 1994; Wang et al. 2006; http://statgen.ncsu.edu/qtlcart/WQTLCart.htm). CIM was used to scan the genetic map and estimate the likelihood of a QTL and its corresponding effect at every 2 cM. The forward regression algorithm was used to get cofactors. The permutation test method was used to obtain the empirical thresholds of the experiment on the basis of 1000 runs of randomly shuffling the trait values, which were expected to have a genomewide type-I error of 0.05 (Churchill and Doerge 1994). Thus, LOD values 3.20–3.54 (DH population) and 3.87–4.13 (RC-F2 population) were used for identifying statistically significant QTL (SL-QTL) in each environment, respectively. In addition, those QTL that appeared repeatedly in at least two environments at a LOD value below the significance level (3.20–4.13) but higher than 2.0 (DH population, P ≤ 0.5) or 2.8 (RC-F2 population, P ≤ 0.5) were considered as micro-real QTL (MR-QTL). Multi-trait QTL mapping (MTM) analysis with CIM method was used to analyze the QTL × environment interaction in the DH population. LOD value 9.17 (P ≤ 0.05) was determined by permutation test (1000 runs of data shuffling) described by Doerge and Churchill (1996). QTL detected in different environments were considered to be the same one if their C.I. overlapped each other with 2-LOD (equal to 95% C.I.). On the other hand, if one QTL's C.I. overlapped that of others in one environment (s) but not in other environment (s), the QTL was considered as an independent QTL.

QTL nomenclature typically used in rice (Mccouch et al. 1997) was adopted. A designation begins with “q,” followed by an abbreviation of the trait name, the linkage group suffixed with the marker “-,” and finally the serial number of QTL in the linkage group.

The maximum-likelihood estimation method in QTLmapper V2.0 software (Wang et al. 1999) was employed to detect the epistatic interactions of flowering time in B. napus. The LR value corresponding to P = 0.005 was used as the threshold for claiming the presence of putative epistatic interacting pairs of loci. The significance of the epistatic effect was further tested by running the submenu of the Bayesian test (using P ≤ 0.005). The genetic effects, including single-locus and two-locus effects, were obtained on the basis of the results of the epistatic interaction detection model.

To distinguish the effect of a specific QTL on budding, bolting, or flowering time, conditional QTL analysis was carried out. The conditional QTL mapping method with the software Genad.exe and Gencond1.exe (Zhu 1995; http://www.cab.zju.edu.cn/ics/faculty/zhujun.htm) was used to analyze the relationship between flowering time, bolting time, and budding time. Conditional phenotypic values, yhk (T1 | T2), were obtained by the mixed model approach for the conditional analysis of quantitative traits, where T1 | T2 represents trait 1 conditioned on trait 2 (for example, Ft | Bud denotes the flowering time conditioned on budding time). The conditional phenotypic values were also analyzed using the software WinQTL cartographer2.5.

RESULTS

Phenotypic variation and genetic effects:

The two parent plants flowered in March after the cold season; Tapidor consistently flowered later than Ningyou7 in winter-cropped environments. In spring-cropped environments Ningyou7 flowered normally; however, the winter-type cultivar, Tapidor, did not flower at all, indicating a strong requirement for vernalization. The two populations showed transgressive segregation of flowering time in all environments (Figure 1; see supplemental Table 3 at http://www.genetics.org/supplemental/). For the nine winter-cropped environments, the later-flowering lines of the two populations grown in Dali (D) flowered much later than in the Wuhan area (W). The normal distribution of flowering times in the two populations indicated a quantitative genetic control mechanism, and the variation in flowering time across environments indicated that different flowering genes responded to different environments. The results of ANOVA analysis (GLM procedure) showed that genotype, environment, and G × E had significant effects on flowering time in both populations (Table 1). The values of sigma square for the environment were the largest in both of the populations. The value of sigma square for the G × E interaction was larger than that of the genotype in DH population, which was grown in both spring-cropped and winter-cropped environments, but the reverse was true in the RC-F2 population, which was only grown in winter-cropped environments. Consequently, QTL analysis was done in each environment separately and the QTL × environment interaction was analyzed with MTM in DH population in the following section. The heritability (h2) of flowering time estimated from the variance components was high (77% of TN DH population and 90% of RC-F2 population).

Figure 1.
Phenotypic variation of flowering time in the two populations in multiple environments. The scales below represent the period from sowing to flowering in days. The solid lines show flowering time spans of plants in the mapping populations in each environment. ...
TABLE 1
Results of ANOVA with GLM procedure on the trait of flowering time from two populations measured in 11 environments

The phenotypic variation in flowering time in different environments was attributable mainly to genetic effects, which accounted for 44–74% (60% on average) of the phenotypic variation (Table 2). The single-locus effect (including additive and dominance effects) and the epistatic effect demonstrated by two-locus analysis were the major influences on flowering time, in a ratio of ~6:4. The dominance effect, which was revealed in the RC-F2 population, contributed 17% of the phenotypic variation at single loci and 10% of the total genetic effect.

TABLE 2
Genetic effects on phenotypic variation and the number of QTL and interacting loci pairs of flowering time detected in multiple environments

Genomewide detection of important QTL and significant epistatic loci from the multi-environments:

A total of 42 unique flowering time QTL were detected from two populations and nine environments with nonredundancy; 36 identified as SL-QTL (had LOD values above the threshold at P ≤ 0.05), and the other 6 designated MR-QTL (had lower LOD values with small genetic effects but repeatedly appeared in multiple environments) (Table 2; Figure 2; see supplemental Table 4 at http://www.genetics.org/supplemental/). Most (32/36) of the SL-QTL could be detected in the DH population, and 27 of 36 SL-QTL were detected in the RC-F2 population. Of the SL-QTL, 23 could be detected in both populations. All the SL-QTL were distributed only on 9 linkage groups, i.e., N1, 2, 3, 9, 10, 12, 16, 18, and N19, but QTL with the highest LOD values in spring-cropped and winter-cropped environments were detected on N10 and N16, respectively.

Figure 2.
Distribution of unique QTL for flowering time in 11 linkage groups of B. napus. Whole or part of linkage group was shown with black line labeled with molecular markers (short vertical bars) on the bottom, and the Arabic numerals listed on both sides show ...

Multiple environments gave higher resolution for QTL screening. There were only 10 SL-QTL detected, on average, in a single environment. However, on average, 2 new SL-QTL were detected with each added environment. Among the total of 36 SL-QTL, 31 could be detected repeatedly in more than one environment. Each SL-QTL explained 3–50% of the phenotypic variation in its most favorable environment. One SL-QTL in N10 was specific to spring-cropped environments, and 23 were specific to winter-cropped environments.

The two mapping populations were combined to analyze MR-QTL. Six MR-QTL were detected, and each located in N1, N3, N5, N10, and N12. Twenty-four QTL could not be counted as MR-QTL since they reached significant level statistically in other environment(s) and thereafter were considered as SL-QTL, such as the QTL located in N18 (Figure 3A; supplemental Table 4).

Figure 3.
QTL scanning results from 11 environments with the TN DH population. Curves of different colors represent QTL scanned from different environments. The triangles on the bottom line show the positions of molecular markers in the linkage group. (A) Examples ...

Sixty-three statistically significant IPs of loci controlling flowering time were detected in the DH and F2 populations in different environments (Table 2; see supplemental Table 5 at http://www.genetics.org/supplemental/). The number of IPs in each genome varied: 22 IPs in the A genome (N1–N10), 8 in the C genome (N10–N19), and half of them occurred between the two genomes of B. napus. Thirteen SL-QTL and 3 MR-QTL were involved in IP, including 2 pairs showing QTL/QTL interaction and 31 pairs showing QTL/non-QTL interaction. Moreover, 50% (30/63) of the IP did not involve any QTL. Eighteen IPs detected in the RC-F2 population were used to analyze the genetic nature of epistatic interaction. The results indicated that two-thirds of the IP were additive × additive interacting pairs. The genes that underlie IP are analyzed by comparative mapping.

Dissection of QTL for budding and bolting time:

According to the data collected from S05H, S06H, and W06W1 environments where the traits of bolting, budding, and flowering time were investigated, 16, 15, and 16 SL-QTL for bolting, budding, and flowering time were detected, respectively, with 7 overlapping QTL for all the three traits. There were 9 SL-QTL detected for both budding time and flowering time (supplemental Table 4). It appears that some QTL could control all three traits with varied effects and the other QTL were specific for one or two traits.

Conditional QTL analysis showed that 90% and 58% of flowering time QTL disappeared after being conditioned on budding and bolting, respectively. This indicates that the majority of flowering time QTL was associated with floral transition (Figure 4). The flowering time QTL with a LOD value of 7, which overlapped the C.I. of bolting or budding time QTL, decreased its LOD significantly after being conditioned on either bolting or budding, implying that such QTL were responsible for bolting and budding time. One QTL in N3, qFT3-3, appears to be responsible for the speed of floral organ development because only a small decrease occurred in its LOD value and genetic effect compared with the phenotypic variation after it was conditioned on budding time.

Figure 4.
The ratio of changeable and unchangeable SL-QTL after conditioned on budding time and bolting time. Ft | Bud, Ft | Bolt, and Bud | Bolt denotes the flowering time conditioned on the budding time, on the bolting time, and the budding ...

Association of QTL and IP with functional genes by large-scale comparative mapping:

Using the in silico mapping approach, 40 synteny blocks and 84 islands between A. thaliana pseudochromosomes and the TN linkage groups were aligned (see supplemental Figure 1 at http://www.genetics.org/supplemental/). Five hundred and forty-eight orthologs of 120 flowering time genes in Arabidopsis were mapped onto the synteny blocks and islands. One hundred fifty-three (28%) and 50 (9%) of the orthologs were mapped in the regions of QTL and nearby IP loci, respectively. There were 126 orthologs associated with SL-QTL and 27 with MR-QTL. On the other hand, 26 SL-QTL (72%), 4 MR-QTL (67%), and 26 IP loci (41%) were associated with the orthologs (supplemental Table 4; supplemental Table 5).

The N3 linkage group contained 24% of the SL-qFT (QTL for flowering time) and 19% of the qBu (QTL for budding) and qBo (QTL for bolting) in the genome. Five SL- qFT on N3 could be detected in both winter- and spring-cropped environments. One hundred flowering time orthologous genes were aligned on N3. Many genes involved in different floral transition pathways were mapped onto the alignment array, e.g., FLC, FLD, and VRN1 from the vernalization pathway; CO, COP9, and CCA from the photoperiod pathway; and TFL2/SOC for the central suppresser/promoter system (Figure 3B). All of the SL-QTL detected in both winter- and spring-cropped environments were concentrated on the first half of the chromosome, from marker E6HM31-450 to marker CNU270 (4.3 cM–76.5 cM). There was just one MR-QTL located on the N3 linkage group. There were 13 IP involved with loci on N3, which occupied 21% of the IP. Among all the IP on the chromosome there were three QTL involved of which two SL-QTL could be detected on both macroenvironments (supplemental Table 5).

The QTL with the largest genetic effect, qFT10-4, was detected in N10 and explained 50% and 26% of the phenotypic variation for flowering time and budding time, respectively, in the spring-cropped environment (Figure 3C). It was actually a major factor for floral transition and might also have an effect on bolting time (LOD value = 3.81 in S05H environment) considering that it dramatically changes in LOD value after conditional analysis. GE analysis showed that this QTL was highly environment dependent (P ≤ 0.05), and the LR value of the QTL × environment were extremely high in two spring environments (11.0 for S05H and 19.7 for S06H environment) and was very low in all of winter environments (0.10–6.67). It is obvious that the major flowering time QTL, qFT10-4, was specific for spring rapeseed environment. An ortholog of FLC, the major suppressor of floral transition in nonvernalization environments in Arabidopsis, underlay the QTL region by in silico mapping. Correspondingly, a RFLP marker, BnFLC10 (previously named as BnFLC1 in B. napus by Pires et al. 2004), was mapped to a position corresponding to the peak of the QTL. In a BC3F2 population segregating at locus BnFLC10, the genotype with homozygous alleles from the early-flowering parent Ningyou7 showed cosegregation with the flowering phenotype (Figure 5). Three Indel markers were found between the two alleles of Tapidor and Ningyou7. One of the Indel markers, BnFLC10-M1, was detected in all 20 winter-type cultivars of B. napus with the winter-type allele and 14 to 66 spring-type cultivars with the spring-type allele (J. Hou, unpublished data). Therefore, BnFLC10 may be the gene that controls flowering time in nonvernalization environments.

Figure 5.
Distribution of flowering time with the segregation of locus BnFLC10 in the BC3F2 population. The open, shaded, solid, and diagonal bars represent plants homozygous with alleles of BnFLC10-Ningyou7, heterozygous, homozygous with the allele of BnFLC10 ...

A QTL cluster was detected in every winter-cropped environment in N16 (Figure 3D). This cluster explained 26%–52% of the phenotypic variation in each winter-cropped environment. In contrast, the cluster could not be detected in any spring-cropped environment and showed great QTL × winter-crop environment interaction (LOD = 19.3 in MTM analysis). Two to four SL-QTL, each explaining 6.30–19.29% of the phenotypic variation, could be identified in the cluster in each biennial crop environment. The peaks of QTL in the cluster varied slightly with environment, which resulted in five SL-QTL being detected in all winter-cropped environments. The additive effect within the cluster was negative indicating that the alleles from the winter cultivar, Tapidor, accelerated flowering.

An STS marker, BnFT-16, designed from the flowering gene FT, was mapped to near the highest peak of the QTL cluster. The corresponding alignment of Arabidopsis contains an inverted repeat DNA sequence that contains at least 14 flowering time genes. Two closely linked loci of FT in Arabidopsis were mapped directly to the position that corresponds to the central peak of the QTL cluster, qFT-16, and the two copies of FKF1, which encode the photoperiod acceptor of Arabidopsis, were located in the QTL region. Two SSR markers (sR12387 and Na12A05), which were located on one side of FT, were used to perform genotype analysis in a BC4F2 population grown in winter-crop environment. It was found that 20% of plants in the population with the donor (Tapidor) allele budded very early and 100% of plants with recurrent parental alleles did not bud at all in early winter (J. Wang, unpublished data). This demonstrated that the alleles from Tapidor in the cluster had a positive effect on flowering transition.

DISCUSSION

There were 42 flowering time QTL revealed in rapeseed in this large-scale experiment, which involved the TN DH population and its derived RC-F2 population grown in 11 environments. Sixty-three interacting pairs of loci were also detected, and 50% of these loci were involved with the QTL regions. Seventy-four percent of the QTL (31/42) and 41% (26/63) of the interacting loci were associated with underlying functional genes by in silico mapping analysis, which were aligned with 120 orthologs of Arabidopsis and were mainly responsible for floral transition. The derived architecture, which consists of QTL, interacting pairs of loci, and underlying genes, may provide a platform for Brassica researchers to associate the dissected flowering time QTL with functional genes. For example, BnFLC10, which marks the division between spring- and winter-type rapeseed crops, and the QTL cluster in N16 function as an accelerator of floral transition in Arabidopsis. The network may also provide a data resource that will allow rapeseed breeders to design new cultivars aimed at particular environments.

It is widely accepted that quantitative traits are controlled by multiple genes. However, it is rare that >10 QTL are detected in one experiment (Gilliland et al. 2006; Li et al. 2006). Valdar et al. (2006) have suggested that many more QTL would be revealed using new approaches. They used a very large heterogeneous sample of mice that had been bred for 50 generations for increased QTL mapping resolution and discovered 843 QTL for 101 traits, with a maximum of 20 QTL per trait. It is necessary to develop a general method of revealing a larger number of QTL in one mapping population in crops. On the basis of the TN DH mapping population, we developed a RC-F2 population for phenotypic evaluation to allow the determination of dominance effects that led to an increase of 18% statistically significant QTL. Moreover, the two sister populations were grown in multiple (11) environments, which resulted in the discovery of 2 novel SL-QTL, on average, in each additional environment.

In QTL mapping practice, two adjacent QTL, detected from two environments, with overlapping C.I.'s (e.g., 10-cM C.I. overlapping by 5 cM) would be considered as one QTL (Udall et al. 2006). However, a 15-cM genetic distance may cover 9 Mb and harbor 1800 genes in the rapeseed genome, and it is probable that at least two of these 1800 genes will respond to different environments. In contrast, one conventionally mapped QTL in rice and Arabidopsis may be subsequently “modified” into two sub-QTL with derived near-isogenic lines (NILs) (Monna et al. 2002; Kroymann and Mitchell-Olds 2005; Thomson et al. 2006). In this experiment, we used the default genetic distance 5 cM to define a QTL in a specific environment. Two or more SL-QTL detected from multi-environments were defined as the same one when they had overlapping C.I.'s. After combining the C.I.'s of QTL in different environments, 36 SL-QTL were incorporated from 131 isolated or overlapping SL-QTL. Four thirds of the QTL were associated with flowering time genes colocated by in silico mapping. It provided a basic genetic network of flowering time for rapeseed.

Large numbers of QTL below a certain level of statistical significance are usually ignored when determining the presence of a QTL (Churchill and Doerge 1994; Van Ooijen 1999). However, a QTL with a low LOD value in one experiment may be significant in another experiment.

Gilliland et al. (2006) used two different populations to map QTL associated with vitamin E content in seeds of Arabidopsis, and found that a QTL that exists above the threshold in one population could be just below the threshold value in another. The spring-crop-specific QTL, qFT10-4, detected in this study could not be detected at a statistically significant level in another mapping population in the spring-cropped environment (Udall et al. 2006). In fact, many QTL may have small phenotypic effects: it is accepted that mutations in genes with very small effects are responsible for adaptive evolution (Fisher 1958). Furthermore, the expressivity of alleles varies with environment. Li et al. (2003) analyzed the QTL × environment interaction in rice in nine environments for heading date and plant height and found that only 56% of QTL appeared repeatedly in multiple environments at P ≤ 0.05. Lander and Kruglyak (1995) postulated four types of linkages when doing QTL mapping: suggestive linkage, significant linkage, highly significant linkage, and confirmed linkage. In this study, a suggestive linkage between phenotype and genetic locus was given with LOD = 2.0 (DH population) and LOD = 2.8 (RC-F2 population). We defined the MR-QTL as those repeatedly detected at a LOD value above the threshold of suggestive linkage while below the threshold of P = 0.05 level, thus leading to six more QTL being detected. Actually, while each SL-QTL was repeatedly detected from approximately four environments on average, it could be repeatedly detected in five environments if the threshold was decreased to the level of suggestive linkage. In other words, one SL-QTL in an environment might be an MR-QTL at another environment, and an MR-QTL might also be a SL-QTL if the environment is good enough to induce the significantly differential expression of the two alleles.

Common molecular markers and genetic linkage maps are powerful tools for comparing the chromosomal organization of different species or exploring homologous chromosomes in polyploids (Van Deynze et al. 1995; Schmidt 2000; Udall et al. 2005; Lohithaswa et al. 2007). The annotated Arabidopsis genome sequence has been exploited as a tool for carrying out comparative analyses of the Arabidopsis and Brassicaceae genomes, and conserved genomic blocks have been identified in different Brassicaceae species after comparing the sequences of molecular markers with the genome sequence of Arabidopsis (Boivin et al. 2004; Parkin et al. 2005; Schranz et al. 2006). It provides a route to align candidate genes underlying QTL controlling agronomically important traits. Using this approach, 548 orthologs of flowering time genes in Arabidopsis were mapped onto the synteny blocks and islands of B. napus in this study, and 28% and 9% of orthologs would be of great assistance to resolve the flowering time QTL and interacting loci, respectively.

A range of important flowering time genes, such as FLC, CO, LFY, and FY, are located on the top of chromosome 5 of Arabidopsis (Koornneef et al. 1994). Many flowering time QTL have been identified in the collinear region of Brassica, i.e., FR1 on R2 (BrFLC2 located), FR2 on R3 (BrFLC3 and BrFLC5 located), and VFR2 on R10 (BrFLC1 located) in B. rapa (Osborn et al. 1997; Kole et al. 2001; Schranz et al. 2002), and VFN1 (N2, BnFLC2 located), VFN3 (N3, BnFLC3 and BnFLC5 located), VFN2 (N10, BnFLC1 located), dft12.5 (N12, BnFLC2 located), and dtf13.3 (N13, BnFLC3 located) in B. napus (Osborn et al. 1997; Butruille et al. 1999; Osborn and Lukens 2003; Udall et al. 2006). In this study, flowering time QTL were identified from all of the regions reported above except N13, and more QTL were detected in N3 and a new QTL appeared in N19. One QTL, qFT10-4, the QTL with the highest LOD score in a spring-cropped environment, was identified on N10. The candidate gene for the QTL, BnFLC10, has been demonstrated to be an important element on determining the type of rapeseed, winter crop or spring crop, with the further evidence of the experiment showing genotype–phenotype cosegregation.

The bottom of chromosome 1 of Arabidopsis harbors key genes for flowering time, i.e., FT, FKF1, AP1, and EFS. The homologous region was inverted and duplicated in chromosome 6 of B. oleracea in which BoAP1-a was associated with cauliflower curd phenotype (Smith and King 2000; Howell et al. 2005). A major flowering time QTL was repeatedly detected in the collinear region of N16 in B. napus (Delourme et al. 2006). Many more QTL controlling three traits, i.e., bolting, budding, and flowering, were revealed in the region given prominence to their specific role in winter-cropped environment in this study. In fact, two copies of AP1 and one copy of FT have been mapped in this QTL cluster. The cluster may represent a key point in the photoperiod network because the later flowering cultivar, Tapidor, was much more sensitive to day length changes than the spring-type cultivar after vernalization (data not shown). The alleles from Tapidor in the cluster accounted for up to 52% of the phenotypic variation in the winter-cropped environment. One major hazard for winter crops is when plants start bolting or flowering in a period during the winter when the temperature rises to spring-like levels. This situation is now more common, due to climate change, and serious loss in yield may occur if cold weather follows. Despite the unpredictability of the weather, the change in photoperiod is always regular. It appears that, apart from the vernalization pathway, a strong suppression factor operates in the QTL cluster during the short-day photoperiod to guarantee that the winter cultivar parent does not flower during a warm winter; the suppression is released rapidly when the long-day photoperiod approaches. The identification of such suppression factors should be the subject of future research.

Epistasis is a major genetic component controlling both qualitative and quantitative traits (Cheverud and Routman 1995; Yu et al. 1997). It is not a surprise that 63 IP were detected at a significant level of P = 0.005 from this experiment considering there are at least four pathways regulating flowering in Arabidopsis. One of the key genes in vernalization pathway, FLC, is revealed to interact with tens of regulating genes that belong to different pathways (Schmitz and Amasino 2007). The numerous IP in rapeseed suggested a complex genetic network controlling flowering time. Since only 39 QTL were recognized from the experiment, it is noteworthy that a considerable number of IP (50%) did not involve any identified QTL. It is accepted that some of IPs have been found without specific QTL involved in different organisms (Eshed and Zamir 1996; Li et al. 1997; Shimomura et al. 2001; Montooth et al. 2003). Carlborg and Haley (2004) pointed out that some statistical methods that were used to dissect epistatic QTL were based on simultaneous scans and randomization tests, which resulted in identifying epistatic QTL that do not have individual effects. The model used in this study also belongs to this type (Wang et al. 1999). In fact, one-third of non-QTL/non-QTL IP (10/30) were located in regions that had flowering time genes beneath them such as FT, VRN1, and PIE1. The candidate genes underlying the IP provide clues for dissecting the biological meaning of IP. For example, the candidate genes MXP5.1 and VRN2 for IP3-24/10-32 (qFT3-5 and qFT10-7 involved), belong to vernalization pathway. MXP5.1 encodes VIL1, and VIL1 along with VIN3 interacted with VRN2-involved PRC2-Like complex (Schmitz and Amasino 2007). The IPs together with QTL, both SL-QTL and MR-QTL, identified from multi-environmental tests, and the associated genes underlying the IPs and QTL suggest considerable biological meaning. Further resolution of these interesting loci in the network would further help us to understand their biological meaning.

Acknowledgments

The authors thank Jingguo Hu, Eddie Arthur, and Gerhard Rakow for their critical reading of the manuscript and Xiaoxiao Zou for technical help. The authors thank an anonymous reviewer for extraordinary assistance with improving the manuscript. Financial support for this work was provided by the National Basic Research and Development Program (2006CB101600) and National 863 High Technology Program, P. R. China (2006AA102108 to J. Zhao). Also this research was supported by grants from the Rural Development Administration (BioGreen 21 Program) and the Korean Science and Engineering Foundation, Seoul, Republic of Korea (R21-2004-000-10010-0).

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