A spruce gene map infers ancient plant genome reshuffling and subsequent slow evolution in the gymnosperm lineage leading to extant conifers
© Pavy et al; licensee BioMed Central Ltd. 2012
Accepted: 26 October 2012
Published: 26 October 2012
Seed plants are composed of angiosperms and gymnosperms, which diverged from each other around 300 million years ago. While much light has been shed on the mechanisms and rate of genome evolution in flowering plants, such knowledge remains conspicuously meagre for the gymnosperms. Conifers are key representatives of gymnosperms and the sheer size of their genomes represents a significant challenge for characterization, sequencing and assembling.
To gain insight into the macro-organisation and long-term evolution of the conifer genome, we developed a genetic map involving 1,801 spruce genes. We designed a statistical approach based on kernel density estimation to analyse gene density and identified seven gene-rich isochors. Groups of co-localizing genes were also found that were transcriptionally co-regulated, indicative of functional clusters. Phylogenetic analyses of 157 gene families for which at least two duplicates were mapped on the spruce genome indicated that ancient gene duplicates shared by angiosperms and gymnosperms outnumbered conifer-specific duplicates by a ratio of eight to one. Ancient duplicates were much more translocated within and among spruce chromosomes than conifer-specific duplicates, which were mostly organised in tandem arrays. Both high synteny and collinearity were also observed between the genomes of spruce and pine, two conifers that diverged more than 100 million years ago.
Taken together, these results indicate that much genomic evolution has occurred in the seed plant lineage before the split between gymnosperms and angiosperms, and that the pace of evolution of the genome macro-structure has been much slower in the gymnosperm lineage leading to extent conifers than that seen for the same period of time in flowering plants. This trend is largely congruent with the contrasted rates of diversification and morphological evolution observed between these two groups of seed plants.
KeywordsAngiosperm duplication evolution gene families genetic map gymnosperm phylogenomics Picea spruce structural genomics
Gene duplication plays an important role in providing raw material to evolution . In plants, gene duplicates arise through diverse molecular mechanisms, ranging from whole-genome duplication to more restricted duplications of smaller chromosomal regions . The evolution of the flowering plant genomes has been intensively studied since the completion of the genome sequence for several angiosperm species. Lineage-specific whole-genome duplications greatly contributed to the expansion of plant genomes and gene families (for examples, see [3–9]), with whole-genome duplications found in basal angiosperms, monocots and eudicots [9–12].
Little is known about the large-scale evolutionary history of gene duplications for other seed plants, as well as before the origin of angiosperms. Spermatophytes encompass the angiosperms and the gymnosperms, whose seeds are not enclosed in an ovary. The two groups diverged around 300 million years ago (Mya) in the Late Carboniferous [13, 14]. Contrary to angiosperms, which underwent massive adaptive radiation to supplant the gymnosperms as the dominant vascular plant group [15, 16], extant gymnosperms are divided into a relatively small number of groups including the Pinophyta (conifers), Cycadophyta (cycads), Gnetophyta (gnetophytes) and Ginkgophyta (Ginkgo), and they contain about 1,000 species . Polyploidy is rare in gymnosperms. Only 5% of them, and 1.5% of the subgroup conifers, have been reported as polyploid species [18, 19], as indicated by cytological analysis , distributions of synonymous substitution rates [19, 20] or phylogenetic analysis . Nevertheless, the genomes of some gymnosperms, such as in the conifer family Pinaceae, are among the largest of all known organisms , with haploid genome sizes up to 37 Gb for Pinus gerardiana [22, 23].
Several issues need to be addressed regarding the evolution of the seed plant genome, and that of the plant genome predating the gymnosperm-angiosperm (GA) divergence. How many gene duplications are shared between angiosperms and gymnosperms, which would predate their divergence and make them ancient? How frequently have gene duplications occurred solely in gymnosperms since their split from angiosperms? Are ancient duplicates, those preceding the GA split, relatively more abundant and more translocated through the gymnosperm genome than most recent duplicates specific to the gymnosperms?
These questions could be addressed through a phylogenomic approach, where the members of different gene families are mapped in a gymnosperm taxon with these families further sampled in completely sequenced angiosperm taxa to reconstruct their multiple phylogenies. Given that the gene complement of model angiosperms has been entirely determined by complete genome sequencing, but not that of a gymnosperm taxon, such gene phylogenies would give rise to mixed angiosperm-gymnosperm nodes and gymnosperm-specific nodes. With respect to the divergence time between pro-angiosperms and pro-gymnosperms (approximately 300 Mya), different grouping of gene duplicates could help determine the relative age of duplications, such that mixed angiosperm-gymnosperm nodes predating the split between angiosperms and gymnosperms would indicate ancient duplications, while gymnosperm-specific nodes postdating this split would indicate more recent duplications. The various proportions of these nodes over a large number of gene phylogenies would provide a glance at the relative frequency of ancient to recent gene duplications in the gymnosperm lineage, and the mapping of these duplicates on a gymnosperm genome would allow assessment of their possible translocation. Because of the incomplete nature of gene inventories in gymnosperms, such an analysis from the perspective of the angiosperm lineage is still not possible, given that nodes containing angiosperm duplicates only might not be truly angiosperm-specific. On a smaller scale, similar approaches have been applied to investigate the deep phylogenies of a few seed plant gene families completely sequenced in the conifers. They have indicated that, while some gene duplications deemed ancient predated the split between gymnosperms and angiosperms, some duplications have occurred more recently that are specific to the gymnosperm lineage (for example, ).
Based on such a phylogenetic approach together with gene mapping, one could also ask if the spread of gene families over the gymnosperm genome is more likely for ancient duplicates predating the GA split than for more recent duplicates postdating this split. Theoretical and empirical approaches have shown that duplicated regions should be translocated with time . As such, one would expect the more recent gymnosperm-specific duplicates to be physically less spread across the genome than more ancient duplicates predating the GA split. Altogether, the relative age of gymnosperm-specific gene duplicates and their degree of translocation would allow an assessment of whether the conservation of genome macro-structure parallels the recognised archaic nature of gymnosperms in terms of morphology, the reproductive system and other phenotypic attributes . Testing these hypotheses requires large catalogues of gene sequences, which have recently become available in conifers , and mapping of a large number of genes in a gymnosperm.
In this study, we assembled a map involving 1,801 spruce-expressed genes and examined the distribution of gene families onto the spruce genome and its level of conservation across Pinaceae and angiosperm genomes. We asked whether ancient gene duplicates shared with angiosperms are more numerous and more reshuffled than more recent duplicates occurring in the gymnosperm lineage leading to extant conifers. We also investigated how stable the genome macro-structure has been between the conifers Picea and Pinus since their divergence 120 to 140 Mya [13, 14, 27], a period of time corresponding to tremendous reshuffling of the angiosperm genome.
Spruce gene map
Testing for gene clustering within spruce chromosomes using the Kolmogorov-Smirnov statistics (D n and D* n ).
Number of mapped genes
Chromosome length (cM)
Tandemly arrayed genes
A total of 125 family members were organised into 51 TAGs (31 arrays within 1 cM and 20 arrays within 5 cM; Figure 1). Most of the arrays included two genes, but arrays were identified including up to eight genes, such as the myb-r2r3 array on chromosome 7 (Figure 1). Based on the GO classification, genes coding for extracellular proteins and cell wall proteins, and genes involved in DNA-binding functions and in secondary metabolism were over-represented among spruce gene arrays (Additional file 8). To test whether this distribution could be observed by chance alone, we randomly redistributed the 664 gene family members and counted the number of chromosomes represented for each family. This simulation was replicated 1,000 times. The observed and the simulated distributions were found to be significantly different (χ2 = 35.7, degrees of freedom = 11, P = 0.00018). The main contribution to the χ2 value was from the families with members mapping to a single chromosome. Seventeen gene families were found to be associated with a unique chromosome more often than would be expected by chance alone. The TAGs were the major contributors to this distribution.
Within 32 gene groups representing 71 genes (3.9% of the mapped genes), no recombinants were observed out of 500 white spruce progeny. These groups encompassed a variety of molecular functions with no significant deviation from the composition of the overall dataset (Additional file 9). In 20 groups, genes were related neither in sequence nor in function. By contrast, 12 groups were made of functionally related genes, including five tandem arrays and seven groups of genes from different families. These twelve groups involved three main functions: metabolism (six groups), regulation of transcription (three groups) and transport (three groups) (Additional file 9).
We obtained assessments of gene expression for co-localizing genes from 10 groups . In three groups, the co-localizing genes were co-regulated across eight tissues (mature xylem, juvenile xylem, phelloderm (including phloem), young needles, vegetative buds, megagametophytes, adventitious roots and embryogenic cells). The first group included one citrate synthase involved in carbohydrate metabolism and a calcineurin B-like protein involved in transduction through calcium binding. The second group included one reductase involved in histidine catabolic process, which was co-expressed with a ribosomal 30S protein. The third group consisted of two chalcone synthases.
Intergeneric map comparisons
Macro-synteny was spread all along the genomes with large conserved segments (Figure 4). The conserved positions of homologous genes allowed us to delineate the respective positions of homoeologous chromosomal regions in spruce, loblolly pine and maritime pine (Additional file 11). The conserved regions represented 82.0% and 86.5% of the lengths of the spruce and loblolly pine maps, respectively (Figure 4 and Additional file 10). The portion of 82.0% of the spruce map conserved with the loblolly pine map could be extended to 87.6% when conservation with the maritime pine map was also considered (Additional file 10). Thus, map comparison with maritime pine provided a significant enrichment in shared genes and homoeologous regions among maps. This high level of conservation enabled us to draw the first comprehensive map for a sizeable part of the gene space of the Pinaceae (Additional file 11).
Phylogenetic analyses of 157 gene families
In total, 527 spruce genes were considered in the phylogenetic analyses. They were distributed in 157 families each containing at least two genes mapped on the spruce genome (Additional file 6). These families were distributed across diverse molecular functions, representative of the distribution of expressed genes found in white spruce (Figure 2, see Methods).
Distribution and relative age of gene pairs
We analysed the distribution patterns found among the gene pairs on the spruce genome. Most spruce gene pairs were translocated (86.3%) and most of these translocations occurred before the GA split (94.5%). We counted the number of duplicates found on each of the 12 chromosomes, and compared the observed distribution to a theoretical distribution that would be expected by chance alone. Out of 688 gene pairs (or nodes) representing 'ancient' duplications, 56 pairs (8.1%) were located on the same chromosome and 632 pairs (91.9%) were duplicates involving a translocation to another chromosome. This difference was highly significant (χ 2 = 482.2; P < 2.2e-16), indicating that ancient gene pairs have been highly dispersed. Out of 87 pairs of genes representing 'recent' duplications, only 37 pairs (42.5%) were translocated and 50 pairs (57.5%) were located on the same chromosome. This difference was not significant (χ2 = 1.9; P = 0.16). For each pair of genes, we computed the distance between the duplicates found on a same chromosome. The mean distance between duplicates arising from a recent duplication event was 4.3 cM; whereas this distance was 47.0 cM between duplicates derived from ancient duplication events. This 10-fold difference was highly significant (Welch t-test t = -7.8; P = 1.1e-11).
Many gene copies found on the same chromosome were forming arrays of genes tandemly duplicated within 5 cM. Within the 51 tamdemly gene arrays that incorporated 6.9% of the mapped genes, 125 gene pairs (duplications) could be classified relative to the GA split: 44 were classified as recent, only 5 were ancient, and 76 were undetermined. Overall, only four gene arrays could be accounted for by ancient duplications predating the GA split (BAM, expansin-like, pectinesterase, tonoplast intrinsic protein), whereas 29 other arrays were generated by duplications after the GA split (χ2 = 18.9; P = 1.3e-05; Figure 6). Thus, the more recent origin of these closely-spaced duplicates has apparently resulted in less time and opportunity for them to be dispersed or translocated.
The completion of several genome sequencing projects in angiosperms has resulted in improved knowledge of the content and organisation of the flowering plant genomes. In gymnosperms, in the absence of a completely sequenced and ordered genome, recent efforts have been put toward improving knowledge of the gene space through several EST sequencing projects ; but the structural organisation of this gene space on the genome remains largely undetermined . The spruce genetic map and analyses presented herein allow better comprehension of the genome macro-structure for a gymnosperm. These results combined with phylogenies reveal the relative proportion of gene duplications shared between angiosperms and gymnosperms or unique to gymnosperms, and how the seed plant genome has been reshuffled over time from a conifer perspective.
Gene distribution and density
To localise the GRRs, we implemented a statistical approach based on the kernel density function. This represents a technical improvement compared with existing methodologies given that we used an adaptive kernel approach to avoid the use of an arbitrarily fixed bandwidth. This approach allowed us to take into account the density observed locally to compute the bandwidth size. Because the number of genes currently positioned on the spruce genome represents around 6% of the estimated total number of genes , we applied stringent parameters in these analyses to reduce the rate of false positives. Thus, we may have underestimated the extent of GRRs. Besides these significant peaks, a few other peaks of kernel density that do not currently reach significance (Figure 3) may do so with an increased number of mapped genes. Indeed, Kolmogorov-Smirnov tests of homogeneity of gene distribution indicated that nine chromosomes had a significantly non-uniform distribution. Even so, there does not seem to be a widespread occurrence of GRRs on the spruce genome. In addition, the seven significant GRRs were distributed among seven chromosomes. This peculiar distribution suggests that GRRs may correspond to centromeric regions where, on genetic maps, markers tend to cluster due to more limited recombination.
In angiosperms, species with small genomes tend to be made of GRRs alternating with gene-poor regions. For example, the genic space of Arabidopsis thaliana represents 45% of the genome while the remaining 55% is 'gene-empty' and interspersed among genes as blocks ranging in size from a few hundred base pairs to 50 kb . By contrast, plant species with larger genomes do not show such a contrasted gene distribution, in line with the pattern found here for the large spruce genome. Rather, they harbour a gradient of gene density along chromosomes, such as in maize , soybean  and wheat [38, 39]. In the soybean genome, a majority of the predicted genes (78%) are found in chromosome ends, whereas repeat-rich sequences are found in centromeric regions . In conifers, retroelements have been reported as a large component of the genome, with some families well dispersed while others occur in centromeric or peri-centromeric regions (for example, see [41–45]). Thus, they might have participated in shaping the distribution of genes along chromosomes by reducing the occurrence of GRRs.
The type of gene distribution along the genome bears consequences for the planning of genome sequencing strategies. For instance, a gene distribution of 'island' type implies that a deeper sequencing effort is necessary to reach a majority of the genes . Though genetic distance does not equate physical distance, the pattern seen here in spruce indicates that genetic maps alone that would include most of the gene complement will be insufficient to anchor a significant portion of physical scaffolds, especially if these are small. In conifers, little is known about physical gene density in genomic sequences. In spruce, two partially sequenced BAC clones had a single gene per 172 kbp and 94 kbp, respectively, which represents a density at least 10-fold lower than the average gene density of the sequenced genomes of Arabidopsis, rice, poplar or grapevine . In addition, the sequencing of four other randomly selected BAC clones in spruce failed to report any gene .
Tandemly arrayed genes and functional clusters
In the present analysis, we identified two types of gene clusters: arrays of gene duplicated in tandem and arrays of unrelated sequences sharing functional annotations. There were 51 arrays (TAGs) encompassing genes from the same family that were duplicated within 5 cM. They incorporated 6.9% (125) of the mapped genes and they could be indicative of small segmental duplications. Such TAGs were also reported in genomic sequences of model angiosperms: they involve 11.7% of the Arabidopsis genes and 6.7% of the rice genes . Most of the spruce arrays (78.0%) included only two genes. Similar proportions were found in genome sequences of model angiosperms . The largest spruce array found consisted of eight myb-r2r3 genes on chromosome 7 (Figure 1). Interestingly, seven of these myb-r2r3 belong to the same subgroup Sg4C . The other genes belonging to Sg4C were not positioned on this linkage map. Spruce TAGs were significantly enriched in functions related to DNA binding, secondary metabolism and structural proteins (Figure 1). In Arabidopsis and rice, TAGs are under-represented among transcription factors and over-represented in enzymes . GO analyses and expression data showed a strong correlation between tandem duplicates and biotic stress genes in Arabidopsis , leading the authors to suggest that 'tandem duplicates are likely important for adaptive evolution to rapidly changing environments'. In the myb-r2r3 gene array, the three genes named PgMyb5, PgMyb10 and PgMyb13 exhibited very different expression patterns . The lack of co-expression of the genes mapped in arrays did not support a gene arrangement oriented by co-regulation. In these arrays, a majority of the genes were derived from duplications occurring after the GA split but were shared between spruce and pine, indicating from the perspective of geological time that expression divergence may occur quite rapidly after gene duplication . Such a pattern is in accordance with the observation that if a new function is not acquired rapidly through neo-functionalisation, one duplicate tends to evolve towards a pseudogene and disappear . A high frequency of pseudogenes has indeed been reported in conifer genomes [26, 42, 52]. However, there are exceptions to this neo-functionalisation trend among surviving duplicates, such as in the conifer Knox-I family. In this family, the closely located kn1 and kn2 arose from a duplication postdating the divergence between gymnosperms and angiosperms; nevertheless, neo-functionalisation has not happened yet between these duplicates in spite of the duplication occurring before the divergence between the spruce and pine lineages, more than 100 Mya . Sub-functionalisation of these duplicates has been noted , conferring partial functional redundancy that might enhance survival and adaptation in these long-lived perennials. Several other cases of conifer-specific duplications might exist that imply partial redundancy of function instead of neo-functionalisation.
We found three clusters made of co-expressed gene sequences that were similar to operon-like structures: two cases made of non-homologous sequences and a third one made of tandemly duplicated chalcone synthases. In angiosperms, only five such structures have been described and were associated with secondary metabolism and defence mechanisms . Such metabolic clusters have emerged as a new and growing theme in plant biology . The three clusters found in our study were similar to these functional clusters, except that their roles were not restricted to secondary metabolism. Among our data, two other cases for which we could not obtain expression data were also good candidates for functional clusters. On chromosome 11, there were also two co-localising pectin methylesterases. Moreover, a single group of two non-homologous sequences was clearly involved in the secondary metabolism. This group encompassed one flavonol synthase and one glutathione synthase on chromosome 6 of Picea. Glutathione plays several important roles in the defence of plants against environmental threats. It is a substrate for glutathione S-transferases, enabling neutralisation of potentially toxic xenobiotics . Thus, these flavonol and glutathione synthases may belong to a cluster of functionally related but non-homologous genes. Similarly, 80 co-expression clusters sharing the same GO term were described along the 3B wheat chromosome, suggesting the existence of adaptive complexes of co-functional genes . In spruce, exhaustive transcriptomic resources have recently been developed . Their analysis combined with the positioning on the genome of additional genes should allow us to pinpoint whether adaptation at the metabolic level has contributed to shaping the organisation of the gene space.
Highly conserved organisation of the gene space between spruce and pine
Before conifer gene catalogues were available, the number of available orthologous markers to enable comparative studies of genome macro-structure between conifer species was highly limited . A substantial conservation of the Pinaceae genome macro-structure was nevertheless suspected [34, 56, 57]. Estimating the extent of conservation in genome macro-structure was more exhaustive in our study, because we identified a much enlarged set of orthologous mapped gene loci (over 200) between spruce and pine. Synteny and collinearity between the spruce and loblolly pine genomes were very high. Lower collinearity was noted with the maritime pine genome, which resulted from likely lower accuracy of the gene order based on the use of a smaller mapping population for this species . Therefore, it is safe to assume that the organisation of the conifer gene space has been largely maintained over a period dating back 120 to 140 Mya, since the early diversification of Pinaceae in its main lineages in the Early Cretaceous [13, 14, 27]. Such a high level of conservation of the genome macro-structure has also been reported among angiosperm genomes from the Rosids and Asterids clades [58, 59], which diverged about 115 Mya [58–60], a time period similar to that of the pine-spruce divergence. By contrast, since the monocot-eudicot split 140 to 150 Mya , which slightly preceded the pine-spruce divergence, synteny has been largely disrupted between model monocots and dicots . Such large discrepancies in apparent rate of evolution of genome macro-structure are largely conspicuous among angiosperm lineages, where it has been shown that the genomes of perennial species such as grape and poplar evolved slower than those of annual species such as Arabidopsis and rice . These differences in evolutionary rates are also reminiscent of those in substitution rates between annual and perennial or woody seed plants, where various hypotheses related to mutation rate, generation time, population size and fixation rate have been proposed [64–68].
Age and organisation of gene duplicates
The phylogenetic analysis of 157 gene families indicated a large imbalance in favour of ancient duplications predating the GA split versus more recent duplications postdating the GA split. Since the genes sampled in the present study were identified after sequencing ESTs, one could argue that the sample might be biased towards expression patterns that are possibly related to high sequence conservation (for example, ), hence artificially increasing the ratio of ancient versus recent gene duplications detected in the present study. First, the ratio was highly asymmetric (eight to one) and we showed that the genes and families involved in our study were representative of a large array of molecular functions and biological processes seen in the most recent spruce gene catalogue, and implicating conserved and less conserved gene families . Gene annotations in conifers [26, 70, 71] also do not favour this hypothesis. Indeed, the most complete catalogue of expressed genes for a conifer, which was based on a large effort involving the sequencing of 23,589 full-length cDNA inserts, has recently enabled the reporting of the most exhaustive comparison of homologous genes sequenced both in angiosperms and gymnosperms . The results indicated that the spruce protein families were largely overlapping with those of angiosperm model plants completely sequenced . Comparing the occurrence of the Pfam domains in the spruce gene catalogue with genomes completely sequenced from model plant species showed that only 28 protein domains were statistically over-represented in spruce and most of them were involved in metabolism, stress response and retrotransposition. Moreover, the gene coding portion of the spruce genome was evaluated at around thirty thousand transcribed genes, a number in the same range as that observed for model angiosperm genomes . The in-depth study of a few transcription factor families also showed that conifers lack some members in specific subfamilies while containing more genes in closely related subfamilies that were derived from duplication events postdating the GA split [24, 48, 72]. These various observations suggest that the conifer genes that are highly divergent from their angiosperm's homologues are rare in the sequence resources developed so far, in spite of the fact that these resources relied on the investigation of a diversity of tissues and conditions . In the future, the availability of the genome sequence may allow the discovery of more conifer-specific genes that could be highly duplicated; but we would (do?) not expect to find them in abundance, as suggested by the present phylogenetic analysis.
A large majority of spruce gene pairs were translocated and most of these translocations occurred before the GA split, affecting a large majority of the 157 gene families analysed. By contrast, genes duplicated after the GA split were located overwhelmingly in close proximity on the same chromosome and often organised in tandem. These trends were consistent with the observation that the physical distances between duplicates on the Caenorhabditis elegans genome increase with time, due to chromosomal rearrangements and other mutational events . Nevertheless, this pattern is not always clear, for instance in Arabidopsis gene families where the occurrence of tandem duplications and segmental duplications are negatively correlated [47, 74]. In this model plant, unequal cross-over and gene loss were proposed as possible mechanisms leading to the counter-selection of tandem duplications .
The observed large excess of ancient duplications predating the GA split over more recent duplications postdating the GA split is consistent with the hypothesis of relative stasis in the gymnosperm lineage leading to conifers and the little evidence for a recent large expansion of the gene space. While a single whole genome duplication has been hypothesised to have affected the common ancestor of seed plants around 350 Mya , evidence for more recent widespread polyploidy in the gymnosperm lineage after its divergence from the angiosperm lineage was not found in the present study, in agreement with results from cytological studies reporting a rare occurrence of this phenomenon in gymnosperms [18, 75]. Variation in basic chromosome number in the diploid gymnosperms would rather be the result of chromosome fusion or fission . For instance, such fission would have led to the additional chromosome seen in Douglas fir, relative to other Pinaceae . If so, some of the translocations hypothesised in the present study after the GA split could also be the result of ancient chromosomal fissions increasing the basic chromosome number in the lineage leading to spruce and pine.
While more recent duplications specific to the gymnosperm lineage leading to extant conifers were detected, the stasis of genome macro-structure noted in this lineage is in concordance with that observed between the spruce and pine genomes, and corresponding to a period exceeding 100 My since the last common ancestor of Pinaceae [13, 14, 27]. Such slow rates of genome evolution parallel the slow rates of speciation and patterns of reticulate evolution noted in Pinaceae taxa [76, 77] and their archaic morphological features and life history [14, 78]. Such multiple coincidences reinforce the idea that perennial nature and large historical population sizes are key factors to the slow evolution of conifers . The large excess of ancient duplications detected in the present study also indicates that much gene expansion has occurred in the plant lineage before the divergence between gymnosperms and angiosperms in the Late Carboniferous. Part of this expansion in the primitive land plants might be related to the major burst of duplications noted in transcription factors and coinciding with the water-to-land transition of plants . Further studies implicating other divisions of green plants are needed to better comprehend the temporal dynamic of gene family expansions and reshuffling of the plant genome before the emergence of modern seed plants.
The high levels of synteny, collinearity and similar ranges of linkage distances noted between the spruce and pine genomes will provide opportunities to transfer genomic information between these genera, especially if such conservation is maintained at a finer scale. The reported genomic features are likely to extend to other genera of the Pinaceae and therefore save time and energy in the deployment of genomic resources necessary to identify orthologous regions in these ecological and economic important species. Such mutual enrichment of genetic maps across species is also significant, with respect to the complexity underlying the analysis of the large conifer genomes. Increasing the number of homologous genes mapped on both the spruce and pine genomes will increase the resolution of comparative mapping efforts, which in turn should highlight the extent of micro-arrangements since the divergence of Pinaceae taxa. Such dense gene maps and high structural correspondence between them will also help identify homoeologous quantitative trait loci responsible for adaptation  and other complex characters, between species and genera. While successfully applied to dissect nitrogen use efficiency in cereals , these efforts in comparative structural genomics should provide insight into the evolutionary trajectories of the conifer genome at the functional level.
Genotyping of gene SNPs
A collection of 27,720 white spruce cDNA clusters  was used to develop gene SNPs (Additional file 4). In silico SNP discovery in expressed white spruce (Picea glauca (Moench) Voss) genes was conducted after Sanger resequencing and according to parameters previously reported . A GoldenGate (Illumina, San Diego, CA, USA) SNP array (PgLM2) specifically designed to map additional white spruce genes was constructed with 1,536 attempted SNPs dispersed through 1,509 expressed genes. The assay was conducted following procedures previously described . Genotyping was done at the Genome Québec Innovation Centre (McGill University, Montreal, QC, Canada, team of A. Montpetit) and by using 250 ng of template DNA per sample (at a rate of 50 ng/μL). The results were analysed with the BeadStudio software (Illumina). We retained the 1,292 SNPs exhibiting a GenTrain quality score of 0.25 or more. Of these, 1,121 (73.0%) SNPs representative of 1,098 genes segregated among the 500 progeny of the white spruce cross D (C94-1-2516, ♀77111 × ♂2388). The average call rate per valid SNP was 99.48%. In total, 773,395 new genotypes were obtained from valid SNPs. Using replicated positive controls, the rate of reproducibility was estimated at 99.994%.
Annotation of the mapped genes
The mapped genes were representative of a large array of molecular functions and biological processes including wood formation, growth, vascular tissues development and differentiation, responses to abiotic and biotic stresses and adaptation [83, 84]. They were mostly derived from expressional and functional studies [83, 84] as well as outlier detection  (Additional files 4, 5 and 6).
GO annotation was performed with the Blast2GO software . GO terms were assigned based on the top 10 blastx hits found against the non redundant (nr) protein database with an E-value below 1e-10. GO annotation was run based on the terms from the PlantGO-Slim classification for molecular functions, biological processes and cellular components. Annotations were used to assess GO term enrichment using the Fisher exact test function implemented in Blast2GO.
In total, 6,923 GO terms including molecular functions, biological processes and cellular components were assigned to the 1,801 mapped genes, for an average of 3.9 GO terms per gene. There were 1,285 sequences associated with a molecular function. At level two of the molecular function classification, most of the terms fell in the 'binding' category (49.0%) or in the 'catalytic activity' category (40.0%). At the level three of the classification, five categories each represented about 15% to 20% of the genes; therefore, most of the terms were related either to hydrolase activity, transferase activity, nucleic acid binding, protein binding or lipid binding (Additional file 5). Also, 1,027 sequences were associated with biological processes with a large diversity of 84 terms involved in the annotations (Additional file 5).
Spruce gene linkage maps
Two parental linkage maps for white spruce were estimated de novo from the 500 progeny above for cross D. Parental maps were assembled as previously described , using the genotyping data of the PgLM2 array, as well as data from a previous GoldenGate SNP array on the same 500 progeny . Anonymous markers and gene markers from previous linkage mapping projects involving a different white spruce cross (cross P: C96-1-2516, ♀80112 × ♂80109; 260 progeny) were also considered [56, 82]. The white spruce maps derived from both crosses D and P  were merged with the JoinMap 4.0 function 'Combine Groups for Map Integration'. Before marker ordering within each integrated linkage group, we compared the recombination frequencies estimated for homologous markers from both data sets. For this purpose, a 'heterogeneity test' was conducted with JoinMap4.0. As a result, pairs of loci showing significantly different recombination frequencies (P < 0.01) were eliminated to avoid erroneous marker ordering.
A black spruce (Picea mariana (Mill.) B.S.P.) map  was also used to position 58 additional genes (3% of the total mapped genes) that were not mapped in white spruce. Because the genomes of the two species are highly syntenic and collinear , white spruce and black spruce genetic maps were aligned based on 258 gene markers mapped in common. The white spruce composite map was taken as the reference map; then, black spruce gene loci not positioned in white spruce were transferred onto the white spruce composite map. The position of a transferred gene on the linkage group was estimated as the middle of the interval between the two closest white spruce anchor loci. Hence, the spruce composite map included 1,743 genes from white spruce and 58 genes from black spruce for a total of 1,801 positioned genes.
Gene distribution along the chromosomes
We tested whether the genes were uniformly distributed along the chromosomes or whether they were clustered. For each chromosome, we compared the observed gene distribution with a uniform distribution by using the Kolmogorov-Smirnov test. We computed the maximum difference (noted D n ) between the observed distribution F(x) and the empirical distribution F n (x). With large sample sizes such as those considered here (in terms of genes), D n follows a complex distribution and the critical value depends on the sample size. D'Agostino and Stephens proposed a modified statistic named D n * with critical values independent of the sample size . The critical values of statistical significance used for D n * were 0.895 (α = 0.05) and 1.035 (α = 0.01).
Gene density analysis
To identify the location and extent of GRRs, we used the kernel density estimation, a non-parametric technique based on the kernel density function . The choice of the bandwidth is arbitrary and affects the smoothness of the distribution. The function asciker available in the software Stata® 10 (College Station, TX, USA) was used to compute the non-parametric density estimator as well as the confidence intervals at two significance levels (α = 0.05 and 0.01) . We used the adaptive kernel method , which is based on a varying bandwidth instead of an arbitrarily fixed one. This approach enabled us to take into account the density observed locally to compute the bandwidth size. The density and the 95% confidence intervals were calculated with the akdensity function in Stata®10. Then, we compared the position of the confidence interval with the uniform function in the R package. If the lower bound of the confidence interval was greater than the uniform distribution, the region was declared as a GRR (P < 0.05).
Synteny with pine genomes
The gene-based linkage map for the loblolly pine genome  was downloaded from the Dendrome database . Out of 1,816 genes mapped onto the loblolly pine genome, a dataset of 1,666 genes was retrieved from the Dendrome database (accession TG091). These sequences were compared with 27,720 spruce unigenes ) and the 1,801 spruce mapped loci by using the blastn program. Also, 426 mapped loci were collected from maritime pine . For each pine sequence, the best hit among the spruce genes with various thresholds from 70% to 95% of identity was retrieved. All the spruce sequences matching a loblolly pine sequence with at least 95% of identity were located on a homoeologous chromosome. However, to increase the number of matches analysed, a minimum identity level of 80% was retained. Under these circumstances, 7.2% of the matches were not found on homoeologous pairs of chromosome. Moreover, we performed the reciprocal comparison involving mapped spruce genes against the pine sequence database. If one link was found between the pine and spruce chromosomes in both reciprocal analyses, we declared this link as homologous. In a few cases, one gene from one species could match several genes from the other species on the homoeologous chromosome. In such cases, a single link was retained between the two homoeologous chromosomes.
Gene families used in phylogenetic analyses
Phylogenetic analyses were conducted by including angiosperm and gymnosperm (conifer) sequences. These involved 527 mapped spruce genes from 157 families. To assess the representativeness of this sample (Figure 2), we compared the distribution of the molecular function GO terms found across these genes and across the white spruce GCAT gene catalogue  with a two-tailed Fisher exact test. Out of the seven terms assigned at the level two of the molecular function's classification, only two terms were over-represented among the mapped families compared with the gene catalogue (False Discovery Rate (FDR) ≤ 0.01). These terms were 'binding' (GO:0005488) and 'transcription regulator activity' (GO:0030528). Indeed, 31 families encompassing 180 sequences were related to transcription activity or regulation. The four largest groups of mapped transcriptional regulators included 21 genes from the myb-r2r3 family, 17 genes from the b-hlh family, 14 nam genes and 11 aux-iaa genes. At the level three of this classification, only two terms were differentially represented among the mapped families and the spruce gene catalogue (FDR ≤ 0.01). Genes related to nucleic acid binding (GO:0003676) were over-represented while the ones related to transferase activity transferring glycosyl groups (GO:0016757) were under-represented among the mapped genes. Thus, there was no large molecular functional class missing in the sample of 157 families used for the phylogenomic analysis, which was quite representative of the overall relative diversity and abundance of molecular functions seen in the spruce GCAT gene catalogue .
The phylogenetic analyses aimed to classify duplications involving mapped spruce genes as recent or ancient by considering the GA split as a reference (see below). We translated the spruce sequences with the getorf program available in the EMBOSS package . Among all possible open reading frames, we selected the sequence with a match with a protein sequence from another species. The Arabidopsis protein dataset (TAIR7 release) from TAIR ) and the rice protein dataset from the rice annotation database ) were retrieved. We also built a protein sequence dataset from pine. Although not a strict requirement for the present purpose of dating spruce gene duplications, we also retrieved two datasets of EST contigs derived from Pinus pinaster and Pinus taeda . We concatenated the 12,901 sequences from Pinus pinaster and the 72,928 sequences from Pinus taeda into a single file. These sequences were translated with the getorf program to obtain a set of all possible protein sequences. For each spruce sequence, we conducted three blastp searches against the protein datasets from Arabidopsis, rice and pine. In the blastp outputs, we screened the five best hits. We aligned the spruce, pine, Arabidopsis and rice homologous sequences with the kalign program  and we selected manually conserved domains for further phylogenetic analysis. Because the considered angiosperm sequences were from complete genomic sequences, it was not necessary to filter out sequences based on absolute sequence similarity. However, the observed e-values in the blastp searches of the spruce sequences against the Arabidopsis proteome were in the range of e-50 to e-100.
Phylogenetic analyses were conducted for 157 gene families (Additional file 5). To be considered, a family had to contain at least two mapped members on the spruce genome and had to be represented in Pinus as well as in both models and completely sequenced angiosperms Arabidopsis and rice, respectively an eudicot and a monocot. Both the NJ method , an approach based on matrices of substitution rates, and MP analysis  were employed using the package Phylip 3.6 . We used MP and NJ methods instead of other more computer-intensive approaches such as Bayesian or maximum-likelihood algorithms given that the metric that we wanted to estimate, the proportion of ancient versus recent spruce gene duplications relative to the GA split, was based on a large sample of gene families (157), and given that our interpretation of the topologies was conservative because it was based on the strict consensus from MP and NJ trees for each gene family. Results obtained for a few gene families using other more computer-intensive phylogenetic methods resulted in essentially the same consensus trees (results not shown). To estimate NJ trees, distance matrices were calculated using the protdist program with the JTT amino acid substitution matrix and submitted to the program neighbor. Parsimony trees were estimated using protpars. For each family and each method, the robustness of the topologies obtained was assessed by means of 500 bootstraps using the program seqboot. For each method, the consensus tree derived from the bootstrap analysis was the majority-rule consensus generated with the program consense. Then, for each gene family phylogeny, only nodes supported minimally by 50% of bootstraps and in concordance between the two phylogenetic methods were retained, that is, the strict consensus of two majority-rule bootstrap MP and NJ trees.
For each gene family phylogeny, we used the unrooted strict consensus of the MP and NJ trees and estimated the relative age of spruce gene duplications by determining if they occurred before or after the GA split. Nodes involving spruce gene sequences but with no intervening angiosperm sequences from Arabidopsis or rice indicated duplications post-dating the GA split and were referred to as recent duplications. On the other hand, when spruce gene sequences were separated by intervening nodes involving angiosperm sequences from rice and/or Arabidopsis, these duplications predated the GA split and were referred to as ancient. In some cases, ancient gene duplicates produced before the GA split may have been lost both in the Arabidopsis and rice genomes, which would bias upward the number of duplications that would be declared specific to the gymnosperm lineage leading to spruce. Based on the literature, one may argue that this bias should be negligible, given that loss of gene duplicates mostly occurs quickly after duplication if processes such as sub-functionalisation or neo-functionalisation do not occur . At the same time, sampling two divergent and completely sequenced angiosperm genomes (representative of the eudicot and the monocot lineages) should keep such a bias low because gene loss would have to occur independently in both angiosperm lineages after their split, or in their common ancestor during the relatively short period between the GA split (around 300 Mya)  and the divergence of monocots from other angiosperms (around 150 Mya) . For instance, the sister lineage of the large knox-1 gene family in conifers has been lost in rice and other monocots but conserved in Arabidopsis and other eudicots . Finally, because this bias would result in the scoring of a number of false-positive gymnosperm-specific duplications, it would tend to reduce the true proportion between ancient duplications predating the GA split and more recent duplications post-dating this split. If real, the bias is likely negligible, given that the observed value of this proportion was already highly skewed toward ancient duplications (ratio of eight to one, see Results).
All circular genetic maps were drawn with the Circos software .
This work was supported by Genome Canada and Génome Québec for the Arborea project. We thank the two anonymous reviewers for their useful comments, S. Beauseigle and S. Blais (Université Laval) for their help in the laboratory, G. Daigle (Université Laval) for support with statistical analyses, P. Belleau (Centre de Recherche du Centre Hospitalier Universitaire de Québec) for helpful discussions, A. Deschênes (Université Laval) for help with GO annotation and gene density analyses, A. Montpetit and his team (McGill University) for performing the genotyping assay and J. Mackay (Université Laval) for sharing gene expression data ahead of publication.
- Lynch M: The origins of genome architecture. 2007, Sunderland, MA: Sinauer AssociatesGoogle Scholar
- Freeling M: Bias in plant gene content following different sorts of duplication: tandem, whole-genome, segmental, or by transposition. Annu Rev Plant Biol. 2009, 60: 433-453. 10.1146/annurev.arplant.043008.092122.PubMedView ArticleGoogle Scholar
- Li WH: Evolution of duplicated genes. Evolution of Genes and Proteins. Edited by: Nei M, Koehin RK. 1983, Sunderland, MA: Sinauer Associates, 14-37.Google Scholar
- Lynch M, Conery JS: The evolutionary fate and consequences of duplicate genes. Science. 2000, 290: 1151-1155. 10.1126/science.290.5494.1151.PubMedView ArticleGoogle Scholar
- Lynch M, O'Hely M, Walsh B, Force A: The probability of preservation of a newly arisen gene duplicate. Genetics. 2001, 159: 1789-1804.PubMedPubMed CentralGoogle Scholar
- Bowers JE, Chapman BA, Rong J, Paterson AH: Unravelling angiosperm genome evolution by phylogenetic analysis of chromosomal duplication events. Nature. 2003, 422: 433-438. 10.1038/nature01521.PubMedView ArticleGoogle Scholar
- Lynch M, Conery JS: The evolutionary demography of duplicate genes. J Struct Funct Genomics. 2003, 3: 35-44. 10.1023/A:1022696612931.PubMedView ArticleGoogle Scholar
- Nei M, Rooney AP: Concerted and birth-and-death evolution of multigene families. Annu Rev Genet. 2005, 39: 121-152. 10.1146/annurev.genet.39.073003.112240.PubMedPubMed CentralView ArticleGoogle Scholar
- Proost S, Pattyn P, Gerats T, van de Peer Y: Journey through the past: 150 million years of plant genome evolution. Plant J. 2011, 66: 58-65. 10.1111/j.1365-313X.2011.04521.x.PubMedView ArticleGoogle Scholar
- Jiao Y, Wickett NJ, Ayyampalayam S, Chanderbali AS, Landherr L, Ralph PE, Tomsho LP, Hu Y, Liang H, Soltis PS, Soltis DE, Clifton SW, Schlarbaum SE, Schuster SC, Ma H, Leebens-Mack J, dePamphilis CW: Ancestral polyploidy in seed plants and angiosperms. Nature. 2011, 473: 97-100. 10.1038/nature09916.PubMedView ArticleGoogle Scholar
- Fawcett JA, Maere S, van de Peer Y: Plants with double genomes might have had a better chance to survive the Cretaceous-Tertiary extinction event. Proc Natl Acad Sci USA. 2009, 106: 5737-5742. 10.1073/pnas.0900906106.PubMedPubMed CentralView ArticleGoogle Scholar
- Wood TE, Takebayashi N, Barker MS, Mayrose I, Greenspoon PB, Rieseberg LH: The frequency of polyploid speciation in vascular plants. Proc Natl Acad Sci USA. 2009, 106: 13875-13879. 10.1073/pnas.0811575106.PubMedPubMed CentralView ArticleGoogle Scholar
- Savard L, Li P, Strauss SH, Chase MW, Michaud N, Bousquet J: Chloroplast and nuclear gene sequences indicate Late Pennsylvanian time for the last common ancestor of extant seed plants. Proc Natl Acad Sci USA. 1984, 91: 5163-5167.View ArticleGoogle Scholar
- Gernandt DS, Willyard A, Syring JV, Liston A: The conifers (Pinophyta). Genetics, Genomics and Breeding of Conifer Trees. Edited by: Plomion C, Bousquet J, Kole C. 2011, New York: Edenbridge Science Publishers and CRC Press, 1-39.Google Scholar
- Sanderson MJ, Donoghue MJ: Shifts in diversification rate with the origin of angiosperms. Science. 1994, 264: 1590-1593. 10.1126/science.264.5165.1590.PubMedView ArticleGoogle Scholar
- Magallon SA, Sanderson MJ: Absolute diversification rates in angiosperm clades. Evolution. 2001, 55: 1762-1780.PubMedView ArticleGoogle Scholar
- Beck CB: Origin and Evolution of Gymnosperms. 1988, New York: Columbia University PressGoogle Scholar
- Khoshoo TN: Chromosome numbers in gymnosperms. Silvae Genetics. 1961, 10: 1-9.Google Scholar
- Cui L, Wall PK, Leebens-Mack JH, Lindsay BG, Soltis DE, Doyle JJ, Soltis S, Carlson JE, Arumuganathan K, Barakat A, Albert VA, Ma H, dePamphilis CW: Widespread genome duplications throughout the history of flowering plants. Genome Res. 2006, 16: 738-749. 10.1101/gr.4825606.PubMedPubMed CentralView ArticleGoogle Scholar
- Jiao Y, Wickett NJ, Ayyampalayam S, Chanderbali AS, Landherr L, Ralph PE, Tomsho LP, Hu Y, Liang H, Soltis PS, Soltis DE, Clifton SW, Schlarbaum SE, Schuster SC, Ma H, Leebens-Mack J, dePamphilis CW: Ancestral polyploidy in seed plants and angiosperms. Nature. 2011, 473: 97-100. 10.1038/nature09916.PubMedView ArticleGoogle Scholar
- Murray BG: Nuclear DNA amounts in gymnosperms. Ann Bot. 1998, 82: 3-15.View ArticleGoogle Scholar
- Ahuja MR: Polyploidy in gymnosperms: revisited. Silvae Genetica. 2005, 54: 59-69.Google Scholar
- Ahuja MR, Neale DB: Evolution of genome size in conifers. Silvae Genetica. 2005, 54: 126-137.Google Scholar
- Guillet-Claude C, Isabel N, Pelgas B, Bousquet J: The evolutionary implications of knox-I gene duplications in conifers: correlated evidence from phylogeny, gene mapping, and analysis of functional divergence. Mol Biol Evol. 2004, 21: 2232-2245. 10.1093/molbev/msh235.PubMedView ArticleGoogle Scholar
- Judd WS, Campbell CS, Kellog EA, Stevens PF, Donoghue MJ: Plant Systematics, a Phylogenetic Approach. 2008, Sunderland, MA: Sinauer AssociatesGoogle Scholar
- Rigault P, Boyle B, Lepage P, Cooke JE, Bousquet J, MacKay JJ: A white spruce gene catalogue for conifer genome analyses. Plant Phys. 2011, 157: 14-28. 10.1104/pp.111.179663.View ArticleGoogle Scholar
- Wang XQ, Tank DC, Sang T: Phylogeny and divergence times in Pinaceae: evidence from three genomes. Mol Biol Evol. 2000, 17: 773-781. 10.1093/oxfordjournals.molbev.a026356.PubMedView ArticleGoogle Scholar
- Pelgas B, Bousquet J, Meirmans PG, Ritland K, Isabel N: QTL mapping in white spruce: gene maps and genomic regions underlying adaptive traits across pedigrees and environments. BMC Genomics. 2011, 12: 145-10.1186/1471-2164-12-145.PubMedPubMed CentralView ArticleGoogle Scholar
- Eckert AJ, van Heerwaarden J, Wegrzyn JL, Nelson CD, Ross-Ibarra J, Gonzalez-Martinez SC, Neale DB: Patterns of population structure and environmental associations to aridity across the range of loblolly pine (Pinus taeda L., Pinaceae). Genetics. 2010, 185: 969-982. 10.1534/genetics.110.115543.PubMedPubMed CentralView ArticleGoogle Scholar
- Raherison E, Rigault P, Caron S, Poulin P-L, Boyle B, Verta J-P, Giguère I, Bomal C, Bohlmann J, Cooke J, Mackay J: Transcriptome profiling in conifer trees reveals interspecific conservation in the vascular gene expression and patterns of gene family diversification. BMC Genomics. 2012, 13 (1): 434-10.1186/1471-2164-13-434.PubMedPubMed CentralView ArticleGoogle Scholar
- Chancerel E, Lepoittevin C, Le Provost G, Lin Y-C, Jaramillo-Correa JP, Eckert A, Wegrzyn JL, Zelenika D, Boland A, Frigerio J-M, Chaumeil P, Garnier-Géré P, Boury C, Grivet D, Gonzalez-Martinez SC, Rouzé P, van de Peer Y, Neale DB, Cervera MT, Kremer A, Plomion C: Development and implementation of a highly multiplexed SNP array for genetic mapping in maritime pine and comparative mapping with loblolly pine. BMC Genomics. 2011, 12: 368-10.1186/1471-2164-12-368.PubMedPubMed CentralView ArticleGoogle Scholar
- MacKay JJ, Dean JFD: Transcriptomics. Genetics, Genomics and Breeding of Conifers Trees. Edited by: Plomion C, Bousquet J, Kole C. 2011, New York: Edenbridge Science Publishers and CRC Press, 323-357.Google Scholar
- Ritland K, Krutovsky K, Tsumura Y, Pelgas B, Isabel N, Bousquet J: Genetic mapping in conifers. Genetics, Genomics and Breeding of Conifer Trees. Edited by: Plomion C, Bousquet J, Kole C. 2011, New York: Edenbridge Science Publishers and CRC Press, 196-238.Google Scholar
- The Arabidopsis Genome Initiative: Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature. 2000, 408: 796-815.36Google Scholar
- Schnable PS, Ware D, Fulton RS, Stein JC, Wei F, Pasternak S, Liang C, Zhang J, Fulton L, Graves TA, Minx P, Reily AD, Courtney L, Kruchowski SS, Tomlinson C, Strong C, Delehaunty K, Fronik C, Courtney B, Rock SM, Belter E, Du F, Kim K, Abbott RM, Cotton M, Levy A, Marchetto P, Ochoa K, Jackson SM, Gillam B, et al: The B73 maize genome: complexity, diversity, and dynamics. Science. 2009, 326: 1112-1115. 10.1126/science.1178534.PubMedView ArticleGoogle Scholar
- Choi IY, Hyten DL, Matukumalli LK, Song Q, Chaky JM, Quigley CV, Chase K, Lark KG, Reiter RS, Yoon M-S, Hwang E-Y, Yi S-I, Young ND, Shoemaker RC, van Tassel CP, Specht JE, Cregan PB: A soybean transcript map: gene distribution, haplotype and single-nucleotide polymorphism analysis. Genetics. 2007, 176: 685-696. 10.1534/genetics.107.070821.PubMedPubMed CentralView ArticleGoogle Scholar
- Choulet F, Wicker T, Rustenholz C, Paux E, Salse J, Leroy P, Schlub S, Le Paslier M-C, Magdelenat G, Gonthier C, Couloux A, Budak H, Breen J, Pumphrey M, Liu S, Kong X, Jia J, Gut M, Brunel D, Anderson JA, Gill BS, Appels R, Keller B, Feuillet C: Megabase level sequencing reveals contrasted organization and evolution patterns of the wheat gene and transposable element spaces. Plant Cell. 2010, 22: 1686-1701. 10.1105/tpc.110.074187.PubMedPubMed CentralView ArticleGoogle Scholar
- Rustenholz C, Choulet F, Laugier C, Safar J, Simkova H, Dolezel J, Magni F, Scalabrin S, Cattonaro F, Vautrin S, Bellec A, Bergès H, Feuillet C, Paux E: A 3000-loci transcription map of chromosome 3B unravels the structural and functional features of gene islands in hexaploid wheat. Plant Phys. 2011, 157: 596-1608.View ArticleGoogle Scholar
- Schmutz J, Cannon SB, Schlueter J, Ma J, Mitros T, Nelson W, Hyten DL, Song Q, Thelen JJ, Cheng J, Xu D, Hellsten U, May GD, Yu Y, Sakurai T, Umezawa T, Bhattacharyya MK, Sandhu D, Valliyodan B, Lindquist E, Peto M, Grant D, Shu S, Goodstein D, Barry K, Futrell-Griggs M, Abernathy B, Du J, Tian Z, Zhu L, et al: Genome sequence of the palaeopolyploid soybean. Nature. 2010, 463: 178-183. 10.1038/nature08670.PubMedView ArticleGoogle Scholar
- Kamm A, Doudrick RL, Heslop-Harrison JS, Schmidt T: The genomic and physical organization of Ty1-copia-like sequences as a component of large genomes in Pinus elliottii var elliottii and other gymnosperms. Proc Natl Acad Sci USA. 1996, 93: 2708-2713. 10.1073/pnas.93.7.2708.PubMedPubMed CentralView ArticleGoogle Scholar
- Morse AM, Peterson DG, Islam-Faridi MN, Smith KE, Magbanua Z, Garcia SA, Kubisiak TL, Amerson HV, Carlson JE, Nelson CD, Davis JM: Evolution of genome size and complexity in Pinus. PloS One. 2009, 4: 1-11. 10.1371/journal.pone.0005361.View ArticleGoogle Scholar
- Kovach A, Wegrzyn JL, Parra G, Holt C, Bruening GE, Loopstra CA, Hartigan J, Yandell M, Langley CH, Korf I, Neale DB: The Pinus taeda genome is characterized by diverse and highly diverged repetitive sequences. BMC Genomics. 2010, 11: 420-10.1186/1471-2164-11-420.PubMedPubMed CentralView ArticleGoogle Scholar
- Magbanua ZV, Ozkan S, Bartlett BD, Chouvarine P, Saski CA, Liston A, Cronn RC, Nelson CD, Peterson DG: Adventures in the enormous: a 1.8 million clone BAC library for the 21.7 Gb genome of loblolly pine. PloS One. 2011, 6: e16214-10.1371/journal.pone.0016214.PubMedPubMed CentralView ArticleGoogle Scholar
- Morgante M, De Paoli E: Toward the conifer genome sequence. Genetics, Genomics and Breeding of Conifers Trees. Edited by: Plomion C, Bousquet J, Kole C. 2011, New York: Edenbridge Science Publishers and CRC Press, 389-403.Google Scholar
- Hamberger B, Hall D, Yuen M, Oddy C, Hamberger B, Keeling CI, Ritland C, Ritland K, Bohlmann J: Targeted isolation, sequence assembly and characterization of two white spruce (Picea glauca) BAC clones for terpenoid synthase and cytochrome P450 genes involved in conifer defence reveal insights into a conifer genome. BMC Plant Biol. 2009, 9: 106-10.1186/1471-2229-9-106.PubMedPubMed CentralView ArticleGoogle Scholar
- Rizzon C, Ponger L, Gaut BS: Striking similarities in the genomic distribution of tandemly arrayed genes in Arabidopsis and rice. PLoS Comput Biol. 2006, 2: e115-10.1371/journal.pcbi.0020115.PubMedPubMed CentralView ArticleGoogle Scholar
- Bedon F, Bomal C, Caron S, Levasseur C, Boyle B, Mansfield SD, Schmidt A, Gershenzon J, Grima-Pettenati J, Séguin A, MacKay J: Subgroup 4 R2R3-MYBs in conifer trees: gene family expansion and contribution to the isoprenoid- and flavonoid-oriented responses. J Exp Bot. 2010, 61: 3847-3864. 10.1093/jxb/erq196.PubMedPubMed CentralView ArticleGoogle Scholar
- Hanada K, Zou C, Lhti-Shiu MD, Shinozaki K, Shiu S-H: Importance of lineage-specific expansion of plant tandem duplicates in the adaptive response to environmental stimuli. Plant Phys. 2008, 148: 993-1003. 10.1104/pp.108.122457.View ArticleGoogle Scholar
- Bedon F, Grima-Pettenati J, Mackay J: Conifer R2R3-MYB transcription factors: sequence analyses and gene expression in wood-forming tissues of white spruce (Picea glauca). BMC Plant Biol. 2007, 30: 17-View ArticleGoogle Scholar
- Moore RC, Purugganan MD: The early stages of duplicate gene evolution. Proc Natl Acad Sci USA. 2003, 100: 15682-15687. 10.1073/pnas.2535513100.PubMedPubMed CentralView ArticleGoogle Scholar
- Kinlaw CS, Neale DB: Complex gene families in pine genomes. Trends Plant Sci. 1997, 2: 356-359. 10.1016/S1360-1385(97)84624-9.View ArticleGoogle Scholar
- Osbourn A: Gene clusters for secondary metabolic pathways: an emerging theme in plant biology. Plant Phys. 2010, 154: 531-535. 10.1104/pp.110.161315.View ArticleGoogle Scholar
- Chu HY, Wegel E, Osbourn A: From hormones to secondary metabolism: the emergence of metabolic gene clusters in plants. Plant J. 2011, 66: 66-79. 10.1111/j.1365-313X.2011.04503.x.PubMedView ArticleGoogle Scholar
- Marrs KA: The functions and regulation of glutathione S-transferases in plants. Annu Rev Plant Physiol Plant Mol Biol. 1996, 47: 127-158. 10.1146/annurev.arplant.47.1.127.PubMedView ArticleGoogle Scholar
- Pelgas B, Beauseigle S, Achere V, Jeandroz S, Bousquet J, Isabel N: Comparative genome mapping among Picea glauca, P. mariana x P. rubens and P. abies, and correspondence with other Pinaceae. Theor Appl Genet. 2006, 113: 1371-1393. 10.1007/s00122-006-0354-7.PubMedView ArticleGoogle Scholar
- Krutovsky KV, Troggio M, Brown GR, Jermstad KD, Neale DB: Comparative mapping in the Pinaceae. Genetics. 2004, 168: 447-461. 10.1534/genetics.104.028381.PubMedPubMed CentralView ArticleGoogle Scholar
- Xu X, Pan S, Cheng S, Zhang B, Mu D, Ni P, Zhang G, Yang S, Li R, Wang J, Orjeda G, Guzman F, Torres M, Lozano R, Ponce O, Martinez D, De la Cruz G, Chakrabarti SK, Patil VU, Skryabin KG, Kuznetsov BB, Ravin NV, Kolanova TV, Beletsky AV, Mardanov AV, Di Genova A, Bolser DM, Martin DMA, Li G, Yang Y, et al: Genome sequence and analysis of the tuber crop potato. Nature. 2011, 475: 189-195. 10.1038/nature10158.PubMedView ArticleGoogle Scholar
- Cenci A, Combes MC, Lashermes P: Comparative sequence analyses indicate that Coffea (Asterids) and Vitis (Rosids) derive from the same paleo-hexaploid ancestral genome. Mol Genet Genomics. 2010, 283: 493-501. 10.1007/s00438-010-0534-7.PubMedView ArticleGoogle Scholar
- Wang H, Moore MJ, Soltis PS, Bell CD, Brockington SF, Alexandre R, Davis CC, Latvis M, Manchester SR, Soltis DE: Rosid radiation and the rapid rise of angiosperm-dominated forests. Proc Natl Acad Sci USA. 2009, 106: 3853-3858. 10.1073/pnas.0813376106.PubMedPubMed CentralView ArticleGoogle Scholar
- Chaw SM, Chang CC, Chen HL, Li WH: Dating the monocot-dicot divergence and the origin of core eudicots using whole chloroplast genomes. J Mol Evol. 2004, 58: 424-441. 10.1007/s00239-003-2564-9.PubMedView ArticleGoogle Scholar
- Liu H, Sachidanandam R, Stein L: Comparative genomics between rice and Arabidopsis shows scant collinearity in gene order. Genome Res. 2001, 11: 2020-2026. 10.1101/gr.194501.PubMedPubMed CentralView ArticleGoogle Scholar
- Zuccolo A, Bowers JE, Estill JC, Xiong Z, Luo M, Sebastian A, Goicoechea JL, Collura K, Yu Y, Jiao Y, Duarte J, Tang H, Ayyampalayam S, Rounsley S, Kudma D, Paterson AH, Pires JC, Chanderbali A, Soltis DE, Chamala S, Barbazuk B, Soltis PS, Albert VA, Ma H, Mandoli D, Banks J, Carlson JE, Tomkins J, Depamphilis CW, Wing RA, et al: A physical map for the Amborella trichopoda genome sheds light on the evolution of angiosperm genome structure. Genome Biol. 2011, 12: R48-10.1186/gb-2011-12-5-r48.PubMedPubMed CentralView ArticleGoogle Scholar
- Bousquet J, Strauss SH, Doerksen AH, Price RA: Extensive variation in evolutionary rate of rbcL sequences among seed plants. Proc Natl Acad Sci USA. 1992, 89: 7844-7848. 10.1073/pnas.89.16.7844.PubMedPubMed CentralView ArticleGoogle Scholar
- Bouillé M, Bousquet J: Trans-species shared polymorphisms at orthologous nuclear gene loci among distant species in the conifer Picea (Pinaceae): implications for the long-term maintenance of genetic diversity in trees. Am J Bot. 2005, 92: 63-73. 10.3732/ajb.92.1.63.PubMedView ArticleGoogle Scholar
- Gaut B, Yang L, Takuno S, Eguiarte LE: The patterns and causes of variation in plant nucleotide substitution rates. Ann Rev Ecol Evol Syst. 2011, 42: 245-266. 10.1146/annurev-ecolsys-102710-145119.View ArticleGoogle Scholar
- Petit RJ, Hampe A: Some evolutionary consequences of being a tree. Ann Rev Ecol Evol Syst. 2006, 37: 187-214. 10.1146/annurev.ecolsys.37.091305.110215.View ArticleGoogle Scholar
- Smith SA, Donoghue MJ: Rates of molecular evolution are linked to life history in flowering plants. Science. 2008, 322: 86-89. 10.1126/science.1163197.PubMedView ArticleGoogle Scholar
- Drummond DA, Bloom JD, Adami C, Wilke CO, Arnold FH: Why highly expressed proteins evolve slowly. Proc Natl Acad Sci USA. 2005, 102: 14338-14343. 10.1073/pnas.0504070102.PubMedPubMed CentralView ArticleGoogle Scholar
- Pavy N, Laroche J, Bousquet J, Mackay J: Large-scale statistical analysis of secondary xylem ESTs in pine. Plant Mol Biol. 2005, 57: 203-224. 10.1007/s11103-004-6969-7.PubMedView ArticleGoogle Scholar
- Pavy N, Paule C, Parsons L, Crow JA, Morency M-J, Johnson JE, Noumen E, Guillet-Claude C, Butterfield Y, Barber S, Yang G, Liu J, Stott J, Kirkpatrick R, Siddiqui A, Holt R, Marra M, Seguin A, Retzel E, Bousquet J, Mackay J: Generation, annotation, analysis and database integration of 16,500 white spruce EST clusters. BMC Genomics. 2005, 6: 144-10.1186/1471-2164-6-144.PubMedPubMed CentralView ArticleGoogle Scholar
- Côté CL, Boileau F, Roy V, Ouellet M, Levasseur C, Morency M-J, Cooke JEK, Séguin A, Mackay JJ: Gene family structure, expression and functional analysis of HD-Zip III genes in angiosperm and gymnosperm forest trees. BMC Plant Biol. 2010, 10: 273-10.1186/1471-2229-10-273.PubMedPubMed CentralView ArticleGoogle Scholar
- Katju V, Lynch M: The structure and early evolution of recently arisen gene duplicates in the Caenorhabditis elegans genome. Genetics. 2003, 165: 1793-1803.PubMedPubMed CentralGoogle Scholar
- Cannon SB, Mitra A, Baumgarten A, Young ND, May G: The roles of segmental and tandem gene duplication in the evolution of large gene families in Arabidopsis thaliana. BMC Plant Biol. 2004, 4: 10-10.1186/1471-2229-4-10.PubMedPubMed CentralView ArticleGoogle Scholar
- Fuchs J, Brandes A, Schubert I: Systematics and evolution telomere sequence localization and karyotype evolution in higher plants. Plant Syst Evol. 1995, 196: 227-241. 10.1007/BF00982962.View ArticleGoogle Scholar
- Perron M, Perry DJ, Andalo C, Bousquet J: Evidence from sequence-tagged-site markers of a recent progenitor-derivative species pair in conifers. Proc Natl Acad Sci USA. 2000, 97: 11331-11336. 10.1073/pnas.200417097.PubMedPubMed CentralView ArticleGoogle Scholar
- Bouillé M, Senneville S, Bousquet J: Discordant mtDNA and cpDNA phylogenies indicate geographic speciation and reticulation as driving factors for the diversification of the genus Picea. Tree Genet Genomes. 2011, 7: 469-484. 10.1007/s11295-010-0349-z.View ArticleGoogle Scholar
- Miller CN: The origin of modern conifer families. Origin and Evolution of Gymnosperms. Edited by: Beck CB. 1988, New York: Columbia University Press, 448-486.Google Scholar
- Lang D, Weiche B, Timmerhaus G, Richardt S, Riano-Pachon DM, Corrêa R, Mueller-Roeber B, Rensing SA: Genome-wide phylogenetic comparative analysis of plant transcriptional regulation: a timeline of loss, gain, expansion, and correlation with complexity. Genome Biol Evol. 2010, 2: 488-503. 10.1093/gbe/evq032.PubMedPubMed CentralView ArticleGoogle Scholar
- Quraishi UM, Abrouk M, Murat F, Pont C, Foucrier S, Desmaizieres G, Confolent C, Rivière N, Charmet G, Paux E, Murigneux A, Guerreiro L, Lafarge S, Le Gouis J, Feuillet C, Salse J: Cross-genome map based dissection of a nitrogen use efficiency ortho-metaQTL in bread wheat unravels concerted cereal genome evolution. Plant J. 2011, 65: 745-756. 10.1111/j.1365-313X.2010.04461.x.PubMedView ArticleGoogle Scholar
- Beaulieu J, Doerksen T, Boyle B, Clément S, Deslauriers M, Beauseigle S, Blais S, Poulin P-L, Lenz P, Caron S, Rigault P, Bicho P, Bousquet J, MacKay J: Association genetics of wood physical traits in the conifer white spruce and relationships with gene expression. Genetics. 2011, 188: 197-214. 10.1534/genetics.110.125781.PubMedPubMed CentralView ArticleGoogle Scholar
- Pavy N, Pelgas B, Beauseigle S, Blais S, Gagnon F, Gosselin I, Lamothe M, Isabel N, Bousquet J: Enhancing genetic mapping of complex genomes through the design of highly-multiplexed SNP arrays: application to the large and unsequenced genomes of white spruce and black spruce. BMC Genomics. 2008, 9: 21-10.1186/1471-2164-9-21.PubMedPubMed CentralView ArticleGoogle Scholar
- Bomal C, Bedon F, Caron S, Mansfield SD, Levasseur C, Cooke JEK, Blais S, Tremblay L, Morency M-J, Pavy N, Grima-Pettenati J, Séguin A, MacKay J: Involvement of Pinus taeda MYB1 and MYB8 in phenylpropanoid metabolism and secondary cell wall biogenesis: a comparative in planta analysis. J Exp Bot. 2008, 59: 3925-3939. 10.1093/jxb/ern234.PubMedPubMed CentralView ArticleGoogle Scholar
- Pavy N, Boyle B, Nelson C, Paule C, Giguère I, Caron S, Parsons LS, Dallaire N, Bedon F, Bérubé H, Cooke J, MacKay J: Identification of conserved core xylem gene sets: conifer cDNA microarray development, transcript profiling and computational analyses. New Phytol. 2008, 180: 766-786. 10.1111/j.1469-8137.2008.02615.x.PubMedView ArticleGoogle Scholar
- Namroud MC, Beaulieu J, Juge N, Laroche J, Bousquet J: Scanning the genome for gene single nucleotide polymorphisms involved in adaptive population differentiation in white spruce. Mol Ecol. 2008, 17: 3599-3613. 10.1111/j.1365-294X.2008.03840.x.PubMedPubMed CentralView ArticleGoogle Scholar
- Conesa A, Götz S: Blast2GO: a comprehensive suite for functional analysis in plant genomics. Int J Plant Genomics. 2008, 2008: 619832-PubMedPubMed CentralView ArticleGoogle Scholar
- D'Agostino RB, Stephens MA: Goodness-of-fit Techniques. 1986, New York: Marcel-Dekker Inc.Google Scholar
- Parzen E: On estimation of a probability density function and mode. Ann Math Stat. 1962, 33: 1065-1076. 10.1214/aoms/1177704472.View ArticleGoogle Scholar
- Fiorio CV: Confidence intervals for kernel density estimation. Stata Journal. 2004, 4: 168-179.Google Scholar
- Van Kerm P: Adaptative kernel density estimation. Stata Journal. 2003, 3: 148-156.Google Scholar
- Rice LI, Bleasby A: EMBOSS: The European Molecular Biology Open Software Suite. Trends Genet. 2000, 16: 276-277. 10.1016/S0168-9525(00)02024-2.PubMedView ArticleGoogle Scholar
- Dong Q, Schlueter SD, Brendel V: PlantGDB, plant genome database and analysis tools. Nucleic Acids Res. 2004, 32: D354-359. 10.1093/nar/gkh046.PubMedPubMed CentralView ArticleGoogle Scholar
- Lassmann T, Sonnhammer EL: Kalign--an accurate and fast multiple sequence alignment algorithm. BMC Bioinformatics. 2005, 6: 298-10.1186/1471-2105-6-298.PubMedPubMed CentralView ArticleGoogle Scholar
- Saitou N, Nei M: The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol. 1987, 4: 406-425.PubMedGoogle Scholar
- Swofford DL, Olsen GJ, Waddell PJ, Hillis DM: Phylogenetic inference. Molecular Systematics. Edited by: Hillis DM, Moritz C, Mable BK. 1996, Sunderland: Sinauer, 407-514. 2Google Scholar
- Felsenstein J: Phylip 3.6. 2005, [http://evolution.genetics.washington.edu/phylip.html]Google Scholar
- Krzywinski M, Schein J, Birol I, Connors J, Gascoyne R, Horsman D, Jones SJ, Marra MA: Circos: an information aesthetic for comparative genomics. Genome Res. 2009, 19: 1639-1645. 10.1101/gr.092759.109.PubMedPubMed CentralView ArticleGoogle Scholar
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