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BMC Genomics. 2011 Mar 10;12(1):145
Betty et al; licensee BioMed Central Ltd.
Pelgas, Betty
Bousquet, Jean
Meirmans, Patrick G
Ritland, Kermit
Isabel, Nathalie
Background: The genomic architecture of bud phenology and height growth remains poorly known in most forest trees. In non model species, QTL studies have shown limited application because most often QTL data could not be validated from one experiment to another. The aim of our study was to overcome this limitation by basing QTL detection on the construction of genetic maps highly-enriched in gene markers, and by assessing QTLs across pedigrees, years, and environments. Results: Four saturated individual linkage maps representing two unrelated mapping populations of 260 and 500 clonally replicated progeny were assembled from 471 to 570 markers, including from 283 to 451 gene SNPs obtained using a multiplexed genotyping assay. Thence, a composite linkage map was assembled with 836 gene markers. For individual linkage maps, a total of 33 distinct quantitative trait loci (QTLs) were observed for bud flush, 52 for bud set, and 52 for height growth. For the composite map, the corresponding numbers of QTL clusters were 11, 13, and 10. About 20% of QTLs were replicated between the two mapping populations and nearly 50% revealed spatial and/or temporal stability. Three to four occurrences of overlapping QTLs between characters were noted, indicating regions with potential pleiotropic effects. Moreover, some of the genes involved in the QTLs were also underlined by recent genome scans or expression profile studies. Overall, the proportion of phenotypic variance explained by each QTL ranged from 3.0 to 16.4% for bud flush, from 2.7 to 22.2% for bud set, and from 2.5 to 10.5% for height growth. Up to 70% of the total character variance could be accounted for by QTLs for bud flush or bud set, and up to 59% for height growth. Conclusions: This study provides a basic understanding of the genomic architecture related to bud flush, bud set, and height growth in a conifer species, and a useful indicator to compare with Angiosperms. It will serve as a basic reference to functional and association genetic studies of adaptation and growth in Picea taxa. The putative QTNs identified will be tested for associations in natural populations, with potential applications in molecular breeding and gene conservation programs. QTLs mapping consistently across years and environments could also be the most important targets for breeding, because they represent genomic regions that may be least affected by G × E interactions.
RESEARCH ARTICLE Open AccessQTL mapping in white spruce: gene maps andgenomic regions underlying adaptive traits acrosspedigrees, years and environmentsBetty Pelgas1,2*, Jean Bousquet2, Patrick G Meirmans1,3, Kermit Ritland4, Nathalie Isabel1,2AbstractBackground: The genomic architecture of bud phenology and height growth remains poorly known in mostforest trees. In non model species, QTL studies have shown limited application because most often QTL data couldnot be validated from one experiment to another. The aim of our study was to overcome this limitation by basingQTL detection on the construction of genetic maps highly-enriched in gene markers, and by assessing QTLs acrosspedigrees, years, and environments.Results: Four saturated individual linkage maps representing two unrelated mapping populations of 260 and 500clonally replicated progeny were assembled from 471 to 570 markers, including from 283 to 451 gene SNPs obtainedusing a multiplexed genotyping assay. Thence, a composite linkage map was assembled with 836 gene markers.For individual linkage maps, a total of 33 distinct quantitative trait loci (QTLs) were observed for bud flush, 52 forbud set, and 52 for height growth. For the composite map, the corresponding numbers of QTL clusters were 11,13, and 10. About 20% of QTLs were replicated between the two mapping populations and nearly 50% revealedspatial and/or temporal stability. Three to four occurrences of overlapping QTLs between characters were noted,indicating regions with potential pleiotropic effects. Moreover, some of the genes involved in the QTLs were alsounderlined by recent genome scans or expression profile studies.Overall, the proportion of phenotypic variance explained by each QTL ranged from 3.0 to 16.4% for bud flush, from2.7 to 22.2% for bud set, and from 2.5 to 10.5% for height growth. Up to 70% of the total character variance couldbe accounted for by QTLs for bud flush or bud set, and up to 59% for height growth.Conclusions: This study provides a basic understanding of the genomic architecture related to bud flush, bud set, andheight growth in a conifer species, and a useful indicator to compare with Angiosperms. It will serve as a basic referenceto functional and association genetic studies of adaptation and growth in Picea taxa. The putative QTNs identified will betested for associations in natural populations, with potential applications in molecular breeding and gene conservationprograms. QTLs mapping consistently across years and environments could also be the most important targets forbreeding, because they represent genomic regions that may be least affected by G × E interactions.BackgroundFor genera where a reference genome is already available[1] or for others with large and unsequenced genomessuch as for the vast majority of forest tree species, quanti-tative trait locus (QTL) mapping still represent an attrac-tive approach that can help improve our comprehensionof the genomic architecture of adaptive traits [2-4].Under certain conditions, QTL studies also permit theidentification of candidate regions for further in-depthgenomic characterization [5]. In non-model plant species,there exists a vast body of literature on the genomicarchitecture of quantitative traits [6-9]. However, theconclusions of those studies on the number and locationof QTLs are often confined to a single given experimentor experimental cross [10]. The comparison of QTLsamong studies and species is also made difficult due tothe large varieties of experimental procedures used toscore phenotypic traits. An additional reason is that* Correspondence: betty.pelgas@NRCan.gc.ca1Natural Resources Canada, Canadian Forest Service, Laurentian ForestryCentre, Québec, Québec, G1V 4C7, CanadaFull list of author information is available at the end of the articlePelgas et al. BMC Genomics 2011, 12:145http://www.biomedcentral.com/1471-2164/12/145© 2011 Betty et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative CommonsAttribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction inany medium, provided the original work is properly cited.syntenic QTLs are difficult to identify and validate due tothe limited number of orthologous markers commonlypositioned among unrelated families or species.With the advent of high-throughput genotyping technol-ogies and gene catalogs, it is now possible to constructdense linkage maps with markers derived from expressedgene sequences. This new generation of gene markerstransferable across pedigrees, including ESTPs, COSs andSNPs [11-14], allows the construction of consensus linkagemaps with hundreds of expressed gene loci [13]. Further-more, such gene-based markers are most often ortholo-gous and should enable comparative mapping studies(synteny and colinearity) and subsequently, comparativeanalysis of QTLs among species and genera [15,16], a taskthat has been hardly initiated in conifers [17,18].In conifers such as loblolly pine (Pinus taeda L.), mar-itime pine (Pinus pinaster Ait.), Douglas fir (Pseudotsugamenziesii [Mirb.] Franco) and sugi (Cryptomeria japo-nica D. Don), the main focus of QTL studies has beenon economical and adaptive traits such as wood proper-ties [8,19-24], and growth characteristics [9,25]. Up tonow, few QTL studies have focused on adaptive charac-ters tied to phenological traits and cold hardiness[26-29]. The context has recently changed, becauseadaptive traits are directly involved with response to cli-mate change. Renewed interest in phenology is how itimpacts on growth-related traits in plants in the contextof global warming, and how it might affect plant pro-ductivity and ecosystem services [30,31].Phenophases correspond to adaptive traits, such as budflush and bud set, are influenced in part by heredity andby the environment [32,33]. They determine to a certainextent the fitness of individuals under particular climaticconditions [34]. Temperature is a main driver of develop-mental processes in plants both alone and through inter-actions with other environmental factors, such asphotoperiod [35,36]. Considering that many factors, suchas the environment and the genetic background, affectQTL detection, QTL comparative mapping betweenunrelated families in different environments throughtime represents an interesting strategy to highlight con-sensus regions of importance for a given trait [18,37].Bud phenology is known to be variable and quitehighly heritable in forest trees [32,38,39]. Heritabilityestimates for bud flush may range from 0.44 to 0.98[33,40-44] while for bud set, heritability values mayrange from 0.37 to 0.72 [33,40]. For height growth, her-itability can reach up to 0.86 [45,46]. Moreover, somegenetic studies on poplar (Populus sp) and willow (Salixsp) have reported that relatively few loci with majoreffects controlled these traits [33,41,44]. Conversely,other studies in Douglas fir and poplar have revealedthat growth and bud development were subject to con-trol by many genetic loci with small to moderate effects[1,2,26,28]. In white spruce, the number and size effectsof genes underlying these adaptive traits have not beencharacterized yet.Recently, genetic maps with some tens to hundreds ofgene-based markers have been constructed in whitespruce for different segregating families [12,13], whichprovides a solid foundation to investigate the genomicarchitecture of adaptive traits. While expanding thesemaps with additional gene-based SNPs, the specificobjectives of the current study were: i) to identify congru-ent QTLs or genomic regions underlying phenologicaltraits across different pedigrees, years, and environmentsand ii) to determine putative genomic regions associatedin part with the photoperiodic control of phenology inwhite spruce. To do so, the present comparative QTLmapping study was conducted with clonally-replicatedmaterial representative of two unrelated white sprucefamilies raised in natural outdoor and environmentally-controlled conditions, and was conducted throughsuccessive growing seasons.MethodsPlant materialTwo large unrelated full-sib families of white spruce(Picea glauca (Moench) Voss) were used separately tomap QTLs. The first QTL mapping population, alreadyused in a previous genetic mapping study [13], was crossC96-1-2856 (♀80112 × ♂80109) hereafter named crossP, which progeny was assessed throughout growing sea-sons 2004 and 2005, i.e. at the age of 2 and 3 years. Thesecond QTL mapping population was cross C94-1-2516(♀77111 × ♂2388) hereafter named cross D, which pro-geny were assessed throughout growing successive sea-sons 2005, 2006, and 2007, i.e. at the age of 3, 4 and5 years. Progeny of both crosses were clonally propagatedby root cutting and maintained at the Valcartier Experi-mental Station near Quebec City, Canada (VES, 46°57′2′′N, 71°29′50′′W; Canadian Forest Service, NaturalResources Canada) under natural outdoor conditions.During the growing seasons, trees were fertilized dailywith a solution 1 g L-1 of a mix 3:1 (w:w) of 20-08-20: 20-20-20 commercial fertilizers (outdoor conditions:Solutech, Coopérative Fédérée, Québec, Canada; con-trolled conditions: Plant-Prod, Brampton, Ontario,Canada). To promote bud set and growth cessation, ferti-lization was stopped during the first three weeks ofAugust, but trees were kept well watered. Fertilizationwas resumed with a solution 1 g L-1 of fertilizer 08-20-30until the end of the experiment.Genetic mapsa. Development of gene based-markers and genotypingSNP markers derived from a collection of 16,500 whitespruce EST clusters previously assembled by Pavy et al.Pelgas et al. BMC Genomics 2011, 12:145http://www.biomedcentral.com/1471-2164/12/145Page 2 of 23[47] were assayed on both crosses. For cross P, the gen-otyping data from a previously developed GoldenGatebead array (Illumina Inc., San Diego, California, USA)[48] of 768 SNPs representing 425 genes (Table 1,Arborea PgLM0 array) was used [13]. As well, a secondset of 384 SNPs representing as many genes were geno-typed for cross P using the GoldenGate assay developedfor white spruce and also for the closely related Sitkaspruce (Treenomix project, [49]), and resulted in 120scorable and segregating SNPs in this white sprucecross. For cross D, a new and larger Illumina bead arrayof 1,536 SNPs representing 822 genes (Table 1, ArboreaPgLM1 array) was specifically constructed using theGoldenGate assay. The array contained 90 SNPs derivedfrom in silico identification in white spruce clusters ofexpressed sequence tags (ESTs) following the methodsof Pavy et al. [50], and 1,416 SNPs and 30 indels (vary-ing from 1 to 30 nucleotides) selected after genomic re-sequencing of the mapping parents and of one haploidmegagemetophyte (for paralogs check) following themethods of Pavy et al. [13]. The 30 indels were locatedin untranslated regions. TreeSNPs was used for datamanagement [51]. To be valid and successfully geno-typed, the SNPs (including the indels) had to get a mini-mum quality GenTrain score of 0.25 [13] and had tosegregate in the mapping population. Gene nomencla-ture follows the Arborea GCAT3.3 white spruce genecatalogue encompassing around 28,000 genes from theclustering of 272,172 ESTs (http://www.arborea.ulaval.ca/research/sequencing/gene_catalogue/index.html).Additional file 1 provides a list of primer sequencesused for PCR amplification and unigene identifiers(https://www.gydle.com/arborea/gcat/) corresponding tothe unigene sequences and their annotations from thegene assembly GCAT3.3 (http://www.arborea.ulaval.ca).b. Map assemblyFor each QTL mapping population P and D, two indivi-dual (parental) and one sub-composite linkage mapswere assembled from 260 and 500 progeny, respectively,using the ‘’two-way pseudo-testcross’’ strategy [52]. Theconstruction of these maps and the assembly of a com-posite map representative of the white spruce genomewere carried out according to the procedures outlinedby Pelgas et al. [11]. Marker grouping and linked lociordering were performed using a minimum LODthreshold value of 6.0 and a minimum recombinationfraction (θ) of 0.35.Experimental design and treatmentsSix ramets for each of 395 and 740 progeny from theQTL mapping populations P and D, respectively, withone ramet placed randomly within each of six blocks(three “indoor” and three “outdoor” blocks), were main-tained over several growing seasons. Growth cessationand bud set occurred under either natural outdoor con-ditions at VES for the three outdoor blocks, or underenvironmentally-controlled conditions at Agricultureand Agri-Food Canada (AAFC, in Quebec City, Canada)for the three indoor blocks. At summer solstice (21stJune), indoor blocks were transferred at AAFC in aTable 1 Genotyping success rates obtained with the 768 and 1,536 SNPs Arborea bead arrays for each of two whitespruce QTL mapping populations using the GoldenGate assayBead array (cross) Number of SNPs(percent success rate)Number of genes(percent success rate)SNPs assayed SNPs withGenTrain score≥ 0.25aSegregating SNPswith GenTrainscore ≥ 0.25aGenes assayed Genes withsegregating SNPsArborea PgLM0 (for mapping cross P)bTotal 768 603 (79%) 516 (67%) 425 330 (78%)Number of resequenced SNPsc 730 572 (78%) 505 (69%)Number of in silico SNPs d 38 31 (82%) 11 (30%)Arborea PgLM1 (for mapping cross D)Total 1,536 1,261 (82%) 1,100 (72%) 822e 672f (82%)Number of resequenced SNPsb 1,416 1,159 (82%) 1,051 (74%)Number of resequenced indelsg 30 21 (70%) 21 (70%)Number of in silico SNPsc 90 77 (86%) 28 (31%)aFor SNPs with a GenTrain score ≥ 0.25, valid GenCall scores were obtained for 99.5% of samples, on average.bAs reported by Pavy et al. [13].cPercentage obtained by using the total of 768 SNPs assayed per cross or the total number of genes assayed as a reference.din silico SNPs were detected in clusters of aligned EST sequences derived from white spruce cDNA libraries from various individuals but not implicating themapping parents Pavy et al. [50], which explains the lower rates of segregating SNPs obtained.eincluding 330 genes shared with PgLM0.fincluding 250 genes shared with PgLM0.gIncluding 21 gene indels of 1 to 2 bp, 6 indels of 3 to 6 bp, and 3 indels of over 10 bp, from untranslated regions.Pelgas et al. BMC Genomics 2011, 12:145http://www.biomedcentral.com/1471-2164/12/145Page 3 of 23large growth chamber and exposed to a declining photo-period to simulate natural changes occurring at VES inorder to help identify QTL clusters responding specifi-cally to photoperiod. For that purpose, the photoperiodinside the growth room was adjusted weekly from thesummer solstice to the end of September, i.e. from 16.0to 12.2 hours. The environmental conditions in thelarge growth chamber were: day/night-time tempera-tures of 24/15C; photosynthetic photon flux density of600-800 mol photons m-2 s-1 provided by a mixture ofhigh pressure sodium and metal halide 400W lamps (PLlight Systems, Beamsville, Ontario, Canada). Measures ofbud flush were completed at VES in the spring for allblocks just before the moving of three of them to AAFC(indoor conditions). Inversely, measures of bud set werecompleted for three blocks at AAFC just before themoving to VES in the fall. For both QTL mappingpopulations (crosses P and D), outdoor and indoorblocks were considered separately during data collectionand for subsequent statistical analyses.Phenotypic data collectiona. Timing of bud flushThe timing of bud flush of the terminal bud of theshoot leader was assessed three times per week by visualinspection, during two successive springs for both QTLmapping populations: in 2004 and 2005 for populationP and in 2006 and 2007 for population D. Seven stagesscoring the bud flushing from 0 to 6 were delineated:stage 0 being the terminal bud completely closed andstage 6 being the bud completely flushed with needleselongating [53]. At each date of bud visual inspection,bud development stage (from 0 to 6) was recorded foreach tree under outdoor conditions at VES for both out-door and indoor blocks.b. Timing of bud setThe timing of bud set of the terminal bud was assessedonce a week during both summers 2004 and 2005 forQTL mapping population P, and once per week duringthe summer 2006 for population D. For the latter popu-lation, the data were also collected two to three timesper week during the 2007 growing season. Six stages ofbud set were defined from 0 to 5 [53]: stage 0 being thevery initiation with small white-green bud and stage 5being the brown bud completely set with the needle fanopened towards the outside [53,54]. Data for bud setwere collected at VES for outdoor blocks and at AAFCunder controlled conditions for indoor blocks. At eachdate of bud visual inspection, the stage associated tobud formation (from 0 to 5) was recorded for each treepositioned under outdoor or indoor conditions.c. GrowthFor both QTL mapping populations, annual growthexpressed as the total height (mm) of the current yearstem was measured at the end of each growing season,once the terminal bud was set: in 2004 and 2005 forpopulation P and in 2005, 2006, and 2007 for populationD. For this latter population, the total height of eachtree was also measured in 2006.Statistical analysesFor each phenotypic trait, statistical analyses were per-formed similarly for both mapping populations. Descrip-tive statistics and correlations were calculated from rawdata for growth traits, whereas for the timings of budflush and bud set, phenological data scores gathered foreach tree were considered as the date (in Julian days) toreach each bud development stage. Then, to consider atonce the various qualitative scores of bud flush or budset datasets, principal component analysis was con-ducted for each of these characters at each site and eachyear before undertaking QTL analyses (see below).a. Quantitative data analyses and correlations amongphenotypic traitsDescriptive statistical parameters (Additional file 2), var-iances and normality (Kolmogorov-Smirnov test) of phe-notypic traits were obtained using UNIVARIATE andGLM with the RANDOM option procedure of SAS (Sta-tistical Analysis System, version 9.1.3, SAS Institute Inc.,Cary, North Carolina, USA). The PLOT (QQPlot) andNORMAL options of the UNIVARIATE procedure(SAS) were further used to judge the normality oferrors. Phenotypic correlation coefficients were calcu-lated with the CORR procedure (SAS) for all pairwisetrait combinations, using either Pearson or Spearmancorrelations, when data followed or not a normal distri-bution, respectively. Correlations for bud flush and budset were estimated based on each developmental stageseparately and thus processed based on the number ofJulian days to reach each stage.b. Principal component analysesPrincipal component analyses (PCA) of phenologicaldata scores were performed separately for bud flush orbud set using the covariance matrix as a tool of linearcompression on each data set (R software [55]; R Devel-opment Core Team 2008). This transformation wasdone on block means to decrease the number multi-data qualitative variables (phenology multi-stages) to asingle, two or three quantitative variables (compositephenotypes), which may reveal different dimensions ineach dataset. This procedure was preferred to the classi-cal approach of estimating an index from the variousphenological scores because it does not suppress tem-poral trends in each dataset. The principal componentswere obtained by calculating the eigenvalues of the cov-ariance matrix, which represent the amount of variancecontributed by each factor. The Kaiser criterion wasapplied [56] in order to retain only factors withPelgas et al. BMC Genomics 2011, 12:145http://www.biomedcentral.com/1471-2164/12/145Page 4 of 23eigenvalues greater than 1: one to three factors per traitcontaining the overwhelming majority of the total var-iance (Table 2) were retained for linkage analyses.A rotation of components was applied when required toimprove component orthogonality in comparison toinitial data. Rotation was done before retaining the mostimportant components. Factorial scores derived fromthese components showing no departure from normalitywere used for QTL analyses.c. QTL analysesAnalyses were performed on all datasets separately toassess QTL consistency over mapping populations, years,and environmental conditions. Thus, single QTLs wereidentified. For height annual growth, clonal means acrossreplications were taken into account separately for eachyear and each environmental condition of both mappingpopulations and it was the same for the total height datarecorded only in 2006 for the population D. For bud flushand bud set, factorial scores derived from componentsretained after PCA were used as phenotypic data for eachtree. Associations between segregating genetic markersand the phenotypic variability of each trait were deter-mined for each year and each environmental condition foreach individual parental map. An interval mappingapproach [57] was used to first seek putative QTLs. Per-mutation tests were performed to set out chromosomeand genome-wide significant thresholds [58]. Single-QTLmodel analyses were carried out with MapQTL software,version 5.0 [59]. Each linkage group was scanned with oneto five markers as maximum number of neighbouringmarkers and 200 iterations. Permutation tests were carriedout on each linkage group to compare hypotheses H1 (pre-sence of one QTL on the linkage group) versus H0 (noQTLs on the linkage group). At least 1,000 data permuta-tions were applied to each linkage group and also gen-ome-wide to determine the chromosome-wise andgenome-wise statistical significance thresholds. Only geno-mic regions exceeding chromosome-wise P ≤ 0.05 (sugges-tive level) or genome-wise P ≤ 0.05 (significance level)significance were reported as supporting the existence of aQTL. Multiple-QTL model analyses were performed withMultiQTL software, version 2.6 (http://www.multiqtl.com)to increase the accuracy of the estimated QTL position.The Kosambi mapping function with the option ‘markerrestoration’ was used to reduce the effect of missing infor-mation. Permutations tests were carried out on each link-age group to compare hypotheses H1 versus H0 and alsoto compare hypotheses H2 (presence of two QTLs on thelinkage group) versus H1. The two-linked QTL model wasrun to prevent the spurious detection of “ghost” QTLsthat can arise when two QTLs are segregating on thesame linkage group [60].Table 2 Proportion of the total phenotypic variance explained by each principal component (PC1, PC2 and PC3)retained for the timings of bud flush and bud setTraits and sitesa MappingpopulationbYear Proportion of total variance (range of phenology stagesc)PC1 PC2 PC3Bud flushVES P 2004 65.4 (4.4-4.6) 9.9 (3.6-4.0) -2005 50.1 (3.9-4.9) 12.9 (2.0-2.7) -D 2006 62.3 (0.4-2.3) 13.6 (3.4-5.2) -2007 56.9 (1.4-3.5) 12.8 (4.2-5.7) -AAFC P 2004 - - - - -2005 - - - - -D 2006 66.8 (1.6-3.4) 12.5 (4.3-5.8) -2007 73.5 (3.8-5.0) 10.9 (1.1-2.5) -Bud setVES P 2004 73.4 (3.9-4.7) 14.3 (3.3-3.9) -2005 64.4 (4.3-4.5) 13.7 (1.7) -D 2006 73.9 (3.1-4.3) - -2007 66.7 (3.7-4.7) 11.9 (1.9-3.0) 6.4 (1.9-2.5)AAFC P 2005 45.8 (4.0-4.7) 34.1 (2.1) -D 2006 80.9 (3.4-4.7) - -2007 64.0 (0.7-3.6) - -aAAFC, indoor controlled conditions at Agriculture and Agri-Food Canada; VES, natural outdoor conditions at Valcartier Experimental Station.bP, first mapping population (cross C96-1-2856); D, second mapping population (cross C94-1-2516).ccorresponds to the range of bud phenology stages explained by each principal component (indeed, PCA were performed from means of stages of three blocksfor each environmental condition).Pelgas et al. BMC Genomics 2011, 12:145http://www.biomedcentral.com/1471-2164/12/145Page 5 of 23d. QTL locationThe central position of each single QTL was determinedby the position where the highest LOD was reached onthe linkage map, i.e QTL-LOD peak. The confidenceinterval in centimorgans of each QTL corresponded to aLOD score drop of 1 and 2 on either side of the likeli-hood peak, i.e. at -1LOD and -2LOD below the QTL-LOD peak, providing at least 95% confidence [61]. Toportray single QTL locations, results were graphicallyconverted with the MapParse 2.0 software (http://mac.softpedia.com/get/Math-Scientific/MapParse.shtml,P. Meirmans, unpublished) and MapChart [62]. A sim-plified visualization of single QTLs observed for the fourdifferent parental maps was plotted onto the compositegenetic map. Single QTLs were projected on the compo-site map, using markers of left and right flanking endsof the confidence intervals shared by individual linkagemaps and the composite map, by means of a homotheticfunction as proposed by Chardon et al. [37]. In a fewcases where anchor markers displayed a discrepancy inordering between an individual map and the compositemap, the projection was processed with the next flank-ing markers. For each trait, single QTLs sharing ortho-logous markers positioned in the same genomic regionat the level of the composite map and in the -1LODconfidence interval were considered as characterizingthe same QTL cluster. Also, to determine whetherQTLs among different traits were significantly co-located, first, the proportion of QTLs within each QTLcluster for different traits that had overlapping confi-dence intervals was determined. Then, QTL confidenceintervals were randomized across the linkage map 1,000times, and the distribution of the proportion of overlap-ping QTLs of different traits determined. If this propor-tion of randomized QTLs was less than the originalQTL overlap 95% of the time, the co-location wasdeemed significant.ResultsGenotyping SNP-array PgLM1For the new data generated by the 1,536-SNP genotyp-ing array PgLM1 for the cross D, 72% of the polymorph-isms assayed (1,100 SNPs) had a Gentrain quality scoreof 0.25 or more and segregated among the progeny(Table 1). They were thus deemed successful. This suc-cess rate was comparable with previous genotyping suc-cess rate on the cross P using a 768-SNP genotypingarray (PgLM0, [13]). A number of SNPs with acceptableGentrain score were found to be monomorphic, pre-sumably because of the failure of one of the two allele-specific oligos (ASO) included in the GoldenGate assay[13]. For the 1,100 polymorphisms successfully geno-typed and segregating, the call rate was 99.5% on aver-age, with the lowest call rate at 95% for any given SNP(or maximum of 5% missing data). Based on the inclu-sion of 14 positive controls, the repeatability of the gen-otyping assay was estimated at 99.47%. The overallsuccess rate was higher for resequenced SNPs than forSNPs derived from the alignment of EST sequences(Table 1). Because a subset of genes (317 out of 822)had more than one SNP genotyped, the success rate wasmarginally higher on a gene basis (82%) (Table 1) andwhen two SNPs were successfully genotyped for a givengene, the one with the least missing data was kept forgene mapping purposes. A number of small indelslocated in untranslated regions could be successfullygenotyped using the GoldenGate assay. Fifteen out of 21indels of 1 to 2 bp assayed could be successfully geno-typed, also 6 indels of 3 to 6 pb, but none of threeindels above 10 bp (12, 21, 30 bp) could be successfullygenotyped. Therefore, for small indels, the genotypingsuccess rate with the GoldenGate assay was comparableto that of SNPs.Genetic mapsFour saturated linkage maps were assembled from 454 to570 markers, including 283 to 451 gene SNPs (Table 3):the parental maps for the female 80112 and the male80109 of cross P were assembled each on 12 major link-age groups (LGs) by positioning respectively 549 and 570markers, including newly genotyped gene SNPs (usingthe Sitka spruce/white spruce SNP array, see Methods)and previously mapped AFLPs, SSRs, ESTPs, and geneSNPs [12,13]; parental maps for the female 77111 andthe male 2388 of cross D were assembled for each of the12 major LGs by positioning respectively 471 and 454markers composed of newly genotyped gene SNPs (usingthe PgLM1 array, see above). The alignment betweeneach homoeologous linkage group of the four individuallinkage maps could be conducted on account of commongene markers between both mapping populations. Syn-teny and colinearity were well conserved between indivi-dual linkage maps within the same mapping population.Indeed, marker order was the same for 159 out of 165(96.4%) homoeologous markers between the male(80109) and female (80112) linkage maps of cross P, andfor 244 out of 258 (94.6%) homoeologous markersbetween the male (2388) and the female (77111) linkagemaps of cross D.The sub-composite maps of crosses P (955 markers:473 AFLPs, 12 SSRs, 31 ESTPs, and 439 gene SNPsincluding 327 SNPs from the PgLM0 array and 112SNPs from the Sitka spruce/white spruce SNP array)and D (639 markers: 17 ESTPs and 622 gene SNPs fromthe PgLM1 array) could be merged using 251 syntenicgene loci (sub-composite maps not shown). Out ofthem, 221 (88.4%) were positioned in the same orderthrough both sub-composite linkage maps, allowing toPelgas et al. BMC Genomics 2011, 12:145http://www.biomedcentral.com/1471-2164/12/145Page 6 of 23assemble the composite linkage map for the species.This composite linkage map consisted of 1,301 markers(453 AFLPs, 12 SSRs, 33 ESTPs, and 803 gene SNPs)positioned on 12 major linkage groups, correspondingto a marker density of 1 marker per 1.6 cM (Figures 1,2, 3 and 4, Table 3). With 836 genes, this compositemap can be considered as the most enriched compositeconifer map in genes up to now.Statistical parameters of phenotypic dataKolmogorov-Smirnov tests and QQplots revealed thatheight growth data followed a normal distribution,whereas bud flush and bud set data did not always followsuch a distribution except for few developmental stages.For phenotypic traits following a normal distribution, sig-nificant differences were observed among clones withincross but not among blocks. Correlation coefficientsrevealed a small number of significant correlationsamong traits ranging from low to high (Additional file 3).Pearson’s and Spearman’s correlation coefficients wereconsidered as small, medium and large when values werebetween 0.1 to 0.3, 0.3 to 0.5, and 0.5 to 1, respectively.QTL detection within crossesQTL results for each trait are tabulated in three addi-tional files 4, 5, 6 following each individual linkage mapfrom both mapping populations. They are also reportedon Figures. 1, 2, 3 and 4 for the composite linkage map.Over all four parental maps, a total of 137 single QTLsrelated to growth and phenology were detected: 33 forbud flush (eight from cross P; 25 from cross D), 52 forbud set (14 from cross P; 38 from cross D) and 52 forgrowth (four from cross P; 48 from cross D). For budflush, bud set, and height growth, respectively, nine,Table 3 Parameters used to assemble the individual and composite white spruce linkage mapsMapping parameters Individual maps Composite mapCross P Cross D♀80112 ♂80109 ♀77111 ♂2388Available markersa 600 671 521 508 1,578bDistorted markersc 10 12 15 19 33Markers without segregation distortion 590 659 506 489 1545Assigned markers 580 650 486 470 1,342AFLP loci 256 289 - - 470SSR loci 9 10 - - 12Gene lociESTPs 22 23 20 20 33SNPs 293 328 466 450 827dTotal 315 351 486 470 860Positioned marker loci (%) 549 (94.7) 570 (87.7) 471 (96.9) 454 (96.6) 1,301 (96.9)AFLP loci 239 235 - - 453SSR loci 8 10 - - 12Gene lociESTPs 19 19 20 20 33SNPs 283e 306f 451 434 803gTotal 302 325 471 454 836Major linkage groups (n > 10 markers) 12 (4h) 12 (1h) 12 12 12Minor linkage groups (3 ≤ n ≤ 10 markers) 1 0 0 0 0Unlinked markers 10 9 20 19 58Map length GF, cM (Kosambi) 2,163.6 2,276.1 2,055.6 1,700.1 2,086.8Average map density, cM (Kosambi) 3.9 4.0 4.4 3.7 1.6Average size for major linkage groups, cM (Kosambi) 143.3 162.6 171.3 141.7 173.9aFor individual linkage maps, only markers segregating 1:1 or 1:1:1:1 were used.b256 markers were shared between the two crosses.cBonferroni correction: P ≤ 0.01/number of loci.d373 gene loci from Arborea PgLM0 SNP array, 339 from Arborea PgLM1 array, and 115 from Treenomix array.e215 gene loci from Arborea PgLM0 SNP array, and 68 from Treenomix array.f239 gene loci from Arborea PgLM0 SNP array, and 67 from Treenomix array.g370 gene loci from Arborea PgLM0 SNP array, 325 from Arborea PgLM1 array, and 108 from Treenomix array.hNumber of linkage groups composed of 2 subgroups having more than 10 markers.Pelgas et al. BMC Genomics 2011, 12:145http://www.biomedcentral.com/1471-2164/12/145Page 7 of 23seven and 25 QTLs were significant at the 5% genome-wide level, 23, 38, and 21 were significant at the 5%chromosome-wide level (i.e. suggestive QTLs), and two,seven, and six were also considered as indicative (even ifnot significant). These latter trivial QTLs were retainedas informative QTLs since they co-localized with at leasttwo significant QTLs, except for one associated withonly one significant QTL on LG IV for height growth(Additional file 6). For bud flush, bud set and heightgrowth, respectively, four, six, and 11 suggestive QTLs(as defined above) co-localized with significant QTLs.Out of the total of 137 single QTLs detected, 26 (19%)were derived from the mapping population P and theother 111 (81%) from the mapping population D.The percent of phenotypic variance accounted for bysingle QTLs ranged from 3.0 to 16.4% for bud flush, 2.7to 22.2% for bud set, and 2.5 to 10.5% for height growth.The proportion of phenotypic explained variance (PPVE)for each QTL detected from cross P was rarely below10% with a maximum of 22.2%, whereas for cross D,PPVE values were often near 5% with a maximum of14.4%. For the first cross P, the sum of PPVE perFigure 1 Genomic architecture of adaptive traits in white spruce: distribution of QTLs significantly associated to bud flush, bud set,and height growth on linkage groups I to III of the composite map of white spruce assembled from both mapping populations P (♀80112 × ♂ 80109) and D (♀ 77111 × ♂ 2388). Genetic distances are indicated in cM (Kosambi) at the left of each linkage group. QTLs forbud flush, bud set and height growth are indicated by green, blue, and orange circles and bars, respectively. Thin and large vertical bars of eachQTL correspond respectively to -1LOD and -2LOD confidence intervals and are positioned compared to marker position on central bar of eachlinkage group. Circles identify gene markers associated to each -1LOD confidence interval of each QTL. Note that because of the densepositioning of markers, physical position of circles and confidence intervals can not be plotted beside each other. QTL names correspond toenvironmental condition (indoor = AAFC or outdoor = VES), year and for bud flush and bud set, principal component identifying each QTL, i.e.PC1, PC2 or PC3 (Table 2). An asterisk was added for QTLs identified from total height growth in order to distinguish them to QTLs identified forannual height growth in 2006 for the cross D. Red stars indicate the correspondence between QTL-marker and outlier candidate gene SNPsinvolved in local adaption for white spruce [108].Pelgas et al. BMC Genomics 2011, 12:145http://www.biomedcentral.com/1471-2164/12/145Page 8 of 23individual based on the maximum LOD value was18.5%, 45% and 19.3%, respectively for bud flush, budset, and height growth for the parent 80109, and 36.1%,70.2% and 21.0%, respectively, for the parent 80112,with generally two to five QTLs per trait, correspondingto an average PPVE value per QTL ranging between 9.3and 14%. For the second cross D, the correspondingnumbers were 69.5%, 53.6% and 51.5% for the parent2388, and 55.0%, 55.5% and 58.6% for the parent 77111,with 9 to 11 QTLs per trait, corresponding to an aver-age PPVE value per QTL ranging between 5.2 and 7.0%.In general for the second cross D, most QTLs eachexplained less than 10% of the total phenotypic varianceon an individual basis for any of the three traits (TablesS2, S3, and S4).QTL comparison across individuals between crossesAt the level of the composite linkage map, a total of 34QTL clusters (11, 13 and 10) regrouping all of 137QTLs (33, 52, 52) were found for the three traits (budflush, bud set, and height growth, respectively). For eachtrait, QTL clusters regrouped all single QTLs positionedin the same genomic regions at the level of the compo-site map, i.e. having shared orthologous loci in their-1LOD confidence intervals. For instance, on LG I, twoQTL clusters, one for bud set and another one forFigure 2 Genomic architecture of adaptive traits in white spruce: distribution of QTLs significantly associated to bud flush, bud set,and height growth on linkage groups IV to VI of the composite map of white spruce assembled from both mapping populations P(♀ 80112 × ♂ 80109) and D (♀ 77111 × ♂ 2388). Genetic distances are indicated in cM (Kosambi) at the left of each linkage group. QTLs forbud flush, bud set and height growth are indicated by green, blue, and orange circles and bars, respectively. Thin and large vertical bars of eachQTL correspond respectively to -1LOD and -2LOD confidence intervals and are positioned compared to marker position on central bar of eachlinkage group. Circles identify gene markers associated to each -1LOD confidence interval of each QTL. Note that because of the densepositioning of markers, physical position of circles and confidence intervals can not be plotted beside each other. QTL names correspond toenvironmental condition (indoor = AAFC or outdoor = VES), year and for bud flush and bud set, principal component identifying each QTL, i.e.PC1, PC2 or PC3 (Table 2). An asterisk was added for QTLs identified from total height growth in order to distinguish them to QTLs identified forannual height growth in 2006 for the cross D. Red stars indicate the correspondence between QTL-marker and outlier candidate gene SNPsinvolved in local adaption for white spruce [108].Pelgas et al. BMC Genomics 2011, 12:145http://www.biomedcentral.com/1471-2164/12/145Page 9 of 23height growth, were identified. The first QTL clusterregrouped four single QTLs observed with the mappingpopulation D, whereas the second QTL clusterregrouped two and four single QTLs detected from bothmapping populations P and D, respectively (Figures. 1,2, 3 and 4). About one third of all QTL clusters (10 outof 34), were detected in cross P (20% for growth, 27%for bud flush, and 39% for bud set), compared to morethan 85% in cross D for each trait (100% for growth,91% for bud flush, and 85% for bud set). Indeed, theanalyses performed on the mapping population Prevealed that the 26 single QTLs mentioned above wereFigure 3 Genomic architecture of adaptive traits in white spruce: distribution of QTLs significantly associated to bud flush, bud set,and height growth on linkage groups VII to IX of the composite map of white spruce assembled from both mapping populations P(♀ 80112 × ♂ 80109) and D (♀ 77111 × ♂ 2388). Genetic distances are indicated in cM (Kosambi) at the left of each linkage group. QTLs forbud flush, bud set and height growth are indicated by green, blue, and orange circles and bars, respectively. Thin and large vertical bars of eachQTL correspond respectively to -1LOD and -2LOD confidence intervals and are positioned compared to marker position on central bar of eachlinkage group. Circles identify gene markers associated to each -1LOD confidence interval of each QTL. Note that because of the densepositioning of markers, physical position of circles and confidence intervals can not be plotted beside each other. QTL names correspond toenvironmental condition (indoor = AAFC or outdoor = VES), year and for bud flush and bud set, principal component identifying each QTL, i.e.PC1, PC2 or PC3 (Table 2). An asterisk was added for QTLs identified from total height growth in order to distinguish them to QTLs identified forannual height growth in 2006 for the cross D. Red stars indicate the correspondence between QTL-marker and outlier candidate gene SNPsinvolved in local adaption for white spruce [108].Pelgas et al. BMC Genomics 2011, 12:145http://www.biomedcentral.com/1471-2164/12/145Page 10 of 23distributed among three distinct QTL clusters for budflush (one onto LG XI and two onto LG IV), five forbud set (LGs III, IV, VI, VIII and X), and two for heightgrowth (LGs I and X). For each trait, bud flush, bud set,and height growth, respectively, two (67%), four (80%),and two (100%) QTL clusters were commonly identifiedin the two parental maps, across all years and for alltested environments. Similar analyses carried out for themapping population D indicated: 10 distinct QTL clus-ters for bud flush (one onto LGs III, IV, X, and XII, andtwo onto LGs V, VI, and XI), 11 for bud set (one ontoLGs I, II, III, VI, VIII, XI, and XII, and two onto LGs Vand X), and 10 for height growth (one onto LGs I, II,III, and VIII, and two onto LGs IV, V, and X). For thissecond cross, nine (i.e. 90%), ten (i.e. 91%), and ten (i.e.100%) QTL clusters, respectively for bud flush, bud set,and height growth, were commonly identified in the twoparental maps, for all years and environments tested. Byconsidering all 34 QTL clusters detected for the threetraits on the composite map, two QTL clusters out ofFigure 4 Genomic architecture of adaptive traits in white spruce: distribution of QTLs significantly associated to bud flush, bud set,and height growth on linkage groups X to XII of the composite map of white spruce assembled from both mapping populations P(♀ 80112 × ♂ 80109) and D (♀ 77111 × ♂ 2388). Genetic distances are indicated in cM (Kosambi) at the left of each linkage group. QTLs forbud flush, bud set and height growth are indicated by green, blue, and orange circles and bars, respectively. Thin and large vertical bars of eachQTL correspond respectively to -1LOD and -2LOD confidence intervals and are positioned compared to marker position on central bar of eachlinkage group. Circles identify gene markers associated to each -1LOD confidence interval of each QTL. Note that because of the densepositioning of markers, physical position of circles and confidence intervals can not be plotted beside each other. QTL names correspond toenvironmental condition (indoor = AAFC or outdoor = VES), year and for bud flush and bud set, principal component identifying each QTL, i.e.PC1, PC2 or PC3 (Table 2). An asterisk was added for QTLs identified from total height growth in order to distinguish them to QTLs identified forannual height growth in 2006 for the cross D. Red stars indicate the correspondence between QTL-marker and outlier candidate gene SNPsinvolved in local adaption for white spruce [108].Pelgas et al. BMC Genomics 2011, 12:145http://www.biomedcentral.com/1471-2164/12/145Page 11 of 2311 (18%) and two out of 10 (20%) were replicatedbetween the two mapping populations for bud flush andheight growth, respectively, while three out of 13 (23%)were replicated between the two mapping populationsfor bud set. In fact, among the QTL clusters identifiedfrom cross P, 66% were also observed in cross D forbud flush, 60% for bud set, and 100% for height growth.Yearly and environmental replications of QTLsOf the total of 10 QTL clusters identified from the map-ping population P for the three traits, three for budflush, five for bud set, and two for height growth, 40%(2, 2, 0) were detected in 2004 and 90% (2, 5, 2) in2005, with 30% (1, 2, 0) shared between the two years(numbers in parentheses refer to bud flush, bud set andheight growth, respectively). For this cross, comparisonsbetween environments were limited because no mea-surements under controlled conditions (indoor atAAFC) could be recorded for any of the three traits dur-ing the first growing season in 2004, but bud set andheight growth could be assessed under controlled indoorconditions for this mapping population in 2005. Indeed,70% (-, 5, 2) of the 10 QTL clusters were identified dur-ing the second year (in 2005) for indoor experimenta-tion under controlled conditions, and they were relatedto bud set and height growth; whereas in natural condi-tions (outdoor at VES), 40% (2, 2, 0) of QTL clusterswere observed in 2004, and 20% (2, 0, 0) in 2005 (forbud flush, bud set, and height growth, respectively). Ingeneral, out the whole of 10 QTL clusters, 45% (-, 2, 1)were commonly detected under outdoor and indoorenvironmental conditions (for bud flush, bud set andheight growth, respectively).For the second mapping population (D), out the sumof 31 QTL clusters detected for bud flush (10), bud set(11), and height growth (10), 61% (3, 9, 7) were detectedin 2006 and 61% (9, 5, 5) in 2007, with 26% (2, 3, 3)found in common between the two years. During thefirst growing season in 2006, 55% (3, 8, 6) of QTL clus-ters were detected under controlled conditions (AAFC)and 42% (7, 2, 4) in 2007 (for the three traits, respec-tively), whereas in natural conditions (VES), 36% ofQTL clusters (1, 4, 6) were observed in 2006 and 39%(5, 4, 3) in 2007. Thus, out of 31 QTL clusters, 52% (6,4, 6) were commonly detected from both environmentalconditions for bud flush, bud set, and height growth,respectively.When considering all mapping populations and years,QTLs were shared 64% of time (7 QTL clusters out of11) between indoor and outdoor environments for budflush, 46% (6 QTL clusters out of 13) for bud set, and60% (6 QTL clusters out of 10) for height growth. Thus,more than 46% of QTL clusters were replicated intoboth environmental conditions. In parallel, three, five,and three QTL clusters (i.e. 32%) were identified as spe-cific to indoor controlled conditions (AAFC) comparedto one, two and one (i.e. 12%) detected only in outdoorconditions (VES), for bud flush, bud set and heightgrowth, respectively. QTL clusters specific to indoorcontrolled conditions were mostly identified for the sec-ond year for bud flush (100%) and height growth (67%),whereas mainly for the first year for bud set (80%).In general, out of the 11, 13 and 10 QTL clustersidentified onto the composite map for bud flush, budset, and height growth, respectively, 3 (27%), 6 (46%),and 6 (60%) were replicated between years. Year-specificQTL clusters were mainly observed during the first yearfor bud set (5 out of 7) and during the second year forbud flush (6 out of 8), and mainly for the mappingpopulation D. Year-specific QTL clusters were observedas often for the first year (2 out of 10) as for the secondyear (2 out of 10) for height growth.Phenological development stages versus QTLsFor bud flush and bud set, the data used (factorial scores)for QTL analyses were generated by PCA analyses (seeMethods). Considering the nature of each componentretained after PCA (Table 2), each single QTL for bud setor bud flush did not necessarily correspond to only onebud development stage [53], but rather to a grouping ofthese different stages with variable weighting in the prin-cipal component. Thus, when considering all singleQTLs characterizing each out of the 11 and 13 QTL clus-ters identified for bud flush and bud set, respectively, itappeared that one (onto LG VI) and two (onto LGs IIIand XII) QTL clusters had a group of single QTLs withcomponents involved in almost all bud flush or bud setdevelopment stages, unlike other QTL clusters thatinvolved principal components more narrowly defined interms of bud development stages (Table 2). For example,the chromosomal region involved in bud set onto LG X(cross D: AAFC 2006 PC1, Figure 4) could play a part inthe transition from stages 3 to 4 (Bud set_AAFC_crossD_2006_PC1, Table 2). For bud flush, the genomic regiondetected onto LG V (cross D: AAFC 2007 PC1 and VES2007 PC2, Figure 2) could be involved into the transitionfrom stages 4 to 5 (Bud flush_AAFC_cross D_2007_PC1/VES_cross D_2007_PC2, Table 2).Shared QTL clusters among traitsPermutation tests conducted onto all -1LOD and -2LODQTL intervals (data not shown) allowed to compareQTL positions of each QTL cluster among traits andestimate the level of overlapping QTLs between pheno-typic traits. Four to five occurrences of overlappingQTLs between characters considered two at a time werehighly significant (with 1-P-value of 0.99 or 1.0). FiveQTL clusters positioned onto LGs III, IV, V, VI, and XPelgas et al. BMC Genomics 2011, 12:145http://www.biomedcentral.com/1471-2164/12/145Page 12 of 23were identified as affecting both bud flush and bud settraits with an overlapping proportion ranging from 14.3to 100% at -1LOD (Figures 1-2, 4). QTLs positionedonto LGs III, IV, V, and X also revealed at -1LOD a sig-nificant overlapping (from 9.1 to 100%) between QTLclusters for bud flush and height growth. QTLs detectedfor bud set and height growth co-located significantlyonto four linkage groups (Figures 1-2, 4: LGs III, IV, V,and X). The overlapping range between QTLs affectingsimultaneously height growth and bud set varied from18.8 to 100% (Figures 1-2, 4). QTLs involving the threeadaptive traits co-located together onto the linkagegroups III, IV, V, and X.To investigate associations among characters at thephenotypic level, correlations within and among thethree adaptive traits were estimated and revealed somesignificant associations (Additional file 3). For bud flush,no or barely significant correlations for the different budstages were observed between years for both crosses andby considering each environmental condition separately.The trend was relatively similar for bud set with a nota-ble exception: in both crosses, a small number of lowpositive and significant correlations were observedunder outdoor environmental conditions (VES) betweenstage 3 of bud set for the first experimental year and allstages of bud set for the second experimental year (from0.192 to 0.228 for cross P and from 0.147 to 0.174 forcross D). For height growth, highly significant correla-tion estimates were observed between years within arange of low to medium values: under outdoor environ-mental conditions at VES, a moderate positive correla-tion was observed (0.424) between 2004 and 2005 forcross P, whereas for cross D, low positive correlationswere identified (from 0.134 to 0.149) between 2005,2006, and 2007. For this latter cross, significant correla-tions were observed (up to 0.340 between years) undercontrolled environmental conditions (at AAFC). As forrelationships among traits, a small number of significantcorrelations ranging from low to high were identified(Additional file 3). For instance, for cross D in 2006,almost all stages of bud flush were slightly to highlynegatively correlated with height growth (-0.179 to-0.546). In 2007, negative significant correlations werealso observed between these traits (up to -0.345). Thesecorrelations reflects the observation that white spruceswith an early bud flush have a longer growing season andconsequently, a greater height growth. No or barely sig-nificant correlations were observed between the differentstages of bud flush and bud set considering the same yearof bud development. Also, almost no significant correla-tions were observed between height growth and bud setduring the first growing season for cross D, whereas in2007, low to moderate positive correlations betweenheight growth and all stages of bud set (0.133 to 0.437)were observed. For cross P, correlation results were rela-tively similar (see details in Additional file 3), with anexception: low negative correlations were detected in2004 between the last stages (5 and 6) of bud flush andthe stages 1 and 3 of bud set (-0.184 to -0.236), suggest-ing that for a number of genotypes, later bud flush andearlier bud set co-occurrred, and vice-versa.DiscussionIn white spruce, a rapidly growing number of genes(nearly 28,000) are being identified through more than270,000 ESTs [47]. Together with SNP discovery, thisresource provides the basis for highly informative QTLmapping studies associated with important adaptivetraits such as bud flush, bud set and height growth,facilitating the identification of potentially causativegenes in the future. In the present study, the genotypingof several hundreds gene SNPs permitted the construc-tion of four linkage maps highly enriched in gene loci.The resulting composite linkage map of the whitespruce genome represents the most dense gene-basedlinkage map yet for a conifer. Until recently, therestricted availability of dense marker linkage mapsthrough several populations was a limiting factor for theaccurate identification of a large number of QTLs. Newhigh throughput genotyping methods have begun toovercome this barrier such as diversity arrays (DArT) orSNP arrays technologies, allowing the construction ofhigh-density genetic maps with hundreds and now thou-sands of gene-based marker loci [13,63,64]. With theexpansion of genetic linkage maps with additional gene-based SNPs, the present study highlighted the genomicarchitecture of the conifer white spruce for three adap-tive traits by the identification of congruent QTLsunderlying bud flush, bud set and height growth acrossdifferent pedigrees, years, and environments.QTL detectionRepeatability values for bud flush (0.33 to 0.57), bud set(0.33 to 0.54), and height growth (0.49 to 0.80) indicatedmoderate to high heritabilities of these complex traits(detailed data not shown). These repeatability values areonly suggestive, because their estimation is based ononly two mapping populations and not on a large num-ber of families. Regardless, they were about in the sameorder or marginally higher than those reported for simi-lar characters in white spruce [e.g. [32]] or for othertree species [65,66]. The clonal nature of the materialused herein could account for these marginally highervalues. Consequently, QTLs were expected to segregatefor these characters in the two mapping populations stu-died, as showed by others [26,28].Of all QTL clusters observed with mapping populationP, most (90%) were detected with population D.Pelgas et al. BMC Genomics 2011, 12:145http://www.biomedcentral.com/1471-2164/12/145Page 13 of 23However, the opposite was not true, with only 24% ofQTLs of population D detected in population P. Onepossible explanation for this asymmetry of QTL detec-tion is the difference of sample size and marker densitybetween the two mapping populations. The difference inprogeny number (260 for cross P versus 500 for crossD) had likely a major effect in the power to detectQTLs. Indeed, after 10 simulation tests performed witha random draw of 250 progeny at a time from 500 pro-geny selectioned for the cross D, the number of QTLsdetected was in the same range than that for cross P(data not shown). Only 29% of all QTLs detected withcross D from 500 progeny were identified from 250 pro-geny and PPVE values were approximately twice as largeas those obtained with 500 progeny. Thus, the smallprogeny size of cross P (or D during simulations)decreased the sensitivity of QTL detection and resultedin an overestimation of the effect size of main QTLs(PPVE >9% per QTL), while QTLs of small effectsremained undetected. For Douglas fir, in the most com-plete QTL studies realized until now on adaptive traitsin conifers across multiple years and/or multiple envir-onments [26,28], the estimated effects of each QTLdetected were relatively in the same range than that inour study using the large cross D, i.e. with PPVE valuesranging between 1.2 to 11.5% using 98 progenies fromone cross and between 0.7 to 9.5% using about 400 pro-genies from a second cross.The present results corroborate previous QTL mappingsimulation studies, where the use of small segregatingpopulations (progeny size of about 200) resulted in lowpower detection, with disproportionately large effects onphenotype and with poor congruence of QTL position[67-70]. Although QTLs detected in the present studygenerally have effects in the same range as that in otherforest trees [26,28,65], more QTLs of small effects (around5%) were revealed by the larger mapping population D inthe present study. Other QTL analyses performed withvery large populations in mouse or maize also revealedthat many genes of relatively small effects characterizedmany quantitative traits, disclosing a complex geneticarchitecture [70-72]. Regarding marker density, which washigh in the present study, simulation studies carried outby Darvasi et al. [73] to determine the effect of markerspacing, gene effect, and population size on the power ofmarker/QTL association indicated that while marker den-sity influenced very little the accuracy of QTL location,the addition of new markers supplied supplementaryalleles that could be associated to the detection of newQTLs. Indeed, even with an infinite number of markers,the confidence intervals of QTLs were more stronglyaffected by population size and gene effect.Another possible explanation of the lack of congru-ence in QTL detection between mapping populationsP and D is genetic heterogeneity between populations, i.e. diverse marker subsets that would segregate in eachpopulation. Indeed, Beavis et al. [74], by observing thatvery few QTLs were common across four Maize popula-tions, found that the lack of congruence among theseindependent linkage populations was associated to dif-ferent sets of polymorphic alleles segregating in the dif-ferent genetic backgrounds. In fact, polymorphic allelesat QTLs in one population can be monomorphic inanother. Thus, the use of different unrelated populationsincreases the power to detect QTLs (see below).Repetition effect of experimental designThe evaluation of replicated QTLs across genetic back-grounds and environments may be essential to confirmthe utility of specific marker/QTL linkage informationprior to the implementation of marker-assisted breedingmethods. The impact of the environment and thegenetic background on quantitative trait variation andthe detection of QTL has been documented early onand suggests that the general conclusions about QTLs,particularly those with small effects discovered on thebasis of single environment and single population, couldbe erroneous [74,75]. QTLs found from experimentsinvolving different crosses and/or different environmentsand/or yearly replicated can increase the number ofQTLs detected and also provide additional confidence intheir locations [9,26,74,76,77]. However, comparisons ofQTLs can become complex due to the probability thatdifferent crosses will be segregating for different QTLs,with perhaps G x E effects, and/or a lack of commonmarkers between crosses.a. QTL stability in relation to genetic backgroundQTL clusters shared by the two unrelated mappingpopulations P and D in the present study correspondto about 20% of the total number of QTL clustersidentified. This level of overlapping, in agreement withthe proportion of shared gene loci between the twomapping populations, i.e. about 40%, allowed the vali-dation of QTLs at the intraspecific level. Chromosomalregions with shared QTLs across different populationsgenerally correspond to greater mapping precision andgreater QTL significance compared to other genomicregions with QTLs discovered from single mappingpopulations [37,78]. In our study, syntenic regionsrevealed a high level of marker colinearity (> 93%)among individual linkage maps of both crosses, madeeasier the identification of QTLs shared between map-ping populations, i.e. QTL clusters, by synteny-basedprojection from a homothetic function. High levels ofcolinearity are a prerequisite for the identification ofcandidate genes for traits of interest using cross-spe-cies information [79]. Meta-analyses are a good illus-tration of integrated QTL maps from synteny-basedPelgas et al. BMC Genomics 2011, 12:145http://www.biomedcentral.com/1471-2164/12/145Page 14 of 23projections [80]. Thus, the conservation of gene orderbetween genome maps provides a framework for thecomparative analysis of genome architecture of com-plex traits. Cereal species, largely syntenic, lend them-selves well to QTL projections across rice, maize,Sorghum, teosinte, wheat, and barley genomes[37,75,78,81,82]. For instance, rice candidate geneswere projected onto the maize genome using a syntenyconservation approach between maps and thence, asso-ciations between maize QTLs and genes involve inflowering time in rice were observed [37].More comprehensive analyses of the genetic architec-ture of complex traits may require the consideration ofmultiple populations that represent a larger sample ofthe standing intraspecific variation and thus provide aframework for comparative analyses [80]. The existenceof QTLs identified for white spruce in other Picea spe-cies remains to be investigated in closely-related andphylogenetically more distant conifer taxa, using homo-eologous genome segments that would allow theunequivocal recognition of QTL regions across taxa.Given the high genome macro-synteny and macro-coli-nearity between the phylogenetically remote whitespruce and black spruce [13], it is expected that a num-ber of QTLs should be conserved across taxa. Whilelogistically and financially demanding, such studies mayalso highlight the control of adaptive/wood traits byhomoeologous chromosomal regions across conifer spe-cies. Recently, Casasoli et al. [16] reported a high con-servation of QTLs for bud flush between two broad-leaved forest species, oak and chestnut, with the help oforthologous markers used to find homoeologous geno-mic regions between both species.However, even if synteny-based projections can beuseful to identify new candidate genes that explain traitvariation across taxa, when orthologous QTLs underlinethe same trait, the involvement of each orthologousgene to the phenotype may vary across taxa. This differ-ential contribution of one gene for a same trait betweenspecies was observed by Doust et al. [83] for the geneticcontrol of branching in foxtail millet and in maize. Aswell, local gene duplications followed by subfunctionali-zation [84] may also contribute to differential geneticcontrol in spite of similar QTL location. In line withthis, recent comparative studies among knox-1 genefamily members across diverse spruce species indicatethat selection patterns vary among family membersacross different species, suggesting the differential evolu-tion of gene duplicates among congeneric species [85].The comparison of genes harbouring outlier SNPs puta-tively involved in adaptation between spruce species alsosuggested parallel evolution and the implication of dif-ferent genes from the same gene families [86]. QTLcomparative studies with more complete maps of thegene space should help understand these trends at thegenome level.b. QTL stability between environmentsThe different environmental conditions tested in thepresent study did not seem to influence the identifica-tion of at least half of the total number of QTL clusters.Environmental conditions differed by the fact that threeblocks of progeny out of six were studied under con-trolled indoor conditions at AAFC (day/night tempera-tures fixed at 24/15°C) from the summer solstice to theend of september to evaluate the impact of the decliningphotoperiod on bud set compared to the three otherblocks maintained under natural outdoor conditions(VES). The identification of more than 46% of QTLclusters across both environments indicates that subsetsof QTLs controlling phenology and growth traits arespatially quite stable. More diverse environmental condi-tions remained to be tested. However, our presentresults are hardly comparable with those observed inDouglas fir for bud flush, which was tested under adozen different environmental conditions, differing inelevation, latitude, moisture, temperature, day length[26,28]. QTL replications for bud flush in Douglas firwere in a range of 0% to 83% following the environmen-tal conditions considered. However, in most cases, avery little overlap across sites was observed, suggestingthat different genes controlling bud flush were expresseddifferently considering the diverse environments testedfor Douglas fir. For bud set and growth traits, Jermstadet al. [28] observed few or no QTL replication (i.e. up to17%) across the four different environments tested,reflecting important genotype x environment interac-tions. These discrepancies with the results of the presentstudy reflect to some extent the variability of environ-mental conditions tested in each study but also, theycorrelate well with observations from analysis of quanti-tative genetic variation in adaptive traits in white spruceand Douglas fir. Indeed, small or null genotype x envir-onment interactions were noted in white spruce [32], ascompared to large genotype x environment interactionsin Douglas fir [87,88], which could be related to morediverse environmental conditions in the Pacific North-west, as compared to those affecting white spruce ineastern Canada [32,89-91]. In hybrid poplar (black cot-tonwood × eastern cottonwood), results more similar toours with about 33% of environmental replication forbud set QTLs were observed across environments differ-ing by temperature regimes between field and green-house conditions [10], suggesting that a large fraction ofphenotypic variation for bud set (1/3) could beexplained by a subset of stable genomic regionsresponding to diverse environmental conditions. Envir-onmentally stable QTLs for bud flush and bud set inour study may correspond to genomic regions putativelyPelgas et al. BMC Genomics 2011, 12:145http://www.biomedcentral.com/1471-2164/12/145Page 15 of 23involved during nearly all the stages of bud developmen-tal processes, considering the nature of the principalcomponents implicated in these QTLs. Thus, almost allstages of bud development may be controlled in part bygenomic regions under moderate to low environmentalinfluence. Considering the moderate to high heritabilityvalues observed for the three adaptive traits (i.e. about0.41, 0.40 and 0.62 on average for both environments,for bud flush, bud set and height growth, respectively;detailed data not shown), these environmentally stableQTLs that explain at least 30% of the total phenotypicvariance would account for more than 48% of thegenetic additive variance.c. QTL stability among yearsTemporal stability has been observed for numerousQTL clusters. Overall, about one third of QTL clustersfor bud flush were replicated across years, while it wasnear 50% and 60% for bud set and height growth,respectively. The repeated detection of QTLs from oneyear to the next suggests that several genes controllingbud flush, bud set, and height growth are repeatedlyexpressed through time for white spruce. QTL replica-tion over multiple growing seasons was not alwaysobserved in previous studies [41]. However, our resultsare congruent with studies of bud flush in Douglas firand for different growth traits in Maritime pine andJapanese cedar [Cryptomeria japonica (D. Don)][25,26,40,92]. Another recent study revealed large tem-poral stability (across at least 10 years) for wood densityQTLs, suggesting a link between tree maturation andtemporal stability of QTLs [9]. In the present study, theinformation from QTLs replicated across years indicatesthat the phenotypic variance explained by each repli-cated QTL is relatively similar to that observed foryear-specific QTLs for bud flush, and about 25% largerthan those year-specific QTLs for bud set and heightgrowth. These temporally stable QTLs may explain alto-gether at least 20%, 48%, and 32% of the total phenoty-pic variance for bud flush, bud set, and height growth,respectively. In view of heritability values for each trait(i.e. about 0.39, 0.40, and 0.61, on average for differentyears, for bud flush, bud set, and height growth, respec-tively; detailed data not shown), these QTLs mayaccount for more than 51% of the genetic additivevariance.d. Overview of QTL stabilityIf our experimental design had been based on the use ofonly one mapping population in a single environmentduring one growing season, two and 13 QTL clusterswould have been detected in the worse and best ofcases, respectively. Such a strategy would have revealedonly 6% to 38% of the total number of QTL clustersidentified in the present study. By assuming a designbased on only one mapping population (i.e. cross Dbecause of its large progeny number) with yearly replica-tion and testing in two the environmental conditions, upto 91% (31/34) of QTL clusters would have been identi-fied in the present study. On the other hand, withoutyear-to-year replication, 44% (15/34) of QTL clusterswould have been observed across the two tested envir-onments in the best case. Generally, QTL mappingdesigns for characters related to forest trees allowed forthe detection of QTLs across different environments,but they rarely relied on replication across mappingpopulations and/or growing seasons [9,10,26,28,76,77].Thus, the experimental design retained in the presentstudy, and based on a repetition effect across mappingpopulations, environments, and years, was favourable tothe detection of a large number of QTL clusters, inwhich one can observed genetic, environmental, andyear-to-year stability of QTLs. In a breeding perspective,QTLs mapping consistently across years and environ-ments could be a most important target for breeding,because they represent genomic regions that may likelybe least affected by G × E interactions.QTL clusters associated to photoperiodic controlQTL clusters specific to natural outdoor conditionsdetected for bud set may highlight genomic regions thatmay be putatively activated by diverse connected envir-onmental signals, mainly temperature and photoperiod.For many temperate and boreal trees including coniferssuch as spruces and Douglas fir, bud flush and shootelongation are mainly triggered by rising air tempera-tures in early spring [93-95], whereas bud emergenceand growth cessation are essentially activated bydecreasing photoperiod [96,97]. In the present study, topossibly disentangle the effects of temperature andphotoperiod, night and day temperatures were fixedunder controlled indoor conditions, while photoperiodconditions were similar as for outdoor conditions.Moreover, soil moisture and nutrients were relativelywell controlled in both environmental conditions toavoid their implication with the variation observed. Con-sequently, QTL clusters detected for bud set only fromtrees grown at AAFC indoor controlled conditionscould not be influenced by temperature but mainly byphotoperiod conditions. While QTL clusters specific tonatural outdoor conditions for bud set may highlightonly genomic regions responding simultaneously andmainly to temperature and photoperiod conditions.QTLs found specific to indoor conditions (AAFC) weremainly detected during the first year of measurementfor cross D and the second year for cross P, and theywere observed for composite phenotypes (after PCA)explaining the transition between the stages 3, 4, and 5of bud set (e.g. QTLs identified as AAFC 2006 on LGsII, IV, V, and X, see Figures 1-2, 4 and Additional filePelgas et al. BMC Genomics 2011, 12:145http://www.biomedcentral.com/1471-2164/12/145Page 16 of 235). Assuming moderate heritability values for bud set (e.g. on average 0.43 and 0.36 for cross P and D, respec-tively; data not shown), these QTLs explaining 20% ofthe total phenotypic variance would account for at least47% of the genetic additive variance. Thus, close to halfof genetic variation in bud set may be associated togenomic regions controlled only by declining photoper-iod. Given that photoperiod is an environmental factoreasily monitorable and stable year after year, genomicregions responding to this factor during bud set couldbe short-listed for the partial control of height growthvia the duration of the growing season (see below thesection on QTL overlapping). Additional analyses couldbe conducted to corroborate with accuracy that thesebud set QTLs are only associated to photoperiod. Arecent study showed that the genes implicated in thephotoperiod pathway may be quite conserved amongdifferent plant species [98]. Thus, orthologues to thecandidate genes of this pathway should be mapped andco-location with bud set QTLs be checked. Thoughlogistically demanding, an additional QTL mappingexperiment with long-day and short-day conditionscould be undertaken on the large mapping population Dto obtain a factual measure of photoperiodic response,as done in wheat [99].For bud flush, QTL clusters identified only from thesecond cross D during the second experimental year,and which were replicated across environments (i.e.QTLs identified as AAFC 2007 or VES 2007 on LGs III,V, and XI, see Figrues 1-2, 4 and Additional file 4), pos-sibly indicate genomic regions responding to tempera-ture variations of the previous winter, or could belinked to the declining photoperiod of the previoussummer season. The main factor initiating bud flush isreportedly temperature [100,101]. However, because budset is associated to the formation of needle primordiathrough the end of the growing season and decliningphotoperiod, the height growth for the following year isthus predetermined [102,103]. Therefore, QTLs for budflush and height growth that were observed only for thesecond year in both environmental conditions could beclosely tied to the declining photoperiod of the previousyear via bud set and the formation of needle primordia.QTL overlapping among traitsA number of significant occurrences of overlappingQTLs among bud flush, bud set, and height growthwere observed after permutation tests among all QTLpositions. For instance, on LG X, the confidence inter-vals of height growth QTLs significantly overlappedwith those detected for bud flush and bud set (Figure 4).Another instance of significant co-locations of QTLswas identified for the three adaptive traits on linkagegroups III, IV, and V (Figures 1-2). Such co-locationsindicate that the shared QTL clusters may bear pleio-trophic effects.The co-locations of QTLs identified for both phenolo-gical traits and height growth in the present study indi-cated a similar number of overlapping QTL clustersbetween height growth and bud flush, and betweenheight growth and bud set. In juvenile eastern whitespruce, early studies have shown for instance that heightgrowth is correlated to bud set and the length of thegrowth season, which itself is partly determined by thetiming of bud set [32]. While QTLs for bud flush andbud set corresponded to composite phenotypes in ourstudy, correlations between the principal coordinatesderived from PCA and the original stages (expressed inJulian days, see Mat&Meth) were also high (data notshown). Thus, establishing the correspondence betweenQTL co-locations and correlations between phenotypiccharacters appears possible. Considering together thethree adaptive traits studied herein, significant correla-tions were observed: up to -0.55 between height growthand bud flush, and up to 0.44 between height growthand bud set. These correlations are in the same rangeand signs as previous estimates in juvenile black spruce[104] and eastern white spruce [32], indicating theopposite effects of these characters on the length of thegrowth season and ensuing height growth [32]. In differ-ent Angiosperms, similar observations could be madewhere some of the QTLs detected for spring flush, stemheight, basal area, and sylleptic branch number inpoplar, for bud flush and height growth in oak, or forbunch number and bunch weight in palms, were over-lapping on the same linkage groups and could be relatedto significant correlations among these traits [41,45,77].Previous quantitative trait genetic studies suggested thattrait correlations may be attributable to either pleiotro-pic effects of single genes or to tight linkage of severalgenes that individually influence specific traits [105,106].It should not be too difficult to disentangle these twoeffects. A few examples of potential quantitative traitnucleotides (QTNs) or SNPs closely link to QTNswould suggest that pleitropy might be involved. Forinstance, on LG X, the gene SNP 5437a (e.i. cluster#GQ03507_B21 in Arborea Gene Catalog GCAT version3.3, ftp://ftp.gydle.com/pub/arborea) was observed ashaving the highest LOD value simultaneously in QTLsfor bud flush, bud set, and height growth. The corre-sponding gene for this SNP was observed as deregulated(up-expressed) in an expression profile study recentlyconducted on growth cessation and bud formation inwhite spruce [107]. On the same linkage group, anothergene SNP, e.i. 4148e (cluster #GQ0203_O13 in ArboreaGene Catalog GCAT version 3.3, ftp://ftp.gydle.com/pub/arborea), was observed as simultaneously associatedto bud set and height growth in -1LOD interval underPelgas et al. BMC Genomics 2011, 12:145http://www.biomedcentral.com/1471-2164/12/145Page 17 of 23the maximal LOD value of QTLs, so in a highly signifi-cant confidence interval. This latter SNP (4148e) wasidentified as candidate for adaptation from a genomescan aimed at finding SNPs with significant differentia-tion among natural populations of white spruce [108]. Ifsuch SNPs influence bud flush or bud set timings, thenthey would also modulate height growth and could beconsidered as putative causal SNPs or QTNs (quantita-tive trait nucleotides). Association studies in unstruc-tured populations should allow to verify thesehypotheses. However, even if the same genes or sets ofgenes are involved in QTL co-locations or overlaps,there should likely be variations in magnitude of effects,as recently observed for common gene sets underlyingthe developmental control of male and female flowers inmaize [106]. Indeed, when orthologous QTLs underliedifferent traits, the contribution of each of the ortholo-gous genes to the traits might vary. Moreover, a variablepart of the covariance between phenotypic characterscan also be accounted for by QTLs mapping to differentregions of the genome [109].QTLs and gene expression profilesTo follow upon the above observations of SNPs possiblytightly linked to QTNs or representing QTNs, we inves-tigated expression profile studies conducted in sprucespecies in an effort to find genes that would co-localizewith QTLs. While there may be several potential candi-date genes that mapped within the confidence limits ofQTL location, and because only a fraction of the genespace was mapped, care must be taken not to blindlyaccept the first suitable candidate with a plausible func-tion. Analyses of comprehensive microarray geneexpression profiling in white spruce and Sitka spruce[110] have shed light on changes of gene expressionduring bud formation, growth cessation, and cold accli-mation, potentially revealing regulating genes. Severalgenes differentially expressed during bud developmentand cold hardiness [110] were found to co-localize withQTLs specifically associated to bud set in the presentstudy. As mentioned in the previous section, the geneSNP 4148e is a good example. Another possible case isthe BAS1/CyP734A gene (targeted by the gene SNP5068f, i.e. cluster #GQ04107_J01 in Arborea’s Gene Cat-alog GCAT version 3.3, ftp://ftp.gydle.com/pub/arborea),which was included in a QTL for bud set positionedonto LG VIII. This gene was also found to be involvedin response to light stimulus and it was up-expressed byshort day induction [107]. The BAS1/CyP734A genelikely belongs to the brassinosteroid family, which wasshown to regulate dormancy [111,112]. Another geneassociated to a bud set QTL detected onto LG VI (withthe gene SNP 10583v1, i.e. cluster #GQ03808_I16 inArborea Gene Catalog GCAT version 3.3, ftp://ftp.gydle.com/pub/arborea) was from the dehydrin family andturned out to be up-expressed after an induction ofshort days [107]. A SNP of this gene was also previouslyidentified as candidate for adaptation from a genomescan aimed at finding SNPs with significant differentia-tion among natural populations of white spruce [108].Its putative role as antifreeze gene may be to stabilizecellular membrane [110]. The up-regulation of this geneafter an induction of seven short days and throughoutall bud stages until the dormancy [107], suggests that ahigh level of freezing tolerance could be rapidlyachieved, that could decrease frost damages during thetiming of bud set.These few examples, though not comprehensive, indi-cate that QTL analysis together with gene mapping,population structure studies, and expression profilingcan reveal candidate genes for bud formation andgrowth cessation, suggesting valuable new paths ofinvestigations at the functional level. Many other genesdifferentially expressed during bud development couldalso be associated to overlapping bud set and bud flushQTLs, suggesting that these genes could also involved inthe biochemical pathways underlying the expression ofboth traits. However, while bridging the gap betweenstructural, population, and functional genomics and pro-viding new research hypotheses, these temptative asso-ciations should not be interpreted as definite proofs ofthe causal role of the genes identified in the underlyingQTLs, because QTL mapping implicates large arrays ofgenes by simple virtue of linkage.ConclusionsSome years ago, Chen et al. [10] suggested that the can-didate gene approach would probably become themethod of choice for identifying QTLs in most organ-isms, including forest trees. The present study is a sig-nificant effort in this direction. Robust QTL mappingwith gene-based linkage maps resulted in a muchimproved estimation of the genetic architecture of aconifer genome in terms of the magnitude of QTLeffects, QTL-environment interactions, and putativepleiotropy. The co-location of candidate genes and QTLintervals cannot to be considered as a strict evidence ofthese genes being causally involved in the quantitativetrait. However, the use of gene maps in conjunctionwith QTL architecture, gene expression and outlierdetection studies also allowed to propose genes andgene SNPs with potential causal roles in the underlyingQTLs. Supplementary validations involving large surveysin natural populations and formal association geneticstudies [31,108,113], as well as expression studies andfunctional characterization [114], appear essential toattest such associations [3]. Recent association mappingstudies have proven useful to reveal significantPelgas et al. BMC Genomics 2011, 12:145http://www.biomedcentral.com/1471-2164/12/145Page 18 of 23associations between genes previously identified in cold-hardiness QTLs [29] and SNPs genotyped in Douglas firassociation populations [113], and between gene SNPsputatively involved in light signal transduction and budset timing variation in Sitka spruce [31].The next challenge should be to enhance the repre-sentation of gene space on genomic maps and narrowdown the intervals to small regions that would includesmall numbers of candidate genes. Such gene mapsshould also be highly useful for comparative genomicsand to orientate and support efforts in sequencing theconifer genome. To instigate this highly challengingtask, different strategies should be put forward. Rese-quencing of expressed sequenced tags (ESTs) using pyr-osequencing should allow to identify segregating SNPsand map all expressed genes at a reasonable cost, whichcurrently add up to nearly 28,000 genes in white spruce(Arborea GCAT3.3 gene catalogue). Currently half ofthe expressed genes is being mapped in white spruceusing such strategies and high-throughput genotypingand mapping populations in the order of thousands ofprogeny. Plans for similar maps are being developed inother conifers, which will facilitate large-scale compara-tive analysis of genome structure and quantitative traitsarchitecture. These maps should also be useful to priori-tize the sequencing of gene-containing BACs corre-sponding to QTLs or gene-rich regions. Shotgunsequencing of these BACs should represent a usefulstarting point to conifer genome sequencing, given themapping of the entire gene space which should facilitatethe assembly of gene-rich regions.A complete understanding of the genes and gene net-works underlying traits involved in the control of heightgrowth and adaptive traits associated to bud phenologyis crucial, to help orientate association genomics effortsand hasten the development of genomics-assisted selec-tion aimed at increasing forest productivity in the faceof an uncertain and rapidly changing environment [18].Selected genotypes should combine an optimum dura-tion of seasonal height growth with frost hardiness,implicating that timings of bud flush and bud set takeplace in time to avoid injuries by late frosts in the springand by early frosts in the fall [93,115-118]. Theserequirements are expected to change dramatically undernorthern latitudes, where climate warming has alreadybeen shown to be proportionally greater [119].In a near future, using one of the present mappingpopulations, a QTL mapping study will integrate theexpression profiles of a large number of white sprucegenes or expression QTLs (eQTLs). Transcript abundancemay act as intermediate phenotype between loci andmacroscopic phenotypes, and can be considered as expres-sion quantitative trait (e-trait) in order to identify chromo-somal regions where genotypes significantly affect geneexpression [120]. By using cis- and trans- mappingapproaches, other interesting questions regarding geneexpression regulation could be addressed by combiningQTL and eQTL: for instance the relative contributions ofcis-regulatory elements versus trans-regulatory elements[121], or the exploration of the effect of gene duplicationson the genetic regulatory network [122]. Because of thevirtually unlimited types of data that can be integrated inQTL mapping for an “overall genomic information sys-tem” (e.g., eQTL, proteomics, metabolomics, associationstudies), the increase of gene mapping efforts in coniferspecies shall represent an important stage for conifer com-parative genomics, simultaneously opening stimulatingperspectives for evolutionary studies and molecular breed-ing applications.Additional materialAdditional file 1: List of gene markers retained for linkage mapconstruction. List of primer sequences used for PCR amplification foreach gene-marker retained for the construction of individual linkagemaps for both crosses P and D.Additional file 2: Measurements of bud flush, bud set and heightgrowth, over three years, two sites and for two unrelated mappingpopulations. Statistical parameters of phenotypic data for bud flush, budset and height growth, over three years, two sites and for two unrelatedmapping populations.Additional file 3: Pearson’s and Spearman’s (grey background)correlation coefficients for each trait measured in outdoor (VES)and indoor (AAFC) conditions for both mapping populations P andD. (* significant at 5% level; ** at 1% level; *** at 0.1% level) Phenotypiccorrelation coefficients within and among the three adaptive traits.Additional file 4: QTLs identified for bud flush. List of QTLs identifiedfor bud flush within each mapping population, P and D, for eachenvironmental condition and year (QTL interval position, PPVE percent ofphenotypic variance explained, LOD value, list of gene loci for each QTL).Additional file 5: QTLs identified for bud set. List of QTLs identifiedfor bud set within each mapping population, P and D, for eachenvironmental condition and year (QTL interval position, PPVE percent ofphenotypic variance explained, LOD value, list of gene loci for each QTL).Additional file 6: QTLs identified for height growth. List of QTLsidentified for growth within each mapping population, P and D, for eachenvironmental condition and year (QTL interval position, PPVE percent ofphenotypic variance explained, LOD value, list of gene loci for each QTL).AcknowledgementsWe would like to thank M.-C. Gros-Louis, P. Sylvestre, C. Laplante and C.Dhont (Arborea and Canadian Forest Service, CFL) for assistance with fieldmeasurements, sample collection, and DNA extractions; S. Beauseigle, S. Blaisand F. Gagnon (Arborea, Univ. Laval) for assistance with gene sequencing,SNP discovery, and the design of SNP arrays and data treatment, S. Clement(Arborea and Canadian Forest Service, CFL) for the design and use of betaversions of the LIMS TreeSNPs, A. Montpetit and his team from the GenomeQuébec Innovation Centre (MGill Univ.) for much of the genotyping work,and A. Labbé (Canadian Forest Service, CFL) for statistical support. We thankA. Bertrand and Y. Castonguay (Arborea and Agriculture and Agri-foodCanada), and J. Cooke (Arborea, Univ. of Alberta) for fruitful discussionsregarding the physiology of adaptive traits in interaction with environment,and C. Dhont and T. Doerksen (Arborea and Canadian Forest Service, CFL)for helpful comments on earlier drafts of this manuscript. We also thank twoanonymous reviewers and the associate editor for their constructivePelgas et al. BMC Genomics 2011, 12:145http://www.biomedcentral.com/1471-2164/12/145Page 19 of 23comments. This research is part of the Arborea research program and wasmade possible by contributions from Genome Canada, Genome Quebec, theCanadian Biotechnology Strategy, and the Canadian Forest Service.Author details1Natural Resources Canada, Canadian Forest Service, Laurentian ForestryCentre, Québec, Québec, G1V 4C7, Canada. 2Arborea and Canada ResearchChair in Forest and Environmental Genomics, Forest Research Centre andInstitute for Systems and Integrative Biology, Université Laval, Québec,Québec, G1V OA6, Canada. 3Current address: Institute of Biodiversity andEcosystem Dynamics, Universiteit van Amsterdam, PO Box 94248, 1090GEAmsterdam, The Netherlands. 4Department of Forest Science, Faculty ofForestry, The University of British Columbia, 2424 Main Mall, Vancouver, BC,V6T 1Z4, Canada.Authors’ contributionsBP produced all individual, sub-composite and composite linkage maps, co-performed all statistical analyses of phenotypic traits and principalcomponent analyses of phenological data, constructed linkage maps, testedand conducted various QTL approaches with MapQTL and MultiQTL,evaluated QTL co-locations among traits, compiled and analysed all results,and drafted the manuscript, figures, tables, and additional files. JB co-conceived the study and co-obtained the funding, supervised the genesequencing, SNP discovery and the design of SNP arrays for white spruce(Arborea PgLM0 and PgLM1 arrays), provided overall guidance during QTLanalyses, co-designed and edited the manuscript. PM co-performed allstatistical analyses of phenotypic traits and principal component analyses ofphenological data and designed a software for QTL visualization. KRsupervised the GoldenGate assay developed for white spruce and Sitkaspruce (Treenomix project), developed permutation programs to test the co-location of QTLs, and edited the manuscript. NI co-conceived the study andco-obtained the funding, supervised phenotypic data collection, providedthe main guidance during linkage mapping and QTL analyses, and co-designed and edited the manuscript. All authors read and approved thefinal manuscript.Received: 19 October 2010 Accepted: 10 March 2011Published: 10 March 2011References1. 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QTL mapping in white spruce: gene maps and genomic regions underlying adaptive traits across pedigrees, years and environments
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