Tree Genetics & Genomes (2006) 2: 10–24DOI 10.1007/s11295-005-0026-9

ORIGINAL PAPER

David Pot . Jose-Carlos Rodrigues .Philippe Rozenberg . Guillaume Chantre .Josquin Tibbits . Christine Cahalan .Frédérique Pichavant . Christophe Plomion

QTLs and candidate genes for wood properties in maritime pine

(Pinus pinaster Ait.)

Received: 8 October 2005 / Accepted: 24 October 2005 / Published online: 3 February 2006# Springer-Verlag 2006

Abstract A three-generation outbred pedigree of 186 in-dividuals was used to identify the genomic regions in-volved in the variability of chemical and physical woodproperties of Pinus pinaster. A total of 54 quantitative traitloci (QTLs) was detected, with an average of 2.4 QTLs pertrait. Clusters of wood properties QTLs were found atseveral points in the genome, suggesting the existence ofpleiotropic effects of a limited number of genes. The co-localizations observed in this study are in accordance with

the genetic correlations previously reported in the litera-ture. In addition, in an attempt to identify the genes under-lying the QTLs, nine wood quality candidate genes involvedin cell wall structure were localized on the genetic map.Only one of them, Korrigan, a gene encoding for a β 1-4endo-glucanase known in Arabidopis thaliana to be involvedin polysaccharide biosynthesis, co-localized with a woodquality QTL cluster involved in hemicellulose content andfibre characteristics. This finding is in accordance with re-sults previously reported for this gene regarding its ex-pression variability (transcriptome and proteome levels)and patterns of molecular evolution. The pertinence of thisresult will be tested in more rigorous designs in order toidentify early selection predictors for wood quality.

Keywords Wood quality . QTL . Candidate gene .Korrigan . Pinus pinaster

Introduction

Wood is one of the world’s most important natural re-sources. It has been exploited for thousands of years as asource of energy, building material and, more recently, as araw material for making paper and fibreboard. In manyregions of the world, large-scale plantations grown on shortrotations are increasingly used to supply timber and fibre.For most of the forest species grown in these plantations,breeding programmes have been initiated, mainly focusingon the improvement of growth and form. These effortshave yielded impressive results, e.g. 30% realized geneticgain for both volume and straightness for Pinus pinaster[1]. Gains in productivity have often been followed by areduction in harvest age (e.g. from 60 to 40 years in P.pinaster), which has led to a greater proportion of lower-quality juvenile wood being harvested [29, 37]. In order tomaintain the utility of plantation-grown wood, there is nowa critical need to focus breeding effort on the improvementof wood quality.

Wood quality is defined in terms of particular end-usesand involves several components that can be studied at the

D. Pot . C. Plomion (*)UMR 1202 BIOGECO, INRA, Equipe de Génétique,69 route d’Arcachon, 33612 Cestas Cédex, Francee-mail: plomion@pierroton.inra.frTel.: +33-5-57122838Fax: +33-5-57122881

D. PotUMR 1096 PIA, CIRAD,Avenue Agropolis, 34398 Montpellier Cédex, France

J.-C. RodriguesInstituto Investigaçao Cientifica Tropical,Tapada da Ajuda, Lisbon 1349-01, Portugal

P. RozenbergINRA, Unité d’Amélioration,Génétique et Physiologie Forestières,Olivet 45166, France

G. ChantreAFOCEL, Station Territoriale Sud Ouest Domaine de Sivaillan,Les Lamberts, 33480 Moulis en Médoc,France

J. TibbitsThe University of Melbourne, Forest Science Centre,Creswick 3363, Australia

C. CahalanSchool of Agricultural and Forest Sciences,University of Wales,Bangor, Gwynedd, LL57 2UW, UK

F. PichavantUniversité de Bordeaux 1, Institut du Pin,351 cours de la Libération,33405 Talence Cédex, France

anatomical (e.g. fibre morphology, cellulose microfibrilangle), chemical (e.g. cell wall composition) and physical(e.g. density) levels. Traditional quantitative genetic stud-ies have shown that wood quality traits and end-use prop-erties are variable and heritable and that significant geneticgains can be achieved [38, 39, 43, 45, 61]. However, theinclusion of wood properties in most breeding programmesis still hampered by the considerable expense and timerequired for screening numerous traits in a large number ofindividuals, as well as the time needed before selection canbe performed on adult trees. In this context, molecularmarker-aided selection opens new possibilities as selectionbecomes cheaper and feasible prior to trait expression [27].

Quantitative trait loci (QTLs) mapping experiments havealready allowed the detection of several chromosomal re-gions involved in the variability of physical and chemicalwood properties in several forest tree species, includingEucalyptus spp. [19, 34], Pinus pinaster [33], Pinus taeda[7, 37], Pinus radiata [26], Pinus sylvestris [31], Larix spp.[2] and Populus spp. [5]. Although this approach has pro-vided essential information for initiating marker-assistedbreeding at the within-family level, it is inefficient for sup-plying breeding tools in the context of multiple-familybreeding programmes. This is especially true in the case ofoutbred species like forest trees which are characterized bya low level of linkage disequilibrium [50]. In this case, theidentification of the precise genes underlying the QTL is asine qua non condition for the initiation of any marker-aided programme.

Candidate genes underlying QTLs may be proposed froman understanding of biochemical or developmental pathwaysaffecting the trait of interest, and associations may then betested between molecular and trait variation (reviewed byPflieger et al. [40]). Wood formation (i.e. xylogenesis) hasfour major steps: cell division, cell expansion, secondarycell wall formation and cell death (reviewed by Plomionet al. [42]). This multi-step process involves expression ofa number of structural genes, coordinated by transcriptionfactors, which are mainly involved in the biosynthesis ofpolysaccharides, lignins and cell wall proteins. A largenumber of the genes that determine cell wall compositionand cell shape have been identified in the past by classicalbiochemical analysis (e.g. lignification genes, reviewed in[56]) and, more recently, by the application of genomictools such as gene and protein expression profiling [17,22]. The screening of large collections of Arabidopsisthaliana mutants [15, 35] has also identified a number ofimportant genes involved in the biosynthesis and deposi-tion of the plant cell wall.

This study focuses on maritime pine (P. pinaster Aït), aforeground species for the French wood industry. Our ob-jectives were twofold: (1) to identify the genomic regionsinvolved in the variability of wood quality componentsusing a QTL approach and (2) to test if these regions cor-responded to the location of candidate genes involved inthe biosynthesis of wood.

Materials and methods

Plant material and quantitative trait measurement

Full-sib pedigree

A three-generation outbred pedigree comprising 186 indi-viduals of a single 15-year-old full-sib (FS) progeny wasused to study the genetic architecture of wood properties inmaritime pine. Four grandparents were phenotypically se-lected in the Aquitaine population for stem growth andstraightness and were classified according to their breedingvalue either as “Vigor +” or “Vigor −”. The two parent treeswere crossed in 1980, and seeds from the controlled crosswere sown in spring 1982. They produced progeny seed-lings (the mapping pedigree) that were planted in autumn1982 in Malente (Gironde, France, 44°30′N 0°47′W) on asemi-humid podzolic soil. Spacing was 4 m betweenrows and 1.1 m between individual trees (2,272 trees perhectare). The 186 trees were felled in March 1997. Stemdiscs were cut from the felled trees and then dried in agreenhouse before being analysed for several wood andend-use properties.

Assessment of wood and end-use properties

As wood quality can only be defined in terms of partic-ular end-uses and may involve several traits (e.g. wooddensity, wood heterogeneity, chemical composition andfibre properties), a range of wood-quality-related traitswere measured on the FS pedigree (Table 1). These traitsand their measurements have been previously described byPot et al. [43] and can be classified into five categories: (1)growth, (2) timber quality, (3) wood chemical composition,(4) kraft pulp production and (5) pulp and fibre properties.In addition to the traits described by Pot et al. [43], pro-portions of lignin monomers were measured in the FSprogeny using the thioacidolysis method described byLapierre [28]. The ratio between H-units (para-hydroxy-phenyl) and G-units (guaiacyl) was used for QTL detec-tion. Trait description, means, coefficients of variation andtests of deviation from normality (Kolmogorov–Smirnov)are presented in Table 1. Phenotypic correlations and theirsignificance are presented in Table 2.

Genotyping and mapping

Amplified fragment length polymorphism

Using the two-way pseudo-testcross mapping strategy [18],two genetic maps, one for each parent of the FS pedigree,were first established by Chagné et al. [9] using amplifiedfragment length polymorphism (AFLP) markers genotypedon a subset of 90 F1 individuals. For the present study, 219

11

evenly spaced AFLP markers segregating 1:1 (116 for thefemale map and 103 for the male map) were selected andgenotyped on the remaining trees to ensure higher power ofQTL detection.

Candidate genes

Ten candidate genes involved in the biosynthesis and de-position of the secondary cell wall were chosen based onfunctional information gathered in tree species or modelorganisms. These genes are involved in polysaccharide(Korrigan, CesA01, CesA3, PFK, Susy; [14, 21]), lignin(PAL, C4H, CAD, CCoA-OMT; [4]) and cell wall protein(AGP) biosynthesis (Table 3). Expressed sequence tagswere available for all of these genes in P. pinaster. For eachparent of the FS pedigree, two sequences (forward andreverse) were obtained using the DYEnamic ET terminatorkit (Amersham Pharmacia Biotech AB, Uppsala, Sweden).Sequences were aligned using SEQUENCHER (http://www.Genecodes.com, Ann Arbor, USA), and nucleotidepolymorphisms were visually identified. For mapping thecandidate genes, a single nucleotide polymorphism (SNP)genotyping procedure based on primer extension was usedacross 90 offsprings, this being a sufficient sample toaccurately map the genes and compare their positions withthe detected QTLs. Primer extension reactions were per-formed using the SNUPe kit from Amersham Bioscience(Uppsala, Sweden) according to the specifications of the

supplied protocol and run on a MegaBACE 1000 se-quencing machine (Amersham Pharmacia Biotech AB).PCR and primer extension primers are listed in Tables 3and 4. Polymorphic candidate genes were then mappedusing Joinmap v3.0 [52].

QTL mapping

QTL analysis was performed independently on each par-ental map under the “backcross” option of the MultiQTLsoftware (http://esti.haifa.ac.il/~poptheor/MultiQtl/MultiQtl.htm). The “marker restoration” option was used to calcu-late the probabilities of missing marker genotypes. Theseprobabilities were then taken into account in the QTL de-tection procedures. In the first step, the interval mappingmethod (1-QTL model) was used to detect QTLs pre-senting a logarithmic odds ratio (LOD score) equal to orgreater than 1.5. Then, significant QTLs (see definitionbelow) were used as cofactors in a Composite IntervalMapping (CIM) algorithm [23, 59]. Additionally, a two-dimensional scan was performed to fit a 2-QTL model foreach linkage group. Significance of the 2-QTL model wastested against a model without genetic effect (no QTL) andagainst a 1-QTL model. For each significant QTL, theproportion of phenotypic variance explained was estimated

as PEV ¼ 14d

2

�2ph; where d is the substitution allelic effect

(d=XQq−Xqq, with XQq and Xqq being the mean values of the

Table 1 Description of the studied traits and summary statistics

Trait category Trait Definition Unit Mean Cv (%)a Departure from normalityin the full-sib pedigreeb

Growth th Total height (15 years old) cm 940.27 15.49 NoTimber quality d Mean density of all the rings kg/m3 491.11 6.68 No

pyl Pilodyn penetration West mm 15.98 9.72 NoEast mm 15.33 11.81 Yes, 2.058×10-09

het Mean standard deviation of all rings kg/m3 126.75 10.10 NoWood chemical composition lignin Lignin content % 28.93 3.21 No

wext Water extractives content % 6.38 27.67 Noaext Acetone extractives content % 0.81 53.48 Yes, 0.002alfacel Alpha cellulose content % 46.99 2.29 Nohemicel Hemicellulose content % 23.98 2.03 Yes, 0.025H/G Ratio of p. hydroxyphenylpropane (H)

and gaiacyl (G)% 0.02 39.02 Yes, 7.32×10-05

Kraft pulp production parameters pulpY Kraft pulping yield adjusted to thekappa number

% 44.35 4.47 No

kappa Kappa index % 31.21 8.63 NoFibre properties afl Arithmetic fibre length mm 0.62 9.91 No

wfl Weighted fibre length mm 1.90 9.59 Nofw Fibre width μm 27.43 2.10 Nocoars Coarseness μg/mm 0.13 8.42 Nocurl Curl % 8.14 9.71 Yes, 0.009zspan Zero span tensile value N/cm 29.57 17.03 No

aCoefficient of variationbEstimated using the Kolmogorov–Smirnov test. When significant deviation was observed, the corresponding P-value is provided betweenparentheses

12

Tab

le2

Correlatio

ncoefficientsbetweenwoo

dqu

ality

compo

nentsandtotalgrow

th

thd

pyl

het

lignin

wext

aext

alfacel

hemicel

pulpY

kappa

afl

wfl

fwcoars

curl

zspan

th1.000

−0.192

(−0.480)

−0.18

(nc)

0.044N

S

(−0.085N

S )−0

.053

NS

(0.395)

−0.035

NS

(nc)

0.067N

S

(nc)

0.151

(−0.366)

−0.190

(nc)

−0.11(nc)

−0.093

(nc)

0.278(0.795)

0.203(0.361)

−0.048

NS

(0.064

NS )

0.278(0.429)

−0.299

(−0.184)

−0.057

NS

(−0.432)

d−0

.199

1.000

−0.13

(nc)

0.098(−0.194)

0.003N

S

(−0.544)

−0.108

(nc)

0.001N

S

(nc)

−0.032

NS

(0.624)

0.083N

S

(nc)

0.066N

S

(nc)

0.041N

S(nc)

−0.200

(−0.679)

−0.229

(−0.39)

−0.285

(−0.763)

−0.124

NS

(−0.649)

0.141(0.653)

−0.004

NS

(−0.117N

S )pyl

0.373

−0.414

1.000

0.003N

S(nc)

−0.007

NS(nc)

0.083N

S

(nc)

−0.052

NS

(nc)

0.016N

S(nc)

−0.049

NS

(nc)

0.010N

S

(nc)

0.007N

S(nc)

−0.061

NS(nc)

−0.050

NS(nc)

−0.158

(nc)

0.015N

S(nc)

−0.027

NS(nc)

−0.042

NS(nc)

het

0.387

−0.010

NS

0.062N

S1.000

−0.064

NS

(−0.291)

−0.070

NS

(nc)

−0.001

NS

(nc)

0.097(0.335)

−0.006

NS

(nc)

−0.044

NS

(nc)

−0.142

(nc)

0.079N

S

(−0.040N

S )0.203N

S(0.520)

0.042N

S(0.522)

0.183

(0.247

NS )

−0.022

NS

(0.157

NS )

0.194N

S(0.516)

lignin

−0.140

−0.064

NS

−0.013

NS

−0.249

1.000

0.164(nc)

0.059N

S

(nc)

−0.740

(−1)

0.386(nc)

−0.167

(nc)

0.158(nc)

−0.065

NS

(0.141

NS )

−0.079

NS

(−0.266N

S )0.091N

S

(0.261

NS )

−0.11

(0.122

NS )

0.092(−0.540)

−0.048

NS

(−0.327N

S )wext

−0.375

0.211

−0.241

−0.139

0.293

1.000

0.290(nc)

−0.081

NS(nc)

−0.165

(nc)

−0.202

(nc)

0.139(nc)

0.040N

S(nc)

0.082N

S(nc)

−0.061

NS(nc)

−0.089

NS(nc)

−0.028

NS(nc)

−0.036

NS(nc)

aext

−0.066

NS

0.138

−0.191

−0.128

0.063N

S0.431

1.000

−0.030

NS(nc)

−0.075

NS

(nc)

−0.080

NS

(nc)

0.001N

S(nc)

−0.054

NS(nc)

−0.044

NS(nc)

0.040N

S(nc)

−0.046

NS(nc)

0.065N

S(nc)

0.028N

S(nc)

alfacel

0.345

−0.001

NS

0.098N

S0.325

−0.596

−0.181

0.056N

S1.000

−0.694

(nc)

0.180(nc)

−0.182

(nc)

0.154

(−0.194N

S )0.178(0.229

NS )

−0.012

NS

(−0.292N

S )0.238

(−0.131N

S )−0

.211

(0.567)

−0.019

NS

(0.370

NS )

hemicel

−0.380

0.109N

S−0

.167

−0.269

0.236

0.169

−0.049

NS

−0.711

1.000

−0.042

NS

(nc)

0.076N

S(nc)

−0.123

(nc)

−0.144

(nc)

−0.015

NS(nc)

−0.138

(nc)

0.151(nc)

0.098N

S(nc)

pulpY

0.118N

S−0

.001

NS

0.019N

S0.266

−0.379

−0.079

NS

−0.076

NS

0.349

−0.153

1.000

−0.300

(nc)

0.238(nc)

0.241(nc)

0.036N

S(nc)

0.344(nc)

−0.260

(nc)

0.117(nc)

kappa

−0.218

0.135

−0.152

−0.196

0.202

0.058N

S0.091N

S−0

.284

0.197

−0.336

1.000

−0.165

(nc)

−0.183

(nc)

−0.007

NS(nc)

−0.208

(nc)

0.144(nc)

−0.137

(nc)

afl

0.448

−0.164

0.300

0.355

−0.101

NS

−0.130

−0.109

NS

0.267

−0.326

0.461

−0.276

1.000

0.803(0.752)

0.014N

S(0.556)

0.644(0.939)

−0.552

(−0.623)

0.154(−0.032N

S )

wfl

0.411

−0.170

0.310

0.390

−0.207

−0.140

−0.109

NS

0.276

−0.280

0.540

−0.290

0.885

1.000

0.208N

S(0.619)

0.670(0.763)

−0.443

(−0.192N

S )0.292(0.387

NS )

fw−0

.146

−0.136

0.070N

S−0

.276

0.117N

S−0

.024

NS

0.038N

S−0

.173

0.123

−0.300

0.119N

S−0

.561

−0.487

1.000

0.181(0.822)

0.213(−0.665)

0.003N

S

(−0.018N

S )coars

0.504

−0.180

0.310

0.404

−0.227

−0.224

−0.051

NS

0.371

−0.378

0.492

−0.279

0.832

0.900

−0.486

1.000

−0.629

(−0.766)

0.142(−0.127N

S )

curl

−0.468

0.142

−0.249

−0.412

0.204

0.135

0.059N

S−0

.353

0.373

−0.457

0.289

−0.800

−0.759

0.701

−0.780

1.000

−0.107

NS

(−0.127N

S )zspan

0.055N

S−0

.035

NS

0.121

0.247

−0.183

0.022N

S−0

.063

NS

0.144

−0.087

NS

0.386

−0.160

0.531

0.698

−0.369

0.552

−0.509

1.000

Pheno

typiccorrelations

andlevelo

fsign

ificance

(NS,no

nsign

ificant;otherw

ise,sign

ificantatthe

5%level)in

thefull-sibpedigree

arepresentedin

thelower

partof

thematrix.Pheno

typic

andgenetic

(inparentheses)

correlations

andtheirsign

ificance,as

obtained

byPot

etal.[43],arepresentedin

theup

perpartof

thematrix

13

trait in the alternative groups of the backcross population)and σ2ph is the phenotypic variance. In addition, the totalproportion of phenotypic variance for one trait explainedby all the QTLs r2Ptot

� �was calculated by adding all the in-

dividual QTL determination coefficients detected in thetwo maps. This calculation assumed that none of the QTLsdetected in the two maps corresponded to the same QTLand that there was no epistatic effect. The confidenceinterval of each QTL position was calculated by bootstrap[54].

As asymptotic distributions of LOD values are not con-sidered a good basis for making genome-wide conclusions,a permutation approach [11] was used to estimate the sig-nificance level of individual QTLs. Considering the het-erogeneity of the data (different numbers of markers perchromosome, various trait distributions, variable amountsof missing data per marker), significance levels were com-puted separately for each chromosome–trait combination.QTLs were finally reported for two type I error rates: a“suggestive” level corresponding to a per-linkage-grouptype I error rate of 5% and a “significant” level corre-sponding to a genome-wide type I error rate of 5%. Errorrates corresponding to a genome-wide type I error of 5%were also calculated for each linkage group taking intoaccount its number of markers. If αm is the individualmarker error rate corresponding to a 5% genome-wide typeI error, then αc, the error rate at the linkage group level for achromosome comprising n markers, would be αc=1−(1−αm)

n. These theoretical rates were compared to theprobabilities associated with the LOD scores obtained byCIM at the linkage group level after 1,000 permutations [6].

In addition, associations between candidate gene poly-morphisms and wood traits variability were tested byANOVA.

Results

Phenotypic data

For all the traits, the levels of phenotypic variability ob-served in the FS experiment corresponded to the onesobserved in a half-diallel (HD) experiment previouslyanalysed by Pot et al. [43]. Wood quality traits usuallypresented lower coefficients of variation than growth traits(Table 1). The phenotypic correlations observed in the FSexperiment (Table 2) also agreed with those obtained in theHD. Total growth (th) presented significant negative corre-lations with wood density (d; HD=−0.192, FS=−0.199),hemicellulose content (hemicel; HD=−0.190, FS=−0.380)and the curvature index of the fibres (curl; HD=−0.299,FS=−0.468). Significant positive correlations were alsoobserved between th and alpha cellulose content (alfacel;HD=0.151, FS=0.346), fibre length (HD=0.203 for wfl and0.278 for afl, FS=0.411 for wfl and 0.448 for afl) andcoarseness (coars; HD=0.278, FS=0.504). Wood densitypresented significant negative phenotypic correlations withfibre dimensions (afl, wfl and fw) and non-significant cor-relations with the chemical traits. The fibre properties werehighly correlated with each other either positively (fibrelength and coarseness) or negatively (fibre length and curlindex). With the exception of the extractive content, which

Table 3 Candidate genes and primer pairs

Gene ID Function PCR primers pairs Accession

Forward Reverse

Korrigan Endo-(1-4)-β-glucanase(EC:2.4.1.12)

GCAGGACTATGGTGTTTTAAGC TATTCCCCCAGTATCACCCC BV079723

CesA01 Cellulose synthase 01(EC:2.4.1.12)

TCTGGACGGGATTGAGGAAGGAGTAGA

CAAGCAGGGTTGTAGGAGGAATGAGGA

AY531556

CesA3 Cellulose synthase 3(IXR1) (EC:2.4.1.12)

GCTTTGAGAAGTCGTTTGGC GTATGCCAGTCTTTCCAGCC BV079715

CAD Cinnamyl alcoholdehydrogenase(EC:1.1.1.1.95)

ACAAGAGCCAGACGATCGAAA TGATTACCTGCACTGGTTGG BX784386ATTTGGAACCAACCAGTGC ACCCCTGCACATAACAGAGG BX784387

PAL Phenylalanineammonia-lyase(EC:4.3.1.5)

CCGAACAGCATAACCAGGAT GCGTCGTAAACCACTTCAATC BX784397

C4H Cinnamate-4-hydrolase(EC:1.14.13.11)

GCAGAGGCATTGAATTCTCC GCCTGTCAAACATCATCCTG BX784398

AGP Arabinogalactane protein TCTTTCTCTGAGTCGCTCGAA TGCGAATAGTGGGAAGAGTG BX784280C-coA-OMT Cafeoyl-CoA

3-O-methyltransferaseCGTCTGATCGATCTGGTGAA CAGAAACTGCGAAAACCTCA BX784390

Susy Sucrose synthase(EC: 2.4.1.13)

GTTTCTCCTGGAGCAGACATGCAGATTTA

CCATCGGAACTGACCATTCAGGTTATATTT

AY531555

PFK Fructokinase(EC: 2.7.1.4)

TTAACAGGAGGTGCAGATCCATTCGACG

AGTGGTGTCTACAGCTTCCACTGCCAGG

Unpublished

14

did not present any significant correlations with other traits,the chemical traits were highly correlated with each other,exhibiting strong negative correlations between lignin andalpha cellulose contents (HD=−0.740, FS=−0.596) and be-tween cellulose and hemicellulose contents (HD=−0.694,FS=−0.711) and a positive correlation between lignin andhemicellulose contents (HD=0.386, FS=0.236).

QTL detection

Overview of the detected QTLs

For a few traits, a significant departure from normality wasdetected using the Kolmogorov–Smirnov test (Table 1).Various transformations were applied to normalize the rawdata, but these did not modify the QTL detection results.Consequently, only the results obtained from the non-

transformed data are presented. Fifty-four QTLs were de-tected with the 1-QTL model, 11 at the 5% genome-widelevel (significant level) and 43 at the 5% linkage grouplevel (suggestive level, Table 5, Fig. 1). Thirty-four QTLssegregated in the male, whereas only 20 segregated in thefemale parent. For three traits (th, pyl-east and aext), noQTL was detected. For two other traits (pyl-west and pulpY-30), QTLs were detected in the male but not in the female.The percentage of phenotypic variance explained by eachQTL varied from 3.7 to 12.3%. The 2-QTL model did notallow the detection of additional QTLs.

Timber quality traits

For the timber quality traits, eight QTLs were detected.Three QTLs, explaining collectively 17.3% of the phe-notypic variation, were detected for density estimated by

Table 4 SNPs and map positions of candidate genes

Candidategenes

SNPa,b Targeted polymorphic site,(female*male), primer sequence

Linkage groupassignment

LOD with the nearestmarkersc

Korrigan ss12709589S

ss12709590S

ss12709593S

ss12709589, (A/G*A/G),GGAAGTAGACATCAGGGTTTATGC

LG 12 male, A164–415, 7.91;female, A90–352, 6.99

CesA01 ss16383697S

ss16383698S

ss16383699S

ss16383699, (G/G*A/G),TCTCAACGACAAAGTTAAGAGGT

LG 6 male, A125–376, 2.24

CesA3 ss12709574S ss12709574, (C/G*G/G),GCTGCTGTTGTTGTCCAAAGTC

LG 3 Ptifg_9136, 12.06;female, A65–327, 10.54

CAD ss16209001S ss16209001, (A/A*A/G) ,CAGACGATCGAATCCTGTGAAGT

LG 9 male, A68–319, 12.24

PAL ss16209021S

ss16209028S

ss16209029NS

ss16209032S

ss16209021, (G/C*G/C),GAAGCAGATTGTTTCTCAGGTAGC

LG 6 48, 8.16; male, A195–446,7.63; female, A166–428,5.91

C4H MonomorphAGP ss16209070S

ss16209071S

ss16209073S

ss16209074S

ss16209071, (G/C*G/C),GAATCAACGAAGTGGAGGAGTACA

LG 4 AS01F03, 28.74;female, A39–301, 13.05

C-coA-OMT ss16209078S

ss16209080Sss16209080, (A/A*A/G),GATTGAAGCCATTTGTTTTGTTTA

LG 6 Pt_NCS_CcoA-OMT, 15.43;male, A131–332, 12.41

Susy ss16383692S

ss16383693S

ss16383694S

ss16383695S

ss16383696S

ss1638369, (A/A*T/A),AGTAATTATTTATTTCATGCTTTC

LG 10 Ptig_98580, 17.43;male, A112–363, 9.99

PFK INDEL-1 INDEL-1, agarose gel polymorphism LG 2 Ptifg_8939, 10.42male, A132–363, 9.99

adbSNP accessionbEffect of the polymorphic site on the amino acid sequence (Synonymous S or non-synonymous NS)cLOD value corresponding to the association with the nearest markers in the map published used by Chagné et al. [10]. Testcross markerssegregating 1:1 in the FS progeny are indicated after the parent names. Intercross markers (3:1 segregation) are indicated without otherreferences

15

pilodyn (pyl-west), whereas only two QTLs explaining9.3% of the phenotypic variation were detected for averagedensity measured by X-ray microdensitometry (d). ThreeQTLs, accounting for 19.4% of the phenotypic variation,were obtained for heterogeneity of density (het). As ex-pected from quantitative genetic studies which showedthe absence of genetic correlations between estimates ofwood density obtained from pilodyn measurements andmicrodensitometric profiles [43, 45], none of these QTLsco-localized.

Chemical composition and industrial productionparameters

With the exception of acetone extractive content (aext),QTLs were detected for all the traits related to woodchemical composition. Seven QTLs were identified forlignin, four for alfacel and four for hemicel. Three QTLsrelated to the monomeric composition of lignin (H/G ratio)were also found. For lignin, 45.9% of the phenotypic var-iation was explained by the detected QTLs, this proportiondropping down to 20–25% for the other chemical traits.

Table 5 Results of the QTL analysis using composite interval mapping

Trait category Sex Traita LGb cMc Lodd P (LG)e df P (genome)g r2p (%)h r2ptot (%)i h2nsj r2g (%)k

Growth Female th No QTL 0 0.456 NAMale th No QTL

Timber quality Female d 11f 7 1.5433 0.04 –14.015 * 4.6 9.4 0.295 31.86Male d 7m 38 1.639 0.043 –14.341 * 4.8Female pyl-east No QTL 0 0.00 NAMale pyl-east No QTLFemale pyl-east No QTL 17.3 0.00 >100Male pyl-east 2am 15 2.9233 0 0.919 ** 8.8Male pyl-east 2bm 19 1.5261 0.029 0.6 * 3.7Male pyl-east 3am 31 1.6879 0.013 0.681 * 4.8Female het 12f 156 1.7498 0.035 -5.717 * 5.1 19.4 0.509 38.11Male het 3bm 113 2.4291 0.009 -7.23 * 8.0Male het 10bm 3 2.3932 0.007 6.385 * 6.3

Wood chemicalcomposition

Female lignin 8f 59 2.5972 0.008 –0.556 * 9.0 45.9 0.471 97.45Female lignin 10f 38 2.1625 0.022 0.469 * 6.4Male lignin 1m 77 2.3203 0.011 –0.454 * 6.0Male lignin 2am 5 1.8293 0.015 0.427 * 5.3Male lignin 3bm 37 2.6676 0.006 0.518 * 7.8Male lignin 4m 30 1.9905 0.019 0.471 * 6.4Male lignin 7m 65 1.8731 0.034 0.414 * 5.0Female H/G 1f 65 2.5489 0.006 –0.005 * 9.0 23.5 ND NAMale H/G 7m 132 2.4752 0.007 0.005 * 9.4Male H/G 10bm 0 1.61 0.023 0.004 * 5.1Female alfacel 8f 38 2.4502 0.007 0.6 * 7.8 24.5 0.343 71.43Female alfacel 11f 65 1.5306 0.041 –0.413 * 3.7Female alfacel 12f 156 1.813 0.04 –0.475 * 7.1Male alfacel 4m 18 1.8069 0.03 –0.525 * 5.9Female hemicel 8f 39 2.0639 0.018 –0.244 * 6.3 25.6 NS >100Female hemicel 12f 156 2.3091 0.011 0.253 * 6.7Male hemicel 11m 96 1.8608 0.02 0.206 * 4.5Male hemicel 12m 9 2.64066 0.001 0.276 ** 8.1Female aext No QTL 0 NS NAMale aext No QTLFemale wext 5f 66 2.7002 0.003 –0.981 ** 7.8 22.4 NS >100Male wext 1m 154 1.8734 0.036 –0.845 * 5.8Male wext 7m 115 2.4947 0.005 –1.045 * 8.8

Kraft pulpproductionparameters

Female kappa 10f 9 1.8952 0.031 1.167 * 4.7 16 NS >100Male kappa 5m 0 1.9014 0.03 –1.189 * 4.9Male kappa 7m 121 1.5601 0.046 1.364 * 6.4Female PulpY no QTL 8.8 NS >100Male PulpY 4m 92 3.4045 0.001 –1.173 ** 8.8

16

Kappa index and pulp yield were also analysed. These twotraits were measured for cooking conditions correspondingto an active alkali of 22% and a sulfidity of 30% for anexpected kappa index of 30. Although strong phenotypiccorrelations are usually expected between these parametersand wood chemical composition (lignin and cellulose), onlyweak ones were observed, probably revealing a lack ofaccuracy in the pulp yield measurements and/or a partialdegradation of the polysaccharides at kappa 30. ThreeQTLs were identified for kappa and one for pulpY.

Several co-localizations were observed between thechemical composition and the kraft production QTLs:lignin and alfacel on LG4m; wext, H/G and Kappa onLG7m; lignin, alfacel and hemicel on LG8f; lignin andKappa on LG10f; and hemicel and alfacel on LG12f.

Fibre properties

For fibre properties, a total of 21 QTLs (three for afl, wfl, fwand coars, two for curl and seven for zspan) were detected.Seven were significant at the genome-wide level. The totalphenotypic variance explained varied between 16.2 and24.3% for the fibre characteristics, while the QTLs detectedfor zspan explained 42.7% of the phenotypic variation.Clusters of QTLs involving different fibre properties weredetected on LG4f, LG8f and LG12f.

Candidate gene polymorphism and mapping

All but one candidate gene (C4H) were polymorphic inthe parents of the mapping pedigree (Table 4) and were

Table 5 (continued)

Trait category Sex Traita LGb cMc Lodd P (LG)e df P (genome)g r2p (%)h r2ptot (%)i h2nsj r2g (%)k

Fibre properties Female afl 8f 43 1.7865 0.035 0.028 * 5.2 22.4 0.172 130.23Male afl 4m 92 2.1005 0.012 –0.027 * 4.9Male afl 12m 5 4.0193 0 –0.043 ** 12.3Female wfl 8f 44 2.1596 0.02 0.09 * 6.1 24.3 0.19 127.89Male wfl 4m 92 2.6189 0.005 –0.092 * 6.3Male wfl 12m 4 3.8538 0 –0.126 ** 11.9Female fw 7f 182 3.0279 0.004 –0.329 ** 8.2 19 0.184 103.26Female fw 8f 43 1.8049 0.041 –0.266 * 5.4Male fw 12m 4 1.6389 0.028 0.268 * 5.4Female coarc 8f 43 2.2095 0.021 0.05 * 6.5 22.8 0.374 60.96Male coarc 4m 92 1.8507 0.022 –0.004 * 4.4Male coarc 12m 6 3.9538 0 –0.007 ** 11.9Female curl 8f 43 2.1738 0.016 –0.387 * 6 16.2 0.249 65.06Male curl 12m 9 3.251 0 0.506 ** 10.2Female zspan 1f 15 2.1658 0.018 2.301 * 5.3 42.7 0.16 266.88Female zspan 2f 159 1.7994 0.049 –2.629 * 6.9Male zspan 2bm 25 2.6549 0 2.601 ** 6.7Male zspan 4m 92 2.9145 0.001 –2.718 ** 7.3Male zspan 5m 9 2.4014 0.01 2.457 * 6Male zspan 7m 141 2.1937 0.015 –2.378 * 5.6Male zspan 12m 26 1.9574 0.017 –2.233 * 4.9

The QTLs are displayed along the linkage groups in Fig. 1aAbbreviation as defined in Table 1bLinkage group (LG) on which the QTL was foundcPosition of the LOD peak on the linkage groupdLOD value at the LOD peakeProbability associated with the presence of the QTL at the linkage group levelfAllelic substitution effect as defined in the “Material and methods”gProbability associated with the presence of the QTL at the genome group levelhPhenotypic variation explained by each QTLiAs defined in the “Materials and methods” sectionjHeritability of the trait as in Pot et al. [43]kGenetic variance explained by the QTLs, calculated as r2g=r

2ptot/h

2ns

*0.05<P<0.5; **P<0.05

17

mapped. A total of 24 polymorphic sites, 23 synonymousand 1 non-synonymous mutations (in PAL), were detected.The polymorphic sites and the primer sequences used tomap the candidate genes are presented in Table 4. Out ofthe nine candidate genes localized on the genetic map, onlyKorrigan co-localized with wood quality QTLs. ANOVAof gene polymorphisms and trait variation in the mappingpedigree revealed two significant associations (p value<0.005): between CAD polymorphism and th (p value=0.0013, PEV=12%) and between Korrigan polymorphismand hemicel content (p value=0.003, PEV=18%).

Discussion

Genetic architecture of wood and end-use properties

QTL detection accuracy

Traits exhibiting continuous variation are expected to beunder polygenic control. However, studies of genetic ar-chitecture of wood properties which have been performedin diverse tree genera (Populus [5], Eucalyptus [19, 53] andPinus [20, 31, 48, 49]) have shown that these complextraits are under oligogenic control, with a few QTLs ac-counting for a significant part of the phenotypic variation.

The present study, which allowed the detection of 2.4QTLs on average for each trait, is consistent with thesefindings.

However, it should be noted that most of the studiespublished so far, including this one (and with the exceptionof [7, 13]), have been carried out using rather small ped-igree sizes (fewer than 400 genotypes) which are not welladapted for detecting small-effect QTLs [3, 51] and thuslead to an underestimation of the QTL number.

In addition, as reported by Beavis [3], small pedigreeslead to an overestimation of the phenotypic variation ex-plained. This deviation is expected to be greater for low-heritability traits [60]. Therefore, estimates of phenotypicand genetic variation explained by the QTLs should bereliable for growth, wood density, wood density hetero-geneity, lignin and cellulose contents, which present rel-atively high heritabilities in maritime pine, whereasthese parameters would be overestimated for pilodyn,fibre properties, extractives content, hemicellulose andkraft production parameters, which are characterized bylower heritabilities. Our results match with these expecta-tions since genetic explained variances (r2g, Table 5) wereoften greater than 100% for low-heritability traits, where-as estimates obtained for high-heritability traits were morerealistic.

Fig. 1 QTLs and candidate gene locations. Each QTL is delineatedby the position of the LOD score peak and the bootstrap mean valueof the LOD score peak. Ninety-five per cent confidence intervalsbased on 1,000 bootstrap samples are indicated as lines. Candidate

gene (as listed in Table 3) positions are indicated by arrowed boxes.When they are only mapped in one parent, they are indicated by asingle arrow box (e.g. PFK); otherwise, they are indicated by adouble arrow box (e.g. Korrigan)

18

Fig. 1 (continued)

19

Maturation effect and QTL detection

In some cases, none or only a few small-effect QTLs weredetected for high-heritability traits (i.e. th and d) for whichgreater phenotypic and genetic explained variances would

have been expected. Three hypotheses could be proposedto explain this result: (1) no genetic variability at the lociinvolved in the genetic determinism of these traits waspresent in the parents of the FS pedigree, and therefore, nosegregating QTLs could be detected in the studied family;

Fig. 1 (continued)

20

(2) the genetic variability of these traits depends on epis-tatic effects that were not explored in the QTL analysis; or(3) the genetic variability of these traits relies on small-effect QTLs that are unstable over the maturation period ofthe trees. If the first explanation is unlikely due to the highlevel of heterozygosity of maritime pine and no empiricaljustification is yet available for the second one, previousstudies have underlined the importance of the maturationeffect on the genetic control of growth and density [24, 41,48, 53]. If many different genes are responsible for thegenetic control of the same character (e.g. annual ringdensity or annual height increment) during a tree’s life, it isnot surprising that the resulting traits (mean density, totalheight) did not display any significant QTLs.

Large QTL effects and apparent pleiotropic effectsdetected for several wood quality components

Large-effect QTLs were detected for several wood qual-ity components. Among the various wood properties, oneof the most important is wood chemical composition(essentially lignin and alpha cellulose contents) whichaffects pulp yield, the energy required during the pulpingprocess and also the physical properties of pulp. Numerouslarge-effect QTLs were detected for lignin and cellulosecontent in this study. The importance of these results re-sides in two points. First, in contrast to height growth andwood density, they suggest that the genetic control of thesetraits is probably stable during the ontogenic develop-ment of the trees, and second, they provide basic infor-mation for the development of marker-assisted selection

in forest trees. Wood chemical traits are among the mostexpensive and difficult traits to measure. Therefore, theavailability of diagnostic tools for these traits based on mo-lecular markers would greatly enhance breeding efficiency.

Furthermore, multiple co-localizations between woodchemical composition QTLs were identified on linkagegroups LG4m (lignin, alfacel), LG8f (alfacel, hemicel,lignin) and LG12f (hemicel, alfacel), underlining the in-volvement of the same chromosomal regions in the geneticcontrol of these different traits. If a single co-localizationbetween two QTLs could be due to chance alone, theobservation of multiple co-localizations could be consideredas a good indication of the common genetic control of thetraits. Such multiple co-occurrences probably also reflect theeffects of pleiotropic genes rather than the existence ofseveral genes in strong linkage disequilibrium at differentgenomic positions. These genes could be involved in carbonallocation either to polysaccharides or lignin, given thestrong negative genetic correlation between these two traits.

Multiple co-locations between QTLs for fibre and pulpproperties were also found on LG4m (afl, wfl, coars, zspan),LG8f (afl, fw, coars, curl, wfl) and LG12m (fw, afl, wfl,coars, curl, zspan). Although the heritabilities of thesetraits are lower than those of the traits reported previouslyand thus lead to an overestimation of the QTL effects, themultiple co-localizations underline the validity of the ge-nomic regions detected.

Other co-localizations were also identified betweenwood physical properties, fibre characteristics and chem-ical composition (e.g. lignin and pyl, and pyl and zspan onLG2m; kappa-30 and lignin on LG10f; kappa-30, chemicalcomposition and fibre properties on LG8f; hemicel and

Fig. 1 (continued)

21

fibre properties on LG12m). As a consequence, somegenomic regions seemed to be not only involved in thegenetic control of a particular class of trait (chemicalcomposition or fibre properties), but also in the control ofdifferent classes.

It is also interesting to notice that all the co-localizationsobserved were consistent with the genetic correlations re-ported previously in maritime pine [43], although suchresults were not always expected from the phenotypiccorrelations. For instance, a co-localization between den-sity (d) and lignin QTLs on LG7m was not expected fromthe phenotypic correlation (not significantly different fromzero in the HD and the FS), but was consistent with thesignificant genetic correlation observed in the HD (rg=−0.544). This congruence between the genetic correlationsobtained in an independent experiment (HD) tends tovalidate the co-localization of the QTLs detected in the FSpedigree.

Interspecific validation of the QTLs

Comparative QTL mapping is a useful strategy for val-idating QTLs [47], allowing the identification of cor-responding chromosomal regions affecting the samequantitative trait in different species. Although the numberof comparative anchor tag markers between P. pinaster andP. taeda (the reference species of the Comparative ConiferGenome Project: http://dendrome.ucdavis.edu/Synteny/about.html) remains low, it was still possible to identifyorthologous regions carrying similar wood quality QTLs,e.g. QTLs for lignin and alpha-cellulose content on LG8 [7,10]. The consistency observed for this chromosomal regionbetween the two species supports the hypothesis that somegenes affecting wood properties have been conserved overa long period of evolution and validates the importance ofthis region in the genetic control of wood quality traits.

Usefulness of the QTLs detected for within familyselection

If QTL verification usually proceeds from the repeatedanalysis of marker–trait associations across time [8, 48],space or independent samples of the same pedigree [7, 13,16, 57], the limited size of our pedigree and the lack ofclonal replicates of the full-sibs did not allow us to performthese types of tests to validate the detected QTLs. Never-theless, (1) the multiple detection of QTL co-localizations,(2) the congruence between QTLs co-localization and ge-netic correlations between traits and (3) the interspecificdetection of the same QTLs can be considered as goodindications of their reliability. Several studies have under-lined the benefits of marker-assisted selection (MAS) inwithin-family breeding schemes [25, 58]. The QTLs de-tected here, keeping in mind that their effects are probablyoverestimated, provide tools for breeders wishing to initiatesuch selection within this particular FS family.

Towards the characterization of wood quality QTLs

QTL mapping is often presented as a gene discovery process.However, Darvasi et al. [12] and Mangin et al. [32] showedthat even using large population sizes and with large-effectQTLs, the confidence interval for any QTL location stillremains rather large, often in the order of 10 cM, i.e. about130 Mb in maritime pine. Marker–trait association defined ina pedigree context will therefore fail to identify the actual generesponsible for quantitative trait variation and as underlinedin “Introduction” will not constitute a useful breeding tool inthe context of multiple-family breeding strategies. This isespecially true in conifers. On the one hand, the megagenomeof these species [55] rules out the characterization of QTLs bychromosomalwalking [46]. On the other hand, their high levelof diversity and their large effective population size [50] willhamper the direct use of QTLs in breeding programmes. Inthis context, identification of markers tightly linked to thepolymorphism responsible for trait variation is an almostcompulsory step. In this perspective, the second aim of thisstudy was to test the co-localization between potential woodquality genes and the QTL detected.

Gene–QTL associations

Although all the candidate genes analysed in this study arepotentially involved in the variability of wood quality com-ponents, only one co-location between Korrigan and woodproperties QTLs (cell wall composition and fibre properties)was found, on LG12 (Fig. 1). This association was con-firmed by a significant ANOVA test between Korriganpolymorphism and hemicellulose variability (p value=0.003,PEV=18%).

Korrigan is involved in cellulose biosynthesis [14]. It isthought to catalyze the cleavage of cellodextrin from sis-tosterol cellodextrin before the synthesis of cellulose micro-fibrils by the cellulose synthase complex. A P. pinasterortholog of Korrigan was first obtained from the randomsequencing of xylem ESTs (accession BX255589 of 693 bpshowing 71% identity and 81% similarity to the Korriganlocus of A. thaliana accession AAC83240, using BlastX)and then extended to 937 bp by PCR. It was also identifiedas differentially expressed between differentiating xylem ofearlywood vs latewood [30], two types of wood differing intheir cellulose and lignin contents. In addition, analysis ofthe molecular evolution of this gene [44] showed that it is apotential target of natural and/or artificial selection in P.pinaster and P. radiata, meaning that its nucleotide vari-ability is potentially linked to the phenotypic variability of atrait submitted to natural and/or artificial selection. Anassociation between Korrigan variability and hemicellulosecontent variability would therefore make sense, given thecentral role of this biochemical compound in the cell wallstructure. However, even if this study has provided aninteresting case of a positional candidate gene, higher res-olution, such as that offered by association study in un-related individuals [36], will be needed to validate thisparticular candidate gene.

22

Acknowledgements We thank Dr. H. Höfte for his helpful com-ments and advice.This work was supported by funding from the European Union

(GENIALITY: FAIR CT98-3953, GEMINI: QLRT-1999-00942), theAquitaine Region (no. 2002 0307002A) and the Ministère de laRecherche (Biotech programme).

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