Fagaceae Trees

CHAPTER 5

5 Fagaceae Trees

Antoine Kremer1, Manuela Casasoli2, Teresa Barreneche3, Catherine Bodénès1, Paul Sisco4, ThomasKubisiak5, Marta Scalfi6, Stefano Leonardi6, Erica Bakker7, Joukje Buiteveld8, Jeanne Romero-Severson9,Kathiravetpillai Arumuganathan10, Jeremy Derory1, Caroline Scotti-Saintagne11, Guy Roussel1, MariaEvangelista Bertocchi1, Christian Lexer12, Ilga Porth13, Fred Hebard14, Catherine Clark15, John Carlson16,Christophe Plomion1, Hans-Peter Koelewijn8, and Fiorella Villani17

1 UMR Biodiversité Gènes & Communautés, INRA, 69 Route d’Arcachon, 33612 Cestas, France, e-mail:[email protected]

2 Dipartimento di Biologia Vegetale, Università “La Sapienza”, Piazza A. Moro 5, 00185 Rome, Italy3 Unité de Recherche sur les Espèces Fruitières et la Vigne, INRA, 71 Avenue Edouard Bourlaux, 33883 Villenave d’Ornon,

France4 The American Chestnut Foundation, One Oak Plaza, Suite 308 Asheville, NC 28801, USA5 Southern Institute of Forest Genetics, USDA-Forest Service, 23332 Highway 67, Saucier, MS 39574-9344, USA6 Dipartimento di Scienze Ambientali, Università di Parma, Parco Area delle Scienze 11/A, 43100 Parma, Italy7 Department of Ecology and Evolution, University of Chicago, 5801 South Ellis Avenue, Chicago, IL 60637, USA8 Alterra Wageningen UR, Centre for Ecosystem Studies, P.O. Box 47, 6700 AA Wageningen, The Netherlands9 Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA

10 Flow Cytometry and Imaging Core Laboratory, Benaroya Research Institute at Virginia Mason, 1201 Ninth Avenue, Seattle,WA 98101, USA

11 UMR Ecologie des Forêts de Guyane, INRA, Campus agronomique BP 709, Avenue de France, 97387 Kourou, French Guyana12 Jodrell Laboratory, Royal Botanic Gardens, Kew, Richmond, Surrey TW9 3DS, UK13 Austrian Research Centre, 2444 Seibersdorf, Austria14 The American Chestnut Foundation Research Farms, 14005 Glenbrook Avenue, Meadowview, VA 24361, USA15 Department of Forestry, North Carolina State University, Box 8008, Raleigh, NC 27695-8008, USA16 The School of Forest Resources and Huck Institutes for Life Sciences, Pennsylvania State University, 323 Forest Resources

Building, University Park, PA 16802, USA17 Istituto per l’Agroselvicoltura, CNR, V.le G. Marconi, 2 - 05010, Porano, Italy

5.1Introduction

Worldwide, there are more than 1,000 species belong-ing to the Fagaceae. All Fagaceae species are woodyplants and are spread throughout the northern hemi-sphere, from the tropical to the boreal regions. Thefamily comprises seven genera (Govaerts and Frodin1998), and the number of species is extremely vari-able among genera: Castanea (12), Castanopsis (100to 200), Chrysolepis (2), Fagus (11), Lithocarpus (300),Quercus (450 to 600), Trigonobalanus (3). Oaks (Quer-cus), chestnuts (Castanea), and beeches (Fagus) arewidely used in forestry for wood products over thethree continents (Asia, Europe, and America) andare important economic species. Consequently, theyhave receivedmoreattention in forest genetic researchthan other genera. In addition to their cultivation in

forestry, chestnuts are also used for their fruit pro-duction and have been partially domesticated for thatpurpose. Castanopsis and Lithocarpus are importantecological components of the Asian flora and haverecently been investigated for their biological diver-sity (Cannon and Manos 2003). The remaining generacompriseonlyavery fewspecies and for the timebeinghave been studied mainly in botany and taxonomy.

Genetic research in Fagaceae has been restrictedto the three genera of economic importance (Cas-tanea, Fagus, and Quercus), although activities inphylogeny and evolutionary genetics have recentlyencompassed the whole family (Manos and Stanford2001; Manos et al. 2001). Because of their long rota-tion times, breeding activities in the three main gen-era have been limited (Kremer et al. 2004). However,population differentiation has been investigated ina very large number of species, with the main aimof identifying geographic patterns or historical foot-

Genome Mapping and Molecular Breeding in Plants, Volume 7Forest TreesC. Kole (Ed.)© Springer-Verlag Berlin Heidelberg 2007

162 A. Kremer et al.

prints for molecular markers and phenotypic traitsof forestry relevance. Population genetics has drivenmost of the research activities in molecular geneticsand also genetic mapping, in contrast to other for-est tree species where tree improvement has been themain goal. Genetic maps have been constructed inat least one species of Quercus, Castanea, and Fagus.Because of their low genetic divergence, it quicklybecame obvious that molecular markers could be eas-ily transferred from Quercus to Castanea (and viceversa) but less easily to Fagus. These earlier find-ings led to further activities on comparative mappingacross genera, especially between Quercus and Cas-tanea.

5.1.1Evolutionary Biology and Phylogenyof the Fagaceae

Fossil remains indicate that the Fagaceae appearedat the transition between the secondary and tertiaryera. Remains of Dryophyllum, which is a fossil genusbelonging to the Fagaceae, were discovered in lay-ers belonging to the early Cretaceous (Jones 1986).Fossil remains that were unequivocally assigned toFagaceae and dated to the Upper Eocene and LateOligocene were found in North America (Herendeenet al. 1995) and Europe (Kvacek and Walther 1989).Differentiation of the various genera occurred dur-ing the mid Tertiary, and reported species of Fagaceaeat the late Tertiary resemble already extant species.The oldest reported genera belonging to the Fagaceaeoccurred in Southeast Asia, where the extant speciesdiversity is also the highest. The family originatedfrom Southeast Asia and radiated toward Europe andAmerica (Wen 1999; Xiang et al. 2000). Migration andmajor continental rearrangements contributed to dis-junction and vicariance within the family, especiallywithinQuercus (ManosandStanford2001). It is gener-ally accepted that most major oak groups essentiallyevolved in the areas where they reside today (Axel-rod 1983).

Phylogenetic investigations based on chloroplastor nuclear DNA data are poorly resolutive, suggest-ing that the differentiation into different genera wasextremely rapid during the mid Tertiary (Manos andSteele 1997; Manos et al. 2001). All genera are usuallyclustered into a “starlike” dendrogram (polytomy),except Fagus, which diverged earlier from the com-mon ancestor. However, there is a strong genomic

similarity between Quercus, Castanea, Lithocarpus,and Castanopsis. Paleontological records suggest thatQuercus and Castanea separated 60 million years ago(Crepet 1989). Interspecific separation within the gen-era Quercus, Fagus, and Castanea occurred between22and3millionyearsagoas inferred fromamolecularclock based on cpDNA divergence (Manos and Stan-ford 2001). The reduced genetic divergence among thedifferent genera was recently confirmed by the resultsobtained in transferring molecular tools and markersamong genera, as it is much more difficult to transfermicrosatellite markers from Quercus to Fagus than itis from Quercus to Castanea (Steinkellner et al. 1997;Barreneche et al. 2004).

5.1.2Ploidy, Karyotype, and Genome Size in Fagaceae

Reported karyotype studies in Quercus, Lithocarpus,Castanopsis, and Castanea (Mehra et al. 1972), inQuercus (D’Emerico et al. 1995), and Fagus (Ohriand Ahuja 1991) indicate that the number of chro-mosomes within the family is remarkably stable (2n= 24). Naturally occurring triploids have been men-tioned occasionally in oaks (Butorina 1993; Naujokset al. 1995). Extra chromosomes (2n = 24+1, 2 or 3)have been reported as consequences of irregular seg-regation in mitoses (Zoldos et al. 1998). Otherwise, C-banding comparisons have shown that the morphol-ogy of the chromosomes of Fagus (Ohri and Ahuja1991) and Quercus (Ohri and Ahuja 1990) are quitesimilar.

The DNA content is variable across genera in theFagaceae: the 2C DNA values varying from a lowof 1.11 pg in Fagus to a high of 2.0 pg in Quercusspecies (Table 1). GC content on the other hand ap-pears constant across genera (40%) and is similarto most higher plants (Table 1). All values reportedin Table 1 were obtained by flow cytometric analy-sis of interphasic nuclei and are slightly higher thanearlier assessments made with the Feulgen micro-densitometry method (Ohri and Ahuja 1990). The31 species in Table 1 represent a cross-section ofthe Fagaceae across the northern hemisphere. Thetwo Fagus species, Fagus grandifolia and F. sylvat-ica, were quite similar in genome size (1.27 and1.11 pg per 2C, respectively) and are at the lowerrange of genome sizes among the Fagaceae, sug-gesting that Fagus has either the most rudimen-tary genome or the most greatly reduced genome

Chapter 5 Fagaceae Trees 163

Table 1. DNA content in 31 Fagaceae species determined by flow cytometric analysis

Species 2C nuclear DNA 1C nuclear DNA GC content Referencepg (mean value) Mbp (%)

Genus CastaneaC. seguinii 1.57 755 – Arumuganathan et al.∗C. sativa (1) 1.61 777 – Arumuganathan et al.C. sativa (2) 1.62 – – Brown and Siljak-Yakovlev

(pers comm)C. crenata 1.65 794 – Arumuganathan et al.C. mollissima 1.65 794 – Arumuganathan et al.C. dentata 1.67 803 – Arumuganathan et al.

Genus FagusF. grandifolia 1.27 610 – Arumuganathan et al.F. sylvatica 1.11 535 40 Gallois et al. 1999

Genus Quercus Subgen Erythrobalanus∗∗Q. velutina 1.17 565 – Arumuganathan et al.Q. nuttallii 1.39 672 – Arumuganathan et al.Q. shumardii 1.47 709 – Arumuganathan et al.Q. nigra 1.52 735 – Arumuganathan et al.Q. rubra 1.58 762 – Arumuganathan et al.Q. palustris 1.60 774 – Arumuganathan et al.Q. coccinea 1.64 791 – Arumuganathan et al.Q. phellos 1.66 799 – Arumuganathan et al.Q. falcata 1.72 832 – Arumuganathan et al.Q. pagoda 1.75 843 – Arumuganathan et al.Q. imbricaria 1.81 871 – Arumuganathan et al.

Genus Quercus Subgen Lepidobalanus∗∗Q. bicolor 1.35 651 – Arumuganathan et al.Q. montana 1.49 719 – Arumuganathan et al.Q. robur 1.53 740 – Arumuganathan et al.Q. stellata 1.55 745 – Arumuganathan et al.Q. alba 1.59 766 – Arumuganathan et al.Q. macrocarpa 1.62 780 – Arumuganathan et al.Q. robur 1.84 885 42 Favre and Brown 1996Q. pubescens 1.86 882 42.1 Favre and Brown 1996Q. petraea 1.87 901 41.7 Favre and Brown 1996Q. robur 1.88 – 39.4 Zoldos et al. 1998Q. petraea 1.90 – 39.8 Zoldos et al. 1998Q. pubescens 1.91 – 39.7 Zoldos et al. 1998

Genus Quercus Subgen Cerris∗∗Q. acutissima 1.42 684 – Arumuganathan et al.Q. cerris 1.91 – 40.2 Zoldos et al. 1998Q. suber 1.91 – 39.7 Zoldos et al. 1998

Genus Quercus Subgen SclerophyllodrysQ. coccifera 2.00 – 40.4 Zoldos et al. 1998Q. ilex 2.00 – 39.8 Zoldos et al. 1998

∗Arumuganathan K, Schlarbaum SE, Carlson JE previously unpublished data (genome sizes are an average of three determinationsof 2 to 4 individuals per species)∗∗ According to Flora Europea (http://rbg-web2.rbge.org.uk/FE/fe.html)


among the Fagaceae. In addition, the small genomeof Quercus velutina at 1.17 pg per 2C is essentiallythe same as that of the Fagus species, again sug-gesting a basal genome size of about 1.2 pg per 2Cfor the Fagaceae. Among the 24 Quercus species pre-sented, the range of genome sizes is essentially con-tinuous up to a maximum of 2.0 pg per 2C in Q. coc-cifera and Q. ilex. We looked for any indication thatthe interspecific variation in the observed genomesizes followed the botanical subdivisions within Quer-cus. We used here the classification into four distinctbotanical subgenera from Flora Europaea (http://rbg-web2.rbge.org.uk/FE/fe.html). This classification cor-responds to earlier botanical descriptions of Schwarz(1964) and Camus (1936-1954) and recent molecu-lar analyses (Manos et al. 1999; Xu et al. 2005). Thespecies that were investigated include representativesof all four subgenera: 12 species in Erythrobalanus(red oaks), seven species in Lepidobalanus (whiteoaks), three species in Cerris, and two in Sclerophyl-lodrys. The average 2C DNA contents were 2.0 pg forsubgenus Sclerophyllodris, 1.75 pg for subgenus Cer-ris, 1.73 pg for subgenus Lepidobalanus, and 1.56 pgfor subgenus Erythrobalanus. The two oak specieswith the largest genomes, Q. coccifera and Q. ilex(2.0 pg), are both evergreen species and are part ofa disputed botanical subgenus (named Sclerophyl-lodrys, according to Schwarz 1964). This is intriguing,given that molecular phylogenetic analysis separatesthe evergreen species from the two sections of de-ciduous oaks (Manos and Steele 1997 and Xu et al.2005), confirming their earlier subdivision in Sclero-phyllodrys by Schwarz (1964).

Among the five Castanea species studied, genomesizes varied much less than among oaks, ranging onlyfrom 1.57 pg in C. seguinii to 1.67 pg per 2C in C. den-tata. In fact DNA content varied as much within thechestnut species as between. For example, unrelatedC. seguinii trees varied from 1.5 to 1.63 pg per 2C,while C. sativa varied from 1.57 to 1.65 pg per 2C(Arumuganathan et al. this study). Thus there maynot be significant differences in average DNA con-tent between Castanea species, and the range of av-erage DNA content reported among species in Ta-ble 1 may just represent the natural variation in DNAcontent among Castanea individuals. The intraspe-cific variation in DNA content in Quercus was alsoas extensive as the amount of variation among thespecies. For example, the 2C DNA content varied be-tween 1.88 pg and 2.0 pg among Q. petraea trees ofthe same populations (Zoldos et al. 1998), between

1.45 and 1.96 in Q. pagoda, and between 1.34 and 1.78in Q. macrocarpa (Arumuganathan et al. this study).The intraspecific variation may be due in part to theoccurrence of extra B chromosomes (Ohri and Ahuja1990; Zoldos et al. 1998). While the range of DNAcontent among oak species appears to be greater thanamong chestnut and beech species, the magnitudeof the differences among oak species may be relatedto experimental issues as well as biological ones. Forexample, the size estimates by Arumuganathan et al.(this study) were consistently smaller than those byFavre and Brown (1996) and Zoldos et al. (1998). Onecould speculate that the differences relate to the factthat Arumuganathan et al. (this study) studied pri-marily New World species, while the other two stud-ies dealt exclusively with Old World species. However,the three groups report different genome sizes forQ. robur (1.53, 1.84, and 1.88 pg per 2C, respectively).Whether this discrepancy has a biological basis (theArumuganathan et al. study sampled three trees ofQ. robur “fastigiata,” the “upright” horticultural va-riety) or resulted from experimental differences insampling, internal size standards, and other method-ologies is not clear.

In general, the genome sizes in the Fagaceae areonly3.5 to6 times larger than thegenomeofArabidop-sis (0.32 pg; Bennett et al. 2003) and are within thesize range of the sequenced rice and poplar genomes(both 1.0 pg; Brunner et al. 2004). Comparative ge-nomics should thus be relatively efficient within theFagaceae. Comparative genomics will lead to a bet-ter understanding of the extent to which the con-tinuous range of DNA content is related to adap-tive radiation of the species during evolution or isthe result of overlapping ranges and interspecies hy-bridizations. Knowledge of the genome sizes revealsthat genome-level comparisons between Fagus syl-vatica, Q. velutina, Q. coccifera, and Q. ilex wouldbe particularly informative and could illuminate therole of genome duplication in the evolution of theFagaceae. When comparative genomics studies areextended to more species within the Fagaceae, itwill be interesting to determine whether or not thebroader range of genome sizes observed in Quer-cus relates to more extensive adaptations and spe-cializations than exist among Fagus and Castaneaspecies. Given the extensive natural populations ofFagaceae species that still exist across the northernhemisphere, such information will certainly provideinsights into the ecology of temperate forest ecosys-tems.


5.2Construction of GeneticLinkage Maps

5.2.1Genetic Mapping in Forest Trees

PCR-based molecular markers and the two-way pseu-dotestcross strategy are useful tools for construct-ing genetic maps in forest trees (Grattapaglia andSederoff 1994). These outbred species are character-ized by long generation times, long life spans, anda high genetic load that often leads to significantinbreeding depression. Although all these elementshinder the development of the type of mapping popu-lations normally used for genetic linkage mapping(for instance inbred lines and backcrosses), the highlevel of heterozygosity in forest species made two-generation full-sib pedigrees suitable populations formarker segregation analysis. Full-sib and half-sibcrosses can, therefore, be used to construct single-tree genetic linkage maps thanks to dominant PCR-based molecular markers. Following this approach,called the two-way pseudotestcross strategy (Gratta-paglia and Sederoff 1994), three types of segregationconfigurations can be obtained for dominant molec-ular markers in the mapping population: (1) maletestcross markers, segregating in a 1:1 ratio and in-herited from the male parent; (2) female testcrossmarkers, segregating in a 1:1 ratio and inherited fromthe female parent; and (3) intercross markers, segre-gating in a 3:1 ratio and inherited from both parentaltrees. Male and female testcross markers are used toconstruct two independent single-tree genetic mapsthat are then aligned thanks to the intercross mark-ers. RAPD (Williams et al. 1990) and AFLP (Vos et al.1995) dominant molecular markers have been usedmost commonly to construct genetic linkage maps inforest tree species (VerhaegenandPlomion1996;Mar-ques et al. 1998; Arcade et al. 2000; Costa et al. 2000;Cervera et al. 2001), as their random distribution inthe genome allows all chromosomes to be coveredmost efficiently.

The two-waypseudotestcross strategywasfirst ap-plied in forest trees by Grattapaglia et al. (1995) toidentify loci controlling quantitative trait loci (QTLs).In forest genetics, QTL analysis has been one of themost important applications of linkage mapping, andseveral studies reported successful QTL detections(Sewell and Neale 2000; van Buijtenen 2001).

5.2.2Genetic Mapping Initiatives in Fagaceae

Oak Mapping Initiatives

European white oaks Starting in 1995, activitiesin genetic mapping were implemented in Europeanwhite oaks at the INRA Research Centre in Bordeaux-Cestas (France). Motivations for genetic mapping inoaks were threefold: (1) the detection of genomic re-gions involved in species differentiation, (2) the de-tection of QTLs controlling traits of adaptive signif-icance, and (3) the comparative analysis of genomicevolution in the Fagaceae. The whole mapping projectis based on three pedigrees: one full-sib family ofQuercus robur (3P × A4), one full-sib family of Q. pe-traea (QS28 × QS21), and one interspecific F1 full-sib family Q. robur × Q. petraea (11P × QS29). Aninterspecific F2 cross is planned as well. Given theobjectives of the mapping experiments, the parentsof the pedigrees were not selected for any particularcriteria. The Q. robur parent trees originated fromthe southwest of France (INRA research station ofBordeaux-Cestas, and Arcachon) and the Q. petraeaparents were from the central part of France (INRAresearch station of Orléans-Ardon). The controlledcrosses were repeatedly done over successive yearsuntil 2004. From 200 to 1,000 seeds were obtainedfor each cross. The young seedlings were installedin a seedbed in a nursery, where they are raised asstool beds. Starting at age 5, the full-sibs were hedgedevery year at the ground level at the end of winter.Following the hedging, stump sprouts developing inspring were harvested and cut in 15- to 20-cm-longcuttings. These cuttings were then transplanted infield tests for phenotypic observations and furtherQTL detection. For the time being, only the Q. roburintraspecific cross has been fully exploited for geneticmapping and QTL detection. The clonal test of thefull-sibs has now been planted in three different sites(two near Bordeaux, southwest France, and one nearNancy, northeastern France). The genetic mapping ofQ. robur mapping was done on a sample of 94 off-spring (pedigree 3P × A4), and the QTL detection ona sample of 278 offspring (replicated on average in fivevegetative propagules).

Another mapping initiative for Q. robur was im-plemented in the Netherlands (ALTERRA, Wagenin-gen). This mapping pedigree consists of 101 full-sibs(Bakker 2001). The sibs were screened by paternityanalysis within an open-pollinated progeny set of 397


sibs collected on a single tree located in an urbanarea (Amsterdam). This tree was surrounded by threeother oak trees within a radius of 10 m. One of theseoak trees was selected to be the paternal tree. Pater-nity analysis revealed that 26% of the collected seedswere siredby thismaleparent. The selected seedsweregerminated, grown individually in pots in a nursery(ALTERRA research station, Wageningen, the Nether-lands), and measured for several morphological andphysiological traits during the next 2 years (1999,2000). The objective of this work was quite similarto the French initiative: detection of QTL controllingmorphological and adaptive traits involved in speciesdifferentiation.

American red oaks Genetic mapping in northernredoak(Quercus rubraL.)was initiatedatPurdueUni-versity (http://www.genomics.purdue.edu/forestry/;Romero-Severson 2003) and has continued at theUniversity of Notre Dame. Using exclusion methodsbased on microsatellite polymorphisms (Aldrichet al. 2002, 2003a), a preliminary mapping populationof 97 full-sibs was identified from the open-pollinatedprogeny of a single tree. The most likely male parentmale was the closest conspecific. Recombinationpatterns revealed (Romero-Severson et al. 2003) sixlinkage groups (LGs) of three or more markers.A second acorn harvest from the same female parentyielded 462 full-sibs. The genetic map under con-struction now includes 15 microsatellites, 66 AFLPmarkers from the first set of progeny, and severalhundred new AFLP markers from the second set ofprogeny. All of the potential pollen parents within200 m of the female parent are being genotypedwith all 15 microsatellite markers to eliminate anydoubt over the full-sib status of the mapping popu-lation. The microsatellite markers used for geneticmapping are the same as those used for studieson interspecific gene flow (Aldrich et al. 2003b)and in northern red oak genetic diversity studies.No map has yet been published for Q. rubra. Thelong-term goal of the red oak mapping project is thedetection of QTLs and genes controlling heartwoodcolor and resistance to specific pests, specificallyPhytophthora ramorum, the agent of sudden oakdeath.

Chestnut Mapping InitiativesEuropean chestnut Starting in 1998, a genetic map-ping project for European chestnut (Castanea sativaMill.) was implemented using a full-sib family ob-

tained fromacontrolledcrossperformedbetween twohighly differentiated trees originating from Turkey.Anatolia Peninsula was shown to be an importantregion for chestnut genetic diversity (Villani et al.1991, 1992). As illustrated by these studies, a re-markably high level of genetic, morphological, andphysiological differentiation was observed betweentwo groups of chestnut populations coming fromtwo phytogeographic regions, characterized by strik-ing climatic differences: the Eurosiberian part of thepeninsula in northeastern Anatolia (humid) and theMediterranean region in western Anatolia (xeric).Common field experiments carried out at the ex-perimental field site of Istituto di Biologia Agroam-bientale e Forestale, CNR (Porano, Italy), showedsignificant differences between these populations ingrowth rate, bud flush, and physiological param-eters, related to the water use efficiency, allowing“drought-adapted” and “wet-adapted” ecotypes to beidentified (Lauteri et al. 1997, 1999). Differences ob-served in theecophysiologicalbehavior suggested thatTurkish chestnut populations are genetically adaptedto contrasting environments, making them a suit-able material to study the adaptive potential of thisspecies.

The controlled cross was performed in 1998between a female parent (Bursa) belonging to the“drought-adapted” type from western Turkey anda male parent (Hopa) belonging to the “wet-adapted”type from eastern Turkey. The parental individualswere 9 years old and were chosen according to theirheterozygosity level at isozymes and high degree ofvariation in physiological traits. An F1 full-sib familyof 186 offsprings was obtained, and 96 F1 individualswere used to construct two separate genetic linkagemaps: a female or Bursa map and a male or Hopamap. The main objective of the project was to exploitthe peculiar genetic and adaptive variation observedin these populations in order to identify the genomicregions affecting carbon isotope discrimination(related to the water use efficiency), bud phenology,and growth by means of QTL analysis.

American and Chinese chestnuts During thelast century, American Chestnut, Castanea dentata(Marsch) Borkh, one of the most important timberand nut-producing tree species in eastern NorthAmerica, was dramatically affected by a cankerdisease (chestnut blight) caused by Cryphonectriaparasitica. American chestnut showed low levelsof resistance to blight, whereas Asian chestnut


species (Castanea crenata (Japanese chestnut) andC. mollissima (Chinese chestnut) exhibited higherlevels of resistance to the disease. During the 1980san important backcross breeding program wasundertaken in the USA in order to obtain selectedmaterial combining the blight resistance of Asianchestnut and good timber qualities of Americanchestnut (Burnham et al. 1986).

In this context, ageneticmap for chestnutwascon-structed. The main objective of this mapping projectwas to identify genomic regions involved in blight re-sistance. In addition, the map was also used to lo-cate loci controlling morphological traits that dif-ferentiated both species. The mapping populationwas F2 progeny derived from a cross between twoC. mollissima × C. dentata F1 hybrids. The femaleparent was the C. mollissima cultivar “Mahogany”and two different American chestnut trees from Rox-bury, CT were used as male to create the F1 hy-brids. One hundred and two F2 individuals were usedfor the map construction, and 185 individuals wereassessed for resistance to Cryphonectria parasitica.

Beech Mapping InitiativeA genetic mapping project for European beech (Fa-gus sylvatica) has been implemented at the Universityof Parma (Italy) during the last 10 years (Scalfi et al.2004). The objective was to dissect important adaptivetraits and to identify their underlying QTLs to detectgenomic regions involved in important quantitativetraits such as growth, phenology, and water-use effi-ciency. The mapping pedigree consisted of a full-sibfamily comprising 143 offsprings. The family was thelargest in a 4 × 4 diallel controlled cross performedin 1995 (Ceroni et al. 1997). The parents originatedfrom a natural population located at high altitude innorthern Italy (1,650 m altitude, just below the treeline).

5.2.3Genetic Linkage Mapsfor Quercus, Castanea, and Fagus

Genetic Map of Q. roburThe first Quercus map was published in 1998 onQ. robur (Barreneche et al. 1998) (pedigree 3P ×A4). Using the pseudotestcross mapping strategy, twomaps were constructed comprising 307 markers (271RAPD, 10 SCARs, 18 SSRs, 1 minisatellite, 6 isozymes,and 1 ribosomal DNA marker). Both maps provided

85 to 90% coverage of the Q. robur genome. Segre-gating markers could be aligned in 12 LGs, and themap size amounted to 893.2 cM for the paternal and921.7 cM for the female map. These maps were furtherupgraded by the inclusion of new SSRs (Barrenecheet al. 2004) and additional AFLP and STS. The up-grading is still ongoing and to date 854 markers (271RAPD, 457 AFLP, 10 SCAR, 59 SSR, 49 EST, 1 min-isatellite, 6 isozymes, and 1 ribosomal DNA marker)have been located (Table 2).

The Dutch Q. robur map (pedigree A1 × A2) wasalso constructed using the two-way pseudotestcrossstrategy (Bakker 2001). Two parental maps were firstestablished comprising 18 SSR and 343 AFLP markers.The total lengths of the maternal and paternal mapswere respectively 496 and 566 cM. Thirteen LGs wereobtained (for 12 chromosomes) and the two mapscould be partially merged using 58 “bridge” mark-ers (2 LGs could not be aligned). One of the paternalLGs (LG 13, 27 cM) was highly dissimilar to the otherLGs in terms of marker density. This LG containedalmost half (48%) of all paternal markers and 22% ofthe segregating (heterozygote) markers. This marker-dense LG was homologous to one of the maternal LGsthat remarkably was composed exclusively of 13 seg-regating markers. Congruence of LGs with the Frenchmap was based on the location of SSR markers (Sect.5.3.1). The total map length of the integrated map was659 cM, map density being one marker per 2.4 cM forthe map without taking the exceptionally dense LG 13into account.

Genetic Map of Castanea sativaA first framework of the chestnut genetic linkage mapwas obtained with RAPD and ISSR markers (Casasoliet al. 2001). Few isozyme loci were integrated in thisfirst versionof themap.A total of 381molecularmark-ers segregated in the chestnut full-sib family coveringa good portion of the chestnut genome (more than70%). Intercross segregating markers allowed 11 ofthe 12 LGs identified to be aligned between the femaleand male maps. This original framework was thenused to map AFLP markers and codominant locus-specific markers such as SSR- and EST-derived mark-ers. Table 2 shows the number and type of molecu-lar markers contained in the chestnut genetic linkagemap. At present, 517 molecular markers have beenmapped in chestnut covering 80% of its genome. The12 LGs were aligned to obtain 12 consensus femaleand male LGs (chestnut linkage consensus groups areavailable at the Web site www.pierroton.inra.fr).


Tab

le2.

Sum

mar

yof

gene

tic

linka

gem

aps

ofQ

uerc

usro

bur

and

Cas

tane

asa

tiva

Spec

ies

Num

ber

Tota

l num

ber

RA

PD,

SSR

STS

Isoz

ymes

%of

Tota

lG

enom

eTo

tal

Ref

.(p

edig

ree)

ofLG

sof

mar

ker

ISSR

,di

stor

ted

gene

tic

satu

rati

onsi

zelo

ciA

FLP

mar

kers

dist

ance

(%)

(cM

)(c

M)

Q.r

obur

1285

472

859

616

1895

080

1200

Bar

rene

che

etal

.200

4;(3

P×

A4)

and

this

stud

y

Q.r

obur

1336

134

318

––

17.5

659

6410

35B

akke

r(2

001)

;(A

1×

A2)

and

this

stud

y

C.s

ativ

a12

517

427

3946

510

865

8210

50C

asas

olie

tal.

2001

;(B

ursa

×H

opa)

and

this

stud

y

C.m

ollis

sim

a×

1255

952

129

18

2552

4–

–K

ubis

iak

etal

1997

;C

.den

tata

Sisc

oet

al.2

005

(Mah

ogan

y)

F.sy

lvat

icaa)

12/1

113

8/12

412

8/11

310

/69

–23

844/

971

78/8

210

81/1

185

Scal

fiet

al.2

004;

(44

×45

)Sc

alfi

2005

a)T

hetw

onu

mbe

rsin

dica

ted

corr

espo

ndto

the

map

ofth

efe

mal

ean

dm

ale

pare

nt


Genetic Map of Castanea mollissima/Castanea dentataThe C. mollissima/C. dentata map was the first to bepublished in the Fagaceae (Kubisiak et al. 1997). Atfirst a total of 241 markers, including 8 isozymes, 17RFLPs, 216 RAPDs, were mapped in the F2 family.Twelve LGs were identified, covering a genetic dis-tance of 530.1 cM (corresponding to 75% of the chest-nut genome). To saturate the map, additional mark-ers were recently added to the initial map: 275 AFLP(Clark et al. 2001) and 30 STS (29 SSR and the 5SrDNAlocus) (Siscoet al. 2005).Todate, a total of 559markershave been located. Relatively high levels of segrega-tion distortion (more than 25%) have been reportedin the C. mollissima/C. dentata family. Skewed seg-regation is a common feature in progenies resultingfrom interspecific crosses.

Genetic Map of Fagus sylvaticaThe Fagus genetic linkage map was based on a total of312 markers: 28 RAPDs, 274 AFLPs, and 10 SSRs. Twomaps were constructed using the “double testcross”strategy. In the female map 132 markers were dis-tributed in 12 LGs covering 844 cM. In the male parentonly 11 LGs were identified, resulting in linkage rela-tionships between 119 markers spanning over 971 cM(Table 2). The two maps cover about 78% and 82% ofthe Fagus genome. Using intercross markers (15 AFLPand 2 SSR) seven homologous LGs could be identified(Scalfi et al. 2004). Ten additional EST markers werethen added to the map since its publication (Scalfi2005).

5.3Comparative Mappingbetween Quercus, Castanea,and Fagus

5.3.1Mapping of Microsatellites in Quercus robur,Castanea sativa, C. mollissima, and C. dentata

Microsatellite markers, which are tandemly repeatedunits of 2 to 6 nucleotides evenly dispersed through-out plant genomes, have been sometimes used forcomparative mapping studies (Marques et al. 2002).AmplificationoforthologousSSRmarkers acrossphy-logenetically related species depends largely on evo-lutionary distance and genome complexity of com-

pared species (Powell et al. 1996). Usually, SSR cross-amplification is more efficient between closely relatedspecies with a low proportion of highly repeated se-quences in their genome. Steinkellner et al. (1997)showed that microsatellite markers specifically de-veloped in Quercus species were cross-amplified inchestnut. For these reasons microsatellites were sup-posed to be useful molecular markers for comparingQ. robur and C. sativa genetic linkage maps. To obtainorthologous markers for comparative mapping, SSRmarkers developed both in Quercus species and inC. sativa were therefore tested for cross-amplificationand transferability between these two genera (Bar-reneche et al. 2004 and references therein) in a re-ciprocal way. We tested a total of 83 primer pairs:53 developed in Quercus species and 30 in C. sativa.Primer pairs giving a strong amplification productwere selected for mapping. Nineteen loci, 15 from oakand 4 from chestnut, were integrated into the two pre-viously established genetic maps, allowing the firstcomparative mapping between LGs of the two species(Barreneche et al. 2004). Figure 1 shows the sevenhomeologous LGs identified by orthologous SSR andall microsatellite loci mapped in Q. robur and C. sativagenetic maps. These same SSR loci were used to alignthe European chestnut genetic linkage map with theC. mollissima × C. dentata interspecific map. Elevenof the 12 LGs of the two maps could be associated,nine LGs were aligned on the basis of pairs, triplets,or quadruplets of common markers, while three addi-tional groups were matched using a single SSR marker(Sisco et al. 2005).

Overall, these findings showed that microsatellitemarkers could be cross-transferred between Quer-cus and Castanea genera and be used to recover or-thologous markers for comparative mapping. Never-theless, cross-transferability efficiency was low andthe number of cross-transferred loci was not suffi-cient to link the 12 LGs of the two species. As ex-pected, SSR loci were extremely useful for compar-ative mapping within the same genus (Castanea),but their cross-transferability efficiency decreasedbetween different genera. SSR loci mapped both inQ. robur and C. sativa were sequenced in order todefinitely demonstrate their orthology. Sequencingresults clearly showed that both orthologous and par-alogous loci could be recovered among the SSR cross-transferred between the two genera. Moreover, indelswere sometimes observed within the flanking regionsof the repeated motif. Therefore, although SSR locican be cross-transferred between Quercus and Cas-


Fig.1

.A

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nmen

tbe

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nQ

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base

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orth

olog

ous

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tes.

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reen

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ree

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and

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ight

blue

onri

ght ,

pedi

gree

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igne

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are

nam

edas

inB

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1998

)an

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001)

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ence

from

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are

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ified

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(Voo

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02)


tanea genera, a sequence analysis is needed to demon-strate orthology and to avoid the risk of paralogy.

5.3.2Mapping of EST-Derived Markers in Q. roburand C. sativa: Alignment of the 12 LinkageGroups between the Two Species

Several factors make EST (expressed sequence tag)-derived markers very useful for comparative map-ping studies (Brown et al. 2001). First, ESTs are se-quence fragments of coding regions; therefore se-quence conservation among species is expected tobe higher than that observed, for instance, in SSRloci. Second, ESTs correspond very often to genesof known function. This is of great interest becausesome ESTs colocalized with QTLs in a genetic link-age map may be putative positional candidate genesfor a given trait. Finally, transcriptome analyses giverise to a high number of EST sequences that are thesource of numerous EST-derived markers distributedthroughout plant genomes. In oak, ESTs were devel-oped by Derory et al. (2006) and Porth et al. (2005a).This gave the opportunity to exploit EST sequence in-formation for marker design in order to complete thecomparative mapping between Q. robur and C. sativa(Casasoli et al. 2006). About 100 EST sequences wereselected from oak databases. Oak sequences werealigned with homologous sequences obtained fromGenBank in order to design primer pairs for am-plification in the most conserved regions of the se-quence and assure a good cross-amplification effi-ciency in chestnut. A total of 82 primer pairs were de-signed. A proportion of about 70% produced by PCRa single and strong band both in oak and chestnutand 51 and 45 ESTs were mapped in oak and chest-nut, respectively, using single strand conformationpolymorphism (SSCP) and denaturing gradient gelelectrophoresis (DGGE) approaches (Casasoli et al.2006). These EST-derived markers, together with SSRmarkers previously mapped, provided 55 orthologousmolecular markers that allowed the 12 LGs of Q. roburand C. sativa to be aligned. As shown in Fig. 2, from2 to 7 common orthologous markers were mappedin the 12 homeologous pairs of LGs. Macrosyntenyand macrocollinearity were well conserved betweenthe two species. Few inversions, probably due to map-ping errors, were observed. Although these data arestill preliminary given the low number of common

molecular markers mapped in the two species, nomajor chromosomal rearrangements have been iden-tified, suggesting that oak and chestnut genomes arequite stable. Thus it appears likely that the “singlegenetic system” model of the grass genomes (Galeand Devos 1998) can also be applied to Q. robur andC. sativa. EST-derived markers were very easily trans-ferred from oak to chestnut. About 50% of them con-tained intron-derived sequences. This increased theprobability of detecting segregating polymorphismsuseful for mapping in both oak and chestnut full-sibfamilies. These markers proved to be ideal markersfor comparative mapping within the Fagaceae family.

5.3.3Mapping of Microsatellites and EST-DerivedMarkers in Fagus sylvatica, Quercus robur,and Castanea sativa

Success of transferability between Fagus sylvatica,Quercus robur, and Castanea sativa was lower. Al-though 86 SSR markers originally developed in otherFagaceae species were tested in Fagus (66 from Quer-cus, 20 from Castanea), only seven produced an inter-pretable banding pattern and only one marker fromQ. rubra and one from C. sativa could be placed onthe beech map (Scalfi 2005). One marker originallydeveloped in Fagus gave good amplification also inQuercus and Castanea but was monomorphic in thecrosses used for these species.

Similarly, 86 EST markers originally developed inQuercus were tested in beech, 46 coming from a bud-burst c-DNA library (Derory et al. 2006), 22 from os-motic stress response (Porth et al. 2005a), and 17 fromhypoxia response cDNA-AFLP markers (C. Bodénèsunpublished results). The success rate was higher thanfor microsatellites. In total 16 were polymorphic usingvarious techniques (SSCP, DGGE, sequencing, CAPS,dCAPS), and 10 were finally mapped onto the beechmap (Scalfi 2005).

Two markers (1T11 and 1T62) that mapped onQuercusandCastaneaonLG10 (Table4)weremappedalso on group 4 in Fagus with the help of a “bridge”marker (1T41): this can be considered as evidenceof synteny between LG10 of Q. robur (3P*A4) andC. sativa with LG 4 of Fagus. For the two markersthe sequence homology of Fagus with Quercus was82% and 43%, respectively; the lower value is due toa large insertion in the beech sequence that was notpresent in the cDNA of Quercus. Eliminating the gap,


Fig.2

.C

ompa

rati

vem

appi

ngbe

twee

nQ

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uran

dC

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iva.

The

12ho

meo

logo

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nQ

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ur(Q

, gre

en,p

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the homology increased to 92%. Synteny could notbe assessed for any other group since none had morethan one marker mapped on it. For example, marker2T32 mapped on LG 2 in Quercus was found linkedto markers on LG 7-F in beech, but more than onecomapping marker is needed to establish synteny.

5.3.4Assignment of Linkage GroupsBetween Quercus and Castanea

Most genetic maps constructed within Fagaceaespecies comprised 12 LGs (Table 2). Cross-transferable SSR- and EST-derived markers madeit possible to assign LGs among the four speciesQ. robur, C. sativa, C. mollissima, and C. dentata(Table 4). However, the assignment is still based ona limited number of markers per LG. Assignment wasdone by pairwise comparisons:

– Between the two Q. robur maps (3P × A4 and A1 ×A2): 11 out of 12 LGs had at least two orthologousSSRs in common; the remaining LG was assignedby default.

– Between Q. robur (3P × A4) and C. sativa (Bursa× Hopa): between two and seven pairs of commonmarkers (either SSR, isozymes, or EST) allowed theLGs to be assigned.

– Between the two chestnut maps (Bursa × Hopaand Mahogany): the assignment is still incompleteas only nine LGs could be assigned so far by at leasttwo pairs of SSRs.

The results obtained so far need to be confirmed byfurther mapping experiments, based mainly on ESTmarkers. They are also encouraging as suggested bythe conservation of the macrosynteny and macro-collinearity that have so far been observed betweenthe two most intensively studied species: Q. robur andC. sativa.

5.4Genes Mapped in Oaks and Chestnut

Transcriptomic investigations and differential geneexpression studies were implemented recently withthe main aim of identifying genes that are involved inthe adaptation of oak or chestnut trees to their envi-ronment.Geneexpressionwasmonitored fordifferenttraits, or tissues:

– Bud burst in oaks– Hypoxia in oaks– Osmotic stress in oaks– Juvenile and mature shoots in oaks– Blight infection in chestnuts

Various techniques were implemented for con-structing expression profiles: cDNA-AFLP, SSH, andQuantitative RT-PCR. We will briefly summarize theexperiments conducted and the functions of genesthat were identified. Table 3 provides a list of EST-derived markers mapped in Q. robur and C. sativa.For each EST the accession number, amplification, se-quencing, and mapping results are reported.

5.4.1Bud Burst

Candidate genes for bud burst were identified in Q. pe-traea using SSH libraries, macroarray experiments,andRT-PCR.Three subtracted libraries (SSHmethod)were constructed, generating 801 ESTs derived fromsix developmental stages of bud burst. Expression pat-terns of these transcripts were monitored in apicalbuds during bud flushing in order to identify genesdifferentially expressed between the quiescent and ac-tive stage of bud development. After bioinformaticprocessing of the ESTs, macroarray experiments re-vealed a total of 233 unique transcripts exhibiting dif-ferential expression during the process, and a puta-tive function was assigned to 65% of them (Deroryet al. 2006). Cell rescue/defense-, metabolism-, pro-tein synthesis-, cell cycle-, and transcription-relatedtranscripts were among the most regulated genes. Re-verse northern and RT-PCR showed that several genesexhibited contrasting expression between quiescentand swelling buds. Among this set of 233 unique tran-scripts, ca. 100 were selected and tentatively amplifiedand mapped in oak and chestnut, as previously de-scribed. In oak and chestnut, 51 and 45 ESTs were suc-cessfully mapped, respectively, using SSCP and DGGEapproaches (Casasoli et al. 2006).

5.4.2Hypoxia

Q. robur and Q. petraea exhibit different responsesto hypoxia, the first one being more tolerant to wa-terlogged conditions. Hypoxia-induced genes wereidentified from vegetative copies of the two species


Tab

le3.

List

ofge

nes

map

ped

inbo

thQ

uerc

usro

bur

and

Cas

tane

asa

tiva

EST

Acc

essi

onR

efer

ence

for

prim

erEx

pect

edO

bser

ved

Iden

tity

cLi

nkag

eFu

ncti

onal

Nam

eanu

mbe

rse

quen

ces

and

size

(bp)

bsi

zegr

oup

cate

gory

PCR

prot

ocol

sQ

–C

d

1T11

CF3

6926

3Po

rth

etal

.200

5b55

555

599

10–

10U

nkno

wn

1T12

CF3

6926

4Po

rth

etal

.200

5b52

252

293

.5∗

nm–

3M

etab

olis

m1T

21C

F369

266

Port

het

al.2

005b

338

770

941

–6

Prot

ein

synt

hesi

s1T

25C

F369

268

Port

het

al.2

005b

187

187

97.2

6–

11U

nkno

wn

1T57

CF3

6927

3Po

rth

etal

.200

5b28

228

293

.54

–2

Tran

scri

ptio

n1T

62C

F369

274

Port

het

al.2

005b

346

705Q

91.9

10–

10M

etab

olis

m60

0C2T

11C

F369

278

Port

het

al.2

005b

397

397Q

89∗

nm–

8C

ellr

escu

e,de

fens

ean

dvi

rule

nce

500C

2T3

CF3

6928

3Po

rth

etal

.200

5b28

463

094

.310

–10

Unc

lass

ified

(pla

sma

mem

bran

ere

late

d?)

2T13

CF3

6928

0Po

rth

etal

.200

5b33

433

494

.511

–3

Met

abol

ism

2T32

CF3

6928

4Po

rth

etal

.200

5b38

660

8Q93

2–

1Pr

otei

nsy

nthe

sis

500C

01A

03C

R62

7501

Cas

asol

ieta

l.20

0638

250

094

.37

–5

Prot

ein

synt

hesi

s1,

00E+

07C

R62

7526

Cas

asol

ieta

l.20

0614

540

0Q86

5d

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T ran

scri

p tio

n14

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F02

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6275

66C

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tal.

2006

164

164

86.3

1–

6Tr

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02G

03C

R62

7575

Cas

asol

ieta

l.20

0620

670

090

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Hyp

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tica

lpro

tein

06B

07C

R62

7724

Cas

asol

ieta

l.20

0630

730

793

.38

–7

Hyp

othe

tica

lpro

tein

6,00

E+10

CR

6277

45C

asas

olie

tal.

2006

333

700

94.5

12–

12Pr

otei

nw

ith

bind

ing

func

tion

orco

fact

orre

quir

emen

t07

°08

CR

6277

71C

asas

olie

tal.

2006

341

700

86.1

∗2

–n i

Hyp

oth e

tica

lpro

tein

07°0

9C

R92

6157

Cas

asol

ieta

l.20

0625

230

095

.75

–4

Cel

lula

rtr

ansp

ort

07B

10C

R62

7781

Der

ory

etal

.200

636

036

08

–na

Tran

scri

ptio

n07

C03

CR

6277

85C

asas

olie

tal.

2006

285

285

96.6

∗ni

–4

Hyp

othe

tica

lpro

tein

aST

Ss08

A01

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10,0

8B04

,Con

s86

,and

08G

04ha

veno

tbee

nse

quen

ced.

bT

heex

pect

edsi

zes

wer

eba

sed

onth

ekn

owle

dge

ofth

eES

Tse

q uen

cean

dpr

imer

desi

gn.T

heob

serv

edsi

zes

wer

eap

prox

imat

ebe

caus

eba

sed

onan

elec

trop

h ore

sis

onag

aros

ege

l.T

heun

map

ped

ampl

ified

ESTs

wer

eei

ther

noni

nfor

mat

ive

orm

appi

ngm

etho

ds(S

SCP

orD

GG

E)ha

veno

tbee

nsu

cces

sful

lyop

tim

ized

.c

Exc

eptf

or01

E07

and

Con

s12

9,al

lST

Sse

quen

ces

mat

ched

the

sam

ege

nein

both

spec

ies

usin

ga

BLA

STX

proc

edur

e.If

STS

was

map

ped

orse

quen

cew

asav

aila

b le

for

only

one

spec

ies,

alig

nmen

thas

been

done

wit

hth

eor

igin

aloa

kES

T(∗

).In

5ca

ses,

sequ

ence

reac

tion

did

notw

ork

(-).

dW

eus

eda

LOD

thre

shol

d³6

.0to

map

STS,

exce

ptfo

rtho

sem

arke

dw

ith

e,fo

rwhi

ch4.

0<

LOD

scor

e<

6.0.

ni:n

onin

form

ativ

e;nm

:non

map

ped.

Q-C

:Que

rcus

robu

r(3P

×A

4)-C

asta

nea

sati

va(B

ursa

×H

opa)


Tab

le3.

(con

tinu

ed)

EST

Acc

essi

onR

efer

ence

for

prim

erEx

pect

edO

bser

ved

Iden

tity

cLi

nkag

eFu

ncti

onal

Nam

eanu

mbe

rse

quen

ces

and

size

(bp)

bsi

zegr

oup

cate

gory

PCR

prot

ocol

sQ

–C

d

08°0

1C

R62

7918

Der

ory

etal

. 200

621

050

03

d–

nmM

etab

olis

m08

°03

CR

6279

20C

asas

olie

tal.

2006

454

454

94.1

∗12

d–

niPr

otei

nw

ith

bind

ing

func

tion

orco

fact

orre

quir

emen

t08

B04

CR

6279

33D

eror

yet

al.2

006

327

327

9–

nmM

etab

olis

m08

C05

CR

6279

43C

asas

olie

tal.

2006

213

213

95.5

∗2

–ni

Hyp

othe

t ica

l pro

t ei n

08C

11C

R62

7947

Der

ory

etal

.200

631

631

694

.42

–1

Hyp

othe

tica

lpro

tein

08D

11C

R62

7958

Cas

asol

ieta

l.20

0634

370

088

.4∗

11–

3M

etab

olis

m08

G04

CR

6279

86D

eror

yet

al.2

006

393

1000

11–

naH

ypot

heti

calp

rote

inC

ons

13C

R62

7506

Cas

asol

ieta

l.20

0630

130

189

.5–9

3.1

1–

6Tr

ansc

ript

ion

Con

s14

CR

6275

08C

asas

olie

tal.

2006

243

1200

–5

–4

Prot

ein

synt

hesi

sC

ons

19C

R62

7517

Cas

asol

ieta

l.20

0617

830

081

.39

–2/

4Pr

otei

nsy

nthe

sis

Con

s21

CR

6275

23C

asas

olie

tal.

2006

333

550

89.3

2–

1Pr

otei

nsy

nthe

sis

Con

s30

CR

6275

41C

asas

olie

tal.

2006

424

1400

Q93

.14

–2

Hyp

othe

tica

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ons

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6277

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61C

R62

7776

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l.20

0626

016

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Cel

lres

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and

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sing

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s74

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6278

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tal.

2006

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137

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9C

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s75

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6279

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tal.

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600

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8M

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08

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8018

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asol

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ons

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6278

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tal.

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250

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calp

rote

in


Tab

le3.

(con

tinu

ed)

EST

Acc

essi

onR

efer

ence

for

prim

erEx

pect

edO

bser

ved

Iden

tity

cLi

nkag

eFu

ncti

onal

Nam

eanu

mbe

rse

quen

ces

and

size

(bp)

bsi

zegr

oup

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gory

PCR

prot

ocol

sQ

–C

d

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s10

5C

R62

7826

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asol

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560

095

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abol

ism

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s10

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7828

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ergy

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s10

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ffer

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atio

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109

CR

6278

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194

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921

992

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ism

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7837

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0621

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rote

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0CC

ons

112

CR

6278

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171

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scri

pti o

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8014

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928

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ctor

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ent

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8019

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asol

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012

0–

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niE

nerg

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ons

129

CR

6280

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210

500

79.6

9d

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lres

cue,

defe

nse,

and

viru

lenc

eC

ons

130

CR

6282

41C

asas

olie

tal.

2006

190

190

91.9

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–n i

En e

rgy

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s13

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8167

Cas

asol

ieta

l.20

0611

520

010

0∗ni

–1

Hyp

othe

tica

lpro

tein


Table 4. Homologous linkage groups (LGs) in genetic maps of Quercus robur, Castanea sativa, and C. mollissima/C. dentata

LG in Quercus robur a LG in Quercus robur b LG in Castanea sativa c LG in C. mollissima/C. dentata d

Pedigree 3P × A4 Pedigree A1 × A2 Pedigree Bursa × Hopa Pedigree Mahogany

1 1 6 H2 2 1 A3∗ 11∗ 8 C4 4 2 K5 5 4 E6 6 11∗∗ B∗∗7 7 5 I8 10 7 F9 8 9 L

10 3 10 D11 9 3 G12 12 12 J

Assignment of linkage groups was made by comparison within the following pairs: 3P × A4 and A1 × A2, 3P × A4 and Bursa ×Hopa, Bursa × Hopa and Mahogany.∗) LG 3 in (3P × A4) and 11 in (A1 × A2) assigned by “default” (all other 11 LGs being assigned by at least 2 markers present ineach species)∗∗) LG 11 in (Bursa × Hopa) and B in (Mahogany) assigned by “default” (all other 11 linkage group being assigned by at leasttwo markers present in each species, except for pairs 7-F and 3-G where only one marker was common).)The numbers or letters of linkage groups (LG) correspond to the following publications:a Barreneche et al. (1998); Barreneche et al. (2004)b Bakker (2001) and this studycCasasoli et al. (2001)d Kubisiak et al. (1997); Sisco et al. (2005)

grown in hydroponic conditions. Gene expression wasmonitored in seedlings raised under reduced oxygen(3%) applied for 24 h. RNA was extracted from roottips before (0 h time stress) and after oxygen reduc-tion, following the protocol of Chang et al. (1993).Stress induction was validated by measuring alcoholdehydrogenase activity. Differentially expressed frag-ments were obtained by cDNA-AFLP, and 170 weresequenced and compared to databanks (C. Bodénèsunpublished results).

5.4.3Osmotic Stress

Osmotic stress induced genes were identified ina Q. petraea cell line grown under moderate stress(Porth et al. 2005a). Two subtraction libraries (SSHmethod) were established from callus cell cultures ex-posed to hyperosmotic stress for 1 h (indicated as 1T)and 2 d (2T), respectively. The differentially expressedESTs were classified according to their putative func-tions. At least five of these gene products were

assumed to be targets for stress tolerance in oak, e.g.,betaine aldehyde dehydrogenase, two trans-actingtranscription factors (one ABA-responsive, the otherABA-independent), a glutathione-S-transferase, anda heat shock cognate protein.

Seven genes were selected, based on their puta-tive functions, to monitor their expression in vivo.Leaf tissue from hyperosmotically grown Q. petraeaand Q. robur plantlets was harvested and investigatedby RT-PCR at time intervals of 1, 6, 24, and 72 h. In-dications of stress adaptation were found in Q. pe-traea based on up-regulation of certain genes relatedto protective functions, whereas in Q. robur down-regulation of those genes was evident (Porth et al.2005a).

Segregating osmo-regulated loci were mapped toten different LGs of Quercus (Porth et al. 2005b).By using orthologous primers, ten of the loci, in-cluding the four putatively water-stress tolerance re-lated genes (1T57, 1T62, 2T11, and 2T13), were suc-cessfully amplified in C. sativa. Sequence analysisshowed an identity of at least 90% (Table 3) withQuercus.


5.4.4Differential Expression in Juvenileand Mature Oak Shoots

A gene named QRCPE (Quercus robur crown pref-erentially expressed) that is differentially expressedbetween mature and juvenilelike shoots was recentlydiscovered in oaks (Gil et al. 2003). QRPCE accumu-lates in ontogenetically older organs of oak trees, al-though it is present in zygotic and somatic embryosbut absent in callus cells. The encodedprotein is small,contains a predicted N-terminal hydrophobic signalpeptide that targets the protein to the cell wall, and isrich in glycine and histidine residues. In C. sativa, theQRCPE homolog is also expressed at different levelsbetween adult and juvenilelike tissues.

5.4.5Blight Infection in Chestnut

A cDNA clone showed similarity to a gene previouslyidentified as encoding a cystatin. A protein shown tohave antifungal activity in C. sativa (Pernas et al. 1998,1999) was isolated from a cDNA library from stem tis-sues of C. dentata (Connors et al. 2001). The expres-sion of this gene was verified by RT-PCR in healthyand diseased tissues of American chestnut (Connorset al. 2002). Amplification of a fragment of the genein American and Chinese chestnuts and comparisonof the sequences of the cloned amplification productsrevealed differences within the intron (SNPs or dele-tion). These differences could be used to locate thecystatin gene on the map of C. mollissima/C. dentataand to verify its putative colocalization with QTLsinvolved in blight resistance (Connors et al. 2002).However, cystatin did not map to any region knownto be involved in resistance to chestnut blight.

5.5QTL Detection

5.5.1Phenotypic Traits Investigated

A common objective in genetic mapping in oak, chest-nut, and beech is the detection of QTLs for adaptivetraits, e.g., phenotypic traits that respond strongly tonatural selection, and particularly to abiotic or bioticstresses. The interest in these traits lies in the issues

raised by global change and the capacity of trees to re-spond to these challenges (Parmesan and Yohe 2003).This capacity depends on the level of genetic diversityfor these traits and their underlying genes in naturalpopulations. Knowledge of the genetic architectureof these traits (number and distribution of QTLs) istherefore of primary importance and has motivatedresearch in QTL in conifers as well (Sewell and Neale2000; van Buijtenen 2001).

In European oak, chestnut, and beech, the geneticcontrol of three different adaptive traits, bud phenol-ogy, growth, and carbon isotope discrimination, werestudied using a QTL approach (Casasoli et al. 2004;Scalfi et al. 2004; Scotti-Saintagne et al. 2004; Bren-del et al. 2007). Bud phenology, growth, and carbonisotope discrimination (delta or ∆, which providesan indirect measure of plant water-use efficiency)are adaptive traits that show great phenotypic vari-ation in natural populations of forest trees (Zhangand Marshall 1995; Tognetti et al. 1997; Lauteri et al.1999; Hurme et al. 2000; Jermstad et al. 2001). Initi-ation and cessation of the growing seasons, definedthrough bud flush and bud set timing, have profoundimplications for adaptation of perennial plants to coldwinter temperatures. Early flushing genotypes mightbe susceptible to spring frost damage. Likewise, budset timing is related to the fall cold acclimation (Howeet al. 2000). Growth traits, such as annual height anddiameter increments, are important components ofplant vigor and biomass production, and they areprofoundly influenced by abiotic and biotic stress oc-currences during the growing season. In addition,they are relevant characteristics from an economicpoint of view and are often evaluated in breeding pro-grams (Bradshaw and Stettler 1995). Carbon isotopediscrimination (∆) is a parameter related to the iso-topic fractionation of carbon stable isotopes duringthe photosynthetic process (for review see Farquharet al. 1989; Brugnoli and Farquhar 2000). Plant mate-rial is always enriched in 13C with respect to the iso-topic composition (δ13C) of atmospheric CO2. This isparticularly evident in C3 plants where the fraction-ation effect mostly occurs during CO2 diffusion fromoutside the leaf to the carboxylation sites into thechloroplasts, and during the carboxylation by ribu-lose 1,5-bisphosphate (RuBP) carboxylase. Due to itsrelationships with the diffusional path of photosyn-thetic gas exchange (for both CO2 and water vaporin reverse directions) and with the photosyntheticsubstrate demand (CO2 fixation by RuBP carboxyla-tion activity), ∆ has been theoretically predicted and


empirically demonstrated to be inversely related toplant water-use efficiency (roughly the ratio of carbongain to water losses; for deeper insights see Farquharet al. 1989; Brugnoli and Farquhar 2000). Despite thecomplexity of this trait, significant heritabilities andlow genotype × environment interactions have beenfound for ∆ in crop species (Hall et al. 1994) encour-aging the use of this parameter for breeding pur-poses.

5.5.2Strategies and Methods Used for QTL Detection

In forest trees, QTLs for several traits have beendetected, clearly showing the usefulness of this ap-proach to dissect genomic regions controlling com-plex traits (Sewell and Neale 2000). With few excep-tions (Brown et al. 2003; Jermstad et al. 2003), thesize of segregating populations used in these stud-ies is often small (150 to 200 individuals). Amongfactors influencing QTL detection power, small sam-ple sizes and low trait heritability were shown tocause an overestimation of QTL effects and the un-derestimation of QTL number and to hamper de-tection of QTLs with low effects (Beavis 1995). Forthese reasons, a single QTL detection experiment doesnot give an exhaustive idea of the genetic architec-ture of a quantitative trait. One possible strategy toovercome these difficulties is to detect QTLs severaltimes across different environments and developmen-tal stages. In this way, environmental and temporalstability of QTLs can be verified and a more completepicture of genetic architecture of the complex traitcan be drawn. Moreover, comparative QTL mappingbetween phylogenetically related species offers an im-portant tool to validate QTLs from the evolutionarypoint of view. In oak and chestnut, a QTL-detectionstrategy based on multiple experiments across dif-ferent environments and years has been performedto give an idea, as much as possible, of the com-plete genetic architecture of adaptive traits in bothspecies. Afterwards, comparative QTL mapping forthe three adaptive traits studied was carried out be-tween the two species in order to identify genomic re-gions conserved through evolution controlling thesetraits.

In oak, QTL detection was done in both the French(3P*A4) and Dutch (A1*A2) Q. robur mapping pedi-grees. In the French studies, phenotypic assessmentswere done over successive years using a clonal test

planted with the vegetative copies of the full-sibs be-longing to the pedigree. The phenotypic data ob-tained so far all originate from the two plantationsinstalled in the southwest of France. The assessmentsfirst addressed the same three major adaptive traits asfor chestnut: phenology, growth, and carbon isotopediscrimination (Scotti-Saintagne et al. 2004; Brendelet al. 2007). In addition, leaf morphology characters(Saintagne et al. 2004) and the ability for vegetative re-production by cutting propagation (Scotti-Saintagneet al. 2005) were assessed. The focus on leaf morpho-logical traits is related to their use in species discrim-ination as shown by previous analyses (Kremer et al.2002). The Dutch study focused on QTL detection formorphological and growth characters in one specificfull-sib cross that was grown for two successive yearsin a nursery (Bakker 2001).

In European chestnut (C. sativa), bud flush,growth, and carbon isotope discrimination mea-surements were performed for 3 years: 2000, 2001,and 2002, corresponding to the growing seasons2, 3, and 4 since seed germination. Bud set timingwas scored only in 2002. During the three years,plants were grown in central Italy (Istituto diBiologia Agroambientale e Forestale, CNR, Porano,central Italy, 42◦ 43′ latitude, 500 m elevation)as previously reported. Details about phenotypicmeasurements are reported in Casasoli et al. (2004)and in Table 5.

In American (C. dentata) and Asian (C. mollis-sima) chestnut, the blight resistance response of F2

progeny was assessed by using the agar-disk cork-borer method (Griffin et al. 1983). During the grow-ing season, each F2 individual was inoculated with twodifferent strains of Cryphonectria parasitica. Cankerevaluations were made over two successive months.The mean canker sizes in each month for each iso-late were used as relative measures of resistance.The degree of association between marker loci andblight resistance trait was investigated using succes-sively single-locus or nonsimultaneous analysis ofvariance (ANOVA) models and multiple marker orsimultaneous analysis of variance (ANOVA) mod-els (Kubisiak et al. 1997). In European beech (F. syl-vatica), leaf traits (size and shape) were assessedover 2 years, whereas growth and carbon isotopediscrimination measurements were done only oneyear.

The MultiQTL software (Britvin et al. 2001,http://esti.haifa.ac.il/∼poptheor) was used for QTLdetection both in oak and chestnut in the French


Tab

le5.

QT

Lda

tain

oak,

ches

tnut

,and

beec

h

Trai

tSp

ecie

sPe

digr

eeN

umbe

rof

Num

ber

offie

ldG

row

ing

Her

itab

ility

Num

ber

Ran

geof

(ref

eren

ce)

offs

prin

gspl

anta

tion

sse

ason

(s)

whe

nor

repe

atab

ility

ofQ

TLs

vari

atio

nof

phen

otyp

edw

here

the

trai

tth

etr

ait

dete

cted

(f)

PEV

was

asse

ssed

was

asse

ssed

(min

imum

–m

axim

um)

Hei

ghtg

row

thQ

uerc

usro

bur

(a)

3P×

A4

207

14

a0.

14–0

.23

c5

9.5–

18.7

Que

rcus

robu

r(b

)A

1×

A2

101

12

–1

31.2

Cas

tane

asa

tiva

(c)

Burs

a×

Hop

a13

5–15

31

3–

67.

0–17

.0Fa

gus

sylv

atic

a(d

)44

×45

118

11

–0

–Bu

dbu

rst

Que

rcus

robu

r(a

)3P

×A

417

4–27

83

8,4,

50.

15–0

.52

c12

3.1–

10.7

Que

rcus

robu

r(b)

A1

×A

210

11

1–

2Tr

aitn

otno

rmal

lydi

stri

bute

d.N

omin

alsc

ale

of1–

5C

asta

nea

sati

va(c

)Bu

rsa

×H

opa

150–

174

13

–9

6.3–

12.2

Fagu

ssy

lvat

ica

(d)

44×

4512

41

1–

127

.3D

elta

Que

rcus

robu

r(e

)3P

×A

412

1–20

72

4,5,

50.

32–0

.80

c5

4.4–

34.4

Cas

tane

asa

tiva

(c)

Burs

a×

Hop

a15

2–15

51

3–

75.

7–13

.2Fa

gus

sylv

atic

a(d

)44

×45

102

11

–0

–Bu

dse

tC

asta

nea

sati

va(c

)Bu

rsa

×H

opa

151

11

–3

8.9–

17.1

Dia

met

erQ

uerc

usro

bur

(b)

A1

×A

210

11

2–

–N

osi

gnifi

cant

grow

thQ

TL

dete

cted

Cas

tane

asa

tiva

(c)

Burs

a×

Hop

a13

6–15

31

3–

45.

9–10

.3

(a)Sc

otti

-Sai

ntag

neet

al.(

2004

)(b

)B

akke

r(2

001)

(c)C

asas

olie

tal.

(200

4)(d

)Sc

alfi

etal

.(20

04)

(e)B

rend

elet

al.(

2007

)(f

)Q

TL

dete

cted

atp

<0.

05at

the

geno

me

leve

l


Fig. 3. Comparative QTL mapping between Q. robur and C.sativa (from Casasoli et al. 2006). Homeologous LGs between Q. robur(Q) and C. sativa (C) are named and ordered as in Figs. 1 and 2. Orthologous markers are linked by dotted lines. Commonintervals between the two genomes identified by orthologous markers are filled with corresponding backgrounds in both oak andchestnut LGs. The figure was drawn using MapChart software (Voorrips 2002). Each QTL is represented on the right of the LGby its confidence interval (95% confidence intervals, black line) and the most probable position (Casasoli et al. 2006). QTLs weredetected for three different phenotypic traits on the male (m) and female map (f): bud burst (Bud), total height (H), and carbonisotope discrimination (∆). The phenotypic traits were observed over three seasons (indicated by subscripts 1 to 3). In oak, thedate of bud burst was assessed as the date when the apical bud flushed. In chestnut, bud burst was assessed in two different ways:A = date of first observed unfolded leaf of a tree; B = date when 70% of buds showed an unfolded leaf BudA2f: QTL for bud burstassessed with method A during season 2 in female map. H2m: QTL for total height measured at season 2 and located on malemap. ∆3f: QTL for carbon isotope discrimination assessed during season 3 and located on female map


Fig. 3. (continued)

studies. This software was chosen for severalreasons. First, the composite interval mappingwas available (CIM, Jansen and Stam 1994; Zeng1994); second, QTL significance thresholds couldbe computed by permutation (Churchill and Do-erge 1994); and finally, confidence intervals forQTL position could be estimated by bootstrap(Visscher et al. 1996). The same statistical analysis

was performed in oak and chestnut; the details arereported in Casasoli et al. (2004) and Scotti-Saintagneet al. (2004). The Dutch study used MapQTL 4.0(http://www.kyazma.nl/index.php/mc.MapQTL) forQTL detection. For beech, QTL Cartographer 1.12(Basten et al. 1994, 2002) and MultiQTL (Britvin et al.2001) softwares were used to detect QTLs (Scalfi et al.2004)


5.5.3Number and Distributionof QTLs and Their Effects

The results of QTL detection are extremely heteroge-neous across pedigrees and species. The survey of theresults has been limited to traits that were assessed inat least two species (Table 5). Heterogeneity amongthe results is most likely related to the reduced sizeof the mapping pedigree. As already mentioned, withon average less than 200 sibs per pedigree, the contri-bution of a given QTL to the phenotypic variance ofthe trait is usually overestimated and exhibits a largesample variance (Beavis 1995). However, because thesampling efforts were similar and the phenotypic as-sessments were the same, a comparative analysis ofQTL detection could be done between Q. robur andC. sativa.

The alignment of the 12 Q. robur and C. sativa LGsgives rise to a logical framework defined by commonorthologous markers for comparing QTL location be-tween the two species. Figure 3 shows the alignmentof the 12 LGs, the common genomic regions identifiedby orthologous markers, and QTLs compared in oakand chestnut. Details about the definition of commongenomic intervals and corresponding unique QTLsbetween the two species (i.e., more individual QTLsdetected several times in the same genomic region ina single species) are reported in Casasoli et al. (2006).A total number of 34 common intervals were iden-tified between the oak and chestnut genetic linkagemaps thanks to the orthologous markers. Followingthe previously described criteria to declare uniqueQTLs, 13 and 10 unique QTLs were identified for tim-ing of bud burst, 5 and 7 unique QTLs were identifiedfor carbon isotope discrimination, and, finally, 5 and6 unique QTLs for height growth were identified inoak and chestnut, respectively (Fig. 3). Among theseunique QTLs, nine controlling timing of bud burst andtwocontrollingheight growth were colocatedbetweenthe two species. No QTL involved in carbon isotopediscrimination was colocated in the oak and chestnutmap. Following Lin et al. (1995), the probability ofobtaining these colocations by chance is p = 0.0002 inthe case of timing of bud burst and p = 0.20 in the caseof height growth. When QTL number and effects werecompared for the three traits between the two species,a similar genetic architecture was observed for adap-tive traits in oak and chestnut (Casasoli et al. 2006).Fromthis simple comparison itwas clear that adaptivetraits are controlled by more loci of low and moderate

than large effect in both species. Timing of bud flushwas the trait showing the higher number of detectedand stable QTLs. Despite this similar genetic architec-ture, most of the QTLs for bud flush were conserved,whereas only a few QTLs were conserved for heightgrowth, and none for carbon isotope discrimination.The different conservation of QTLs may be explainedtaking into account differences for the three adaptivetraits investigated in trait heritability values, QTL sta-bility across experiments, and QTL-by-environmentinteractions. The striking conservation of QTLs forbud flush is very interesting from an evolutionarypoint of view. Although correspondence of QTLs doesnot imply correspondence of genes underlying theQTLs, as already reported in other species (Doustet al. 2004), these findings showed that loci controllingbud flush have remained highly polymorphic in bothspecies. This high polymorphism of loci controllingbud flush, despite strong natural selection acting onthis adaptive trait, may be explained with selectionpressures able to maintain diversity over long evo-lutionary times (balancing, disruptive, or frequency-dependent selection) as discussed in Casasoli et al.(2006).

5.6Conclusion

Mapping experiments in Fagaceae were hampered byvarious biological constraints that have limited re-search activities in this field. First, for most species,it was not possible to find adequate F2 pedigreesthat would allow us to screen the genome for QTLsof interest. This is somehow compensated by thehigh level of within-population diversity, which wouldallow segregation for QTLs of interest in F1 pedi-grees as well. Second, controlled crosses to obtainmapping F1 pedigrees has been challenging in thesespecies, and alternatives based on open-pollinatedprogeny screening using parentage analysis were im-plemented. Third, the development of mapping ac-tivities was restrained by the limited genomic re-sources available (genetic markers, ESTs) within thisgroup of species. Despite these limitations, impor-tant progress has been made in the recent years asa result of international cooperation. Maps have beendeveloped for each economically important genus(Quercus, Castanea, and Fagus), and the ongoing ac-tivities in comparative mapping suggest that there


is a strong macrosynteny between phylogeneticallyclose genera (Quercus and Castanea). For some traits,e.g., bud burst, there is even a strong conservationof the QTL position between the two genera. Ex-tension of comparative mapping to Fagus might bemore problematic as illustrated by difficulties de-scribed in this review. However, comparative map-ping should be much easier with Lithocarpus andCastanopsis, as these genera are close to Quercusand Castanea. Furthermore, the genome of the Fa-gaceae is of small enough size (e.g., only 3.5 to 6times larger than Arabidopsis) to make comparativegenomics easily applicable to this family. These expec-tations should enhance research activities in geneticswithin a large group of ecologically and economicallyimportant species growing throughout the northernhemisphere.

Acknowledgement. The construction of the genetic linkagemaps in European species Quercus robur, Castanea sativa andFagus sylvatica was carried out with the financial support of theEuropean Commission, DG Research (OAKFLOW, QLK5-2000-00960 for oaks; CASCADE, EVK2-1999-00065P for chestnut,and DYNABEECH, QLRT-1999-01210 for beech). The study onthe Dutch Q. robur map was carried out with financial supportfrom Programm 381 Functions of Nature, Forest and Landscapeof the Dutch Ministry of Agriculture, Nature and Food quality.Jeremy Derory received a PhD grant from INRA to develop theEST used for the comparative mapping between Quercus andCastanea. The authors are grateful to Scott Schlarbaum (Uni-versity of Tennessee-Knoxville) for providing the material forthe genome size determination of different Fagaceae species,to Preston Aldrich, Kevin McAbee, David Chagne, Paolo Pi-ovani, Weilin Sun, Michela Troggio, for their helpful contri-bution. Jeanne Romero thanks Antoine Kremer for suggestingto screen open-pollinated progenies by exclusion methods inorder to identify full-sib progeny for the mapping.

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