WP 3-Comparative Architecture of wood quality in Eucalyptus and Populus
The objective of WP3 is to identify and characterise genomic regions in eucalypts (E) and poplar (P) that control wood properties relevant for bioenergy. Comparative analyses will be performed between E and P at the level of whole-genome sequences as well as for genomic and phenotypic data obtained in WP1 and WP2, respectively, and in previous projects. Comparisons in E and P at the structural (comparative genetic and physical mapping) and at the functional (comparative QTL mapping) levels will offer a unique opportunity to understand the genetic basis of wood properties suitable for bioenergy production in these two model hardwoods.
Task 3.1: Comparative Genomics between Eucalyptus and Populus
To facilitate comparisons of E and P genome architecture (gene organization into linkage groups, distribution of recombination across genomes comparing physical length vs genetic distances), here we will increase the density of available genetic maps listed in Table 4 using common markers that link the different genetic maps from the same genus and then anchor them on both physical maps of E and P. Table 3 describes the plant material and data available within the consortium.
Subtask 3.1.1: Identification of Consensus Orthologous Sequences (COS) between E and P.
Taking advantage of the genome sequence of poplar (annotation V1.1 and V2.0) and eucalyptus (V1.0), we will define a set of COS markers that will be designed with TribeMCL (Enright et al. 2002) based on all-against-all BLASTP search between both gene models (E-value cutoff of < e-10), as described by Ming et al. (2008). Regarding the 1800 COS identified between Quercus and Populus genomes (Plomion C., personal communication) we can expect around 1000 to 2000 COS between E and P.
Subtask 3.1.2: Alignment of shotgun DNA sequences onto the reference genomes
Shotgun DNA sequences will be also available for the project (from P2 and P3) for parental genotypes of the mapping pedigrees of E and P. For one E. gunnii parent, shotgun sequencing will be performed by partner P4. The alignment of shotgun DNA sequences onto the reference genomes will be carried out by P2 and P3. This subtask will allow large-scale identification of gene polymorphisms among mapping pedigrees. Further, it will allow the selection of a representative set of polymorphism (SNPs, INDELs) based on three criteria: i) maximizing genome coverage, ii) detected into COS (Subt-3.1.1), and iii) detected into candidate genes (WP1, Subt-1.1.2).
Subtask 3.1.3: High throughput genotyping and genetic mapping
A SNP array will be developed for E and P, based on Subt-3.1.2 and according to primer design specifications. While golden gate technology (Illumina, US) seems to be the most efficient genotyping method today, new techniques that become available may be used in lieu of the Illumina platform available at P3 laboratory (beadXpress station). This genotyping step will be done on a total of 400 individuals for P3 pedigrees, 180 for P4, and 480 individuals for one of the P2 pedigrees. The genetic mapping of SNPs will be done by P2 and P3 using available genetic maps and JoinMap software. This mapping step will enable comparative analyses between genetic maps at the intragenera (E. urophylla vs E. grandis vs E. gunnii; P. deltoides vs P. trichocarpa vs P. tremula) and intergenera levels (P vs E) using the COS markers.
Task 3.2: Comparative Architecture of Wood quality traits in Eucalyptus and Populus
QTL analyses will be undertaken to provide a detailed description of the genetic architecture (number, location, and effects of major genes) of the key wood quality traits of cellulose hemicellulose and lignin content using the different pedigrees listed in table 4. We will take advantage of the development of NIRS calibration models in WP2 to phenotype a large number of progenies and perform accurate QTL detection.
Subtask 3.2.1: QTL mapping for each mapping pedigree
Phenotyping of the pedigrees for wood quality traits will be performed in WP2. In eucalyptus, we will focus on cellulose and hemicellulose contents, using NIRs measurements (QTL for lignin contents are already available). This analysis will be done using 600 individuals from three trials of the same E. urophylla x E. grandis pedigree. In P, we will focus on cellulose, hemicellulose and lignin contents. 700 individuals from a P. deltoides x P. trichocarpa pedigree and 300 individuals from a P. nigra x P. nigra pedigree will be analysed. Wood density will be analysed in each pedigree of E and P. QTL analysis will be done by interval mapping and multiple interval mapping algorithms for all traits and for each parental genotype. Analysis will be performed using the MultiQTL or MapQTL software (available for the project). Co–locations analysis between CGs and QTLs will be performed for each genetic map.
Subtask 3.2.2: Meta QTL analysis and comparison with others QTL results
Using the data generated in Subt-3.1.3 and Subt-3.2.1, a meta-QTL analysis will also be undertaken. QTLs previously reported from other E and P species will be included in this meta-QTL analysis. This step will help identifying orthologous regions harbouring QTL in different pedigrees of the same genus and potentially between genera.
Task 3.3: A case study: Dissection of a major QTL for lignin
The objective of this task is to characterize one of the two-linked QTLs for lignin content previously detected on linkage group 6 of E. urophylla (Gion et al. 2010, submitted). These two linked QTLs explained 15% and 37% of the variation of lignin content and composition, respectively (Confidence interval around 15 cM).
Subtask 3.3.1: High density mapping of the region harboring a major QTL for lignin
Using high throughput technologies, we will genotype a large number of available individuals into the two larger pedigrees (Table 3) for SNPs located in the targeted region (Subt.3.2.1). If necessary, a Bulk Segregant Analysis based on the flanking markers of the QTL will be used to select a large number of markers in the targeted region (DARTs polymorphisms are available in E). This will allow increasing the marker density into the region of interest (confidence interval of the QTL). P8 will make available E. globulus BAC sequences, BAC libraries and 3D pools (GenEglobwq project, FCT funded) that will help the genomic dissection of this QTL in E. urophylla.
Subtask 3.3.2: Fine mapping of major QTL for lignin
We will use selective phenotyping (for lignin content) based on a sample of a full sib family maximizing the recombination rate in the genomic region (MapPop software). QTL detection will be performed for the high marker density region to map precisely the position of the QTL. The identification of the number and the nature of genes underlying the QTL will be possible using the corresponding genome sequence of E and P.
Subtask 3.3.3: QTL validation in different genetic backgrounds
Polymorphism underlying the QTL will be genotyped in two groups of E. globulus and E.gunnii genotypes (from P4,P7,P10) contrasted for lignin content, previously characterized and selected in WP2.
Subtask 3.3.4: Genetic control of gene regulation underlying QTL
For positional candidate genes (PGCs), the genetic control of gene expression will be carried out following an eQTL analysis. The transcript levels of PCGs will be analysed on differentiating xylem of 500 full-sibs using RealTimePCR (Fluidigm system available at P1 laboratory).
