RNAseq based variant dataset in a black poplar association panel.

OBJECTIVE: Black poplar (Populus nigra L.) is a species native to Eurasia with a wide distribution area. It is an ecologically important species from riparian ecosystems, that is used as a parent of interspecific (P. deltoides x P. nigra) cultivated poplar hybrids. Variant detection from transcriptomics sequences of 241 P. nigra individuals, sampled in natural populations from 11 river catchments (in four European countries) is described here. These data provide new valuable resources for population structure analysis, population genomics and genome-wide association studies. DATA DESCRIPTION: We generated transcriptomics data from a mixture of young differentiating xylem and cambium tissues of 480 Populus nigra trees sampled in a common garden experiment located at Orléans (France), corresponding to 241 genotypes (2 clonal replicates per genotype, at maximum) by using RNAseq technology. We launched on the resulting sequences an in-silico pipeline that allowed us to obtain 878,957 biallelic polymorphisms without missing data. More than 99% of these positions are annotated and 98.8% are located on the 19 chromosomes of the P. trichocarpa reference genome. The raw RNAseq sequences are available at the NCBI Sequence Read Archive SPR188754 and the variant dataset at the Recherche Data Gouv repository under https://doi.org/10.15454/8DQXK5 .

Favorable Conditions for Genomic Evaluation to Outperform Classical Pedigree Evaluation Highlighted by a Proof-of-Concept Study in Poplar.

Forest trees like poplar are particular in many ways compared to other domesticated species. They have long juvenile phases, ongoing crop-wild gene flow, extensive outcrossing, and slow growth. All these particularities tend to make the conduction of breeding programs and evaluation stages costly both in time and resources. Perennials like trees are therefore good candidates for the implementation of genomic selection (GS) which is a good way to accelerate the breeding process, by unchaining selection from phenotypic evaluation without affecting precision. In this study, we tried to compare GS to pedigree-based traditional evaluation, and evaluated under which conditions genomic evaluation outperforms classical pedigree evaluation. Several conditions were evaluated as the constitution of the training population by cross-validation, the implementation of multi-trait, single trait, additive and non-additive models with different estimation methods (G-BLUP or weighted G-BLUP). Finally, the impact of the marker densification was tested through four marker density sets. The population under study corresponds to a pedigree of 24 parents and 1,011 offspring, structured into 35 full-sib families. Four evaluation batches were planted in the same location and seven traits were evaluated on 1 and 2 years old trees. The quality of prediction was reported by the accuracy, the Spearman rank correlation and prediction bias and tested with a cross-validation and an independent individual test set. Our results show that genomic evaluation performance could be comparable to the already well-optimized pedigree-based evaluation under certain conditions. Genomic evaluation appeared to be advantageous when using an independent test set and a set of less precise phenotypes. Genome-based methods showed advantages over pedigree counterparts when ranking candidates at the within-family levels, for most of the families. Our study also showed that looking at ranking criteria as Spearman rank correlation can reveal benefits to genomic selection hidden by biased predictions.

Gene expression predictions and networks in natural populations supports the omnigenic theory.

BACKGROUND: Recent literature on the differential role of genes within networks distinguishes core from peripheral genes. If previous works have shown contrasting features between them, whether such categorization matters for phenotype prediction remains to be studied. RESULTS: We measured 17 phenotypic traits for 241 cloned genotypes from a Populus nigra collection, covering growth, phenology, chemical and physical properties. We also sequenced RNA for each genotype and built co-expression networks to define core and peripheral genes. We found that cores were more differentiated between populations than peripherals while being less variable, suggesting that they have been constrained through potentially divergent selection. We also showed that while cores were overrepresented in a subset of genes statistically selected for their capacity to predict the phenotypes (by Boruta algorithm), they did not systematically predict better than peripherals or even random genes. CONCLUSION: Our work is the first attempt to assess the importance of co-expression network connectivity in phenotype prediction. While highly connected core genes appear to be important, they do not bear enough information to systematically predict better quantitative traits than other gene sets.

Sequence imputation from low density single nucleotide polymorphism panel in a black poplar breeding population.

BACKGROUND: Genomic selection accuracy increases with the use of high SNP (single nucleotide polymorphism) coverage. However, such gains in coverage come at high costs, preventing their prompt operational implementation by breeders. Low density panels imputed to higher densities offer a cheaper alternative during the first stages of genomic resources development. Our study is the first to explore the imputation in a tree species: black poplar. About 1000 pure-breed Populus nigra trees from a breeding population were selected and genotyped with a 12K custom Infinium Bead-Chip. Forty-three of those individuals corresponding to nodal trees in the pedigree were fully sequenced (reference), while the remaining majority (target) was imputed from 8K to 1.4 million SNPs using FImpute. Each SNP and individual was evaluated for imputation errors by leave-one-out cross validation in the training sample of 43 sequenced trees. Some summary statistics such as Hardy-Weinberg Equilibrium exact test p-value, quality of sequencing, depth of sequencing per site and per individual, minor allele frequency, marker density ratio or SNP information redundancy were calculated. Principal component and Boruta analyses were used on all these parameters to rank the factors affecting the quality of imputation. Additionally, we characterize the impact of the relatedness between reference population and target population. RESULTS: During the imputation process, we used 7540 SNPs from the chip to impute 1,438,827 SNPs from sequences. At the individual level, imputation accuracy was high with a proportion of SNPs correctly imputed between 0.84 and 0.99. The variation in accuracies was mostly due to differences in relatedness between individuals. At a SNP level, the imputation quality depended on genotyped SNP density and on the original minor allele frequency. The imputation did not appear to result in an increase of linkage disequilibrium. The genotype densification not only brought a better distribution of markers all along the genome, but also we did not detect any substantial bias in annotation categories. CONCLUSIONS: This study shows that it is possible to impute low-density marker panels to whole genome sequence with good accuracy under certain conditions that could be common to many breeding populations.

Accuracy of RNAseq based SNP discovery and genotyping in Populusnigra.

BACKGROUD: Populus nigra is a major tree species of ecological and economic importance for which several initiatives have been set up to create genomic resources. In order to access the large number of Single Nucleotide Polymorphisms (SNPs) typically needed to carry out a genome scan, the present study aimed at evaluating RNA sequencing as a tool to discover and type SNPs in genes within natural populations of P. nigra. RESULTS: We have devised a bioinformatics pipeline to call and type SNPs from RNAseq reads and applied it to P. nigra transcriptomic data. The accuracy of the resulting RNAseq-based SNP calling and typing has been evaluated by (i) comparing their position and alleles to those previously reported in candidate genes, (ii) assessing their genotyping accuracy with respect to a previously available SNP chip and (iii) evaluating their inter-annual repeatability. We found that a combination of several callers yields a good compromise between the number of variants type and the accuracy of genotyping. We further used the resulting genotypic data to carry out basic genetic analyses whose results confirm the quality of the RNAseq-based SNP dataset. CONCLUSIONS: We demonstrated the potential and accuracy of RNAseq as an efficient way to genotype SNPs in P. nigra. These results open prospects towards the use of this technology for quantitative and population genomics studies.

Characterization of the Poplar Pan-Genome by Genome-Wide Identification of Structural Variation.

Many recent studies have emphasized the important role of structural variation (SV) in determining human genetic and phenotypic variation. In plants, studies aimed at elucidating the extent of SV are still in their infancy. Evidence has indicated a high presence and an active role of SV in driving plant genome evolution in different plant species.With the aim of characterizing the size and the composition of the poplar pan-genome, we performed a genome-wide analysis of structural variation in three intercrossable poplar species: Populus nigra, Populus deltoides, and Populus trichocarpa We detected a total of 7,889 deletions and 10,586 insertions relative to the P. trichocarpa reference genome, covering respectively 33.2 Mb and 62.9 Mb of genomic sequence, and 3,230 genes affected by copy number variation (CNV). The majority of the detected variants are inter-specific in agreement with a recent origin following separation of species.Insertions and deletions (INDELs) were preferentially located in low-gene density regions of the poplar genome and were, for the majority, associated with the activity of transposable elements. Genes affected by SV showed lower-than-average expression levels and higher levels of dN/dS, suggesting that they are subject to relaxed selective pressure or correspond to pseudogenes.Functional annotation of genes affected by INDELs showed over-representation of categories associated with transposable elements activity, while genes affected by genic CNVs showed enrichment in categories related to resistance to stress and pathogens. This study provides a genome-wide catalogue of SV and the first insight on functional and structural properties of the poplar pan-genome.

Integrating genome annotation and QTL position to identify candidate genes for productivity, architecture and water-use efficiency in Populus spp.

BACKGROUND: Hybrid poplars species are candidates for biomass production but breeding efforts are needed to combine productivity and water use efficiency in improved cultivars. The understanding of the genetic architecture of growth in poplar by a Quantitative Trait Loci (QTL) approach can help us to elucidate the molecular basis of such integrative traits but identifying candidate genes underlying these QTLs remains difficult. Nevertheless, the increase of genomic information together with the accessibility to a reference genome sequence (Populus trichocarpa Nisqually-1) allow to bridge QTL information on genetic maps and physical location of candidate genes on the genome. The objective of the study is to identify QTLs controlling productivity, architecture and leaf traits in a P. deltoides x P. trichocarpa F1 progeny and to identify candidate genes underlying QTLs based on the anchoring of genetic maps on the genome and the gene ontology information linked to genome annotation. The strategy to explore genome annotation was to use Gene Ontology enrichment tools to test if some functional categories are statistically over-represented in QTL regions. RESULTS: Four leaf traits and 7 growth traits were measured on 330 F1 P. deltoides x P. trichocarpa progeny. A total of 77 QTLs controlling 11 traits were identified explaining from 1.8 to 17.2% of the variation of traits. For 58 QTLs, confidence intervals could be projected on the genome. An extended functional annotation was built based on data retrieved from the plant genome database Phytozome and from an inference of function using homology between Populus and the model plant Arabidopsis. Genes located within QTL confidence intervals were retrieved and enrichments in gene ontology (GO) terms were determined using different methods. Significant enrichments were found for all traits. Particularly relevant biological processes GO terms were identified for QTLs controlling number of sylleptic branches: intervals were enriched in GO terms of biological process like 'ripening' and 'adventitious roots development'. CONCLUSION: Beyond the simple identification of QTLs, this study is the first to use a global approach of GO terms enrichment analysis to fully explore gene function under QTLs confidence intervals in plants. This global approach may lead to identification of new candidate genes for traits of interest.

Qualitative and quantitative resistances to leaf rust finely mapped within two nucleotide-binding site leucine-rich repeat (NBS-LRR)-rich genomic regions of chromosome 19 in poplar.

• R(US) is a major dominant gene controlling quantitative resistance, inherited from Populus trichocarpa, whereas R(1) is a gene governing qualitative resistance, inherited from P. deltoides. • Here, we report a reiterative process of concomitant fine-scale genetic and physical mapping guided by the P. trichocarpa genome sequence. The high-resolution linkage maps were developed using a P. deltoides × P. trichocarpa progeny of 1415 individuals. R(US) and R(1) were mapped in a peritelomeric region of chromosome 19. Markers closely linked to R(US) were used to screen a bacterial artificial chromosome (BAC) library constructed from the P. trichocarpa parent, heterozygous at the locus R(US) . • Two local physical maps were developed, one encompassing the R(US) allele and the other spanning r(US) . The alignment of the two haplophysical maps showed structural differences between haplotypes. The genetic and physical maps were anchored to the genome sequence, revealing genome sequence misassembly. Finally, the R(US) locus was localized within a 0.8-cM interval, whereas R(1) was localized upstream of R(US) within a 1.1-cM interval. • The alignment of the genetic and physical maps with the local reorder of the chromosome 19 sequence indicated that R(US) and R(1) belonged to a genomic region rich in nucleotide-binding site leucine-rich repeat (NBS-LRR) and serine threonine kinase (STK) genes.

Bud set in poplar--genetic dissection of a complex trait in natural and hybrid populations.

• The seasonal timing of growth events is crucial to tree distribution and conservation. The seasonal growth cycle is strongly adapted to the local climate that is changing because of global warming. We studied bud set as one cornerstone of the seasonal growth cycle in an integrative approach. • Bud set was dissected at the phenotypic level into several components, and phenotypic components with most genetic variation were identified. While phenotypic variation resided in the timing of growth cessation, and even so more in the duration from growth cessation to bud set, the timing of growth cessation had a stronger genetic component in both natural and hybrid populations. • Quantitative trait loci (QTL) were identified for the most discriminative phenotypic bud-set components across four poplar pedigrees. The QTL from different pedigrees were recurrently detected in six regions of the poplar genome. • These regions of 1.83-4.25 Mbp in size, containing between 202 and 394 genes, form the basis for further molecular-genetic dissection of bud set.

An EST resource for cassava and other species of Euphorbiaceae.

Cassava (Manihot esculenta) is a major food staple for nearly 600 million people in Africa, Asia, and Latin America. Major losses in yield result from biotic and abiotic stresses that include diseases such as Cassava Mosaic Disease (CMD) and Cassava Bacterial Blight (CBB), drought, and acid soils. Additional losses also occur from deterioration during the post-harvest storage of roots. To help cassava breeders overcome these obstacles, the scientific community has turned to modern genomics approaches to identify key genetic characteristics associated with resistance to these yield-limiting factors. One approach for developing a genomics program requires the development of ESTs (expressed sequence tags). To date, nearly 23,000 ESTs have been developed from various cassava tissues, and genotypes. Preliminary analysis indicates existing EST resources contain at least 6000-7000 unigenes. Data presented in this report indicate that the cassava ESTs will be a valuable resource for the study of genetic diversity, stress resistance, and growth and development, not only in cassava, but also other members of the Euphorbiaceae family.

Recent progress in the characterization of molecular determinants in the Xanthomonas axonopodis pv. manihotis-cassava interaction.

Cassava bacterial blight, caused by Xanthomonas axonopodis pv. manihotis (Xam), is a widespread disease that affects cassava (Manihot esculenta Crantz). Studies on the pathogen population structure, pathogen diagnosis, identification and expression of plant genes involved in resistance have been carried out. Different molecular techniques were developed to assess the genetic diversity among the Xampopulations. Characterization of Xam population dynamics over time had enable us to determine the different factors that are associated with resistance breakdown and those that influence the genetic structure or virulence phenotypes of the pathogen's population. Methods for detecting the pathogen in vegetative planting materials and true seeds were developed and contributed to reduce the impact of the disease. To better understand the genetics of resistance a quantitative trait loci (QTLs) approach was developed. Using a PCR-based strategy with degenerate primers we isolated two resistance gene candidates in cassava. We also characterized a region of a chromosome rich in R-gene like sequence. In this review we also report the main results obtained by transcript profiling methodologies, cDNA-AFLP and ESTs developed by the authors to characterize the genes involved in disease resistance. All together these techniques allowed the identification of molecular markers either associated to CBB resistance or that may represent putative genes involved in disease resistance. This article reviews current knowledge on the molecular cassava-Xam interactions.

A unigene catalogue of 5700 expressed genes in cassava.

Two economically important characters, starch content and cassava bacterial blight resistance, were targeted to generate a large collection of cassava ESTs. Two libraries were constructed from cassava root tissues of varieties with high and low starch contents. Other libraries were constructed from plant tissues challenged by the pathogen Xanthomonas axonopodis pv.manihotis. We report here the single pass sequencing of 11,954 cDNA clones from the 5' ends, including 111 from the 3' ends. Cluster analysis permitted the identification of a unigene set of 5,700 sequences. Sequence analyses permitted the assignment of a putative functional category for 37% of sequences whereas approximately 16% sequences did not show any significant similarity with other proteins present in the database and therefore can be considered as cassava specific genes. A group of genes belonging to a large multigene family was identified. We characterize a set of genes detected only in infected libraries putatively involved in the defense response to pathogen infection. By comparing two libraries obtained from cultivars contrasting in their starch content a group of genes associated to starch biosynthesis and differentially expressed was identified. This is the first large cassava EST resource developed today and publicly available thus making a significant contribution to genomic knowledge of cassava.