Genome-wide association study for in vitro digestibility and related traits in triticale forage.

BACKGROUND: Triticale is making its way on dairy farms as an alternative forage crop. This requires the availability of high-yielding triticale varieties with good digestibility. Triticale forage breeding mainly focussed on biomass yield, but efforts to improve digestibility are increasing. We previously investigated the interrelationships among different quality traits in soft dough triticale: starch, acid detergent fibre and in vitro digestibility of organic matter (IVOMD) and of neutral detergent fibre (IVNDFD) of the total plant, IVNDFD and Klason lignin of the stems, and ear proportion and stem length. Here we determine the genetic control of these traits, using a genome-wide association (GWAS) approach. A total of 33,231 DArTseq SNP markers assessed in a collection of 118 winter triticale genotypes, including 101 varieties and 17 breeding lines, were used. RESULTS: The GWAS identified a total of 53 significant marker-trait associations (MTAs). The highest number of significantly associated SNP markers (n = 10) was identified for total plant IVNDFD. A SNP marker on chromosome 1A (4211801_19_C/T; 474,437,796 bp) was found to be significantly associated with ear proportion, and plant and stem IVNDFD, with the largest phenotypic variation for ear proportion (R²p = 0.23). Based on MTAs, candidate genes were identified which were of particular relevance for variation in in vitro digestibility (IVD) because they are putatively involved in plasma membrane transport, cytoskeleton organisation, carbohydrate metabolic processes, protein phosphorylation, and sterol and cell wall biogenesis. Interestingly, a xyloglucan-related candidate gene on chromosome 2R, SECCE2Rv1G0126340, was located in close proximity of a SNP significantly associated with stem IVNDFD. Furthermore, quantitative trait loci previously reported in wheat co-localized with significantly associated SNP markers in triticale. CONCLUSIONS: A collection of 118 winter triticale genotypes combined with DArTseq SNP markers served as a source for identifying 53 MTAs and several candidate genes for forage IVD and related traits through a GWAS approach. Taken together, the results of this study demonstrate that the genetic diversity available in this collection can be further exploited for research and breeding purposes to improve the IVD of triticale forage.

Predicting yield of individual field-grown rapeseed plants from rosette-stage leaf gene expression.

In the plant sciences, results of laboratory studies often do not translate well to the field. To help close this lab-field gap, we developed a strategy for studying the wiring of plant traits directly in the field, based on molecular profiling and phenotyping of individual plants. Here, we use this single-plant omics strategy on winter-type Brassica napus (rapeseed). We investigate to what extent early and late phenotypes of field-grown rapeseed plants can be predicted from their autumnal leaf gene expression, and find that autumnal leaf gene expression not only has substantial predictive power for autumnal leaf phenotypes but also for final yield phenotypes in spring. Many of the top predictor genes are linked to developmental processes known to occur in autumn in winter-type B. napus accessions, such as the juvenile-to-adult and vegetative-to-reproductive phase transitions, indicating that the yield potential of winter-type B. napus is influenced by autumnal development. Our results show that single-plant omics can be used to identify genes and processes influencing crop yield in the field.

Genetic composition and diversity of Arabica coffee in the crop's centre of origin and its impact on four major fungal diseases.

Conventional wisdom states that genetic variation reduces disease levels in plant populations. Nevertheless, crop species have been subject to a gradual loss of genetic variation through selection for specific traits during breeding, thereby increasing their vulnerability to biotic stresses such as pathogens. We explored how genetic variation in Arabica coffee sites in southwestern Ethiopia was related to the incidence of four major fungal diseases. Sixty sites were selected along a gradient of management intensity, ranging from nearly wild to intensively managed coffee stands. We used genotyping-by-sequencing of pooled leaf samples (pool-GBS) derived from 16 individual coffee shrubs in each of the 60 sites to assess the variation in genetic composition (multivariate: reference allele frequency) and genetic diversity (univariate: mean expected heterozygosity) between sites. We found that genetic composition had a clear spatial pattern and that genetic diversity was higher in less managed sites. The incidence of the four fungal diseases was related to the genetic composition of the coffee stands, but in a specific way for each disease. In contrast, genetic diversity was only related to the within-site variation of coffee berry disease, but not to the mean incidence of any of the four diseases across sites. Given that fungal diseases are major challenges of Arabica coffee in its native range, our findings that genetic composition of coffee sites impacted the major fungal diseases may serve as baseline information to study the molecular basis of disease resistance in coffee. Overall, our study illustrates the need to consider both host genetic composition and genetic diversity when investigating the genetic basis for variation in disease levels.

Flemish soils contain rhizobia partners for Northwestern Europe-adapted soybean cultivars.

In Europe, soybean (Glycine max) used for food and feed has to be imported, causing negative socioeconomic and environmental impacts. To increase the local production, breeding generated varieties that grow in colder climates, but the yield using the commercial inoculants is not satisfactory in Belgium because of variable nodulation efficiencies. To look for indigenous nodulating strains possibly adapted to the local environment, we initiated a nodulation trap by growing early-maturing cultivars under natural and greenhouse conditions in 107 garden soils in Flanders. Nodules occurred in 18 and 21 soils in the garden and greenhouse experiments respectively. By combining 16S rRNA PCR on single isolates with HiSeq 16S metabarcoding on nodules, we found a large bacterial richness and diversity from different soils. Furthermore, using Oxford Nanopore Technologies sequencing of DNA from one nodule, we retrieved the entire genome of a Bradyrhizobium species, not previously isolated, but profusely present in that nodule. These data highlight the need of combining diverse identification techniques to capture the true nodule rhizobial community. Eight selected rhizobial isolates were subdivided by whole-genome analysis in three genera containing six genetically distinct species that, except for two, aligned with known type strains and were all able to nodulate soybean in the laboratory.

Genetic control of tolerance to drought stress in soybean.

BACKGROUND: Drought stress limits the production of soybean [Glycine max (L.) Merr.], which is the most grown high-value legume crop worldwide. Breeding for drought tolerance is a difficult endeavor and understanding the genetic basis of drought tolerance in soybean is therefore crucial for harnessing the genomic regions involved in the tolerance mechanisms. A genome-wide association study (GWAS) analysis was applied in a soybean germplasm collection (the EUCLEG collection) of 359 accessions relevant for breeding in Europe, to identify genomic regions and candidate genes involved in the response to short duration and long duration drought stress (SDS and LDS respectively) in soybean. RESULTS: The phenotypic response to drought was stronger in the long duration drought (LDS) than in the short duration drought (SDS) experiment. Over the four traits considered (canopy wilting, leaf senescence, maximum absolute growth rate and maximum plant height) the variation was in the range of 8.4-25.2% in the SDS, and 14.7-29.7% in the LDS experiments. The GWAS analysis identified a total of 17 and 22 significant marker-trait associations for four traits in the SDS and LDS experiments, respectively. In the genomic regions delimited by these markers we identified a total of 12 and 16 genes with putative functions that are of particular relevance for drought stress responses including stomatal movement, root formation, photosynthesis, ABA signaling, cellular protection and cellular repair mechanisms. Some of these genomic regions co-localized with previously known QTLs for drought tolerance traits including water use efficiency, chlorophyll content and photosynthesis. CONCLUSION: Our results indicate that the mechanism of slow wilting in the SDS might be associated with the characteristics of the root system, whereas in the LDS, slow wilting could be due to low stomatal conductance and transpiration rates enabling a high WUE. Drought-induced leaf senescence was found to be associated to ABA and ROS responses. The QTLs related to WUE contributed to growth rate and canopy height maintenance under drought stress. Co-localization of several previously known QTLs for multiple agronomic traits with the SNPs identified in this study, highlights the importance of the identified genomic regions for the improvement of agronomic performance in addition to drought tolerance in the EUCLEG collection.

Phenotypic variation and quantitative trait loci for resistance to southern anthracnose and clover rot in red clover.

KEY MESSAGE: High variability for and candidate loci associated with resistance to southern anthracnose and clover rot in a worldwide collection of red clover provide a first basis for genomics-assisted breeding. Red clover (Trifolium pratense L.) is an important forage legume of temperate regions, particularly valued for its high yield potential and its high forage quality. Despite substantial breeding progress during the last decades, continuous improvement of cultivars is crucial to ensure yield stability in view of newly emerging diseases or changing climatic conditions. The high amount of genetic diversity present in red clover ecotypes, landraces, and cultivars provides an invaluable, but often unexploited resource for the improvement of key traits such as yield, quality, and resistance to biotic and abiotic stresses. A collection of 397 red clover accessions was genotyped using a pooled genotyping-by-sequencing approach with 200 plants per accession. Resistance to the two most pertinent diseases in red clover production, southern anthracnose caused by Colletotrichum trifolii, and clover rot caused by Sclerotinia trifoliorum, was assessed using spray inoculation. The mean survival rate for southern anthracnose was 22.9% and the mean resistance index for clover rot was 34.0%. Genome-wide association analysis revealed several loci significantly associated with resistance to southern anthracnose and clover rot. Most of these loci are in coding regions. One quantitative trait locus (QTL) on chromosome 1 explained 16.8% of the variation in resistance to southern anthracnose. For clover rot resistance we found eight QTL, explaining together 80.2% of the total phenotypic variation. The SNPs associated with these QTL provide a promising resource for marker-assisted selection in existing breeding programs, facilitating the development of novel cultivars with increased resistance against two devastating fungal diseases of red clover.

Automatic wheat ear counting using machine learning based on RGB UAV imagery.

In wheat (Triticum aestivum L) and other cereals, the number of ears per unit area is one of the main yield-determining components. An automatic evaluation of this parameter may contribute to the advance of wheat phenotyping and monitoring. There is no standard protocol for wheat ear counting in the field, and moreover it is time consuming. An automatic ear-counting system is proposed using machine learning techniques based on RGB (red, green, blue) images acquired from an unmanned aerial vehicle (UAV). Evaluation was performed on a set of 12 winter wheat cultivars with three nitrogen treatments during the 2017-2018 crop season. The automatic system uses a frequency filter, segmentation and feature extraction, with different classification techniques, to discriminate wheat ears in micro-plot images. The relationship between the image-based manual counting and the algorithm counting exhibited high levels of accuracy and efficiency. In addition, manual ear counting was conducted in the field for secondary validation. The correlations between the automatic and the manual in-situ ear counting with grain yield were also compared. Correlations between the automatic ear counting and grain yield were stronger than those between manual in-situ counting and GY, particularly for the lower nitrogen treatment. Methodological requirements and limitations are discussed.

Gloxinia-An Open-Source Sensing Platform to Monitor the Dynamic Responses of Plants.

The study of the dynamic responses of plants to short-term environmental changes is becoming increasingly important in basic plant science, phenotyping, breeding, crop management, and modelling. These short-term variations are crucial in plant adaptation to new environments and, consequently, in plant fitness and productivity. Scalable, versatile, accurate, and low-cost data-logging solutions are necessary to advance these fields and complement existing sensing platforms such as high-throughput phenotyping. However, current data logging and sensing platforms do not meet the requirements to monitor these responses. Therefore, a new modular data logging platform was designed, named Gloxinia. Different sensor boards are interconnected depending upon the needs, with the potential to scale to hundreds of sensors in a distributed sensor system. To demonstrate the architecture, two sensor boards were designed-one for single-ended measurements and one for lock-in amplifier based measurements, named Sylvatica and Planalta, respectively. To evaluate the performance of the system in small setups, a small-scale trial was conducted in a growth chamber. Expected plant dynamics were successfully captured, indicating proper operation of the system. Though a large scale trial was not performed, we expect the system to scale very well to larger setups. Additionally, the platform is open-source, enabling other users to easily build upon our work and perform application-specific optimisations.

Use of identifiability analysis in designing phenotyping experiments for modelling forage production and quality.

Agricultural systems models are complex and tend to be over-parameterized with respect to observational datasets. Practical identifiability analysis based on local sensitivity analysis has proved effective in investigating identifiable parameter sets in environmental models, but has not been applied to agricultural systems models. Here, we demonstrate that identifiability analysis improves experimental design to ensure independent parameter estimation for yield and quality outputs of a complex grassland model. The Pasture Simulation model (PaSim) was used to demonstrate the effectiveness of practical identifiability analysis in designing experiments and measurement protocols within phenotyping experiments with perennial ryegrass. Virtual experiments were designed combining three factors: frequency of measurements, duration of the experiment. and location of trials. Our results demonstrate that (i) PaSim provides sufficient detail in terms of simulating biomass yield and quality of perennial ryegrass for use in breeding, (ii) typical breeding trials are insufficient to parameterize all influential parameters, (iii) the frequency of measurements is more important than the number of growing seasons to improve the identifiability of PaSim parameters, and (iv) identifiability analysis provides a sound approach for optimizing the design of multi-location trials. Practical identifiability analysis can play an important role in ensuring proper exploitation of phenotypic data and cost-effective multi-location experimental designs. Considering the growing importance of simulation models, this study supports the design of experiments and measurement protocols in the phenotyping networks that have recently been organized.

Introducing turgor-driven growth dynamics into functional-structural plant models.

Background and Aims: In many scenarios the availability of assimilated carbon is not the constraining factor of plant growth. Rather, organ growth appears driven by sink activity in which water availability plays a determinant role. Current functional-structural plant models (FSPMs) mainly focus on plant-carbon relations and largely disregard the importance of plant water status in organogenesis. Consequently, incorporating a turgor-driven growth concept, coupling carbon and water dynamics in an FSPM, presents a significant improvement towards capturing plant development in a more mechanistic manner. Methods: An existing process-based water flow and storage model served as a basis for implementing water control in FSPMs. Its concepts were adjusted to the scale of individual plant organs and interwoven with the basic principles of modelling carbon dynamics to allow evaluation of turgor pressure across the entire plant. This was then linked to plant organ growth by applying the principles of the widely used Lockhart equation. Key results: This model successfully integrates a mechanistic understanding of plant water transport dynamics coupled with simple carbon dynamics within a dynamically developing plant architecture. It allows evaluation of turgor pressure on the scale of plant organs, resulting in clear diel and long-term patterns, directly linked to plant organ growth. Conclusions: A conceptual sap flow and turgor-driven growth model was introduced for functional-structural plant modelling. It is applicable to any plant architecture and allows visual exploration of the diel patterns of organ water content and growth. Integrated in existing FSPMs, this new concept fosters an array of possibilities for FSPMs, as it presents a different formulation of growth in terms of local processes, influenced by local and external conditions.

Modelling leaf spectral properties in a soybean functional-structural plant model by integrating the prospect radiative transfer model.

Background and Aims: Currently, functional-structural plant models (FSPMs) mostly resort to static descriptions of leaf spectral characteristics, which disregard the influence of leaf physiological changes over time. In many crop species, including soybean, these time-dependent physiological changes are of particular importance as leaf chlorophyll content changes with leaf age and vegetative nitrogen is remobilized to the developing fruit during pod filling. Methods: PROSPECT, a model developed to estimate leaf biochemical composition from remote sensing data, is well suited to allow a dynamic approximation of leaf spectral characteristics in terms of leaf composition. In this study, measurements of the chlorophyll content index (CCI) were linked to leaf spectral characteristics within the 400-800 nm range by integrating the PROSPECT model into a soybean FSPM alongside a wavelength-specific light model. Key Results: Straightforward links between the CCI and the parameters of the PROSPECT model allowed us to estimate leaf spectral characteristics with high accuracy using only the CCI as an input. After integration with an FSPM, this allowed digital reconstruction of leaf spectral characteristics on the scale of both individual leaves and the whole canopy. As a result, accurate simulations of light conditions within the canopy were obtained. Conclusions: The proposed approach resulted in a very accurate representation of leaf spectral properties, based on fast and simple measurements of the CCI. Integration of accurate leaf spectral characteristics into a soybean FSPM leads to a better, dynamic understanding of the actual perceived light within the canopy in terms of both light quantity and quality.

Physiological basis of chilling tolerance and early-season growth in miscanthus.

Background and Aims: The high productivity of Miscanthus × giganteus has been at least partly ascribed to its high chilling tolerance compared with related C4 crops, allowing for a longer productive growing season in temperate climates. However, the chilling tolerance of M. × giganteus has been predominantly studied under controlled environmental conditions. The understanding of the underlying mechanisms contributing to chilling tolerance in the field and their variation in different miscanthus genotypes is largely unexplored. Methods: Five miscanthus genotypes with different sensitivities to chilling were grown in the field and scored for a comprehensive set of physiological traits throughout the spring season. Chlorophyll fluorescence was measured as an indication of photosynthesis, and leaf samples were analysed for biochemical traits related to photosynthetic activity (chlorophyll content and pyruvate, Pi dikinase activity), redox homeostasis (malondialdehyde, glutathione and ascorbate contents, and catalase activity) and water-soluble carbohydrate content. Key Results: Chilling-tolerant genotypes were characterized by higher levels of malondialdehyde, raffinose and sucrose, and higher catalase activity, while the chilling-sensitive genotypes were characterized by higher concentrations of glucose and fructose, and higher pyruvate, Pi dikinase activity later in the growing season. On the early sampling dates, the biochemical responses of M. × giganteus were similar to those of the chilling-tolerant genotypes, but later in the season they became more similar to those of the chilling-sensitive genotypes. Conclusions: The overall physiological response of chilling-tolerant genotypes was distinguishable from that of chilling-sensitive genotypes, while M. × giganteus was intermediate between the two. There appears to be a trade-off between high and efficient photosynthesis and chilling stress tolerance. Miscanthus × giganteus is able to overcome this trade-off and, while it is more similar to the chilling-sensitive genotypes in early spring, its photosynthetic capacity is similar to that of the chilling-tolerant genotypes later on.

Introduction of a natural resource balance indicator to assess soil organic carbon management: Agricultural Biomass Productivity Benefit.

The rising demand for feed and food has put an increasing pressure on agriculture, with agricultural intensification as a direct response. Notwithstanding the higher crop productivity, intensive agriculture management entails many adverse environmental impacts. Worldwide, soil organic carbon (SOC) decline is hereby considered as a main danger which affects soil fertility and productivity. The life cycle perspective helps to get a holistic overview when evaluating the environmental sustainability of agricultural systems, though the impact of farm management on soil quality aspects is often not integrated. In this paper, we introduce an indicator called Agricultural Biomass Productivity Benefit of SOC management (ABB_SOC), which, relying on natural resource consumption, enables to estimate the net effect of the efforts made to attain a better soil quality. Hereby the focus is put on SOC. First, we introduce a framework to describe the SOC trend due to farm management decisions. The extent to which remediation measures are required are used as a measure for the induced SOC losses. Next, ABB_SOC values are calculated as the balance between the natural resource consumption of the inputs (including remediation efforts) and the desired output of arable crop production systems. The models RothC and EU-Rotate_N are used to simulate the SOC evolution due to farm management and the response of the biomass productivity, respectively. The developed indicator is applied on several rotation systems in Flanders, comparing different remediation strategies. The indicator could be used as a base for a method to account for soil quality in life cycle analysis.

The domestication and evolutionary ecology of apples.

The cultivated apple is a major fruit crop in temperate zones. Its wild relatives, distributed across temperate Eurasia and growing in diverse habitats, represent potentially useful sources of diversity for apple breeding. We review here the most recent findings on the genetics and ecology of apple domestication and its impact on wild apples. Genetic analyses have revealed a Central Asian origin for cultivated apple, together with an unexpectedly large secondary contribution from the European crabapple. Wild apple species display strong population structures and high levels of introgression from domesticated apple, and this may threaten their genetic integrity. Recent research has revealed a major role of hybridization in the domestication of the cultivated apple and has highlighted the value of apple as an ideal model for unraveling adaptive diversification processes in perennial fruit crops. We discuss the implications of this knowledge for apple breeding and for the conservation of wild apples.

Overexpression of GA20-OXIDASE1 impacts plant height, biomass allocation and saccharification efficiency in maize.

Increased biomass yield and quality are of great importance for the improvement of feedstock for the biorefinery. For the production of bioethanol, both stem biomass yield and the conversion efficiency of the polysaccharides in the cell wall to fermentable sugars are of relevance. Increasing the endogenous levels of gibberellic acid (GA) by ectopic expression of GA20-OXIDASE1 (GA20-OX1), the rate-limiting step in GA biosynthesis, is known to affect cell division and cell expansion, resulting in larger plants and organs in several plant species. In this study, we examined biomass yield and quality traits of maize plants overexpressing GA20-OX1 (GA20-OX1). GA20-OX1 plants accumulated more vegetative biomass than control plants in greenhouse experiments, but not consistently over two years of field trials. The stems of these plants were longer but also more slender. Investigation of GA20-OX1 biomass quality using biochemical analyses showed the presence of more cellulose, lignin and cell wall residue. Cell wall analysis as well as expression analysis of lignin biosynthetic genes in developing stems revealed that cellulose and lignin were deposited earlier in development. Pretreatment of GA20-OX1 biomass with NaOH resulted in a higher saccharification efficiency per unit of dry weight, in agreement with the higher cellulose content. On the other hand, the cellulose-to-glucose conversion was slower upon HCl or hot-water pretreatment, presumably due to the higher lignin content. This study showed that biomass yield and quality traits can be interconnected, which is important for the development of future breeding strategies to improve lignocellulosic feedstock for bioethanol production.

New insight into the history of domesticated apple: secondary contribution of the European wild apple to the genome of cultivated varieties.

The apple is the most common and culturally important fruit crop of temperate areas. The elucidation of its origin and domestication history is therefore of great interest. The wild Central Asian species Malus sieversii has previously been identified as the main contributor to the genome of the cultivated apple (Malus domestica), on the basis of morphological, molecular, and historical evidence. The possible contribution of other wild species present along the Silk Route running from Asia to Western Europe remains a matter of debate, particularly with respect to the contribution of the European wild apple. We used microsatellite markers and an unprecedented large sampling of five Malus species throughout Eurasia (839 accessions from China to Spain) to show that multiple species have contributed to the genetic makeup of domesticated apples. The wild European crabapple M. sylvestris, in particular, was a major secondary contributor. Bidirectional gene flow between the domesticated apple and the European crabapple resulted in the current M. domestica being genetically more closely related to this species than to its Central Asian progenitor, M. sieversii. We found no evidence of a domestication bottleneck or clonal population structure in apples, despite the use of vegetative propagation by grafting. We show that the evolution of domesticated apples occurred over a long time period and involved more than one wild species. Our results support the view that self-incompatibility, a long lifespan, and cultural practices such as selection from open-pollinated seeds have facilitated introgression from wild relatives and the maintenance of genetic variation during domestication. This combination of processes may account for the diversification of several long-lived perennial crops, yielding domestication patterns different from those observed for annual species.

Rapid genetic adaptation precedes the spread of an exotic plant species.

Human activities have increasingly introduced plant species far outside their native ranges under environmental conditions that can strongly differ from those originally met. Therefore, before spreading, and potentially causing ecological and economical damage, non-native species may rapidly evolve. Evidence of genetically based adaptation during the process of becoming invasive is very scant, however, which is due to the lack of knowledge regarding the historical genetic makeup of the introduced populations and the lack of genomic resources. Capitalizing on the availability of old non-native herbarium specimens, we examined frequency shifts in genic SNPs of the Pyrenean Rocket (Sisymbrium austriacum subsp. chrysanthum), comparing the (i) native, (ii) currently spreading non-native and (iii) historically introduced gene pool. Results show strong divergence in flowering time genes during the establishment phase, indicating that rapid genetic adaptation preceded the spread of this species and possibly assisted in overcoming environmental constraints.

Quantification of species composition in grass-clover swards using RGB and multispectral UAV imagery and machine learning.

Introduction: Growing grass-legume mixtures for forage production improves both yield productivity and nutritional quality, while also benefiting the environment by promoting species biodiversity and enhancing soil fertility (through nitrogen fixation). Consequently, assessing legume proportions in grass-legume mixed swards is essential for breeding and cultivation. This study introduces an approach for automated classification and mapping of species in mixed grass-clover swards using object-based image analysis (OBIA). Methods: The OBIA procedure was established for both RGB and ten band multispectral (MS) images capturedby an unmanned aerial vehicle (UAV). The workflow integrated structural (canopy heights) and spectral variables (bands, vegetation indices) along with a machine learning algorithm (Random Forest) to perform image segmentation and classification. Spatial k-fold cross-validation was employed to assess accuracy. Results and discussion: Results demonstrated good performance, achieving an overall accuracy of approximately 70%, for both RGB and MS-based imagery, with grass and clover classes yielding similar F1 scores, exceeding 0.7 values. The effectiveness of the OBIA procedure and classification was examined by analyzing correlations between predicted clover fractions and dry matter yield (DMY) proportions. This quantification revealed a positive and strong relationship, with R2 values exceeding 0.8 for RGB and MS-based classification outcomes. This indicates the potential of estimating (relative) clover coverage, which could assist breeders but also farmers in a precision agriculture context.

Orthology Guided Assembly in highly heterozygous crops: creating a reference transcriptome to uncover genetic diversity in Lolium perenne.

Despite current advances in next-generation sequencing data analysis procedures, de novo assembly of a reference sequence required for SNP discovery and expression analysis is still a major challenge in genetically uncharacterized, highly heterozygous species. High levels of polymorphism inherent to outbreeding crop species hamper De Bruijn Graph-based de novo assembly algorithms, causing transcript fragmentation and the redundant assembly of allelic contigs. If multiple genotypes are sequenced to study genetic diversity, primary de novo assembly is best performed per genotype to limit the level of polymorphism and avoid transcript fragmentation. Here, we propose an Orthology Guided Assembly procedure that first uses sequence similarity (tBLASTn) to proteins of a model species to select allelic and fragmented contigs from all genotypes and then performs CAP3 clustering on a gene-by-gene basis. Thus, we simultaneously annotate putative orthologues for each protein of the model species, resolve allelic redundancy and fragmentation and create a de novo transcript sequence representing the consensus of all alleles present in the sequenced genotypes. We demonstrate the procedure using RNA-seq data from 14 genotypes of Lolium perenne to generate a reference transcriptome for gene discovery and translational research, to reveal the transcriptome-wide distribution and density of SNPs in an outbreeding crop and to illustrate the effect of polymorphisms on the assembly procedure. The results presented here illustrate that constructing a non-redundant reference sequence is essential for comparative genomics, orthology-based annotation and candidate gene selection but also for read mapping and subsequent polymorphism discovery and/or read count-based gene expression analysis.

The impact of extensive clonal growth on fine-scale mating patterns: a full paternity analysis of a lily-of-the-valley population (Convallaria majalis).

BACKGROUND AND AIMS: The combination of clonality and a mating system promoting outcrossing is considered advantageous because outcrossing avoids the fitness costs of selfing within clones (geitonogamy) while clonality assures local persistence and increases floral display. The spatial spread of genetically identical plants (ramets) may, however, also decrease paternal diversity (the number of sires fertilizing a given dam) and fertility, particularly towards the centre of large clumped clones. This study aimed to quantify the impact of extensive clonal growth on fine-scale paternity patterns in a population of the allogamous Convallaria majalis. METHODS: A full analysis of paternity was performed by genotyping all flowering individuals and all viable seeds produced during a single season using AFLP. Mating patterns were examined and the spatial position of ramets was related to the extent of multiple paternity, fruiting success and seed production. KEY RESULTS: The overall outcrossing rate was high (91 %) and pollen flow into the population was considerable (27 %). Despite extensive clonal growth, multiple paternity was relatively common (the fraction of siblings sharing the same father was 0·53 within ramets). The diversity of offspring collected from reproductive ramets surrounded by genetically identical inflorescences was as high as among offspring collected from ramets surrounded by distinct genets. There was no significant relationship between the similarity of the pollen load received by two ramets and the distance between them. Neither the distance of ramets with respect to distinct genets nor the distance to the genet centre significantly affected fruiting success or seed production. CONCLUSIONS: Random mating and considerable pollen inflow most probably implied that pollen dispersal distances were sufficiently high to mitigate local mate scarcity despite extensive clonal spread. The data provide no evidence for the intrusion of clonal growth on fine-scale plant mating patterns.