Extensive allele mining discovers novel genetic diversity in the loci controlling frost tolerance in barley.

KEY MESSAGE: Exome sequencing-based allele mining for frost tolerance suggests HvCBF14 rather than CNV at Fr-H2 locus is the main responsible of frost tolerance in barley. Wild relatives, landraces and old cultivars of barley represent a reservoir of untapped and potentially important genes for crop improvement, and the recent sequencing technologies provide the opportunity to mine the existing genetic diversity and to identify new genes/alleles for the traits of interest. In the present study, we use frost tolerance and vernalization requirement as case studies to demonstrate the power of allele mining carried out on exome sequencing data generated from > 400 barley accessions. New deletions in the first intron of VRN-H1 were identified and linked to a reduced vernalization requirement, while the allelic diversity of HvCBF2a, HvCBF4b and HvCBF14 was investigated by combining the analysis of SNPs and read counts. This approach has proven very effective to identify gene paralogs and copy number variants of HvCBF2 and the HvCBF4b-HvCBF2a segment. A multiple linear regression model which considers allelic variation at these genes suggests a major involvement of HvCBF14, rather than copy number variation of HvCBF4b-HvCBF2a, in controlling frost tolerance in barley. Overall, the present study provides powerful resource and tools to discover novel alleles at relevant genes in barley.

Exome sequences and multi-environment field trials elucidate the genetic basis of adaptation in barley.

Broadening the genetic base of crops is crucial for developing varieties to respond to global agricultural challenges such as climate change. Here, we analysed a diverse panel of 371 domesticated lines of the model crop barley to explore the genetics of crop adaptation. We first collected exome sequence data and phenotypes of key life history traits from contrasting multi-environment common garden trials. Then we applied refined statistical methods, including some based on exomic haplotype states, for genotype-by-environment (G×E) modelling. Sub-populations defined from exomic profiles were coincident with barley's biology, geography and history, and explained a high proportion of trial phenotypic variance. Clear G×E interactions indicated adaptation profiles that varied for landraces and cultivars. Exploration of circadian clock-related genes, associated with the environmentally adaptive days to heading trait (crucial for the crop's spread from the Fertile Crescent), illustrated complexities in G×E effect directions, and the importance of latitudinally based genic context in the expression of large-effect alleles. Our analysis supports a gene-level scientific understanding of crop adaption and leads to practical opportunities for crop improvement, allowing the prioritisation of genomic regions and particular sets of lines for breeding efforts seeking to cope with climate change and other stresses.

Double Gamers-Can Modified Natural Regulators of Higher Plants Act as Antagonists against Phytopathogens? The Case of Jasmonic Acid Derivatives.

As key players in biotic stress response of plants, jasmonic acid (JA) and its derivatives cover a specific and prominent role in pathogens-mediated signaling and hence are promising candidates for a sustainable management of phytopathogenic fungi. Recently, JA directed antimicrobial effects on plant pathogens has been suggested, supporting the theory of oxylipins as double gamers in plant-pathogen interaction. Based on these premises, six derivatives (dihydrojasmone and cis-jasmone, two thiosemicarbazonic derivatives and their corresponding complexes with copper) have been evaluated against 13 fungal species affecting various economically important herbaceous and woody crops, such as cereals, grapes and horticultural crops: Phaeoacremonium minimum, Neofusicoccum parvum, Phaeomoniella chlamydospora, Fomitiporia mediterranea, Fusarium poae, F. culmorum, F. graminearum, F. oxysporum f. sp. lactucae,F. sporotrichioides, Aspergillus flavus, Rhizoctonia solani,Sclerotinia spp. and Verticillium dahliae. The biological activity of these compounds was assessed in terms of growth inhibition and, for the two mycotoxigenic species A. flavus and F. sporotrichioides, also in terms of toxin containment. As expected, the inhibitory effect of molecules greatly varied amongst both genera and species; cis-jasmone thiosemicarbazone in particular has shown the wider range of effectiveness. However, our results show that thiosemicarbazones derivatives are more effective than the parent ketones in limiting fungal growth and mycotoxins production, supporting possible applications for the control of pathogenic fungi.

A Molecular Toolbox to Identify and Quantify Grape Varieties: On the Trace of "Glera".

A pillar of wine authenticity is the variety/ies used. Ampelographic descriptors and SSR markers, included in several national and international databases, are extensively used for varietal identification purposes. Recently, SNP markers have been proposed as useful for grape varietal identification and traceability. Our study has been directed toward the development of a molecular toolbox able to track grape varieties from the nursery to the must. Two complementary approaches were developed, exploiting SNP markers with two different technologies, i.e., a high-throughput platform for varietal identification and a digital PCR system for varietal quantification. As proof-of-concept, the toolbox was successfully applied to the identification and quantification of the "Glera" variety along the Prosecco wine production chain. The assays developed found their limits in commercial, aged wines.

Multi-environment genome -wide association mapping of culm morphology traits in barley.

In cereals with hollow internodes, lodging resistance is influenced by morphological characteristics such as internode diameter and culm wall thickness. Despite their relevance, knowledge of the genetic control of these traits and their relationship with lodging is lacking in temperate cereals such as barley. To fill this gap, we developed an image analysis-based protocol to accurately phenotype culm diameters and culm wall thickness across 261 barley accessions. Analysis of culm trait data collected from field trials in seven different environments revealed high heritability values (>50%) for most traits except thickness and stiffness, as well as genotype-by-environment interactions. The collection was structured mainly according to row-type, which had a confounding effect on culm traits as evidenced by phenotypic correlations. Within both row-type subsets, outer diameter and section modulus showed significant negative correlations with lodging (<-0.52 and <-0.45, respectively), but no correlation with plant height, indicating the possibility of improving lodging resistance independent of plant height. Using 50k iSelect SNP genotyping data, we conducted multi-environment genome-wide association studies using mixed model approach across the whole panel and row-type subsets: we identified a total of 192 quantitative trait loci (QTLs) for the studied traits, including subpopulation-specific QTLs and 21 main effect loci for culm diameter and/or section modulus showing effects on lodging without impacting plant height. Providing insights into the genetic architecture of culm morphology in barley and the possible role of candidate genes involved in hormone and cell wall-related pathways, this work supports the potential of loci underpinning culm features to improve lodging resistance and increase barley yield stability under changing environments.

Genomic Prediction of Grain Yield in a Barley MAGIC Population Modeling Genotype per Environment Interaction.

Multi-parent Advanced Generation Inter-crosses (MAGIC) lines have mosaic genomes that are generated shuffling the genetic material of the founder parents following pre-defined crossing schemes. In cereal crops, these experimental populations have been extensively used to investigate the genetic bases of several traits and dissect the genetic bases of epistasis. In plants, genomic prediction models are usually fitted using either diverse panels of mostly unrelated accessions or individuals of biparental families and several empirical analyses have been conducted to evaluate the predictive ability of models fitted to these populations using different traits. In this paper, we constructed, genotyped and evaluated a barley MAGIC population of 352 individuals developed with a diverse set of eight founder parents showing contrasting phenotypes for grain yield. We combined phenotypic and genotypic information of this MAGIC population to fit several genomic prediction models which were cross-validated to conduct empirical analyses aimed at examining the predictive ability of these models varying the sizes of training populations. Moreover, several methods to optimize the composition of the training population were also applied to this MAGIC population and cross-validated to estimate the resulting predictive ability. Finally, extensive phenotypic data generated in field trials organized across an ample range of water regimes and climatic conditions in the Mediterranean were used to fit and cross-validate multi-environment genomic prediction models including G×E interaction, using both genomic best linear unbiased prediction and reproducing kernel Hilbert space along with a non-linear Gaussian Kernel. Overall, our empirical analyses showed that genomic prediction models trained with a limited number of MAGIC lines can be used to predict grain yield with values of predictive ability that vary from 0.25 to 0.60 and that beyond QTL mapping and analysis of epistatic effects, MAGIC population might be used to successfully fit genomic prediction models. We concluded that for grain yield, the single-environment genomic prediction models examined in this study are equivalent in terms of predictive ability while, in general, multi-environment models that explicitly split marker effects in main and environmental-specific effects outperform simpler multi-environment models.