Genomic prediction accounting for genotype by environment interaction offers an effective framework for breeding simultaneously for adaptation to an abiotic stress and performance under normal cropping conditions in rice

Developing rice varieties adapted to alternate wetting and drying water management is crucial for the sustainability of irrigated rice cropping systems. Here we report the first study exploring the feasibility of breeding rice for adaptation to alternate wetting and drying using genomic prediction methods that account for genotype by environment interactions. Two breeding populations (a reference panel of 284 accessions and a progeny population of 97 advanced lines) were evaluated under alternate wetting and drying and continuous flooding management systems. The predictive ability of genomic prediction for response variables (index of relative performance and the slope of the joint regression) and for multi-environment genomic prediction models were compared. For the three traits considered (days to flowering, panicle weight and nitrogen-balance index), significant genotype by environment interactions were observed in both populations. In cross validation, predictive ability for the index was on average lower (0.31) than that of the slope of the joint regression (0.64) whatever the trait considered. Similar results were found for progeny validation. Both cross-validation and progeny validation experiments showed that the performance of multi-environment models predicting unobserved phenotypes of untested entrees was similar to the performance of single environment models with differences in predictive ability ranging from -6–4% depending on the trait and on the statistical model concerned. The predictive ability of multi-environment models predicting unobserved phenotypes of entrees evaluated under both water management systems outperformed single environment models by an average of 30%. Practical implications for breeding rice for adaptation to alternate wetting and drying system are discussed

Exploring the potential of endophyte-plant interactions for improving crop sustainable yields in a changing climate

Climate change poses a major threat to global food security, significantly reducing crop yields as cause of abiotic stresses, and for boosting the spread of new and old pathogens and pests. Sustainable crop management as a route to mitigation poses the challenge of recruiting an array of solutions and tools for the new aims. Among these, the deployment of positive interactions between the micro-biotic components of agroecosystems and plants can play a highly significant role, as part of the agro-ecological revolution. Endophytic microorganisms have emerged as a promising solution to tackle this challenge. Among these, Arbuscular Mycorrhizal Fungi (AMF) and endophytic bacteria and fungi have demonstrated their potential to alleviate abiotic stresses such as drought and heat stress, as well as the impacts of biotic stresses. They can enhance crop yields in a sustainable way also by other mechanisms, such as improving the nutrient uptake, or by direct effects on plant physiology. In this review we summarize and update on the main types of endophytes, we highlight several studies that demonstrate their efficacy in improving sustainable yields and explore possible avenues for implementing crop-microbiota interactions. The mechanisms underlying these interactions are highly complex and require a comprehensive understanding. For this reason, omic technologies such as genomics, transcriptomics, proteomics, and metabolomics have been employed to unravel, by a higher level of information, the complex network of interactions between plants and microorganisms. Therefore, we also discuss the various omic approaches and techniques that have been used so far to study plant-endophyte interactions

Knockdown of MLO genes reduces susceptibility to powdery mildew in grapevine

10openInternationalItalian coauthor/editorErysiphe necator is the causal agent of powdery mildew (PM), one of the most destructive diseases of grapevine. PM is controlled by sulfur-based and synthetic fungicides, which every year are dispersed into the environment. This is why PM-resistant varieties should become a priority for sustainable grapevine and wine production. PM resistance can be achieved in other crops by knocking out susceptibility S-genes, such as those residing at genetic loci known as MLO (Mildew Locus O). All MLO S-genes of dicots belong to the phylogenetic clade V, including grapevine genes VvMLO7, 11 and 13, which are upregulated during PM infection, and VvMLO6, which is not upregulated. Before adopting a gene-editing approach to knockout candidate S-genes, the evidence that loss of function of MLO genes can reduce PM susceptibility is necessary. This paper reports the knockdown through RNA interference of VvMLO6, 7, 11 and 13. The knockdown of VvMLO6, 11 and 13 did not decrease PM severity, whereas the knockdown of VvMLO7 in combination with VvMLO6 and VvMLO11 reduced PM severity up to 77%. The knockdown of VvMLO7 and VvMLO6 seemed to be important for PM resistance, whereas a role for VvMLO11 does not seem likely. Cell wall appositions (papillae) were present in both resistant and susceptible lines in response to PM attack. Thirteen genes involved in defense were less upregulated in infected mlo plants, highlighting the early mlo-dependent disruption of PM invasionopenPessina, S.; Lenzi, L.; Perazzolli, M.; Campa, M.; Dalla Costa, L.; Urso, S.; Vale, G.; Salamini, F.; Velasco, R.; Malnoy, M.Pessina, S.; Lenzi, L.; Perazzolli, M.; Campa, M.; Dalla Costa, L.; Urso, S.; Vale, G.; Salamini, F.; Velasco, R.; Malnoy, M.A

High accuracy of genome-enabled prediction of belowground and physiological traits in barley seedlings

In plants, the study of belowground traits is gaining momentum due to their importance on yield formation and the uptake of water and nutrients. In several cereal crops, seminal root number and seminal root angle are proxy traits of the root system architecture at the mature stages, which in turn contributes to modulating the uptake of water and nutrients. Along with seminal root number and seminal root angle, experimental evidence indicates that the transpiration rate response to evaporative demand or vapor pressure deficit is a key physiological trait that might be targeted to cope with drought tolerance as the reduction of the water flux to leaves for limiting transpiration rate at high levels of vapor pressure deficit allows to better manage soil moisture. In the present study, we examined the phenotypic diversity of seminal root number, seminal root angle, and transpiration rate at the seedling stage in a panel of 8-way Multiparent Advanced Generation Inter-Crosses lines of winter barley and correlated these traits with grain yield measured in different site-by-season combinations. Second, phenotypic and genotypic data of the Multiparent Advanced Generation Inter-Crosses population were combined to fit and cross-validate different genomic prediction models for these belowground and physiological traits. Genomic prediction models for seminal root number were fitted using threshold and log-normal models, considering these data as ordinal discrete variable and as count data, respectively, while for seminal root angle and transpiration rate, genomic prediction was implemented using models based on extended genomic best linear unbiased predictors. The results presented in this study show that genome-enabled prediction models of seminal root number, seminal root angle, and transpiration rate data have high predictive ability and that the best models investigated in the present study include first-order additive × additive epistatic interaction effects. Our analyses indicate that beyond grain yield, genomic prediction models might be used to predict belowground and physiological traits and pave the way to practical applications for barley improvement

Rice diversity panel provides accurate genomic predictions for complex traits in the progenies of biparental crosses involving members of the panel

So far, most potential applications of genomic prediction in plant improvement have been explored using cross validation approaches. This is the first empirical study to evaluate the accuracy of genomic prediction of the performances of progenies in a typical rice breeding program. Using a cross validation approach, we first analyzed the effects of marker selection and statistical methods on the accuracy of prediction of three different heritability traits in a reference population (RP) of 284 inbred accessions. Next, we investigated the size and the degree of relatedness with the progeny population (PP) of sub-sets of the RP that maximize the accuracy of prediction of phenotype across generations, i.e., for 97 F5–F7 lines derived from biparental crosses between 31 accessions of the RP. The extent of linkage disequilibrium was high (r2 = 0.2 at 0.80 Mb in RP and at 1.1 Mb in PP). Consequently, average marker density above one per 22 kb did not improve the accuracy of predictions in the RP. The accuracy of progeny prediction varied greatly depending on the composition of the training set, the trait, LD and minor allele frequency. The highest accuracy achieved for each trait exceeded 0.50 and was only slightly below the accuracy achieved by cross validation in the RP. Our results thus show that relatively high accuracy (0.41–0.54) can be achieved using only a rather small share of the RP, most related to the PP, as the training set. The practical implications of these results for rice breeding programs are discussed. (Résumé d'auteur

Physical Mapping of Bread Wheat Chromosome 5A: An Integrated Approach

The huge size, redundancy, and highly repetitive nature of the bread wheat [Triticum aestivum (L.)] genome, makes it among the most difficult species to be sequenced. To overcome these limitations, a strategy based on the separation of individual chromosomes or chromosome arms and the subsequent production of physical maps was established within the frame of the International Wheat Genome Sequence Consortium (IWGSC). A total of 95,812 bacterial artificial chromosome (BAC) clones of short-arm chromosome 5A (5AS) and long-arm chromosome 5A (5AL) arm-specific BAC libraries were fingerprinted and assembled into contigs by complementary analytical approaches based on the FingerPrinted Contig (FPC) and Linear Topological Contig (LTC) tools. Combined anchoring approaches based on polymerase chain reaction (PCR) marker screening, microarray, and sequence homology searches applied to several genomic tools (i. e., genetic maps, deletion bin map, neighbor maps, BAC end sequences (BESs), genome zipper, and chromosome survey sequences) allowed the development of a high-quality physical map with an anchored physical coverage of 75% for 5AS and 53% for 5AL with high portions (64 and 48%, respectively) of contigs ordered along the chromosome. In the genome of grasses, Brachypodium [Brachypodium distachyon (L.) Beauv.], rice (Oryza sativa L.), and sorghum [Sorghum bicolor (L.) Moench] homologs of genes on wheat chromosome 5A were separated into syntenic blocks on different chromosomes as a result of translocations and inversions during evolution. The physical map presented represents an essential resource for fine genetic mapping and map-based cloning of agronomically relevant traits and a reference for the 5A sequencing projects