Enhancing grapevine breeding efficiency through genomic prediction and selection index.

Grapevine (Vitis vinifera) breeding reaches a critical point. New cultivars are released every year with resistance to powdery and downy mildews. However, the traditional process remains time-consuming, taking 20-25 years, and demands the evaluation of new traits to enhance grapevine adaptation to climate change. Until now, the selection process has relied on phenotypic data and a limited number of molecular markers for simple genetic traits such as resistance to pathogens, without a clearly defined ideotype, and was carried out on a large scale. To accelerate the breeding process and address these challenges, we investigated the use of genomic prediction, a methodology using molecular markers to predict genotypic values. In our study, we focused on 2 existing grapevine breeding programs: Rosé wine and Cognac production. In these programs, several families were created through crosses of emblematic and interspecific resistant varieties to powdery and downy mildews. Thirty traits were evaluated for each program, using 2 genomic prediction methods: Genomic Best Linear Unbiased Predictor and Least Absolute Shrinkage Selection Operator. The results revealed substantial variability in predictive abilities across traits, ranging from 0 to 0.9. These discrepancies could be attributed to factors such as trait heritability and trait characteristics. Moreover, we explored the potential of across-population genomic prediction by leveraging other grapevine populations as training sets. Integrating genomic prediction allowed us to identify superior individuals for each program, using multivariate selection index method. The ideotype for each breeding program was defined collaboratively with representatives from the wine-growing sector.

Efficient Bayesian automatic calibration of a functional-structural wheat model using an adaptive design and a metamodelling approach.

Functional-structural plant models are increasingly being used by plant scientists to address a wide variety of questions. However, the calibration of these complex models is often challenging, mainly because of their high computational cost, and, as a result, error propagation is usually ignored. Here we applied an automatic method to the calibration of WALTer: a functional-structural wheat model that simulates the plasticity of tillering in response to competition for light. We used a Bayesian calibration method to jointly estimate the values of five parameters and quantify their uncertainty by fitting the model outputs to tillering dynamics data. We made recourse to Gaussian process metamodels in order to alleviate the computational cost of WALTer. These metamodels are built from an adaptive design that consists of successive runs of WALTer chosen by an efficient global optimization algorithm specifically adapted to this particular calibration task. The method presented here performed well on both synthetic and experimental data. It is an efficient approach for the calibration of WALTer and should be of interest for the calibration of other functional-structural plant models.

The ecologically relevant genetics of plant-plant interactions.

Interactions among plants have been long recognized as a major force driving plant community dynamics and crop yield. Surprisingly, our knowledge of the ecological genetics associated with variation of plant-plant interactions remains limited. In this opinion article by scientists from complementary disciplines, the international PLANTCOM network identified four timely questions to foster a better understanding of the mechanisms mediating plant assemblages. We propose that by identifying the key relationships among phenotypic traits involved in plant-plant interactions and the underlying adaptive genetic and molecular pathways, while considering environmental fluctuations at diverse spatial and time scales, we can improve predictions of genotype-by-genotype-by-environment interactions and modeling of productive and stable plant assemblages in wild habitats and crop fields.

A genome-wide association and prediction study in grapevine deciphers the genetic architecture of multiple traits and identifies genes under many new QTLs.

To cope with the challenges facing agriculture, speeding-up breeding programs is a worthy endeavor, especially for perennial species such as grapevine, but requires understanding the genetic architecture of target traits. To go beyond the mapping of quantitative trait loci in bi-parental crosses, we exploited a diversity panel of 279 Vitis vinifera L. cultivars planted in 5 blocks in the vineyard. This panel was phenotyped over several years for 127 traits including yield components, organic acids, aroma precursors, polyphenols, and a water stress indicator. The panel was genotyped for 63k single nucleotide polymorphisms by combining an 18K microarray and genotyping-by-sequencing. The experimental design allowed to reliably assess the genotypic values for most traits. Marker densification via genotyping-by-sequencing markedly increased the proportion of genetic variance explained by single nucleotide polymorphisms, and 2 multi-single nucleotide polymorphism models identified quantitative trait loci not found by a single nucleotide polymorphism-by-single nucleotide polymorphism model. Overall, 489 reliable quantitative trait loci were detected for 41% more response variables than by a single nucleotide polymorphism-by-single nucleotide polymorphism model with microarray-only single nucleotide polymorphisms, many new ones compared with the results from bi-parental crosses. A prediction accuracy higher than 0.42 was obtained for 50% of the response variables. Our overall approach as well as quantitative trait locus and prediction results provide insights into the genetic architecture of target traits. New candidate genes and the application into breeding are discussed.

Across-population genomic prediction in grapevine opens up promising prospects for breeding.

Crop breeding involves two selection steps: choosing progenitors and selecting individuals within progenies. Genomic prediction, based on genome-wide marker estimation of genetic values, could facilitate these steps. However, its potential usefulness in grapevine (Vitis vinifera L.) has only been evaluated in non-breeding contexts mainly through cross-validation within a single population. We tested across-population genomic prediction in a more realistic breeding configuration, from a diversity panel to ten bi-parental crosses connected within a half-diallel mating design. Prediction quality was evaluated over 15 traits of interest (related to yield, berry composition, phenology and vigour), for both the average genetic value of each cross (cross mean) and the genetic values of individuals within each cross (individual values). Genomic prediction in these conditions was found useful: for cross mean, average per-trait predictive ability was 0.6, while per-cross predictive ability was halved on average, but reached a maximum of 0.7. Mean predictive ability for individual values within crosses was 0.26, about half the within-half-diallel value taken as a reference. For some traits and/or crosses, these across-population predictive ability values are promising for implementing genomic selection in grapevine breeding. This study also provided key insights on variables affecting predictive ability. Per-cross predictive ability was well predicted by genetic distance between parents and when this predictive ability was below 0.6, it was improved by training set optimization. For individual values, predictive ability mostly depended on trait-related variables (magnitude of the cross effect and heritability). These results will greatly help designing grapevine breeding programs assisted by genomic prediction.

From cultivar mixtures to allelic mixtures: opposite effects of allelic richness between genotypes and genotype richness in wheat.

Agroecosystem diversification through increased crop genetic diversity could provide multiple services such as improved disease control or increased productivity. However, we still poorly understand how genetic diversity affects agronomic performance. We grew 179 inbred lines of durum wheat in pure stands and in 202 binary mixtures in field conditions. We then tested the effect of allelic richness between genotypes and genotype richness on grain yield and Septoria tritici blotch disease. Allelic richness was tested at 19K single nucleotide polymorphisms distributed along the durum wheat genome. Both genotype richness and allelic richness could be equal to 1 or 2. Mixtures were overall more productive and less diseased than their pure stand components. Yet, we identified one locus at which allelic richness between genotypes was associated with increased disease severity and decreased grain yield. The effect of allelic richness at this locus was stronger than the effect of genotype richness on grain yield (-7.6% vs +5.7%). Our results suggest that positive effects of crop diversity can be reversed by unfavourable allelic associations. This highlights the need to integrate genomic data into crop diversification strategies. More generally, investigating plant-plant interactions at the genomic level is promising to better understand biodiversity-ecosystem functioning relationships.

Harnessing multivariate, penalized regression methods for genomic prediction and QTL detection of drought-related traits in grapevine.

Viticulture has to cope with climate change and to decrease pesticide inputs, while maintaining yield and wine quality. Breeding is a key lever to meet this challenge, and genomic prediction a promising tool to accelerate breeding programs. Multivariate methods are potentially more accurate than univariate ones. Moreover, some prediction methods also provide marker selection, thus allowing quantitative trait loci (QTLs) detection and the identification of positional candidate genes. To study both genomic prediction and QTL detection for drought-related traits in grapevine, we applied several methods, interval mapping (IM) as well as univariate and multivariate penalized regression, in a bi-parental progeny. With a dense genetic map, we simulated two traits under four QTL configurations. The penalized regression method Elastic Net (EN) for genomic prediction, and controlling the marginal False Discovery Rate on EN selected markers to prioritize the QTLs. Indeed, penalized methods were more powerful than IM for QTL detection across various genetic architectures. Multivariate prediction did not perform better than its univariate counterpart, despite strong genetic correlation between traits. Using 14 traits measured in semi-controlled conditions under different watering conditions, penalized regression methods proved very efficient for intra-population prediction whatever the genetic architecture of the trait, with predictive abilities reaching 0.68. Compared to a previous study on the same traits, these methods applied on a denser map found new QTLs controlling traits linked to drought tolerance and provided relevant candidate genes. Overall, these findings provide a strong evidence base for implementing genomic prediction in grapevine breeding.

Adoption and Optimization of Genomic Selection To Sustain Breeding for Apricot Fruit Quality.

Genomic selection (GS) is a breeding approach which exploits genome-wide information and whose unprecedented success has shaped several animal and plant breeding schemes through delivering their genetic progress. This is the first study assessing the potential of GS in apricot (Prunus armeniaca) to enhance postharvest fruit quality attributes. Genomic predictions were based on a F1 pseudo-testcross population, comprising 153 individuals with contrasting fruit quality traits. They were phenotyped for physical and biochemical fruit metrics in contrasting climatic conditions over two years. Prediction accuracy (PA) varied from 0.31 for glucose content with the Bayesian LASSO (BL) to 0.78 for ethylene production with RR-BLUP, which yielded the most accurate predictions in comparison to Bayesian models and only 10% out of 61,030 SNPs were sufficient to reach accurate predictions. Useful insights were provided on the genetic architecture of apricot fruit quality whose integration in prediction models improved their performance, notably for traits governed by major QTL. Furthermore, multivariate modeling yielded promising outcomes in terms of PA within training partitions partially phenotyped for target traits. This provides a useful framework for the implementation of indirect selection based on easy-to-measure traits. Thus, we highlighted the main levers to take into account for the implementation of GS for fruit quality in apricot, but also to improve the genetic gain in perennial species.

The wild grape genome sequence provides insights into the transition from dioecy to hermaphroditism during grape domestication.

BACKGROUND: A key step in domestication of the grapevine was the transition from separate sexes (dioecy) in wild Vitis vinifera ssp. sylvestris (V. sylvestris) to hermaphroditism in cultivated Vitis vinifera ssp. sativa (V. vinifera). It is known that V. sylvestris has an XY system and V. vinifera a modified Y haplotype (Yh) and that the sex locus is small, but it has not previously been precisely characterized. RESULTS: We generate a high-quality de novo reference genome for V. sylvestris, onto which we map whole-genome re-sequencing data of a cross to locate the sex locus. Assembly of the full X, Y, and Yh haplotypes of V. sylvestris and V. vinifera sex locus and examining their gene content and expression profiles during flower development in wild and cultivated accessions show that truncation and deletion of tapetum and pollen development genes on the X haplotype likely causes male sterility, while the upregulation of a Y allele of a cytokinin regulator (APRT3) may cause female sterility. The downregulation of this cytokinin regulator in the Yh haplotype may be sufficient to trigger reversal to hermaphroditism. Molecular dating of X and Y haplotypes is consistent with the sex locus being as old as the Vitis genus, but the mechanism by which recombination was suppressed remains undetermined. CONCLUSIONS: We describe the genomic and evolutionary characterization of the sex locus of cultivated and wild grapevine, providing a coherent model of sex determination in the latter and for transition from dioecy to hermaphroditism during domestication.

VviUCC1 Nucleotide Diversity, Linkage Disequilibrium and Association with Rachis Architecture Traits in Grapevine.

Cluster compactness is a trait with high agronomic relevance, affecting crop yield and grape composition. Rachis architecture is a major component of cluster compactness determinism, and is a target trait toward the breeding of grapevine varieties less susceptible to pests and diseases. Although its genetic basis is scarcely understood, a preliminary result indicated a possible involvement of the VviUCC1 gene. The aim of this study was to characterize the VviUCC1 gene in grapevine and to test the association between the natural variation observed for a series of rachis architecture traits and the polymorphisms detected in the VviUCC1 sequence. This gene encodes an uclacyanin plant-specific cell-wall protein involved in fiber formation and/or lignification processes. A high nucleotide diversity in the VviUCC1 gene promoter and coding regions was observed, but no critical effects were predicted in the protein domains, indicating a high level of conservation of its function in the cultivated grapevine. After correcting statistical models for genetic stratification and linkage disequilibrium effects, marker-trait association results revealed a series of single nucleotide polymorphisms (SNPs) significantly associated with cluster compactness and rachis traits variation. Two of them (Y-984 and K-88) affected two common cis-transcriptional regulatory elements, suggesting an effect on phenotype via gene expression regulation. This work reinforces the interest of further studies aiming to reveal the functional effect of the detected VviUCC1 variants on grapevine rachis architecture.

Advances in Breeding for Mixed Cropping - Incomplete Factorials and the Producer/Associate Concept.

Mixed cropping has been suggested as a resource-efficient approach to meet high produce demands while maintaining biodiversity and minimizing environmental impact. Current breeding programs do not select for enhanced general mixing ability (GMA) and neglect biological interactions within species mixtures. Clear concepts and efficient experimental designs, adapted to breeding for mixed cropping and encoded into appropriate statistical models, are lacking. Thus, a model framework for GMA and SMA (specific mixing ability) was established. Results of a simulation study showed that an incomplete factorial design combines advantages of two commonly used full factorials, and enables to estimate GMA, SMA, and their variances in a resource-efficient way. This model was extended to the Producer (Pr) and Associate (As) concept to exploit additional information based on fraction yields. It was shown that the Pr/As concept allows to characterize genotypes for their contribution to total mixture yield, and, when relating to plant traits, allows to describe biological interaction functions (BIF) in a mixed crop. Incomplete factorial designs show the potential to drastically improve genetic gain by testing an increased number of genotypes using the same amount of resources. The Pr/As concept can further be employed to maximize GMA in an informed and efficient way. The BIF of a trait can be used to optimize species ratios at harvest as well as to extend our understanding of competitive and facilitative interactions in a mixed plant community. This study provides an integrative methodological framework to promote breeding for mixed cropping.

A novel high-density grapevine (Vitis vinifera L.) integrated linkage map using GBS in a half-diallel population.

KEY MESSAGE: A half-diallel population involving five elite grapevine cultivars was generated and genotyped by GBS, and highly-informative segregation data was used to construct a high-density genetic map for Vitis vinifera L. Grapevine is one of the most relevant fruit crops in the world. Deeper genetic knowledge could assist modern grapevine breeding programs to develop new wine grape varieties able to face climate change effects. To assist in the rapid identification of markers for crop yield components, grape quality traits and adaptation potential, we generated a large Vitis vinifera L. population (N = 624) by crossing five red wine cultivars in a half-diallel scheme, which was subsequently sequenced by an efficient GBS procedure. A high number of fully informative genetic variants was detected using a novel mapping approach capable of reconstructing local haplotypes from adjacent biallelic SNPs, which were subsequently used to construct the densest consensus genetic map available for the cultivated grapevine to date. This 1378.3-cM map integrates 10 bi-parental consensus maps and orders 4437 markers in 3353 unique positions on 19 chromosomes. Markers are well distributed all along the grapevine reference genome, covering up to 98.8% of its genomic sequence. Additionally, a good agreement was observed between genetic and physical orders, adding confidence in the quality of this map. Collectively, our results pave the way for future genetic studies (such as fine QTL mapping) aimed to understand the complex relationship between genotypic and phenotypic variation in the cultivated grapevine. In addition, the method used (which efficiently delivers a high number of fully informative markers) could be of interest to other outbred organisms, notably perennial fruit crops.

Quantitative Assessment of Grapevine Wood Colonization by the Dieback Fungus Eutypa lata.

Eutypa lata is a fungal pathogen causing severe dieback in vineyards worldwide. This fungus colonizes vines through pruning wounds, eventually causing a brown sectorial necrosis in wood as well as stunted vegetative growth. Several years may pass between infection and the expression of external symptoms, hindering the rapid evaluation of both grapevine cultivars susceptibility and E. lata variation in aggressiveness. We aimed to develop a rapid quantitative method for the assessment of wood colonization after inoculation of cuttings in controlled conditions. We used several grape cultivars varying in susceptibility in the vineyard and fungal isolates with different levels of aggressiveness to monitor wood colonization during a maximum period of 2 months. Re-isolation allowed demonstration of the effects of both cultivars and fungal isolates on the rate of wood colonization. We also developed a real-time PCR method that was efficient in measuring fungal biomass, which was found to be correlated with isolate aggressiveness based on foliar symptom severity. The real-time PCR approach appears to be a useful technology to evaluate grapevine susceptibility to E. lata, and could be adapted to other pathogens associated with grapevine trunk diseases.

The epigenomic landscape of African rainforest hunter-gatherers and farmers.

The genetic history of African populations is increasingly well documented, yet their patterns of epigenomic variation remain uncharacterized. Moreover, the relative impacts of DNA sequence variation and temporal changes in lifestyle and habitat on the human epigenome remain unknown. Here we generate genome-wide genotype and DNA methylation profiles for 362 rainforest hunter-gatherers and sedentary farmers. We find that the current habitat and historical lifestyle of a population have similarly critical impacts on the methylome, but the biological functions affected strongly differ. Specifically, methylation variation associated with recent changes in habitat mostly concerns immune and cellular functions, whereas that associated with historical lifestyle affects developmental processes. Furthermore, methylation variation--particularly that correlated with historical lifestyle--shows strong associations with nearby genetic variants that, moreover, are enriched in signals of natural selection. Our work provides new insight into the genetic and environmental factors affecting the epigenomic landscape of human populations over time.

A statistical framework for joint eQTL analysis in multiple tissues.

Mapping expression Quantitative Trait Loci (eQTLs) represents a powerful and widely adopted approach to identifying putative regulatory variants and linking them to specific genes. Up to now eQTL studies have been conducted in a relatively narrow range of tissues or cell types. However, understanding the biology of organismal phenotypes will involve understanding regulation in multiple tissues, and ongoing studies are collecting eQTL data in dozens of cell types. Here we present a statistical framework for powerfully detecting eQTLs in multiple tissues or cell types (or, more generally, multiple subgroups). The framework explicitly models the potential for each eQTL to be active in some tissues and inactive in others. By modeling the sharing of active eQTLs among tissues, this framework increases power to detect eQTLs that are present in more than one tissue compared with "tissue-by-tissue" analyses that examine each tissue separately. Conversely, by modeling the inactivity of eQTLs in some tissues, the framework allows the proportion of eQTLs shared across different tissues to be formally estimated as parameters of a model, addressing the difficulties of accounting for incomplete power when comparing overlaps of eQTLs identified by tissue-by-tissue analyses. Applying our framework to re-analyze data from transformed B cells, T cells, and fibroblasts, we find that it substantially increases power compared with tissue-by-tissue analysis, identifying 63% more genes with eQTLs (at FDR = 0.05). Further, the results suggest that, in contrast to previous analyses of the same data, the majority of eQTLs detectable in these data are shared among all three tissues.

The ABO blood group is a trans-species polymorphism in primates.

The ABO histo-blood group, the critical determinant of transfusion incompatibility, was the first genetic polymorphism discovered in humans. Remarkably, ABO antigens are also polymorphic in many other primates, with the same two amino acid changes responsible for A and B specificity in all species sequenced to date. Whether this recurrence of A and B antigens is the result of an ancient polymorphism maintained across species or due to numerous, more recent instances of convergent evolution has been debated for decades, with a current consensus in support of convergent evolution. We show instead that genetic variation data in humans and gibbons as well as in Old World monkeys are inconsistent with a model of convergent evolution and support the hypothesis of an ancient, multiallelic polymorphism of which some alleles are shared by descent among species. These results demonstrate that the A and B blood groups result from a trans-species polymorphism among distantly related species and has remained under balancing selection for tens of millions of years-to date, the only such example in hominoids and Old World monkeys outside of the major histocompatibility complex.

TriAnnot: A Versatile and High Performance Pipeline for the Automated Annotation of Plant Genomes.

In support of the international effort to obtain a reference sequence of the bread wheat genome and to provide plant communities dealing with large and complex genomes with a versatile, easy-to-use online automated tool for annotation, we have developed the TriAnnot pipeline. Its modular architecture allows for the annotation and masking of transposable elements, the structural, and functional annotation of protein-coding genes with an evidence-based quality indexing, and the identification of conserved non-coding sequences and molecular markers. The TriAnnot pipeline is parallelized on a 712 CPU computing cluster that can run a 1-Gb sequence annotation in less than 5 days. It is accessible through a web interface for small scale analyses or through a server for large scale annotations. The performance of TriAnnot was evaluated in terms of sensitivity, specificity, and general fitness using curated reference sequence sets from rice and wheat. In less than 8 h, TriAnnot was able to predict more than 83% of the 3,748 CDS from rice chromosome 1 with a fitness of 67.4%. On a set of 12 reference Mb-sized contigs from wheat chromosome 3B, TriAnnot predicted and annotated 93.3% of the genes among which 54% were perfectly identified in accordance with the reference annotation. It also allowed the curation of 12 genes based on new biological evidences, increasing the percentage of perfect gene prediction to 63%. TriAnnot systematically showed a higher fitness than other annotation pipelines that are not improved for wheat. As it is easily adaptable to the annotation of other plant genomes, TriAnnot should become a useful resource for the annotation of large and complex genomes in the future.

Roadmap for annotating transposable elements in eukaryote genomes.

Current high-throughput techniques have made it feasible to sequence even the genomes of non-model organisms. However, the annotation process now represents a bottleneck to genome analysis, especially when dealing with transposable elements (TE). Combined approaches, using both de novo and knowledge-based methods to detect TEs, are likely to produce reasonably comprehensive and sensitive results. This chapter provides a roadmap for researchers involved in genome projects to address this issue. At each step of the TE annotation process, from the identification of TE families to the annotation of TE copies, we outline the tools and good practices to be used.

In search of lost trajectories: Recovering the diversification of transposable elements.

Transposable elements (TEs) are DNA sequences that have the capacity to move and duplicate within genomes, and occasionally between them. They are present in almost all species and are especially prevalent in eukaryotes where they can account for most of the genomic content. As a result of their dynamics and their mere presence, TEs can profoundly shape genomes and gene expression. With the current pace of sequencing technology improvement, a rapidly increasing number of genomes, particularly from non-model species, are being sequenced. However, the complete annotation of these genomes and especially of the TEs they contain, still poses fundamental difficulties. In a recent article, we presented a combined method that automatically annotates TEs with accuracy and sensitivity, and takes their diversification dynamics into account in the de novo annotation process. Here, we further discuss several additional aspects of our results, notably in the light of our knowledge of TE dynamics and the conceptual model behind the TE detection algorithms currently in use. In addition, we propose a new approach that uses simulations to improve algorithm performance.