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Conceived as a general introduction to the book, this chapter is a reminder of the core concepts of genetic mapping and molecular marker-based prediction. It provides an overview of the principles and the evolution of methods for mapping the variation of complex traits, and methods for QTL-based prediction of human disease risk and animal and plant breeding value. The principles of linkage-based and linkage disequilibrium-based QTL mapping methods are described in the context of the simplest, single-marker, methods. Methodological evolutions a.re analysed in relation with their ability to account for the complexity of the genotype-phenotype relations. Main characteristics of the genetic architecture of complex traits, drawn from QTL mapping works using large populations of unrelated individuals, are presented. Methods combining marker-QTL association data into polygenic risk score that captures pa.rt of an individual's susceptibility to complex diseases are reviewed. Principles of best linear mixed model-based prediction of breeding value in animal- and plant-breeding programs using phenotypic and pedigree data, are summarized and methods for moving from BLUP to marker-QTL BLUP are presented. Factors influencing the additional genetic progress achieved by using molecular data and rules for their optimization are discussed.

This volume explores the conceptual framework and the practical issues related to genomic prediction of complex traits in human medicine and in animal and plant breeding. The book is organized into five parts. Part One reminds molecular genetics approaches intending to predict phenotypic variations. Part Two presents the principles of genomic prediction of complex traits, and reviews factors that affect its reliability. Part Three describes genomic prediction methods, including machine-learning approaches, accounting for different degree of biological complexity, and reviews the associated computer-packages. Part Four reports on emerging trends such as phenomic prediction and incorporation into genomic prediction models of “omics” data and crop growth models. Part Five is dedicated to lessons learned from cases studies in the fields of human health and animal and plant breeding, and to methods for analysis of the economic effectiveness of genomic prediction. Written in the highly successful Methods in Molecular Biology series format, the book provides theoretical bases and practical guidelines for an informed decision making of practitioners and identifies pertinent routes for further methodological researches.

Ce Forum avait pour objet une diminution des asymétries de connaissances et d'informations, des besoins de renforcement des capacités entre des différentes communautés d'acteurs, sur les conditions d'évaluation des techniques d'édition du génome CRISPR/Cas9, dans le cadre de leur usage potentiel sur une varièté de riz à Madagascar. L'organisation de débats reposait sur des grilles de question et différents ateliers pour qualifier les convergences et divergences. Le rapport présent à été soumis à validation aux participants du forum qui a réuni 50 à 60 participants pendant 2 jours. Ces derniers représentaient 4 communautés d'acteurs : les Organisation de producteurs en provenance du territoire Malgache (FITAME Morondava, MTF Ambilobe, APDIP Tsiroanimandidy, CRAM Fianarantsoa, VFTM Fianarantsoa, TTM Chambre d'Agriculture, réseau SOA, FIFATA, Coalition Paysanne, CEFFEL), les services publics et institutions internationales : ministères : agriculture, environnement, recherche ; AFD, FAO; le secteur privé : ONG (Inter-aide, AFDI…), entreprises et la recherche agronomique : Fofifa, Inera (Burkina), Africa rice, Cirad et universitaires facultés : d'agronomie, d'économie et de droit de Tana.

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.

International audience; Salinity tolerance is an important quality for European rice grown in river deltas. We evaluated the salinity tolerance of a panel of 235 temperate japonica rice accessions genotyped with 30,000 SNP markers. The panel was exposed to mild salt stress (50 mM NaCI; conductivity of 6 dS m(-1)) at the seedling stage. Eight different root and shoot growth parameters were measured for both the control and stressed treatments. The Na+ and K+ mass fractions of the stressed plants were measured using atomic absorption spectroscopy. The salt treatment affected plant growth, particularly the shoot parameters. The panel showed a wide range of Na+/K+ ratio and the temperate accessions were distributed over an increasing axis, from the most resistant to the most susceptible checks. We conducted a genome-wide association study on indices of stress response and ion mass fractions in the leaves using a classical mixed model controlling structure and kinship. A total of 27 QTLs validated by sub sampling were identified. For indices of stress responses, we also used another model that focused on marker x treatment interactions and detected 50 QTLs, three of which were also identified using the classical method. We compared the positions of the significant QTLs to those of approximately 300 genes that play a role in rice salt tolerance. The positions of several QTLs were close to those of genes involved in calcium signaling and metabolism, while other QTLs were close to those of kinases. These results reveal the salinity tolerance of accessions with a temperate japonica background. Although the detected QTLs must be confirmed by other approaches, the number of associations linked to candidate genes involved in calcium-mediated ion homeostasis highlights pathways to explore in priority to understand the salinity tolerance of temperate rice.

The increase in rice production needed to meet future demand requires renewed cropping systems and rice varieties with enhanced resources use efficiency and adaptation to environmental stresses in the context of climate change. Genomic selection (GS) has the potential to accelerate the development of such varieties. We present an overview of the proof-of-concept studies conducted during the last decade with the aim of providing rice breeders with tailored GS methods and tools. These studies involved complementary breeding programs (pedigree breeding, population improvement, etc.), mobilising different compartments of rice genetic diversity (indica, tropical and temperate japonica), and targeting a wide range of traits (yield potential, nitrogen use efficiency, adaptation to alternate watering and drying, salinity tolerance, drought tolerance, exclusion of heavy metals, outcrossing and hybrid seed production abilities, etc.). The objective was to evaluate the importance of different factors known to influence the accuracy of genomic prediction: marker density, linkage disequilibrium, trait heritability and genetic architecture, characteristics of the training population, relatedness between training and candidate populations, statistical models, etc.). Our results showed notably that GS can accelerate genetic gain in both pedigree and population breeding schemes by increasing selection intensity and by shortening the selection cycle. Rice diversity panels provide accurate genomic predictions for complex traits in the progenies of biparental crosses involving members of the panel. Genomic prediction accounting for genotype-by-environment interactions offers an effective framework for breeding simultaneously for adaptation to an abiotic stress and for performance under normal cropping conditions. The degree of relatedness between the training and the candidate population matter more than the size of the training set per se. Whatever the genetic background of the training and the candidate populations (and the associated linkage disequilibrium), average marker density of more than one SNP every 20 kb does not improve prediction accuracy. Further simulation studies are needed to assess the impact of GS on long-term genetic gain and diversity, to adjust the GS strategy accordingly. In the light of these results, we propose a strategy for embedding international rice gene discovery and ecophysiological ideotype modeling research in a GS based rice breeding program.

Effective harnessing by crop breeding programs of genetic gains expected from the integration of genomic resources, high throughput phenotyping capabilities, and efficient statistical methods to model the complexity of empirical data requires adjustments of the breeding strategies. Tools that simulate breeding features are needed to explore the effects of those adjustments and their potential interactions. Simulations are useful because they allow rapid replicated testing of a wide range of hypotheses at low cost, for example, the initial feasibility of genomic selection or the impact of the reference population structure. Simulation of breeding strategies is well adapted to investigate long-term effects of selection, which are often infeasible using real experiments due to time and cost requirements. Different tools have been proposed to reflect the complexity of the studied population (genomes structure, genetic architecture of the traits, and relatedness among individuals). However, few of them are specifically designed to simulate breeding strategies in crop species. We have therefore developed breedgenr, a simulation tool dedicated to meet breeder's needs to rationalize the different steps of a breeding scheme. breedgenr is an R package that present two distinctive features compared to existing tools. First, breedgenr uses real genotypic and phenotypic data from breeding programs to generate the reference population. Thus, it limits the number of possible hypotheses and scenarios regarding the evolutionary history and the structure of the breeding populations. Second breedgenr is based on non-parametric approaches to calibrate genotype-phenotype relationship. Consequently, it does not rest on specific assumptions regarding the genetic model and the genetic architecture of the phenotypic traits considered. Robustness and functionality of breedgenr were evaluated using real datasets on rice and different breeding schemes. The objective was to design breeding schemes that integrate different genomic selection scenarios. The results confirmed the robustness of the calibration of genotype-phenotype relationships based on the chosen non-parametric kernel methods, and the capability of breedgenr to simulate breeding populations and different breeding schemes: pedigree breeding and recurrent selection. Further ascertaining of breedgenr robustness and adjustment-expansion of its functionalities are presented.

Background Plant root systems play a major role in anchoring and in water and nutrient uptake from the soil. The root cone angle is an important parameter of the root system architecture because, combined with root depth, it helps to determine the volume of soil explored by the plant. Two genes, DRO1 and SOR1, and several QTLs for root cone angle have been discovered in the last 5 years. Results To find other QTLs linked to root cone angle, a genome-wide association mapping study was conducted on two panels of 162 indica and 169 japonica rice accessions genotyped with two sets of SNP markers (genotyping-by-sequencing set with approximately 16,000 markers and high-density-rice-array set with approximately 300,000 markers). The root cone angle of all accessions was measured using a screen protractor on images taken after 1 month of plant growth in the Rhizoscope phenotyping system. The distribution of the root cone angle in the indica panel was Gaussian, but several accessions of the japonica panel (all the bulus from Indonesia and three temperate japonicas from Nepal or India) appeared as outliers with a very wide root cone angle. The data were submitted to association mapping using a mixed model with control of structure and kinship. A total of 15 QTLs for the indica panel and 40 QTLs for the japonica panel were detected. Genes underlying these QTLs (+/−50 kb from the significant markers) were analyzed. We focused our analysis on auxin-related genes, kinases, and genes involved in root developmental processes and identified 8 particularly interesting genes. Conclusions The present study identifies new sources of wide root cone angle in rice, proposes ways to bypass some drawbacks of association mapping to further understand the genetics of the trait and identifies candidate genes deserving further investigation. Electronic supplementary material The online version of this article (10.1186/s12284-017-0184-z) contains supplementary material, which is available to authorized users.