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Motivation Inferring gene regulatory networks in non-independent genetically related panels is a methodological challenge. This hampers evolutionary and biological studies using heterozygote individuals such as in wild sunflower populations or cultivated hybrids. Results First, we simulated 100 datasets of gene expressions and polymorphisms, displaying the same gene expression distributions, heterozygosities and heritabilities as in our dataset including 173 genes and 353 genotypes measured in sunflower hybrids. Secondly, we performed a meta-analysis based on six inference methods [least absolute shrinkage and selection operator (Lasso), Random Forests, Bayesian Networks, Markov Random Fields, Ordinary Least Square and fast inference of networks from directed regulation (Findr)] and selected the minimal density networks for better accuracy with 64 edges connecting 79 genes and 0.35 area under precision and recall (AUPR) score on average. We identified that triangles and mutual edges are prone to errors in the inferred networks. Applied on classical datasets without heterozygotes, our strategy produced a 0.65 AUPR score for one dataset of the DREAM5 Systems Genetics Challenge. Finally, we applied our method to an experimental dataset from sunflower hybrids. We successfully inferred a network composed of 105 genes connected by 106 putative regulations with a major connected component. Availability and implementation Our inference methodology dedicated to genomic and transcriptomic data is available at https://forgemia.inra.fr/sunrise/inference_methods. Supplementary information Supplementary data are available at Bioinformatics online.

Crop wild relatives represent valuable sources of alleles for crop improvement, including adaptation to climate change and emerging diseases. However, introgressions from wild relatives might have deleterious effects on desirable traits, including yield, due to linkage drag. Here, we analyzed the genomic and phenotypic impacts of wild introgressions in inbred lines of cultivated sunflower to estimate the impacts of linkage drag. First, we generated reference sequences for seven cultivated and one wild sunflower genotype, as well as improved assemblies for two additional cultivars. Next, relying on previously generated sequences from wild donor species, we identified introgressions in the cultivated reference sequences, as well as the sequence and structural variants they contain. We then used a ridge-regression best linear unbiased prediction (BLUP) model to test the effects of the introgressions on phenotypic traits in the cultivated sunflower association mapping population. We found that introgression has introduced substantial sequence and structural variation into the cultivated sunflower gene pool, including >3,000 new genes. While introgressions reduced genetic load at protein-coding sequences, they mostly had negative impacts on yield and quality traits. Introgressions found at high frequency in the cultivated gene pool had larger effects than low-frequency introgressions, suggesting that the former likely were targeted by artificial selection. Also, introgressions from more distantly related species were more likely to be maladaptive than those from the wild progenitor of cultivated sunflower. Thus, breeding efforts should focus, as far as possible, on closely related and fully compatible wild relatives.

Crop wild relatives represent valuable sources of alleles for crop improvement, including adaptation to climate change and emerging diseases. However, introgressions from wild relatives might have deleterious effects on desirable traits, including yield, due to linkage drag. Here we comprehensively analyzed the genomic and phenotypic impacts of wild introgressions into cultivated sunflower to estimate the impacts of linkage drag. First, we generated new reference sequences for seven cultivated and one wild sunflower genotype, as well as improved assemblies for two additional cultivars. Next, relying on previously generated sequences from wild donor species, we identified introgressions in the cultivated reference sequences, as well as the sequence and structural variants they contain. We then used a ridge regression model to test the effects of the introgressions on phenotypic traits in the cultivated sunflower association mapping population. We found that introgression has introduced substantial sequence and structural variation into the cultivated sunflower gene pool, including > 3,000 new genes. While introgressions reduced genetic load at protein-coding sequences and positively affected traits associated with abiotic stress resistance, they mostly had negative impacts on yield and quality traits. Introgressions found at high frequency in the cultivated gene pool had larger effects than low frequency introgressions, suggesting that the former likely were targeted by artificial selection. Also, introgressions from more distantly related species were more likely to be maladaptive than those from the wild progenitor of cultivated sunflower. Thus, pre-breeding efforts should focus, as far as possible, on closely related and fully compatible wild relatives.

This article describes how RNA expression data of 173 genes were produced on 384 sunflowerhybrids grown infield conditions. Sunflower hybrids were selected to represent genetic diversity withincultivated sunflower. The RNA was extracted from mature leaves at one time seven days after anthesis.These data allow to differentiate the different genotype behaviours and constitute a valuable resource to thecommunity to study the adaptation of crops tofield conditions and the molecular basis of heterosis. It isavailable on data.inra.fr repository.; Cet article décrit la production des niveaux d’expression de 173 gènes dans 384 hybrides de tournesol cultivés en conditions de champ. Les hybrides sont issus de parents choisis pour représenter la diversité génétique dans le tournesol cultivé. Les ARN ont été extraits à partir de feuilles matures environ sept jours après la floraison. Ces données permettent de différencier les comportements des différents génotypes et constituent une ressource importante pour les chercheurs intéressés dans l’adaptation des espèces cultivées aux conditions agronomiques et aux bases moléculaires de l’hétérosis. Elles sont disponibles sur le portail Data INRAE : data.inra.fr.

International audience; Genomic selection, a hot topic in genetics, consists in predicting breeding values of selection candidates, using a large number of genetic markers , due to the recent progress in molecular biology. One of the most popular method chosen by geneticists is Ridge regression. In this context, we focus on some predictive aspects of Ridge regression and present theoretical results regarding the accuracy criteria, i.e., the correlation between predicted value and true value. We show the influence of the singular values, the regularization parameter , and the projection of the signal on the space spanned by the rows of the design matrix. Asymptotic results, in a high dimensional framework, are also given, and we prove that the convergence to an optimal accuracy highly depends on a weighted projection of the signal on each subspace. We discuss also on how to improve the prediction. Last, illustrations on simulated and real data are proposed.

Understanding the genetic basis of phenotypic plasticity is crucial for predicting and managing climate change effects on wild plants and crops. Here, we combined crop modeling and quantitative genetics to study the genetic control of oil yield plasticity for multiple abiotic stresses in sunflower. First we developed stress indicators to characterize 14 environments for three abiotic stresses (cold, drought and nitrogen) using the SUNFLO crop model and phenotypic variations of three commercial varieties. The computed plant stress indicators better explain yield variation than descriptors at the climatic or crop levels. In those environments, we observed oil yield of 317 sunflower hybrids and regressed it with three selected stress indicators. The slopes of cold stress norm reaction were used as plasticity phenotypes in the following genome-wide association study. Among the 65,534 tested SNP, we identified nine QTL controlling oil yield plasticity to cold stress. Associated SNP are localized in genes previously shown to be involved in cold stress responses: oligopeptide transporters, LTP, cystatin, alternative oxidase, or root development. This novel approach opens new perspectives to identify genomic regions involved in genotype-by-environment interaction of a complex traits to multiple stresses in realistic natural or agronomical conditions.

International audience

The comparison of QTL detection performed on an elite panel and an (elite $$\times$$ exotic) progeny shows that introducing exotic germplasm into breeding programs can bring new interesting allelic diversity. Selection of stable varieties producing the highest amount of extractable sugar per hectare (ha), resistant to diseases, and respecting environmental criteria is undoubtedly the main target for sugar beet breeding. As sodium, potassium, and $$\alpha$$ -amino nitrogen in sugar beets are the impurities that have the biggest negative impact on white sugar extraction, it is interesting to reduce their concentration in further varieties. However, domestication history and strong selection pressures have affected the genetic diversity needed to achieve this goal. In this study, quantitative trait locus (QTL) detection was performed on two populations, an (elite $$\times$$ exotic) sugar beet progeny and an elite panel, to find potentially new interesting regions brought by the exotic accession. The three traits linked with impurities content were studied. Some QTLs were detected in both populations, the majority in the elite panel because of most statistical power. Some of the QTLs were colocated and had favorable effect in the progeny since the exotic allele was linked with a decrease in the impurity content. A few number of favorable QTLs were detected in the progeny, only. Consequently, introgressing exotic genetic material into sugar beet breeding programs can allow the incorporation of new interesting alleles.

This article describes how the transcriptomic data were produced on sunflower plants subjected to water deficit. Twenty-four sunflower (Helianthus annuus) genotypes were selected to represent genetic diversity within cultivated sunflower and included both inbred lines and their hybrids. Drought stress was applied to plants in pots at the vegetative stage using the high-throughput phenotyping platform Heliaphen. Here, we provide transcriptomic data from sunflower leaves. These data differentiate both plant water status and the different genotypes. They constitute a valuable resource to the community to study adaptation of crops to drought and the transcriptomic basis of heterosis.; Données transcriptomiques de feuilles de huit lignées de tournesol et de leurs 16 hybrides soumis à un stress hydrique. Cet article décrit la production de données transcriptomiques sur des plantes de tournesol soumises à un stress hydrique. Vingt-quatre génotypes de tournesol (Helianthus annuus) ont été sélectionnés pour représenter la diversité génétique parmi le tournesol cultivé et comprennent à la fois des lignées et leurs hybrides. Le stress hydrique a été appliqué sur des plantes en pots au stade végétatif grâce à la plateforme de phénotypage haut-débit Heliaphen. Ici, nous mettons à disposition les données transcritomiques de feuilles de ces plantes. Les données permettent de différencier à la fois le statut hydrique et les différents génotypes. Elles constituent une ressource importante pour la communauté pour étudier l'adaptation des plantes à la sécheresse et les bases de l'hétérosis au niveau transcriptomique.

Heliaphen is an outdoor pot platform designed for high-throughput phenotyping. It allows automated management of drought scenarios and plant monitoring during the whole plant cycle. A robot moving between plants growing in 15L pots monitors plant water status and phenotypes plant or leaf morphology, from which we can compute more complex traits such as the response of leaf expansion (LE) or plant transpiration (TR) to water deficit. Here, we illustrate the platform capabilities for sunflower on two practical cases: a genetic and genomics study for the response to drought of yield-related traits and a simulation study, where we use measured parameters as inputs for a crop simulation model. For the genetic study, classical measurements of thousand-kernel weight (TKW) were done on a sunflower bi-parental population under water stress and control conditions managed automatically. The association study using the TKW drought-response highlighted five genetic markers. A complementary transcriptomic experiment identified closeby candidate genes differentially expressed in the parental backgrounds in drought conditions. For the simulation study, we used the SUNFLO crop simulation model to assess the impact of two traits measured on the platform (LE and TR) on crop yield in a large population of environments. We conducted simulations in 42 contrasted locations across Europe and 21 years of climate data. We defined the pattern of abiotic stresses occurring at this continental scale and identified ideotypes (i.e. genotypes with specific traits values) that are more adapted to specific environment types. This study exemplifies how phenotyping platforms can help with the identification of the genetic architecture of complex response traits and the estimation of eco-physiological model parameters in order to define ideotypes adapted to different environmental conditions.

Key message This study compares five models of GWAS, to show the added value of non-additive modeling of allelic effects to identify genomic regions controlling flowering time of sunflower hybrids. Abstract Genome-wide association studies are a powerful and widely used tool to decipher the genetic control of complex traits. One of the main challenges for hybrid crops, such as maize or sunflower, is to model the hybrid vigor in the linear mixed models, considering the relatedness between individuals. Here, we compared two additive and three non-additive association models for their ability to identify genomic regions associated with flowering time in sunflower hybrids. A panel of 452 sunflower hybrids, corresponding to incomplete crossing between 36 male lines and 36 female lines, was phenotyped in five environments and genotyped for 2,204,423 SNPs. Intra-locus effects were estimated in multi-locus models to detect genomic regions associated with flowering time using the different models. Thirteen quantitative trait loci were identified in total, two with both model categories and one with only non-additive models. A quantitative trait loci on LG09, detected by both the additive and non-additive models, is located near a GAI homolog and is presented in detail. Overall, this study shows the added value of non-additive modeling of allelic effects for identifying genomic regions that control traits of interest and that could participate in the heterosis observed in hybrids. Electronic supplementary material The online version of this article (doi:10.1007/s00122-017-3003-4) contains supplementary material, which is available to authorized users.

Soybean [Glycine max (L.) Merr.] is a major crop with high seed protein content. Genomic selection is expected to be a valuable tool in improving the efficiency of breeding programs, especially for complex traits such as yield. This study aimed to evaluate the accuracy of genomic selection for yield and seed protein content in a soybean breeding population. Having a structured population, we compared genomic prediction accuracy obtained using models calibrated across or within two subpopulations: early lines and late lines. Calibrations within subpopulations were more efficient. Using a medium density of markers and genomic best linear unbiased prediction(GBLUP) model, which assumes an additive polygenic architecture, we predicted ~32 and39% of phenotypic variation among late lines for seed protein content and yield, respectively. Prediction accuracy was further improved by including epistasis in the GBLUP model. Further, we assessed accuracies obtained using several Bayesian models that assume different distributions for marker effects: Bayesian ridge regression, Bayesian LASSO, Bayes Cp, and Bayes R. Overall, these approaches did not improve prediction accuracy. In this study, were ported preliminary results relevant to the study of the efficiency of genomic selection use in a breeding program.

SNP genotyping of 114 cultivated sunflower populations showed that the multiplication process and the main traits selected during breeding of sunflower cultivars drove molecular diversity of the populations. The molecular diversity in a set of 114 cultivated sunflower populations was studied by single-nucleotide polymorphism genotyping. These populations were chosen as representative of the 400 entries in the INRA collection received or developed between 1962 and 2011 and made up of land races, open-pollinated varieties, and breeding pools. Mean allele number varied from 1.07 to 1.90. Intra-population variability was slightly reduced according to the number of multiplications since entry but some entries were probably largely homozygous when received. A principal component analysis was used to study inter-population variability. The first 3 axes accounted for 17% of total intra-population variability. The first axis was significantly correlated with seed oil content, more closely than just the distinction between oil and confectionary types. The second axis was related to the presence or absence of restorer genes and the third axis to flowering date and possibly to adaptation to different climates. Our results provide arguments highlighting the effect of the maintenance process on the within population genetic variability as well as on the impact of breeding for major agronomic traits on the between population variability of the collection. Propositions are made to improve sunflower population maintenance procedures to keep maximum genetic variability for future breeding.

International audience; Coffea canephora is subject to enormous competitive challenges from other crops, especially for farmer sustainability and consumer requirements. Coffee breeding programs have to focus on specific traits linked to these two key targets, such as quality character, largely depending on the bean's biochemical composition and field yield. Two segregating populations A and B, from crosses between a hybrid (Congolese x Guinean) FRT58 parental clone and a Congolese FRT51 genotype and between two Congolese parents FRT67 and FRT51, respectively, were used to characterize the quantitative trait loci (QTL) involved in agronomic and biochemical traits. A consensus genetic map was established using 249 SSRs covering 1,201 cM. Three QTL detection models per population with MapQTL (model I) and MCQTL (model II) followed by a connected population approach with MCQTL (model III) were compared based on their efficiency, precision for QTL detection, and their genetic effect assessment (additive, dominance, and parental-favorable allele). The analysis detected a total of 143 QTLs, 60 of which were shared between the three models; 28 found with two models; and two, 13, and 40 specific from models I, II, and III, respectively. The last model III based on connected populations is much more efficient in detecting QTLs with low variance explained and led to the genetic characterization of favorable allele. Thanks to this comparison of three QTL detection models on our quantitative genetic study, we will give a new insight for coffee breeding programs dedicated to managing complex agronomic or qualitative traits.

Key message We enhance power and accuracy of QTL mapping in multiple related families, by clustering the founders of the families on their local genomic similarity. Abstract MCQTL is a linkage mapping software application that allows the joint QTL mapping of multiple related families. In its current implementation, QTLs are modeled with one or two parameters for each parent that is a founder of the multi-cross design. The higher the number of parents, the higher the number of model parameters which can impact the power and the accuracy of the mapping. We propose to make use of the availability of denser and denser genotyping information on the founders to lessen the number of MCQTL parameters and thus boost the QTL discovery. We developed clusthaplo, an R package (http://cran.r-project.org/web/packages/clusthaplo/index.html), which aims to cluster haplotypes using a genomic similarity that reflects the probability of sharing the same ancestral allele. Computed in a sliding window along the genome and followed by a clustering method, the genomic similarity allows the local clustering of the parent haplotypes. Our assumption is that the haplotypes belonging to the same class transmit the same ancestral allele. So their putative QTL allelic effects can be modeled with the same parameter, leading to a parsimonious model, that is plugged in MCQTL. Intensive simulations using three maize data sets showed the significant gain in power and in accuracy of the QTL mapping with the ancestral allele model compared to the classical MCQTL model. MCQTL_LD (clusthaplo outputs plug in MCQTL) is a versatile and powerful tool for QTL mapping in multiple related families that makes use of linkage and linkage disequilibrium (web site http://carlit.toulouse.inra.fr/MCQTL/). Electronic supplementary material The online version of this article (doi:10.1007/s00122-014-2267-1) contains supplementary material, which is available to authorized users.

Background As for many crops, new high-quality grapevine varieties requiring less pesticide and adapted to climate change are needed. In perennial species, breeding is a long process which can be speeded up by gaining knowledge about quantitative trait loci linked to agronomic traits variation. However, due to the long juvenile period of these species, establishing numerous highly recombinant populations for high resolution mapping is both costly and time-consuming. Genome wide association studies in germplasm panels is an alternative method of choice, since it allows identifying the main quantitative trait loci with high resolution by exploiting past recombination events between cultivars. Such studies require adequate panel design to represent most of the available genetic and phenotypic diversity. Assessing linkage disequilibrium extent and panel power is also needed to determine the marker density required for association studies. Results Starting from the largest grapevine collection worldwide maintained in Vassal (France), we designed a diversity panel of 279 cultivars with limited relatedness, reflecting the low structuration in three genetic pools resulting from different uses (table vs wine) and geographical origin (East vs West), and including the major founders of modern cultivars. With 20 simple sequence repeat markers and five quantitative traits, we showed that our panel adequately captured most of the genetic and phenotypic diversity existing within the entire Vassal collection. To assess linkage disequilibrium extent and panel power, we genotyped single nucleotide polymorphisms: 372 over four genomic regions and 129 distributed over the whole genome. Linkage disequilibrium, measured by correlation corrected for kinship, reached 0.2 for a physical distance between 9 and 458 Kb depending on genetic pool and genomic region, with varying size of linkage disequilibrium blocks. This panel achieved reasonable power to detect associations between traits with high broad-sense heritability (> 0.7) and causal loci with intermediate allelic frequency and strong effect (explaining > 10 % of total variance). Conclusions Our association panel constitutes a new, highly valuable resource for genetic association studies in grapevine, and deserves dissemination to diverse field and greenhouse trials to gain more insight into the genetic control of many agronomic traits and their interaction with the environment. Electronic supplementary material The online version of this article (doi:10.1186/s12870-016-0754-z) contains supplementary material, which is available to authorized users.

Chantier qualité GA; Variations in meat quality traits are under complex genetic control and improvement has been hampered by the difficulty in their measurement. Several QTL have been reported for different meat quality related traits, but few genes have been described which explain large amounts of the phenotypic variation. The use of single nucleotide polymorphism (SNP) marker panels with predictive value for carcass traits have been evaluated for cattle and SNP are commercially available even though their predictive accuracy may be low in different breeds. To identify new molecular markers for meat quality, an association study was performed in 15 breeds of cattle using 389 SNP belonging to 206 candidate genes known to be involved in muscle development, metabolism and structure. Fifty-four SNP belonging to 20 different genes were found associated with different growth, carcass and meat quality traits. Some of them were novel associations and other were replications of known associations. Among the former, the gene-network associated with the calpain/calpastatin system was shown to be associated with meat texture, although small effects are found for the examined polymorphisms. Novel associations also included SNP in AANAT which was associated with collagen (P=0.006), CAST with fatty acid muscle composition (P=0.00003), CYP1A1 with juiciness (P=0.0005), DGAT2 with physical traits (P=0.0009) and lipid content (P=0.01) in muscle, MADH3 with the myofibrilar fragmentation index (MFI) (P=0.01), NEB with weight (P=0.00009), PCSK1 with juiciness (P=0.002), PLOD3 with carcass performance (P=0.0009) and fatty acids (P=0.04), and PGAM2 and VIM with post-mortem maturation (P=0.00008 and 0.000005, respectively). These data provide a starting point to investigate the complex gene-networks underlying economically important traits which are of importance to the beef industry for the improvement of production efficiency and meat quality.

Understanding the genetic basis of phenotypic plasticity is crucial for predicting and managing climate change effects on wild plants and crops. Here, we combined crop modelling and quantitative genetics to study the genetic control of oil yield plasticity for multiple abiotic stresses in sunflower. First, we developed stress indicators to characterize 14 environments for three abiotic stresses (cold, drought and nitrogen) using the SUNFLO crop model and phenotypic variations of three commercial varieties. The computed plant stress indicators better explain yield variation than descriptors at the climatic or crop levels. In those environments, we observed oil yield of 317 sunflower hybrids and regressed it with three selected stress indicators. The slopes of cold stress norm reaction were used as plasticity phenotypes in the following genome-wide association study. Among the 65 534 tested Single Nucleotide Polymorphisms (SNPs), we identified nine quantitative trait loci controlling oil yield plasticity to cold stress. Associated single nucleotide polymorphisms are localized in genes previously shown to be involved in cold stress responses: oligopeptide transporters, lipid transfer protein, cystatin, alternative oxidase or root development. This novel approach opens new perspectives to identify genomic regions involved in genotype-by-environment interaction of a complex traits to multiple stresses in realistic natural or agronomical conditions.

Genetic diversity in worldwide population of beets is strongly affected by the domestication history, and the comparison of linkage disequilibrium in worldwide and elite populations highlights strong selection pressure. Genetic relationships and linkage disequilibrium (LD) were evaluated in a set of 2035 worldwide beet accessions and in another of 1338 elite sugar beet lines, using 320 and 769 single nucleotide polymorphisms, respectively. The structures of the populations were analyzed using four different approaches. Within the worldwide population, three of the methods gave a very coherent picture of the population structure. Fodder beet and sugar beet accessions were grouped together, separated from garden beets and sea beets, reflecting well the origins of beet domestication. The structure of the elite panel, however, was less stable between clustering methods, which was probably because of the high level of genetic mixing in breeding programs. For the linkage disequilibrium analysis, the usual measure (r 2) was used, and compared with others that correct for population structure and relatedness (r 2 , r 2 , r 2 ). The LD as measured by r 2 persisted beyond 10 cM within the elite panel and fell below 0.1 after less than 2 cM in the worldwide population, for almost all chromosomes. With correction for relatedness, LD decreased under 0.1 by 1 cM for almost all chromosomes in both populations, except for chromosomes 3 and 9 within the elite panel. In these regions, the larger extent of LD could be explained by strong selection pressure.

Adaptation to pollinators is a key factor of diversification in angiosperms. The Caribbean sister genera Rhytidophyllum and Gesneria present an important diversification of floral characters. Most of their species can be divided in two major pollination syndromes. Large-open flowers with pale colours and great amount of nectar represent the generalist syndrome, while the hummingbird-specialist syndrome corresponds to red tubular flowers with a less important nectar volume. Repeated convergent evolution toward the generalist syndrome in this group suggests that such transitions rely on few genes of moderate to large effect. To test this hypothesis, we built a linkage map and performed a QTL detection for divergent pollination syndrome traits by crossing one specimen of the generalist species Rhytidophyllum auriculatum with one specimen of the hummingbird pollinated R. rupincola. Using geometric morphometrics and univariate traits measurements, we found that floral shape among the second-generation hybrids is correlated with morphological variation observed between generalist and hummingbird-specialist species at the genus level. The QTL analysis showed that colour and nectar volume variation between syndromes involve each one major QTL while floral shape has a more complex genetic basis and rely on few genes of moderate effect. Finally we did not detect any genetic linkage between the QTLs underlying those traits. This genetic independence of traits could have facilitated evolution toward optimal syndromes.

Quantitative trait loci (QTL) detection experiments have often been restricted to large biallelic populations. Use of connected multiparental crosses has been proposed to increase the genetic variability addressed and to test for epistatic interactions between QTL and the genetic background. We present here the results of a QTL detection performed on six connected F2 populations of 150 F2:3 families each, derived from four maize inbreds and evaluated for three traits of agronomic interest. The QTL detection was carried out by composite interval mapping on each population separately, then on the global design either by taking into account the connections between populations or not. Epistatic interactions between loci and with the genetic background were tested. Taking into account the connections between populations increased the number of QTL detected and the accuracy of QTL position estimates. We detected many epistatic interactions, particularly for grain yield QTL (R 2 increase of 9.6%). Use of connections for the QTL detection also allowed a global ranking of alleles at each QTL. Allelic relationships and epistasis both contribute to the lack of consistency for QTL positions observed among populations, in addition to the limited power of the tests. The potential benefit of assembling favorable alleles by marker-assisted selection are discussed.

Bacterial wilt, one of the most devastating bacterial diseases of plants worldwide, is caused by Ralstonia solanacearum and affects many important crop species. We show that several strains isolated from solanaceous crops in Europe are pathogenic in different accessions of Arabidopsis thaliana. One of these strains, 14.25, causes wilting symptoms in A. thaliana accession Landsberg erecta (Ler) and no apparent symptoms in accession Columbia (Col-0). Disease development and bacterial multiplication in the susceptible Ler accession depend on functional hypersensitive response and pathogenicity (hrp) genes, key elements for bacterial pathogenicity. Genetic analysis using Ler x Col-0 recombinant inbred lines showed that resistance is governed by at least three loci: QRS1 (Quantitative Resistance to R. solanacearum) and QRS2 on chromosome 2, and QRS3 on chromosome 5. These loci explain about 90% of the resistance carried by the Col-0 accession. The ERECTA gene, which encodes a leucine-rich repeat receptor-like kinase (LRR-RLK) and affects development of aerial organs, is dimorphic in our population and lies close to QRS1. Susceptible Ler plants transformed with a wild-type ERECTA gene, and the LER line showed increased disease resistance to R. solanacearum as indicated by reduced wilt symptoms and impaired bacterial growth, suggesting unexpected cross-talk between resistance and developmental pathways.

The establishment of the nitrogen-fixing symbiosis between rhizobia and legumes requires an exchange of signals between the two partners. In response to flavonoids excreted by the host plant, rhizobia synthesize Nod factors (NFs) which elicit, at very low concentrations and in a specific manner, various symbiotic responses on the roots of the legume hosts. NFs from several rhizobial species have been characterized. They all are lipo-chitooligosaccharides, consisting of a backbone of generally four or five glucosamine residues N-acylated at the non-reducing end, and carrying various O-substituents. The N-acyl chain and the other substituents are important determinants of the rhizobial host specificity. A number of nodulation genes which specify the synthesis of NFs have been identified. All rhizobia, in spite of their diversity, possess conserved nodABC genes responsible for the synthesis of the N-acylated oligosaccharide core of NFs, which suggests that these genes are of a monophyletic origin. Other genes, the host specific nod genes, specify the substitutions of NFs. The central role of NFs and nod genes in the Rhizobium-legume symbiosis suggests that these factors could be used as molecular markers to study the evolution of this symbiosis. We have studied a number of NFs which are N-acylated by alpha,beta-unsaturated fatty acids. We found that the ability to synthesize such NFs does not correlate with taxonomic position of the rhizobia. However, all rhizobia that produce NFs such nodulate plants belonging to related tribes of legumes, the Trifolieae, Vicieae, and Galegeae, all of them being members of the so-called galegoid group. This suggests that the ability to recognize the NFs with alpha-beta-unsaturated fatty acids is limited to this group of legumes, and thus might have appeared only once in the course of legume evolution, in the galegoid phylum.

Bacterial wilt caused by the soilborne bacterium Ralstonia solanacearum attacks hundreds of plant species, including many agriculturally important crops. Natural resistance to this disease has been found in some species and is usually inherited as a polygenic trait. In tomato, a model crop plant, genetic analysis previously revealed the involvement of several QTL (quantitative trait loci) controlling resistance and, in all of these studies with different strains of the pathogen, loci on chromosome 6 played the predominant role in controlling this trait. Using quantitative data collected from a greenhouse test F3 population, we identified a new locus on chromosome 12 that appears to be active specifically against a race 1 biovar 3 Pss4 bacterial strain endemic to Taiwan. Chromosome 6 still contributes significantly to the control of the resistance, and weaker associations of the trait to other regions of the genome are observed. These results are discussed in the context of current molecular knowledge about the strain specificity of disease resistance genes.

Ralstonia solanacearum is a soil-borne bacterium that causes the serious disease known as bacterial wilt in many plant species. In tomato, several QTL controlling resistance have been found, but in different studies, markers spanning a large region of chromosome 6 showed strong association with the resistance. By using two different approaches to analyze the data from a field test F3 population, we show that at least two separate loci ∼30 cM apart on this chromosome are most likely involved in the resistance. First, a temporal analysis of the progression of symptoms reveals a distal locus early in the development of the disease. As the disease progresses, the maximum LOD peak observed shifts toward the proximal end of the chromosome, obscuring the distal locus. Second, although classical interval mapping could only detect the presence of one locus, a statistical “two-QTL model” test, specifically adapted for the resolution of linked QTL, strongly supported the hypothesis for the presence of two loci. These results are discussed in the context of current molecular knowledge about disease resistance genes on chromosome 6 and observations made by tomato breeders during the production of bacterial wilt-resistant varieties.

The aim of this paper was to compare different methods for testing the presence of one versus multiple QTLs on a same chromosome. We describe different methods that have partially been taken out of the literature. We perform simulations covering different situations to compare the power of these methods for detecting more than one QTL. None of the tests considered appear to be similar; that is, the first-type error depends on the value of the parameters concerning the first QTL. The method starting with a two-QTL model is the most powerful in many situations.

Alfalfa (Medicago sativa L.) is a forage legume of world-wide importance whose both allogamous and autotetraploid nature maximizes the genetic diversity within natural and cultivated populations. This genetic diversity makes difficult the discrimination between two related populations. We analyzed this genetic diversity by screening DNA from individual plants of eight cultivated and natural populations of M. sativa and M. falcata using the RAPD method. A high level of genetic variation was found within and between populations. Using five primers, 64 intense bands were scored as present or absent across all populations. Most of the loci were revealed to be highly polymorphic whereas very few population-specific polymorphisms were identified. From these observations, we adopted a method based on the Roger's genetic distance between populations using the observed frequency of bands to discriminate populations pairwise. Except for one case, the between-population distances were all significantly different from zero. We have also determined the minimal number of bands and individuals required to test for the significance of between-population distances.

International audience; Association mapping and linkage mapping were used to identify quantitative trait loci (QTL) and/or causative mutations involved in the control of flowering time in cultivated sunflower Helianthus annuus. A panel of 384 inbred lines was phenotyped through testcrosses with two tester inbred lines across 15 location × year combinations. A recombinant inbred line (RIL) population comprising 273 lines was phenotyped both per se and through testcrosses with one or two testers in 16 location × year combinations. In the association mapping approach, kinship estimation using 5,923 single nucleotide polymorphisms was found to be the best covariate to correct for effects of panel structure. Linkage disequilibrium decay ranged from 0.08 to 0.26 cM for a threshold of 0.20, after correcting for structure effects, depending on the linkage group (LG) and the ancestry of inbred lines. A possible hitchhiking effect is hypothesized for LG10 and LG08. A total of 11 regions across 10 LGs were found to be associated with flowering time, and QTLs were mapped on 11 LGs in the RIL population. Whereas eight regions were demonstrated to be common between the two approaches, the linkage disequilibrium approach did not detect a documented QTL that was confirmed using the linkage mapping approach.

Chantier qualité GA; Consumers demand healthy and palatable meat, both factors being affected by fat composition. However, red meat has relatively high concentration of saturated fatty acids and low concentration of the beneficial polyunsaturated fatty acids. To select animals prone to produce particular fat types, it is necessary to identify the genes influencing muscle lipid composition. This paper describes an association study in which a large panel of candidate genes involved in adipogenesis, lipid metabolism and energy homoeostasis was tested for effects on fat composition in 15 European cattle breeds. Sixteen genes were found to have significant effects on different lipid traits, and among these, CFL1 and MYOZ1 were found to have large effects on the ratio of 18: 2/18: 3, CRI1 on the amount of neutral adrenic acid (22: 4 n-6), MMP1 on docosahexaenoic acid (22: 6 n-3) and conjugated linoleic acid, PLTP on the ratio of n-6: n-3 and IGF2R on flavour. Several genes - ALDH2, CHRNE, CRHR2, DGAT1, IGFBP3, NEB, SOCS2, SUSP1, TCF12 and FOXO1 - also were found to be associated with both lipid and organoleptic traits although with smaller effect. The results presented here help in understanding the genetic and biochemical background underlying variations in fatty acid composition and flavour in beef.

In this chapter, we study different gene regulatory network learning methods based on penalized linear regressions (the Lasso regression and the Dantzig Selector), Bayesian networks, and random forests. We also replicated the learning scheme using bootstrapped sub-samples of the observations. The biological motivation relies on a tough nut to crack in Systems Biology: understanding the intertwined action of genome elements and gene activity to model gene regulatory features of an organism. We introduce the used methodologies, and then assess the methods on simulated “Systems Genetics” (or genetical genomics) datasets. Our results show that methods have very different performances depending on tested simulation settings: total number of genes in the considered network, sample size, gene expression heritability, and chromosome length. We observe that the proposed approaches are able to capture important interaction patterns, but parameter tuning or ad hoc pre- and post-processing may also have an important effect on the overall learning quality.

International audience; Advancements in genotyping are rapidly decreasing marker costs and increasing marker density. This opens new possibilities for mapping quantitative trait loci (QTL), in particular by combining linkage disequilibrium information and linkage analysis (LDLA). In this study, we compared different approaches to detect QTL for four traits of agronomical importance in two large multi-parental datasets of maize (Zea mays L.) of 895 and 928 testcross progenies composed of 7 and 21 biparental families, respectively, and genotyped with 491 markers. We compared to traditional linkage-based methods two LDLA models relying on the dense genotyping of parental lines with 17,728 SNP: one based on a clustering approach of parental line segments into ancestral alleles and one based on single marker information. The two LDLA models generally identified more QTL (60 and 52 QTL in total) than classical linkage models (49 and 44 QTL in total). However, they performed inconsistently over datasets and traits suggesting that a compromise must be found between the reduction of allele number for increasing statistical power and the adequacy of the model to potentially complex allelic variation. For some QTL, the model exclusively based on linkage analysis, which assumed that each parental line carried a different QTL allele, was able to capture remaining variation not explained by LDLA models. These complementarities between models clearly suggest that the different QTL mapping approaches must be considered to capture the different levels of allelic variation at QTL involved in complex traits.

Summary: The aim of the MCQTL software package is to perform QTL mapping in multi-cross designs. It allows the analysis of the usual populations derived from inbred lines and can link the families by assuming that the QTL locations are the same in all of them. Moreover, a diallel modelling of the QTL genotypic effects is allowed in multiple related families. The implemented model is a linear regression model. A composite interval mapping and an iterative QTL mapping are implemented to deal with multiple QTL models. Marker cofactor selections by forward or backward stepwise methods are implemented as well as computation of threshold test value by permutation. Availability: The program is available on request after signing a licence agreement; free of charge for academic and non-profit organizations at http://www.genoplante.org(Bioinformatics products). Contact: contactbioinf@genoplante.com

Inferring gene regulatory networks tends to use several biological information. Here we use data from genetic markers and expression data in the framework of discrete static bayesian networks. We compare several scores and also the impact of a network connectivity a priori. We propose and compare two models with existing approaches of gene regulatory network inference. On simulated data one of our models reached better results in the case of small sample size. We use this model on real data in Arabidopsis thaliana.; L’inférence de réseaux de régulation de gènes s’oriente actuellement vers l’utilisation conjointe d’informations biologiques complémentaires. Nous utilisons ici des données de marqueurs génétiques en plus des classiques données d’expression dans le cadre des réseaux bayésiens statiques discrets. Nous comparons les qualités de différents scores ainsi que l’impact d’un a priori lié à la connectivité des réseaux. Nous proposons et comparons deux modélisations aux approches existantes pour l’inférence de réseaux de régulation. Sur des données simulées, l’un de nos modèles obtient les meilleurs résultats dans le cas d’échantillons de petites tailles. Nous utilisons ce même modèle sur des données réelles d’Arabidopsis thaliana.

UMR 1334 AGAP : Equipe AFEF ‘Architecture et Fonctionnement des Espèces fruitières’ ; Team AFFS ‘Architecture and Functioning of Fruit Species’; Among the several linkage disequilibrium measures known to capture different features of the non-independence between alleles at different loci, the most commonly used for diallelic loci is the r(2) measure. In the present study, we tackled the problem of the bias of r(2) estimate, which results from the sample structure and/or the relatedness between genotyped individuals. We derived two novel linkage disequilibrium measures for diallelic loci that are both extensions of the usual r(2) measure. The first one, r(S)(2), uses the population structure matrix, which consists of information about the origins of each individual and the admixture proportions of each individual genome. The second one, r(V)(2), includes the kinship matrix into the calculation. These two corrections can be applied together in order to correct for both biases and are defined either on phased or unphased genotypes. We proved that these novel measures are linked to the power of association tests under the mixed linear model including structure and kinship corrections. We validated them on simulated data and applied them to real data sets collected on Vitis vinifera plants. Our results clearly showed the usefulness of the two corrected r(2) measures, which actually captured 'true' linkage disequilibrium unlike the usual r(2) measure.

Genome-wide association (GWA) genetics is potentially of great interest in grapevine to find much more rapidly and efficiently the main loci (QTLs) and alleles involved in the variation of traits of interest to breeding, compared to searches based on individual segregating progenies. Knowledge of the structure of genetic diversity and linkage disequilibrium (LD) is a necessary prerequisite to design association genetics studies. Vitis vinifera genetic diversity and structure are already well characterized, but LD in this species has not been thoroughly estimated yet. Other Vitis species are a large potential source of resistance to biotic and abiotic stresses for introgression into V. vinifera, but relatively few genetic resources are available in germplasm repositories and the genetic diversity of these species is hardly known. The objectives of the DL-Vitis project (ANR 2009-2011) are to assess genetic structure and linkage disequilibrium in V. vinifera and 3 American species (V. riparia, V. cinerea, V. aestivalis). For V. vinifera, 3 samples of 93 cultivars were defined, based on available SSR data, by minimizing structure and kinship and maximizing genetic distances. An additional sample of 93 accessions of wild V. vinifera (subsp. silvestris) was also defined. For each American species, 150-200 seeds were collected in natura last autumn. Genetic diversity and structure will be assessed this year using SSRs, and a sample of 93 accessions will be defined for each species. Four genomic regions of 2 Mb each were chosen to estimate LD between specific SNPs in ca. 1 gene out of 3 in each sample of 93 individuals described above. Novel estimates of LD taking structure and kinship into account were developed. The first results obtained in V. vinifera in 2 genomic regions showed differences in the rate of LD decay between samples and regions, suggesting past occurrence of differential selection.

A quantitative trait locus (QTL) analysis designed for a multi-parent population was carried out and tested in oil palm (Elaeis guineensis Jacq.), which is a diploid cross-fertilising perennial species. A new extension of the MCQTL package was especially designed for crosses between heterozygous parents. The algorithm, which is now available for any allogamous species, was used to perform and compare two types of QTL search for small size families, within-family analysis and across-family analysis, using data from a 2 × 2 complete factorial mating experiment involving four parents from three selected gene pools. A consensus genetic map of the factorial design was produced using 251 microsatellite loci, the locus of the Sh major gene controlling fruit shell presence, and an AFLP marker of that gene. A set of 76 QTLs involved in 24 quantitative phenotypic traits was identified. A comparison of the QTL detection results showed that the across-family analysis proved to be efficient due to the interconnected families, but the family size issue is just partially solved. The identification of QTL markers for small progeny numbers and for marker-assisted selection strategies is discussed. Electronic supplementary material The online version of this article (doi:10.1007/s00122-010-1284-y) contains supplementary material, which is available to authorized users.

Chantier qualité GA; International audience; Population structure is a recurrent problem for the detection of associations between a marker and a trait, because it can lead to an excess of false positives of the association tests. One popular way of circumventing this problem is the use of family based tests, which consider the transmission of the genotype from the parents to the offspring. Here we focus on quantitative traits and study the Abecasis “orthogonal" quantitative transmission disequilibrium test, which is commonly used in family based association studies. We derive the probability distribution of this test under a general model of structured population. Our derivations show that this test leads to a small excess of false positives due to population structure. They also illustrate and quantify how the heterogeneity in genotypes and phenotypes between populations affect the power of the test. We finally show that the excess of false positives observed for the Abecasis “orthogonal" test may also be found for the Allison “linear" test, though at a lower extent.

Background Since 2001, the use of more and more dense maps has made researchers aware that combining linkage and linkage disequilibrium enhances the feasibility of fine-mapping genes of interest. So, various method types have been derived to include concepts of population genetics in the analyses. One major drawback of many of these methods is their computational cost, which is very significant when many markers are considered. Recent advances in technology, such as SNP genotyping, have made it possible to deal with huge amount of data. Thus the challenge that remains is to find accurate and efficient methods that are not too time consuming. The study reported here specifically focuses on the half-sib family animal design. Our objective was to determine whether modelling of linkage disequilibrium evolution improved the mapping accuracy of a quantitative trait locus of agricultural interest in these populations. We compared two methods of fine-mapping. The first one was an association analysis. In this method, we did not model linkage disequilibrium evolution. Therefore, the modelling of the evolution of linkage disequilibrium was a deterministic process; it was complete at time 0 and remained complete during the following generations. In the second method, the modelling of the evolution of population allele frequencies was derived from a Wright-Fisher model. We simulated a wide range of scenarios adapted to animal populations and compared these two methods for each scenario. Results Our results indicated that the improvement produced by probabilistic modelling of linkage disequilibrium evolution was not significant. Both methods led to similar results concerning the location accuracy of quantitative trait loci which appeared to be mainly improved by using four flanking markers instead of two. Conclusions Therefore, in animal half-sib designs, modelling linkage disequilibrium evolution using a Wright-Fisher model does not significantly improve the accuracy of the QTL location when compared to a simpler method assuming complete and constant linkage between the QTL and the marker alleles. Finally, given the high marker density available nowadays, the simpler method should be preferred as it gives accurate results in a reasonable computing time.

National audience

National audience