The domesticated sunflower, Helianthus annuus L., is a global oil crop that has promise for climate change adaptation, because it can maintain stable yields across a wide variety of environmental conditions, including drought 1 . Even greater resilience is achievable through the mining of resistance alleles from compatible wild sunflower relatives 2,3 , including numerous extremophile species 4 . Here we report a high-quality reference for the sunflower genome (3.6 gigabases), together with extensive transcriptomic data from vegetative and floral organs. The genome mostly consists of highly similar, related sequences 5 and required single-molecule realtime sequencing technologies for successful assembly. Genome analyses enabled the reconstruction of the evolutionary history of the Asterids, further establishing the existence of a whole-genome triplication at the base of the Asterids II clade 6 and a sunflowerspecific whole-genome duplication around 29 million years ago 7 . An integrative approach combining quantitative genetics, expression and diversity data permitted development of comprehensive gene networks for two major breeding traits, flowering time and oil metabolism, and revealed new candidate genes in these networks. We found that the genomic architecture of flowering time has been shaped by the most recent whole-genome duplication, which suggests that ancient paralogues can remain in the same regulatory networks for dozens of millions of years. This genome represents a cornerstone for future research programs aiming to exploit genetic diversity to improve biotic and abiotic stress resistance and oil production, while also considering agricultural constraints and human nutritional needs 8,9 .As the only major crop domesticated in North America, with its sunlike inflorescence that inspired artists, the sunflower is both a social icon and a major research focus for scientists. In evolutionary biology, the Helianthus genus is a long-time model for hybrid speciation and adaptive introgression 10 . In plant science, the sunflower is a model for understanding solar tracking 11 and inflorescence development 12 .Despite this large interest, assembling its genome has been extremely difficult as it mainly consists of long and highly similar repeats. This complexity has challenged leading-edge assembly protocols for close to a decade 13 .To finally overcome this challenge, we generated a 102× sequencing coverage of the genome of the inbred line XRQ using 407 singlemolecule real-time (SMRT) cells on the PacBio RS II platform. Production of 32 million very long reads allowed us to generate a genome assembly that captures 3 gigabases (Gb) (80% of the estimated genome size) in 13,957 sequence contigs. Four high-density genetic maps were combined with a sequence-based physical map to build the sequences of the 17 pseudo-chromosomes that anchor 97% of the gene content (Fig.
-Sunflower (Helianthus annuus L.) crop is often labelled as environmental-friendly for many objective reasons: limited amounts of N fertiliser, no irrigation, and limited use of pesticides. In addition, sunflower has a potential for providing multiple ecosystem services in diverse cropping systems (e.g. pollinators feeding). However agroecological innovations have been less developed or disseminated than for cereals or oilseed rape. Based on results from the sunflower research consortium in Toulouse (Mestries and Debaeke. 2016. Journées d'échanges Tournesol, 28 et 29 juin 2016, Toulouse (France)), we illustrate some innovating and promising approaches for more agroecological practices in sunflower cropping. Our results suggested that: integrated crop management could be proposed to limit the use of pesticides and mitigate crop damages; cover crops could be used as biofumigants to control soilborne diseases in sunflower; intercropping sunflower with soybean could be a valuable option for maximizing resource-use efficiency in low-input environments; sunflower yield could be maintained at good level in very low input cropping systems. Previous examples point out how agroecological principles could be applied to sunflower crop to improve its production in low-input conditions, and enhance the ecosystem services deliverable by this oilseed crop.Keywords: cultural control / intercropping / ecosystem services / cropping systems / low-input Résumé -La culture de tournesol : respectueuse de l'environnement et agroécologique. La culture de tournesol (Helianthus annuus L.) est souvent qualifiée de respectueuse de l'environnement en raison de sa faible dépendance aux apports d'engrais, à l'eau d'irrigation et aux applications de pesticides en végétation. Par ailleurs, le tournesol contribue à de nombreux services écosystémiques au premier rang desquels l'alimentation des pollinisateurs. Cependant, très peu d'innovations agroécologiques ont été mises en avant sur cette culture contrairement à ce qui a été développé en céréales ou en colza. En nous basant sur les travaux récents du consortium de recherche sur le tournesol basé à Toulouse (Mestries et Debaeke. 2016. Journées d'échanges Tournesol, 28 et 29 juin 2016, Toulouse (France)), nous illustrerons plusieurs approches innovantes et prometteuses pour augmenter la performance agroécologique de la culture de tournesol. Ainsi nos résultats montrent que : des méthodes de protection intégrée peuvent être proposées pour limiter l'application de pesticides et atténuer les pertes de rendement ; des cultures intermédiaires (brassicacées) pourraient être utilisées pour la biofumigation des sols et le contrôle de maladies telluriques en tournesol ; l'association culturale avec le soja pourrait constituer une option intéressante pour maximiser l'utilisation des ressources en bas intrants ; le rendement du tournesol peut être maintenu à un bon niveau dans des systèmes à très bas niveaux d'intrants. Les exemples précédents illustrent comment les principes de l'agroécologie peuv...
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. AbstractGenome-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 materialThe online version of this article (doi:10.1007/s00122-017-3003-4) contains supplementary material, which is available to authorized users.
Prediction of hybrid performance using incomplete factorial mating designs is widely used in breeding programs including different heterotic groups. Based on the general combining ability (GCA) of the parents, predictions are accurate only if the genetic variance resulting from the specific combining ability is small and both parents have phenotyped descendants. Genomic selection (GS) can predict performance using a model trained on both phenotyped and genotyped hybrids that do not necessarily include all hybrid parents. Therefore, GS could overcome the issue of unknown parent GCA. Here, we compared the accuracy of classical GCA-based and genomic predictions for oil content of sunflower seeds using several GS models. Our study involved 452 sunflower hybrids from an incomplete factorial design of 36 female and 36 male lines. Re-sequencing of parental lines allowed to identify 468,194 non-redundant SNPs and to infer the hybrid genotypes. Oil content was observed in a multi-environment trial (MET) over 3 years, leading to nine different environments. We compared GCA-based model to different GS models including female and male genomic kinships with the addition of the female-by-male interaction genomic kinship, the use of functional knowledge as SNPs in genes of oil metabolic pathways, and with epistasis modeling. When both parents have descendants in the training set, the predictive ability was high even for GCA-based prediction, with an average MET value of 0.782. GS performed slightly better (+0.2%). Neither the inclusion of the female-by-male interaction, nor functional knowledge of oil metabolism, nor epistasis modeling improved the GS accuracy. GS greatly improved predictive ability when one or both parents were untested in the training set, increasing GCA-based predictive ability by 10.4% from 0.575 to 0.635 in the MET. In this scenario, performing GS only considering SNPs in oil metabolic pathways did not improve whole genome GS prediction but increased GCA-based prediction ability by 6.4%. Our results show that GS is a major improvement to breeding efficiency compared to the classical GCA modeling when either one or both parents are not well-characterized. This finding could therefore accelerate breeding through reducing phenotyping efforts and more effectively targeting for the most promising crosses.
Genome-wide association studies are a powerful and widely used tool to decipher the genetic 10 control of complex traits. One of the main challenges for hybrid crops, such as maize or sunflower, is 11 to model the hybrid vigor in the linear mixed models, considering the relatedness between individuals. 12Here, we compared two additive and three non-additive association models for their ability to identify 18A quantitative trait loci on LG09, detected by both the additive and non-additive models, is located near a 19 GAI homolog and is presented in detail. Overall, this study shows the added value of non-additive modeling 20 of allelic effects for identifying genomic regions that control traits of interest and that could participate in 21 the heterosis observed in hybrids. 22Keywords Genome-wide association study · sunflower · multi-locus · non-additive effect 23This work was supported by SUNRISE project, we thank all partners.
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