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.

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.

SummaryOver the last 40 years, new sunflower downy mildew isolates (Plasmopara halstedii) have overcome major gene resistances in sunflower, requiring the identification of additional and possibly more durable broad‐spectrum resistances. Here, 354 RXLR effectors defined in silico from our new genomic data were classified in a network of 40 connected components sharing conserved protein domains. Among 205 RXLR effector genes encoding conserved proteins in 17 P. halstedii pathotypes of varying virulence, we selected 30 effectors that were expressed during plant infection as potentially essential genes to target broad‐spectrum resistance in sunflower. The transient expression of the 30 core effectors in sunflower and in Nicotiana benthamiana leaves revealed a wide diversity of targeted subcellular compartments, including organelles not so far shown to be targeted by oomycete effectors such as chloroplasts and processing bodies. More than half of the 30 core effectors were able to suppress pattern‐triggered immunity in N. benthamiana, and five of these induced hypersensitive responses (HR) in sunflower broad‐spectrum resistant lines. HR triggered by PhRXLRC01 co‐segregated with Pl22 resistance in F3 populations and both traits localized in 1.7 Mb on chromosome 13 of the sunflower genome. Pl22 resistance was physically mapped on the sunflower genome recently sequenced, unlike all the other downy mildew resistances published so far. PhRXLRC01 and Pl22 are proposed as an avirulence/resistance gene couple not previously described in sunflower. Core effector recognition is a successful strategy to accelerate broad‐spectrum resistance gene identification in complex crop genomes such as sunflower.