Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher-throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally.

Quinoa germplasm preserves useful and substantial genetic variation, yet it remains untapped due to a lack of implementation of modern breeding tools. We have integrated field and sequence data to characterize a large diversity panel of quinoa. Whole-genome sequencing of 310 accessions revealed 2.9 million polymorphic high confidence single nucleotide polymorphism (SNP) loci. Highland and Lowland quinoa were clustered into two main groups, with F_ST divergence of 0.36 and linkage disequilibrium (LD) decay of 6.5 and 49.8 kb, respectively. A genome-wide association study using multi-year phenotyping trials uncovered 600 SNPs stably associated with 17 traits. Two candidate genes are associated with thousand seed weight, and a resistance gene analog is associated with downy mildew resistance. We also identified pleiotropically acting loci for four agronomic traits important for adaptation. This work demonstrates the use of re-sequencing data of an orphan crop, which is partially domesticated to rapidly identify marker-trait association and provides the underpinning elements for genomics-enabled quinoa breeding.

Response to photoperiod is of major importance in crop production. It defines the adaptation of plants to local environments. Quinoa is a short‐day plant which had been domesticated in the Andeans regions. We wanted to understand the adaptation to long‐day conditions by studying orthologues of two major flowering time regulators of Arabidopsis, FLOWERING LOCUS T (FT) and CONSTANS (CO) in quinoa accessions with contrasting photoperiod response. By searching the quinoa reference genome sequence, we identified 24 FT and six CO homologs. CqFT genes displayed remarkably different expression patterns between long‐ and short‐day conditions, whereas the influence of the photoperiod on CqCOL expressions was moderate. Cultivation of 276 quinoa accessions under short‐ and long‐day conditions revealed great differences in photoperiod sensitivity. After sequencing their genomes, we identified large sequence variations in 12 flowering time genes. We found non‐random distribution of haplotypes across accessions from different geographical origins, highlighting the role of CqFT and CqCOL genes in the adaptation to different day‐length conditions. We identified five haplotypes causing early flowering under long days. This study provides assets for quinoa breeding because superior haplotypes can be assembled in a predictive breeding approach to produce well‐adapted early flowering lines under long‐day photoperiods.

Random mutagenesis is a standard procedure to increase allelic variation in a crop species, especially in countries where the use of genetically modified crops is limited due to legal constraints. The chemical mutagen EMS is used in many species to induce random mutations throughout the genome with high mutation density. The major drawback for functional analysis is a high background mutation load in a single plant that must be eliminated by subsequent backcrossing, a time and resource-intensive activity. Here, we demonstrate that genomic background selection combined with marker-assisted selection is an efficient way to select individuals with reduced background mutations within a short period. We identified BC₁ plants with a significantly higher share of the recurrent parent genome, thus saving one backcross generation. Furthermore, spring rapeseed as the recurrent parent in a backcrossing program could accelerate breeding by reducing the generation cycle. Our study depicts the potential for reducing the background mutation load while accelerating the generation cycle in EMS-induced winter oilseed rape populations by integrating genomic background selection.

Quinoa germplasm preserves useful and substantial genetic variation, yet it remains untapped due to a lack of implementation of modern breeding tools. We have integrated field and sequence data to characterize a large diversity panel of quinoa. Whole-genome sequencing of 310 accessions revealed 2.9 million polymorphic high confidence SNP loci. Highland and Lowland quinoa were clustered into two main groups, with F _ST divergence of 0.36 and fast LD decay of 6.5 and 49.8 Kb, respectively. A genome-wide association study uncovered 600 SNPs stably associated with 17 agronomic traits. Two candidate genes are associated with thousand seed weight, and a resistance gene analog is associated with downy mildew resistance. We also identified pleiotropically acting loci for four agronomic traits that are highly responding to photoperiod hence important for the adaptation to different environments. This work demonstrates the use of re-sequencing data of an orphan crop, which is partially domesticated to rapidly identify marker-trait association and provides the underpinning elements for genomics-enabled quinoa breeding.