Plant Breeding Research Articles

Understanding genome structure facilitates the use of wild lentil germplasm for breeding: A case study with shattering loci.

Plant breeders are generally reluctant to cross elite crop cultivars with their wild relatives to introgress novel desirable traits due to associated negative traits such as pod shattering. This results in a genetic bottleneck that could be reduced through better understanding of the genomic locations of the gene(s) controlling this trait. We integrated information on parental genomes, pod shattering data from multiple environments, and high-density genetic linkage maps to identify pod shattering quantitative trait loci (QTLs) in three lentil interspecific recombinant inbred line populations. The broad-sense heritability on a multi-environment basis varied from 0.46 (in LR-70, Lens culinaris × Lens odemensis) to 0.77 (in LR-68, Lens orientalis × L. culinaris). Genetic linkage maps of the interspecific populations revealed reciprocal translocations of chromosomal segments that differed among the populations, and which were associated with reduced recombination. LR-68 had a 2-5 translocation, LR-70 had 1-5, 2-6, and 2-7 translocations, and LR-86 had a 2-7 translocation in one parent relative to the other. Segregation distortion was also observed for clusters of single nucleotide polymorphisms on multiple chromosomes per population, further affecting introgression. Two major QTL, on chromosomes 4 and 7, were repeatedly detected in the three populations and contain several candidate genes. These findings will be of significant value for lentil breeders to strategically access novel superior alleles while minimizing the genetic impact of pod shattering from wild parents.

Residual network improves the prediction accuracy of genomic selection.

Genetic improvement of complex traits in animal and plant breeding depends on the efficient and accurate estimation of breeding values. Deep learning methods have been shown to be not superior over traditional genomic selection (GS) methods, partially due to the degradation problem (i.e. with the increase of the model depth, the performance of the deeper model deteriorates). Since the deep learning method residual network (ResNet) is designed to solve gradient degradation, we examined its performance and factors related to its prediction accuracy in GS. Here we compared the prediction accuracy of conventional genomic best linear unbiased prediction, Bayesian methods (BayesA, BayesB, BayesC, and Bayesian Lasso), and two deep learning methods, convolutional neural network and ResNet, on three datasets (wheat, simulated and real pig data). ResNet outperformed other methods in both Pearson's correlation coefficient (PCC) and mean squared error (MSE) on the wheat and simulated data. For the pig backfat depth trait, ResNet still had the lowest MSE, whereas Bayesian Lasso had the highest PCC. We further clustered the pig data into four groups and, on one separated group, ResNet had the highest prediction accuracy (both PCC and MSE). Transfer learning was adopted and capable of enhancing the performance of both convolutional neural network and ResNet. Taken together, our findings indicate that ResNet could improve GS prediction accuracy, affected potentially by factors such as the genetic architecture of complex traits, data volume, and heterogeneity.

The complex relationship between disease resistance and yield in crops.

In plants, growth and defence are controlled by many molecular pathways that are antagonistic to one another. This results in a 'growth-defence trade-off', where plants temporarily reduce growth in response to pests or diseases. Due to this antagonism, genetic variants that improve resistance often reduce growth and vice versa. Therefore, in natural populations, the most disease resistant individuals are often the slowest growing. In crops, slow growth may translate into a yield penalty, but resistance is essential for protecting yield in the presence of disease. Therefore, plant breeders must balance these traits to ensure optimal yield potential and yield stability. In crops, both qualitative and quantitative disease resistance are often linked with genetic variants that cause yield penalties, but this is not always the case. Furthermore, both crop yield and disease resistance are complex traits influenced by many aspects of the plant's physiology, morphology and environment, and the relationship between the molecular growth-defence trade-off and disease resistance-yield antagonism is not well-understood. In this article, we highlight research from the last 2 years on the molecular mechanistic basis of the antagonism between defence and growth. We then discuss the interaction between disease resistance and crop yield from a breeding perspective, outlining the complexity and nuances of this relationship and where research can aid practical methods for simultaneous improvement of yield potential and disease resistance.

Genetic tapestry of Capsicum fruit colors: a comparative analysis of four cultivated species.

Genome-wide association study of color spaces across the four cultivated Capsicum spp. revealed a shared set of genes influencing fruit color, suggesting mechanisms and pathways across Capsicum species are conserved during the speciation. Notably, Cytochrome P450 of the carotenoid pathway, MYB transcription factor, and pentatricopeptide repeat-containing protein are the major genes responsible for fruit color variation across the Capsicum species. Peppers (Capsicum spp.) rank among the most widely consumed spices globally. Fruit color, serving as a determinant for use in food colorants and cosmeceuticals and an indicator of nutritional contents, significantly influences market quality and price. Cultivated Capsicum species display extensive phenotypic diversity, especially in fruit coloration. Our study leveraged the genetic variance within four Capsicum species (Capsicum baccatum, Capsicum chinense, Capsicum frutescens, and Capsicum annuum) to elucidate the genetic mechanisms driving color variation in peppers and related Solanaceae species. We analyzed color metrics and chromatic attributes (Red, Green, Blue, L*, a*, b*, Luminosity, Hue, and Chroma) on samples cultivated over six years (2015-2021). We resolved genomic regions associated with fruit color diversity through the sets of SNPs obtained from Genotyping by Sequencing (GBS) and genome-wide association study (GWAS) with a Multi-Locus Mixed Linear Model (MLMM). Significant SNPs with FDR correction were identified, within the Cytochrome P450, MYB-related genes, Pentatricopeptide repeat proteins, and ABC transporter family were the most common among the four species, indicating comparative evolution of fruit colors. We further validated the role of a pentatricopeptide repeat-containing protein (Chr01:31,205,460) and a cytochrome P450 enzyme (Chr08:45,351,919) via competitive allele-specific PCR (KASP) genotyping. Our findings advance the understanding of the genetic underpinnings of Capsicum fruit coloration, with developed KASP assays holding potential for applications in crop breeding and aligning with consumer preferences. This study provides a cornerstone for future research into exploiting Capsicum's diverse fruit color variation.

Breeding for Resistance against Pest and Diseases in Tomatoes: A Review

Diseases and pests have a substantial effect on tomato production, greatly affecting both the quantity and quality of this crucial vegetable crop. Although fungicides and insecticides have been important in controlling plant diseases and pests, their excessive usage raises significant environmental issues. Vegetable breeders are increasingly concentrating on developing cultivars with natural tolerance to biotic stresses to promote sustainability and environmental friendliness. The change in focus is intended to cultivate tomato cultivars with inherent resistance to diseases and pests, hence decreasing the need for chemical treatments. Advancements in creating high-yielding genetically resistant tomato cultivars are a result of detailed study on the genetic basis of pest and disease resistance in tomato crops, as well as the complex interactions between the host plant and pathogens. For effective breeding programs and pre-breeding activities, scientists and breeders must have access to sources of resistance and a thorough grasp of the genetic complexities involved. This requires examining the genetic composition of both the tomato plants and the different infections that are impacting them. Breeders may generate tomato cultivars with strong resistance to common diseases and pests by using the inherent defensive mechanisms found in certain tomato types via selective crossing. Continuing to study how hosts and pathogens interact and the molecular processes involved in resistance is crucial. This information offers vital insights on how to improve and expand resistance, leading to the creation of cultivars with long-lasting and wide-ranging resistance. Currently, the emphasis on breeding is a proactive and sustainable strategy for transfer of resistances in high yielding tomato cultivars. Researchers aim to develop tomato cultivars that provide high yield and demonstrate tolerance to changing disease and pest stresses by integrating genetic knowledge with sophisticated breeding methods. This comprehensive method protects tomato crops and encourages environmental sustainability by decreasing the need on chemical inputs in agriculture.

Enhancing genome-wide populus trait prediction through deep convolutional neural networks.

As a promising model, genome-based plant breeding has greatly promoted the improvement of agronomic traits. Traditional methods typically adopt linear regression models with clear assumptions, neither obtaining the linkage between phenotype and genotype nor providing good ideas for modification. Nonlinear models are well characterized in capturing complex nonadditive effects, filling this gap under traditional methods. Taking populus as the research object, this paper constructs a deep learning method, DCNGP, which can effectively predict the traits including 65 phenotypes. The method was trained on three datasets, and compared with other four classic models-Bayesian ridge regression (BRR), Elastic Net, support vector regression, and dualCNN. The results show that DCNGP has five typical advantages in performance: strong prediction ability on multiple experimental datasets; the incorporation of batch normalization layers and Early-Stopping technology enhancing the generalization capabilities and prediction stability on test data; learning potent features from the data and thus circumventing the tedious steps of manual production; the introduction of a Gaussian Noise layer enhancing predictive capabilities in the case of inherent uncertainties or perturbations; fewer hyperparameters aiding to reduce tuning time across datasets and improve auto-search efficiency. In this way, DCNGP shows powerful predictive ability from genotype to phenotype, which provide an important theoretical reference for building more robust populus breeding programs.

Quantifying Nitrogen Uptake Rates of Maize Roots Using Stable Isotopes.

Nitrogen is an essential element for plant growth and development; however, application of nitrogen (N)-based fertilizers comes with a high environmental cost. This includes the energy required for production, volatilization from fields, and runoff or leaching to waterways triggering algal blooms. As such, a key goal in plant breeding programs is to develop varieties that maintain yield while requiring less fertilization. Central to this goal is understanding how roots take up nitrogen and finding traits that represent improvements in the net uptake. Maize, one of the most widely produced crops in the world, has seminal, crown, and brace root types, each under independent developmental control. Recent evidence suggests that these independent developmental patterns may result in different nutrient uptake characteristics. As such, understanding the uptake dynamics of each root type under different environmental conditions is an essential aspect for the selection of new maize varieties. A key method for tracking nitrogen uptake is the use of the 15N stable isotope, which is naturally less abundant than the main 14N isotope. This method involves replacing the 14N in nutrient solutions with 15N, exogenously providing it to the plant tissues (roots in this case), and then measuring the 15N content of the tissues after a fixed amount of time. Here, we provide a brief overview of nitrogen uptake and remobilization in maize, and discuss current techniques for measuring nutrient uptake, with a focus on methods using stable isotopes of nitrogen.

Speed Breeding in Cereal Crops: Accelerating Genetic Improvement for Rapid Agricultural Advancement

The global demand for food is rising due to population growth, climate change, and evolving consumer preferences. Traditional plant breeding programs typically require a decade or more to develop and release new crop varieties. Speed breeding is a cutting-edge technique designed to expedite genetic improvement by significantly reducing the seed-to-seed cycle. This review explores the principles, applications, and potential of this method in accelerating agricultural advancement. Originating from NASA's experiments for food production in space, the approach has evolved into a highly efficient strategy for speeding up breeding cycles in cereal crops. By manipulating light, photoperiodic regimes, temperature, and humidity within controlled environments, it is possible to achieve up to six generations of photo-insensitive crops and two to three generations of other crops per year. This rapid generation turnover provides a unique opportunity for accelerated genetic mapping, trait stacking, and enhanced genomic selection. Speed breeding complements modern breeding technologies such as genome editing, high-throughput genotyping, and CRISPR, facilitating quicker development of crop varieties with improved traits. Its applications extend to boosting transgenic pipelines and understanding critical physiological traits in crops. Furthermore, it aligns with breeding methods like single plant selection and single seed descent, offering a more efficient path to desirable outcomes. However, challenges remain, including the need for specialized infrastructure, the impact of genotypic variations, and potential stress responses due to accelerated conditions. Despite these hurdles, speed breeding represents a promising tool in the quest for food security and resilient agricultural systems. This review examines its potential to transform plant breeding, reduce cultivar development times, and contribute to rapid agricultural advancement in a changing world.

CsMIEL1 effectively inhibits the accumulation of anthocyanins under low temperatures in tea plants (Camelliasinensis)

Tea is one of the most prevalent non-alcoholic beverages. The leaves of tea plants hyperaccumulate anthocyanins under cold stress, resulting in enhanced bitterness. Previously, we determined that the RING-type E3 ubiquitin ligase CsMIEL1 from the tea plant (Camellia sinensis (L.) O. Kuntze) is involved in the response to stress conditions. This study aimed to determine the role of CsMIEL1 in anthocyanin accumulation at the post-translational modification level. The results showed that the heterologous expression of CsMIEL1 led to an 86% decrease in anthocyanin levels, resulting in a significant decrease in the mRNA levels of related genes in Arabidopsis at low temperatures but no significant differences in other phenotypes. Furthermore, multi-omics analysis and yeast two-hybrid library screening were performed to identify potential downstream targets of CsMIEL1. The results showed that the overexpression of CsMIEL1 resulted in 45% (448) of proteins being differentially expressed, of which 8% (36) were downregulated in A.thaliana, and most of these differentially expressed proteins (DEPs) were clustered in the plant growth and secondary metabolic pathways. Among the 71 potential targets that may interact with CsMIEL1, CsMYB90 and CsGSTa, which are related to anthocyanin accumulation, were selected. In subsequent analyses, these two proteins were verified to interact with CsMIEL1 via yeast two-hybrid (Y2H) and pull-down analyses in vitro. In summary, we explored the potential mechanism by which the E3 ligase relieves anthocyanin hyperaccumulation at low temperatures in tea plants. These results provide a new perspective on the mechanisms of anthocyanin regulation and the molecular breeding of tea plants.

Genome-Wide Profile of Mutations Induced by Carbon Ion Beam Irradiation of Dehulled Rice Seeds

As a physical mutagen, carbon ion beam (CIB) irradiation can induce high-frequency mutation, which is user-friendly and environment-friendly in plant breeding. In this study, we resequenced eight mutant lines which were screened out from the progeny of the CIB-irradiated dehulled rice seeds. Among these mutants, CIB induced 135,535 variations, which include single base substitutions (SBSs), and small insertion and deletion (InDels). SBSs are the most abundant mutation, and account for 88% of all variations. Single base conversion is the main type of SBS, and the average ratio of transition and transversion is 1.29, and more than half of the InDels are short-segmented mutation (1–2 bp). A total of 69.2% of the SBSs and InDels induced by CIBs occurred in intergenic regions on the genome. Surprisingly, the average mutation frequency in our study is 9.8 × 10−5/bp and much higher than that of the previous studies, which may result from the relatively high irradiation dosage and the dehulling of seeds for irradiation. By analyzing the mutation of every 1 Mb in the genome of each mutant strain, we found some unusual high-frequency (HF) mutation regions, where SBSs and InDels colocalized. This study revealed the mutation mechanism of dehulled rice seeds by CIB irradiation on the genome level, which will enrich our understanding of the mutation mechanism of CIB radiation and improve mutagenesis efficiency.

The first high-quality genome assembly and annotation of Lantana camara, an important ornamental plant and a major invasive species

This study presents the first annotated, haplotype-resolved, chromosome-scale genome of Lantana camara, a flowering shrub native to Central America and known for its dual role as an ornamental plant and an invasive species. Despite its widespread cultivation and ecological impact, the lack of a high-quality genome has hindered the investigation of traits of both ornamental and invasive. This research bridges the gap in genomic resources for L. camara, which is crucial for both ornamental breeding programs and invasive species management. Whole-genome and transcriptome sequencing were utilized to elucidate the genetic complexity of a diploid L. camara breeding line UF-T48. The genome was assembled de novo using HiFi and Hi-C reads, resulting in two phased genome assemblies with high Benchmarking Universal Single-Copy Orthologs (BUSCO) scores of 97.7%, indicating their quality. All 22 chromosomes were assembled with pseudochromosomes averaging 117 Mb. The assemblies revealed 29 telomeres and an extensive presence of repetitive sequences, primarily long terminal repeat transposable elements. The genome annotation identified 83,775 protein-coding genes, with 83% functionally annotated. In particular, the study mapped 42 anthocyanin and carotenoid candidate gene clusters and 12 herbicide target genes to the assembly, identifying 38 genes spread across the genome that are integral to flower color development and 53 genes for herbicide targeting in L. camara. This comprehensive genomic study not only enhances the understanding of L. camara’s genetic makeup but also sets a precedent for genomic research in the Verbenaceae family, offering a foundation for future studies in plant genetics, conservation, and breeding.

Improving Crop Yield through Increasing Carbon Gain and Reducing Carbon Loss

Photosynthesis is a process where solar energy is utilized to convert atmospheric CO2 into carbohydrates, which forms the basis for plant productivity. The increasing demand for food has created a global urge to enhance yield. Earlier, the plant breeding program was targeting the yield and yield-associated traits to enhance the crop yield. However, the yield cannot be further improved without improving the leaf photosynthetic rate. Hence, in this review, various strategies to enhance leaf photosynthesis were presented. The most promising strategies were the optimization of Rubisco carboxylation efficiency, the introduction of a CO2 concentrating mechanism in C3 plants, and the manipulation of photorespiratory bypasses in C3 plants, which are discussed in detail. Improving Rubisco’s carboxylation efficiency is possible by engineering targets such as Rubisco subunits, chaperones, and Rubisco activase enzyme activity. Carbon-concentrating mechanisms can be introduced in C3 plants by the adoption of pyrenoid and carboxysomes, which can increase the CO2 concentration around the Rubisco enzyme. Photorespiration is the process by which the fixed carbon is lost through an oxidative process. Different approaches to reduce carbon and nitrogen loss were discussed. Overall, the potential approaches to improve the photosynthetic process and the way forward were discussed in detail.

Enhancing genetic variability in Trigonella species through sodium azide induction: morpho-physiological and chromosomal amelioration

Plant breeding, aimed at enhancing desired traits, depends on genetic diversity. Mutation breeding is a powerful method of rapidly expanding genetic diversity, facilitating crop improvement, and ensuring food security. In a recent study, researchers evaluated the genetic variability of Trigonella species using different doses of sodium azide (SA) (0.2%, 0.4%, 0.6%, 0.8%, and 1.0%) through morphological, physiological, and cytogenetic studies. Morphological variations were observed in cotyledonary leaves, vegetative leaves, and overall plant growth and habit. Several quantitative parameters, such as plant height, fertile branches per plant, pods per plant (or clusters), seeds per pod, and seed yield, increased when treated with 0.2% and 0.4% SA compared to the control. Furthermore, the total chlorophyll content and carotenoids increased in the sample treated with 0.2% SA over the control but decreased with higher concentrations. Scanning electron microscopy revealed that stomatal aperture and seed dimensions increased at lower concentrations of sodium azide treatment. The study found a positive correlation between the different parameters studied in the Trigonella species, as indicated by high r-values. Based on their findings, it was concluded that the genotype of fenugreek can be improved by using 0.2% and 0.4% concentrations of sodium azide. However, the evaluation of observed variants in successive generations is a critical and necessary process to validate their potential as keystones for crop genetic improvements.

For a Colorful Life: Recent Advances in Anthocyanin Biosynthesis during Leaf Senescence

Leaf senescence is the last stage of leaf development, and it is accompanied by a leaf color change. In some species, anthocyanins are accumulated during leaf senescence, which are vital indicators for both ornamental and commercial value. Therefore, it is essential to understand the molecular mechanism of anthocyanin accumulation during leaf senescence, which would provide new insight into autumn coloration and molecular breeding for more colorful plants. Anthocyanin accumulation is a surprisingly complex process, and significant advances have been made in the past decades. In this review, we focused on leaf coloration during senescence. We emphatically discussed several networks linked to genetic, hormonal, environmental, and nutritional factors in regulating anthocyanin accumulation during leaf senescence. This paper aims to provide a regulatory model for leaf coloration and to put forward some prospects for future development.

Cultivars and Production Environments Shape Shoot Endophyte Profiles of Boxwood with Different Blight Resistance

Phyllosphere colonizers, including bacteria and fungi, are critical for plant growth and health. Yet, how they are affected simultaneously by host plant cultivar, local environment and agricultural practices was not well understood. We used boxwood, an iconic landscape plant and a major evergreen shrub crop in the U.S. nursery industry, as a model plant, and sequenced 16S rRNA and ITS amplicons to examine the assemblages of endophytic bacteria and fungi in the shoots of four cultivars representing three levels of boxwood blight resistance under two distinct climates and production systems in Oregon and Virginia. Cultivar and local environment were the two main drivers shaping the composition and structure of the boxwood endophytic microbial community, particularly the fungal community. Three bacterial and seven fungal genera were consistently identified with high prevalence and abundance as the core taxa from four cultivars and two locations across three sampling times. The microbial composition varied among the levels of boxwood blight resistance and taxa specific to the tolerant cultivar were fewer compared to the susceptible one. Identification of these microbial indicators, along with the core taxa, is foundational for developing a microbiome-based plant breeding program and a systems approach to improve boxwood health and production under a changing climate.

Transcriptome dynamics of Gossypium purpurascens in response to abiotic stresses by Iso-seq and RNA-seq data

Gossypium purpurascens is a member of the Malvaceae family, holds immense economic significance as a fiber crop worldwide. Abiotic stresses harm cotton crops, reduce yields, and cause economic losses. Generating high-quality reference genomes and large-scale transcriptomic datasets across diverse conditions can offer valuable insights into identifying preferred agronomic traits for crop breeding. The present research used leaf tissues to conduct PacBio Iso-seq and RNA-seq analysis. We carried out an in-depth analysis of DEGs using both correlations with cluster analysis and principal component analysis. Additionally, the study also involved the identification of both lncRNAs and CDS. We have prepared RNA-seq libraries from 75 RNA samples to study the effects of drought, salinity, alkali, and saline-alkali stress, as well as control conditions. A total of 454.06 Gigabytes of transcriptome data were effectively validated through the identification of differentially expressed genes and KEGG and GO analysis. Overwhelmingly, gene expression profiles and full-length transcripts from cotton tissues will aid in understanding the genetic mechanism of abiotic stress tolerance in G. purpurascens.

Genome-wide association study in Chickpea (Cicer arietinum L.) for yield and nutritional components

The increase in the global human population and the accompanying challenges in meeting nutritional needs amidst climate change are a worldwide concern. Widespread protein and micronutrient deficiencies contribute to a significant number of individuals experiencing malnutrition, leading to severe health repercussions. This issue can be addressed through genomics-assisted breeding, particularly in enhancing the nutritional profile of vital staple crops like chickpea (Cicer arietinum L.). Chickpea, beyond being a rich source of protein, provides a diverse nutritional spectrum encompassing carbohydrates, fats, and minerals. To explore and improve the genetic basis of nutritional traits in chickpea, a study was conducted using 93 kabuli-type single plant derived lines and five cultivars in 2018, 2022, and 2023. Genotyping by sequencing revealed a total of 165K single nucleotide polymorphisms (SNPs) within this kabuli chickpea mini-core collection. After filtering for a minor allele frequency greater than 5%, 113,512 SNPs were utilized, distributed across eight chromosomes of the chickpea genome. Marker-trait associations were analyzed using genome wide association study, leading to the identification of 27 significantly associated SNPs from across all eight chromosomes linked to three seed nutritional concentrations and 100-seed weight. To unravel the molecular mechanisms governing seed protein, fiber, fat concentrations, and 100-seed weight, 31 candidate genes were determined within a 30 kb window size. This comprehensive approach holds promise for advancing crop breeding strategies to combat malnutrition and improve global food security.

Exploiting viral vectors to deliver genome editing reagents in plants

AbstractGenome editing holds great promise for the molecular breeding of plants, yet its application is hindered by the shortage of simple and effective means of delivering genome editing reagents into plants. Conventional plant transformation-based methods for delivery of genome editing reagents into plants often involve prolonged tissue culture, a labor-intensive and technically challenging process for many elite crop cultivars. In this review, we describe various virus-based methods that have been employed to deliver genome editing reagents, including components of the CRISPR/Cas machinery and donor DNA for precision editing in plants. We update the progress in these methods with recent successful examples of genome editing achieved through virus-based delivery in different plant species, highlight the advantages and limitations of these delivery approaches, and discuss the remaining challenges.

Preharvest Sprouting in Quinoa: A New Screening Method Adapted to Panicles and GWAS Components

The introduction of quinoa into new growing regions and environments is of interest to farmers, consumers, and stakeholders around the world. Many plant breeding programs have already started to adapt quinoa to the environmental and agronomic conditions of their local fields. Formal quinoa breeding efforts in Washington State started in 2010, led by Professor Kevin Murphy out of Washington State University. Preharvest sprouting appeared as the primary obstacle to increased production in the coastal regions of the Pacific Northwest. Preharvest sprouting (PHS) is the undesirable sprouting of seeds that occurs before harvest, is triggered by rain or humid conditions, and is responsible for yield losses and lower nutrition in cereal grains. PHS has been extensively studied in wheat, barley, and rice, but there are limited reports for quinoa, partly because it has only recently emerged as a problem. This study aimed to better understand PHS in quinoa by adapting a PHS screening method commonly used in cereals. This involved carrying out panicle-wetting tests and developing a scoring scale specific for panicles to quantify sprouting. Assessment of the trait was performed in a diversity panel (N = 336), and the resulting phenotypes were used to create PHS tolerance rankings and undertake a GWAS analysis (n = 279). Our findings indicate that PHS occurred at varying degrees across a subset of the quinoa germplasm tested and that it is possible to access PHS tolerance from natural sources. Ultimately, these genotypes can be used as parental lines in future breeding programs aiming to incorporate tolerance to PHS.

Deep genotyping reveals specific adaptation footprints of conventional and organic farming in barley populations—an evolutionary plant breeding approach

Sustainable food production for a growing world population will pose a central challenge in the coming decades. Organic farming is among the feasible approaches to achieving this goal if the yield gap to conventional farming can be decreased. However, uncertainties exist to which extend—and for which phenotypes in particular—organic and conventional agro-ecosystems require differentiated breeding strategies. To answer this question, a heterogeneous spring barley population was established between a wild barley and an elite cultivar to examine this question. This initial population was divided into two sets and sown one in organic and the other in conventional managed agro-ecosystems, without any artificial selection for two decades. A fraction of seeds harvested each year was sown the following year. Various generations, up to the 23th were whole-genome pool-sequenced to identify adaptation patterns towards ecosystem and climate conditions in the allele frequency shifts. Additionally, a meta-data analysis was conducted to link genomic regions’ increased fitness to agronomically related traits. This long-term experiment highlights for the first time that allele frequency pattern difference between the conventional and organic populations grew with subsequent generations. Further, the organic-adapted population showed a higher genetic heterogeneity. The data indicate that adaptations towards new environments happen in few generations. Drastic interannual changes in climate are manifested in significant allele frequency changes. Particular wild form alleles were positively selected in both environments. Clustering these revealed an increased fitness associated with biotic stress resistance, yield physiology, and yield components in both systems. Additionally, the introduced wild alleles showed increased fitness related to root morphology, developmental processes, and abiotic stress responses in the organic agro-ecosystem. Concluding the genetic analysis, we demonstrate that breeding of organically adapted varieties should be conducted in an organically managed agro-ecosystem, focusing on root-related traits, to close the yield gap towards conventional farming.