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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Populus_trichocarpa_Breeding_Population_SNPs

These data are from the manuscript “Application of Genomic Prediction in a Populus trichocarpa Breeding Program”, by Brian J. Stanton, David Macaya-Sanz, Chanaka Roshan Abeyratne, David Kainer, Kathy Haiby, Austin Himes, Carlos Gantz, Gerald A. Tuskan, and Stephen P. DiFazio. The data are based on genome resequencing to approximately 10X depth on two collections of Populus trichocarpa trees from Oregon, Washington, California, and British Columbia. The first collection consists of 293 genets collected by Poplar Innovations LLC for a breeding program. The second collection consists of 961 trees collected for the purpose of genome-wide association studies. These genets were sequenced using short, paired-end Illumina sequence reads (Chhetri et al. 2019). Reads were aligned to the P. trichocarpa ′Stettler-14′ reference (Hofmeister et al. 2020), with minor modifications to correct mis-assemblies (Zhou et al. 2020), and variants were called as per methods described in (Abeyratne et al. 2023). Identified variants were filtered using GATK’s VariantFiltration tool (DePristo et al. 2011), with filter expression flag set to “AF < 0.01 || AF > 0.99 || QD < 10.0 || ExcessHet > 20.0 || FS > 10.0 || MQ < 58.0”. SNPs with severe departures from Hardy−Weinberg expectations (exact-test p< 0.01) were also removed using vcftools --hwe flag (Danecek et al. 2011), resulting in 15,627,211 bi-allelic SNPs. The data included here consist of 141,903 high quality bi-allelic genome-wide SNPs obtained by further filtering the original SNP dataset using vcftools with flags --maf 0.05, --max-maf 0.95, --max-missing 0.95, --min-meanDP 10.75, --max-meanDP 43.00, --thin 2000. Collectively, these filtering parameters removed SNPs with 1) a minor allele frequency ≤ 0.05; 2) proportion of missing data for individual loci exceeding 5%; 3) sequencing depth more than 2X mean-depth or less than 0.5X mean-depth; or 4) a distance of

09 BIOMASS FUELS↗
DOE OSTI2025

Ecological connectivity and in-kind mitigation in a regulatory decision framework: A case study with an amphibian habitat specialist

Ecological connectivity is critical to the survival and long-term viability of populations but is often overlooked in regulatory frameworks. We integrated landscape-level processes into a mitigation strategy for impacts to aquatic resources on the U.S. Department of Energy (DOE) Oak Ridge Reservation (ORR) in eastern Tennessee. Wetlands on the ORR, which contain significant breeding populations of the imperiled four-toed salamander (Hemidactylium scutatum) and tubercled rein orchid (Platanthera flava var. herbiola), will be impacted by construction of an environmental waste disposal facility under the Comprehensive Environmental Response, Compensation, and Liability Act of 1980 (CERCLA). Here, we used a modified Kepner-Tregoe decision analysis to select general mitigation options that balanced regulatory requirements and interest group perspectives. We emphasized habitat connectivity through models that prioritized an area's importance to natural area connectivity (centrality) and maintenance of population structure for an affected habitat specialist (four-toed salamanders). We also emphasized in-kind mitigation through the preservation and enhancement of ecologically similar resources and the translocation and establishment of a new subpopulation of four-toed salamanders elsewhere on the ORR. We ultimately released over 500 juvenile salamanders that originated from the impacted site into the chosen mitigation wetlands. By doing so under the constraints of a time-sensitive CERCLA remediation effort and exceeding its substantive requirements, this work underscores feasibility. Ecological connectivity and the conservation of species that are not afforded explicit regulatory processes can be effectively and efficiently integrated into environmental decision-making and land use planning.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Breaking the barrier of human-annotated training data for machine learning-aided plant research using aerial imagery

Machine learning (ML) can accelerate biological research. However, the adoption of such tools to facilitate phenotyping based on sensor data has been limited by (i) the need for a large amount of human-annotated training data for each context in which the tool is used and (ii) phenotypes varying across contexts defined in terms of genetics and environment. This is a major bottleneck because acquiring training data is generally costly and time-consuming. This study demonstrates how a ML approach can address these challenges by minimizing the amount of human supervision needed for tool building. A case study was performed to compare ML approaches that examine images collected by an uncrewed aerial vehicle to determine the presence/absence of panicles (i.e. “heading”) across thousands of field plots containing genetically diverse breeding populations of 2 Miscanthus species. Automated analysis of aerial imagery enabled the identification of heading approximately 9 times faster than in-field visual inspection by humans. Leveraging an Efficiently Supervised Generative Adversarial Network (ESGAN) learning strategy reduced the requirement for human-annotated data by 1 to 2 orders of magnitude compared to traditional, fully supervised learning approaches. The ESGAN model learned the salient features of the data set by using thousands of unlabeled images to inform the discriminative ability of a classifier so that it required minimal human-labeled training data. This method can accelerate the phenotyping of heading date as a measure of flowering time in Miscanthus across diverse contexts (e.g. in multistate trials) and opens avenues to promote the broad adoption of ML tools.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

pixelvar79/ESGAN-Flowering-Detection-paper

Machine learning (ML) can accelerate biological research. However, the adoption of such tools to facilitate phenotyping based on sensor data has been limited by (i) the need for a large amount of human-annotated training data for each context in which the tool is used and (ii) phenotypes varying across contexts defined in terms of genetics and environment. This is a major bottleneck because acquiring training data is generally costly and time-consuming. This study demonstrates how a ML approach can address these challenges by minimizing the amount of human supervision needed for tool building. A case study was performed to compare ML approaches that examine images collected by an uncrewed aerial vehicle to determine the presence/absence of panicles (i.e. “heading”) across thousands of field plots containing genetically diverse breeding populations of 2 Miscanthus species. Automated analysis of aerial imagery enabled the identification of heading approximately 9 times faster than in-field visual inspection by humans. Leveraging an Efficiently Supervised Generative Adversarial Network (ESGAN) learning strategy reduced the requirement for human-annotated data by 1 to 2 orders of magnitude compared to traditional, fully supervised learning approaches. The ESGAN model learned the salient features of the data set by using thousands of unlabeled images to inform the discriminative ability of a classifier so that it required minimal human-labeled training data. This method can accelerate the phenotyping of heading date as a measure of flowering time in Miscanthus across diverse contexts (e.g. in multistate trials) and opens avenues to promote the broad adoption of ML tools.

Varela, Sebastian↗
DOE OSTI2025

Global interfertility and heterosis in sugar kelp populations: a next step in sugar kelp breeding

Abstract The potential of seaweed aquaculture is restricted by high labor, production and processing costs, leading to low economic viability. Selective breeding can improve yields and cultivation efficiency, thereby decreasing production costs. Until now, genetic resources as input for Saccharina latissimabreeding trials have been sourced strictly locally, due to concerns regarding outplanting genetically exogenous material in local waters. Here we study, for the first time, worldwide interregional fertility of the seaweedS. latissima,in order to assess the potential of including globalS. latissimagenetic resources for selective breeding with regard to heterosis. We quantified the yield (as an indicative aquacultural performance) and morphological traits of intra- and interregionalS. latissimahybrids originating from a broad range of locations in a common garden experiment. Our results show that the practical application of worldwideS. latissimagenetic resources in breeding programs is feasible based on global interfertility. We found a wide morphological diversity of hybrids and observed significant heterosis in interregional hybrids. The degree of heterosis could not be linked to geographic distance. These findings reveal that worldwide genetic resources can considerably contribute toS. latissimabreeding programs and could offer a major next step in improving yields and quality traits.

Biotechnology & Applied Microbiology↗
DOE OSTI2026

Genomic approaches to accelerate American chestnut restoration

More than a century after two introduced pathogens killed billions of American chestnut trees, introgression of resistance alleles from Chinese chestnuts has contributed to the recovery of self-sustaining populations. However, progress has been slow because of the complex genetic architecture of resistance. To better understand blight resistance, we compared reference genomes, gene expression responses, and stem metabolite profiles of the resistant Chinese and susceptible American chestnut species. To accelerate resistance breeding, we conducted large-scale phenotyping and genotyping in hybrids of these species. Simulation and inoculation experiments suggest that significant resistance gains are possible through selectively breeding trees with an average of 70 to 85% American chestnut ancestry. In conclusion, the resources developed in this work are foundational for breeding to create diverse restoration populations with sufficient disease resistance and competitive growth.

Westbrook, Jared W. [The American Chestnut Foundat↗
DOE OSTI2025

Demonstration of closed shell breeding of cesium ions with an electron beam ion source

An electron beam ion source serves as a charge breeder for the Californium Rare Isotope Breeder Upgrade (CARIBU) at the Argonne Tandem Linac Accelerator System (ATLAS). The source accepts radioactive beams of 1+ or 2+ ions and raises their charge state for post-acceleration by ATLAS. The efficiency of this process impacts the length of each experiment and, hence, the type and number of experiments that can be run in each program cycle. Recent efforts to improve the charge breeding efficiency for the 80 < A < 160 species typical of CARIBU have focused on utilizing the closed shell breeding technique. Here, with this technique, the electron beam energy is manipulated to take advantage of the large gap in ionization energies at shell closures and selectively populate a single charge state. Charge breeding studies with stable cesium ions have demonstrated an absolute efficiency of 72% for Cs 27+ and a total efficiency of 93%. The Cs 27+ efficiency result represents a factor of 3 improvement over the previous best charge breeding result of 23% for Cs 27+ .

Vondrasek, R. [Argonne National Laboratory (ANL), ↗
DOE OSTI2026

Ornamental origins and genomic frontiers: a review of big-bracted dogwood research

The big-bracted (Benthamidia) dogwood clade consists of small- to medium-sized deciduous trees within the genus Cornus, known for their showy spring-time floral bract display. Cornus is within the family Cornaceae and order Cornales, and as Cornales is one of the earliest diverging asterids, these taxa have been important for phylogenetic research. Three species within the big-bracted clade, flowering (Cornus florida), kousa (C. kousa), and Pacific (C. nuttallii) dogwoods, are popular ornamental landscape plants in North America, with more than 130 cultivars released. Despite their commercial popularity, numerous research gaps have limited the expansion of fundamental research and dogwood breeding programs. In this present review, we aim to provide a thorough overview of our current understanding of 1) the phylogenetic and biogeographic context, 2) plant biology and major pests and pathogens impacting commercialization, 3) historical commercialization and propagation methods, and 4) genetic and genomic resources and how they have been implemented to understand these species. Research gaps and future directions to advance basic research and breeding of big-bracted ornamental dogwoods are discussed throughout.

Cornus florida↗
DOE OSTI2025

Scaffolded and annotated nuclear and organelle genomes of the North American brown alga Saccharina latissima

Increasing the genomic resources of emerging aquaculture crop targets can expedite breeding processes as seen in molecular breeding advances in agriculture. High quality annotated reference genomes are essential to implement this relatively new molecular breeding scheme and benefit research areas such as population genetics, gene discovery, and gene mechanics by providing a tool for standard comparison. The brown macroalga Saccharina latissima (sugar kelp) is an ecologically and economically important kelp that is found in both the northern Pacific and Atlantic Oceans. Cultivation of Saccharina latissima for human consumption has increased significantly this century in both North America and Europe, and its single blade morphology allows for dense seeding practices used in the cultivation of its Asian sister species, Saccharina japonica. While Saccharina latissima has potential as a human food crop, insufficient information from genetic resources has limited molecular breeding in sugar kelp aquaculture. We present scaffolded and annotated Saccharina latissima nuclear and organelle genomes from a female gametophyte collected from Black Ledge, Groton, Connecticut. This Saccharina latissima genome compares well with other published kelp genomes and contains 218 scaffolds with a scaffold N50 of 1.35 Mb, a GC content of 49.84%, and 25,012 predicted genes. We also validated this genome by comparing the synteny and completeness of this Saccharina latissima genome to other kelp genomes. Our team has successfully performed initial genomic selection trials with sugar kelp using a draft version of this genome. This Saccharina latissima genome expands the genetic toolkit for the economically and ecologically important sugar kelp and will be a fundamental resource for future foundational science, breeding, and conservation efforts.

DeWeese, Kelly↗
DOE OSTI2026

Synthetic Biology of Plants and Microbes for Agriculture, Environment, and Future Applications

Agriculture is under pressure to provide food for a growing population and the feedstock required to drive the bioeconomy. Methods to breed and genetically modify plants are inadequate to keep pace. When engineering crops, traits are painstakingly introduced into plants one-at-a-time, combine unpredictably, and are continuously expressed. Synthetic biology is changing these paradigms with new genome construction tools, computer aided design (CAD), and artificial intelligence (AI). “Smart plants” contain circuits that respond to environmental change, alter morphology, or respond to threats. Further, the plant and associated microbes (fungi, bacteria, archaea) are now being viewed by genetic engineers as a holistic system. Historically, plant health has been enhanced by many natural and laboratory-evolved soil microbes marketed to enhance growth, provide nutrients, or confer pest/stress resistance. Synthetic biology has expanded the number of species that can be engineered, increased the complexity of engineered functions, controlled environmental release, and assembled stable consortia. New CAD tools will manage genetic engineering projects spanning multiple plant genomes (nucleus, chloroplast, mitochondrion) and the thousands of genomes of associated bacteria/fungi. Here, this review covers advanced genetic engineering techniques to drive the next agricultural revolution, as well as push plant engineering into new realms for manufacturing, infrastructure, sensing, and remediation.

Clauer, Phillip [Massachusetts Inst. of Technology↗
DOE OSTI2025

Optimizing genomic prediction for complex traits via investigating multiple factors in switchgrass

Genomic prediction has accelerated breeding processes and provided mechanistic insights into the genetic bases of complex traits. To further optimize genomic prediction, we assess the impact of genome assemblies, genotyping approaches, variant types, allelic complexities, polyploidy levels, and population structures on the prediction of 20 complex traits in switchgrass (Panicum virgatum L.), a perennial biofuel feedstock. Surprisingly, short read-based genome assembly performs comparably to or even better than long read-based assembly. Due to higher gene coverage, exome capture and multi-allelic variants outperform genotyping-by-sequencing and bi-allelic variants, respectively. Tetraploid models show higher prediction accuracy than octoploid models for most traits, likely due to the greater genetic distances among tetraploids. Depending on the trait in question, different types of variants need to be integrated for optimal predictions. Furthermore, our study provides insights into the factors influencing genomic prediction outcomes, guiding best practices for future studies and for improving agronomic traits in switchgrass and other species through selective breeding.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Rapid quantification of whole seed fatty acid amount, composition, and shape phenotypes from diverse oilseed species with large differences in seed size

Seed oils are widely used in the food, biofuel, and industrial feedstock industries, with their utility and value determined by total oil content and fatty acid composition. Current high throughput seed oil analysis methods either lack accuracy in total fatty acid profiling or require extensive labor for lipid extraction prior to derivatization to fatty acid methyl esters (FAME) and quantification by gas chromatography (GC). Alternatively, direct whole seed FAME production methods have been developed for the very small seeds in the model species Arabidopsis thaliana but these have generally not been adapted to larger seeds of most oilseed crops. High-throughput direct whole seed FAME production methods were optimized for seeds up to 5 mg each utilizing acid-catalyzed esterification. For the oilseed species Camelina sativa, Thlaspi avernse (pennycress), Cuphea viscosissima, and Brassica napus (var. Canola), the total seed fatty acid content and composition from direct seed esterification to FAME matched that of lipid extract derivatization demonstrating the accuracy of the methods. In combination with seed phenotyping using GridFree, this approach enabled the development of a rapid pipeline for simultaneous seed weight, count, size/shape phenotyping, and oil analysis. For the larger and tougher seeds produced by Limnanthes alba (Meadowfoam) and Cannabis sativa L. (hemp) the whole seed acid-based method proved insufficient, and prior laborious homogenization of seeds was required. Therefore, a rapid one-tube bead homogenization and base catalyzed-esterification method was developed. Base-derived fatty acid esterification cannot derivatize free fatty acids leading to slightly lower total seed fatty acid than acid-catalyzed methods, however the seed oil content and fatty acid composition that is valuable for screening large numbers of samples in research populations was accurately measured. New rapid whole seed fatty acid esterification and phenotyping protocols were developed to accurately assess oilseed lipid content. These methods are particularly valuable in oilseed research, breeding, and engineering applications where efficient analysis of large numbers of samples and accurate oil fatty acid profiling is essential. While having been developed for current and emerging oilseed crops, these methods also provide a foundation from which protocols might be established for new and emerging crop species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

An overview of switchgrass phenotypes variability across diverse populations and their implications for conversion to fuels

There have been substantial changes to the human lifestyle over the past two centuries, which are reflected in the amount of fuel we consume to power our day-to-day needs. The way we use these resources has indeed manifested in an overdependence on non-renewable energy sources, such as coal and petroleum, for generating electricity and powering our transportation needs. There is a pressing need to explore alternative ways of fueling our current lifestyle without impacting the environment. Biofuels have long been touted as a sustainable solution for use as drop-in fuels in aviation and maritime applications. Still, they have yet to establish themselves as a competitive commercial alternative, necessitating further research and development. Lignocellulosic biomass is an underutilized resource that is widely accessible for the commercial processing of renewable biofuels. Bioenergy crops, such as switchgrass (Panicum virgatum L.), which can be cultivated on marginal lands with minimal competition for agricultural land, are an ideal and promising candidate for bulk-scale biofuel synthesis. Over the past 30 years, significant progress has been made in breeding and genetically modifying these grasses to enhance their drought resilience and subsequent yields. However, discrepancies in biomass composition can lead to irregular feedstocks for downstream operations, which in turn affect overall production targets for biofuels. Here, this review examines the variability in switchgrass (P. virgatum L.) biomass phenotypes across diverse populations and plant components, and their implications for biofuel conversion. The study highlights significant variations in biomass yield, composition, and cell wall chemistry both between switchgrass genotypes and within individual cultivars. Key findings include differences in cellulose, hemicellulose, and lignin content between leaves and stems, which affect biomass digestibility and ethanol yield. The review also discusses the impact of lignin chemistry, particularly the syringyl/guaicyl (S/G) ratio, on the efficiency of biomass saccharification. Furthermore, it explores how these variations respond differently to various pretreatment techniques, affecting overall biofuel production. We conclude that understanding and quantifying this variability is crucial for optimizing switchgrass as a feedstock for commercial biofuel production, thereby potentially addressing the pressing need for sustainable energy sources in sectors such as aviation.

Kousika, Rohit [Univ. of Tennessee, Knoxville, TN ↗
DOE OSTI2026

Constitutive and inducible oleoresin defenses share genetic architectures and mechanisms in Pinus taeda

The oleoresin defense system of loblolly pine (Pinus taeda) protects trees from insects and pathogens and is an important source of renewable biofuels and chemicals, but the genetic basis of oleoresin production is poorly understood. We characterized the genetic architecture of oleoresin flow, resin canal number, stem wood terpene content, and monoterpene composition in two clonal populations of P. taeda. We used quantitative genetic analyses, genome-wide association studies (GWASs), multiplex network learning, and gene expression profiling to elucidate shared gene networks underlying defense traits and to identify high-quality candidates for breeding and engineering loblolly pine. Genetic analyses revealed polygenic inheritance and trait-to-trait correlations provide strong evidence for shared genes regulating constitutive and induced oleoresin flow. We identified 236 single nucleotide polymorphisms associated with oleoresin flow, resin canal number, and terpene composition and highlight candidate genes likely involved in terpene biosynthesis, cambial meristem reprogramming, and pathogen perception and immune signaling. Fourteen GWAS candidates were methyl jasmonate-responsive in tissues where resin canals initiate and terpene production occurs. Integrating quantitative genetics, GWAS, gene expression, and multiplex network analyses enabled the prioritization of high-quality candidate genes. This work advances the development of more resilient loblolly pine optimized for ecological performance, renewable chemical, and biofuel production.

genome-wide association study↗
DOE OSTI2026

Ecological connectivity and habitat loss shape patterns of genetic diversity in a threatened salamander

Context The maintenance of genetic diversity is essential for preserving adaptive potential in populations, yet it is increasingly threatened by landscape alteration. The field of landscape genetics offers a framework for assessing how patch-level landscape conditions, modeled at multiple scales, influence genetic diversity. Objectives We sought to assess how local environmental features and connectivity influence genetic diversity across 74 four-toed salamander (Hemidactylium scutatum) breeding wetlands in the southeastern United States. Methods Using next-generation sequencing data and hierarchical Bayesian models, we examined genome-wide heterozygosity in relation to local landscape features and ecological connectivity. We also assessed the scale of effect of landscape features and tested for temporal lag effects. Results Genetic diversity was lower in wetlands with higher levels of historic deforestation and lower connectivity. An interaction between deforestation and connectivity indicated that deforestation had stronger negative effects in isolated wetlands but weaker effects in well-connected wetlands. Accounting for scale of effect and temporal lags was critical for detecting these relationships. Conclusions Our analyses highlight the importance of assessing the spatial scale (scale of effect) and temporal lag of landscape features to detect key drivers of genetic diversity. In line with population genetic theory, our results indicate that the genetic consequences of habitat loss do not affect populations uniformly and are most severe in isolated populations where gene flow cannot buffer against loss of diversity. Altogether, we highlight the importance of considering the interaction of habitat loss and connectivity in conservation genetic management.

Hemidactylium scutatum↗
DOE OSTI2026

A sorghum pangenome reference improves global crop trait discovery

Although the green revolution adapted a handful of crops to homogeneous and high-input industrialized agriculture, much of the global population still relies on the local production of variable crop cultivars by low-input smallholder farms. This diversity of unhomogenized crops, like that of the grain and bioenergy crop sorghum, offers raw materials for genetic gain and cultivar improvement. However, breeding efforts can be constrained by highly specialized traits and breeding targets Here, to bridge this diversity, we constructed a 33-member pangenome reference and a diversity panel across 1,984 cultivars and landraces. We leveraged these resources to explore the complex interplay among historical contingency, ongoing adaptation and previously uncharacterized structural diversity. Specifically, our analyses conclusively demonstrated multiple nested and deeply diverged structural variants in the domestication gene SHATTERING1, which distinguish the previously established multicentric origin of sorghum. We then applied landscape genomics to reveal how gene flow and secondary contact created the complex genetic mosaic in contemporary breeding networks. As proof of concept for pangenome-accelerated trait discovery, we connected biosynthetic gene cluster structural variation to phenotypic leaf concentration of the cyanogenic glucoside dhurrin. Combined, these approaches will accelerate breeding and trait discovery and provide a framework for similar applications in other crops.

agricultural genetics↗
DOE OSTI2025

The mutation atlas of giant kelp (Macrocystis pyrifera): a mutation database resource for natural knockouts

Giant kelp (Macrocystis pyrifera) is a paramount species of immense ecological and economic importance. It forms dense underwater forests, providing crucial habitat and serving as a foundation species for diverse marine ecosystems. Understanding the genetics of giant kelp is essential for conservation and sustainable farming, safeguarding these valuable ecosystems and their benefits. By analyzing mutations based on their impact, we can gain insights into the potential functional consequences and implications for the organism, helping to identify critical genes or regions that may play a significant role in adaptation, development, and environmental response. To achieve this, we annotated the effects and impact of spontaneous mutations in 559 giant kelp individuals from four different populations. We found over 15.9 million mutations in genes of giant kelp, and classified them into modifier, low, moderate, and high impact depending on their predicted effects. The creation of this mutation effect database, attached to the seedbank of these individuals, offers several applications, including enhancing breeding programs, aiding genetic engineering with naturally occurring mutations, and developing strategies to mitigate the impact of environmental changes.

Plant Sciences↗
DOE OSTI2025

Integrative path modeling and QTL mapping identify maturity, stem strength, and cell wall composition driving lettuce resistance to Sclerotinia minor

Lettuce ( Lactuca sativa ) is highly vulnerable to Sclerotinia minor , the pathogen causing lettuce drop. Breeding for resistance is the most effective control strategy; however, full resistance has not been achieved, and current partial resistance sources are often linked with undesirable traits, such as early bolting. This study aimed to unravel the genetic basis of partial resistance to S. minor and its relationship with plant maturity (bolting), stem mechanical strength (SMS), and cell wall composition (CWC) using a recombinant inbred line (RIL) population derived from a cross between the susceptible iceberg cv. ‘Salinas’ and the resistant oil-seed accession PI 251246. Field evaluations indicated that resistance was linked to earlier bolting, stronger stems, and higher pentose content. Path analysis demonstrated that earlier-maturing plants exhibited increased resistance through enhanced SMS and modified CWC, particularly with higher xylose and lower arabinose levels. Further analysis indicated a significant relationship between syringyl lignin content and resistance, especially in plants with varying bolting responses. Three key quantitative trait loci (QTLs) on linkage groups (LG) 2, 6, and 7 were consistently associated with resistance, bolting, and SMS. Importantly, residual QTL analysis revealed that the resistance locus on LG7 acted independently of maturity, suggesting a distinct resistance mechanism. Callose synthase emerged as a key candidate gene within the LG7 resistance QTL, located near - but distinct from - genes associated with plant maturity and flowering. These findings provide valuable insights into decoupling resistance from early bolting, suggesting a pathway for breeding lettuce cultivars with improved disease resistance and delayed bolting.

Lactuca↗