Jean-Luc Jannink

Breedbase ( https://breedbase.org/ ) is a comprehensive, web-based, open source breeding management and analysis software for plant breeding that was introduced to address gaps with existing software in dealing with modern plant breeding data. Breedbase is continually improved and developed, with new features being added as required by the breeding community. Since its inception, the capabilities of Breedbase were improved to better serve the diverse data modeling needs of additional crops beyond our original ensemble of cassava, banana, yam, sweet potato, tomato, rice, and wheat. We describe here major updates that have been made in the past 4 years, including: (1) improved handling of repetitive measurements; (2) a redesigned experimental treatments interface; (3) a more fine-grained user permission system; (4) improved trial quality control workflows; (5) intercropping support; (6) the addition of a “decision meeting” feature that helps decision makers make the best selection with the available data; (7) transgenics and tracking tools and finally, (8) an improved field layout tool which consolidates several trials in one field. These new features integrate seamlessly with the previous feature set of Breedbase - making it one of the most feature rich breeding management softwares available.

Large-scale chromosomal rearrangements or structural variants (SVs) have shaped the evolution of Avena species, driving domestication and adaptation in oats ( Avena sativa L.). The legacy of these reorganizations, including translocations and inversions, remains evident, yet their frequency and impact on phenotypic and genotypic variability in oat breeding populations remain unclear. The objective of this study was to characterize chromosome rearrangements in 1230 oat breeding lines using genotyping-by-sequencing and to utilize this information in genomic loci discovery and prediction. Principal component analysis (PCA) based on single-nucleotide polymorphisms (SNPs) identified distinct clusters at the known locations of 1A/1C translocation, 3C inversion, 4C inversion, and 7D inversion. The distribution of the clusters of the four rearrangements in the genome-wide diversity space of our panel confirmed the role of SVs in population structure, with 1A/1C showing the most significant divergence. Suppression of recombination due to SVs was shown by higher linkage disequilibrium (LD) observed in all samples compared to lines within individual SV clusters. Genome-wide association studies (GWAS) identified markers for grain yield and agronomic traits, with some differences among cluster-based populations. Genomic prediction accuracies improved when cross-validation was performed within SV clusters rather than across the entire dataset. However, using SV clusters as covariates did not improve GWAS and prediction accuracies. These results suggest that SVs cause population stratification, reduce recombination rates, hinder gene discovery, and affect genomic prediction accuracy. Characterizing oat germplasm for SVs can improve breeding decisions, from optimizing parental selection to enhancing genomic predictions, accelerating genetic gains for key traits.

Genomic selection has become an important strategy in cassava breeding, enabling faster selection cycles and sustained genetic progress. Despite its widespread adoption, long-term evaluations integrating predictive performance, realized genetic gain, and genetic diversity remain scarce, particularly in clonally propagated crops. We present a comprehensive assessment of genomic selection outcomes in the Brazilian cassava breeding program across four recurrent selection cycles (C0 to C3) implemented between 2011 and 2024, using historical phenotypic and genomic data from 210 multi-environment trials. Predictive ability of genomic best linear unbiased prediction models ranged from low to moderate, depending on the trait’s genetic architecture and heritability. Prediction accuracies were highest in early cycles (C0 and C1) and showed modest declines in later cycles (C2 and C3). Root yield, shoot yield, plant height, starch content, and dry matter content exhibited stable predictive performance across cycles, with a gradual reduction in RMSE, indicating improved model calibration as training populations expanded. Regression analyses of genomic estimated breeding values revealed significant realized genetic gains for most yield-related traits. In contrast, dry matter content and starch content exhibited small, non-significant negative trends, consistent with known unfavorable genetic correlations with yield. Targeted reductions in plant architecture scores reflected deliberate selection for ideotypes suited to mechanized production systems. At the same time, analyses of genetic diversity revealed a slight decrease in observed heterozygosity, with higher values in the most advanced selection cycle. These results provide an integrated framework for monitoring predictive performance, realized genetic gain, and population genetic dynamics under long-term genomic selection. Collectively, they offer valuable insights into balancing short-term genetic improvement with long-term sustainability and support the development of strategies to optimize selection decisions, breeding planning, and population management in Brazilian cassava breeding programs.

Provitamin A biofortification of cassava ( Manihot esculenta ) is constrained by a negative genetic correlation between total carotenoid (TC) and dry matter (DM) contents, but its underlying biological mechanism remains unclear. We examined this relationship in 24 African, Latin American, and hybrid cassava genotypes by measuring gene expression and metabolites in inner and outer storage root layers that varied spatially in carotenoid accumulation, enabling comparisons across and within genotypes. TC, and particularly upstream cis- carotenes, were negatively associated with DM after accounting for genotype, and this relationship strengthened across developmental timepoints, consistent with a physiological component of the trade-off. Gene-metabolite co-expression networks constructed from genetic, non-genetic, and overall phenotypic trait components showed no significant topological overlap between carotenoid- and starch-related gene sets, indicating that the TC/DM relationship is not driven by transcriptional co-regulation of the main pathways, although individual associations between carotenoids and carbohydrate-related genes suggested possible indirect metabolic links. Instead, TC accumulation was most strongly associated with regulatory, stress-, redox-, and plastid-related genes, enriched for heme-binding and oxidoreductase functions. These findings suggest that the TC/DM trade-off is not a fixed constraint, and identify candidate regulators of carotenoid content for future functional validation to support cassava provitamin A biofortification. Highlight: Genetic, spatial, and developmental variation in cassava storage roots implicate cis- carotenes, but not direct transcriptional regulation of the carotenoid and starch pathways, in the trade-off between carotenoids and dry matter.

Multi-trait genomic prediction offers a practical route to improve selection for costly, complex traits in clonally propagated crops such as cassava. In a Brazilian breeding panel of 1,078 cassava clones genotyped with 25,923 SNPs and phenotyped for six agronomic traits, we compared single-trait (ST) and multi-trait (MT) GBLUP models. Stage-wise mixed models produced BLUEs that fed into ST and MT-GBLUP. We tested five cross-validation schemes that mimic breeder realities: ST baseline (CV1); naive all-traits MT prediction for unphenotyped candidates (CV2); MT prediction using auxiliary trait phenotypes in the test set (CV3); and two sparse-phenotyping regimes with missingness by trait (CV4) or by clone (CV5) at 25%, 50%, and 75% levels. The main results were that, under the ST baseline (CV1), predictive ability ranged from 0.50 for DMC and 0.45 for FRY down to 0.13 for Le.Dis. A naive full MT model (CV2) performed approximately on par with ST-GBLUP. In contrast, MT designs (CV3) that included informative auxiliary traits, such as shoot yield and combinations with plant vigor and leaf disease severity, yielded small gains for DMC with predictive ability of approximately 0.51 (+2%), while FRY predictive ability increased to approximately 0.65 (+44%), accompanied by RMSE reductions for FRY up to approximately 13.5% (e.g. RMSE approximately 6.2). Sparse-phenotyping simulations (CV4/CV5) demonstrated that MT models sustain or even improve predictive ability under realistic missing-data regimes ≈ 0.62λ0.65). Selection concordance between MT and ST top-10% sets was generally high (>0.80), and MT configurations produced measurable improvements in expected selection response and genetic gain per cycle for several target traits. These results indicate that strategically implemented MT-GBLUP, using a small set of biologically and operationally informative auxiliary traits and optimized sparse phenotyping, can materially increase predictive accuracy and selection efficiency for economically critical cassava traits while reducing phenotyping burden.

Cassava is a major staple crop in tropical regions, and improving its root nutritional quality, particularly carotenoid and dry matter content (DMC), remains a central breeding goal. To elucidate the genetic basis of these traits by locating genomic regions associated with them, we analyzed 3,043 cassava clones from the Brazilian Agricultural Research Corporation (Embrapa) breeding program, phenotyped across 188 multi-environment trials conducted from 2011 to 2022 in Brazil. All clones were genotyped using Genotyping-by-Sequencing (27,045 Single Nucleotide Polymorphism - SNPs) and Diversity Arrays Technology - DArTseq (25,923 SNPs). Trait values were estimated using a two-stage mixed model to obtain deregressed BLUPs (Best Linear Unbiased Predictions), and genome-wide association analyses were performed using both the Mixed Linear Model (MLM) and Multi-Locus Mixed Model (MLMM). We detected six significant SNPs consistently associated with carotenoid content and DMC after Bonferroni correction. These SNPs mapped to six candidate genes involved in pathways relevant to root physiology, including Abscisic Acid ABA-related signaling, hydrolase activity affecting carotenoid conversion, fatty-acid biosynthesis within plastids, cell-wall remodeling, and glycolytic energy metabolism. The loci jointly explained 75.56 % of the phenotypic variance for carotenoids and 76.23 % for DMC, with individual SNP effects ranging from ∼17 % to ∼42 % PVE (Proportion of Variance Explained). Broad-sense heritability was H2 = 0.78 for carotenoids and H² = 0.34 for DMC, confirming substantial genetic control and suitability for molecular breeding. Haplotype analyses revealed four superior haplotypes for carotenoids and one key haplotype for DMC, each showing significantly higher trait values compared with other allelic combinations. These haplotypes represent promising targets for marker-assisted selection and genomic selection, with direct applicability for accelerating genetic gain in elite breeding populations. The results provide actionable genomic resources for breeding programs aiming to develop biofortified and high-root quality cultivars and establish a foundation for future multi-omics and functional validation studies.

Post-harvest physiological deterioration (PPD) represents a significant challenge of cassava production and commercialization. This multifaceted biological process involves a series of mechanisms, including enzymatic stress responses, alterations in gene expression, protein synthesis, accumulation of secondary metabolites, and ultimately, programmed cell death. These changes render the storage roots unpalatable and unmarketable. Therefore, unraveling the genetic architecture of PPD and exploring the interactions of associated genes during its early and late stages is essential for the crop production. We used modern genetic resources to unravel the genetic basis of PPD, based on a genome-wide association study (GWAS), utilizing a combination of different models, including BLINK (Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway), SUPER (Settlement of MLM Under Progressively Exclusive Relationship), and MLMM (Multi-locus mixed models). The phenotyping dataset spanned five years and included evaluations from 42 different trials, evaluating the Embrapa (Brazilian Agricultural Research Corporation) germplasm along with a population derivative from a genomic selection cycle. We utilized a genotype dataset comprising 26,000 high-quality SNPs (single nucleotide polymorphisms). Our findings indicated four significant genetic variants located on chromosomes 2, 5, and 13, which together explain 35.83 % of the phenotypic variation. These variants are associated with genes that are closely linked to the pathways activated during the early and late symptoms of PPD. The identification of these three key genes provides valuable insights into the genetic architecture of PPD and lays a strong foundation for molecular breeding, supporting the efforts to identify cassava genotypes with enhanced PPD tolerance, the identified genomic regions may be incorporated into genomic selection models, thereby enhancing marker-assisted selection (MAS) and improving breeding strategies for long shelf life and high-quality agronomic cassava cultivars for the cassava community.

Plant architecture, the spatial configuration of stems, branches, leaves, and inflorescences underpins essential physiological functions such as light capture, assimilate partitioning, flowering, and ultimately, yield. In cassava ( Manihot esculenta ), architectural traits such us plant height, branching level, and plant shape are agronomically important yet remain underexploited in breeding. Here, a large-scale analysis was conducted using phenotypic and genomic data from more than 14,000 cassava accessions evaluated across 34 field locations in Nigeria between 2010 and 2021, encompassing the national breeding programs of the National Root Crops Research Institute and the International Institute of Tropical Agriculture. The study aimed to dissect the genetic architecture, environmental stability, and breeding relevance of four key traits: plant full height, height to first branching, the branching level number (BranchlevelNum) and plant shape. Phenotypic analyses across breeding stages revealed consistent variation in plant height, branching height, and branching intensity, reflecting the cumulative effects of selection and evaluation across environments. Broad-sense heritability estimates ranged from 0.41 to 0.72, with BranchlevelNum and Cylindrical shape exhibiting strong genetic control and weak correlations with yield components, indicating their suitability for independent improvement. Genome-wide association analyses identified significant loci associated with BranchlevelNum, including a major region on chromosome 2 and an additional locus on chromosome 13, collectively explaining approximately 11% of the phenotypic variance. Candidate genes within these regions included regulators of meristem activity and hormone-related pathways, supporting a developmental basis for branching variation. Genomic prediction accuracy for BranchlevelNum reached 0.44, comparable to values reported for key agronomic traits in cassava. These results demonstrate that branching-related architectural traits are genetically tractable, largely independent of yield, and amenable to genomic selection. The findings support the integration of BranchlevelNum and plant shape into ideotype-driven breeding frameworks aimed at improving flowering efficiency, canopy structure, and field performance in cassava. Author Summary: Cassava is a major food crop, and its plant shape plays an important role in how easily it can be grown, harvested, and improved through breeding. Traits such as plant height, branching, and canopy form affect flowering, seed production, and field management, yet they have received much less attention than yield or disease resistance. In this study, we examined plant architecture using field and genetic data from more than 14,000 cassava plants grown across Nigeria over twelve years. We focused on key traits describing plant height, branching level, and overall plant shape. We found that branching level is strongly controlled by genetics, remains stable across environments, and can be predicted accurately using genomic data. We also identified specific regions of the cassava genome linked to branching behavior. Our findings show that plant architecture can be improved using modern breeding tools without compromising yield. Incorporating branching traits into breeding programs can help develop cassava varieties that flower more reliably and perform better in farmers’ fields.

As one of the flagship databases of the US Department of Agriculture, GrainGenes is positioned at the critical juncture of agricultural data crossroads. GrainGenes (https://graingenes.org; https://wheat.pw.usda.gov) is a centralized location for curated data and web-based tools for wheat, barley, rye, and oat in the service of a global user base. Since 1992, GrainGenes has been serving plant researchers in their quest to improve traits, including biotic and abiotic resistance, as well as high nutrition content. Starting with genetic markers and maps, GrainGenes has evolved to acquire genomic sequences, assemblies, and annotations, leading to an ever-increasing number of pangenomes. Over the years, new web-based tools and capabilities were added to the website to increase the access and utility of peer-reviewed datasets for researchers, plant geneticists and breeders at various stages of their careers, from high school students to emeritus professors. Here we provide a comprehensive overview of the curated content and customized tools available in GrainGenes, whose resources are designed to benefit researchers, growers, and farmers in their efforts to develop more nutritious food for the growing human population and high-quality animal feed.