Biology subjects

Jannink, J.-L.

Publications and source records attributed to Jannink, J.-L..

At least 19 recordsLinked to original sources

bioRxiv2026

Breedbase 2.0 - expanding the functionality for traditional breeding and transgenics

Breedbase ( is a comprehensive, web-based, open-source breeding management and analysis software for plant breeding that was introduced to address gaps with existing software for 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.

bioinformatics↗

bioRxiv2026

Long-term realized genetic gain and population dynamics under genomic selection in Brazilian cassava germplasm

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 traits 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.

genetics↗

bioRxiv2026

Gene-metabolite networks reveal physiological trade-offs but not transcriptional co-regulation between carotenoids and dry matter in cassava (Manihot esculenta) roots

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. HighlightGenetic, 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.

plant biology↗

bioRxiv2026

Enhancing predictive accuracy of yield traits in cassava through multi-trait genomic prediction

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 {approx} 0.62{lambda}0.65). 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.

genetics↗

bioRxiv2026

Uncovering genomic regions controlling root quality traits in Cassava (Manihot esculenta Crantz) using different GWAS models

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 H{superscript 2} = 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.

genetics↗

bioRxiv2026

Unraveling candidate genomic regions responsible for delayed post-harvest deterioration in Cassava (Manihot esculenta Crantz)

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.

genetics↗

bioRxiv2026

Introgression from the wild relative Manihot glaziovii on cassava (M. esculenta) chromosome 1 exhibits segregation distortion and no direct effect on dry matter

The cassava (Manihot esculenta) genome has two large introgressions from its wild relative M. glaziovii on chromosomes 1 and 4 that originate from historical hybridization efforts. The 10 Mbp chromosome 1 introgression has been increasing in frequency in African breeding populations due to its statistical association with higher dry matter content and root number. However, the region also exhibits suppressed recombination, hindering breeders ability to combine favorable glaziovii alleles with the cultivated esculenta background. Since homozygous introgressed lines are rarely selected for advanced trials, dominance effects have not been well-characterized. To analyze the effects of the introgression with higher resolution, we generated a population of over 5000 seedlings from crosses between heterozygous introgressed parents and screened for recombinants using ten KASP markers tagging glaziovii-specific alleles. An optimized subset of 453 lines was then selected and evaluated over two years for yield and vigor traits. Unlike previous studies, composite interval mapping and mixed linear models showed no significant associations between glaziovii alleles and dry matter content or root number. Small, opposing effects on clonal vigor were observed at different ends of the introgression. The region showed significant segregation distortion and enrichment of putative deleterious alleles. Genome alignment of M. esculenta and M. glaziovii assemblies did not show any major structural variants in the introgression region, suggesting that suppressed recombination is likely driven by sequence-level divergence rather than structural rearrangements. These results indicate that the glaziovii introgression does not directly contribute to dry matter, supporting the need for recombination and purging of the glaziovii introgression to aid cassava improvement. Plain language summaryA large chromosome segment from a wild relative of cassava is an important structural aspect in the cassava genome. Since the chromosome segment tends to be inherited as one block, its effects on cassava traits were not well resolved. Through genetic mapping at higher resolution, we identified that the wild segment impacts early vigor and does not appear to impact dry yield, as was previously thought. While there are no major structural differences between the wild and cultivated chromosome segments, their overall divergence seems to suppress the wild chromosome segment from pairing with the cultivated chromosome segment during reproduction. In the apparent absence of any major benefits from the wild segment, removing it from the breeding population may be beneficial. Core ideasO_LIA set of glaziovii allele-specific markers were designed to track the chromosome 1 introgression haplotype. C_LIO_LISegregation distortion suggests the presence of recessive deleterious or lethal alleles in the introgression. C_LIO_LIIncreased recombination is needed to purge deleterious alleles enriched in introgression region. C_LIO_LIThe glaziovii introgression was associated with slightly lower vigor rating and stem diameter. C_LIO_LIThe effects of the previously-identified glaziovii DM QTL were not detected in this population. C_LI

genetics↗

bioRxiv2026

Genetic basis of Cassava (Manihot esculenta Crantz) plant architecture and its relevance for selection of farmer-preferred varieties

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 SummaryCassava 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.

genetics↗

bioRxiv2025

Bivariate genome-wide association study reveals polygenic contributions to covariance between total carotenoid and dry matter contents in yellow-fleshed cassava (Manihot esculenta)

Cassava breeders aim to increase the provitamin A carotenoid content of storage roots to help combat vitamin A deficiency in sub-Saharan Africa, but a negative genetic correlation between total carotenoid (TC) and dry matter (DM) contents hinders breeding efforts. Genetic linkage between a major-effect variant in the phytoene synthase 2 (PSY2) gene and nearby candidate gene(s) has been thought to drive this correlation. Evidence from molecular experiments, however, suggest there may be a metabolic relationship between TC and DM, which we predicted would create genome-wide mediated pleiotropy. Bivariate genome-wide associations were used to test the hypothesis of pleiotropy and examine the genetic architecture of the negative covariance between TC and DM. A population of 378 accessions in the yellow-fleshed cassava breeding program at the International Institute of Tropical Agriculture (IITA) in Ibadan, Nigeria was genotyped with DArTseqLD. TC measured by iCheck spectrometer and DM data were available from field trials over ten years across three locations in Nigeria. Mixed linear models controlling for the previously-identified PSY2 causal variant were used to identify multiple new quantitative trait loci (QTL) jointly associated with both traits. The majority of 17 jointly-associated loci identified at a relaxed significance threshold affected TC and DM in opposite directions, although this pattern did not reach statistical significance in a binomial test. Even after accounting for the effects of these 17 loci as covariates, there was significantly negative polygenic covariance between TC and DM remaining. These findings support the hypothesis that mediated pleiotropy rather than genetic linkage drives the negative genetic correlation between TC and DM in cassava and demonstrate a new application of multivariate GWAS for interrogating the genetic architecture of correlated traits. Plain language summaryIncreasing provitamin A in cassava roots has reduced their dry matter content, making vitamin-enriched cassava varieties less desirable. This study used multi-trait models to identify shared genetic factors, most of which had opposing effects on the two traits. The negative relationship was distributed across the genome, suggesting an inherent physiological trade-off. These findings will guide breeders in developing selection strategies for vitamin-enriched cassava and other starchy crops. More broadly, this study demonstrates the use of multi-trait associations to help distinguish whether traits are associated due to separate, nearby genes (genetic linkage) or if the same genes affect multiple traits (pleiotropy).

genetics↗

bioRxiv2024

Evaluating a Cassava Crop Growth Model by Optimizing Genotypic-Specific Parameters Using Multi-environment Trial Breeding Data

Cassava (Manihot esculenta Crantz) is a critical food security crop for sub-Saharan Africa. Efforts to improve cassava through breeding have expanded over the past decade. At the same time, crop growth models (CGM) are becoming common place in breeding efforts to expand the inference of evaluations of breeding germplasm to environments that have not been tested and to prepare for breeding for adaptation to future climates. We parameterized a CGM, the CROPGRO-MANIHOT-Cassava model in the DSSAT family of models, using data on 67 clones from the International Institute of Tropical Agriculture cassava breeding program evaluated from 2017 to 2020 and over eight locations in Nigeria using trial and error parameter adjustments and the General Likelihood Uncertainty Estimation method. Our objectives were to assess the feasibility of this large-scale calibration in the context of a cassava breeding program and to identify systematic biases of the model. For each cultivar we calculated the Pearson correlation between model prediction and observation across the environments, as well as root mean squared error and d statistics. As a result of calibration, the correlation coefficient increased from -0.03 to +0.08, the RMSE dropped from 21 t ha-1 to 5 t ha-1 while d increased from 0.23 to 0.44. We found that the model underestimated root yield in dry environments (low precipitation and high temperature) and overestimated root yield in wet environments (high precipitation and low temperature). Our experience suggests both that CGM calibration could become a routine component of the cassava breeding data analysis cycle and that there are opportunities for model improvement.

plant biology↗

bioRxiv2024

A pangenome and pantranscriptome of hexaploid oat

Oat grain is a traditional human food rich in dietary fiber that contributes to improved human health. Interest in the crop has surged in recent years owing to its use as the basis for plant-based milk analogs. Oat is an allohexaploid with a large, repeat-rich genome that was shaped by subgenome exchanges over evolutionary timescales. In contrast to many other cereal species, genomic research in oat is still at an early stage, and surveys of structural genome diversity and gene expression variability are scarce. Here, we present annotated chromosome-scale sequence assemblies of 33 wild and domesticated oats along with an atlas of gene expression across six tissues of different developmental stages in 23 accessions. We describe the interplay of gene expression diversity across subgenomes, accessions and tissues. Gene loss in the hexaploid is accompanied by compensatory up-regulation of the remaining homeologs, but this process is constrained by subgenome divergence. Chromosomal rearrangements have significantly impacted recent oat breeding. A large pericentric inversion associated with early flowering explains distorted segregation on chromosome 7D and a homeologous sequence exchange between chromosomes 2A and 2C in a semidwarf mutant has risen to prominence in Australian elite varieties. The oat pangeome will promote the adoption of genomic approaches to understanding the evolution and adaptation of domesticated oats and will accelerate their improvement.

genomics↗

bioRxiv2022

Genomic selection in organisms with biphasic lifecycles: a Saccharina latissima (sugar kelp) case study

Sugar kelp (Saccharina latissima) has a biphasic life cycle, allowing selection on both the diploid sporophytes (SPs) and haploid gametophytes (GPs). We trained a genomic selection (GS) model from farm-tested SP phenotypic data and used a mixed-ploidy additive relationship matrix to predict GP breeding values. Top-ranked GPs were used to make crosses for further farm evaluation. The relationship matrix included 866 individuals: a) founder SPs sampled from the wild; b) progeny GPs from founders; c) Farm-tested SPs crossed from b); and d) progeny GPs from farm-tested SPs. The complete pedigree-based relationship matrix was estimated for all individuals. A subset of founder SPs (n = 58) and GPs (n = 276) were genotyped with Diversity Array Technology and whole genome sequencing, respectively. We evaluated GS prediction accuracy via cross validation on farm-tested SPs in two years using a basic GBLUP model. We also estimated the general combining ability (GCA) and specific combining ability (SCA) variances of parental GPs. A total of 11 yield-related and morphology traits were evaluated. The cross validation accuracies for dry weight per meter (r ranged from 0.16 to 0.35) and wet weight per meter (r ranged 0.19 to 0.35) were comparable to GS accuracy for yield traits in terrestrial crops. For morphology traits, cross validation accuracy exceeded 0.18 in all scenarios except for blade thickness in the second year. Accuracy in a third validation year for dry weight per meter over a confirmation set of 87 individuals was 0.31.

genetics↗

bioRxiv2021

Sexual dimorphism and the effect of wild introgressions on recombination in cassava (Manihot esculenta Crantz) breeding germplasm

Recombination has essential functions in meiosis, evolution, and breeding. The frequency and distribution of crossovers dictate the generation of new allele combinations and can vary across species and between sexes. Here, we examine recombination landscapes across the 18 chromosomes of cassava (Manihot esculenta Crantz) with respect to male and female meioses and known introgressions from the wild relative Manihot glaziovii. We used SHAPEIT2 and duoHMM to infer crossovers from genotyping-by-sequencing (GBS) data and a validated multi-generational pedigree from the International Institute of Tropical Agriculture (IITA) cassava breeding germplasm consisting of 7,020 informative meioses. We then constructed new genetic maps and compared them to an existing map previously constructed by the International Cassava Genetic Map Consortium (ICGMC). We observed higher recombination rates in females compared to males, and lower recombination rates in M. glaziovii introgression segments on chromosomes 1 and 4, with suppressed recombination along the entire length of the chromosome in the case of the chromosome 4 introgression. Finally, we discuss hypothesized mechanisms underlying our observations of heterochiasmy and crossover suppression and discuss the broader implications for plant breeding.

genomics↗

bioRxiv2021

Development of the Wheat Practical Haplotype Graph Database as a Resource for Genotyping Data Storage and Genotype Imputation

To improve the efficiency of high-density genotype data storage and imputation in bread wheat (Triticum aestivum L.), we applied the Practical Haplotype Graph (PHG) tool. The wheat PHG database was built using whole-exome capture sequencing data from a diverse set of 65 wheat accessions. Population haplotypes were inferred for the reference genome intervals defined by the boundaries of the high-quality gene models. Missing genotypes in the inference panels, composed of wheat cultivars or recombinant inbred lines genotyped by exome capture, genotyping-by-sequencing (GBS), or whole-genome skim-seq sequencing approaches, were imputed using the wheat PHG database. Though imputation accuracy varied depending on the method of sequencing and coverage depth, we found 93% imputation accuracy with 0.01x sequence coverage, which was only slightly lower than the accuracy obtained using the 0.5x sequence coverage (96.9%). Compared to Beagle, on average, PHG imputation was ~4% (p-value = 0.00027) more accurate, and showed 27% higher accuracy at imputing a rare haplotype introgressed from a wild relative into wheat. The reduced accuracy of imputation with GBS data (90.4%) is likely associated with the small overlap between GBS markers and the exome capture dataset, which was used for constructing PHG. The highest imputation accuracy was obtained with exome capture for the wheat D genome, which also showed the highest levels of linkage disequlibrium and proportion of identity-by-descent regions among accessions in our reference panel. We demonstrate that genetic mapping based on genotypes imputed using PHG identifies SNPs with a broader range of effect sizes that together explain a higher proportion of genetic variance for heading date and meiotic crossover rate compared to previous studies.

genomics↗

bioRxiv2021

Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations

Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly-related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G+T, G+M and G+T+M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17 and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly-related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.

genetics↗

bioRxiv2021

Genomic mating in outbred species: predicting cross usefulness with additive and total genetic covariance matrices

Diverse crops are both outbred and clonally propagated. Breeders typically use truncation selection of parents and invest significant time, land and money evaluating the progeny of crosses to find exceptional genotypes. We developed and tested genomic mate selection criteria suitable for organisms of arbitrary homozygosity level where the full-sibling progeny are of direct interest as future parents and/or cultivars. We extended cross variance and covariance variance prediction to include dominance effects and predicted the multivariate selection index genetic variance of crosses based on haplotypes of proposed parents, marker effects and recombination frequencies. We combined the predicted mean and variance into usefulness criteria for parent and variety development. We present an empirical study of cassava (Manihot esculenta), a staple tropical root crop. We assessed the potential to predict the multivariate genetic distribution (means, variances and trait covariances) of 462 cassava families in terms of additive and total value using cross-validation. We were able to predict all genetic variances and most covariances with non-zero accuracy. We also tested a directional dominance model and found significant inbreeding depression for most traits and a boost in total merit accuracy for root yield. We predicted 47,083 possible crosses of 306 parents and contrasted them to those previously tested to show how mate selection can reveal new potential within the germplasm. We enable breeders to consider the potential of crosses to produce future parents (progeny with excellent breeding values) and varieties (progeny with top performance). Author SummaryBreeders typically use truncation selection and invest significant resources evaluating progeny to find exceptional genotypes. We extended genetic variance and trait covariance prediction to include dominance and predicting the multivariate selection index variance. We enable mate selection based on potential to produce future parents (progeny with excellent breeding values) and/or varieties (progeny with top performance). Using cross-validation, we demonstrate that genetic variances and covariances can be predicted with non-zero accuracy in cassava, a staple tropical root crop.

genetics↗

bioRxiv2020

Translating insights from the seed metabolome into improved prediction for healthful compounds in oat

Oat (Avena sativa L.) seed is a rich resource of beneficial lipids, soluble fiber, protein, and antioxidants, and is considered a healthful food for humans. Despite these characteristics, little is known regarding the genetic controllers of variation for these compounds in oat seed. We sought to characterize natural variation in the mature seed metabolome using untargeted metabolomics on 367 diverse lines and leverage this information to improve prediction for seed quality traits. We used a latent factor approach to define unobserved variables that may drive covariance among metabolites. One hundred latent factors were identified, of which 21% were enriched for compounds associated with lipid metabolism. Through a combination of whole-genome regression and association mapping, we show that latent factors that generate covariance for many metabolites tend to have a complex genetic architecture. Nonetheless, we recovered significant associations for 23% of the latent factors. These associations were used to inform a multi-kernel genomic prediction model, which was used to predict seed lipid and protein traits in two independent studies. Predictions for eight of the 12 traits were significantly improved compared to genomic best linear unbiased prediction when this prediction model was informed using associations from lipid-enriched factors. This study provides new insights into variation in the oat seed metabolome and provides genomic resources for breeders to improve selection for health-promoting seed quality traits. More broadly, we outline an approach to distill high-dimensional ‘omics’ data to a set of biologically-meaningful variables and translate inferences on these data into improved breeding decisions.View Full Text

genomics↗

bioRxiv2020

Genome-wide association analysis reveals new insights into the genetic architecture of defensive, agro-morphological and quality-related traits in cassava

Cassava (Manihot esculenta) is one of the most important starchy root crops in the tropics due to its adaptation to marginal environments. Genetic progress in this clonally propagated crop can be accelerated through the discovery of markers and candidate genes that could be used in cassava breeding programs. We carried out a genome-wide association study (GWAS) using a panel of 5,310 clones developed at the International Institute of Tropical Agriculture - Nigeria. The population was genotyped at more than 100,000 SNP markers via genotyping-by-sequencing (GBS). Genomic regions underlying genetic variation for 14 traits classified broadly into four categories: biotic stress (cassava mosaic disease and cassava green mite severity); quality (dry matter content and carotenoid content) and plant agronomy (harvest index and plant type). We also included several agro-morphological traits related to leaves, stems and roots with high heritability. In total, 41 significant associations were uncovered. While some of the identified loci matched with those previously reported, we present additional association signals for the traits. We provide a catalogue of favourable alleles at the most significant SNP for each trait-locus combination and candidate genes occurring within the GWAS hits. These resources provide a foundation for the development of markers that could be used in cassava breeding programs and candidate genes for functional validation.

genetics↗