Genomic prediction in Brassica napus: evaluating the benefit of imputed whole-genome sequencing data.

Advances in sequencing technology allow whole plant genomes to be sequenced with high quality. Combining genotypic and phenotypic data in genomic prediction helps breeders to select crossing partners in partially phenotyped populations. In plant breeding programs, the cost of sequencing entire breeding populations still exceeds available genotyping budgets. Hence, the method for genotyping is still mainly single nucleotide polymorphism (SNP) arrays; however, arrays are unable to assess the entire genome- and population-wide diversity. A compromise involves genotyping the entire population using an SNP array and a subset of the population with whole-genome sequencing. Both datasets can then be used to impute markers from whole-genome sequencing onto the entire population. Here, we evaluate whether imputation of whole-genome sequencing data enhances genomic predictions, using data from a nested association mapping population of rapeseed (Brassica napus). Employing two cross-validation schemes that mimic scenarios for the prediction of close and distant relatives, we show that imputed marker data do not significantly improve prediction accuracy, likely due to redundancy in relationship estimates and imputation errors. In simulation studies, only small improvements were observed, further corroborating the findings. We conclude that SNP arrays are already equipped with the information that is added by imputation through relationship and linkage disequilibrium.

haploMAGIC: Accurate phasing and detection of recombination in multiparental populations despite genotyping errors.

Recombination is a key mechanism in breeding for promoting genetic variability. Multiparental populations constitute an excellent platform for precise genotype phasing, identification of genome-wide crossovers, estimation of recombination frequencies and construction of recombination maps. Here, we introduce haploMAGIC, a pipeline to detect crossovers in multiparental populations with single-nucleotide polymorphism (SNP) data by exploiting the pedigree relationships for accurate genotype phasing and inference of grandparental haplotypes. haploMAGIC applies filtering to prevent false positive crossovers due to genotyping errors, a common problem in high-throughput SNP analysis of complex plant genomes. Hence it discards haploblocks not reaching a specified minimum number of informative alleles. A performance analysis using populations simulated with AlphaSimR revealed that haploMAGIC improves upon existing methods of crossover detection in terms of recall and precision, most notably when genotyping error rates are high. Furthermore, we constructed recombination maps using haploMAGIC with high-resolution genotype data from two large multi-parental populations of winter rapeseed (Brassica napus). The results demonstrate the applicability of the pipeline in real-world scenarios and showed good correlations in recombination frequency compared with alternative software. Therefore, we propose haploMAGIC as an accurate tool at crossover detection with multiparental populations that shows robustness against genotyping errors.

Novel PHOTOPERIOD-1 gene variants associate with yield-related and root-angle traits in European bread wheat.

KEY MESSAGE: PHOTOPERIOD-1 homoeologous gene copies play a pivotal role in regulation of flowering time in wheat. Here, we show that their influence also extends to spike and shoot architecture and even impacts root development. The sequence diversity of three homoeologous copies of the PHOTOPERIOD-1 gene in European winter wheat was analyzed by Oxford Nanopore amplicon-based multiplex sequencing and molecular markers in a panel of 194 cultivars representing breeding progress over the past 5 decades. A strong, consistent association with an average 8% increase in grain yield was observed for the PpdA1-Hap1 haplotype across multiple environments. This haplotype was found to be linked in 51% of cultivars to the 2NS/2AS translocation, originally introduced from Aegilops ventricosa, which leads to an overestimation of its effect. However, even in cultivars without the 2NS/2AS translocation, PpdA1-Hap1 was significantly associated with increased grain yield, kernel per spike and kernel per m2 under optimal growth conditions, conferring a 4% yield advantage compared to haplotype PpdA1-Hap4. In contrast to Ppd-B1 and Ppd-D1, the Ppd-A1 gene exhibits novel structural variations and a high number of SNPs, highlighting the evolutionary changes that have occurred in this region over the course of wheat breeding history. Additionally, cultivars carrying the photoperiod-insensitive Ppd-D1a allele not only exhibit earlier heading, but also deeper roots compared to those with photoperiod-sensitive alleles under German conditions. PCR and KASP assays have been developed that can be effectively employed in marker-assisted breeding programs to introduce these favorable haplotypes.

Effect of a QTL on wheat chromosome 5B associated with enhanced root dry mass on transpiration and nitrogen uptake under contrasting drought scenarios in wheat.

BACKGROUND: A sufficient nitrogen supply is crucial for high-quality wheat yields. However, the use of nitrogen fertilization can also negatively influence ecosystems due to leaching or volatile atmospheric emissions. Drought events, increasingly prevalent in many crop production areas, significantly impact nitrogen uptake. Breeding more efficient wheat varieties is necessary to achieve acceptable yields with limited nitrogen and water. Crop root systems play a crucial role as the primary organ for absorbing water and nutrients. To investigate the impact of an enhanced root system on nitrogen and water use efficiency in wheat under various irrigation conditions, this study conducted two experiments using precision phenotyping platforms for controlled drought stress treatment. Experiment 1 involved four contrasting winter wheat genotypes. It included the Chinese variety Ning0604, carrying a quantitative trait locus (QTL) on chromosome 5B associated with a higher root dry biomass, and three elite German varieties, Elixer, Genius, and Leandrus. Experiment 2 compared near-isogenic lines (NIL) of the three elite varieties, each containing introgressions of the QTL on chromosome 5B linked to root dry mass. In both experiments, nitrogen partitioning was tracked via isotope discrimination after fertilization with 5 Atom % 15N-labeled KNO3-. RESULTS: In experiment 1 the quantification by 15N isotope discrimination revealed significantly (p < 0.05) higher nitrogen derived from fertilizer in the root organ for Ning0604 than those of the three German varieties. In experiment 2, two out of three NILs showed a significantly (p < 0.05) higher uptake of N derived from fertilizer than their respective recipient line under well-watered conditions. Furthermore, significantly lower transpiration rates (p < 0.1) were observed in one NIL compared to its respective recipient. CONCLUSIONS: The combination of the DroughtSpotter facility coupled with 15N tracer-based tracking of N uptake and remobilization extends the insight into the impact of genetically altered root biomass on wheat NUE and WUE under different water availability scenarios. The study shows the potential for how a modified genetic constitution of the locus on wheat chromosome 5B can reduce transpiration and enhance N uptake. The dependence of the observations on the recipient and water availability suggests a need for further research to investigate the interaction with genetic background traits.

Integrated multi-omics analyses and genome-wide association studies reveal prime candidate genes of metabolic and vegetative growth variation in canola.

Genome-wide association studies (GWAS) identified thousands of genetic loci associated with complex plant traits, including many traits of agronomical importance. However, functional interpretation of GWAS results remains challenging because of large candidate regions due to linkage disequilibrium. High-throughput omics technologies, such as genomics, transcriptomics, proteomics and metabolomics open new avenues for integrative systems biological analyses and help to nominate systems information supported (prime) candidate genes. In the present study, we capitalise on a diverse canola population with 477 spring-type lines which was previously analysed by high-throughput phenotyping of growth-related traits and by RNA sequencing and metabolite profiling for multi-omics-based hybrid performance prediction. We deepened the phenotypic data analysis, now providing 123 time-resolved image-based traits, to gain insight into the complex relations during early vegetative growth and reanalysed the transcriptome data based on the latest Darmor-bzh v10 genome assembly. Genome-wide association testing revealed 61 298 robust quantitative trait loci (QTL) including 187 metabolite QTL, 56814 expression QTL and 4297 phenotypic QTL, many clustered in pronounced hotspots. Combining information about QTL colocalisation across omics layers and correlations between omics features allowed us to discover prime candidate genes for metabolic and vegetative growth variation. Prioritised candidate genes for early biomass accumulation include A06p05760.1_BnaDAR (PIAL1), A10p16280.1_BnaDAR, C07p48260.1_BnaDAR (PRL1) and C07p48510.1_BnaDAR (CLPR4). Moreover, we observed unequal effects of the Brassica A and C subgenomes on early biomass production.

Accurate prediction of quantitative traits with failed SNP calls in canola and maize.

In modern plant breeding, genomic selection is becoming the gold standard to select superior genotypes in large breeding populations that are only partially phenotyped. Many breeding programs commonly rely on single-nucleotide polymorphism (SNP) markers to capture genome-wide data for selection candidates. For this purpose, SNP arrays with moderate to high marker density represent a robust and cost-effective tool to generate reproducible, easy-to-handle, high-throughput genotype data from large-scale breeding populations. However, SNP arrays are prone to technical errors that lead to failed allele calls. To overcome this problem, failed calls are often imputed, based on the assumption that failed SNP calls are purely technical. However, this ignores the biological causes for failed calls-for example: deletions-and there is increasing evidence that gene presence-absence and other kinds of genome structural variants can play a role in phenotypic expression. Because deletions are frequently not in linkage disequilibrium with their flanking SNPs, permutation of missing SNP calls can potentially obscure valuable marker-trait associations. In this study, we analyze published datasets for canola and maize using four parametric and two machine learning models and demonstrate that failed allele calls in genomic prediction are highly predictive for important agronomic traits. We present two statistical pipelines, based on population structure and linkage disequilibrium, that enable the filtering of failed SNP calls that are likely caused by biological reasons. For the population and trait examined, prediction accuracy based on these filtered failed allele calls was competitive to standard SNP-based prediction, underlying the potential value of missing data in genomic prediction approaches. The combination of SNPs with all failed allele calls or the filtered allele calls did not outperform predictions with only SNP-based prediction due to redundancy in genomic relationship estimates.

Stage-specific genotype-by-environment interactions determine yield components in wheat.

In cereal crops, environmental fluctuations affect different physiological processes during various developmental phases associated with the formation of yield components. Because these effects are coupled with cultivar-specific phenology, studies investigating environmental responses in different cultivars can give contradictory results regarding key phases impacting yield performance. To dissect how genotype-by-environment interactions affect grain yield in winter wheat, we estimated the sensitivities of yield components to variation in global radiation, temperature and precipitation in 220 cultivars across 81 time-windows ranging from double ridge to seed desiccation. Environmental sensitivity responses were prominent in the short-term physiological subphases of spike and kernel development, causing phenologically dependent, stage-specific genotype-by-environment interactions. Here we reconcile contradicting findings from previous studies and show previously undetected effects; for example, the positive impact of global radiation on kernel weight during canopy senescence. This deep insight into the three-way interactions between phenology, yield formation and environmental fluctuations provides comprehensive new information for breeding and modelling cereal crops.

Haplotype blocks for genomic prediction: a comparative evaluation in multiple crop datasets.

In modern plant breeding, genomic selection is becoming the gold standard for selection of superior genotypes. The basis for genomic prediction models is a set of phenotyped lines along with their genotypic profile. With high marker density and linkage disequilibrium (LD) between markers, genotype data in breeding populations tends to exhibit considerable redundancy. Therefore, interest is growing in the use of haplotype blocks to overcome redundancy by summarizing co-inherited features. Moreover, haplotype blocks can help to capture local epistasis caused by interacting loci. Here, we compared genomic prediction methods that either used single SNPs or haplotype blocks with regards to their prediction accuracy for important traits in crop datasets. We used four published datasets from canola, maize, wheat and soybean. Different approaches to construct haplotype blocks were compared, including blocks based on LD, physical distance, number of adjacent markers and the algorithms implemented in the software "Haploview" and "HaploBlocker". The tested prediction methods included Genomic Best Linear Unbiased Prediction (GBLUP), Extended GBLUP to account for additive by additive epistasis (EGBLUP), Bayesian LASSO and Reproducing Kernel Hilbert Space (RKHS) regression. We found improved prediction accuracy in some traits when using haplotype blocks compared to SNP-based predictions, however the magnitude of improvement was very trait- and model-specific. Especially in settings with low marker density, haplotype blocks can improve genomic prediction accuracy. In most cases, physically large haplotype blocks yielded a strong decrease in prediction accuracy. Especially when prediction accuracy varies greatly across different prediction models, prediction based on haplotype blocks can improve prediction accuracy of underperforming models. However, there is no "best" method to build haplotype blocks, since prediction accuracy varied considerably across methods and traits. Hence, criteria used to define haplotype blocks should not be viewed as fixed biological parameters, but rather as hyperparameters that need to be adjusted for every dataset.

Genomic prediction with haplotype blocks in wheat.

Haplotype blocks might carry additional information compared to single SNPs and have therefore been suggested for use as independent variables in genomic prediction. Studies in different species resulted in more accurate predictions than with single SNPs in some traits but not in others. In addition, it remains unclear how the blocks should be built to obtain the greatest prediction accuracies. Our objective was to compare the results of genomic prediction with different types of haplotype blocks to prediction with single SNPs in 11 traits in winter wheat. We built haplotype blocks from marker data from 361 winter wheat lines based on linkage disequilibrium, fixed SNP numbers, fixed lengths in cM and with the R package HaploBlocker. We used these blocks together with data from single-year field trials in a cross-validation study for predictions with RR-BLUP, an alternative method (RMLA) that allows for heterogeneous marker variances, and GBLUP performed with the software GVCHAP. The greatest prediction accuracies for resistance scores for B. graminis, P. triticina, and F. graminearum were obtained with LD-based haplotype blocks while blocks with fixed marker numbers and fixed lengths in cM resulted in the greatest prediction accuracies for plant height. Prediction accuracies of haplotype blocks built with HaploBlocker were greater than those of the other methods for protein concentration and resistances scores for S. tritici, B. graminis, and P. striiformis. We hypothesize that the trait-dependence is caused by properties of the haplotype blocks that have overlapping and contrasting effects on the prediction accuracy. While they might be able to capture local epistatic effects and to detect ancestral relationships better than single SNPs, prediction accuracy might be reduced by unfavorable characteristics of the design matrices in the models that are due to their multi-allelic nature.

Transgressive and parental dominant gene expression and cytosine methylation during seed development in Brassica napus hybrids.

KEY MESSAGE: Transcriptomic and epigenomic profiling of gene expression and small RNAs during seed and seedling development reveals expression and methylation dominance levels with implications on early stage heterosis in oilseed rape. The enhanced performance of hybrids through heterosis remains a key aspect in plant breeding; however, the underlying mechanisms are still not fully elucidated. To investigate the potential role of transcriptomic and epigenomic patterns in early expression of hybrid vigor, we investigated gene expression, small RNA abundance and genome-wide methylation in hybrids from two distant Brassica napus ecotypes during seed and seedling developmental stages using next-generation sequencing. A total of 31117, 344, 36229 and 7399 differentially expressed genes, microRNAs, small interfering RNAs and differentially methylated regions were identified, respectively. Approximately 70% of the differentially expressed or methylated features displayed parental dominance levels where the hybrid followed the same patterns as the parents. Via gene ontology enrichment and microRNA-target association analyses during seed development, we found copies of reproductive, developmental and meiotic genes with transgressive and paternal dominance patterns. Interestingly, maternal dominance was more prominent in hypermethylated and downregulated features during seed formation, contrasting to the general maternal gamete demethylation reported during gametogenesis in angiosperms. Associations between methylation and gene expression allowed identification of putative epialleles with diverse pivotal biological functions during seed formation. Furthermore, most differentially methylated regions, differentially expressed siRNAs and transposable elements were in regions that flanked genes without differential expression. This suggests that differential expression and methylation of epigenomic features may help maintain expression of pivotal genes in a hybrid context. Differential expression and methylation patterns during seed formation in an F1 hybrid provide novel insights into genes and mechanisms with potential roles in early heterosis.

Benchmarking Oxford Nanopore read alignment-based insertion and deletion detection in crop plant genomes.

Structural variations (SVs) are larger polymorphisms (> 50 bp in length), which consist of insertions, deletions, inversions, duplications, and translocations. They can have a strong impact on agronomical traits and play an important role in environmental adaptation. The development of long-read sequencing technologies, including Oxford Nanopore, allows for comprehensive SV discovery and characterization even in complex polyploid crop genomes. However, many of the SV discovery pipeline benchmarks do not include complex plant genome datasets. In this study, we benchmarked insertion and deletion detection by popular long-read alignment-based SV detection tools for crop plant genomes. We used real and simulated Oxford Nanopore reads for two crops, allotetraploid Brassica napus (oilseed rape) and diploid Solanum lycopersicum (tomato), and evaluated several read aligners and SV callers across 5×, 10×, and 20× coverages typically used in re-sequencing studies. We further validated our findings using maize and soybean datasets. Our benchmarks provide a useful guide for designing Oxford Nanopore re-sequencing projects and SV discovery pipelines for crop plants.

The giant diploid faba genome unlocks variation in a global protein crop.

Increasing the proportion of locally produced plant protein in currently meat-rich diets could substantially reduce greenhouse gas emissions and loss of biodiversity1. However, plant protein production is hampered by the lack of a cool-season legume equivalent to soybean in agronomic value2. Faba bean (Vicia faba L.) has a high yield potential and is well suited for cultivation in temperate regions, but genomic resources are scarce. Here, we report a high-quality chromosome-scale assembly of the faba bean genome and show that it has expanded to a massive 13 Gb in size through an imbalance between the rates of amplification and elimination of retrotransposons and satellite repeats. Genes and recombination events are evenly dispersed across chromosomes and the gene space is remarkably compact considering the genome size, although with substantial copy number variation driven by tandem duplication. Demonstrating practical application of the genome sequence, we develop a targeted genotyping assay and use high-resolution genome-wide association analysis to dissect the genetic basis of seed size and hilum colour. The resources presented constitute a genomics-based breeding platform for faba bean, enabling breeders and geneticists to accelerate the improvement of sustainable protein production across the Mediterranean, subtropical and northern temperate agroecological zones.

Frequent spontaneous structural rearrangements promote rapid genome diversification in a Brassica napus F1 generation.

In a cross between two homozygous Brassica napus plants of synthetic and natural origin, we demonstrate that novel structural genome variants from the synthetic parent cause immediate genome diversification among F1 offspring. Long read sequencing in twelve F1 sister plants revealed five large-scale structural rearrangements where both parents carried different homozygous alleles but the heterozygous F1 genomes were not identical heterozygotes as expected. Such spontaneous rearrangements were part of homoeologous exchanges or segmental deletions and were identified in different, individual F1 plants. The variants caused deletions, gene copy-number variations, diverging methylation patterns and other structural changes in large numbers of genes and may have been causal for unexpected phenotypic variation between individual F1 sister plants, for example strong divergence of plant height and leaf area. This example supports the hypothesis that spontaneous de novo structural rearrangements after de novo polyploidization can rapidly overcome intense allopolyploidization bottlenecks to re-expand crops genetic diversity for ecogeographical expansion and human selection. The findings imply that natural genome restructuring in allopolyploid plants from interspecific hybridization, a common approach in plant breeding, can have a considerably more drastic impact on genetic diversity in agricultural ecosystems than extremely precise, biotechnological genome modifications.

Comprehensive transcriptional variability analysis reveals gene networks regulating seed oil content of Brassica napus.

BACKGROUND: Regulation of gene expression plays an essential role in controlling the phenotypes of plants. Brassica napus (B. napus) is an important source for the vegetable oil in the world, and the seed oil content is an important trait of B. napus. RESULTS: We perform a comprehensive analysis of the transcriptional variability in the seeds of B. napus at two developmental stages, 20 and 40 days after flowering (DAF). We detect 53,759 and 53,550 independent expression quantitative trait loci (eQTLs) for 79,605 and 76,713 expressed genes at 20 and 40 DAF, respectively. Among them, the local eQTLs are mapped to the adjacent genes more frequently. The adjacent gene pairs are regulated by local eQTLs with the same open chromatin state and show a stronger mode of expression piggybacking. Inter-subgenomic analysis indicates that there is a feedback regulation for the homoeologous gene pairs to maintain partial expression dosage. We also identify 141 eQTL hotspots and find that hotspot87-88 co-localizes with a QTL for the seed oil content. To further resolve the regulatory network of this eQTL hotspot, we construct the XGBoost model using 856 RNA-seq datasets and the Basenji model using 59 ATAC-seq datasets. Using these two models, we predict the mechanisms affecting the seed oil content regulated by hotspot87-88 and experimentally validate that the transcription factors, NAC13 and SCL31, positively regulate the seed oil content. CONCLUSIONS: We comprehensively characterize the gene regulatory features in the seeds of B. napus and reveal the gene networks regulating the seed oil content of B. napus.

Novel candidate loci for morpho-agronomic and seed quality traits detected by targeted genotyping-by-sequencing in common bean.

Phaseolus vulgaris L., known as common bean, is one of the most important grain legumes cultivated around the world for its immature pods and dry seeds, which are rich in protein and micronutrients. Common bean offers a cheap food and protein sources to ameliorate food shortage and malnutrition around the world. However, the genetic basis of most important traits in common bean remains unknown. This study aimed at identifying QTL and candidate gene models underlying twenty-six agronomically important traits in common bean. For this, we assembled and phenotyped a diversity panel of 200 P. vulgaris genotypes in the greenhouse, comprising determinate bushy, determinate climbing and indeterminate climbing beans. The panel included dry beans and snap beans from different breeding programmes, elite lines and landraces from around the world with a major focus on accessions of African, European and South American origin. The panel was genotyped using a cost-conscious targeted genotyping-by-sequencing (GBS) platform to take advantage of highly polymorphic SNPs detected in previous studies and in diverse germplasm. The detected single nucleotide polymorphisms (SNPs) were applied in marker-trait analysis and revealed sixty-two quantitative trait loci (QTL) significantly associated with sixteen traits. Gene model identification via a similarity-based approach implicated major candidate gene models underlying the QTL associated with ten traits including, flowering, yield, seed quality, pod and seed characteristics. Our study revealed six QTL for pod shattering including three new QTL potentially useful for breeding. However, the panel was evaluated in a single greenhouse environment and the findings should be corroborated by evaluations across different field environments. Some of the detected QTL and a number of candidate gene models only elucidate the understanding of the genetic nature of these traits and provide the basis for further studies. Finally, the study showed the possibility of using a limited number of SNPs in performing marker-trait association in common bean by applying a highly scalable targeted GBS approach. This targeted GBS approach is a cost-efficient strategy for assessment of the genetic basis of complex traits and can enable geneticists and breeders to identify novel loci and targets for marker-assisted breeding more efficiently.

Long-Amplicon Single-Molecule Sequencing Reveals Novel, Trait-Associated Variants of VERNALIZATION1 Homoeologs in Hexaploid Wheat.

The gene VERNALIZATION1 (VRN1) is a key controller of vernalization requirement in wheat. The genome of hexaploid wheat (Triticum aestivum) harbors three homoeologous VRN1 loci on chromosomes 5A, 5B, and 5D. Structural sequence variants including small and large deletions and insertions and single nucleotide polymorphisms (SNPs) in the three homoeologous VRN1 genes not only play an important role in the control of vernalization requirement, but also have been reported to be associated with other yield related traits of wheat. Here we used single-molecule sequencing of barcoded long-amplicons to assay the full-length sequences (∼13 kbp plus 700 bp from the promoter sequence) of the three homoeologous VRN1 genes in a panel of 192 predominantly European winter wheat cultivars. Long read sequences revealed previously undetected duplications, insertions and single-nucleotide polymorphisms in the three homoeologous VRN1 genes. All the polymorphisms were confirmed by Sanger sequencing. Sequence analysis showed the predominance of the winter alleles vrn-A1, vrn-B1, and vrn-D1 across the investigated cultivars. Associations of SNPs and structural variations within the three VRN1 genes with 20 economically relevant traits including yield, nodal root-angle index and quality related traits were evaluated at the levels of alleles, haplotypes, and copy number variants. Cultivars carrying structural variants within VRN1 genes showed lower grain yield, protein yield and biomass compared to those with intact genes. Cultivars carrying a single vrn-A1 copy and a unique haplotype with a high number of SNPs were found to have elevated grain yield, kernels per spike and kernels per m2 along with lower grain sedimentation values. In addition, we detected a novel SNP polymorphism within the G-quadruplex region of the promoter of vrn-A1 that was associated with deeper roots in winter wheat. Our findings show that multiplex, single-molecule long-amplicon sequencing is a useful tool for detecting variants in target genes within large plant populations, and can be used to simultaneously assay sequence variants among target multiple gene homoeologs in polyploid crops. Numerous novel VRN1 haplotypes and alleles were identified that showed significantly associations to economically important traits. These polymorphisms were converted into PCR or KASP assays for use in marker-assisted breeding.

Genetic and genomic diversity in the sorghum gene bank collection of Uganda.

BACKGROUND: The Plant Genetic Resources Centre at the Uganda National Gene Bank houses has over 3000 genetically diverse landraces and wild relatives of Sorghum bicolor accessions. This genetic diversity resource is untapped, under-utilized, and has not been systematically incorporated into sorghum breeding programs. In this study, we characterized the germplasm collection using whole-genome SNP markers (DArTseq). Discriminant analysis of principal components (DAPC) was implemented to study the racial ancestry of the accessions in comparison to a global sorghum diversity set and characterize the sub-groups present in the Ugandan (UG) germplasm. RESULTS: Population structure and phylogenetic analysis revealed the presence of five subgroups among the Ugandan accessions. The samples from the highlands of the southwestern region were genetically distinct as compared to the rest of the population. This subset was predominated by the caudatum race and unique in comparison to the other sub-populations. In this study, we detected QTL for juvenile cold tolerance by genome-wide association studies (GWAS) resulting in the identification of 4 markers associated (-log10p > 3) to survival under cold stress under both field and climate chamber conditions, located on 3 chromosomes (02, 06, 09). To our best knowledge, the QTL on Sb09 with the strongest association was discovered for the first time. CONCLUSION: This study demonstrates how genebank genomics can potentially facilitate effective and efficient usage of valuable, untapped germplasm collections for agronomic trait evaluation and subsequent allele mining. In face of adverse climate change, identification of genomic regions potentially involved in the adaptation of Ugandan sorghum accessions to cooler climatic conditions would be of interest for the expansion of sorghum production into temperate latitudes.

Machine Learning Applied to the Search for Nonlinear Features in Breeding Populations.

Large plant breeding populations are traditionally a source of novel allelic diversity and are at the core of selection efforts for elite material. Finding rare diversity requires a deep understanding of biological interactions between the genetic makeup of one genotype and its environmental conditions. Most modern breeding programs still rely on linear regression models to solve this problem, generalizing the complex genotype by phenotype interactions through manually constructed linear features. However, the identification of positive alleles vs. background can be addressed using deep learning approaches that have the capacity to learn complex nonlinear functions for the inputs. Machine learning (ML) is an artificial intelligence (AI) approach involving a range of algorithms to learn from input data sets and predict outcomes in other related samples. This paper describes a variety of techniques that include supervised and unsupervised ML algorithms to improve our understanding of nonlinear interactions from plant breeding data sets. Feature selection (FS) methods are combined with linear and nonlinear predictors and compared to traditional prediction methods used in plant breeding. Recent advances in ML allowed the construction of complex models that have the capacity to better differentiate between positive alleles and the genetic background. Using real plant breeding program data, we show that ML methods have the ability to outperform current approaches, increase prediction accuracies, decrease the computing time drastically, and improve the detection of important alleles involved in qualitative or quantitative traits.

High-Resolution Mapping of Barley mild mosaic virus Resistance Gene rym15.

Barley yellow mosaic virus (BaYMV) and Barley mild mosaic virus (BaMMV), which are transmitted by the soil-borne plasmodiophorid Polymyxa graminis, cause high yield losses in barley. In previous studies, the recessive BaMMV resistance gene rym15, derived from the Japanese landrace Chikurin Ibaraki 1, was mapped on chromosome 6HS of Hordeum vulgare. In this study, 423 F4 segmental recombinant inbred lines (RILs) were developed from crosses of Chikurin Ibaraki 1 with two BaMMV-susceptible cultivars, Igri (139 RILs) and Uschi (284 RILs). A set of 32 competitive allele-specific PCR (KASP) assays, designed using single nucleotide polymorphisms (SNPs) from the barley 50 K Illumina Infinium iSelect SNP chip, genotyping by sequencing (GBS) and whole-genome sequencing (WGS), was used as a backbone for construction of two high-resolution maps. Using this approach, the target locus was narrowed down to 0.161 cM and 0.036 cM in the Igri × Chikurin Ibaraki 1 (I × C) and Chikurin Ibaraki 1 × Uschi (C × U) populations, respectively. Corresponding physical intervals of 11.3 Mbp and 0.281 Mbp were calculated for I × C and C × U, respectively, according to the Morex v3 genome sequence. In the 0.281 Mbp target region, six high confidence (HC) and two low confidence (LC) genes were identified. Genome assemblies of BaMMV-susceptible cultivars Igri and Golden Promise from the barley pan-genome, and a HiFi assembly of Chikurin Ibaraki 1 together with re-sequencing data for the six HC and two LC genes in susceptible parental cultivar Uschi revealed functional SNPs between resistant and susceptible genotypes only in two of the HC genes. These SNPs are the most promising candidates for the development of functional markers and the two genes represent promising candidates for functional analysis.

Pangenomics in crop improvement-from coding structural variations to finding regulatory variants with pangenome graphs.

Since the first reported crop pangenome in 2014, advances in high-throughput and cost-effective DNA sequencing technologies facilitated multiple such studies including the pangenomes of oilseed rape (Brassica napus L.), soybean [Glycine max (L.) Merr.], rice (Oryza sativa L.), wheat (Triticum aestivum L.), and barley (Hordeum vulgare L.). Compared with single-reference genomes, pangenomes provide a more accurate representation of the genetic variation present in a species. By combining the genomic data of multiple accessions, pangenomes allow for the detection and annotation of complex DNA polymorphisms such as structural variations (SVs), one of the major determinants of genetic diversity within a species. In this review we summarize the current literature on crop pangenomics, focusing on their application to find candidate SVs involved in traits of agronomic interest. We then highlight the potential of pangenomes in the discovery and functional characterization of noncoding regulatory sequences and their variations. We conclude with a summary and outlook on innovative data structures representing the complete content of plant pangenomes including annotations of coding and noncoding elements and outcomes of transcriptomic and epigenomic experiments.