Integrating de novo QTL-seq and linkage mapping to identify quantitative trait loci conditioning physiological resistance and avoidance to white mold disease in dry bean.

White mold (WM), caused by the ubiquitous fungus Sclerotinia sclerotiorum, is a devastating disease that limits production and quality of dry bean globally. In the present study, classic linkage mapping combined with QTL-seq were employed in two recombinant inbred line (RIL) populations, "Montrose"/I9365-25 (M25) and "Raven"/I9365-31 (R31), with the initial goal of fine-mapping QTL WM5.4 and WM7.5 that condition WM resistance. The RILs were phenotyped for WM reactions under greenhouse (straw test) and field environments. The general region of WM5.4 and WM7.5 were reconfirmed with both mapping strategies within each population. Combining the results from both mapping strategies, WM5.4 was delimited to a 22.60-36.25 Mb interval in the heterochromatic regions on Pv05, while WM7.5 was narrowed to a 0.83 Mb (3.99-4.82 Mb) region on the Pv07 chromosome. Furthermore, additional QTL WM2.2a (3.81-7.24 Mb), WM2.2b (11.18-17.37 Mb, heterochromatic region), and WM2.2c (23.33-25.94 Mb) were mapped to a narrowed genomic interval on Pv02 and WM4.2 in a 0.89 Mb physical interval at the distal end of Pv04 chromosome. Gene models encoding gibberellin 2-oxidase proteins regulating plant architecture are likely candidate genes associated with WM2.2a resistance. Nine gene models encoding a disease resistance protein (quinone reductase family protein and ATWRKY69) found within the WM5.4 QTL interval are putative candidate genes. Clusters of 13 and 5 copies of gene models encoding cysteine-rich receptor-like kinase and receptor-like protein kinase-related family proteins, respectively, are potential candidate genes associated with WM7.5 resistance and most likely trigger physiological resistance to WM. Acquired knowledge of the narrowed major QTL intervals, flanking markers, and candidate genes provides promising opportunities to develop functional molecular markers to implement marker-assisted selection for WM resistant dry bean cultivars.

Linked candidate genes of different functions for white mold resistance in common bean (Phaseolus vulgaris L) are identified by multiple QTL mapping approaches.

White mold (WM) is a major disease in common bean (Phaseolus vulgaris L.), and its complex quantitative genetic control limits the development of WM resistant cultivars. WM2.2, one of the nine meta-QTL with a major effect on WM tolerance, explains up to 35% of the phenotypic variation and was previously mapped to a large genomic interval on Pv02. Our objective was to narrow the interval of this QTL using combined approach of classic QTL mapping and QTL-based bulk segregant analysis (BSA), and confirming those results with Khufu de novo QTL-seq. The phenotypic and genotypic data from two RIL populations, 'Raven'/I9365-31 (R31) and 'AN-37'/PS02-029C-20 (Z0726-9), were used to select resistant and susceptible lines to generate subpopulations for bulk DNA sequencing. The QTL physical interval was determined by considering overlapping interval of the identified QTL or peak region in both populations by three independent QTL mapping analyses. Our findings revealed that meta-QTL WM2.2 consists of three regions, WM2.2a (4.27-5.76 Mb; euchromatic), WM 2.2b (12.19 to 17.61 Mb; heterochromatic), and WM2.2c (23.01-25.74 Mb; heterochromatic) found in both populations. Gene models encoding for gibberellin 2-oxidase 8, pentatricopeptide repeat, and heat-shock proteins are the likely candidate genes associated with WM2.2a resistance. A TIR-NBS-LRR class of disease resistance protein (Phvul.002G09200) and LRR domain containing family proteins are potential candidate genes associated with WM2.2b resistance. Nine gene models encoding disease resistance protein [pathogenesis-related thaumatin superfamily protein and disease resistance-responsive (dirigent-like protein) family protein etc] found within the WM2.2c QTL interval are putative candidate genes. WM2.2a region is most likely associated with avoidance mechanisms while WM2.2b and WM2.2c regions trigger physiological resistance based on putative candidate genes.

Legacy genetics of Arachis cardenasii in the peanut crop shows the profound benefits of international seed exchange.

The narrow genetics of most crops is a fundamental vulnerability to food security. This makes wild crop relatives a strategic resource of genetic diversity that can be used for crop improvement and adaptation to new agricultural challenges. Here, we uncover the contribution of one wild species accession, Arachis cardenasii GKP 10017, to the peanut crop (Arachis hypogaea) that was initiated by complex hybridizations in the 1960s and propagated by international seed exchange. However, until this study, the global scale of the dispersal of genetic contributions from this wild accession had been obscured by the multiple germplasm transfers, breeding cycles, and unrecorded genetic mixing between lineages that had occurred over the years. By genetic analysis and pedigree research, we identified A. cardenasii-enhanced, disease-resistant cultivars in Africa, Asia, Oceania, and the Americas. These cultivars provide widespread improved food security and environmental and economic benefits. This study emphasizes the importance of wild species and collaborative networks of international expertise for crop improvement. However, it also highlights the consequences of the implementation of a patchwork of restrictive national laws and sea changes in attitudes regarding germplasm that followed in the wake of the Convention on Biological Diversity. Today, the botanical collections and multiple seed exchanges which enable benefits such as those revealed by this study are drastically reduced. The research reported here underscores the vital importance of ready access to germplasm in ensuring long-term world food security.

Gene disruption by structural mutations drives selection in US rice breeding over the last century.

The genetic basis of general plant vigor is of major interest to food producers, yet the trait is recalcitrant to genetic mapping because of the number of loci involved, their small effects, and linkage. Observations of heterosis in many crops suggests that recessive, malfunctioning versions of genes are a major cause of poor performance, yet we have little information on the mutational spectrum underlying these disruptions. To address this question, we generated a long-read assembly of a tropical japonica rice (Oryza sativa) variety, Carolina Gold, which allowed us to identify structural mutations (>50 bp) and orient them with respect to their ancestral state using the outgroup, Oryza glaberrima. Supporting prior work, we find substantial genome expansion in the sativa branch. While transposable elements (TEs) account for the largest share of size variation, the majority of events are not directly TE-mediated. Tandem duplications are the most common source of insertions and are highly enriched among 50-200bp mutations. To explore the relative impact of various mutational classes on crop fitness, we then track these structural events over the last century of US rice improvement using 101 resequenced varieties. Within this material, a pattern of temporary hybridization between medium and long-grain varieties was followed by recent divergence. During this long-term selection, structural mutations that impact gene exons have been removed at a greater rate than intronic indels and single-nucleotide mutations. These results support the use of ab initio estimates of mutational burden, based on structural data, as an orthogonal predictor in genomic selection.

Two New Aspergillus flavus Reference Genomes Reveal a Large Insertion Potentially Contributing to Isolate Stress Tolerance and Aflatoxin Production.

Efforts in genome sequencing in the Aspergillus genus have led to the development of quality reference genomes for several important species including A. nidulans, A. fumigatus, and A. oryzae However, less progress has been made for A. flavus As part of the effort of the USDA-ARS Annual Aflatoxin Workshop Fungal Genome Project, the isolate NRRL3357 was sequenced and resulted in a scaffold-level genome released in 2005. Our goal has been biologically driven, focusing on two areas: isolate variation in aflatoxin production and drought stress exacerbating aflatoxin production by A. flavus Therefore, we developed two reference pseudomolecule genome assemblies derived from chromosome arms for two isolates: AF13, a MAT1-2, highly stress tolerant, and highly aflatoxigenic isolate; and NRRL3357, a MAT1-1, less stress tolerant, and moderate aflatoxin producer in comparison to AF13. Here, we report these two reference-grade assemblies for these isolates through a combination of PacBio long-read sequencing and optical mapping, and coupled them with comparative, functional, and phylogenetic analyses. This analysis resulted in the identification of 153 and 45 unique genes in AF13 and NRRL3357, respectively. We also confirmed the presence of a unique 310 Kb insertion in AF13 containing 60 genes. Analysis of this insertion revealed the presence of a bZIP transcription factor, named atfC, which may contribute to isolate pathogenicity and stress tolerance. Phylogenomic analyses comparing these and other available assemblies also suggest that the species complex of A. flavus is polyphyletic.

The genome sequence of segmental allotetraploid peanut Arachis hypogaea.

Like many other crops, the cultivated peanut (Arachis hypogaea L.) is of hybrid origin and has a polyploid genome that contains essentially complete sets of chromosomes from two ancestral species. Here we report the genome sequence of peanut and show that after its polyploid origin, the genome has evolved through mobile-element activity, deletions and by the flow of genetic information between corresponding ancestral chromosomes (that is, homeologous recombination). Uniformity of patterns of homeologous recombination at the ends of chromosomes favors a single origin for cultivated peanut and its wild counterpart A. monticola. However, through much of the genome, homeologous recombination has created diversity. Using new polyploid hybrids made from the ancestral species, we show how this can generate phenotypic changes such as spontaneous changes in the color of the flowers. We suggest that diversity generated by these genetic mechanisms helped to favor the domestication of the polyploid A. hypogaea over other diploid Arachis species cultivated by humans.

Machine Learning as an Effective Method for Identifying True Single Nucleotide Polymorphisms in Polyploid Plants.

Single nucleotide polymorphisms (SNPs) have many advantages as molecular markers since they are ubiquitous and codominant. However, the discovery of true SNPs in polyploid species is difficult. Peanut ( L.) is an allopolyploid, which has a very low rate of true SNP calling. A large set of true and false SNPs identified from the Axiom_ 58k array was leveraged to train machine-learning models to enable identification of true SNPs directly from sequence data to reduce ascertainment bias. These models achieved accuracy rates above 80% using real peanut RNA sequencing (RNA-seq) and whole-genome shotgun (WGS) resequencing data, which is higher than previously reported for polyploids and at least a twofold improvement for peanut. A 48K SNP array, Axiom_2, was designed using this approach resulting in 75% accuracy of calling SNPs from different tetraploid peanut genotypes. Using the method to simulate SNP variation in several polyploids, models achieved >98% accuracy in selecting true SNPs. Additionally, models built with simulated genotypes were able to select true SNPs at >80% accuracy using real peanut data. This work accomplished the objective to create an effective approach for calling highly reliable SNPs from polyploids using machine learning. A novel tool was developed for predicting true SNPs from sequence data, designated as SNP machine learning (SNP-ML), using the described models. The SNP-ML additionally provides functionality to train new models not included in this study for customized use, designated SNP machine learner (SNP-MLer). The SNP-ML is publicly available.

Crossword: A data-driven simulation language for the design of genetic-mapping experiments and breeding strategies.

Quantitative genetic simulations can save time and resources by optimizing the logistics of an experiment. Current tools are difficult to use by those unfamiliar with programming, and these tools rarely address the actual genetic structure of the population under study. Here, we introduce crossword, which utilizes the widely available re-sequencing and genomics data to create more realistic simulations and to reduce user burden. The software was written in R, to simplify installation and implementation. Because crossword is a domain-specific language, it allows complex and unique simulations to be performed, but the language is supported by a graphical interface that guides users through functions and options. We first show crossword's utility in QTL-seq design, where its output accurately reflects empirical data. By introducing the concept of levels to reflect family relatedness, crossword can simulate a broad range of breeding programs and crops. Using levels, we further illustrate crossword's capabilities by examining the effect of family size and number of selfing generations on phenotyping accuracy and genomic selection. Additionally, we explore the ramifications of large phenotypic difference between parents in a QTL mapping cross, a scenario that is common in crop genetics but often difficult to simulate.

Haplotype-Based Genotyping in Polyploids.

Accurate identification of polymorphisms from sequence data is crucial to unlocking the potential of high throughput sequencing for genomics. Single nucleotide polymorphisms (SNPs) are difficult to accurately identify in polyploid crops due to the duplicative nature of polyploid genomes leading to low confidence in the true alignment of short reads. Implementing a haplotype-based method in contrasting subgenome-specific sequences leads to higher accuracy of SNP identification in polyploids. To test this method, a large-scale 48K SNP array (Axiom Arachis2) was developed for Arachis hypogaea (peanut), an allotetraploid, in which 1,674 haplotype-based SNPs were included. Results of the array show that 74% of the haplotype-based SNP markers could be validated, which is considerably higher than previous methods used for peanut. The haplotype method has been implemented in a standalone program, HAPLOSWEEP, which takes as input bam files and a vcf file and identifies haplotype-based markers. Haplotype discovery can be made within single reads or span paired reads, and can leverage long read technology by targeting any length of haplotype. Haplotype-based genotyping is applicable in all allopolyploid genomes and provides confidence in marker identification and in silico-based genotyping for polyploid genomics.

Insight into Genes Regulating Postharvest Aflatoxin Contamination of Tetraploid Peanut from Transcriptional Profiling.

Postharvest aflatoxin contamination is a challenging issue that affects peanut quality. Aflatoxin is produced by fungi belonging to the Aspergilli group, and is known as an acutely toxic, carcinogenic, and immune-suppressing class of mycotoxins. Evidence for several host genetic factors that may impact aflatoxin contamination has been reported, e.g., genes for lipoxygenase (PnLOX1 and PnLOX2/PnLOX3 that showed either positive or negative regulation with Aspergillus infection), reactive oxygen species, and WRKY (highly associated with or differentially expressed upon infection of maize with Aspergillus flavus); however, their roles remain unclear. Therefore, we conducted an RNA-sequencing experiment to differentiate gene response to the infection by A. flavus between resistant (ICG 1471) and susceptible (Florida-07) cultivated peanut genotypes. The gene expression profiling analysis was designed to reveal differentially expressed genes in response to the infection (infected vs. mock-treated seeds). In addition, the differential expression of the fungal genes was profiled. The study revealed the complexity of the interaction between the fungus and peanut seeds as the expression of a large number of genes was altered, including some in the process of plant defense to aflatoxin accumulation. Analysis of the experimental data with "keggseq," a novel designed tool for Kyoto Encyclopedia of Genes and Genomes enrichment analysis, showed the importance of α-linolenic acid metabolism, protein processing in the endoplasmic reticulum, spliceosome, and carbon fixation and metabolism pathways in conditioning resistance to aflatoxin accumulation. In addition, coexpression network analysis was carried out to reveal the correlation of gene expression among peanut and fungal genes. The results showed the importance of WRKY, toll/Interleukin1 receptor-nucleotide binding site leucine-rich repeat (TIR-NBS-LRR), ethylene, and heat shock proteins in the resistance mechanism.

Genotypic Regulation of Aflatoxin Accumulation but Not Aspergillus Fungal Growth upon Post-Harvest Infection of Peanut (Arachis hypogaea L.) Seeds.

Aflatoxin contamination is a major economic and food safety concern for the peanut industry that largely could be mitigated by genetic resistance. To screen peanut for aflatoxin resistance, ten genotypes were infected with a green fluorescent protein (GFP)-expressing Aspergillus flavus strain. Percentages of fungal infected area and fungal GFP signal intensity were documented by visual ratings every 8 h for 72 h after inoculation. Significant genotypic differences in fungal growth rates were documented by repeated measures and area under the disease progress curve (AUDPC) analyses. SICIA (Seed Infection Coverage and Intensity Analyzer), an image processing software, was developed to digitize fungal GFP signals. Data from SICIA image analysis confirmed visual rating results validating its utility for quantifying fungal growth. Among the tested peanut genotypes, NC 3033 and GT-C20 supported the lowest and highest fungal growth on the surface of peanut seeds, respectively. Although differential fungal growth was observed on the surface of peanut seeds, total fungal growth in the seeds was not significantly different across genotypes based on a fluorometric GFP assay. Significant differences in aflatoxin B levels were detected across peanut genotypes. ICG 1471 had the lowest aflatoxin level whereas Florida-07 had the highest. Two-year aflatoxin tests under simulated late-season drought also showed that ICG 1471 had reduced aflatoxin production under pre-harvest field conditions. These results suggest that all peanut genotypes support A. flavus fungal growth yet differentially influence aflatoxin production.