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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 (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.

Domestication of fruit and vegetables resulted in a huge diversity of shapes and sizes of the produce. Selections that took place over thousands of years of alleles that increased fruit weight and altered shape for specific culinary uses provide a wealth of resources to study the molecular bases of this diversity. Tomato (Solanum lycopersicum) evolved from a wild ancestor (S. pimpinellifolium) bearing small and round edible fruit. Molecular genetic studies led to the identification of two genes selected for fruit weight: FW2.2 encoding a member of the Cell Number Regulator family; and FW3.2 encoding a P450 enzyme and the ortholog of KLUH. Four genes were identified that were selected for fruit shape: SUN encoding a member of the IQD family of calmodulin-binding proteins leading to fruit elongation; OVATE encoding a member of the OVATE family proteins involved in transcriptional repression leading to fruit elongation; LC encoding most likely the ortholog of WUSCHEL controlling meristem size and locule number; FAS encoding a member in the YABBY family controlling locule number leading to flat or oxheart shape. For this article, we will provide an overview of the putative function of the known genes, when during floral and fruit development they are hypothesized to act and their potential importance in regulating morphological diversity in other fruit and vegetable crops.

Cultivated peanut ( L.) is an important oilseed crop that is grown extensively in Africa, Asia and America. The diseases early and late leaf spot severely constrains peanut production worldwide. Because multiple genes control resistance to leaf spot diseases, conventional breeding is a time-consuming approach for pyramiding resistance genes into a single genotype. Marker-assisted selection (MAS) would complement and accelerate conventional breeding once molecular markers tightly associated with the resistance genes are identified. In this study, we have generated a large number of SNPs through genotyping by sequencing (GBS) and constructed a high-resolution map with an average distance of 1.34 cM among 2,753 SNP markers distributed on 20 linkage groups. QTL mapping has revealed that major QTL within a confidence interval could provide an efficient way to detect putative resistance genes. Analysis of the interval sequences has indicated that a major QTL for resistance to late leaf spot anchored by two NBS-LRR resistance genes on chromosome B05. Two major QTLs located on chromosomes A03 and B04 were associated with resistance genes for early leaf spot. Sequences within the confidence interval would facilitate identifying resistance genes and applying marker-assisted selection for resistance.

Background Peanut ( Arachis hypogaea L.) is a vital global crop, frequently threatened by both abiotic and biotic stresses. Among the most damaging biotic stresses is Tomato spotted wilt virus (TSWV), which causes peanut spotted wilt disease resulting in significant yield loss. Developing TSWV-resistant cultivars is crucial to new cultivar release. Previous studies have used a subset of the “S” recombinant inbred line (RIL) population derived from SunOleic 97R and NC94022 and identified quantitative trait loci (QTLs) for resistance to TSWV. These studies utilized different genotyping techniques and found large consistent genomic regions on chromosome A01. The objective of this study was to fine map the QTL and identify candidate genes using the entire population of 352 RILs and high-density, high-quality peanut SNP arrays. Results We used both versions of the peanut SNP arrays with five years of disease ratings, and successfully mapped the long-sought peanut spotted wilt disease resistance locus, PSWDR-1 . QTL analyses identified two major QTLs, explaining 41.43% and 43.69% of the phenotypic variance within 3.6 cM and 0.28 cM intervals using the peanut Axiom_ Arachis -v1 and Axiom_ Arachis -v2 SNP arrays, respectively, on chromosome A01. These QTLs corresponded to 295 kb and 235 kb physical intervals. The unique overlap region of these two QTLs was 488 kb. A comparison of the genetic linkage map with the reference genome revealed a 1.3 Mb recombination “cold spot” (11.325–12.646 Mb) with only two recombination events of RIL-S1 and RIL-S17, which displayed contrasting phenotypes. Sequencing of these two recombinants confirmed the cold spot with only five SNPs detected within this region. Conclusions This study successfully identified a peanut spotted wilt disease resistance locus, PSWDR-1 , on chromosome A01 within a recombination “cold spot”. The PSWDR-1 locus contains three candidate genes, a TIR-NBS-LRR gene ( Arahy.1PK53M ), a glutamate receptor-like gene ( Arahy.RI1BYW ), and an MLO-like protein ( Arahy.FX71XI ). These findings provide a foundation for future functional studies to validate the roles of these candidate genes in resistance and application in breeding TSWV-resistant peanut cultivars.

High-throughput next-generation sequence-based genotyping and single nucleotide polymorphism (SNP) detection opens the door for emerging genomics-based breeding strategies such as genome-wide association analysis and genomic selection. In polyploids, SNP detection is confounded by a highly similar homeologous sequence where a polymorphism between subgenomes must be differentiated from a SNP. We have developed and implemented a novel tool called SWEEP: Sliding Window Extraction of Explicit Polymorphisms. SWEEP uses subgenome polymorphism haplotypes as contrast to identify true SNPs between genotypes. The tool is a single command script that calls a series of modules based on user-defined options and takes sorted/indexed bam files or vcf files as input. Filtering options are highly flexible and include filtering based on sequence depth, alternate allele ratio, and SNP quality on top of the SWEEP filtering procedure. Using real and simulated data we show that SWEEP outperforms current SNP filtering methods for polyploids. SWEEP can be used for high-quality SNP discovery in polyploid crops.

Core Ideas Finding reliable SNPs in polyploids is challenging Machine learning is an efficient tool to refine SNP calling from NGS data of polyploids SNP‐ML tool was designed to facilitate SNP calling 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 (Arachis hypogaea 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_Arachis 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_Arachis2, 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.

Background Aspergillus flavus is an important agricultural and food safety threat due to its production of carcinogenic aflatoxins. It has high level of genetic diversity that is adapted to various environments. Recently, we reported two reference genomes of A. flavus isolates, AF13 ( MAT1-2 and highly aflatoxigenic isolate) and NRRL3357 ( MAT1-1 and moderate aflatoxin producer). Where, an insertion of 310 kb in AF13 included an aflatoxin producing gene bZIP transcription factor, named atfC . Observations of significant genomic variants between these isolates of contrasting phenotypes prompted an investigation into variation among other agricultural isolates of A. flavus with the goal of discovering novel genes potentially associated with aflatoxin production regulation. Present study was designed with three main objectives: (1) collection of large number of A. flavus isolates from diverse sources including maize plants and field soils; (2) whole genome sequencing of collected isolates and development of a pangenome; and (3) pangenome-wide association study (Pan-GWAS) to identify novel secondary metabolite cluster genes. Results Pangenome analysis of 346 A. flavus isolates identified a total of 17,855 unique orthologous gene clusters, with mere 41% (7,315) core genes and 59% (10,540) accessory genes indicating accumulation of high genomic diversity during domestication. 5,994 orthologous gene clusters in accessory genome not annotated in either the A. flavus AF13 or NRRL3357 reference genomes. Pan-genome wide association analysis of the genomic variations identified 391 significant associated pan-genes associated with aflatoxin production. Interestingly, most of the significantly associated pan-genes (94%; 369 associations) belonged to accessory genome indicating that genome expansion has resulted in the incorporation of new genes associated with aflatoxin and other secondary metabolites. Conclusion In summary, this study provides complete pangenome framework for the species of Aspergillus flavus along with associated genes for pathogen survival and aflatoxin production. The large accessory genome indicated large genome diversity in the species A. flavus , however AflaPan is a closed pangenome represents optimum diversity of species A. flavus . Most importantly, the newly identified aflatoxin producing gene clusters will be a new source for seeking aflatoxin mitigation strategies and needs new attention in research.

The advent of the genome sequences of and has ushered in a new era for peanut genomics. With the goal of producing a gene atlas for cultivated peanut ( ), 22 different tissue types and ontogenies that represent the full development of peanut were sequenced, including a complete reproductive series from flower to peg elongation and peg tip immersion in the soil to fully mature seed. Using a genome-guided assembly pipeline, a homeolog-specific transcriptome assembly for was assembled and its accuracy was validated. The assembly was used to annotate 21 developmental co-expression networks as tools for gene discovery. Using a set of 8816 putative homeologous gene pairs, homeolog expression bias was documented, and although bias was mostly balanced, there were striking differences in expression bias in a tissue-specific context. Over 9000 alterative splicing events and over 6000 non-coding RNAs were further identified and profiled in a developmental context. Together, this work represents a major new resource for cultivated peanut and will be integrated into peanutbase.org as an available resource for all peanut researchers.

Different species of edible seed watermelons ( spp.) are cultivated in Asia and Africa for their colorful nutritious seeds. Consumer preference varies for watermelon seed coat color. Therefore, it is an important consideration for watermelon breeders. In 1940s, a genetic model of four genes, , , and , was proposed to elucidate the inheritance of seed coat color in watermelon. In this study, we developed three segregating F populations: Sugar Baby (dotted black seed, ) × plant introduction (PI) 482379 (green seed, ), Charleston Gray (dotted black seed, ) × PI 189225 (red seed, ), and Charleston Gray (dotted black seed, ) × UGA147 (clump seed, ) to re-examine the four-gene model and to map the four genes. In the dotted black × green population, the dotted black seed coat color ( ) is dominant to green seed coat color ( ). In the dotted black × red population, the dominant dotted black seed coat color and the recessive red seed coat color segregate for the and genes, where the gene is dominantly epistatic to the gene. However, the inheritance of the locus did not fit the four-gene model, thus we named it . In the dotted black × clump population, the clump seed coat color and the dotted black seed coat color segregate for and , where is recessively epistatic to . The , , , and loci were mapped on chromosomes 3, 5, 6, and 8, respectively, using QTL-seq and genotyping-by-sequencing (GBS). Kompetitive Allele Specific PCR (KASP™) assays and SNP markers linked to the four loci were developed to facilitate maker-assisted selection (MAS) for watermelon seed coat color.

Late leaf spot (LLS; ) is a major fungal disease of cultivated peanut ( ). A recombinant inbred line population segregating for quantitative field resistance was used to identify quantitative trait loci (QTL) using QTL-seq. High rates of false positive SNP calls using established methods in this allotetraploid crop obscured significant QTLs. To resolve this problem, robust parental SNPs were first identified using polyploid-specific SNP identification pipelines, leading to discovery of significant QTLs for LLS resistance. These QTLs were confirmed over 4 years of field data. Selection with markers linked to these QTLs resulted in a significant increase in resistance, showing that these markers can be immediately applied in breeding programs. This study demonstrates that QTL-seq can be used to rapidly identify QTLs controlling highly quantitative traits in polyploid crops with complex genomes. Markers identified can then be deployed in breeding programs, increasing the efficiency of selection using molecular tools. Field resistance to late leaf spot is a quantitative trait controlled by many QTLs. Using polyploid-specific methods, QTL-seq is faster and more cost effective than QTL mapping.