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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 92% imputation accuracy with 0.01× sequence coverage, which was slightly lower than the accuracy obtained using the 0.5× sequence coverage (96.6%). Compared to Beagle, on average, PHG imputation was ∼3.5% (P-value < 2 × 10-14) more accurate, and showed 27% higher accuracy at imputing a rare haplotype introgressed from a wild relative into wheat. We found reduced accuracy of imputation with independent 2× GBS data (88.6%), which increases to 89.2% with the inclusion of parental haplotypes in the database. The accuracy reduction with GBS 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 disequilibrium and proportion of identity-by-descent regions among accessions in the PHG database. 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.

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 92% imputation accuracy with 0.01x sequence coverage, which was slightly lower than the accuracy obtained using the 0.5x sequence coverage (96.6%). Compared to Beagle, on average, PHG imputation was ∼3.5% (p-value < 2 x 10-14) more accurate, and showed 27% higher accuracy at imputing a rare haplotype introgressed from a wild relative into wheat. We found reduced accuracy of imputation with independent 2x GBS data (88.6%), which increases to 89.2% with the inclusion of parental haplotypes in the database. The accuracy reduction with GBS 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 the PHG database. 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.

Switchgrass (Panicum virgatum L.) is a warm-season perennial grass with promising potential as a bioenergy crop in the United States. However, the lack of genomic resources has slowed the development of plant lines with optimal characteristics for sustainable feedstock production. We generated high-density single nucleotide polymorphism (SNP) linkage maps using a reduced-representation sequencing approach by genotyping 231 F1 progeny of a cross between two parents of lowland ecotype from the cultivars Kanlow and Alamo. Over 350 million reads were generated and aligned, which enabled identification and ordering of 4611 high-quality SNPs. The total lengths of the resulting framework maps were 1770 cM for the Kanlow parent and 2059 cM for the Alamo parent. These maps show collinearity with maps generated with polymerase chain reaction (PCR)-based simple-sequence repeat (SSR) markers, and new SNP markers were identified in previously unpopulated regions of the genome. Transmission segregation distortion affected all linkage groups (LGs) to differing degrees, and ordering of distorted markers highlighted several regions of unequal inheritance. Framework maps were adversely affected by the addition of distorted markers with varying severity, but distorted maps were of higher marker density and provided additional information for analysis. Alignment of these linkage maps with a draft version of the switchgrass genome assembly demonstrated high levels of collinearity and provides greater confidence in the validity of both resources. This methodology has proven to be a rapid and cost-effective way to generate high-quality linkage maps of an outcrossing species.

There is limited information regarding the morphometric relationships of panicle traits in oat (Avena sativa) and their contribution to phenology and growth, physiology, and pathology traits important for yield. To model panicle growth and development and identify genomic regions associated with corresponding traits, 10 diverse spring oat mapping populations (n=2,993) were evaluated in the field and 9 genotyped via genotyping-by-sequencing. Representative panicles from all progeny individuals, parents, and check lines were scanned, and images were analyzed using manual and automated techniques, resulting in over 60 unique panicle, rachis, and spikelet variables. Spatial modeling and days to heading were used to account for environmental and phenological variances, respectively. Panicle variables were intercorrelated, providing reproducible archetypal and growth models. Notably, adult plant resistance for oat crown rust was most prominent for taller, stiff stalked plants having a more open panicle structure. Within and among family variance for panicle traits reflected the moderate-to-high heritability and mutual genome-wide associations (hotspots) with numerous high-effect loci. Candidate genes and potential breeding applications are discussed. This work adds to the growing genetic resources for oat and provides a unique perspective on the genetic basis of panicle architecture in cereal crops. [ABSTRACT FROM AUTHOR]

Summary Aegilops umbellulata serve as an important reservoir for novel biotic and abiotic stress tolerance for wheat improvement. However, chromosomal rearrangements and evolutionary trajectory of this species remain to be elucidated. Here, we present a comprehensive investigation into Ae. umbellulata genome by generating a high‐quality near telomere‐to‐telomere genome assembly of PI 554389 and resequencing 20 additional Ae. umbellulata genomes representing diverse geographical and phenotypic variations. Our analysis unveils complex chromosomal rearrangements, most prominently in 4U and 6U chromosomes, delineating a distinct evolutionary trajectory of Ae. umbellulata from wheat and its relatives. Furthermore, our data rectified the erroneous naming of chromosomes 4U and 6U in the past and highlighted multiple major evolutionary events that led to the present‐day U‐genome. Resequencing of diverse Ae. umbellulata accessions revealed high genetic diversity within the species, partitioning into three distinct evolutionary sub‐populations and supported by extensive phenotypic variability in resistance against several races/pathotypes of five major wheat diseases. Disease evaluations indicated the presence of several novel resistance genes in the resequenced lines for future studies. Resequencing also resulted in the identification of six new haplotypes for Lr9, the first resistance gene cloned from Ae. umbellulata. The extensive genomic and phenotypic resources presented in this study will expedite the future genetic exploration of Ae. umbellulata, facilitating efforts aimed at enhancing resiliency and productivity in wheat. [ABSTRACT FROM AUTHOR]

Sunflower (Helianthus annuus L.) production is challenged by different biotic and abiotic stresses, among which downy mildew (DM) is a severe biotic stress that is detrimental to sunflower yield and quality in many sunflower-growing regions worldwide. Resistance against its infestation in sunflower is commonly regulated by single dominant genes. Pl17 and Pl19 are two broad-spectrum DM resistance genes that have been previously mapped to a gene cluster spanning a 3.2 Mb region at the upper end of sunflower chromosome 4. Using a whole-genome resequencing approach combined with a reference sequence-based chromosome walking strategy and high-density mapping populations, we narrowed down Pl17 to a 15-kb region flanked by SNP markers C4_5711524 and SPB0001. A prospective candidate gene HanXRQChr04g0095641 for Pl17 was identified, encoding a typical TNL resistance gene protein. Pl19 was delimited to a 35-kb region and was approximately 1 Mb away from Pl17, flanked by SNP markers C4_6676629 and C4_6711381. The only gene present within the delineated Pl19 locus in the reference genome, HanXRQChr04g0095951, was predicted to encode an RNA methyltransferase family protein. Six and eight SNP markers diagnostic for Pl17 and Pl19, respectively, were identified upon evaluation of 96 diverse sunflower lines, providing a very useful tool for marker-assisted selection in sunflower breeding programs. [ABSTRACT FROM AUTHOR]

Spot blotch (SB) caused by Bipolaris sorokiniana and powdery mildew (PM) caused by Blumeria graminis f. sp. hordei are two important diseases of barley. To map genetic loci controlling susceptibility and resistance to these diseases, a mapping population consisting of 138 recombinant inbred lines (RILs) was developed from the cross between Bowman and ND5883. A genetic map was constructed for the population with 852 unique single nucleotide polymorphism markers generated by sequencing-based genotyping. Bowman and ND5883 showed distinct infection responses at the seedling stage to two isolates (ND90Pr and ND85F) of Bipolaris sorokiniana and one isolate (Race I) of Blumeria graminis f. sp. hordei. Genetic analysis of the RILs revealed that one major gene (Scs6) controls susceptibility to Bipolaris sorokiniana isolate ND90Pr, and another major gene (Mla8) confers resistance to Blumeria graminis f. sp. hordei isolate Race I, respectively. Scs6 was mapped on chromosome 1H of Bowman, as previously reported. Mla8 was also mapped to the short arm of 1H, which was tightly linked but not allelic to the Rcs6/Scs6 locus. Quantitative trait locus (QTL) analysis identified two QTLs, QSbs-1H-P1 and QSbs-7H-P1, responsible for susceptibility to spot blotch caused by Bipolaris sorokiniana isolate ND85F in ND5883, which are located on chromosome 1H and 7H, respectively. QSbs-7H-P1 was mapped to the same region as Rcs5, whereas QSbs-1H-P1 may represent a novel allele conferring seedling stage susceptibility to isolate ND85F. Identification and molecular mapping of the loci for SB susceptibility and PM resistance will facilitate development of barley cultivars with resistance to the diseases. [ABSTRACT FROM AUTHOR]

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 92% imputation accuracy with 0.01× sequence coverage, which was slightly lower than the accuracy obtained using the 0.5× sequence coverage (96.6%). Compared to Beagle, on average, PHG imputation was ∼3.5% (P-value < 2 × 10-14) more accurate, and showed 27% higher accuracy at imputing a rare haplotype introgressed from a wild relative into wheat. We found reduced accuracy of imputation with independent 2× GBS data (88.6%), which increases to 89.2% with the inclusion of parental haplotypes in the database. The accuracy reduction with GBS 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 disequilibrium and proportion of identity-by-descent regions among accessions in the PHG database. 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., (© The Author(s) 2021. Published by Oxford University Press on behalf of Genetics Society of America.)

Spot form net blotch (SFNB), caused by the necrotrophic fungal pathogen Pyrenophora teres f. maculata (Ptm), is a foliar disease of barley that results in significant yield losses in major growing regions worldwide. Understanding the host-parasite interactions between pathogen virulence/avirulence genes and the corresponding host susceptibility/resistance genes is important for the deployment of genetic resistance against SFNB. Two recombinant inbred mapping populations were developed to characterize genetic resistance/susceptibility to the Ptm isolate 13IM8.3, which was collected from Idaho (ID). An Illumina Infinium array was used to produce a genome-wide marker set. Quantitative trait loci (QTL) analysis identified ten significant resistance/susceptibility loci, with two of the QTL being common to both populations. One of the QTL on 5H appears to be novel, while the remaining loci have been reported previously. Single nucleotide polymorphisms (SNPs) closely linked to or delimiting the significant QTL have been converted to user-friendly markers. Loci and associated molecular markers identified in this study will be useful in genetic mapping and deployment of the genetic resistance to SFNB in barley., (Published by Oxford University Press on behalf of Genetics Society of America 2021.)