Diversity and population structure of northern switchgrass as revealed through exome capture sequencing

Genome-Wide Associations with Resistance to Bipolaris Leaf Spot (Bipolaris oryzae (Breda de Haan) Shoemaker) in a Northern Switchgrass Population (Panicum virgatum L.)

Switchgrass (Panicum virgatum L.), a northern native perennial grass, suffers from yield reduction from Bipolaris leaf spot caused by Bipolaris oryzae (Breda de Haan) Shoemaker. This study aimed to determine the resistant populations via multiple phenotyping approaches and identify potential resistance genes from genome-wide association studies (GWAS) in the switchgrass northern association panel. The disease resistance was evaluated from both natural (field evaluations in Ithaca, New York and Phillipsburg, Philadelphia) and artificial inoculations (detached leaf and leaf disk assays). The most resistant populations based on a combination of three phenotyping approaches—detached leaf, leaf disk, and mean from two locations—were ‘SW788’, ‘SW806’, ‘SW802’, ‘SW793’, ‘SW781’, ‘SW797’, ‘SW798’, ‘SW803’, ‘SW795’, ‘SW805’. The GWAS from the association panel showed 27 significant SNPs on 12 chromosomes: 1K, 2K, 2N, 3K, 3N, 4N, 5K, 5N, 6N, 7K, 7N, and 9N. These markers accumulatively explained the phenotypic variance of the resistance ranging from 3.28 to 26.52%. Within linkage disequilibrium of 20 kb, these SNP markers linked with the potential resistance genes included the genes encoding for NBS-LRR, PPR, cell-wall related proteins, homeostatic proteins, anti-apoptotic proteins, and ABC transporter.

Genomic Prediction of Complex Traits in Forage Plants Species: Perennial Grasses Case

The majority of forage grass species are obligate outbreeders. Their breeding classically consists of an initial selection on spaced plants for highly heritable traits such as disease resistances and heading date, followed by familial selection on swards for forage yield and quality traits. The high level of diversity and heterozygosity, and associated decay of linkage disequilibrium (LD) over very short genomic distances, has hampered the implementation of genomic selection (GS) in these species. However, next generation sequencing technologies in combination with the development of genomic resources have recently facilitated implementation of GS in forage grass species such as perennial ryegrass (Lolium perenne L.), switchgrass (Panicum virgatum L.), and timothy (Phleum pratense L.). Experimental work and simulations have shown that GS can increase significantly the genetic gain per unit of time for traits with different levels of heritability. The main reasons are (1) the possibility to select single plants based on their genomic estimated breeding values (GEBV) for traits measured at sward level, (2) a reduction in the duration of selection cycles, and less importantly (3) an increase in the selection intensity associated with an increase in the genetic variance used for selection. Nevertheless, several factors should be taken into account for the successful implementation of GS in forage grasses. For example, it has been shown that the level of relatedness between the training and the selection population is particularly critical when working with highly structured meta-populations consisting of several genetic groups. A sufficient number of markers should be used to estimate properly the kinship between individuals and to reflect the variability of major QTLs. It is also important that the prediction models are trained for relevant environments when dealing with traits with high genotype × environment interaction (G × E). Finally, in these outbreeding species, measures to reduce inbreeding should be used to counterbalance the high selection intensity that can be achieved in GS.

Network-based approaches for understanding gene regulation and function in plants

Expression reprogramming directed by transcription factors is a primary gene regulation underlying most aspects of the biology of any organism. Our views of how gene regulation is coordinated are dramatically changing thanks to the advent and constant improvement of high-throughput profiling and transcriptional network inference methods: from activities of individual genes to functional interactions across genes. These technical and analytical advances can reveal the topology of transcriptional networks in which hundreds of genes are hierarchically regulated by multiple transcription factors at systems level. Here we review the state of the art of experimental and computational methods used in plant biology research to obtain large-scale datasets and model transcriptional networks. Examples of direct use of these network models and perspectives on their limitations and future directions are also discussed.

Segmental duplications are hot spots of copy number variants affecting barley gene content

Copy number variants (CNVs) are pervasive in several animal and plant genomes and contribute to shaping genetic diversity. In barley, there is evidence that changes in gene copy number underlie important agronomic traits. The recently released reference sequence of barley represents a valuable genomic resource for unveiling the incidence of CNVs that affect gene content and for identifying sequence features associated with CNV formation. Using exome sequencing and read count data, we detected 16 605 deletions and duplications that affect barley gene content by surveying a diverse panel of 172 cultivars, 171 landraces, 22 wild relatives and other 32 uncategorized domesticated accessions. The quest for segmental duplications (SDs) in the reference sequence revealed many low-copy repeats, most of which overlap predicted coding sequences. Statistical analyses revealed that the incidence of CNVs increases significantly in SD-rich regions, indicating that these sequence elements act as hot spots for the formation of CNVs. The present study delivers a comprehensive genome-wide study of CNVs affecting barley gene content and implicates SDs in the molecular mechanisms that lead to the formation of this class of CNVs.

Gas exchange characteristics and their influencing factors for halophytic plant communities on west coast of Bohai Sea

Water-salt stress and nutrient limitation may affect leaf economic spectrum of halophytes and confuse our understanding on plant physiological principles in a changing world. In this study, three halophytic plant communities of Phragmites australis, Suaeda salsa, and Tamarix chinensis, were selected in two sites (sites 1 and 2) on the west coast of Bohai Sea. The net photosynthetic rate (Pn), transpiration rate (Tr), stomatal conductance (Gs), leaf vapor pressure deficit (VPDleaf) and their influencing factors were studied to test the possible carbon assimilation strategies of the halophytes. P. australis had higher Pn, Tr, and Gs than S. salsa and T. chinensis in both sites. Similar trends were found for leaf P and photosynthetic N and P efficiency (PNUE and PPUE, respectively) in one or both sites. By contrast, the leaf dry mass per area (LMA) increased in the order of P. australis < S. salsa < T. chinensis in both sites. For identical species in different sites, Pn, leaf P, and PNUE were lower but Tr, VPDleaf, leaf N, leaf N:P, and PPUE were higher in site 1 than in site 2 for one or more halophytes. Although soil physicochemical properties in different sites explained several variations among the halophytes, two-way ANOVA indicated that the species can explain most of the leaf traits compared with the site. LMA also had significant nonlinear relationships with Pn, Tr, Gs, and VPDleaf. PNUE and PPUE showed positive correlation with Pn in both sites, but they decreased in the power-law function with increasing LMA. Overall, the redundancy analysis showed that the gas exchange capacity of the halophytic plant communities was significantly affected by PPUE (60.0% of explanation), PNUE (57.1%), LMA (35.0%), leaf P (22.0%), and soil N (15.8%).

Benchmarking Parametric and Machine Learning Models for Genomic Prediction of Complex Traits

The usefulness of genomic prediction in crop and livestock breeding programs has prompted efforts to develop new and improved genomic prediction algorithms, such as artificial neural networks and gradient tree boosting. However, the performance of these algorithms has not been compared in a systematic manner using a wide range of datasets and models. Using data of 18 traits across six plant species with different marker densities and training population sizes, we compared the performance of six linear and six non-linear algorithms. First, we found that hyperparameter selection was necessary for all non-linear algorithms and that feature selection prior to model training was critical for artificial neural networks when the markers greatly outnumbered the number of training lines. Across all species and trait combinations, no one algorithm performed best, however predictions based on a combination of results from multiple algorithms (i.e., ensemble predictions) performed consistently well. While linear and non-linear algorithms performed best for a similar number of traits, the performance of non-linear algorithms vary more between traits. Although artificial neural networks did not perform best for any trait, we identified strategies (i.e., feature selection, seeded starting weights) that boosted their performance to near the level of other algorithms. Our results highlight the importance of algorithm selection for the prediction of trait values.

Quantitative Trait Loci for Freezing Tolerance in a Lowland x Upland Switchgrass Population

Low-temperature related abiotic stress is an important factor affecting winter survival in lowland switchgrass when grown in northern latitudes in the United States. A better understanding of the genetic architecture of freezing tolerance in switchgrass will aid the development of lowland switchgrass cultivars with improved winter survival. The objectives of this study were to conduct a freezing tolerance assessment, generate a genetic map using single nucleotide polymorphism (SNP) markers, and identify QTL (quantitative trait loci) associated with freezing tolerance in a lowland × upland switchgrass population. A pseudo-F2 mapping population was generated from an initial cross between the lowland population Ellsworth and the upland cultivar Summer. The segregating progenies were screened for freezing tolerance in a controlled-environment facility. Two clonal replicates of each genotype were tested at six different treatment temperatures ranging from -15 to -5°C at an interval of 2°C for two time periods. Tiller emergence (days) and tiller number were recorded following the recovery of each genotype with the hypothesis that upland genotype is the source for higher tiller number and early tiller emergence. Survivorship of the pseudo-F2 population ranged from 89% at -5°C to 5% at -15°C with an average LT50 of -9.7°C. Genotype had a significant effect on all traits except tiller number at -15°C. A linkage map was constructed from bi-allelic single nucleotide polymorphism markers generated using exome capture sequencing. The final map consisted of 1618 markers and 2626 cM, with an average inter-marker distance of 1.8 cM. Six significant QTL were identified, one each on chromosomes 1K, 5K, 5N, 6K, 6N, and 9K, for the following traits: tiller number, tiller emergence days and LT50. A comparative genomics study revealed important freezing tolerance genes/proteins, such as COR47, DREB2B, zinc finger-CCCH, WRKY, GIGANTEA, HSP70, and NRT2, among others that reside within the 1.5 LOD confidence interval of the identified QTL.

Extensions of BLUP Models for Genomic Prediction in Heterogeneous Populations: Application in a Diverse Switchgrass Sample

Genomic prediction is a useful tool to accelerate genetic gain in selection using DNA marker information. However, this technology typically relies on standard prediction procedures, such as genomic BLUP, that are not designed to accommodate population heterogeneity resulting from differences in marker effects across populations. In this study, we assayed different prediction procedures to capture marker-by-population interactions in genomic prediction models. Prediction procedures included genomic BLUP and two kernel-based extensions of genomic BLUP which explicitly accounted for population heterogeneity. To model population heterogeneity, dissemblance between populations was either depicted by a unique coefficient (as previously reported), or a more flexible function of genetic distance between populations (proposed herein). Models under investigation were applied in a diverse switchgrass sample under two validation schemes: whole-sample calibration, where all individuals except selection candidates are included in the calibration set, and cross-population calibration, where the target population is entirely excluded from the calibration set. First, we showed that using fixed effects, from principal components or putative population groups, appeared detrimental to prediction accuracy, especially in cross-population calibration. Then we showed that modeling population heterogeneity by our proposed procedure resulted in highly significant improvements in model fit. In such cases, gains in accuracy were often positive. These results suggest that population heterogeneity may be parsimoniously captured by kernel methods. However, in cases where improvement in model fit by our proposed procedure is null-to-moderate, ignoring heterogeneity should probably be preferred due to the robustness and simplicity of the standard genomic BLUP model.

Effect of Switchgrass Ecotype and Cultivar on Establishment, Feeding, and Development of Fall Armyworm (Lepidoptera: Noctuidae)

As interest in production of second-generation biofuels increases, dedicated biomass crops are likely to be called upon to help meet feedstock demands. Switchgrass (Panicum virgatum L.) is a North American native perennial grass that as a candidate biomass crop, combines high biomass yields with other desirable ecosystem services. At present, switchgrass is produced on limited acres in the United States and experiences relatively minor insect pest problems. However, as switchgrass undergoes breeding to increase biomass yield and quality, and is grown on more acres, insect pest pressure will probably increase. To investigate how currently available switchgrass ecotypes and cultivars may influence herbivory by generalist insect herbivores, we performed feeding trials using neonate and late-instar fall armyworm [Spodoptera frugiperda JE Smith (Lepidoptera: Noctuidae)]. No-choice feeding experiments were used to explore how switchgrass varieties influence larval establishment, consumption levels, and life-history traits in contrast to a preferred host, corn (Zea mays L.). Neonate S. frugiperda consumed greater amounts of corn than switchgrass and increased amounts of upland versus lowland ecotypes. Late-instar larvae, which do the majority of the larval feeding, exhibited lower consumption of lowland ecotypes, which led to increased development time and reduced pupal weights. The exception to these trends was the upland cultivar 'Trailblazer', which unexpectedly performed similarly to lowland cultivars. These results suggest that both switchgrass ecotype and cultivar can influence feeding damage by a common generalist herbivore. These findings can be used to help inform current switchgrass planting decisions as well as future breeding efforts.

Photosynthesis capacity diversified by leaf structural and physiological regulation between upland and lowland switchgrass in different growth stages

Understanding and enhancing switchgrass (Panicum virgatum L.) photosynthesis will help to improve yield and quality for bio-industrial applications on cellulosic biofuel production. In the present study, leaf anatomical traits and physiological characteristics related to photosynthetic capacity of both lowland and upland switchgrass were recorded from four varieties across the vegetative, elongation and reproductive growth stages. Compared with the upland varieties, the lowland switchgrass showed 37-59, 22-64 and 27-73% higher performance on height, stem and leaf over all three growth stages. Leaf anatomical traits indicated that the leaves of lowland varieties provided more space for carbon assimilation and transportation caused by enhanced cell proliferation with more bundles sheath cells and larger contact areas between the bundle sheath and mesophyll cells (CAMB), which lead to the 32-72% higher photosynthetic capacity found in the lowland varieties during vegetative and elongation growth. However, photosynthetic capacity became 22-51% higher in the upland varieties during the reproductive stage, which is attributed to more photosynthetic pigment. In conclusion, lowland varieties gain a photosynthetic advantage with enhanced bundle sheath cell proliferation, while the upland varieties preserved more photosynthetic pigments. Our study provides new insights for improving the yield in crops by enhancing photosynthesis with anatomical and physiological strategies.

Candidate Variants for Additive and Interactive Effects on Bioenergy Traits in Switchgrass (Panicum virgatum L.) Identified by Genome-Wide Association Analyses

Switchgrass ( L.) is a promising herbaceous energy crop, but further gains in biomass yield and quality must be achieved to enable a viable bioenergy industry. Developing DNA markers can contribute to such progress, but depiction of genetic bases should be reliable, involving simple additive marker effects and also interactions with genetic backgrounds (e.g., ecotypes) or synergies with other markers. We analyzed plant height, C content, N content, and mineral concentration in a diverse panel consisting of 512 genotypes of upland and lowland ecotypes. We performed association analyses based on exome capture sequencing and tested 439,170 markers for marginal effects, 83,290 markers for marker × ecotype interactions, and up to 311,445 marker pairs for pairwise interactions. Analyses of pairwise interactions focused on subsets of marker pairs preselected on the basis of marginal marker effects, gene ontology annotation, and pairwise marker associations. Our tests identified 12 significant effects. Homology and gene expression information corroborated seven effects and indicated plausible causal pathways: flowering time and lignin synthesis for plant height; plant growth and senescence for C content and mineral concentration. Four pairwise interactions were detected, including three interactions preselected on the basis of pairwise marker correlations. Furthermore, a marker × ecotype interaction and a pairwise interaction were confirmed in an independent switchgrass panel. Our analyses identified reliable candidate variants for important bioenergy traits. Moreover, they exemplified the importance of interactive effects for depicting genetic bases and illustrated the usefulness of preselecting marker pairs for identifying pairwise marker interactions in association studies.

Genome-Wide Association Study in Pseudo-F2 Populations of Switchgrass Identifies Genetic Loci Affecting Heading and Anthesis Dates

Switchgrass (Panicum virgatum) is a native prairie grass and valuable bio-energy crop. The physiological change from juvenile to reproductive adult can draw important resources away from growth into producing reproductive structures, thereby limiting the growth potential of early flowering plants. Delaying the flowering of switchgrass is one approach by which to increase total biomass. The objective of this research was to identify genetic variants and candidate genes for controlling heading and anthesis in segregating switchgrass populations. Four pseudo-F₂ populations (two pairs of reciprocal crosses) were developed from lowland (late flowering) and upland (early flowering) ecotypes, and heading and anthesis dates of these populations were collected in Lafayette, IN and DeKalb, IL in 2015 and 2016. Across 2 years, there was a 34- and 73-day difference in heading and a 52- and 75-day difference in anthesis at the Lafayette and DeKalb locations, respectively. A total of 37,901 single nucleotide polymorphisms obtained by exome capture sequencing of the populations were used in a genome-wide association study (GWAS) that identified five significant signals at three loci for heading and two loci for anthesis. Among them, a homolog of FLOWERING LOCUS T on chromosome 5b associated with heading date was identified at the Lafayette location across 2 years. A homolog of ARABIDOPSIS PSEUDO-RESPONSE REGULATOR 5, a light modulator in the circadian clock associated with heading date was detected on chromosome 8a across locations and years. These results demonstrate that genetic variants related to floral development could lend themselves to a long-term goal of developing late flowering varieties of switchgrass with high biomass yield.

Genomic prediction accuracy for switchgrass traits related to bioenergy within differentiated populations

BACKGROUND

Switchgrass breeders need to improve the rates of genetic gain in many bioenergy-related traits in order to create improved cultivars that are higher yielding and have optimal biomass composition. One way to achieve this is through genomic selection. However, the heritability of traits needs to be determined as well as the accuracy of prediction in order to determine if efficient selection is possible.

RESULTS

Using five distinct switchgrass populations comprised of three lowland, one upland and one hybrid accession, the accuracy of genomic predictions under different cross-validation strategies and prediction methods was investigated. Individual genotypes were collected using GBS while kin-BLUP, partial least squares, sparse partial least squares, and BayesB methods were employed to predict yield, morphological, and NIRS-based compositional data collected in 2012-2013 from a replicated Nebraska field trial. Population structure was assessed by F statistics which ranged from 0.3952 between lowland and upland accessions to 0.0131 among the lowland accessions. Prediction accuracy ranged from 0.57-0.52 for cell wall soluble glucose and fructose respectively, to insignificant for traits with low repeatability. Ratios of heritability across to within-population ranged from 15 to 0.6.

CONCLUSIONS

Accuracy was significantly affected by both cross-validation strategy and trait. Accounting for population structure with a cross-validation strategy constrained by accession resulted in accuracies that were 69% lower than apparent accuracies using unconstrained cross-validation. Less accurate genomic selection is anticipated when most of the phenotypic variation exists between populations such as with spring regreening and yield phenotypes.

Quantitative Trait Locus Mapping for Flowering Time in a Lowland × Upland Switchgrass Pseudo-F2 Population

Flowering is an important developmental event in switchgrass (), as the time to complete the life cycle affects overall biomass accumulation. The objective of this study was to generate a linkage map using single nucleotide polymorphism (SNP) markers to identify quantitative trait loci (QTL) associated with flowering time. A pseudo-F population was created by crossing two siblings derived from an initial cross between the lowland population Ellsworth and the upland cultivar Summer. Heading and anthesis dates were collected for 2 yr at two locations: DeKalb, IL and Lafayette, IN. Nine QTL for flowering time were detected, two of which were heading-associated, four anthesis-associated, and three associated with both heading and anthesis. One QTL on linkage group (LG) 2a was detected for heading and anthesis in each location and year when environments were analyzed separately, and in a combined analysis across both locations and years. The effect on heading and anthesis of the QTL on LG 2a ranged from 4 to 13 and 5 to 9 d, respectively, depending on environment. Our findings validate QTL for switchgrass flowering time from previous research and identified additional QTL. Based on the switchgrass reference genome version 1.1, flowering time gene homologs reside near the LG 2a QTL and include PSEUDO RESPONSE REGULATOR 5, SUPPRESSOR OF FRIGIDA 4, and APETALA 1, respectively involved in the circadian clock, vernalization, and floral meristem identity. Markers linked to the QTL can be used to improve the efficiency of breeding switchgrass for delayed flowering to increase biomass yield.

Natural variation in genes potentially involved in plant architecture and adaptation in switchgrass (Panicum virgatum L.)

Background

Advances in genomic technologies have expanded our ability to accurately and exhaustively detect natural genomic variants that can be applied in crop improvement and to increase our knowledge of plant evolution and adaptation. Switchgrass (Panicum virgatum L.), an allotetraploid (2n = 4× = 36) perennial C4 grass (Poaceae family) native to North America and a feedstock crop for cellulosic biofuel production, has a large potential for genetic improvement due to its high genotypic and phenotypic variation. In this study, we analyzed single nucleotide polymorphism (SNP) variation in 372 switchgrass genotypes belonging to 36 accessions for 12 genes putatively involved in biomass production to investigate signatures of selection that could have led to ecotype differentiation and to population adaptation to geographic zones.

Results

A total of 11,682 SNPs were mined from ~ 15 Gb of sequence data, out of which 251 SNPs were retained after filtering. Population structure analysis largely grouped upland accessions into one subpopulation and lowland accessions into two additional subpopulations. The most frequent SNPs were in homozygous state within accessions. Sixty percent of the exonic SNPs were non-synonymous and, of these, 45% led to non-conservative amino acid changes. The non-conservative SNPs were largely in linkage disequilibrium with one haplotype being predominantly present in upland accessions while the other haplotype was commonly present in lowland accessions. Tajima’s test of neutrality indicated that PHYB, a gene involved in photoperiod response, was under positive selection in the switchgrass population. PHYB carried a SNP leading to a non-conservative amino acid change in the PAS domain, a region that acts as a sensor for light and oxygen in signal transduction.

Conclusions

Several non-conservative SNPs in genes potentially involved in plant architecture and adaptation have been identified and led to population structure and genetic differentiation of ecotypes in switchgrass. We suggest here that PHYB is a key gene involved in switchgrass natural selection. Further analyses are needed to determine whether any of the non-conservative SNPs identified play a role in the differential adaptation of upland and lowland switchgrass.

Electronic supplementary material

The online version of this article (10.1186/s12862-018-1193-2) contains supplementary material, which is available to authorized users.

Extensive Genetic Diversity is Present within North American Switchgrass Germplasm

Switchgrass ( is a perennial native North American grass present in two ecotypes: upland, found primarily in the northern range of switchgrass habitats, and lowland, found largely in the southern reaches of switchgrass habitats. Previous studies focused on a diversity panel of primarily northern switchgrass, so to expand our knowledge of genetic diversity in a broader set of North American switchgrass, exome capture sequence data were generated for 632 additional, primarily lowland individuals. In total, over 37 million single nucleotide polymorphisms (SNPs) were identified and a set of 1.9 million high-confidence SNPs were obtained from 1169 individuals from 140 populations (67 upland, 65 lowland, 8 admixed) were used in downstream analyses of genetic diversity and population structure. Seven separate population groups were identified with moderate genetic differentiation [mean fixation index (Fst) estimate of 0.06] between the lowland and the upland populations. Ecotype-specific and population-specific SNPs were identified for use in germplasm evaluations. Relative to rice ( L.), maize ( L.), soybean [ (L.) Merr.], and Gaertn., analyses of nucleotide diversity revealed a high degree of genetic diversity (0.0135) across all individuals, consistent with the outcrossing mode of reproduction and the polyploidy of switchgrass. This study supports the hypothesis that repeated glaciation events, ploidy barriers, and restricted gene flow caused by flowering time differences have resulted in distinct gene pools across ecotypes and geographic regions. These data provide a resource to associate alleles with traits of interest for forage, restoration, and biofuel feedstock efforts in switchgrass.

Crop-associated virus reduces the rooting depth of non-crop perennial native grass more than non-crop-associated virus with known viral suppressor of RNA silencing (VSR)

As agricultural acreage expanded and came to dominate landscapes across the world, viruses gained opportunities to move between crop and wild native plants. In the Midwestern USA, virus exchange currently occurs between widespread annual Poaceae crops and remnant native perennial prairie grasses now under consideration as bioenergy feedstocks. In this region, the common aphid species Rhopalosiphum padi L. (the bird cherry-oat aphid) transmits several virus species in the family Luteoviridae, including Barley yellow dwarf virus (BYDV-PAV, genus Luteovirus) and Cereal yellow dwarf virus (CYDV-RPV and -RPS, genus Polerovirus). The yellow dwarf virus (YDV) species in these two genera share genetic similarities in their 3'-ends, but diverge in the 5'-regions. Most notably, CYDVs encode a P0 viral suppressor of RNA silencing (VSR) absent in BYDV-PAV. Because BYDV-PAV has been reported more frequently in annual cereals and CYDVs in perennial non-crop grasses, we examine the hypothesis that the viruses' genetic differences reflect different affinities for crop and non-crop hosts. Specifically, we ask (i) whether CYDVs might persist within and affect a native non-crop grass more strongly than BYDV-PAV, on the grounds that the polerovirus VSR could better moderate the defenses of a well-defended perennial, and (ii) whether the opposite pattern of effects might occur in a less defended annual crop. Because previous work found that the VSR of CYDV-RPS possessed greater silencing suppressor efficiency than that of CYDV-RPV, we further explored (iii) whether a novel grass-associated CYDV-RPS isolate would influence a native non-crop grass more strongly than a comparable CYDV-RPV isolate. In growth chamber studies, we found support for this hypothesis: only grass-associated CYDV-RPS stunted the shoots and crowns of Panicum virgatum L. (switchgrass), a perennial native North American prairie grass, whereas crop-associated BYDV-PAV (and coinfection with BYDV-PAV and CYDV-RPS) most stunted annual Avena sativa L. (oats). These findings suggest that some of the diversity in grass-infecting Luteoviridae reflects viral capacity to modulate defenses in different host types. Intriguingly, while all virus treatments also reduced root production in both host species, only crop-associated BYDV-PAV (or co-infection) reduced rooting depths. Such root effects may increase host susceptibility to drought, and indicate that BYDV-PAV pathogenicity is determined by something other than a P0 VSR. These findings contribute to growing evidence that pathogenic crop-associated viruses may harm native species as well as crops. Critical next questions include the extent to which crop-associated selection pressures drive viral pathogenesis.

Genome-wide associations with flowering time in switchgrass using exome-capture sequencing data

Flowering time is a major determinant of biomass yield in switchgrass (Panicum virgatum), a perennial bioenergy crop, because later flowering allows for an extended period of vegetative growth and increased biomass production. A better understanding of the genetic regulation of flowering time in switchgrass will aid the development of switchgrass varieties with increased biomass yields, particularly at northern latitudes, where late-flowering but southern-adapted varieties have high winter mortality. We use genotypes derived from recently published exome-capture sequencing, which mitigates challenges related to the large, highly repetitive and polyploid switchgrass genome, to perform genome-wide association studies (GWAS) using flowering time data from a switchgrass association panel in an effort to characterize the genetic architecture and genes underlying flowering time regulation in switchgrass. We identify associations with flowering time at multiple loci, including in a homolog of FLOWERING LOCUS T and in a locus containing TIMELESS, a homolog of a key circadian regulator in animals. Our results suggest that flowering time variation in switchgrass is due to variation at many positions across the genome. The relationship of flowering time and geographic origin indicates likely roles for genes in the photoperiod and autonomous pathways in generating switchgrass flowering time variation.

Accuracy of Genomic Prediction in Switchgrass (Panicum virgatum L.) Improved by Accounting for Linkage Disequilibrium

Switchgrass is a relatively high-yielding and environmentally sustainable biomass crop, but further genetic gains in biomass yield must be achieved to make it an economically viable bioenergy feedstock. Genomic selection (GS) is an attractive technology to generate rapid genetic gains in switchgrass, and meet the goals of a substantial displacement of petroleum use with biofuels in the near future. In this study, we empirically assessed prediction procedures for genomic selection in two different populations, consisting of 137 and 110 half-sib families of switchgrass, tested in two locations in the United States for three agronomic traits: dry matter yield, plant height, and heading date. Marker data were produced for the families’ parents by exome capture sequencing, generating up to 141,030 polymorphic markers with available genomic-location and annotation information. We evaluated prediction procedures that varied not only by learning schemes and prediction models, but also by the way the data were preprocessed to account for redundancy in marker information. More complex genomic prediction procedures were generally not significantly more accurate than the simplest procedure, likely due to limited population sizes. Nevertheless, a highly significant gain in prediction accuracy was achieved by transforming the marker data through a marker correlation matrix. Our results suggest that marker-data transformations and, more generally, the account of linkage disequilibrium among markers, offer valuable opportunities for improving prediction procedures in GS. Some of the achieved prediction accuracies should motivate implementation of GS in switchgrass breeding programs.

Genome size variation in the genus Avena

Genome size is an indicator of evolutionary distance and a metric for genome characterization. Here, we report accurate estimates of genome size in 99 accessions from 26 species of Avena. We demonstrate that the average genome size of C genome diploid species (2C = 10.26 pg) is 15% larger than that of A genome species (2C = 8.95 pg), and that this difference likely accounts for a progression of size among tetraploid species, where AB < AC < CC (average 2C = 16.76, 18.60, and 21.78 pg, respectively). All accessions from three hexaploid species with the ACD genome configuration had similar genome sizes (average 2C = 25.74 pg). Genome size was mostly consistent within species and in general agreement with current information about evolutionary distance among species. Results also suggest that most of the polyploid species in Avena have experienced genome downsizing in relation to their diploid progenitors. Genome size measurements could provide additional quality control for species identification in germplasm collections, especially in cases where diploid and polyploid species have similar morphology.