Genomic prediction of switchgrass winter survivorship across diverse lowland populations.

In the North-Central United States, lowland ecotype switchgrass can increase yield by up to 50% compared with locally adapted but early flowering cultivars. However, lowland ecotypes are not winter tolerant. The mechanism for winter damage is unknown but previously has been associated with late flowering time. This study investigated heading date (measured for two years) and winter survivorship (measured for three years) in a multi-generation population generated from two winter-hardy lowland individuals and diverse southern lowland populations. Sequencing data (311,776 markers) from 1,306 individuals were used to evaluate genome-wide trait prediction through cross-validation and progeny prediction (n = 52). Genetic variance for heading date and winter survivorship was additive with high narrow-sense heritability (0.64 and 0.71, respectively) and reliability (0.68 and 0.76, respectively). The initial negative correlation between winter survivorship and heading date degraded across generations (F1r = -0.43, pseudo-F2r = -0.28, pseudo-F2 progeny r = -0.15). Within-family predictive ability was moderately high for heading date and winter survivorship (0.53 and 0.52, respectively). A multi-trait model did not improve predictive ability for either trait. Progeny predictive ability was 0.71 for winter survivorship and 0.53 for heading date. These results suggest that lowland ecotype populations can obtain sufficient survival rates in the northern United States with two or three cycles of effective selection. Despite accurate genomic prediction, naturally occurring winter mortality successfully isolated winter tolerant genotypes and appears to be an efficient method to develop high-yielding, cold-tolerant switchgrass cultivars.

Genetic loci associated with winter survivorship in diverse lowland switchgrass populations.

High winter mortality limits biomass yield of lowland switchgrass (Panicum virgatum L.) planted in the northern latitudes of North America. Breeding of cold tolerant switchgrass cultivars requires many years due to its perennial growth habit and the unpredictable winter selection pressure that is required to identify winter-hardy individuals. Identification of causal genetic variants for winter survivorship would accelerate the improvement of switchgrass biomass production. The objective of this study was to identify allelic variation associated with winter survivorship in lowland switchgrass populations using bulk segregant analysis (BSA). Twenty-nine lowland switchgrass populations were evaluated for winter survival at two locations in southern Wisconsin and 21 populations with differential winter survivorship were used for BSA. A maximum of 10% of the individuals (8-20) were bulked to create survivor and nonsurvivor DNA pools from each population and location. The DNA pools were evaluated using exome capture sequencing, and allele frequencies were used to conduct statistical tests. The BSA tests revealed nine quatitative trait loci (QTL) from tetraploid populations and seven QTL from octoploid populations. Many QTL were population-specific, but some were identified in multiple populations that originated across a broad geographic landscape. Four QTL (at positions 88 Mb on chromosome 2N, 115 Mb on chromosome 5K, and 1 and 100 Mb on chromosome 9N) were potentially the most useful QTL. Markers associated with winter survivorship in this study can be used to accelerate breeding cycles of lowland switchgrass populations and should lead to improvements in adaptation within USDA hardiness zones 4 and 5.

Subsampling and DNA pooling can increase gains through genomic selection in switchgrass.

Genomic selection (GS) can accelerate breeding cycles in perennial crops such as the bioenergy grass switchgrass (Panicum virgatum L.). The sequencing costs of GS can be reduced by pooling DNA samples in the training population (TP), only sequencing TP phenotypic outliers, or pooling candidate population (CP) samples. These strategies were simulated for two traits (spring vigor and anthesis date) in three breeding populations. Sequencing only the outlier 50% of the TP phenotype distribution resulted in a penalty of <5% of the predictive ability, measured using cross-validation. Predictive ability also decreased when sequencing progressively fewer TP DNA pools, but TPs constructed from only two phenotypically contrasting DNA samples retained a mean of >80% predictive ability relative to individual TP sequencing. Novel group testing methods allowed greater than one CP individual to be screened per sequenced DNA sample but resulted in a predictive ability penalty. To determine the impact of reduced sequencing, genetic gain was calculated for seven GS scenarios with variable sequencing budgets. Reduced TP sequencing and most CP pooling methods were superior to individual sequence-based GS when sequencing resources were restricted (2,000 DNA samples per 5-yr cycle). Only one scenario was superior to individual sequencing when sequencing budgets were large (8,000 DNA samples per 5-yr cycle). This study highlights multiple routes for reduced sequencing costs in GS.

Fall Tillage Reduced Nutrient Loads from Liquid Manure Application during the Freezing Season.

Reducing agricultural runoff is important year round, particularly on landscapes that receive wintertime applications of manure. No-tillage systems are typically associated with reduced runoff loads during the growing season, but surface roughness from fall tillage may aid infiltration on frozen soils by providing surface depressional storage. The timing of winter manure applications may also affect runoff, depending on snow and soil frost conditions. Therefore, the objective of this study was to evaluate runoff and nutrient loads during the freezing season from combinations of tillage and manure application timings. Six management treatments were tested in south-central Wisconsin during the winters of 2015-2016 and 2016-2017 with a complete factorial design: two tillage treatments (fall chisel plow vs. no-tillage) and three manure application timings (early December, late January, and unmanured). Nutrient loads from winter manure application were lower on chisel-plowed versus untilled soils during both monitoring years. Loads were also lower from manure applied to soils with less frost development. Wintertime manure applications pose a risk of surface nutrient losses, but fall tillage and timing applications to thawed soils can help reduce loads.

Genomic Prediction for Winter Survival of Lowland Switchgrass in the Northern USA.

The lowland ecotype of switchgrass has generated considerable interest because of its higher biomass yield and late flowering characteristics compared to the upland ecotype. However, lowland ecotypes planted in northern latitudes exhibit very low winter survival. Implementation of genomic selection could potentially enhance switchgrass breeding for winter survival by reducing generation time while eliminating the dependence on weather. The objectives of this study were to assess the potential of genomic selection for winter survival in lowland switchgrass by combining multiple populations in the training set and applying the selected model in two independent testing datasets for validation. Marker data were generated using exome capture sequencing. Validation was conducted using (1) indirect indicators of winter adaptation based on geographic and climatic variables of accessions from different source locations and (2) winter survival estimates of the phenotype. The prediction accuracies were significantly higher when the training dataset comprising all populations was used in fivefold cross validation but its application was not useful in the independent validation dataset. Nevertheless, modeling for population heterogeneity improved the prediction accuracy to some extent but the genetic relationship between the training and validation populations was found to be more influential. The predicted winter survival of lowland switchgrass indicated latitudinal and longitudinal variability, with the northeast USA the region for most cold tolerant lowland populations. Our results suggested that GS could provide valuable opportunities for improving winter survival and accelerate the lowland switchgrass breeding programs toward the development of cold tolerant cultivars suitable for northern latitudes.

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.

Breeding progress and preparedness for mass-scale deployment of perennial lignocellulosic biomass crops switchgrass, miscanthus, willow and poplar.

Genetic improvement through breeding is one of the key approaches to increasing biomass supply. This paper documents the breeding progress to date for four perennial biomass crops (PBCs) that have high output-input energy ratios: namely Panicum virgatum (switchgrass), species of the genera Miscanthus (miscanthus), Salix (willow) and Populus (poplar). For each crop, we report on the size of germplasm collections, the efforts to date to phenotype and genotype, the diversity available for breeding and on the scale of breeding work as indicated by number of attempted crosses. We also report on the development of faster and more precise breeding using molecular breeding techniques. Poplar is the model tree for genetic studies and is furthest ahead in terms of biological knowledge and genetic resources. Linkage maps, transgenesis and genome editing methods are now being used in commercially focused poplar breeding. These are in development in switchgrass, miscanthus and willow generating large genetic and phenotypic data sets requiring concomitant efforts in informatics to create summaries that can be accessed and used by practical breeders. Cultivars of switchgrass and miscanthus can be seed-based synthetic populations, semihybrids or clones. Willow and poplar cultivars are commercially deployed as clones. At local and regional level, the most advanced cultivars in each crop are at technology readiness levels which could be scaled to planting rates of thousands of hectares per year in about 5 years with existing commercial developers. Investment in further development of better cultivars is subject to current market failure and the long breeding cycles. We conclude that sustained public investment in breeding plays a key role in delivering future mass-scale deployment of PBCs.

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.

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.

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.

Genomic prediction of crown rust resistance in Lolium perenne.

BACKGROUND: Genomic selection (GS) can accelerate genetic gains in breeding programmes by reducing the time it takes to complete a cycle of selection. Puccinia coronata f. sp lolli (crown rust) is one of the most widespread diseases of perennial ryegrass and can lead to reductions in yield, persistency and nutritional value. Here, we used a large perennial ryegrass population to assess the accuracy of using genome wide markers to predict crown rust resistance and to investigate the factors affecting predictive ability. RESULTS: Using these data, predictive ability for crown rust resistance in the complete population reached a maximum of 0.52. Much of the predictive ability resulted from the ability of markers to capture genetic relationships among families within the training set, and reducing the marker density had little impact on predictive ability. Using permutation based variable importance measure and genome wide association studies (GWAS) to identify and rank markers enabled the identification of a small subset of SNPs that could achieve predictive abilities close to those achieved using the complete marker set. CONCLUSION: Using a GWAS to identify and rank markers enabled a small panel of markers to be identified that could achieve higher predictive ability than the same number of randomly selected markers, and predictive abilities close to those achieved with the entire marker set. This was particularly evident in a sub-population characterised by having on-average higher genome-wide linkage disequilibirum (LD). Higher predictive abilities with selected markers over random markers suggests they are in LD with QTL. Accuracy due to genetic relationships will decay rapidly over generations whereas accuracy due to LD will persist, which is advantageous for practical breeding applications.

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.

Amplification and adaptation of centromeric repeats in polyploid switchgrass species.

Centromeres in most higher eukaryotes are composed of long arrays of satellite repeats from a single satellite repeat family. Why centromeres are dominated by a single satellite repeat and how the satellite repeats originate and evolve are among the most intriguing and long-standing questions in centromere biology. We identified eight satellite repeats in the centromeres of tetraploid switchgrass (Panicum virgatum). Seven repeats showed characteristics associated with classical centromeric repeats with monomeric lengths ranging from 166 to 187 bp. Interestingly, these repeats share an 80-bp DNA motif. We demonstrate that this 80-bp motif may dictate translational and rotational phasing of the centromeric repeats with the cenH3 nucleosomes. The sequence of the last centromeric repeat, Pv156, is identical to the 5S ribosomal RNA genes. We demonstrate that a 5S ribosomal RNA gene array was recruited to be the functional centromere for one of the switchgrass chromosomes. Our findings reveal that certain types of satellite repeats, which are associated with unique sequence features and are composed of monomers in mono-nucleosomal length, are favorable for centromeres. Centromeric repeats may undergo dynamic amplification and adaptation before the centromeres in the same species become dominated by the best adapted satellite repeat.

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.

Selection Signatures in Four Lignin Genes from Switchgrass Populations Divergently Selected for In Vitro Dry Matter Digestibility.

Switchgrass is undergoing development as a dedicated cellulosic bioenergy crop. Fermentation of lignocellulosic biomass to ethanol in a bioenergy system or to volatile fatty acids in a livestock production system is strongly and negatively influenced by lignification of cell walls. This study detects specific loci that exhibit selection signatures across switchgrass breeding populations that differ in in vitro dry matter digestibility (IVDMD), ethanol yield, and lignin concentration. Allele frequency changes in candidate genes were used to detect loci under selection. Out of the 183 polymorphisms identified in the four candidate genes, twenty-five loci in the intron regions and four loci in coding regions were found to display a selection signature. All loci in the coding regions are synonymous substitutions. Selection in both directions were observed on polymorphisms that appeared to be under selection. Genetic diversity and linkage disequilibrium within the candidate genes were low. The recurrent divergent selection caused excessive moderate allele frequencies in the cycle 3 reduced lignin population as compared to the base population. This study provides valuable insight on genetic changes occurring in short-term selection in the polyploid populations, and discovered potential markers for breeding switchgrass with improved biomass quality.

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.

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

Panicum virgatum L. (switchgrass) is a polyploid, perennial grass species that is native to North America, and is being developed as a future biofuel feedstock crop. Switchgrass is present primarily in two ecotypes: a northern upland ecotype, composed of tetraploid and octoploid accessions, and a southern lowland ecotype, composed of primarily tetraploid accessions. We employed high-coverage exome capture sequencing (~2.4 Tb) to genotype 537 individuals from 45 upland and 21 lowland populations. From these data, we identified ~27 million single-nucleotide polymorphisms (SNPs), of which 1 590 653 high-confidence SNPs were used in downstream analyses of diversity within and between the populations. From the 66 populations, we identified five primary population groups within the upland and lowland ecotypes, a result that was further supported through genetic distance analysis. We identified conserved, ecotype-restricted, non-synonymous SNPs that are predicted to affect the protein function of CONSTANS (CO) and EARLY HEADING DATE 1 (EHD1), key genes involved in flowering, which may contribute to the phenotypic differences between the two ecotypes. We also identified, relative to the near-reference Kanlow population, 17 228 genes present in more copies than in the reference genome (up-CNVs), 112 630 genes present in fewer copies than in the reference genome (down-CNVs) and 14 430 presence/absence variants (PAVs), affecting a total of 9979 genes, including two upland-specific CNV clusters. In total, 45 719 genes were affected by an SNP, CNV, or PAV across the panel, providing a firm foundation to identify functional variation associated with phenotypic traits of interest for biofuel feedstock production.

Genome-wide association study based on multiple imputation with low-depth sequencing data: application to biofuel traits in reed canarygrass.

Genotyping by sequencing allows for large-scale genetic analyses in plant species with no reference genome, but sets the challenge of sound inference in presence of uncertain genotypes. We report an imputation-based genome-wide association study (GWAS) in reed canarygrass (Phalaris arundinacea L., Phalaris caesia Nees), a cool-season grass species with potential as a biofuel crop. Our study involved two linkage populations and an association panel of 590 reed canarygrass genotypes. Plants were assayed for up to 5228 single nucleotide polymorphism markers and 35 traits. The genotypic markers were derived from low-depth sequencing with 78% missing data on average. To soundly infer marker-trait associations, multiple imputation (MI) was used: several imputes of the marker data were generated to reflect imputation uncertainty and association tests were performed on marker effects across imputes. A total of nine significant markers were identified, three of which showed significant homology with the Brachypodium dystachion genome. Because no physical map of the reed canarygrass genome was available, imputation was conducted using classification trees. In general, MI showed good consistency with the complete-case analysis and adequate control over imputation uncertainty. A gain in significance of marker effects was achieved through MI, but only for rare cases when missing data were <45%. In addition to providing insight into the genetic basis of important traits in reed canarygrass, this study presents one of the first applications of MI to genome-wide analyses and provides useful guidelines for conducting GWAS based on genotyping-by-sequencing data.

Accelerating the switchgrass (Panicum virgatum L.) breeding cycle using genomic selection approaches.

Switchgrass (Panicum virgatum L.) is a perennial grass undergoing development as a biofuel feedstock. One of the most important factors hindering breeding efforts in this species is the need for accurate measurement of biomass yield on a per-hectare basis. Genomic selection on simple-to-measure traits that approximate biomass yield has the potential to significantly speed up the breeding cycle. Recent advances in switchgrass genomic and phenotypic resources are now making it possible to evaluate the potential of genomic selection of such traits. We leveraged these resources to study the ability of three widely-used genomic selection models to predict phenotypic values of morphological and biomass quality traits in an association panel consisting of predominantly northern adapted upland germplasm. High prediction accuracies were obtained for most of the traits, with standability having the highest ten-fold cross validation prediction accuracy (0.52). Moreover, the morphological traits generally had higher prediction accuracies than the biomass quality traits. Nevertheless, our results suggest that the quality of current genomic and phenotypic resources available for switchgrass is sufficiently high for genomic selection to significantly impact breeding efforts for biomass yield.