Toward plant breeding for multicrop systems.

Increasing cropping system diversity has great potential to address environmental problems associated with modern agriculture, such as erosion, soil carbon loss, nutrient runoff, water pollution, and loss of biodiversity. As with other agricultural sciences, plant breeding has primarily been conducted in the context of dominant monoculture cropping systems, with little focus on multicrop systems. Multicrop systems have increased temporal and/or spatial diversity and include a diverse set of crops and practices. In order to support a transition to multicrop systems, plant breeders must shift their breeding programs and objectives to better represent more diverse systems, including diverse rotations, alternate-season crops, ecosystem service crops, and intercropping systems. The degree to which breeding methods need to change will depend on the cropping system context in question. Plant breeding alone, however, cannot drive adoption of multicrop systems. Alongside shifts in breeding approaches, changes are needed within broader research, private sector, and policy contexts. These changes include policies and investments that support a transition to multicrop systems, increased collaboration across disciplines to support cropping system development, and leadership from both the public and private sectors to develop and promote adoption of new cultivars.

Early Detection of the Spinach Downy Mildew Pathogen in Leaves by Recombinase Polymerase Amplification.

Downy mildew of spinach, caused by Peronospora effusa, is a major economic threat to both organic and conventional spinach production. Symptomatic spinach leaves are unmarketable and spinach with latent infections are problematic because symptoms can develop postharvest. Therefore, early detection methods for P. effusa could help producers identify infection before visible symptoms appear. Recombinase polymerase amplification (RPA) provides sensitive and specific detection of pathogen DNA and is a rapid, field-applicable method that does not require advanced technical knowledge or equipment-heavy DNA extraction. Here, we used comparative genomics to identify a unique region of the P. effusa mitochondrial genome to develop an RPA assay for the early detection of P. effusa in spinach leaves. In tandem, we established a TaqMan quantitative PCR (qPCR) assay and used this assay to validate the P. effusa specificity of the locus across Peronospora spp. and to compare assay performance. Neither the TaqMan qPCR nor the RPA showed cross reactivity with the closely related beet downy mildew pathogen, P. schachtii. TaqMan qPCR and RPA have detection thresholds of 100 and 900 fg of DNA, respectively. Both assays could detect P. effusa in presymptomatic leaves, with RPA-based detection occurring as early as 5 days before the appearance of symptoms and TaqMan qPCR-based detection occurring after 24 h of plant exposure to airborne spores. Implementation of the RPA detection method could provide real-time information for point-of-care management strategies at field sites.

Annual and perennial Medicago show signatures of parallel adaptation to climate and soil in highly conserved genes.

Human induced environmental change may require rapid adaptation of plant populations and crops, but the genomic basis of environmental adaptation remain poorly understood. We analysed polymorphic loci from the perennial crop Medicago sativa (alfalfa or lucerne) and the annual legume model species M. truncatula to search for a common set of candidate genes that might contribute to adaptation to abiotic stress in both annual and perennial Medicago species. We identified a set of candidate genes of adaptation associated with environmental gradients along the distribution of the two Medicago species. Candidate genes for each species were detected in homologous genomic linkage blocks using genome-environment (GEA) and genome-phenotype association analyses. Hundreds of GEA candidate genes were species-specific, of these, 13.4% (M. sativa) and 24% (M. truncatula) were also significantly associated with phenotypic traits. A set of 168 GEA candidates were shared by both species, which was 25.4% more than expected by chance. When combined, they explained a high proportion of variance for certain phenotypic traits associated with adaptation. Genes with highly conserved functions dominated among the shared candidates and were enriched in gene ontology terms that have shown to play a central role in drought avoidance and tolerance mechanisms by means of cellular shape modifications and other functions associated with cell homeostasis. Our results point to the existence of a molecular basis of adaptation to abiotic stress in Medicago determined by highly conserved genes and gene functions. We discuss these results in light of the recently proposed omnigenic model of complex traits.

Evolutionary networks from RADseq loci point to hybrid origins of Medicago carstiensis and Medicago cretacea.

PREMISE: Although hybridization has played an important role in the evolution of many plant species, phylogenetic reconstructions that include hybridizing lineages have been historically constrained by the available models and data. Restriction-site-associated DNA sequencing (RADseq) has been a popular sequencing technique for the reconstruction of hybridization in the next-generation sequencing era. However, the utility of RADseq for the reconstruction of complex evolutionary networks has not been thoroughly investigated. Conflicting phylogenetic relationships in the genus Medicago have been mainly attributed to hybridization, but the specific hybrid origins of taxa have not been yet clarified. METHODS: We obtained new molecular data from diploid species of Medicago section Medicago using single-digest RADseq to reconstruct evolutionary networks from gene trees, an approach that is computationally tractable with data sets that include several species and complex hybridization patterns. RESULTS: Our analyses revealed that assembly filters to exclusively select a small set of loci with high phylogenetic information led to the most-divergent network topologies. Conversely, alternative clustering thresholds or filters on the number of samples per locus had a lower impact on networks. A strong hybridization signal was detected for M. carstiensis and M. cretacea, while signals were less clear for M. rugosa, M. rhodopea, M. suffruticosa, M. marina, M. scutellata, and M. sativa. CONCLUSIONS: Complex network reconstructions from RADseq gene trees were not robust under variations of the assembly parameters and filters. But when the most-divergent networks were discarded, all remaining analyses consistently supported a hybrid origin for M. carstiensis and M. cretacea.

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.

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.

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.

Selection Mapping Identifies Loci Underpinning Autumn Dormancy in Alfalfa (Medicago sativa).

Autumn dormancy in alfalfa (Medicago sativa) is associated with agronomically important traits including regrowth rate, maturity, and winter survival. Historical recurrent selection experiments have been able to manipulate the dormancy response. We hypothesized that artificial selection for dormancy phenotypes in these experiments had altered allele frequencies of dormancy-related genes. Here, we follow this hypothesis and analyze allele frequency changes using genome-wide polymorphisms in the pre- and postselection populations from one historical selection experiment. We screened the nondormant cultivar CUF 101 and populations developed by three cycles of recurrent phenotypic selection for taller and shorter plants in autumn with markers derived from genotyping-by-sequencing (GBS). We validated the robustness of our GBS-derived allele frequency estimates using an empirical approach. Our results suggest that selection mapping is a powerful means of identifying genomic regions associated with traits, and that it can be exploited to provide regions on which to focus further mapping and cloning projects.

GBS-Based Genomic Selection for Pea Grain Yield under Severe Terminal Drought.

Terminal drought is the main stress that limits pea ( L.) grain yield in Mediterranean-climate regions. This study provides an unprecedented assessment of the predictive ability of genomic selection (GS) for grain yield under severe terminal drought using genotyping-by-sequencing (GBS) data. Additional aims were to assess the GS predictive ability for different GBS data quality filters and GS models, comparing intrapopulation with interpopulation GS predictive ability and to perform genome-wide association (GWAS) studies. The yield and onset of flowering of 315 lines from three recombinant inbred line (RIL) populations issued by connected crosses between three elite cultivars were assessed under a field rainout shelter. We defined an adjusted yield, which is associated with intrinsic drought tolerance, as the yield deviation from the value expected as a function of onset of flowering (which correlated negatively with grain yield). Total polymorphic markers ranged from approximately 100 (minimum of eight reads per locus, maximum 10% genotype missing data) to over 7500 markers (minimum of four reads, maximum 50% missing rate). Best predictions were provided by Bayesian Lasso (BL) or ridge regression best linear unbiased prediction (rrBLUP), rather than support vector regression (SVR) models, with at least 400-500 markers. Intrapopulation GS predictive ability exceeded 0.5 for yield and onset of flowering in all populations and approached 0.4 for the adjusted yield of a population with high trait variation. Genomic selection was preferable to phenotypic selection in terms of predicted yield gains. Interpopulation GS predictive ability varied largely depending on the pair of populations. GWAS revealed extensive colocalization of markers associated with high yield and early flowering and suggested that they are concentrated in a few genomic regions.

Genome-Wide Association Mapping and Genomic Selection for Alfalfa (Medicago sativa) Forage Quality Traits.

Genetic progress for forage quality has been poor in alfalfa (Medicago sativa L.), the most-grown forage legume worldwide. This study aimed at exploring opportunities for marker-assisted selection (MAS) and genomic selection of forage quality traits based on breeding values of parent plants. Some 154 genotypes from a broadly-based reference population were genotyped by genotyping-by-sequencing (GBS), and phenotyped for leaf-to-stem ratio, leaf and stem contents of protein, neutral detergent fiber (NDF) and acid detergent lignin (ADL), and leaf and stem NDF digestibility after 24 hours (NDFD), of their dense-planted half-sib progenies in three growing conditions (summer harvest, full irrigation; summer harvest, suspended irrigation; autumn harvest). Trait-marker analyses were performed on progeny values averaged over conditions, owing to modest germplasm × condition interaction. Genomic selection exploited 11,450 polymorphic SNP markers, whereas a subset of 8,494 M. truncatula-aligned markers were used for a genome-wide association study (GWAS). GWAS confirmed the polygenic control of quality traits and, in agreement with phenotypic correlations, indicated substantially different genetic control of a given trait in stems and leaves. It detected several SNPs in different annotated genes that were highly linked to stem protein content. Also, it identified a small genomic region on chromosome 8 with high concentration of annotated genes associated with leaf ADL, including one gene probably involved in the lignin pathway. Three genomic selection models, i.e., Ridge-regression BLUP, Bayes B and Bayesian Lasso, displayed similar prediction accuracy, whereas SVR-lin was less accurate. Accuracy values were moderate (0.3-0.4) for stem NDFD and leaf protein content, modest for leaf ADL and NDFD, and low to very low for the other traits. Along with previous results for the same germplasm set, this study indicates that GBS data can be exploited to improve both quality traits (by genomic selection or MAS) and forage yield.

Identification of loci controlling forage yield and nutritive value in diploid alfalfa using GBS-GWAS.

KEY MESSAGE: We attempted to identify genomic regions controlling forage yield and nutritive value in alfalfa. Several candidate genes and associated genetic markers were identified that could potentially be useful for alfalfa breeding to more efficiently develop improved cultivars. Alfalfa is one of the most widely cultivated forage legumes worldwide and improving alfalfa forage yield and nutritive value is a major global breeding goal. Genotyping-by-sequencing (GBS) provides cost-effective molecular marker genotyping for genome-wide association studies (GWAS). Using more than 15,000 genome-wide single nucleotide polymorphisms (SNP) identified from GBS, we conducted a GWAS to investigate forage yield and nutritive value-related traits. We have detected a number of associations for all the traits evaluated and a number of associations detected were located on the Medicago truncatula genome. The SNP in a coding region of a cell wall biosynthesis gene was associated with several cell wall-related traits, and we suggest that it may be the causative polymorphism. Two other SNPs residing in meristematic development and early growth genes were found to associate with the total biomass yield. None of the SNPs associated with regrowth after harvest or with spring regrowth were mapped to the M. truncatula genome, possibly reflecting the fact that M. truncatula is an annual species related to alfalfa that typically has limited ability to regrow. The alleles we identify with the major impact on forage yield and nutritive value can be rapidly incorporated into our breeding program.

Assessment of Cultivar Distinctness in Alfalfa: A Comparison of Genotyping-by-Sequencing, Simple-Sequence Repeat Marker, and Morphophysiological Observations.

Cultivar registration agencies typically require morphophysiological trait-based distinctness of candidate cultivars. This requirement is difficult to achieve for cultivars of major perennial forages because of their genetic structure and ever-increasing number of registered material, leading to possible rejection of agronomically valuable cultivars. This study aimed to explore the value of molecular markers applied to replicated bulked plants (three bulks of 100 independent plants each per cultivar) to assess alfalfa ( L. subsp. ) cultivar distinctness. We compared genotyping-by-sequencing information based on 2902 polymorphic single-nucleotide polymorphism (SNP) markers (>30 reads per DNA sample) with morphophysiological information based on 11 traits and with simple-sequence repeat (SSR) marker information from 41 polymorphic markers for their ability to distinguish 11 alfalfa landraces representative of the germplasm from northern Italy. Three molecular criteria, one based on cultivar differences for individual SSR bands and two based on overall SNP marker variation assessed either by statistically significant cultivar differences on principal component axes or discriminant analysis, distinctly outperformed the morphophysiological criterion. Combining the morphophysiological criterion with either molecular marker method increased discrimination among cultivars, since morphophysiological diversity was unrelated to SSR marker-based diversity ( = 0.04) and poorly related to SNP marker-based diversity ( = 0.23, < 0.15). The criterion based on statistically significant SNP allele frequency differences was less discriminating than morphophysiological variation. Marker-based distinctness, which can be assessed at low cost and without interactions with testing conditions, could validly substitute for (or complement) morphophysiological distinctness in alfalfa cultivar registration schemes. It also has interest in sui generis registration systems aimed at marketing alfalfa landraces.

Accuracy of genomic selection for alfalfa biomass yield in different reference populations.

BACKGROUND: Genomic selection based on genotyping-by-sequencing (GBS) data could accelerate alfalfa yield gains, if it displayed moderate ability to predict parent breeding values. Its interest would be enhanced by predicting ability also for germplasm/reference populations other than those for which it was defined. Predicting accuracy may be influenced by statistical models, SNP calling procedures and missing data imputation strategies. RESULTS: Landrace and variety material from two genetically-contrasting reference populations, i.e., 124 elite genotypes adapted to the Po Valley (sub-continental climate; PV population) and 154 genotypes adapted to Mediterranean-climate environments (Me population), were genotyped by GBS and phenotyped in separate environments for dry matter yield of their dense-planted half-sib progenies. Both populations showed no sub-population genetic structure. Predictive accuracy was higher by joint rather than separate SNP calling for the two data sets, and using random forest imputation of missing data. Highest accuracy was obtained using Support Vector Regression (SVR) for PV, and Ridge Regression BLUP and SVR for Me germplasm. Bayesian methods (Bayes A, Bayes B and Bayesian Lasso) tended to be less accurate. Random Forest Regression was the least accurate model. Accuracy attained about 0.35 for Me in the range of 0.30-0.50 missing data, and 0.32 for PV at 0.50 missing data, using at least 10,000 SNP markers. Cross-population predictions based on a smaller subset of common SNPs implied a relative loss of accuracy of about 25% for Me and 30% for PV. Genome-wide association analyses based on large subsets of M. truncatula-aligned markers revealed many SNPs with modest association with yield, and some genome areas hosting putative QTLs. A comparison of genomic vs. conventional selection for parent breeding value assuming 1-year vs. 5-year selection cycles, respectively, indicated over three-fold greater predicted yield gain per unit time for genomic selection. CONCLUSIONS: Genomic selection for alfalfa yield is promising, based on its moderate prediction accuracy, moderate value of cross-population predictions, and lack of sub-population structure. There is limited scope for searching individual QTLs with overwhelming effect on yield. Some of our results can contribute to better design of genomic selection experiments for alfalfa and other crops with similar mating systems.

Genomic Prediction of Biomass Yield in Two Selection Cycles of a Tetraploid Alfalfa Breeding Population.

Alfalfa (Medicago sativa L.) is a widely planted perennial forage legume grown throughout temperate and dry subtropical regions in the world. Long breeding cycles limit genetic improvement of alfalfa, particularly for complex traits such as biomass yield. Genomic selection (GS), based on predicted breeding values obtained using genome-wide molecular markers, could enhance breeding efficiency in terms of gain per unit time and cost. In this study, we genotyped tetraploid alfalfa plants that had previously been evaluated for yield during two cycles of phenotypic selection using genotyping-by-sequencing (GBS). We then developed prediction equations using yield data from three locations. Approximately 10,000 single nucleotide polymorphism (SNP) markers were used for GS modeling. The genomic prediction accuracy of total biomass yield ranged from 0.34 to 0.51 for the Cycle 0 population and from 0.21 to 0.66 for the Cycle 1 population, depending on the location. The GS model developed using Cycle 0 as the training population in predicting total biomass yield in Cycle 1 resulted in accuracies up to 0.40. Both genotype × environment interaction and the number of harvests and years used to generate yield phenotypes had effects on prediction accuracy across generations and locations, Based on our results, the selection efficiency per unit time for GS is higher than phenotypic selection, although accuracies will likely decline across multiple selection cycles. This study provided evidence that GS can accelerate genetic gain in alfalfa for biomass yield.

A saturated genetic linkage map of autotetraploid alfalfa (Medicago sativa L.) developed using genotyping-by-sequencing is highly syntenous with the Medicago truncatula genome.

A genetic linkage map is a valuable tool for quantitative trait locus mapping, map-based gene cloning, comparative mapping, and whole-genome assembly. Alfalfa, one of the most important forage crops in the world, is autotetraploid, allogamous, and highly heterozygous, characteristics that have impeded the construction of a high-density linkage map using traditional genetic marker systems. Using genotyping-by-sequencing (GBS), we constructed low-cost, reasonably high-density linkage maps for both maternal and paternal parental genomes of an autotetraploid alfalfa F1 population. The resulting maps contain 3591 single-nucleotide polymorphism markers on 64 linkage groups across both parents, with an average density of one marker per 1.5 and 1.0 cM for the maternal and paternal haplotype maps, respectively. Chromosome assignments were made based on homology of markers to the M. truncatula genome. Four linkage groups representing the four haplotypes of each alfalfa chromosome were assigned to each of the eight Medicago chromosomes in both the maternal and paternal parents. The alfalfa linkage groups were highly syntenous with M. truncatula, and clearly identified the known translocation between Chromosomes 4 and 8. In addition, a small inversion on Chromosome 1 was identified between M. truncatula and M. sativa. GBS enabled us to develop a saturated linkage map for alfalfa that greatly improved genome coverage relative to previous maps and that will facilitate investigation of genome structure. GBS could be used in breeding populations to accelerate molecular breeding in alfalfa.

Development of an alfalfa SNP array and its use to evaluate patterns of population structure and linkage disequilibrium.

A large set of genome-wide markers and a high-throughput genotyping platform can facilitate the genetic dissection of complex traits and accelerate molecular breeding applications. Previously, we identified about 0.9 million SNP markers by sequencing transcriptomes of 27 diverse alfalfa genotypes. From this SNP set, we developed an Illumina Infinium array containing 9,277 SNPs. Using this array, we genotyped 280 diverse alfalfa genotypes and several genotypes from related species. About 81% (7,476) of the SNPs met the criteria for quality control and showed polymorphisms. The alfalfa SNP array also showed a high level of transferability for several closely related Medicago species. Principal component analysis and model-based clustering showed clear population structure corresponding to subspecies and ploidy levels. Within cultivated tetraploid alfalfa, genotypes from dormant and nondormant cultivars were largely assigned to different clusters; genotypes from semidormant cultivars were split between the groups. The extent of linkage disequilibrium (LD) across all genotypes rapidly decayed to 26 Kbp at r(2) = 0.2, but the rate varied across ploidy levels and subspecies. A high level of consistency in LD was found between and within the two subpopulations of cultivated dormant and nondormant alfalfa suggesting that genome-wide association studies (GWAS) and genomic selection (GS) could be conducted using alfalfa genotypes from throughout the fall dormancy spectrum. However, the relatively low LD levels would require a large number of markers to fully saturate the genome.

Prevalence of single nucleotide polymorphism among 27 diverse alfalfa genotypes as assessed by transcriptome sequencing.

BACKGROUND: Alfalfa, a perennial, outcrossing species, is a widely planted forage legume producing highly nutritious biomass. Currently, improvement of cultivated alfalfa mainly relies on recurrent phenotypic selection. Marker assisted breeding strategies can enhance alfalfa improvement efforts, particularly if many genome-wide markers are available. Transcriptome sequencing enables efficient high-throughput discovery of single nucleotide polymorphism (SNP) markers for a complex polyploid species. RESULT: The transcriptomes of 27 alfalfa genotypes, including elite breeding genotypes, parents of mapping populations, and unimproved wild genotypes, were sequenced using an Illumina Genome Analyzer IIx. De novo assembly of quality-filtered 72-bp reads generated 25,183 contigs with a total length of 26.8 Mbp and an average length of 1,065 bp, with an average read depth of 55.9-fold for each genotype. Overall, 21,954 (87.2%) of the 25,183 contigs represented 14,878 unique protein accessions. Gene ontology (GO) analysis suggested that a broad diversity of genes was represented in the resulting sequences. The realignment of individual reads to the contigs enabled the detection of 872,384 SNPs and 31,760 InDels. High resolution melting (HRM) analysis was used to validate 91% of 192 putative SNPs identified by sequencing. Both allelic variants at about 95% of SNP sites identified among five wild, unimproved genotypes are still present in cultivated alfalfa, and all four US breeding programs also contain a high proportion of these SNPs. Thus, little evidence exists among this dataset for loss of significant DNA sequence diversity from either domestication or breeding of alfalfa. Structure analysis indicated that individuals from the subspecies falcata, the diploid subspecies caerulea, and the tetraploid subspecies sativa (cultivated tetraploid alfalfa) were clearly separated. CONCLUSION: We used transcriptome sequencing to discover large numbers of SNPs segregating in elite breeding populations of alfalfa. Little loss of SNP diversity was evident between unimproved and elite alfalfa germplasm. The EST and SNP markers generated from this study are publicly available at the Legume Information System ( http://medsa.comparative-legumes.org/) and can contribute to future alfalfa research and breeding applications.

Is genetic engineering ever going to take off in forage, turf and bioenergy crop breeding?

BACKGROUND: Genetic engineering offers the opportunity to generate unique genetic variation that is either absent in the sexually compatible gene pool or has very low heritability. The generation of transgenic plants, coupled with breeding, has led to the production of widely used transgenic cultivars in several major cash crops, such as maize, soybean, cotton and canola. The process for regulatory approval of genetically engineered crops is slow and subject to extensive political interference. The situation in forage grasses and legumes is more complicated. SCOPE: Most widely grown forage, turf and bioenergy species (e.g. tall fescue, perennial ryegrass, switchgrass, alfalfa, white clover) are highly self-incompatible and outcrossing. Compared with inbreeding species, they have a high potential to pass their genes to adjacent plants. A major biosafety concern in these species is pollen-mediated transgene flow. Because human consumption is indirect, risk assessment of transgenic forage, turf and bioenergy species has focused on their environmental or ecological impacts. Although significant progress has been made in genetic modification of these species, commercialization of transgenic cultivars is very limited because of the stringent and costly regulatory requirements. To date, the only transgenic forage crop deregulated in the US is 'Roundup Ready' (RR) alfalfa. The approval process for RR alfalfa was complicated, involving several rounds of regulation, deregulation and re-regulation. Nevertheless, commercialization of RR alfalfa is an important step forward in regulatory approval of a perennial outcrossing forage crop. As additional transgenic forage, turf and bioenergy crops are generated and tested, different strategies have been developed to meet regulatory requirements. Recent progress in risk assessment and deregulation of transgenic forage and turf species is summarized and discussed.

Complex patterns of autopolyploid evolution in alfalfa and allies (Medicago sativa; Leguminosae).

PREMISE OF THE STUDY: Although there is growing evidence that autopolyploidy is a widespread and important evolutionary phenomenon, it has received less attention than allopolyploidy. Medicago sativa comprises several diploid and autopolyploid taxa, including autotetraploid cultivated alfalfa, and affords an opportunity to elucidate the evolutionary history of a morphologically and genetically complex autopolyploid system. METHODS: Phylogenies and haplotype networks were constructed from two chloroplast noncoding regions (rpl20-rps12 and trnS-trnG spacers) across seven diploid and polyploid infraspecific taxa of M. sativa and five additional closely related Medicago species, and genetic differentiation was estimated. KEY RESULTS: The two most prominent M. sativa autopolyploids have contrasting evolutionary histories. Chloroplast data support a simple autopolyploid origin of subsp. sativa (alfalfa) from diploid subsp. caerulea, from which it is distinguishable in several quantitative characters. In contrast, morphologically identical diploid and autopolyploid cytotypes of subsp. falcata were found to possess very different chloroplast haplotypes, suggesting past introgression from M. prostrata into the polyploid. Despite the presence of hybrids between tetraploid subspecies falcata and sativa, there was little evidence of introgression of chloroplast genomes from either subspecies into the other. CONCLUSIONS: Autopolyploid evolution in M. sativa is complicated and has followed very different paths in different subspecific taxa. The potential exists for gene flow in virtually all combinations of subspecies both within and between ploidies, yet despite the existence of hybrids, morphologically and genetically distinctive subspecies persist.

Post-glacial evolution of Panicum virgatum: centers of diversity and gene pools revealed by SSR markers and cpDNA sequences.

Switchgrass (Panicum virgatum), a central and Eastern USA native, is highly valued as a component in tallgrass prairie and savanna restoration and conservation projects and a potential bioenergy feedstock. The purpose of this study was to identify regional diversity, gene pools, and centers-of-diversity of switchgrass to gain an understanding of its post-glacial evolution and to identify both the geographic range and potential overlap between functional gene pools. We sampled a total of 384 genotypes from 49 accessions that included the three main taxonomic groups of switchgrass (lowland 4x, upland 4x, and upland 8x) along with one accession possessing an intermediate phenotype. We identified primary centers of diversity for switchgrass in the eastern and western Gulf Coast regions. Migration, drift, and selection have led to adaptive radiation in switchgrass, creating regional gene pools within each of the main taxa. We estimate that both upland-lowland divergence and 4x-to-8x polyploidization within switchgrass began approximately 1.5-1 M ybp and that subsequent ice age cycles have resulted in gene flow between ecotype lineages and between ploidy levels. Gene flow has resulted in "hot spots" of genetic diversity in the southeastern USA and along the Atlantic Seaboard.