Nucleotide polymorphism and copy number variant detection using exome capture and next-generation sequencing in the polyploid grass Panicum virgatum

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.

Genotyping-by-Sequencing and QTL Mapping of Biomass Yield in Two Switchgrass F1 Populations (Lowland x Coastal and Coastal x Upland)

The prevalence of genetic diversity in switchgrass germplasm can be exploited to capture favorable alleles that increase its range of adaptation and biomass yield. The objectives of the study were to analyze the extent of polymorphism and patterns of segregation distortion in two F1 populations and use the linkage maps to locate QTL for biomass yield. We conducted genotyping-by-sequencing on two populations derived from crosses between the allotetraploid lowland genotype AP13 (a selection from "Alamo") and coastal genotype B6 (a selection from PI 422001) with 285 progeny (AB population) and between B6 and the allotetraploid upland VS16 (a selection from "Summer") with 227 progeny (BV population). As predictable from the Euclidean distance between the parents, a higher number of raw variants was discovered in the coastal × upland BV cross (6 M) compared to the lowland × coastal AB cross (2.5 M). The final number of mapped markers was 3,107 on the BV map and 2,410 on the AB map. More segregation distortion of alleles was seen in the AB population, with 75% distorted loci compared to 11% distorted loci in the BV population. The distortion in the AB population was seen across all chromosomes in both the AP13 and B6 maps and likely resulted from zygotic or post-zygotic selection for increased levels of heterozygosity. Our results suggest lower genetic compatibility between the lowland AP13 and the coastal B6 ecotype than between B6 and the upland ecotype VS16. Four biomass QTLs were mapped in the AB population (LG 2N, 6K, 6N, and 8N) and six QTLs in the BV population [LG 1N (2), 8N (2), 9K, and 9N]. The QTL, with the largest and most consistent effect across years, explaining between 8.4 and 11.5% of the variation, was identified on 6N in the AP13 map. The cumulative effect of all the QTLs explained a sizeable portion of the phenotypic variation in both AB and BV populations and the markers associated with them may potentially be used for the marker-assisted improvement of biomass yield. Since switchgrass improvement is based on increasing favorable allele frequencies through recurrent selection, the transmission bias within individuals and loci needs to be considered as this may affect the genetic gain if the favorable alleles are distorted.

Identification of QTL Associated with Regrowth Vigor Using the Nested Association Mapping Population in Switchgrass

Switchgrass (Panicum virgatum L.) is a warm-season perennial grass species that is utilized as forage for livestock and biofuel feedstock. The stability of biomass yield and regrowth vigor under changing harvest frequency would help manage potential fluctuations in the feedstock market and would provide a continuous supply of quality forage for livestock. This study was conducted to (i) assess the genetic variation and (ii) identify the quantitative trait loci (QTL) associated with regrowth vigor after multiple cuttings in lowland switchgrass. A nested association mapping (NAM) population comprising 2000 pseudo F₂ progenies was genotyped with single nucleotide polymorphism (SNP) markers derived from exome-capture sequencing and was evaluated for regrowth vigor in 2017 and 2018. The results showed significant variation among the NAM families in terms of regrowth vigor (p < 0.05). A total of 10 QTL were detected on 6 chromosomes: 1B, 5A, 5B, 6B, 7B, and 8A, explaining the phenotypic variation by up to 4.7%. The additive genetic effects of an individual QTL ranged from −0.13 to 0.26. No single QTL showed a markedly large effect, suggesting complex genetics underlying regrowth vigor in switchgrass. The homologs of candidate genes that play a variety of roles in developmental processes, including plant hormonal signal transduction, nucleotide biosynthesis, secondary metabolism, senescence, and responses to both biotic and abiotic stresses, were identified in the vicinity of QTL.

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.

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.

New Transcriptome-Based SNP Markers for Noug (Guizotia abyssinica) and Their Conversion to KASP Markers for Population Genetics Analyses

The development and use of genomic resources are essential for understanding the population genetics of crops for their efficient conservation and enhancement. Noug (Guizotia abyssinica) is an economically important oilseed crop in Ethiopia and India. The present study sought to develop new DNA markers for this crop. Transcriptome sequencing was conducted on two genotypes and 628 transcript sequences containing 959 single nucleotide polymorphisms (SNPs) were developed. A competitive allele-specific PCR (KASP) assay was developed for the SNPs and used for genotyping of 24 accessions. A total of 554 loci were successfully genotyped across the accessions, and 202 polymorphic loci were used for population genetics analyses. Polymorphism information content (PIC) of the loci varied from 0.01 to 0.37 with a mean of 0.24, and about 49% of the loci showed significant deviation from the Hardy-Weinberg equilibrium. The mean expected heterozygosity was 0.27 suggesting moderately high genetic variation within accessions. Low but significant differentiation existed among accessions (FST = 0.045, p < 0.0001). Landrace populations from isolated areas may have useful mutations and should be conserved and used in breeding this crop. The genomic resources developed in this study were shown to be useful for population genetics research and can also be used in, e.g., association genetics.

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.

Progress in plant genome sequencing: research directions

Since the first plant genome of Arabidopsis thaliana has been sequenced and published, genome sequencing technologies have undergone significant changes. New algorithms, sequencing technologies and bioinformatic approaches were adopted to obtain genome, transcriptome and exome sequences for model and crop species, which have permitted deep inferences into plant biology. As a result of an improved genome assembly and analysis methods, genome sequencing costs plummeted and the number of high-quality plant genome sequences is constantly growing. Consequently, more than 300 plant genome sequences have been published over the past twenty years. Although many of the published genomes are considered incomplete, they proved to be a valuable tool for identifying genes involved in the formation of economically valuable plant traits, for marker-assisted and genomic selection and for comparative analysis of plant genomes in order to determine the basic patterns of origin of various plant species. Since a high coverage and resolution of a genome sequence is not enough to detect all changes in complex samples, targeted sequencing, which consists in the isolation and sequencing of a specific region of the genome, has begun to develop. Targeted sequencing has a higher detection power (the ability to identify new differences/variants) and resolution (up to one basis). In addition, exome sequencing (the method of sequencing only protein-coding genes regions) is actively developed, which allows for the sequencing of non-expressed alleles and genes that cannot be found with RNA-seq. In this review, an analysis of sequencing technologies development and the construction of “reference” genomes of plants is performed. A comparison of the methods of targeted sequencing based on the use of the reference DNA sequence is accomplished.

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.

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.

Liquid-phase sequence capture and targeted re-sequencing revealed novel polymorphisms in tomato genes belonging to the MEP carotenoid pathway

Tomato (Solanum lycopersicum L.) plants are characterized by having a variety of fruit colours that reflect the composition and accumulation of diverse carotenoids in the berries. Carotenoids are extensively studied for their health-promoting effects and this explains the great attention these pigments received by breeders and researchers worldwide. In this work we applied Agilent’s SureSelect liquid-phase sequence capture and Illumina targeted re-sequencing of 34 tomato genes belonging to the methylerythritol phosphate (MEP) carotenoid pathway on a panel of 48 genotypes which differ for carotenoid content calculated as the sum of β-carotene, cis- and trans-lycopene. We targeted 230 kb of genomic regions including all exons and regulatory regions and observed ~40% of on-target capture. We found ample genetic variation among all the genotypes under study and generated an extensive catalog of SNPs/InDels located in both genic and regulatory regions. SNPs/InDels were also classified based on genomic location and putative biological effect. With our work we contributed to the identification of allelic variations possibly underpinning a key agronomic trait in tomato. Results from this study can be exploited for the promotion of novel studies on tomato bio-fortification as well as of breeding programs related to carotenoid accumulation in fruits.

Genotyping-by-sequencing approaches to characterize crop genomes: choosing the right tool for the right application

In the last decade, the revolution in sequencing technologies has deeply impacted crop genotyping practice. New methods allowing rapid, high-throughput genotyping of entire crop populations have proliferated and opened the door to wider use of molecular tools in plant breeding. These new genotyping-by-sequencing (GBS) methods include over a dozen reduced-representation sequencing (RRS) approaches and at least four whole-genome resequencing (WGR) approaches. The diversity of methods available, each often producing different types of data at different cost, can make selection of the best-suited method seem a daunting task. We review the most common genotyping methods used today and compare their suitability for linkage mapping, genomewide association studies (GWAS), marker-assisted and genomic selection and genome assembly and improvement in crops with various genome sizes and complexity. Furthermore, we give an outline of bioinformatics tools for analysis of genotyping data. WGR is well suited to genotyping biparental cross populations with complex, small- to moderate-sized genomes and provides the lowest cost per marker data point. RRS approaches differ in their suitability for various tasks, but demonstrate similar costs per marker data point. These approaches are generally better suited for de novo applications and more cost-effective when genotyping populations with large genomes or high heterozygosity. We expect that although RRS approaches will remain the most cost-effective for some time, WGR will become more widespread for crop genotyping as sequencing costs continue to decrease.

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.

Sequence capture by hybridization to explore modern and ancient genomic diversity in model and nonmodel organisms

The recent expansion of next-generation sequencing has significantly improved biological research. Nevertheless, deep exploration of genomes or metagenomic samples remains difficult because of the sequencing depth and the associated costs required. Therefore, different partitioning strategies have been developed to sequence informative subsets of studied genomes. Among these strategies, hybridization capture has proven to be an innovative and efficient tool for targeting and enriching specific biomarkers in complex DNA mixtures. It has been successfully applied in numerous areas of biology, such as exome resequencing for the identification of mutations underlying Mendelian or complex diseases and cancers, and its usefulness has been demonstrated in the agronomic field through the linking of genetic variants to agricultural phenotypic traits of interest. Moreover, hybridization capture has provided access to underexplored, but relevant fractions of genomes through its ability to enrich defined targets and their flanking regions. Finally, on the basis of restricted genomic information, this method has also allowed the expansion of knowledge of nonreference species and ancient genomes and provided a better understanding of metagenomic samples. In this review, we present the major advances and discoveries permitted by hybridization capture and highlight the potency of this approach in all areas of biology.

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.

Characterization of drought- and heat-responsive microRNAs in switchgrass

Recent investigations revealed that microRNAs (miRNAs) play crucial roles in plant acclimation to stress conditions. Switchgrass, one of the important biofuel crop species can withstand hot and dry climates but the molecular basis of stress tolerance is relatively unknown. To identify miRNAs that are important for tolerating drought or heat, small RNAs were profiled in leaves of adult plants exposed to drought or heat. Sequence analysis enabled the identification of 29 conserved and 62 novel miRNA families. Notably, the abundances of several conserved and novel miRNAs were dramatically altered following drought or heat. Using at least one fold (log2) change as cut off, we observed that 13 conserved miRNA families were differentially regulated by both stresses, and, five and four families were specifically regulated by drought and heat, respectively. Similarly, using a more stringent cut off of two fold (log2) regulation, we found 5 and 16 novel miRNA families were upregulated but 6 and 7 families were downregulated under drought and heat, respectively. The stress-altered expression of a subset of miRNAs and their targets was confirmed using quantitative PCR. Overall, the switchgrass plants exposed to drought or heat revealed similarities as well as differences with respect to miRNA regulation, which could be important for enduring different stress conditions.

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.

Development of highly reliable in silico SNP resource and genotyping assay from exome capture and sequencing: an example from black spruce (Picea mariana)

Picea mariana is a widely distributed boreal conifer across Canada and the subject of advanced breeding programmes for which population genomics and genomic selection approaches are being developed. Targeted sequencing was achieved after capturing P. mariana exome with probes designed from the sequenced transcriptome of Picea glauca, a distant relative. A high capture efficiency of 75.9% was reached although spruce has a complex and large genome including gene sequences interspersed by some long introns. The results confirmed the relevance of using probes from congeneric species to perform successfully interspecific exome capture in the genus Picea. A bioinformatics pipeline was developed including stringent criteria that helped detect a set of 97,075 highly reliable in silico SNPs. These SNPs were distributed across 14,909 genes. Part of an Infinium iSelect array was used to estimate the rate of true positives by validating 4267 of the predicted in silico SNPs by genotyping trees from P. mariana populations. The true positive rate was 96.2% for in silico SNPs, compared to a genotyping success rate of 96.7% for a set 1115 P. mariana control SNPs recycled from previous genotyping arrays. These results indicate the high success rate of the genotyping array and the relevance of the selection criteria used to delineate the new P. mariana in silico SNP resource. Furthermore, in silico SNPs were generally of medium to high frequency in natural populations, thus providing high informative value for future population genomics applications.

Biological invasions, climate change and genomics

The rate of biological invasions is expected to increase as the effects of climate change on biological communities become widespread. Climate change enhances habitat disturbance which facilitates the establishment of invasive species, which in turn provides opportunities for hybridization and introgression. These effects influence local biodiversity that can be tracked through genetic and genomic approaches. Metabarcoding and metagenomic approaches provide a way of monitoring some types of communities under climate change for the appearance of invasives. Introgression and hybridization can be followed by the analysis of entire genomes so that rapidly changing areas of the genome are identified and instances of genetic pollution monitored. Genomic markers enable accurate tracking of invasive species' geographic origin well beyond what was previously possible. New genomic tools are promoting fresh insights into classic questions about invading organisms under climate change, such as the role of genetic variation, local adaptation and climate pre-adaptation in successful invasions. These tools are providing managers with often more effective means to identify potential threats, improve surveillance and assess impacts on communities. We provide a framework for the application of genomic techniques within a management context and also indicate some important limitations in what can be achieved.