Population structure and genome-wide association analysis for frost tolerance in oat using continuous SNP array signal intensity ratios

Genome-wide association for multiple quantitative traits in forage oat germplasm based on specific length amplified fragment sequencing

Oats (Avena sativa L.) is an ideal forage species due to its excellent production performance, high nutritional value, and robust adaptability. In the present study, we analyzed plant height (PH), panicle length (PL), stem diameter (SD), node number (NN), flag leaf length (FLL), flag leaf width (FLW), second leaf length (SLL), and second leaf width (SLW) in a collection containing 340 forage oat accessions, in order to perform a genome-wide association study (GWAS) for identifying markers associated with the eight traits. We genotyped this collection using specific length amplified fragment sequencing (SLAF-seq). Phenotypes for PH, PL, SD, NN, FLL, FLW, SLL, and SLW were collected under natural conditions in four environments. GWAS analyses detected six significant associations for NN and three for FLL. Candidate genes of the nine associations were screened and discussed. Several genes were found to be associated with node number, including zinc finger MYM-type protein 1-like isoform X1, ervatamin-B-like, Pimeloyl-ACP methyl ester carboxylesterase, and ACT domain-containing protein ACR4-like, involved in cell division and organ development. Additionally, three genes were linked to flag leaf length-putative aquaporin PIP2-2, triacylglycerol lipase OBL1-like, and scarecrow-like protein 21-involved in the regulation of plant development and stress response. These SNP markers may be useful to accelerate the breeding progress of forage oat in temperate monsoon environments.

Utilizing Genomics to Characterize the Common Oat Gene Pool—The Story of More than a Century of Polish Breeding

This study was undertaken to investigate the diversity and population structure of 487 oat accessions, including breeding lines from the ongoing programs of the three largest Polish breeding companies, along with modern and historical Polish and foreign cultivars. The analysis was based on 7411 DArTseq-derived SNPs distributed among three sub-genomes (A, C, and D). The heterogeneity of the studied material was very low, as only cultivars and advanced breeding lines were examined. Principal component analysis (PCA), principal coordinate analysis (PCoA), and cluster and STRUCTURE analyses found congruent results, which show that most of the examined cultivars and materials from Polish breeding programs formed major gene pools, that only some accessions derived from Strzelce Plant Breeding, and that foreign cultivars were outside of the main group. During the 120 year oat breeding process, only 67 alleles from the old gene pool were lost and replaced by 67 new alleles. The obtained results indicate that no erosion of genetic diversity was observed within the Polish native oat gene pool. Moreover, current oat breeding programs have introduced 673 new alleles into the gene pool relative to historical cultivars. The analysis also showed that most of the changes in relation to historical cultivars occurred within the A sub-genome with emphasis on chromosome 6A. The targeted changes were the rarest in the C sub-genome. This study showed that Polish oat breeding based mainly on traditional breeding methods—although focused on improving traits typical to this crop, i.e., enhancing the grain yield and quality and improving adaptability—did not significantly narrow the oat gene pool and in fact produced cultivars that are not only competitive in the European market but are also reservoirs of new alleles that were not found in the analyzed foreign materials.

Population structure analysis and genome-wide association study of a hexaploid oat landrace and cultivar collection

INTRODUCTION

Oat (Avena sativa L.) is an important cereal crop grown worldwide for grain and forage, owing to its high adaptability to diverse environments. However, the genetic and genomics research of oat is lagging behind that of other staple cereal crops.

METHODS

In this study, a collection of 288 oat lines originating worldwide was evaluated using 2,213 single nucleotide polymorphism (SNP) markers obtained from an oat iSelect 6K-beadchip array to study its genetic diversity, population structure, and linkage disequilibrium (LD) as well as the genotype-phenotype association for hullessness and lemma color.

RESULTS

The average gene diversity and polymorphic information content (PIC) were 0.324 and 0.262, respectively. The first three principal components (PCs) accounted for 30.33% of the genetic variation, indicating that the population structure of this panel of oat lines was stronger than that reported in most previous studies. In addition, accessions could be classified into two subpopulations using a Bayesian clustering approach, and the clustering pattern of accessions was closely associated with their region of origin. Additionally, evaluation of LD decay using 2,143 mapped markers revealed that the intrachromosomal whole-genome LD decayed rapidly to a critical r² value of 0.156 for marker pairs separated by a genetic distance of 1.41 cM. Genome-wide association study (GWAS) detected six significant associations with the hullessness trait. Four of these six markers were located on the Mrg21 linkage group between 194.0 and 205.7 cM, while the other two significant markers mapped to Mrg05 and Mrg09. Three significant SNPs, showing strong association with lemma color, were located on linkage groups Mrg17, Mrg18, and Mrg20.

DISCUSSION

Our results discerned relevant patterns of genetic diversity, population structure, and LD among members of a worldwide collection of oat landraces and cultivars proposed to be 'typical' of the Qinghai-Tibetan Plateau. These results have important implications for further studies on association mapping and practical breeding in high-altitude oat.

Exploration of the Genetic Diversity of Solina Wheat and Its Implication for Grain Quality

Different Solina wheat accessions (n = 24) collected in the Abruzzo region (Italy) were studied using 45,000 SNP markers generated from the DarTseq platform. The structure of genetic data was analyzed by Principal Component Analysis and Hierarchical Cluster analysis that revealed the existence of two main clusters (Clu1 and Clu2) characterized by samples with different geographical origin. The Solina genetic dataset was further merged and analyzed with a public genetic one provided by CIMMYT containing 25,963 genotypes from all over the world. The Solina accessions occupied a vast space, thus confirming a high heterogeneity of this landrace that, nevertheless, is considerably unique and placed quite far from other clusters. Clu1 and Clu2 divergence were clearly visible. Solina clusters were genetically closer to landraces from Turkey and the central fertile crescent than to the Italian genotypes present in the dataset. Selected commercial quality traits of accessions of the two Solina clusters were analyzed (yield, thousand kernel weight, test weight, and protein content), and significant differences were found between clusters. The results of this investigation did not highlight any relationships of Solina with Italian genotypes, and confirmed its wide genetic diversity by permitting to identify two genetic groups with distinct origin and quality traits.

Identification of Candidate Genes for Salinity and Anaerobic Tolerance at the Germination Stage in Rice by Genome-Wide Association Analyses

Multiple stress tolerance at the seed germination stage is crucial for better crop establishment in the direct-seeded rice ecosystem. Therefore, identifying rice genes/quantitative trait loci (QTLs) associated with salinity and anaerobic tolerance at the germination stage is a prerequisite for adaptive breeding. Here, we studied 498 highly diverse rice accessions Xian (Indica) and Geng (Japonica), and six traits that are highly associated with salinity and anaerobic tolerance at germination stage were measured. A high-density 2.8M Single Nucleotide Polymorphisms (SNP) genotype map generated from the 3,000 Rice Genomes Project (3KRGP) was used for mapping through a genome-wide association study. In total, 99 loci harboring 117 QTLs were detected in different populations, 54, 21, and 42 of which were associated with anaerobic, salinity, and combined (anaerobic and salinity) stress tolerance. Nineteen QTLs were close to the reported loci for abiotic stress tolerance, whereas two regions on chromosome 4 (qSGr4a/qCL4c/qRI4d and qAGr4/qSGr4b) and one region on chromosome 10 (qRI10/qCL10/ qSGr10b/qBM10) were associated with anaerobic and salinity related traits. Further haplotype analysis detected 25 promising candidates genes significantly associated with the target traits. Two known genes (OsMT2B and OsTPP7) significantly associated with grain yield and its related traits under saline and anaerobic stress conditions were identified. In this study, we identified the genes involved in auxin efflux (Os09g0491740) and transportation (Os01g0976100), whereas we identified multistress responses gene OsMT2B (Os01g0974200) and a major gene OsTPP7 (Os09g0369400) involved in anaerobic germination and coleoptile elongation on chromosome 9. These promising candidates provide valuable resources for validating potential salt and anaerobic tolerance genes and will facilitate direct-seeded rice breeding for salt and anaerobic tolerance through marker-assisted selection or gene editing.

Genomic prediction and training set optimization in a structured Mediterranean oat population

The strong genetic structure observed in Mediterranean oats affects the predictive ability of genomic prediction as well as the performance of training set optimization methods. In this study, we investigated the efficiency of genomic prediction and training set optimization in a highly structured population of cultivars and landraces of cultivated oat (Avena sativa) from the Mediterranean basin, including white (subsp. sativa) and red (subsp. byzantina) oats, genotyped using genotype-by-sequencing markers and evaluated for agronomic traits in Southern Spain. For most traits, the predictive abilities were moderate to high with little differences between models, except for biomass for which Bayes-B showed a substantial gain compared to other models. The consistency between the structure of the training population and the population to be predicted was key to the predictive ability of genomic predictions. The predictive ability of inter-subspecies predictions was indeed much lower than that of intra-subspecies predictions for all traits. Regarding training set optimization, the linear mixed model optimization criteria (prediction error variance (PEVmean) and coefficient of determination (CDmean)) performed better than the heuristic approach "partitioning around medoids," even under high population structure. The superiority of CDmean and PEVmean could be explained by their ability to adapt the representation of each genetic group according to those represented in the population to be predicted. These results represent an important step towards the implementation of genomic prediction in oat breeding programs and address important issues faced by the genomic prediction community regarding population structure and training set optimization.

Using probabilistic genotypes in linkage analysis of polyploids

KEY MESSAGE

In polyploids, linkage mapping is carried out using genotyping with discrete dosage scores. Here, we use probabilistic genotypes and we validate it for the construction of polyploid linkage maps. Marker genotypes are generally called as discrete values: homozygous versus heterozygous in the case of diploids, or an integer allele dosage in the case of polyploids. Software for linkage map construction and/or QTL analysis usually relies on such discrete genotypes. However, it may not always be possible, or desirable, to assign definite values to genotype observations in the presence of uncertainty in the genotype calling. Here, we present an approach that uses probabilistic marker dosages for linkage map construction in polyploids. We compare our method to an approach based on discrete dosages, using simulated SNP array and sequence reads data with varying levels of data quality. We validate our approach using experimental data from a potato (Solanum tuberosum L.) SNP array applied to an F1 mapping population. In comparison to the approach based on discrete dosages, we mapped an additional 562 markers. All but three of these were mapped to the expected chromosome and marker position. For the remaining three markers, no physical position was known. The use of dosage probabilities is of particular relevance for map construction in polyploids using sequencing data, as these often result in a higher level of uncertainty regarding allele dosage.

Population genomics of Mediterranean oat (A. sativa) reveals high genetic diversity and three loci for heading date

Genomic analysis of Mediterranean oats reveals high genetic diversity and three loci for adaptation to this environment. This information together with phenotyping and passport data, gathered in an interactive map, will be a vital resource for oat genetic improvement. During the twentieth century, oat landraces have increasingly been replaced by modern cultivars, resulting in loss of genetic diversity. However, landraces have considerable potential to improve disease and abiotic stress tolerance and may outperform cultivars under low input systems. In this work, we assembled a panel of 669 oat landraces from Mediterranean rim and 40 cultivated oat varieties and performed the first large-scale population genetic analysis of both red and white oat types of Mediterranean origin. We created a public database associated with an interactive map to visualize information for each accession. The oat collection was genotyped with 17,288 single-nucleotide polymorphism (SNP) loci to evaluate population structure and linkage disequilibrium (LD); to perform a genome-wide association study (GWAs) for heading date, a key character closely correlated with performance in this drought-prone area. Population genetic analysis using both structure and PCA distinguished two main groups composed of the red and white oats, respectively. The white oat group was further divided into two subgroups. LD decay was slower within white lines in linkage groups Mrg01, 02, 04, 12, 13, 15, 23, 33, whereas it was slower within red lines in Mrg03, 05, 06, 11, 21, 24, and 28. Association analysis showed several significant markers associated with heading date on linkage group Mrg13 in white oats and on Mrg01 and Mrg08 in red oats.

Population genomics of Mediterranean oat (A. sativa) reveals high genetic diversity and three loci for heading date

KEY MESSAGE

Genomic analysis of Mediterranean oats reveals high genetic diversity and three loci for adaptation to this environment. This information together with phenotyping and passport data, gathered in an interactive map, will be a vital resource for oat genetic improvement. During the twentieth century, oat landraces have increasingly been replaced by modern cultivars, resulting in loss of genetic diversity. However, landraces have considerable potential to improve disease and abiotic stress tolerance and may outperform cultivars under low input systems. In this work, we assembled a panel of 669 oat landraces from Mediterranean rim and 40 cultivated oat varieties and performed the first large-scale population genetic analysis of both red and white oat types of Mediterranean origin. We created a public database associated with an interactive map to visualize information for each accession. The oat collection was genotyped with 17,288 single-nucleotide polymorphism (SNP) loci to evaluate population structure and linkage disequilibrium (LD); to perform a genome-wide association study (GWAs) for heading date, a key character closely correlated with performance in this drought-prone area. Population genetic analysis using both structure and PCA distinguished two main groups composed of the red and white oats, respectively. The white oat group was further divided into two subgroups. LD decay was slower within white lines in linkage groups Mrg01, 02, 04, 12, 13, 15, 23, 33, whereas it was slower within red lines in Mrg03, 05, 06, 11, 21, 24, and 28. Association analysis showed several significant markers associated with heading date on linkage group Mrg13 in white oats and on Mrg01 and Mrg08 in red oats.

Harnessing Crop Wild Diversity for Climate Change Adaptation

Warming and drought are reducing global crop production with a potential to substantially worsen global malnutrition. As with the green revolution in the last century, plant genetics may offer concrete opportunities to increase yield and crop adaptability. However, the rate at which the threat is happening requires powering new strategies in order to meet the global food demand. In this review, we highlight major recent 'big data' developments from both empirical and theoretical genomics that may speed up the identification, conservation, and breeding of exotic and elite crop varieties with the potential to feed humans. We first emphasize the major bottlenecks to capture and utilize novel sources of variation in abiotic stress (i.e., heat and drought) tolerance. We argue that adaptation of crop wild relatives to dry environments could be informative on how plant phenotypes may react to a drier climate because natural selection has already tested more options than humans ever will. Because isolated pockets of cryptic diversity may still persist in remote semi-arid regions, we encourage new habitat-based population-guided collections for genebanks. We continue discussing how to systematically study abiotic stress tolerance in these crop collections of wild and landraces using geo-referencing and extensive environmental data. By uncovering the genes that underlie the tolerance adaptive trait, natural variation has the potential to be introgressed into elite cultivars. However, unlocking adaptive genetic variation hidden in related wild species and early landraces remains a major challenge for complex traits that, as abiotic stress tolerance, are polygenic (i.e., regulated by many low-effect genes). Therefore, we finish prospecting modern analytical approaches that will serve to overcome this issue. Concretely, genomic prediction, machine learning, and multi-trait gene editing, all offer innovative alternatives to speed up more accurate pre- and breeding efforts toward the increase in crop adaptability and yield, while matching future global food demands in the face of increased heat and drought. In order for these 'big data' approaches to succeed, we advocate for a trans-disciplinary approach with open-source data and long-term funding. The recent developments and perspectives discussed throughout this review ultimately aim to contribute to increased crop adaptability and yield in the face of heat waves and drought events.

Genome-Wide Identification of Loci Associated With Phenology-Related Traits and Their Adaptive Variations in a Highbush Blueberry Collection

Genetic variation in phenological traits is the key in expanding production areas of crops. Southern highbush blueberry (SHB) is a blueberry cultivar group adapted to warmer climates and has been developed by multiple interspecific hybridizations between elite northern highbush blueberry (NHB) (Vaccinium corymbosum L.) and low-chill Vaccinium species native to the southern United States. In this study, we employed a collection of diverse SHB accessions and performed a genome-wide association study (GWAS) for five phenology-related traits [chilling requirement (CR), flowering date, ripening date, fruit development period, and continuous flowering] using polyploid GWAS models. Phenology-related traits showed higher heritability and larger correlation coefficients between year replications, which resulted in the detection of robust phenotype-genotype association peaks. Notably, a single association peak for the CR was detected on Chromosome 4. Comparison of genotypes at the GWAS peaks between NHB and SHB revealed the putative introgression of low-chill and late-flowering alleles into the highbush genetic pool. Our results provide basic insights into the diversity of phenological traits in blueberry and the genetic establishment of current highbush cultivar groups.

Genome-Wide Association Study Reveals the Genetic Architecture of Seed Vigor in Oats

Seed vigor is crucial for crop early establishment in the field and is particularly important for forage crop production. Oat (Avena sativa L.) is a nutritious food crop and also a valuable forage crop. However, little is known about the genetics of seed vigor in oats. To investigate seed vigor-related traits and their genetic architecture in oats, we developed an easy-to-implement image-based phenotyping pipeline and applied it to 650 elite oat lines from the Collaborative Oat Research Enterprise (CORE). Root number, root surface area, and shoot length were measured in two replicates. Variables such as growth rate were derived. Using a genome-wide association (GWA) approach, we identified 34 and 16 unique loci associated with root traits and shoot traits, respectively, which corresponded to 41 and 16 unique SNPs at a false discovery rate < 0.1. Nine root-associated loci were organized into four sets of homeologous regions, while nine shoot-associated loci were organized into three sets of homeologous regions. The context sequences of five trait-associated markers matched to the sequences of rice, Brachypodium and maize (E-value < 10-10), including three markers matched to known gene models with potential involvement in seed vigor. These were a glucuronosyltransferase, a mitochondrial carrier protein domain containing protein, and an iron-sulfur cluster protein. This study presents the first GWA study on oat seed vigor and data of this study can provide guidelines and foundation for further investigations.

Genetic diversity and genome-wide association analysis in Chinese hulless oat germplasm

Genotyping-by-sequencing (GBS)-derived molecular markers reveal the distinct genetic population structure and relatively narrow genetic diversity of Chinese hulless oat landraces. Four markers linked to the naked grain gene (N1) are identified by genome-wide association study (GWAS). Interest in hulless oat (Avena sativa ssp. nuda), a variant of common oat (A. sativa) domesticated in Western Asia, has increased in recent years due to its free-threshing attribute and its domestication history. However, the genetic diversity and population structure of hulless oat, as well as the genetic mechanism of hullessness, are poorly understood. In this study, the genetic diversity and population structure of a worldwide sample of 805 oat lines including 186 hulless oats were investigated using genotyping-by-sequencing. Population structure analyses showed a strong genetic differentiation between hulless landraces vs other oat lines, including the modern hulless cultivars. The distinct subpopulation stratification of hulless landraces and their low genetic diversity suggests that a domestication bottleneck existed in hulless landraces. Additionally, low genetic diversity within European oats and strong differentiation between the spring oats and southern origin oat lines revealed by previous studies were also observed in this study. Genomic regions contributing to these genetic differentiations suggest that genetic loci related to growth habit and stress resistance may have been under intense selection, rather than the hulless-related genomic regions. Genome-wide association analysis detected four markers that were highly associated with hullessness. Three of these were mapped on linkage group Mrg21 at a genetic position between 195.7 and 212.1 cM, providing robust evidence that the dominant N1 locus located on Mrg21 is the single major factor controlling this trait.

Genome-wide association study identifies favorable SNP alleles and candidate genes for frost tolerance in pea

BACKGROUND

Frost is a limiting abiotic stress for the winter pea crop (Pisum sativum L.) and identifying the genetic determinants of frost tolerance is a major issue to breed varieties for cold northern areas. Quantitative trait loci (QTLs) have previously been detected from bi-parental mapping populations, giving an overview of the genome regions governing this trait. The recent development of high-throughput genotyping tools for pea brings the opportunity to undertake genetic association studies in order to capture a higher allelic diversity within large collections of genetic resources as well as to refine the localization of the causal polymorphisms thanks to the high marker density. In this study, a genome-wide association study (GWAS) was performed using a set of 365 pea accessions. Phenotyping was carried out by scoring frost damages in the field and in controlled conditions. The association mapping collection was also genotyped using an Illumina Infinium® BeadChip, which allowed to collect data for 11,366 single nucleotide polymorphism (SNP) markers.

RESULTS

GWAS identified 62 SNPs significantly associated with frost tolerance and distributed over six of the seven pea linkage groups (LGs). These results confirmed 3 QTLs that were already mapped in multiple environments on LG III, V and VI with bi-parental populations. They also allowed to identify one locus, on LG II, which has not been detected yet and two loci, on LGs I and VII, which have formerly been detected in only one environment. Fifty candidate genes corresponding to annotated significant SNPs, or SNPs in strong linkage disequilibrium with the formers, were found to underlie the frost damage (FD)-related loci detected by GWAS. Additionally, the analyses allowed to define favorable haplotypes of markers for the FD-related loci and their corresponding accessions within the association mapping collection.

CONCLUSIONS

This study led to identify FD-related loci as well as corresponding favorable haplotypes of markers and representative pea accessions that might to be used in winter pea breeding programs. Among the candidate genes highlighted at the identified FD-related loci, the results also encourage further attention to the presence of C-repeat Binding Factors (CBF) as potential genetic determinants of the frost tolerance locus on LG VI.

Genome-wide association mapping of Fusarium langsethiae infection and mycotoxin accumulation in oat (Avena sativa L.)

Fusarium langsethiae is a symptomless pathogen of oat panicles that produces T-2 and HT-2 mycotoxins, two of the most potent trichothecenes produced by Fusarium fungi in cereals. In the last few years, the levels of these mycotoxin in oat grain has increased and the European commission have already recommended a maximum level for of 1000 μg kg^-1 for unprocessed oat for human consumption. The optimal and most sustainable way of combating infection and mycotoxin contamination is by releasing resistant oat varieties. Here the objective was to determine if we could identify any genomic loci associated with either the accumulation of F. langsethiae DNA or mycotoxins in the grain. In each of two years, field trials were conducted wherein 190 spring oat varieties were inoculated with a mixture of three isolate of the pathogen. Mycotoxins were quantified using liquid chromatography-tandem mass spectrometry. Varieties were genotyped using 16,863 genotyping by sequencing markers. Genome-wide association studies associated 5 SNPs in the linkage group Mr06 with T-2 + HT-2 mycotoxin accumulation. Markers were highly correlated, and a single QTL was identified. The marker avgbs_6K_95238.1 mapped within genes showing similarity to lipase, lipase-like or lipase precursor mRNA sequences and zinc-finger proteins. These regions have previously been shown to confer a significant increase in resistance to Fusarium species.

Role of NGS and SNP genotyping methods in sugarcane improvement programs

Sugarcane (Saccharum spp.) is one of the most economically significant crops because of its high sucrose content and it is a promising biomass feedstock for biofuel production. Sugarcane genome sequencing and analysis is a difficult task due to its heterozygosity and polyploidy. Long sequence read technologies, PacBio Single-Molecule Real-Time (SMRT) sequencing, the Illumina TruSeq, and the Oxford Nanopore sequencing could solve the problem of genome assembly. On the applications side, next generation sequencing (NGS) technologies played a major role in the discovery of single nucleotide polymorphism (SNP) and the development of low to high throughput genotyping platforms. The two mainstream high throughput genotyping platforms are the SNP microarray and genotyping by sequencing (GBS). This paper reviews the NGS in sugarcane genomics, genotyping methodologies, and the choice of these methods. Array-based SNP genotyping is robust, provides consistent SNPs, and relatively easier downstream data analysis. The GBS method identifies large scale SNPs across the germplasm. A combination of targeted GBS and array-based genotyping methods should be used to increase the accuracy of genomic selection and marker-assisted breeding.

Genome-wide Association Study and Genomic Prediction for Fusarium graminearum Resistance Traits in Nordic Oat (Avena sativa L.)

Fusarium head blight (FHB) and the accumulation of deoxynivalenol (DON) mycotoxin induced by Fusarium graminearum and other Fusarium fungi cause serious problems for oat production in the Nordic region (Scandinavia, Fennoscandia). Besides toxin accumulation, FHB causes reduction in grain yield and in germination capacity. Here, genomic approaches for accelerating breeding efforts against FHB and DON accumulation were studied. Resistance-related traits included DON content, F. graminearum DNA (relative to oat DNA) content (qFUSG) measured with real-time quantitative polymerase chain reaction (PCR), Fusarium-infected kernels (FIKs) and germination capacity (GC). Plant germplasm used in the study consisted of mostly breeding lines, and additionally, a few cultivars and exotic accessions. Genome-wide association study (GWAS) and genomic prediction, enabling genomic selection (GS) on the resistance-related and collected agronomic traits, were performed. Considerable genetic correlations between resistance-related traits were observed: DON content had a positive correlation (0.60) with qFUSG and a negative correlation (−0.63) with germination capacity. With the material in hand, we were not able to find any significant associations between markers and resistance-related traits. On the other hand, in genomic prediction, some resistance-related traits showed favorable accuracy in fivefold cross-validation (GC = 0.57). Genomic prediction is a promising method and genomic estimated breeding values (GEBVs) generated for germination capacity are applicable in oat breeding programs.

Modeling Crop Genetic Resources Phenotyping Information Systems

Documentation of phenotype information is a priority need in biodiversity, crop modeling, breeding, ecology, and evolution research, for association studies, gene discovery, retrospective statistical analysis and data mining, QTL re-mapping, choosing cultivars, and planning crosses. Lack of access to phenotype information is still seen as a limiting factor for the use of plant genetic resources. Phenotype data are complex. Information on the context, under which they were collected, is indispensable, and the domain is continuously evolving. This study describes comprehensive data and object models supporting web interfaces for multi-site field phenotyping and data acquisition, which have been developed for Central Crop Databases within the European Cooperative Programme for Plant Genetic Resources over the years and which can be used as blueprints for phenotyping information systems. We start from the hypothesis, that entity relationship and object models useful for software development can picture domain expertise, similar as domain ontologies, and encourage a discussion of scientific information systems on modeling level. Starting from information requirements for statistical analysis, meta-analysis, and knowledge discovery, models are discussed in consideration of several standardization and modeling approaches including crop ontologies. Following an object-oriented modeling approach, we keep data and object models close together and to domain concepts. This will make database and software design better understandable and usable for domain experts and support a modular use of software artifacts to be shared across various domains of expertise. Classes and entities represent domain concepts with attributes naturally assigned to them. Field experiments with randomized plots, as typically used in the evaluation of plant genetic resources and in plant breeding, are in the focus. Phenotype observations, which can be listed as raw or aggregated data, are linked to explanatory metadata describing experimental treatments and agronomic interventions, observed traits and observation methodology, field plan and plot design, and the experiment site as a geographical entity. Based on clearly defined types, potential links to information systems in other domains (e.g., geographic information systems) can be better identified. Work flows are shown as web applications for the generation of field plans, field books, templates, upload of spreadsheet data, and images.

Haplotype-based genotyping-by-sequencing in oat genome research

In a de novo genotyping-by-sequencing (GBS) analysis of short, 64-base tag-level haplotypes in 4657 accessions of cultivated oat, we discovered 164741 tag-level (TL) genetic variants containing 241224 SNPs. From this, the marker density of an oat consensus map was increased by the addition of more than 70000 loci. The mapped TL genotypes of a 635-line diversity panel were used to infer chromosome-level (CL) haplotype maps. These maps revealed differences in the number and size of haplotype blocks, as well as differences in haplotype diversity between chromosomes and subsets of the diversity panel. We then explored potential benefits of SNP vs. TL vs. CL GBS variants for mapping, high-resolution genome analysis and genomic selection in oats. A combined genome-wide association study (GWAS) of heading date from multiple locations using both TL haplotypes and individual SNP markers identified 184 significant associations. A comparative GWAS using TL haplotypes, CL haplotype blocks and their combinations demonstrated the superiority of using TL haplotype markers. Using a principal component-based genome-wide scan, genomic regions containing signatures of selection were identified. These regions may contain genes that are responsible for the local adaptation of oats to Northern American conditions. Genomic selection for heading date using TL haplotypes or SNP markers gave comparable and promising prediction accuracies of up to r = 0.74. Genomic selection carried out in an independent calibration and test population for heading date gave promising prediction accuracies that ranged between r = 0.42 and 0.67. In conclusion, TL haplotype GBS-derived markers facilitate genome analysis and genomic selection in oat.

Tools for Genetic Studies in Experimental Populations of Polyploids

Polyploid organisms carry more than two copies of each chromosome, a condition rarely tolerated in animals but which occurs relatively frequently in the plant kingdom. One of the principal challenges faced by polyploid organisms is to evolve stable meiotic mechanisms to faithfully transmit genetic information to the next generation upon which the study of inheritance is based. In this review we look at the tools available to the research community to better understand polyploid inheritance, many of which have only recently been developed. Most of these tools are intended for experimental populations (rather than natural populations), facilitating genomics-assisted crop improvement and plant breeding. This is hardly surprising given that a large proportion of domesticated plant species are polyploid. We focus on three main areas: (1) polyploid genotyping; (2) genetic and physical mapping; and (3) quantitative trait analysis and genomic selection. We also briefly review some miscellaneous topics such as the mode of inheritance and the availability of polyploid simulation software. The current polyploid analytic toolbox includes software for assigning marker genotypes (and in particular, estimating the dosage of marker alleles in the heterozygous condition), establishing chromosome-scale linkage phase among marker alleles, constructing (short-range) haplotypes, generating linkage maps, performing genome-wide association studies (GWAS) and quantitative trait locus (QTL) analyses, and simulating polyploid populations. These tools can also help elucidate the mode of inheritance (disomic, polysomic or a mixture of both as in segmental allopolyploids) or reveal whether double reduction and multivalent chromosomal pairing occur. An increasing number of polyploids (or associated diploids) are being sequenced, leading to publicly available reference genome assemblies. Much work remains in order to keep pace with developments in genomic technologies. However, such technologies also offer the promise of understanding polyploid genomes at a level which hitherto has remained elusive.

Development and Applications of a High Throughput Genotyping Tool for Polyploid Crops: Single Nucleotide Polymorphism (SNP) Array

Polypoid species play significant roles in agriculture and food production. Many crop species are polyploid, such as potato, wheat, strawberry, and sugarcane. Genotyping has been a daunting task for genetic studies of polyploid crops, which lags far behind the diploid crop species. Single nucleotide polymorphism (SNP) array is considered to be one of, high-throughput, relatively cost-efficient and automated genotyping approaches. However, there are significant challenges for SNP identification in complex, polyploid genomes, which has seriously slowed SNP discovery and array development in polyploid species. Ploidy is a significant factor impacting SNP qualities and validation rates of SNP markers in SNP arrays, which has been proven to be a very important tool for genetic studies and molecular breeding. In this review, we (1) discussed the pros and cons of SNP array in general for high throughput genotyping, (2) presented the challenges of and solutions to SNP calling in polyploid species, (3) summarized the SNP selection criteria and considerations of SNP array design for polyploid species, (4) illustrated SNP array applications in several different polyploid crop species, then (5) discussed challenges, available software, and their accuracy comparisons for genotype calling based on SNP array data in polyploids, and finally (6) provided a series of SNP array design and genotype calling recommendations. This review presents a complete overview of SNP array development and applications in polypoid crops, which will benefit the research in molecular breeding and genetics of crops with complex genomes.

Multi-Environmental Trials Reveal Genetic Plasticity of Oat Agronomic Traits Associated With Climate Variable Changes

Although oat cultivation around the Mediterranean basin is steadily increasing, its yield in these regions lags far behind those of Northern Europe. This results mainly from the poor adaptation of current oat cultivars to Mediterranean environments. Local landraces may act as reservoirs of favorable traits that could contribute to increase oat resilience in this region. To aid selection of suitable agro-climate adapted genotypes we integrated genome-wide association approaches with analysis of field assessed phenotypes of genetic variants and of the weight of associated markers across different environmental variables. Association models accounting for oat population structure were applied on either arithmetic means or best linear unbiased prediction (BLUPs) to ensure robust identification of associations with the agronomic traits evaluated. The meta-analysis of the six joint environments (mega-environment) identified several markers associated with several agronomic traits and crown rust severity. Five of these associated markers were located within expressed genes. These associations were only mildly influenced by climatic variables indicating that these markers are good candidates to improve the genetic potential of oat under Mediterranean conditions. The models also highlighted several marker-trait associations, strongly affected by particular climatic variables including high rain pre- or post-heading dates and high temperatures, revealing strong potential for oat adaptation to specific agro-climatic conditions. These results will contribute to increase oat resilience for particular climatic conditions and facilitate breeding for plant adaptation to a wider range of climatic conditions in the current scenario of climate change.

An Integration of Genome-Wide Association Study and Gene Co-expression Network Analysis Identifies Candidate Genes of Stem Lodging-Related Traits in Brassica napus

Lodging is a persistent problem which severely reduce yield and impair seed quality in rapeseed (Brassica napus L.). Enhancing stem strength (SS) has proven to be an effective approach to decrease lodging risk. In the present study, four interrelated stem lodging-related traits, including stem breaking resistance (SBR), stem diameter (SD), SS, and lodging coefficient (LC), were investigated among 472 rapeseed accessions. A genome-wide association study (GWAS) using Brassica 60K SNP array for stem lodging-related traits identified 67 significantly associated quantitative trait loci (QTLs) and 71 candidate genes. In parallel, a gene co-expression network based on transcriptome sequencing was constructed. The module associated with cellulose biosynthesis was highlighted. By integrating GWAS and gene co-expression network analysis, some promising candidate genes, such as ESKIMO1 (ESK1, BnaC08g26920D), CELLULOSE SYNTHASE 6 (CESA6, BnaA09g06990D), and FRAGILE FIBER 8 (FRA8, BnaC04g39510D), were prioritized for further research. These findings revealed the genetic basis underlying stem lodging and provided worthwhile QTLs and genes information for genetic improvement of stem lodging resistance in B. napus.

Oats in healthy gluten-free and regular diets: A perspective

During the 20th century, the economic position of oats (Avena sativa L.) decreased strongly in favour of higher yielding crops including winter wheat and maize. Presently, oat represents only ~1.3% of the total world grain production, and its production system is fragmented. Nonetheless, current interest is growing because of recent knowledge on its potential benefits in food, feed and agriculture. This perspective will serve as a further impetus, with special focus on the recently valued advantages of oats in human food and health. Five approved European Food Safety Authority (EFSA) health claims apply to oats. Four relate to the oat-specific soluble fibres, the beta-glucans, and concern the maintenance and reduction of blood cholesterol, better blood glucose balance and increased faecal bulk. The fifth claim concerns the high content of unsaturated fatty acids, especially present in the endosperm, which reduces the risks of heart and vascular diseases. Furthermore, oat starch has a low glycemic index, which is favourable for weight control. Oat-specific polyphenols and avenanthramides have antioxidant and anti-inflammatory properties. Thus, oats can contribute significantly to the presently recommended whole-grain diet. Next to globulins, oats contain a small fraction of prolamin storage proteins, called 'avenins', but at a much lower quantity than gluten proteins in wheat, barley and rye. Oat avenins do not contain any of the known coeliac disease epitopes from gluten of wheat, barley and rye. Long-term food studies confirm the safety of oats for coeliac disease patients and the positive health effects of oat products in a gluten-free diet. These effects are general and independent of oat varieties. In the EU (since 2009), the USA (since 2013) and Canada (since 2015) oat products may be sold as gluten-free provided that any gluten contamination level is below 20ppm. Oats are, however, generally not gluten-free when produced in a conventional production chain, because of regular contamination with wheat, barley or rye. Therefore, establishing a separate gluten-free oat production chain requires controlling all steps in the chain; the strict conditions will be discussed. Genomic tools, including a single nucleotide polymorphism (SNP) marker array and a dense genetic map, have recently been developed and will support marker-assisted breeding. In 2015, the Oat Global initiative emerged enabling a world-wide cooperation starting with a data sharing facility on genotypic, metabolic and phenotypic characteristics. Further, the EU project TRAFOON (Traditional Food Networks) facilitated the transfer of knowledge to small- and medium-sized enterprises (SMEs) to stimulate innovations in oat production, processing, products and marketing, among others with regard to gluten-free. Finally, with focus on counteracting market fragmentation of the global oat market and production chains, interactive innovation strategies between customers (consumers) and companies through co-creation are discussed.

Marker-trait association analysis of frost tolerance of 672 worldwide pea (Pisum sativum L.) collections

Frost stress is one of the major abiotic stresses causing seedling death and yield reduction in winter pea. To improve the frost tolerance of pea, field evaluation of frost tolerance was conducted on 672 diverse pea accessions at three locations in Northern China in three growing seasons from 2013 to 2016 and marker-trait association analysis of frost tolerance were performed with 267 informative SSR markers in this study. Sixteen accessions were identified as the most winter-hardy for their ability to survive in all nine field experiments with a mean survival rate of 0.57, ranging from 0.41 to 0.75. Population structure analysis revealed a structured population of two sub-populations plus some admixtures in the 672 accessions. Association analysis detected seven markers that repeatedly had associations with frost tolerance in at least two different environments with two different statistical models. One of the markers is the functional marker EST1109 on LG VI which was predicted to co-localize with a gene involved in the metabolism of glycoproteins in response to chilling stress and may provide a novel mechanism of frost tolerance in pea. These winter-hardy germplasms and frost tolerance associated markers will play a vital role in marker-assisted breeding for winter-hardy pea cultivar.

Breeding approaches and genomics technologies to increase crop yield under low-temperature stress

Improved knowledge about plant cold stress tolerance offered by modern omics technologies will greatly inform future crop improvement strategies that aim to breed cultivars yielding substantially high under low-temperature conditions. Alarmingly rising temperature extremities present a substantial impediment to the projected target of 70% more food production by 2050. Low-temperature (LT) stress severely constrains crop production worldwide, thereby demanding an urgent yet sustainable solution. Considerable research progress has been achieved on this front. Here, we review the crucial cellular and metabolic alterations in plants that follow LT stress along with the signal transduction and the regulatory network describing the plant cold tolerance. The significance of plant genetic resources to expand the genetic base of breeding programmes with regard to cold tolerance is highlighted. Also, the genetic architecture of cold tolerance trait as elucidated by conventional QTL mapping and genome-wide association mapping is described. Further, global expression profiling techniques including RNA-Seq along with diverse omics platforms are briefly discussed to better understand the underlying mechanism and prioritize the candidate gene (s) for downstream applications. These latest additions to breeders' toolbox hold immense potential to support plant breeding schemes that seek development of LT-tolerant cultivars. High-yielding cultivars endowed with greater cold tolerance are urgently required to sustain the crop yield under conditions severely challenged by low-temperature.