Including marker x environment interactions improves genomic prediction in red clover (Trifolium pratense L.).

Genomic prediction has mostly been used in single environment contexts, largely ignoring genotype x environment interaction, which greatly affects the performance of plants. However, in the last decade, prediction models including marker x environment (MxE) interaction have been developed. We evaluated the potential of genomic prediction in red clover (Trifolium pratense L.) using field trial data from five European locations, obtained in the Horizon 2020 EUCLEG project. Three models were compared: (1) single environment (SingleEnv), (2) across environment (AcrossEnv), (3) marker x environment interaction (MxE). Annual dry matter yield (DMY) gave the highest predictive ability (PA). Joint analyses of DMY from years 1 and 2 from each location varied from 0.87 in Britain and Switzerland in year 1, to 0.40 in Serbia in year 2. Overall, crude protein (CP) was predicted poorly. PAs for date of flowering (DOF), however ranged from 0.87 to 0.67 for Britain and Switzerland, respectively. Across the three traits, the MxE model performed best and the AcrossEnv worst, demonstrating that including marker x environment effects can improve genomic prediction in red clover. Leaving out accessions from specific regions or from specific breeders' material in the cross validation tended to reduce PA, but the magnitude of reduction depended on trait, region and breeders' material, indicating that population structure contributed to the high PAs observed for DMY and DOF. Testing the genomic estimated breeding values on new phenotypic data from Sweden showed that DMY training data from Britain gave high PAs in both years (0.43-0.76), while DMY training data from Switzerland gave high PAs only for year 1 (0.70-0.87). The genomic predictions we report here underline the potential benefits of incorporating MxE interaction in multi-environment trials and could have perspectives for identifying markers with effects that are stable across environments, and markers with environment-specific effects.

A genome-wide association study of freezing tolerance in red clover (Trifolium pratense L.) germplasm of European origin.

Improvement of persistency is an important breeding goal in red clover (Trifolium pratense L.). In areas with cold winters, lack of persistency is often due to poor winter survival, of which low freezing tolerance (FT) is an important component. We conducted a genome wide association study (GWAS) to identify loci associated with freezing tolerance in a collection of 393 red clover accessions, mostly of European origin, and performed analyses of linkage disequilibrium and inbreeding. Accessions were genotyped as pools of individuals using genotyping-by-sequencing (pool-GBS), generating both single nucleotide polymorphism (SNP) and haplotype allele frequency data at accession level. Linkage disequilibrium was determined as a squared partial correlation between the allele frequencies of pairs of SNPs and found to decay at extremely short distances (< 1 kb). The level of inbreeding, inferred from the diagonal elements of a genomic relationship matrix, varied considerably between different groups of accessions, with the strongest inbreeding found among ecotypes from Iberia and Great Britain, and the least found among landraces. Considerable variation in FT was found, with LT50-values (temperature at which 50% of the plants are killed) ranging from -6.0°C to -11.5°C. SNP and haplotype-based GWAS identified eight and six loci significantly associated with FT (of which only one was shared), explaining 30% and 26% of the phenotypic variation, respectively. Ten of the loci were found within or at a short distance (<0.5 kb) from genes possibly involved in mechanisms affecting FT. These include a caffeoyl shikimate esterase, an inositol transporter, and other genes involved in signaling, transport, lignin synthesis and amino acid or carbohydrate metabolism. This study paves the way for a better understanding of the genetic control of FT and for the development of molecular tools for the improvement of this trait in red clover through genomics assisted breeding.

Multi-location trials and population-based genotyping reveal high diversity and adaptation to breeding environments in a large collection of red clover.

Red clover (Trifolium pratense L.) is an outcrossing forage legume that has adapted to a wide range of climatic and growing conditions across Europe. Red clover is valued for its high yield potential and its forage quality. The high amount of genetic diversity present in red clover provides an invaluable, but often poorly characterized resource to improve key traits such as yield, quality, and resistance to biotic and abiotic stresses. In this study, we examined the genetic and phenotypic diversity within a diverse set of 395 diploid red clover accessions via genome wide allele frequency fingerprinting and multi-location field trials across Europe. We found that the genetic structure of accessions mostly reflected their geographic origin and only few cases were detected, where breeders integrated foreign genetic resources into their local breeding pools. The mean dry matter yield of the first main harvesting season ranged from 0.74 kg m-2 in Serbia and Norway to 1.34 kg m-2 in Switzerland. Phenotypic performance of accessions in the multi-location field trials revealed a very strong accession x location interaction. Local adaptation was especially prominent in Nordic red clover accessions that showed a distinct adaptation to the growing conditions and cutting regime of the North. The traits vigor, dry matter yield and plant density were negatively correlated between the trial location in Norway and the locations Great Britain, Switzerland, Czech Republic and Serbia. Notably, breeding material and cultivars generally performed well at the location where they were developed. Our results confirmed that red clover cultivars were bred from regional ecotypes and show a narrow adaptation to regional conditions. Our study can serve as a valuable basis for identifying interesting materials that express the desired characteristics and contribute to the adaptation of red clover to future climatic conditions.

Phenotypic variation and quantitative trait loci for resistance to southern anthracnose and clover rot in red clover.

KEY MESSAGE: High variability for and candidate loci associated with resistance to southern anthracnose and clover rot in a worldwide collection of red clover provide a first basis for genomics-assisted breeding. Red clover (Trifolium pratense L.) is an important forage legume of temperate regions, particularly valued for its high yield potential and its high forage quality. Despite substantial breeding progress during the last decades, continuous improvement of cultivars is crucial to ensure yield stability in view of newly emerging diseases or changing climatic conditions. The high amount of genetic diversity present in red clover ecotypes, landraces, and cultivars provides an invaluable, but often unexploited resource for the improvement of key traits such as yield, quality, and resistance to biotic and abiotic stresses. A collection of 397 red clover accessions was genotyped using a pooled genotyping-by-sequencing approach with 200 plants per accession. Resistance to the two most pertinent diseases in red clover production, southern anthracnose caused by Colletotrichum trifolii, and clover rot caused by Sclerotinia trifoliorum, was assessed using spray inoculation. The mean survival rate for southern anthracnose was 22.9% and the mean resistance index for clover rot was 34.0%. Genome-wide association analysis revealed several loci significantly associated with resistance to southern anthracnose and clover rot. Most of these loci are in coding regions. One quantitative trait locus (QTL) on chromosome 1 explained 16.8% of the variation in resistance to southern anthracnose. For clover rot resistance we found eight QTL, explaining together 80.2% of the total phenotypic variation. The SNPs associated with these QTL provide a promising resource for marker-assisted selection in existing breeding programs, facilitating the development of novel cultivars with increased resistance against two devastating fungal diseases of red clover.

An image analysis pipeline for automated classification of imaging light conditions and for quantification of wheat canopy cover time series in field phenotyping.

BACKGROUND: Robust segmentation of canopy cover (CC) from large amounts of images taken under different illumination/light conditions in the field is essential for high throughput field phenotyping (HTFP). We attempted to address this challenge by evaluating different vegetation indices and segmentation methods for analyzing images taken at varying illuminations throughout the early growth phase of wheat in the field. 40,000 images taken on 350 wheat genotypes in two consecutive years were assessed for this purpose. RESULTS: We proposed an image analysis pipeline that allowed for image segmentation using automated thresholding and machine learning based classification methods and for global quality control of the resulting CC time series. This pipeline enabled accurate classification of imaging light conditions into two illumination scenarios, i.e. high light-contrast (HLC) and low light-contrast (LLC), in a series of continuously collected images by employing a support vector machine (SVM) model. Accordingly, the scenario-specific pixel-based classification models employing decision tree and SVM algorithms were able to outperform the automated thresholding methods, as well as improved the segmentation accuracy compared to general models that did not discriminate illumination differences. CONCLUSIONS: The three-band vegetation difference index (NDI3) was enhanced for segmentation by incorporating the HSV-V and the CIE Lab-a color components, i.e. the product images NDI3*V and NDI3*a. Field illumination scenarios can be successfully identified by the proposed image analysis pipeline, and the illumination-specific image segmentation can improve the quantification of CC development. The integrated image analysis pipeline proposed in this study provides great potential for automatically delivering robust data in HTFP.

Terrestrial 3D laser scanning to track the increase in canopy height of both monocot and dicot crop species under field conditions.

BACKGROUND: Plant growth is a good indicator of crop performance and can be measured by different methods and on different spatial and temporal scales. In this study, we measured the canopy height growth of maize (Zea mays), soybean (Glycine max) and wheat (Triticum aestivum) under field conditions by terrestrial laser scanning (TLS). We tested the hypotheses whether such measurements are capable to elucidate (1) differences in architecture that exist between genotypes; (2) genotypic differences between canopy height growth during the season and (3) short-term growth fluctuations (within 24 h), which could e.g. indicate responses to rapidly fluctuating environmental conditions. The canopies were scanned with a commercially available 3D laser scanner and canopy height growth over time was analyzed with a novel and simple approach using spherical targets with fixed positions during the whole season. This way, a high precision of the measurement was obtained allowing for comparison of canopy parameters (e.g. canopy height growth) at subsequent time points. RESULTS: Three filtering approaches for canopy height calculation from TLS were evaluated and the most suitable approach was used for the subsequent analyses. For wheat, high coefficients of determination (R(2)) of the linear regression between manually measured and TLS-derived canopy height were achieved. The temporal resolution that can be achieved with our approach depends on the scanned crop. For maize, a temporal resolution of several hours can be achieved, whereas soybean is ideally scanned only once per day, after leaves have reached their most horizontal orientation. Additionally, we could show for maize that plant architectural traits are potentially detectable with our method. CONCLUSIONS: The TLS approach presented here allows for measuring canopy height growth of different crops under field conditions with a high temporal resolution, depending on crop species. This method will enable advances in automated phenotyping for breeding and precision agriculture applications. In future studies, the TLS method can be readily applied to detect the effects of plant stresses such as drought, limited nutrient availability or compacted soil on different genotypes or on spatial variance in fields.

Evidence and consequences of self-fertilisation in the predominantly outbreeding forage legume Onobrychis viciifolia.

BACKGROUND: Sainfoin (Onobrychis viciifolia) is a promising alternative forage plant of good quality, moderate nutrient demand and a high content of polyphenolic compounds. Its poor adoption is caused by the limited availability of well performing varieties. Sainfoin is characterised as tetraploid and mainly outcrossing, but the extent of self-fertilisation and its consequences was not investigated so far. This study aimed at assessing the rate of self-fertilisation in sainfoin under different pollination regimes and at analysing the consequences on plant performance in order to assist future breeding efforts. METHODS: The self-fertilisation rate was assessed in three sainfoin populations with artificially directed pollination (ADP) and in three populations with non-directed pollination (NDP). Dominant SRAP (sequence-related amplified polymorphism) and codominant SSR (simple sequence repeats) markers were used to detect self-fertilisation in sainfoin for the first time based on molecular marker data. RESULTS: High rates of self-fertilisation of up to 64.8% were observed for ADP populations in contrast to only up to 3.9% for NDP populations. Self-fertilisation in ADP populations led to a reduction in plant height, plant vigour and, most severely, for seed yield. CONCLUSIONS: Although sainfoin is predominantly outcrossing, self-fertilisation can occur to a high degree under conditions of limited pollen availability. These results will influence future breeding efforts because precautions have to be taken when crossing breeding material. The resulting inbreeding depression can lead to reduced performance in self-fertilised offspring. Nevertheless the possibility of self-fertilisation also offers new ways for hybrid breeding based on the development of homogenous inbred lines.

Image based phenotyping during winter: a powerful tool to assess wheat genetic variation in growth response to temperature.

Having a strong effect on plant growth, temperature adaption has become a major breeding aim. Due to a lack of efficient methods, we developed an image-based approach to characterise genotypes for their temperature behaviour in the field. Twenty-nine winter wheat (Triticum aestivum L.) genotypes were continuously monitored at 3-day intervals on a plot basis during early growth from November to March using a modified digital camera. Canopy cover (CC) was determined by segmentation of leaves in calibrated images. Relative growth rates (RGR) of CC were then calculated for each measurement interval and related to the respective temperature. Also, classical traits used in plant breeding were assessed. Measurements of CC at single dates were highly repeatable with respect to genotype. For the tested range of temperatures (0-7°C), a linear relation between RGR and temperature was observed. Genotypes differed for base temperature and increase in RGR with rising temperature, these two traits showing a strong positive correlation with each other but being independent of CC at a single date. Our simple approach is suitable to screen large populations for differences in growth response to environmental stimuli. Furthermore, the derived parameters reveal additional information that cannot be assessed by usual measurements of static size.

Association mapping for chilling tolerance in elite flint and dent maize inbred lines evaluated in growth chamber and field experiments.

Chilling sensitivity of maize is a strong limitation for its cultivation in the cooler areas of the northern and southern hemisphere because reduced growth in early stages impairs on later biomass accumulation. Efficient breeding for chilling tolerance is hampered by both the complex physiological response of maize to chilling temperatures and the difficulty to accurately measure chilling tolerance in the field under fluctuating climatic conditions. For this research, we used genome-wide association (GWA) mapping to identify genes underlying chilling tolerance under both controlled and field conditions in a broad germplasm collection of 375 maize inbred lines genotyped with 56 110 single nucleotide polymorphism (SNP). We identified 19 highly significant association signals explaining between 5.7 and 52.5% of the phenotypic variance observed for early growth and chlorophyll fluorescence parameters. The allelic effect of several SNPs identified for early growth was associated with temperature and incident radiation. Candidate genes involved in ethylene signalling, brassinolide, and lignin biosynthesis were found in their vicinity. The frequent involvement of candidate genes into signalling or gene expression regulation underlines the complex response of photosynthetic performance and early growth to climatic conditions, and supports pleiotropism as a major cause of co-locations of quantitative trait loci for these highly polygenic traits.

Genome-wide association mapping of leaf metabolic profiles for dissecting complex traits in maize.

The diversity of metabolites found in plants is by far greater than in most other organisms. Metabolic profiling techniques, which measure many of these compounds simultaneously, enabled investigating the regulation of metabolic networks and proved to be useful for predicting important agronomic traits. However, little is known about the genetic basis of metabolites in crops such as maize. Here, a set of 289 diverse maize inbred lines was genotyped with 56,110 SNPs and assayed for 118 biochemical compounds in the leaves of young plants, as well as for agronomic traits of mature plants in field trials. Metabolite concentrations had on average a repeatability of 0.73 and showed a correlation pattern that largely reflected their functional grouping. Genome-wide association mapping with correction for population structure and cryptic relatedness identified for 26 distinct metabolites strong associations with SNPs, explaining up to 32.0% of the observed genetic variance. On nine chromosomes, we detected 15 distinct SNP-metabolite associations, each of which explained more then 15% of the genetic variance. For lignin precursors, including p-coumaric acid and caffeic acid, we found strong associations (P values to ) with a region on chromosome 9 harboring cinnamoyl-CoA reductase, a key enzyme in monolignol synthesis and a target for improving the quality of lignocellulosic biomass by genetic engineering approaches. Moreover, lignin precursors correlated significantly with lignin content, plant height, and dry matter yield, suggesting that metabolites represent promising connecting links for narrowing the genotype-phenotype gap of complex agronomic traits.

Genomic and metabolic prediction of complex heterotic traits in hybrid maize.

Maize is both an exciting model organism in plant genetics and also the most important crop worldwide for food, animal feed and bioenergy production. Recent genome-wide association and metabolic profiling studies aimed to resolve quantitative traits to their causal genetic loci and key metabolic regulators. Here we present a complementary approach that exploits large-scale genomic and metabolic information to predict complex, highly polygenic traits in hybrid testcrosses. We crossed 285 diverse Dent inbred lines from worldwide sources with two testers and predicted their combining abilities for seven biomass- and bioenergy-related traits using 56,110 SNPs and 130 metabolites. Whole-genome and metabolic prediction models were built by fitting effects for all SNPs or metabolites. Prediction accuracies ranged from 0.72 to 0.81 for SNPs and from 0.60 to 0.80 for metabolites, allowing a reliable screening of large collections of diverse inbred lines for their potential to create superior hybrids.

Breeding maize as biogas substrate in Central Europe: I. Quantitative-genetic parameters for testcross performance.

Biofuels have gained importance recently and the use of maize biomass as substrate in biogas plants for production of methane has increased tremendously in Germany. The objectives of our research were to (1) estimate variance components and heritability for different traits relevant to biogas production in testcrosses (TCs) of maize, (2) study correlations among traits, and (3) discuss strategies to breed maize as a substrate for biogas fermenters. We evaluated 570 TCs of 285 diverse dent maize lines crossed with two flint single-cross testers in six environments. Data were recorded on agronomic and quality traits, including dry matter yield (DMY), methane fermentation yield (MFY), and methane yield (MY), the product of DMY and MFY, as the main target trait. Estimates of variance components showed general combining ability (GCA) to be the major source of variation. Estimates of heritability exceeded 0.67 for all traits and were even much greater in most instances. Methane yield was perfectly correlated with DMY but not with MFY, indicating that variation in MY is primarily determined by DMY. Further, DMY had a larger heritability and coefficient of genetic variation than MFY. Hence, for improving MY, selection should primarily focus on DMY rather than MFY. Further, maize breeding for biogas production may diverge from that for forage production because in the former case, quality traits seem to be of much lower importance.

Breeding maize as biogas substrate in Central Europe: II. Quantitative-genetic parameters for inbred lines and correlations with testcross performance.

Breeding maize for use as a biogas substrate (biogas maize) has recently gained considerable importance. To optimize hybrid breeding programs, information about line per se performance (LP) of inbreds and its relation to their general combining ability (GCA) is required. The objectives of our research were to (1) estimate variance components and heritability of LP for agronomic and quality traits relevant to biogas production, (2) study correlations among traits as well as between LP and GCA, and (3) discuss implications for breeding of biogas maize. We evaluated 285 diverse dent maize inbred lines in six environments. Data were recorded on agronomic and quality traits, including dry matter yield (DMY), methane fermentation yield (MFY), and their product, methane yield (MY), as the main target trait. In agreement with observations made for GCA in a companion study, variation in MY was mainly determined by DMY. MFY, which showed moderate correlation with lignin but only weak correlation with starch, revealed only low genotypic variation. Thus, our results favor selection of genotypes with high DMY and less focus on ear proportion for biogas maize. Genotypic correlations between LP and GCA [r (g) (LP, GCA)] were highest (≥0.94) for maturity traits (days to silking, dry matter concentration) and moderate (≥0.65) for DMY and MY. Multistage selection is recommended. Selection for GCA of maturity traits, plant height, and to some extent also quality traits and DMY on the level of LP looks promising.