Ingo Lenk

A high-throughput Agrobacterium-mediated transformation system for the grass model species Brachypodium distachyon L.

In the ongoing process of developing Brachypodium distachyon as a model plant for temperate cereals and forage grasses, we have developed a high-throughput Agrobacterium-mediated transformation system for a diploid accession. Embryogenic callus, derived from immature embryos of the accession BDR018, were transformed with Agrobacterium tumefaciens strain AGL1 carrying two T-DNA plasmids, pDM805 and pWBV-Ds-Ubi-bar-Ds. Transient and stable transformation efficiencies were optimised by varying the pre-cultivation period, which had a strong effect on stable transformation efficiency. On average 55% of 17-day-old calli co-inoculated with Agrobacterium regenerated stable transgenic plants. Stable transformation frequencies of up to 80%, which to our knowledge is the highest transformation efficiency reported in graminaceous species, were observed. In a study of 177 transgenic lines transformed with pDM805, all of the regenerated transgenic lines were resistant to BASTA®, while the gusA gene was expressed in 88% of the transgenic lines. Southern blot analysis revealed that 35% of the tested plants had a single T-DNA integration. Segregation analysis performed on progenies of ten selected T0 plants indicated simple Mendelian inheritance of the two transgenes. Furthermore, the presence of two selection marker genes, bar and hpt, on the T-DNA of pWBV-Ds-Ubi-bar-Ds allowed us to characterize the developed transformation protocol with respect to full-length integration rate. Even when not selected for, full-length integration occurred in 97% of the transformants when using bialaphos as selection agent.

Genetic variation, population structure, and linkage disequilibrium in European elite germplasm of perennial ryegrass.

Perennial ryegrass (Lolium perenne L.) is a highly valued temperate climate grass species grown as forage crop and for amenity uses. Due to its outbreeding nature and recent domestication, a high degree of genetic diversity is expected among cultivars. The aim of this study was to assess the extent of linkage disequilibrium (LD) within European elite germplasm and to evaluate the appropriate methodology for genetic association mapping in perennial ryegrass. A high level of genetic diversity was observed in a set of 380 perennial ryegrass elite genotypes when genotyped with 40 SSRs and 2 STS markers. A Bayesian structure analysis identified two subpopulations, which were confirmed by principal coordinate analysis (PCoA). One subpopulation consisted mainly of genotypes originating from the UK, while germplasm mostly from Continental Europe was grouped into the second subpopulation. LD (r(2)) decay was rapid and occurred within 0.4cM across European varieties, when population structure was taken into consideration. However, an extended LD of up to 6.6cM was detected within the variety Aberdart. High genetic diversity and rapid LD decay provide means for high resolution association mapping in elite materials of perennial ryegrass. However, different strategies need to be applied depending on the material used. Genome-wide association study (GWAS) with several hundred markers can be applied within synthetic varieties to identify large (up to 10cM) genomic regions affecting trait variation. A combination of available and novel DNA markers is needed to achieve resolution required for GWAS in elite breeding materials. An even higher marker density of several million SNPs might be needed for GWAS in diverse ecotype collections, potentially resulting in quantitative trait polymorphism (QTP) identification.

Nucleotide diversity and linkage disequilibrium of nine genes with putative effects on flowering time in perennial ryegrass (Lolium perenne L.).

Optimization of flowering is an important breeding goal in forage and turf grasses, such as perennial ryegrass (Lolium perenne L.). Nine floral control genes including Lolium perenne CONSTANS (LpCO), SISTER OF FLOWERING LOCUS T (LpSFT), TERMINAL FLOWER1 (LpTFL1), VERNALIZATION1 (LpVRN1, identical to LpMADS1) and five additional MADS-box genes, were analyzed for nucleotide diversity and linkage disequilibrium (LD). For each gene, about 1 kb genomic fragments were isolated from 10 to 20 genotypes of perennial ryegrass of diverse origin. Four to twelve haplotypes per gene were observed. On average, one single nucleotide polymorphism (SNP) was present per 127 bp between two randomly sampled sequences for the nine genes (π = 0.00790). Two MADS-box genes, LpMADS1 and LpMADS10, involved in timing of flowering showed high nucleotide diversity and rapid LD decay, whereas MADS-box genes involved in floral organ identity were found to be highly conserved and showed extended LD. For LpMADS4, LpMADS5, LpCO, LpSFT and LpTFL1, LD extended over the entire region analyzed. The results are compared to previously published results on resistance genes within the same collection of genotypes and the prospects for association mapping of floral control in perennial ryegrass are discussed.

Genomic dissection and prediction of heading date in perennial ryegrass.

BackgroundGenomic selection (GS) has become a commonly used technology in animal breeding. In crops, it is expected to significantly improve the genetic gains per unit of time. So far, its implementation in plant breeding has been mainly investigated in species farmed as homogeneous varieties. Concerning crops farmed in family pools, only a few theoretical studies are currently available. Here, we test the opportunity to implement GS in breeding of perennial ryegrass, using real data from a forage breeding program. Heading date was chosen as a model trait, due to its high heritability and ease of assessment. Genome Wide Association analysis was performed to uncover the genetic architecture of the trait. Then, Genomic Prediction (GP) models were tested and prediction accuracy was compared to the one obtained in traditional Marker Assisted Selection (MAS) methods.ResultsSeveral markers were significantly associated with heading date, some locating within or proximal to genes with a well-established role in floral regulation. GP models gave very high accuracies, which were significantly better than those obtained through traditional MAS. Accuracies were higher when predictions were made from related families and from larger training populations, whereas predicting from unrelated families caused the variance of the estimated breeding values to be biased downwards.ConclusionsWe have demonstrated that there are good perspectives for GS implementation in perennial ryegrass breeding, and that problems resulting from low linkage disequilibrium (LD) can be reduced by the presence of structure and related families in the breeding population. While comprehensive Genome Wide Association analysis is difficult in species with extremely low LD, we did identify variants proximal to genes with a known role in flowering time (e.g. CONSTANS and Phytochrome C).

Accuracy of Genomic Prediction in a Commercial Perennial Ryegrass Breeding Program

High accuracies for genomic prediction in a perennial ryegrass breeding program The additive genetic variance can be traced by genotyping assays Predictions work across different generations and in different traits Good prospects for the implementation of genomic selection in perennial ryegrass

Optimized Use of Low-Depth Genotyping-by-Sequencing for Genomic Prediction Among Multi-Parental Family Pools and Single Plants in Perennial Ryegrass (Lolium perenne L.)

Ryegrass single plants, bi-parental family pools, and multi-parental family pools are often genotyped, based on allele-frequencies using genotyping-by-sequencing (GBS) assays. GBS assays can be performed at low-coverage depth to reduce costs. However, reducing the coverage depth leads to a higher proportion of missing data, and leads to a reduction in accuracy when identifying the allele-frequency at each locus. As a consequence of the latter, genomic relationship matrices (GRMs) will be biased. This bias in GRMs affects variance estimates and the accuracy of GBLUP for genomic prediction (GBLUP-GP). We derived equations that describe the bias from low-coverage sequencing as an effect of binomial sampling of sequence reads, and allowed for any ploidy level of the sample considered. This allowed us to combine individual and pool genotypes in one GRM, treating pool-genotypes as a polyploid genotype, equal to the total ploidy-level of the parents of the pool. Using simulated data, we verified the magnitude of the GRM bias at different coverage depths for three different kinds of ryegrass breeding material: individual genotypes from single plants, pool-genotypes from F2 families, and pool-genotypes from synthetic varieties. To better handle missing data, we also tested imputation procedures, which are suited for analyzing allele-frequency genomic data. The relative advantages of the bias-correction and the imputation of missing data were evaluated using real data. We examined a large dataset, including single plants, F2 families, and synthetic varieties genotyped in three GBS assays, each with a different coverage depth, and evaluated them for heading date, crown rust resistance, and seed yield. Cross validations were used to test the accuracy using GBLUP approaches, demonstrating the feasibility of predicting among different breeding material. Bias-corrected GRMs proved to increase predictive accuracies when compared with standard approaches to construct GRMs. Among the imputation methods we tested, the random forest method yielded the highest predictive accuracy. The combinations of these two methods resulted in a meaningful increase of predictive ability (up to 0.09). The possibility of predicting across individuals and pools provides new opportunities for improving ryegrass breeding schemes.

Genomic Prediction in Tetraploid Ryegrass Using Allele Frequencies Based on Genotyping by Sequencing

Perennial ryegrass is an outbreeding forage species and is one of the most widely used forage grasses in temperate regions. The aim of this study was to investigate the possibility of implementing genomic prediction in tetraploid perennial ryegrass, to study the effects of different sequencing depth when using genotyping-by-sequencing (GBS), and to determine optimal number of single-nucleotide polymorphism (SNP) markers and sequencing depth for GBS data when applied in tetraploids. A total of 1,515 F2 tetraploid ryegrass families were included in the study and phenotypes and genotypes were scored on family-pools. The traits considered were dry matter yield (DM), rust resistance (RUST), and heading date (HD). The genomic information was obtained in the form of allele frequencies of pooled family samples using GBS. Different SNP filtering strategies were designed. The strategies included filtering out SNPs having low average depth (FILTLOW), having high average depth (FILTHIGH), and having both low average and high average depth (FILTBOTH). In addition, SNPs were kept randomly with different data sizes (RAN). The accuracy of genomic prediction was evaluated by using a “leave single F2 family out” cross validation scheme, and the predictive ability and bias were assessed by correlating phenotypes corrected for fixed effects with predicted additive breeding values. Among all the filtering scenarios, the highest estimates for genomic heritability of family means were 0.45, 0.74, and 0.73 for DM, HD and RUST, respectively. The predictive ability generally increased as the number of SNPs included in the analysis increased. The highest predictive ability for DM was 0.34 (137,191 SNPs having average depth higher than 10), for HD was 0.77 (185,297 SNPs having average depth lower than 60), and for RUST was 0.55 (188,832 SNPs having average depth higher than 1). Genomic prediction can help to optimize the breeding of tetraploid ryegrass. GBS data including about 80–100 K SNPs are needed for accurate prediction of additive breeding values in tetraploid ryegrass. Using only SNPs with sequencing depth between 10 and 20 gave highest predictive ability, and showed the potential to obtain accurate prediction from medium-low coverage GBS in tetraploids.