Rhizobium nodule diversity and composition are influenced by clover host selection and local growth conditions.

While shaping of plant microbiome composition through 'host filtering' is well documented in legume-rhizobium symbioses, it is less clear to what extent different varieties and genotypes of the same plant species differentially influence symbiont community diversity and composition. Here, we compared how clover host varieties and genotypes affect the structure of Rhizobium populations in root nodules under conventional field and controlled greenhouse conditions. We first grew four Trifolium repens (white clover) F2 crosses and one variety in a conventional field trial and compared differences in root nodule Rhizobium leguminosarum symbiovar trifolii (Rlt) genotype diversity using high-throughput amplicon sequencing of chromosomal housekeeping (rpoB and recA) genes and auxiliary plasmid-borne symbiosis genes (nodA and nodD). We found that Rlt nodule diversities significantly differed between clover crosses, potentially due to host filtering. However, variance in Rlt diversity largely overlapped between crosses and was also explained by the spatial distribution of plants in the field, indicative of the role of local environmental conditions for nodule diversity. To test the effect of host filtering, we conducted a controlled greenhouse trial with a diverse Rlt inoculum and several host genotypes. We found that different clover varieties and genotypes of the same variety selected for significantly different Rlt nodule communities and that the strength of host filtering (deviation from the initial Rhizobium inoculant composition) was positively correlated with the efficiency of symbiosis (rate of plant greenness colouration). Together, our results suggest that selection by host genotype and local growth conditions jointly influence white clover Rlt nodule diversity and community composition.

Major effect loci for plant size before onset of nitrogen fixation allow accurate prediction of yield in white clover.

KEY MESSAGE: Accurate genomic prediction of yield within and across generations was achieved by estimating the genetic merit of individual white clover genotypes based on extensive genetic replication using cloned material. White clover is an agriculturally important forage legume grown throughout temperate regions as a mixed clover-grass crop. It is typically cultivated with low nitrogen input, making yield dependent on nitrogen fixation by rhizobia in root nodules. Here, we investigate the effects of clover and rhizobium genetic variation by monitoring plant growth and quantifying dry matter yield of 704 combinations of 145 clover genotypes and 170 rhizobium inocula. We find no significant effect of rhizobium variation. In contrast, we can predict yield based on a few white clover markers strongly associated with plant size prior to nitrogen fixation, and the prediction accuracy for polycross offspring yield is remarkably high. Several of the markers are located near a homolog of Arabidopsis thaliana GIGANTUS 1, which regulates growth rate and biomass accumulation. Our work provides fundamental insight into the genetics of white clover yield and identifies specific candidate genes as breeding targets.

Determination of Loline Alkaloids and Mycelial Biomass in Endophyte-Infected Schedonorus Pratensis by Near-Infrared Spectroscopy and Chemometrics.

Near infrared spectroscopy (NIRS) is an accurate, fast and nondestructive technique whose use in predicting forage quality has become increasingly relevant in recent decades. Epichloë-infected grass varieties are commonly used in areas with high pest pressure due to their better performances compared to endophyte-free varieties. The insect resistance of Epichloë-infected grasses has been associated with four main groups of endophyte secondary metabolites: ergot alkaloids, indole-diterpenes, lolines and peramine. Concentrations of these alkaloids are usually measured with high performance liquid chromatography or gas chromatography analysis, which are accurate methods but relatively expensive and laborious. In this paper, we developed a rapid method based on NIRS to detect and quantify loline alkaloids in wild accessions of Schedonorus pratensis infected with the fungal endophyte Epichloë uncinata. The quantitative NIR equations obtained by modified partial least squares algorithm had coefficients of correlation of 0.90, 0.78, 0.85, 0.90 for N-acetylloline, N-acetylnorloline and N-formylloline and the sum of the three, respectively. The acquired NIR spectra were also used for developing an equation to predict in planta fungal biomass with a coefficient of correlation of 0.75. These results showed that the use of NIRS and chemometrics allows the quantification of loline alkaloids and mycelial biomass in a heterogeneous set of endophyte-infected meadow fescue samples.

Large Scale Screening of Epichloë Endophytes Infecting Schedonorus pratensis and Other Forage Grasses Reveals a Relation Between Microsatellite-Based Haplotypes and Loline Alkaloid Levels.

Species belonging to the Festuca-Lolium complex are often naturally infected with endophytic fungi of genus Epichloë. Recent studies on endophytes have shown the beneficial roles of host-endophyte associations as protection against insect herbivores in agriculturally important grasses. However, large-scale screenings are crucial to identify animal friendly strains suitable for agricultural use. In this study we analyzed collected populations of meadow fescue (Schedonorus pratensis) from 135 different locations across Europe, 255 accessions from the United States Department of Agriculture and 96 accessions from The Nordic Genetic Resource Centre. The analysis also included representatives of S. arundinaceus, S. giganteus, and Lolium perenne. All plants were screened for the presence of Epichloë endophytes, resulting in a nursery of about 2500 infected plants from 176 different locations. Genetic diversity was investigated on 250 isolates using a microsatellite-based PCR fingerprinting assay at 7 loci, 5 of which were uncharacterized for these species. Phylogenetic and principal components analysis showed a strong interspecific genetic differentiation among isolates, and, with E. uncinata isolates, a small but significant correlation between genetic diversity and geographical effect (r = 0.227) was detected. Concentrations of loline alkaloids were measured in 218 infected meadow fescue plants. Average amount of total loline and the proportions of the single loline alkaloids differed significantly among endophyte haplotypes (P < 0.005). This study provides insight into endophyte genetic diversity and geographic variation in Europe and a reference database of allele sizes for fast discrimination of isolates. We also discuss the possibility of multiple hybridization events as a source of genetic and alkaloid variation observed in E. uncinata.

Accuracy of Genomic Prediction in a Commercial Perennial Ryegrass Breeding Program.

The implementation of genomic selection (GS) in plant breeding, so far, has been mainly evaluated in crops farmed as homogeneous varieties, and the results have been generally positive. Fewer results are available for species, such as forage grasses, that are grown as heterogenous families (developed from multiparent crosses) in which the control of the genetic variation is far more complex. Here we test the potential for implementing GS in the breeding of perennial ryegrass ( L.) using empirical data from a commercial forage breeding program. Biparental F and multiparental synthetic (SYN) families of diploid perennial ryegrass were genotyped using genotyping-by-sequencing, and phenotypes for five different traits were analyzed. Genotypes were expressed as family allele frequencies, and phenotypes were recorded as family means. Different models for genomic prediction were compared by using practically relevant cross-validation strategies. All traits showed a highly significant level of genetic variance, which could be traced using the genotyping assay. While there was significant genotype × environment (G × E) interaction for some traits, accuracies were high among F families and between biparental F and multiparental SYN families. We have demonstrated that the implementation of GS in grass breeding is now possible and presents an opportunity to make significant gains for various traits.

Estimating genomic heritabilities at the level of family-pool samples of perennial ryegrass using genotyping-by-sequencing.

KEYMESSAGE: By using the genotyping-by-sequencing method, it is feasible to characterize genomic relationships directly at the level of family pools and to estimate genomic heritabilities from phenotypes scored on family-pools in outbreeding species. Genotyping-by-sequencing (GBS) has recently become a promising approach for characterizing plant genetic diversity on a genome-wide scale. We use GBS to extend the concept of heritability beyond individuals by genotyping family-pool samples by GBS and computing genomic relationship matrices (GRMs) and genomic heritabilities directly at the level of family-pools from pool-frequencies obtained by sequencing. The concept is of interest for species where breeding and phenotyping is not done at the individual level but operates uniquely at the level of (multi-parent) families. As an example we demonstrate the approach using a set of 990 two-parent F2 families of perennial ryegrass (Lolium Perenne). The families were phenotyped as a family-unit in field plots for heading date and crown rust resistance. A total of 728 K single nucleotide polymorphism (SNP) variants were available and were divided in groups of different sequencing depths. GRMs based on GBS data showed diagonal values biased upwards at low sequencing depth, while off-diagonals were little affected by the sequencing depth. Using variants with high sequencing depth, genomic heritability for crown rust resistance was 0.33, and for heading date 0.22, and these genomic heritabilities were biased downwards when using variants with lower sequencing depth. Broad sense heritabilities were 0.61 and 0.66, respectively. Underestimation of genomic heritability at lower sequencing depth was confirmed with simulated data. We conclude that it is feasible to use GBS to describe relationships between family-pools and to estimate genomic heritability directly at the level of F2 family-pool samples, but estimates are biased at low sequencing depth.

Genomic dissection and prediction of heading date in perennial ryegrass.

BACKGROUND: Genomic 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. RESULTS: Several 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. CONCLUSIONS: We 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).