QuLinePlus: extending plant breeding strategy and genetic model simulation to cross-pollinated populations-case studies in forage breeding.

Plant breeders are supported by a range of tools that assist them to make decisions about the conduct or design of plant breeding programs. Simulations are a strategic tool that enables the breeder to integrate the multiple components of a breeding program into a number of proposed scenarios that are compared by a range of statistics measuring the efficiency of the proposed systems. A simulation study for the trait growth score compared two major strategies for breeding forage species, among half-sib family selection and among and within half-sib family selection. These scenarios highlighted new features of the QuLine program, now called QuLinePlus, incorporated to enable the software platform to be used to simulate breeding programs for cross-pollinated species. Each strategy was compared across three levels of half-sib family mean heritability (0.1, 0.5, and 0.9), across three sizes of the initial parental population (10, 50, and 100), and across three genetic effects models (fully additive model, a mixture of additive, partial and over dominance model, and a mixture of partial dominance and over dominance model). Among and within half-sib selection performed better than among half-sib selection for all scenarios. The new tools introduced into QuLinePlus should serve to accurately compare among methods and provide direction on how to achieve specific goals in the improvement of plant breeding programs for cross breeding species.

Predictive ability of genomic selection models in a multi-population perennial ryegrass training set using genotyping-by-sequencing.

KEY MESSAGE: Genomic prediction models for multi-year dry matter yield, via genotyping-by-sequencing in a composite training set, demonstrate potential for genetic gain improvement through within-half sibling family selection. Perennial ryegrass (Lolium perenne L.) is a key source of nutrition for ruminant livestock in temperate environments worldwide. Higher seasonal and annual yield of herbage dry matter (DMY) is a principal breeding objective but the historical realised rate of genetic gain for DMY is modest. Genomic selection was investigated as a tool to enhance the rate of genetic gain. Genotyping-by-sequencing (GBS) was undertaken in a multi-population (MP) training set of five populations, phenotyped as half-sibling (HS) families in five environments over 2 years for mean herbage accumulation (HA), a measure of DMY potential. GBS using the ApeKI enzyme yielded 1.02 million single-nucleotide polymorphism (SNP) markers from a training set of n = 517. MP-based genomic prediction models for HA were effective in all five populations, cross-validation-predictive ability (PA) ranging from 0.07 to 0.43, by trait and target population, and 0.40-0.52 for days-to-heading. Best linear unbiased predictor (BLUP)-based prediction methods, including GBLUP with either a standard or a recently developed (KGD) relatedness estimation, were marginally superior or equal to ridge regression and random forest computational approaches. PA was principally an outcome of SNP modelling genetic relationships between training and validation sets, which may limit application for long-term genomic selection, due to PA decay. However, simulation using data from the training experiment indicated a twofold increase in genetic gain for HA, when applying a prediction model with moderate PA in a single selection cycle, by combining among-HS family selection, based on phenotype, with within-HS family selection using genomic prediction.

Using non-targeted direct analysis in real time-mass spectrometry (DART-MS) to discriminate seeds based on endogenous or exogenous chemicals.

Forage seeds are a highly traded agricultural commodity, and therefore, quality control and assurance is high priority. In this study, we have used direct analysis in real time-mass spectrometry (DART-MS) as a tool to discriminate forage seeds based on their non-targeted chemical profiles. In the first experiment, two lots of perennial ryegrass (Lolium perenne L.) seed were discriminated based on exogenous residues of N-(3, 4-dichlorophenyl)-N,N-dimethylurea (Diuron(TM)), a herbicide. In a separate experiment, washed and unwashed seeds of the forage legumes white clover (Trifolium repens L.) and alfalfa (Medicago sativa L.) were discriminated based on the presence or absence of oxylipins, a class of endogenous antimicrobial compounds. Unwashed seeds confer toxicity towards symbiotic, nitrogen-fixing rhizobia which are routinely coated on legume seeds before planting, resulting in reduced rhizobial count. This is the first report of automatic introduction of intact seeds in the DART ion source and detecting oxylipins using DART-MS. Apart from providing scope to investigate legume-rhizobia symbiosis further in the context of oxylipins, the results presented here will enable future studies aimed at classification of seeds based on chemicals bound to the seed coat, thereby offering an efficient screening device for industry.

An integrated genetic linkage map for white clover (Trifolium repens L.) with alignment to Medicago.

BACKGROUND: White clover (Trifolium repens L.) is a temperate forage legume with an allotetraploid genome (2n=4×=32) estimated at 1093 Mb. Several linkage maps of various sizes, marker sources and completeness are available, however, no integrated map and marker set has explored consistency of linkage analysis among unrelated mapping populations. Such integrative analysis requires tools for homoeologue matching among populations. Development of these tools provides for a consistent framework map of the white clover genome, and facilitates in silico alignment with the model forage legume, Medicago truncatula. RESULTS: This is the first report of integration of independent linkage maps in white clover, and adds to the literature on methyl filtered GeneThresher®-derived microsatellite (simple sequence repeat; SSR) markers for linkage mapping. Gene-targeted SSR markers were discovered in a GeneThresher® (TrGT) methyl-filtered database of 364,539 sequences, which yielded 15,647 SSR arrays. Primers were designed for 4,038 arrays and of these, 465 TrGT-SSR markers were used for parental consensus genetic linkage analysis in an F1 mapping population (MP2). This was merged with an EST-SSR consensus genetic map of an independent population (MP1), using markers to match homoeologues and develop a multi-population integrated map of the white clover genome. This integrated map (IM) includes 1109 loci based on 804 SSRs over 1274 cM, covering 97% of the genome at a moderate density of one locus per 1.2 cM. Eighteen candidate genes and one morphological marker were also placed on the IM. Despite being derived from disparate populations and marker sources, the component maps and the derived IM had consistent representations of the white clover genome for marker order and genetic length. In silico analysis at an E-value threshold of 1e-20 revealed substantial co-linearity with the Medicago truncatula genome, and indicates a translocation between T. repens groups 2 and 6 relative to M. truncatula. CONCLUSIONS: This integrated genetic linkage analysis provides a consistent and comprehensive linkage analysis of the white clover genome, with alignment to a model forage legume. Associated marker locus information, particularly the homoeologue-specific markers, offers a new resource for forage legume research to enable genetic analysis and improvement of this forage and grassland species.

An improved reference genome for Trifolium subterraneum L. provides insight into molecular diversity and intra-specific phylogeny.

Subterranean clover (Trifolium subterraneum L., Ts) is a geocarpic, self-fertile annual forage legume with a compact diploid genome (n = x = 8, 544 Mb/1C). Its resilience and climate adaptivity have made it an economically important species in Mediterranean and temperate zones. Using the cultivar Daliak, we generated higher resolution sequence data, created a new genome assembly TSUd_3.0, and conducted molecular diversity analysis for copy number variant (CNV) and single-nucleotide polymorphism (SNP) among 36 cultivars. TSUd_3.0 substantively improves prior genome assemblies with new Hi-C and long-read sequence data, covering 531 Mb, containing 41,979 annotated genes and generating a 94.4% BUSCO score. Comparative genomic analysis among select members of the tribe Trifolieae indicated TSUd 3.0 corrects six assembly-error inversion/duplications and confirmed phylogenetic relationships. Its synteny with T. pratense, T. repens, Medicago truncatula and Lotus japonicus genomes were assessed, with the more distantly related T. repens and M. truncatula showing higher levels of co-linearity with Ts than between Ts and its close relative T. pratense. Resequencing of 36 cultivars discovered 7,789,537 SNPs subsequently used for genomic diversity assessment and sequence-based clustering. Heterozygosity estimates ranged from 1% to 21% within the 36 cultivars and may be influenced by admixture. Phylogenetic analysis supported subspecific genetic structure, although it indicates four or five groups, rather than the three recognized subspecies. Furthermore, there were incidences where cultivars characterized as belonging to a particular subspecies clustered with another subspecies when using genomic data. These outcomes suggest that further investigation of Ts sub-specific classification using molecular and morpho-physiological data is needed to clarify these relationships. This upgraded reference genome, complemented with comprehensive sequence diversity analysis of 36 cultivars, provides a platform for future gene functional analysis of key traits, and genome-based breeding strategies for climate adaptation and agronomic performance. Pangenome analysis, more in-depth intra-specific phylogenomic analysis using the Ts core collection, and functional genetic and genomic studies are needed to further augment knowledge of Trifolium genomes.

Multi-Trait Genomic Prediction Improves Predictive Ability for Dry Matter Yield and Water-Soluble Carbohydrates in Perennial Ryegrass.

In perennial ryegrass (Lolium perenne L), annual and seasonal dry matter yield (DMY) and nutritive quality of herbage are high-priority traits targeted for improvement through selective breeding. Genomic prediction (GP) has proven to be a valuable tool for improving complex traits and may be further enhanced through the use of multi-trait (MT) prediction models. In this study, we evaluated the relative performance of MT prediction models to improve predictive ability for DMY and key nutritive quality traits, using two different training populations (TP1, n = 463 and TP2, n = 517) phenotyped at multiple locations. MT models outperformed single-trait (ST) models by 24% to 59% for DMY and 67% to 105% for nutritive quality traits, such as low, high, and total WSC, when a correlated secondary trait was included in both the training and test set (MT-CV2) or in the test set alone (MT-CV3) (trait-assisted genomic selection). However, when a secondary trait was included in training set and not the test set (MT-CV1), the predictive ability was not statistically significant (p > 0.05) compared to the ST model. We evaluated the impact of training set size when using a MT-CV2 model. Using a highly correlated trait (rg = 0.88) as the secondary trait in the MT-CV2 model, there was no loss in predictive ability for DMY even when the training set was reduced to 50% of its original size. In contrast, using a weakly correlated secondary trait (rg = 0.56) in the MT-CV2 model, predictive ability began to decline when the training set size was reduced by only 11% from its original size. Using a ST model, genomic predictive ability in a population unrelated to the training set was poor (rp = -0.06). However, when using an MT-CV2 model, the predictive ability was positive and high (rp = 0.76) for the same population. Our results demonstrate the first assessment of MT models in forage species and illustrate the prospects of using MT genomic selection in forages, and other outcrossing plant species, to accelerate genetic gains for complex agronomical traits, such as DMY and nutritive quality characteristics.

Genomic Predictive Ability for Foliar Nutritive Traits in Perennial Ryegrass.

Forage nutritive value impacts animal nutrition, which underpins livestock productivity, reproduction and health. Genetic improvement for nutritive traits in perennial ryegrass has been limited, as they are typically expensive and time-consuming to measure through conventional methods. Genomic selection is appropriate for such complex and expensive traits, enabling cost-effective prediction of breeding values using genome-wide markers. The aims of the present study were to assess the potential of genomic selection for a range of nutritive traits in a multi-population training set, and to quantify contributions of family, location and family-by-location variance components to trait variation and heritability for nutritive traits. The training set consisted of a total of 517 half-sibling (half-sib) families, from five advanced breeding populations, evaluated in two distinct New Zealand grazing environments. Autumn-harvested samples were analyzed for 18 nutritive traits and maternal parents of the half-sib families were genotyped using genotyping-by-sequencing. Significant (P < 0.05) family variance was detected for all nutritive traits and genomic heritability (h2g ) was moderate to high (0.20 to 0.74). Family-by-location interactions were significant and particularly large for water soluble carbohydrate (WSC), crude fat, phosphorus (P) and crude protein. GBLUP, KGD-GBLUP and BayesCπ genomic prediction models displayed similar predictive ability, estimated by 10-fold cross validation, for all nutritive traits with values ranging from r = 0.16 to 0.45 using phenotypes from across two locations. High predictive ability was observed for the mineral traits sulfur (0.44), sodium (0.45) and magnesium (0.45) and the lowest values were observed for P (0.16), digestibility (0.22) and high molecular weight WSC (0.23). Predictive ability estimates for most nutritive traits were retained when marker number was reduced from one million to as few as 50,000. The moderate to high predictive abilities observed suggests implementation of genomic selection is feasible for most of the nutritive traits examined.

Multivariate associations of flavonoid and biomass accumulation in white clover (Trifolium repens) under drought.

White clover (Trifolium repens L.) is an important pasture legume in temperate regions, but growth is often strongly reduced under summer drought. Cloned individuals from a full-sib progeny of a pair cross between two phenotypically distinct white clover populations were exposed to water deficit in pots under outdoor conditions for 9 weeks, while control pots were maintained at field capacity. Water deficit decreased leaf water potential by more than 50% overall, but increased the levels of the flavonol glycosides of quercetin (Q) and the ratio of quercetin and kaempferol glycosides (QKR) by 111% and by 90%, respectively. Water deficit reduced dry matter (DM) by 21%, with the most productive genotypes in the controls showing the greatest proportional reduction. The full-sib progeny displayed a significant increase in the root:shoot ratio by 53% under water deficit. Drought-induced changes in plant morphology were associated with changes in Q, but not kaempferol (K) glycosides. The genotypes with high QKR levels reduced their DM production least under water deficit and increased their Q glycoside levels and QKR most. These data show, at the individual genotype level, that increased Q glycoside accumulation in response to water deficit stress can be positively associated with retaining higher levels of DM production.