Transcriptome sequencing of field pea and faba bean for discovery and validation of SSR genetic markers

BACKGROUND: Field pea (Pisum sativum L.) and faba bean (Vicia faba L.) are cool-season grain legume species that provide rich sources of food for humans and fodder for livestock. To date, both species have been relative ‘genomic orphans’ due to limited availability of genetic and genomic information. A significant enrichment of genomic resources is consequently required in order to understand the genetic architecture of important agronomic traits, and to support germplasm enhancement, genetic diversity, population structure and demographic studies. RESULTS: cDNA samples obtained from various tissue types of specific field pea and faba bean genotypes were sequenced using 454 Roche GS FLX Titanium technology. A total of 720,324 and 304,680 reads for field pea and faba bean, respectively, were de novo assembled to generate sets of 70,682 and 60,440 unigenes. Consensus sequences were compared against the genome of the model legume species Medicago truncatula Gaertn., as well as that of the more distantly related, but better-characterised genome of Arabidopsis thaliana L.. In comparison to M. truncatula coding sequences, 11,737 and 10,179 unique hits were obtained from field pea and faba bean. Totals of 22,057 field pea and 18,052 faba bean unigenes were subsequently annotated from GenBank. Comparison to the genome of soybean (Glycine max L.) resulted in 19,451 unique hits for field pea and 16,497 unique hits for faba bean, corresponding to c. 35% and 30% of the known gene space, respectively. Simple sequence repeat (SSR)- containing expressed sequence tags (ESTs) were identified from consensus sequences, and totals of 2,397 and 802 primer pairs were designed for field pea and faba bean. Subsets of 96 EST-SSR markers were screened for validation across modest panels of field pea and faba bean cultivars, as well as related non-domesticated species. For field pea, 86 primer pairs successfully obtained amplification products from one or more template genotypes, of which 59% revealed polymorphism between 6 genotypes. In the case of faba bean, 81 primer pairs displayed successful amplification, of which 48% detected polymorphism. CONCLUSIONS: The generation of EST datasets for field pea and faba bean has permitted effective unigene identification and functional sequence annotation. EST-SSR loci were detected at incidences of 14-17%, permitting design of comprehensive sets of primer pairs. The subsets from these primer pairs proved highly useful for polymorphism detection within Pisum and Vicia germplasm.Sukhjiwan Kaur, Luke W. Pembleton, Noel O.I. Cogan, Keith W. Savin, Tony Leonforte, Jeffrey Paull, Michael Materne and John W. Forste

Transcriptome sequencing of lentil based on second-generation technology permits large-scale unigene assembly and SSR marker discovery

<p>Abstract</p> <p>Background</p> <p>Lentil (<it>Lens culinaris </it>Medik.) is a cool-season grain legume which provides a rich source of protein for human consumption. In terms of genomic resources, lentil is relatively underdeveloped, in comparison to other Fabaceae species, with limited available data. There is hence a significant need to enhance such resources in order to identify novel genes and alleles for molecular breeding to increase crop productivity and quality.</p> <p>Results</p> <p>Tissue-specific cDNA samples from six distinct lentil genotypes were sequenced using Roche 454 GS-FLX Titanium technology, generating c. 1.38 × 10⁶expressed sequence tags (ESTs). <it>De novo </it>assembly generated a total of 15,354 contigs and 68,715 singletons. The complete unigene set was sequence-analysed against genome drafts of the model legume species <it>Medicago truncatula </it>and <it>Arabidopsis thaliana </it>to identify 12,639, and 7,476 unique matches, respectively. When compared to the genome of <it>Glycine max</it>, a total of 20,419 unique hits were observed corresponding to c. 31% of the known gene space. A total of 25,592 lentil unigenes were subsequently annoated from GenBank. Simple sequence repeat (SSR)-containing ESTs were identified from consensus sequences and a total of 2,393 primer pairs were designed. A subset of 192 EST-SSR markers was screened for validation across a panel 12 cultivated lentil genotypes and one wild relative species. A total of 166 primer pairs obtained successful amplification, of which 47.5% detected genetic polymorphism.</p> <p>Conclusions</p> <p>A substantial collection of ESTs has been developed from sequence analysis of lentil genotypes using second-generation technology, permitting unigene definition across a broad range of functional categories. As well as providing resources for functional genomics studies, the unigene set has permitted significant enhancement of the number of publicly-available molecular genetic markers as tools for improvement of this species.</p

Evidence for Heterosis in Italian Ryegrass (Lolium multiflorum Lam.) Based on Inbreeding Depression in F2 Generation Offspring from Biparental Crosses

Italian ryegrass is one of the most important temperate forage grasses on a global basis. Improvement of both dry matter yield and quality of herbage have been major objectives of pasture grass breeding over the last century. F1 and F2 progeny sets derived from controlled pair-crosses between selected Italian ryegrass genotypes have been evaluated for yield and nutritive quality under field conditions. Linear regression of the performance of F1 families under sward conditions on parental genotype means in a spaced plant trial was significant for quality characteristics, but not for herbage yield. This result suggests that phenotypic selection of individual plants from spaced plant nursery is feasible for improvement of nutritive quality traits, but not for yield. The presence of significant heterosis within F1 populations was demonstrated by reduced herbage production in subsequent F2 populations (generated by one cycle of full-sib mating), an up to 22.1% total herbage yield in fresh weight, and a 30.5% survival rate at the end of the second reproductive cycle. Potential optimal crosses for exploiting such heterosis are discussed, based on construction and the inter-mating of complementary parental pools, for the implementation of a novel F1 hybrid production strategy

Prospects for genomic selection in forage plant species

Genomic selection (GS) is a powerful method for exploitation of DNA sequence polymorphisms in breeding improvement, through the prediction of breeding values based on all markers distributed genome-wide. Forage grasses and legumes provide important targets for GS implementation, as many key traits are difficult or expensive to assess, and are measured late in the breeding cycle. Generic attributes of forage breeding programmes are described, along with status of genomic resources for a representative species group (ryegrasses). Two schemes for implementing GS in ryegrass breeding are described. The first requires relatively little modification of current schemes, but could lead to significant reductions in operating cost. The second scheme would allow two rounds of selection for key agronomic traits within a time period previously required for a single round, potentially leading to doubling of genetic gain rate, but requires a purpose-designed reference population. In both schemes, the limited extent of linkage disequilibrium (LD), which is the major challenge for GS implementation in ryegrass breeding, is addressed. The strategies also incorporate recent advances in DNA sequencing technology to minimize costs

Genetic gain and inbreeding from genomic selection in a simulated commercial breeding program for perennial ryegrass

Genomic selection (GS) provides an attractive option for accelerating genetic gain in perennial ryegrass (Lolium perenne L.) improvement given the long cycle times of most current breeding programs. The present study used simulation to investigate the level of genetic gain and inbreeding obtained from GS breeding strategies compared with traditional breeding strategies for key traits (persistency, yield, and flowering time). Base population genomes were simulated through random mating for 60,000 generations at an effective population size of 10,000. The degree of linkage disequilibrium (LD) in the resulting population was compared with that obtained from empirical studies. Initial parental varieties were simulated to match diversity of current commercial cultivars. Genomic selection was designed to fit into a company breeding program at two selection points in the breeding cycle (spaced plants and miniplot). Genomic estimated breeding values (GEBVs) for productivity traits were trained with phenotypes and genotypes from plots. Accuracy of GEBVs was 0.24 for persistency and 0.36 for yield for single plants, while for plots it was lower (0.17 and 0.19, respectively). Higher accuracy of GEBVs was obtained for flowering time (up to 0.7), partially as a result of the larger reference population size that was available from the clonal row stage. The availability of GEBVs permit a 4-yr reduction in cycle time, which led to at least a doubling and trebling genetic gain for persistency and yield, respectively, than the traditional program. However, a higher rate of inbreeding per cycle among varieties was also observed for the GS strategy

Boosting genetic gain in allogamous crops via speed breeding and genomic selection

Breeding schemes that utilize modern breeding methods like genomic selection (GS) and speed breeding (SB) have the potential to accelerate genetic gain for different crops. We investigated through stochastic computer simulation the advantages and disadvantages of adopting both GS and SB (SpeedGS) into commercial breeding programs for allogamous crops. In addition, we studied the effect of omitting one or two selection stages from the conventional phenotypic scheme on GS accuracy, genetic gain, and inbreeding. As an example, we simulated GS and SB for five traits (heading date, forage yield, seed yield, persistency, and quality) with different genetic architectures and heritabilities (0.7, 0.3, 0.4, 0.1, and 0.3; respectively) for a tall fescue breeding program. We developed a new method to simulate correlated traits with complex architectures of which effects can be sampled from multiple distributions, e.g. to simulate the presence of both minor and major genes. The phenotypic selection scheme required 11 years, while the proposed SpeedGS schemes required four to nine years per cycle. Generally, SpeedGS schemes resulted in higher genetic gain per year for all traits especially for traits with low heritability such as persistency. Our results showed that running more SB rounds resulted in higher genetic gain per cycle when compared to phenotypic or GS only schemes and this increase was more pronounced per year when cycle time was shortened by omitting cycle stages. While GS accuracy declined with additional SB rounds, the decline was less in round three than in round two, and it stabilized after the fourth SB round. However, more SB rounds resulted in higher inbreeding rate, which could limit long-term genetic gain. The inbreeding rate was reduced by approximately 30% when generating the initial population for each cycle through random crosses instead of generating half-sib families. Our study demonstrated a large potential for additional genetic gain from combining GS and SB. Nevertheless, methods to mitigate inbreeding should be considered for optimal utilization of these highly accelerated breeding programs

The role of forage management in addressing challenges facing Australasian dairy farming

Forage management underpins the viability of pastoral dairy systems. This review investigated recent developments in forage research and their potential to enable pastoral dairy systems to meet the challenges that will be faced over the next 10 years. Grazing management, complementary forages, pasture diversity, fertiliser use, chemical restriction, irrigation management and pasture breeding are considered. None of these areas of research are looking to increase production directly through increased inputs, but, rather, they aim to lift maximum potential production, defend against production decline or improve the efficiency of the resource base and inputs. Technology approaches consistently focus on improving efficiency, while genetic improvement or the use of complementary forages and species diversity aim to lift production. These approaches do not require additional labour to implement, but many will require an increase in skill level. Only a few areas will help address animal welfare (e.g. the use of selected complementary forages and novel endophytes) and only complementary forages will help address increased competition from non-dairy alternatives, by positively influencing the properties of milk. Overall, the diversity of activity and potential effects will provide managers of pastoral dairy systems with the best tools to respond to the production and environmental challenges they face over the next 10 years

Optimizing resource allocation in a genomic breeding program for perennial ryegrass to balance genetic gain, cost, and inbreeding

Genomic selection (GS) has been recognized as offering numerous potential benefits for ryegrass (Lolium perenne L.) breeding. While the theoretical benefits of GS in ryegrass breeding are clear, the best way to apply GS in practical breeding programs remains to be determined. The present study aimed to investigate genomic breeding options that best balance genetic gain, breeding costs, and the level of inbreeding using stochastic simulation. Nine GS scenarios were tested, including different numbers of selection candidates (10,000, 5000, and 2000 F seedlings) and three reference population sizes for GS composed of plots representing a sward-based trial (500, 200, and 100 plots). Low to moderate prediction accuracy was achieved for productivity traits across cycles (i.e., 0.1–0.45 for yield [h = 0.3]). Scenarios with larger reference populations (i.e., 500 plots) achieved higher prediction accuracy but, when considering field trial costs, were more expensive per unit of genetic gain. All nine GS scenarios delivered significantly higher genetic gain (up to fourfold) than the conventional breeding scenario over a 20-yr period. Scenarios with moderate selection intensity on F seedlings with fewer plots tested in field gave the most genetic gain per dollar invested (i.e., 2000 or 5000 F seedlings and 100 plots). However, all GS scenarios reduced genetic diversity in the breeding population more than phenotypic selection, highlighting the need to mitigate inbreeding when applying GS in perennial ryegrass