Perennial ryegrass (Lolium perenne L.) is one of the most widely grown forage grasses in temperate agriculture. In order to maintain and increase its usage as forage in livestock agriculture, there is a continued need for improvement in biomass yield, quality, disease resistance, and seed yield. Genetic gain for traits such as biomass yield has been relatively modest. This has been attributed to its long breeding cycle, and the necessity to use population based breeding methods. Thanks to recent advances in genotyping techniques there is increasing interest in genomic selection from which genomically estimated breeding values are derived. In this paper we compare the classical RRBLUP model with state-of-the-art machine learning techniques that should yield themselves easily to use in GS and demonstrate their application to predicting quantitative traits in a breeding population of L. perenne. Prediction accuracies varied from 0 to 0.59 depending on trait, prediction model and composition of the training population. The BLUP model produced the highest prediction accuracies for most traits and training populations. Forage quality traits had the highest accuracies compared to yield related traits. There appeared to be no clear pattern to the effect of the training population composition on the prediction accuracies. The heritability of the forage quality traits was generally higher than for the yield related traits, and could partly explain the difference in accuracy. Some population structure was evident in the breeding populations, and probably contributed to the varying effects of training population on the predictions. The average linkage disequilibrium between adjacent markers ranged from 0.121 to 0.215. Higher marker density and larger training population closely related with the test population are likely to improve the prediction accuracy.
In many cultivated crop species there is limited genetic variation available for the development of new higher yielding varieties adapted to climate change and sustainable farming practises. The distant relatives of crop species provide a vast and largely untapped reservoir of genetic variation for a wide range of agronomically important traits that can be exploited by breeders for crop improvement. In this paper, in what we believe to be the largest introgression programme undertaken in the monocots, we describe the transfer of the entire genome of Festuca pratensis into Lolium perenne in overlapping chromosome segments. The L. perenne/F. pratensis introgressions were identified and characterised via 131 simple sequence repeats and 1612 SNPs anchored to the rice genome. Comparative analyses were undertaken to determine the syntenic relationship between L. perenne/ F. pratensis and rice, wheat, barley, sorghum and Brachypodium distachyon. Analyses comparing recombination frequency and gene distribution indicated that a large proportion of the genes within the genome are located in the proximal regions of chromosomes which undergo low/very low frequencies of recombination. Thus, it is proposed that past breeding efforts to produce improved varieties have centred on the subset of genes located in the distal regions of chromosomes where recombination is highest. The use of alien introgression for crop improvement is important for meeting the challenges of global food supply and the monocots such as the forage grasses and cereals, together with recent technological advances in molecular biology, can help meet these challenges.
BackgroundSainfoin (Onobrychis viciifolia) is a highly nutritious tannin-containing forage legume. In the diet of ruminants sainfoin can have anti-parasitic effects and reduce methane emissions under in vitro conditions. Many of these benefits have been attributed to condensed tannins or proanthocyanidins in sainfoin. A combination of increased use of industrially produced nitrogen fertilizer, issues with establishment and productivity in the first year and more reliable alternatives, such as red clover led to a decline in the use of sainfoin since the middle of the last century. In recent years there has been a resurgence of interest in sainfoin due to its potential beneficial nutraceutical and environmental attributes. However, genomic resources are scarce, thus hampering progress in genetic analysis and improvement. To address this we have used next generation RNA sequencing technology to obtain the first transcriptome of sainfoin. We used the library to identify gene-based simple sequence repeats (SSRs) and potential single nucleotide polymorphisms (SNPs).ResultsOne genotype from each of five sainfoin accessions was sequenced. Paired-end (PE) sequences were generated from cDNA libraries of RNA extracted from 7 day old seedlings. A combined assembly of 92,772 transcripts was produced de novo using the Trinity programme. About 18,000 transcripts were annotated with at least one GO (gene ontology) term. A total of 63 transcripts were annotated as involved in the tannin biosynthesis pathway. We identified 3786 potential SSRs. SNPs were identified by mapping the reads of the individual assemblies against the combined assembly. After stringent filtering a total of 77,000 putative SNPs were identified. A phylogenetic analysis of single copy number genes showed that sainfoin was most closely related to red clover and Medicago truncatula, while Lotus japonicus, bean and soybean are more distant relatives.ConclusionsThis work describes the first transcriptome assembly in sainfoin. The 92 K transcripts provide a rich source of SNP and SSR polymorphisms for future use in genetic studies of this crop. Annotation of genes involved in the condensed tannin biosynthesis pathway has provided the basis for further studies of the genetic control of this important trait in sainfoin.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3083-6) contains supplementary material, which is available to authorized users.
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