Dataset Information

Genomic Prediction for 25 Agronomic and Quality Traits in Alfalfa (Medicago sativa).

ABSTRACT: Agronomic and quality traits in alfalfa are very important to forage industry. Genomic prediction (GP) based on genotyping-by-sequencing (GBS) data could shorten the breeding cycles and accelerate the genetic gains of these complex traits, if they display moderate to high prediction accuracies. The aim of this study was to investigate the predictive potentials of these traits in alfalfa. A total of 322 genotypes from 75 alfalfa accessions were used for GP of the agronomic and quality traits, which were related to yield and nutrition value, respectively, using BayesA, BayesB, and BayesC? methods. Ten-fold cross validation was used to evaluate the accuracy of GP represented by the correlation between genomic estimated breeding value (GEBV) and estimated breeding value (EBV). The accuracies ranged from 0.0021 to 0.6485 for different traits. For each trait, three GP methods displayed similar prediction accuracies. Among 15 quality traits, mineral element Ca had a moderate and the highest prediction accuracy (0.34). NDF digestibility after 48 h (NDFD 48 h) and 30 h (NDFD 30 h) and mineral element Mg had prediction accuracies varying from 0.20 to 0.25. Other traits, for example, fat and crude protein, showed low prediction accuracies (0.05 to 0.19). Among 10 agronomic traits, however, some displayed relatively high prediction accuracies. Plant height (PH) in fall (FH) had the highest prediction accuracy (0.65), followed by flowering date (FD) and plant regrowth (PR) with accuracies at 0.52 and 0.51, respectively. Leaf to stem ratio (LS), plant branch (PB), and biomass yield (BY) reached to moderate prediction accuracies ranging from 0.25 to 0.32. Our results revealed that a few agronomic traits, such as FH, FD, and PR, had relatively high prediction accuracies, therefore it is feasible to apply genomic selection (GS) for these traits in alfalfa breeding programs. Because of the limitations of population size and density of SNP markers, several traits displayed low accuracies which could be improved by a bigger reference population, higher density of SNP markers, and more powerful statistic tools.

SUBMITTER: Jia C

PROVIDER: S-EPMC6109793 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Genomic Prediction for 25 Agronomic and Quality Traits in Alfalfa (<i>Medicago sativa</i>).

Jia Congjun C Zhao Fuping F Wang Xuemin X Han Jianlin J Zhao Haiming H Liu Guibo G Wang Zan Z

Frontiers in plant science 20180820

Agronomic and quality traits in alfalfa are very important to forage industry. Genomic prediction (GP) based on genotyping-by-sequencing (GBS) data could shorten the breeding cycles and accelerate the genetic gains of these complex traits, if they display moderate to high prediction accuracies. The aim of this study was to investigate the predictive potentials of these traits in alfalfa. A total of 322 genotypes from 75 alfalfa accessions were used for GP of the agronomic and quality traits, whi ...[more]

PMID: 30177947

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Project description:Genetic progress for forage quality has been poor in alfalfa (Medicago sativa L.), the most-grown forage legume worldwide. This study aimed at exploring opportunities for marker-assisted selection (MAS) and genomic selection of forage quality traits based on breeding values of parent plants. Some 154 genotypes from a broadly-based reference population were genotyped by genotyping-by-sequencing (GBS), and phenotyped for leaf-to-stem ratio, leaf and stem contents of protein, neutral detergent fiber (NDF) and acid detergent lignin (ADL), and leaf and stem NDF digestibility after 24 hours (NDFD), of their dense-planted half-sib progenies in three growing conditions (summer harvest, full irrigation; summer harvest, suspended irrigation; autumn harvest). Trait-marker analyses were performed on progeny values averaged over conditions, owing to modest germplasm × condition interaction. Genomic selection exploited 11,450 polymorphic SNP markers, whereas a subset of 8,494 M. truncatula-aligned markers were used for a genome-wide association study (GWAS). GWAS confirmed the polygenic control of quality traits and, in agreement with phenotypic correlations, indicated substantially different genetic control of a given trait in stems and leaves. It detected several SNPs in different annotated genes that were highly linked to stem protein content. Also, it identified a small genomic region on chromosome 8 with high concentration of annotated genes associated with leaf ADL, including one gene probably involved in the lignin pathway. Three genomic selection models, i.e., Ridge-regression BLUP, Bayes B and Bayesian Lasso, displayed similar prediction accuracy, whereas SVR-lin was less accurate. Accuracy values were moderate (0.3-0.4) for stem NDFD and leaf protein content, modest for leaf ADL and NDFD, and low to very low for the other traits. Along with previous results for the same germplasm set, this study indicates that GBS data can be exploited to improve both quality traits (by genomic selection or MAS) and forage yield.

Project description:BackgroundNucleorhabdoviruses possess bacilliform particles which contain a single-stranded negative-sense RNA genome. They replicate and mature in the nucleus of infected cells. Together with viruses of three other genera of the family Rhabdoviridae, they are known to infect plants and can be transmitted by arthropod vectors, during vegetative propagation, or by mechanical means. In 2010, an alfalfa (Medicago sativa) plant showing virus-like symptoms was collected from Stadl-Paura, Austria and sent to Julius Kühn Institute for analysis.MethodsElectron microscopy (EM) of leaf extracts from infected plants revealed the presence of rhabdovirus-like particles and was further used for ultrastructural analyses of infected plant tissue. Partially-purified preparations of rhabdovirus nucleocapsids were used for raising an antiserum. To determine the virus genome sequence, high throughput sequencing (HTS) was performed. RT-PCR primers were designed to confirm virus infection and to be used as a diagnostic tool.ResultsEM revealed bacilliform virions resembling those of plant-infecting rhabdoviruses. HTS of ribosomal RNA-depleted total RNA extracts revealed a consensus sequence consisting of 13,875 nucleotides (nt) and containing seven open reading frames (ORFs). Homology and phylogenetic analyses suggest that this virus isolate represents a new species of the genus Nucleorhabdovirus (family Rhabdoviridae). Since the virus originated from an alfalfa plant in Austria, the name alfalfa-associated nucleorhabdovirus (AaNV) is proposed. Viroplasms (Vp) and budding virions were observed in the nuclei of infected cells by EM, thus confirming its taxonomic assignment based on sequence data.ConclusionsIn this study, we identified and characterised a new nucleorhabdovirus from alfalfa. It shared only 39.8% nucleotide sequence identity with its closest known relative, black currant-associated rhabdovirus 1. The virus contains an additional open reading frame (accessory gene) with unknown function, located between the matrix protein and the glycoprotein genes. Serological and molecular diagnostic assays were designed for future screening of field samples. Further studies are needed to identify other natural hosts and potential vectors.

Dataset Information

Genomic Prediction for 25 Agronomic and Quality Traits in Alfalfa (Medicago sativa).

Publications

Genomic Prediction for 25 Agronomic and Quality Traits in Alfalfa (<i>Medicago sativa</i>).

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