scholarly journals Genomic prediction models trained with historical records enable populating the German ex situ genebank bio-digital resource center of barley (Hordeum sp.) with information on resistances to soilborne barley mosaic viruses

2021 ◽  
Author(s):  
Maria Y. Gonzalez ◽  
Yusheng Zhao ◽  
Yong Jiang ◽  
Nils Stein ◽  
Antje Habekuss ◽  
...  
Keyword(s):  
Genetic Resources ◽  
Mosaic Virus ◽  
Genomic Prediction ◽  
Prediction Models ◽  
Ex Situ ◽  
Yellow Mosaic Virus ◽  
Plant Responses ◽  
Resource Center ◽  
Digital Resource ◽  
Best Linear Unbiased

AbstractKey messageGenomic prediction with special weight of major genes is a valuable tool to populate bio-digital resource centers.AbstractPhenotypic information of crop genetic resources is a prerequisite for an informed selection that aims to broaden the genetic base of the elite breeding pools. We investigated the potential of genomic prediction based on historical screening data of plant responses against theBarley yellow mosaic virusesfor populating the bio-digital resource center of barley. Our study includes dense marker data for 3838 accessions of winter barley, and historical screening data of 1751 accessions forBarley yellow mosaic virus(BaYMV) and of 1771 accessions forBarley mild mosaic virus(BaMMV). Linear mixed models were fitted by considering combinations for the effects of genotypes, years, and locations. The best linear unbiased estimations displayed a broad spectrum of plant responses against BaYMV and BaMMV. Prediction abilities, computed as correlations between predictions and observed phenotypes of accessions, were low for the marker-assisted selection approach amounting to 0.42. In contrast, prediction abilities of genomic best linear unbiased predictions were high, with values of 0.62 for BaYMV and 0.64 for BaMMV. Prediction abilities of genomic prediction were improved by up to ~ 5% using W-BLUP, in which more weight is given to markers with significant major effects found by association mapping. Our results outline the utility of historical screening data and W-BLUP model to predict the performance of the non-phenotyped individuals in genebank collections. The presented strategy can be considered as part of the different approaches used in genebank genomics to valorize genetic resources for their usage in disease resistance breeding and research.

scholarly journals Accounting for Group-Specific Allele Effects and Admixture in Genomic Predictions: Theory and Experimental Evaluation in Maize

Genetics ◽  
2020 ◽  
Vol 216 (1) ◽  
pp. 27-41
Author(s):  
Simon Rio ◽  
Laurence Moreau ◽  
Alain Charcosset ◽  
Tristan Mary-Huard
Keyword(s):  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Best Linear Unbiased Prediction ◽  
Linear Unbiased Prediction ◽  
Modeling Group ◽  
A Genome ◽  
Specific Allele ◽  
Best Linear Unbiased ◽  
Unbiased Prediction

Populations structured into genetic groups may display group-specific linkage disequilibrium, mutations, and/or interactions between quantitative trait loci and the genetic background. These factors lead to heterogeneous marker effects affecting the efficiency of genomic prediction, especially for admixed individuals. Such individuals have a genome that is a mosaic of chromosome blocks from different origins, and may be of interest to combine favorable group-specific characteristics. We developed two genomic prediction models adapted to the prediction of admixed individuals in presence of heterogeneous marker effects: multigroup admixed genomic best linear unbiased prediction random individual (MAGBLUP-RI), modeling the ancestry of alleles; and multigroup admixed genomic best linear unbiased prediction random allele effect (MAGBLUP-RAE), modeling group-specific distributions of allele effects. MAGBLUP-RI can estimate the segregation variance generated by admixture while MAGBLUP-RAE can disentangle the variability that is due to main allele effects from the variability that is due to group-specific deviation allele effects. Both models were evaluated for their genomic prediction accuracy using a maize panel including lines from the Dent and Flint groups, along with admixed individuals. Based on simulated traits, both models proved their efficiency to improve genomic prediction accuracy compared to standard GBLUP models. For real traits, a clear gain was observed at low marker densities whereas it became limited at high marker densities. The interest of including admixed individuals in multigroup training sets was confirmed using simulated traits, but was variable using real traits. Both MAGBLUP models and admixed individuals are of interest whenever group-specific SNP allele effects exist.

scholarly journals Benchmarking between item based collaborative filtering algorithm and genomic best linear unbiased prediction (GBLUP) model in terms of prediction accuracy for wheat and maize//Estudio comparativo en términos de capacidad predictiva para datos de trigo y maíz entre el algoritmo de filtrado colaborativo y el modelo genómico mejor predictor lineal insesgado (GBLUP)

Biotecnia ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. 136-146
Author(s):  
Osval A. Montesinos-López ◽  
Emeterio Franco-Pérez ◽  
Francisco J. Luna-Vázquez ◽  
Josafat Salinas-Ruiz ◽  
Sara Sandoval-Carrillo ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Genomic Prediction ◽  
Prediction Models ◽  
Best Linear Unbiased Prediction ◽  
Linear Unbiased Prediction ◽  
Collaborative Filtering Algorithm ◽  
New Methodologies ◽  
Best Linear Unbiased ◽  
Better Than ◽  
Unbiased Prediction

Aim/background: in view of the growing demand for food, new methodologies are needed to improve the genomic selection (GS) methodology to obtain more productive plant varieties and there is empirical evidence that GS it is revolutionizing plant breeding for food production around the world. Methods: since the prediction models play a key role in GS, for this reason Montesinos-López et al. (2018) proposed the item based collaborative filtering (IBCF) algorithm for Genomic prediction. For this reason, in this paper we compare the IBCF algorithm with the most popular genomic prediction model called the Genomic Best Linear Unbiased Prediction (GBLUP). Results: We found that the GBLUP is superior than the IBCF model, but the IBCF is competitive to the GBLUP model since produced very similar predictions, but with the large advantage that it is extremely efficient in terms of time for implementation. Conclusions: we found that the GBLUP is better than the IBCF algorithm but the IBCF is more than 400 times more efficient than the GBLUP model in terms of time for implementation. Limitations: The main limitation of the study is that it was performed in univariate terms and it is possible that the IBCF will perform better with multivariate data.RESUMENObjetivo / antecedentes: en vista de la creciente demanda de alimentos, se necesitan nuevas metodologías para mejorar la selección genómica (GS) para obtener variedades de plantas más productivas y en menor tiempo y existe evidencia que la SG está revolucionando el mejoramiento de plantas que ayudará a incrementar la producción de alimentos a nivel mundial. Métodos: dado que los modelos de predicción juegan un papel clave en GS, Montesinos-López et al. (2018) propusieron el algoritmo de filtrado colaborativo (IBCF) para la predicción genómica. Por esta razón, en este artículo comparamos el algoritmo IBCF con el modelo de predicción genómica más popular denominado mejor predictor lineal insesgado Bayesiano (GBLUP). Resultados: Encontramos que el GBLUP es superior en capacidad predictiva al modelo IBCF, pero el IBCF es competitivo con el modelo GBLUP ya que produjo predicciones muy similares, pero con la ventaja de que es eficiente en términos de tiempo de implementación. Conclusiones: encontramos que el GBLUP es mejor que el algoritmo IBCF, pero el IBCF es 400 veces más eficiente que el modelo GBLUP en términos de tiempo de implementación. Limitaciones: la principal limitación del estudio es que se realizó en términos univariados y es posible que el IBCF se desempeñe mejor con datos multivariados.

scholarly journals Accounting for epistasis improves genomic prediction of phenotypes with univariate and bivariate models across environments

2021 ◽  
Author(s):  
Elaheh Vojgani ◽  
Torsten Pook ◽  
Johannes W. R. Martini ◽  
Armin C. Hölker ◽  
Manfred Mayer ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Prediction Models ◽  
Additive Model ◽  
Predictive Ability ◽  
Random Regression ◽  
Average Increase ◽  
Bivariate Model ◽  
Bivariate Models ◽  
Best Linear Unbiased ◽  
Selected Subset

Abstract Key Message The accuracy of genomic prediction of phenotypes can be increased by including the top-ranked pairwise SNP interactions into the prediction model. Abstract We compared the predictive ability of various prediction models for a maize dataset derived from 910 doubled haploid lines from two European landraces (Kemater Landmais Gelb and Petkuser Ferdinand Rot), which were tested at six locations in Germany and Spain. The compared models were Genomic Best Linear Unbiased Prediction (GBLUP) as an additive model, Epistatic Random Regression BLUP (ERRBLUP) accounting for all pairwise SNP interactions, and selective Epistatic Random Regression BLUP (sERRBLUP) accounting for a selected subset of pairwise SNP interactions. These models have been compared in both univariate and bivariate statistical settings for predictions within and across environments. Our results indicate that modeling all pairwise SNP interactions into the univariate/bivariate model (ERRBLUP) is not superior in predictive ability to the respective additive model (GBLUP). However, incorporating only a selected subset of interactions with the highest effect variances in univariate/bivariate sERRBLUP can increase predictive ability significantly compared to the univariate/bivariate GBLUP. Overall, bivariate models consistently outperform univariate models in predictive ability. Across all studied traits, locations and landraces, the increase in prediction accuracy from univariate GBLUP to univariate sERRBLUP ranged from 5.9 to 112.4 percent, with an average increase of 47 percent. For bivariate models, the change ranged from −0.3 to + 27.9 percent comparing the bivariate sERRBLUP to the bivariate GBLUP, with an average increase of 11 percent. This considerable increase in predictive ability achieved by sERRBLUP may be of interest for “sparse testing” approaches in which only a subset of the lines/hybrids of interest is observed at each location.

Organization of the Genome of Turnip Yellow Mosaic Virus deduced from its Messenger Properties

1979 ◽  
Vol 7 (5) ◽  
pp. 1095-1097 ◽  
Author(s):  
CLAUDE BÉNICOURT ◽  
MARIE-DOMINIQUE MORCH
Keyword(s):  
Mosaic Virus ◽  
Yellow Mosaic Virus ◽  
Turnip Yellow Mosaic Virus

Ex-situ conservation of medicinal plant genetic resources managed by national agrobiodiversity center of Korea

Planta Medica ◽  
2012 ◽  
Vol 78 (11) ◽  
Author(s):  
JS Sung ◽  
CW Jeong ◽  
YY Lee ◽  
HS Lee ◽  
YA Jeon ◽  
...  
Keyword(s):  
Medicinal Plant ◽  
Genetic Resources ◽  
Ex Situ Conservation ◽  
Plant Genetic Resources ◽  
Ex Situ

scholarly journals Towards plant resistance to viruses using protein-only RNase P

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Anthony Gobert ◽  
Yifat Quan ◽  
Mathilde Arrivé ◽  
Florent Waltz ◽  
Nathalie Da Silva ◽  
...  
Keyword(s):  
Mosaic Virus ◽  
Virus Replication ◽  
Yield Loss ◽  
Rnase P ◽  
Plant Viruses ◽  
Yellow Mosaic Virus ◽  
Resistance To Viruses ◽  
Target Plant

AbstractPlant viruses cause massive crop yield loss worldwide. Most plant viruses are RNA viruses, many of which contain a functional tRNA-like structure. RNase P has the enzymatic activity to catalyze the 5′ maturation of precursor tRNAs. It is also able to cleave tRNA-like structures. However, RNase P enzymes only accumulate in the nucleus, mitochondria, and chloroplasts rather than cytosol where virus replication takes place. Here, we report a biotechnology strategy based on the re-localization of plant protein-only RNase P to the cytosol (CytoRP) to target plant viruses tRNA-like structures and thus hamper virus replication. We demonstrate the cytosol localization of protein-only RNase P in Arabidopsis protoplasts. In addition, we provide in vitro evidences for CytoRP to cleave turnip yellow mosaic virus and oilseed rape mosaic virus. However, we observe varied in vivo results. The possible reasons have been discussed. Overall, the results provided here show the potential of using CytoRP for combating some plant viral diseases.

scholarly journals High Prevalence of Three Potyviruses Infecting Cucurbits in Oklahoma and Phylogenetic Analysis of Cucurbit Aphid-Borne Yellows Virus Isolated from Pumpkins

Pathogens ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 53
Author(s):  
Vivek Khanal ◽  
Harrington Wells ◽  
Akhtar Ali
Keyword(s):  
Phylogenetic Analysis ◽  
Mosaic Virus ◽  
Zucchini Yellow Mosaic Virus ◽  
Yellow Mosaic Virus ◽  
Watermelon Mosaic Virus ◽  
High Prevalence ◽  
The Us ◽  
Close Relationship ◽  
The Status ◽  
Low Incidence

Field information about viruses infecting crops is fundamental for understanding the severity of the effects they cause in plants. To determine the status of cucurbit viruses, surveys were conducted for three consecutive years (2016–2018) in different agricultural districts of Oklahoma. A total of 1331 leaf samples from >90 fields were randomly collected from both symptomatic and asymptomatic cucurbit plants across 11 counties. All samples were tested with the dot-immunobinding assay (DIBA) against the antisera of 10 known viruses. Samples infected with papaya ringspot virus (PRSV-W), watermelon mosaic virus (WMV), zucchini yellow mosaic virus (ZYMV), and cucurbit aphid-borne-yellows virus (CABYV) were also tested by RT-PCR. Of the 10 viruses, PRSV-W was the most widespread, with an overall prevalence of 59.1%, present in all 11 counties, followed by ZYMV (27.6%), in 10 counties, and WMV (20.7%), in seven counties, while the remaining viruses were present sporadically with low incidence. Approximately 42% of the infected samples were positive, with more than one virus indicating a high proportion of mixed infections. CABYV was detected for the first time in Oklahoma, and the phylogenetic analysis of the first complete genome sequence of a CABYV isolate (BL-4) from the US showed a close relationship with Asian isolates.

scholarly journals Genomic Prediction Using Alternative Strategies of Weighted Single-Step Genomic BLUP for Yearling Weight and Carcass Traits in Hanwoo Beef Cattle

Genes ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 266
Author(s):  
Hossein Mehrban ◽  
Masoumeh Naserkheil ◽  
Deuk Hwan Lee ◽  
Chungil Cho ◽  
Taejeong Choi ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Beef Cattle ◽  
Genomic Prediction ◽  
Quantitative Trait ◽  
Carcass Traits ◽  
Best Linear Unbiased Prediction ◽  
Single Step ◽  
Linear Unbiased Prediction ◽  
Single Nucleotide ◽  
Best Linear Unbiased

The weighted single-step genomic best linear unbiased prediction (GBLUP) method has been proposed to exploit information from genotyped and non-genotyped relatives, allowing the use of weights for single-nucleotide polymorphism in the construction of the genomic relationship matrix. The purpose of this study was to investigate the accuracy of genetic prediction using the following single-trait best linear unbiased prediction methods in Hanwoo beef cattle: pedigree-based (PBLUP), un-weighted (ssGBLUP), and weighted (WssGBLUP) single-step genomic methods. We also assessed the impact of alternative single and window weighting methods according to their effects on the traits of interest. The data was comprised of 15,796 phenotypic records for yearling weight (YW) and 5622 records for carcass traits (backfat thickness: BFT, carcass weight: CW, eye muscle area: EMA, and marbling score: MS). Also, the genotypic data included 6616 animals for YW and 5134 for carcass traits on the 43,950 single-nucleotide polymorphisms. The ssGBLUP showed significant improvement in genomic prediction accuracy for carcass traits (71%) and yearling weight (99%) compared to the pedigree-based method. The window weighting procedures performed better than single SNP weighting for CW (11%), EMA (11%), MS (3%), and YW (6%), whereas no gain in accuracy was observed for BFT. Besides, the improvement in accuracy between window WssGBLUP and the un-weighted method was low for BFT and MS, while for CW, EMA, and YW resulted in a gain of 22%, 15%, and 20%, respectively, which indicates the presence of relevant quantitative trait loci for these traits. These findings indicate that WssGBLUP is an appropriate method for traits with a large quantitative trait loci effect.

Conservation of animal genetic resources: approaches and technologies for in situ and ex situ conservation

2008 ◽  
Vol 42 ◽  
pp. 71-85 ◽  
Author(s):  
J.A. Woolliams ◽  
O. Matika ◽  
J. Pattison
Keyword(s):  
Rural Communities ◽  
Genetic Resources ◽  
Large Scale ◽  
Production Systems ◽  
Regional Distribution ◽  
Livestock Production ◽  
Ex Situ ◽  
Animal Genetic Resources ◽  
The Face ◽  
Livestock Products

SummaryLivestock production faces major challenges through the coincidence of major drivers of change, some with conflicting directions. These are:1. An unprecedented global change in demands for traditional livestock products such as meat, milk and eggs.2. Large changes in the demographic and regional distribution of these demands.3. The need to reduce poverty in rural communities by providing sustainable livelihoods.4. The possible emergence of new agricultural outputs such as bio-fuels making a significant impact upon traditional production systems.5. A growing awareness of the need to reduce the environmental impact of livestock production.6. The uncertainty in the scale and impact of climate change. This paper explores these challenges from a scientific perspective in the face of the large-scale and selective erosion of our animal genetic resources, and concludes thai there is a stronger and more urgent need than ever before to secure the livestock genetic resources available to humankind through a comprehensive global conservation programme.

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