scholarly journals Effect of Trait Heritability, Training Population Size and Marker Density on Genomic Prediction Accuracy Estimation in 22 bi-parental Tropical Maize Populations

2017 ◽  
Vol 8 ◽  
Author(s):  
Ao Zhang ◽  
Hongwu Wang ◽  
Yoseph Beyene ◽  
Kassa Semagn ◽  
Yubo Liu ◽  
...  
Keyword(s):  
Population Size ◽  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Marker Density ◽  
Tropical Maize ◽  
Training Population ◽  
Accuracy Estimation ◽  
Maize Populations

scholarly journals Genomic Prediction in Pea: Effect of Marker Density and Training Population Size and Composition on Prediction Accuracy

2015 ◽  
Vol 6 ◽  
Author(s):  
Nadim Tayeh ◽  
Anthony Klein ◽  
Marie-Christine Le Paslier ◽  
Françoise Jacquin ◽  
Hervé Houtin ◽  
...  
Keyword(s):  
Population Size ◽  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Marker Density ◽  
Training Population ◽  
And Training

scholarly journals The effects of training population design on genomic prediction accuracy in wheat

2019 ◽  
Author(s):  
Stefan McKinnon Edwards ◽  
Jaap B. Buntjer ◽  
Robert Jackson ◽  
Alison R. Bentley ◽  
Jacob Lage ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Training Population ◽  
Population Design

scholarly journals Evaluation of Genomic Prediction for Pasmo Resistance in Flax

2019 ◽  
Vol 20 (2) ◽  
pp. 359 ◽  
Author(s):  
Liqiang He ◽  
Jin Xiao ◽  
Khalid Rashid ◽  
Gaofeng Jia ◽  
Pingchuan Li ◽  
...  
Keyword(s):  
Population Size ◽  
Genomic Prediction ◽  
Field Experiments ◽  
Training Dataset ◽  
Nucleotide Polymorphisms ◽  
Training Population ◽  
Yield And Quality ◽  
Genome Wide ◽  
Best Linear Unbiased ◽  
Breeding Efficiency

Pasmo (Septoria linicola) is a fungal disease causing major losses in seed yield and quality and stem fibre quality in flax. Pasmo resistance (PR) is quantitative and has low heritability. To improve PR breeding efficiency, the accuracy of genomic prediction (GP) was evaluated using a diverse worldwide core collection of 370 accessions. Four marker sets, including three defined by 500, 134 and 67 previously identified quantitative trait loci (QTL) and one of 52,347 PR-correlated genome-wide single nucleotide polymorphisms, were used to build ridge regression best linear unbiased prediction (RR-BLUP) models using pasmo severity (PS) data collected from field experiments performed during five consecutive years. With five-fold random cross-validation, GP accuracy as high as 0.92 was obtained from the models using the 500 QTL when the average PS was used as the training dataset. GP accuracy increased with training population size, reaching values >0.9 with training population size greater than 185. Linear regression of the observed PS with the number of positive-effect QTL in accessions provided an alternative GP approach with an accuracy of 0.86. The results demonstrate the GP models based on marker information from all identified QTL and the 5-year PS average is highly effective for PR prediction.

scholarly journals Optimization of Genomic Selection to Improve Disease Resistance in Two Marine Fishes, the European Sea Bass (Dicentrarchus labrax) and the Gilthead Sea Bream (Sparus aurata)

2021 ◽  
Vol 12 ◽  
Author(s):  
Ronan Griot ◽  
François Allal ◽  
Florence Phocas ◽  
Sophie Brard-Fudulea ◽  
Romain Morvezen ◽  
...  
Keyword(s):  
Population Size ◽  
Genomic Selection ◽  
Sparus Aurata ◽  
Sea Bass ◽  
Gilthead Sea Bream ◽  
Sea Bream ◽  
Full Density ◽  
Marker Density ◽  
Training Population ◽  
European Sea Bass

Disease outbreaks are a major threat to the aquaculture industry, and can be controlled by selective breeding. With the development of high-throughput genotyping technologies, genomic selection may become accessible even in minor species. Training population size and marker density are among the main drivers of the prediction accuracy, which both have a high impact on the cost of genomic selection. In this study, we assessed the impact of training population size as well as marker density on the prediction accuracy of disease resistance traits in European sea bass (Dicentrarchus labrax) and gilthead sea bream (Sparus aurata). We performed a challenge to nervous necrosis virus (NNV) in two sea bass cohorts, a challenge to Vibrio harveyi in one sea bass cohort and a challenge to Photobacterium damselae subsp. piscicida in one sea bream cohort. Challenged individuals were genotyped on 57K–60K SNP chips. Markers were sampled to design virtual SNP chips of 1K, 3K, 6K, and 10K markers. Similarly, challenged individuals were randomly sampled to vary training population size from 50 to 800 individuals. The accuracy of genomic-based (GBLUP model) and pedigree-based estimated breeding values (EBV) (PBLUP model) was computed for each training population size using Monte-Carlo cross-validation. Genomic-based breeding values were also computed using the virtual chips to study the effect of marker density. For resistance to Viral Nervous Necrosis (VNN), as one major QTL was detected, the opportunity of marker-assisted selection was investigated by adding a QTL effect in both genomic and pedigree prediction models. As training population size increased, accuracy increased to reach values in range of 0.51–0.65 for full density chips. The accuracy could still increase with more individuals in the training population as the accuracy plateau was not reached. When using only the 6K density chip, accuracy reached at least 90% of that obtained with the full density chip. Adding the QTL effect increased the accuracy of the PBLUP model to values higher than the GBLUP model without the QTL effect. This work sets a framework for the practical implementation of genomic selection to improve the resistance to major diseases in European sea bass and gilthead sea bream.

scholarly journals Evaluation of Genomic Prediction for Pasmo Resistance in Flax

2018 ◽  
Author(s):  
Liqiang He ◽  
Jin Xiao ◽  
Khalid Y. Rashid ◽  
Gaofeng Jia ◽  
PingChuan Li ◽  
...  
Keyword(s):  
Population Size ◽  
Genomic Prediction ◽  
Field Experiments ◽  
Training Dataset ◽  
Nucleotide Polymorphisms ◽  
Training Population ◽  
Yield And Quality ◽  
Genome Wide ◽  
Best Linear Unbiased ◽  
Breeding Efficiency

Pasmo (Septoria linicola) is a fungal disease causing major losses in seed yield and quality, and stem fibre quality in flax. Pasmo resistance (PR) is quantitative and has low heritability. To improve PR breeding efficiency, the accuracy of genomic prediction (GP) was evaluated using a diverse worldwide core collection of 370 accessions. Four marker sets, including three defined by 500, 134, and 67 previously identified quantitative trait loci (QTL) and one of 52,347 PR-correlated genome-wide single nucleotide polymorphisms, were used to build ridge regression best linear unbiased prediction (RR-BLUP) models using pasmo severity (PS) data collected from field experiments performed during five consecutive years. With five-fold random cross-validation, GP accuracy as high as 0.92 was obtained from the models using the 500 QTL when the average PS was used as the training dataset. GP accuracy increased with training population size, reaching values >0.9 with training population size greater than 185. Linear regression of the observed PS with the number of positive-effect QTL in accessions provided an alternative GP approach with an accuracy of 0.86. The results demonstrate the GP models based on marker information from all identified QTL and the 5-year PS average is highly effective for PR prediction.

scholarly journals Optimizing whole-genomic prediction for autotetraploid blueberry breeding

Heredity ◽  
2020 ◽  
Vol 125 (6) ◽  
pp. 437-448 ◽  
Author(s):  
Ivone de Bem Oliveira ◽  
Rodrigo Rampazo Amadeu ◽  
Luis Felipe Ventorim Ferrão ◽  
Patricio R. Muñoz
Keyword(s):  
Genomic Prediction ◽  
Total Yield ◽  
Substantial Reduction ◽  
Breeding Population ◽  
Fruit Weight ◽  
Sequencing Depth ◽  
Breeding Cycle ◽  
Marker Density ◽  
Training Population ◽  
The Impact

Abstract Blueberry (Vaccinium spp.) is an important autopolyploid crop with significant benefits for human health. Apart from its genetic complexity, the feasibility of genomic prediction has been proven for blueberry, enabling a reduction in the breeding cycle time and increasing genetic gain. However, as for other polyploid crops, sequencing costs still hinder the implementation of genome-based breeding methods for blueberry. This motivated us to evaluate the effect of training population sizes and composition, as well as the impact of marker density and sequencing depth on phenotype prediction for the species. For this, data from a large real breeding population of 1804 individuals were used. Genotypic data from 86,930 markers and three traits with different genetic architecture (fruit firmness, fruit weight, and total yield) were evaluated. Herein, we suggested that marker density, sequencing depth, and training population size can be substantially reduced with no significant impact on model accuracy. Our results can help guide decisions toward resource allocation (e.g., genotyping and phenotyping) in order to maximize prediction accuracy. These findings have the potential to allow for a faster and more accurate release of varieties with a substantial reduction of resources for the application of genomic prediction in blueberry. We anticipate that the benefits and pipeline described in our study can be applied to optimize genomic prediction for other diploid and polyploid species.

scholarly journals Genomic prediction for malting quality traits in practical barley breeding programs

2020 ◽  
Author(s):  
Pernille Sarup ◽  
Vahid Edriss ◽  
Nanna Hellum Kristensen ◽  
Jens Due Jensen ◽  
Jihad Orabi ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Cross Validation ◽  
Spring Barley ◽  
Malting Quality ◽  
Breeding Cycle ◽  
Quality Traits ◽  
Training Population ◽  
Barley Breeding ◽  
Breeding Cycles

AbstractGenomic prediction can be advantageous in barley breeding for traits such as yield and malting quality to increase selection accuracy and minimize expensive phenotyping. In this paper, we investigate the possibilities of genomic selection for malting quality traits using a limited training population. The size of the training population is an important factor in determining the prediction accuracy of a trait. We investigated the potential for genomic prediction of malting quality within breeding cycles with leave one out (LOO) cross-validation, and across breeding cycles with leave set out (LSO) cross-validation. In addition, we investigated the effect of training population size on prediction accuracy by random two, four, and ten-fold cross-validation. The material used in this study was a population of 1329 spring barley lines from four breeding cycles. We found medium to high narrow sense heritabilities of the malting traits (0.31 to 0.65). Accuracies of predicting breeding values from LOO tests ranged from 0.6 to 0.9 making it worth the effort to use genomic prediction within breeding cycles. Accuracies from LSO tests ranged from 0.39 to 0.70 showing that genomic prediction across the breeding cycles were possible as well. Accuracy of prediction increased when the size of the training population increased. Therefore, prediction accuracy might be increased both within and across breeding cycle by increasing size of the training population

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