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

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.

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Optimizing Training Population Size and Genotyping Strategy for Genomic Prediction Using Association Study Results and Pedigree Information. A Case of Study in Advanced Wheat Breeding Lines

PLoS ONE ◽

10.1371/journal.pone.0169606 ◽

2017 ◽

Vol 12 (1) ◽

pp. e0169606 ◽

Cited By ~ 43

Author(s):

Fabio Cericola ◽

Ahmed Jahoor ◽

Jihad Orabi ◽

Jeppe R. Andersen ◽

Luc L. Janss ◽

...

Keyword(s):

Association Study ◽

Population Size ◽

Genomic Prediction ◽

Wheat Breeding ◽

Pedigree Information ◽

Training Population ◽

Breeding Lines ◽

Study Results

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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)

Frontiers in Genetics ◽

10.3389/fgene.2021.665920 ◽

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.

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Improving Prediction Accuracy Using Multi-allelic Haplotype Prediction and Training Population Optimization in Wheat

G3 Genes|Genome|Genetics ◽

10.1534/g3.120.401165 ◽

2020 ◽

Vol 10 (7) ◽

pp. 2265-2273 ◽

Cited By ~ 1

Author(s):

Ahmad H. Sallam ◽

Emily Conley ◽

Dzianis Prakapenka ◽

Yang Da ◽

James A. Anderson

Keyword(s):

Population Structure ◽

Protein Content ◽

Prediction Accuracy ◽

Cross Validation ◽

Predictive Ability ◽

Training Population ◽

Percentage Points ◽

And Training ◽

Fold Cross Validation ◽

Single Snps

The use of haplotypes may improve the accuracy of genomic prediction over single SNPs because haplotypes can better capture linkage disequilibrium and genomic similarity in different lines and may capture local high-order allelic interactions. Additionally, prediction accuracy could be improved by portraying population structure in the calibration set. A set of 383 advanced lines and cultivars that represent the diversity of the University of Minnesota wheat breeding program was phenotyped for yield, test weight, and protein content and genotyped using the Illumina 90K SNP Assay. Population structure was confirmed using single SNPs. Haplotype blocks of 5, 10, 15, and 20 adjacent markers were constructed for all chromosomes. A multi-allelic haplotype prediction algorithm was implemented and compared with single SNPs using both k-fold cross validation and stratified sampling optimization. After confirming population structure, the stratified sampling improved the predictive ability compared with k-fold cross validation for yield and protein content, but reduced the predictive ability for test weight. In all cases, haplotype predictions outperformed single SNPs. Haplotypes of 15 adjacent markers showed the best improvement in accuracy for all traits; however, this was more pronounced in yield and protein content. The combined use of haplotypes of 15 adjacent markers and training population optimization significantly improved the predictive ability for yield and protein content by 14.3 (four percentage points) and 16.8% (seven percentage points), respectively, compared with using single SNPs and k-fold cross validation. These results emphasize the effectiveness of using haplotypes in genomic selection to increase genetic gain in self-fertilized crops.

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Genomic prediction of fruit texture and training population optimization towards the application of genomic selection in apple

Horticulture Research ◽

10.1038/s41438-020-00370-5 ◽

2020 ◽

Vol 7 (1) ◽

Cited By ~ 1

Author(s):

Morgane Roth ◽

Hélène Muranty ◽

Mario Di Guardo ◽

Walter Guerra ◽

Andrea Patocchi ◽

...

Keyword(s):

Genomic Selection ◽

Genomic Prediction ◽

Training Population ◽

Fruit Texture ◽

And Training

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Optimising Genomic Selection in Wheat: Effect of Marker Density, Population Size and Population Structure on Prediction Accuracy

G3 Genes|Genome|Genetics ◽

10.1534/g3.118.200311 ◽

2018 ◽

Vol 8 (9) ◽

pp. 2889-2899 ◽

Cited By ~ 33

Author(s):

Adam Norman ◽

Julian Taylor ◽

James Edwards ◽

Haydn Kuchel

Keyword(s):

Population Structure ◽

Population Size ◽

Genomic Selection ◽

Prediction Accuracy ◽

Marker Density ◽

Density Population

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Effects of marker density and population structure on the genomic prediction accuracy for growth trait in Pacific white shrimp Litopenaeus vannamei

BMC Genetics ◽

10.1186/s12863-017-0507-5 ◽

2017 ◽

Vol 18 (1) ◽

Cited By ~ 29

Author(s):

Quanchao Wang ◽

Yang Yu ◽

Jianbo Yuan ◽

Xiaojun Zhang ◽

Hao Huang ◽

...

Keyword(s):

Population Structure ◽

Litopenaeus Vannamei ◽

Genomic Prediction ◽

Prediction Accuracy ◽

Growth Trait ◽

Pacific White Shrimp ◽

White Shrimp ◽

Marker Density

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Evaluation of Genomic Prediction for Pasmo Resistance in Flax

10.20944/preprints201811.0623.v1 ◽

2018 ◽

Cited By ~ 1

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.

Download Full-text