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

Cericola, Fabio; Jahoor, A.; Orabi, Jihad; Andersen, Jeppe Reitan; Janss, Luc; Jensen, Just

doi:10.1371/journal.pone.0169606

Cited by 91 publications

(129 citation statements)

References 42 publications

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“…In theory, the prediction accuracy is positively related to the training population size, as established by simulation studies [49–51]. This was confirmed in many empirical studies such as those already mentioned [42, 46, 47]. As expected from the theory, optimizing a subset of training lines gave higher PA than random selection.…”

Section: Discussionsupporting

confidence: 56%

“…This was reported many times. For example, in a wheat study [47] selected markers by GWAS on the training set only, as we did also in Fig 4. However, they observed a gain in predictive ability of up to 0.2, particularly for very small number of markers (<100), while we only had small improvement of about 0.01 with a maximum gain for 2000 markers.…”

Section: Discussionmentioning

confidence: 99%

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BWGS: a R package for genomic selection and its application to a wheat breeding programme

Charmet

Tran

Auzanneau³

et al. 2019

Preprint

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AbstractWe developed an integrated R library called BWGS to enable easy computation of Genomic Estimates of Breeding values (GEBV) for genomic selection. BWGS relies on existing R-libraries, all freely available from CRAN servers. The two main functions enable to run 1) replicated random cross validations within a training set of genotyped and phenotyped lines and 2) GEBV prediction, for a set of genotyped-only lines. Options are available for 1) missing data imputation, 2) markers and training set selection and 3) genomic prediction with 15 different methods, either parametric or semi-parametric.The usefulness and efficiency of BWGS are illustrated using a population of wheat lines from a real breeding programme. Adjusted yield data from historical trials (highly unbalanced design) were used for testing the options of BWGS. On the whole, 760 candidate lines with adjusted phenotypes and genotypes for 47 839 robust SNP were used. With a simple desktop computer, we obtained results which compared with previously published results on wheat genomic selection. As predicted by the theory, factors that are most influencing predictive ability, for a given trait of moderate heritability, are the size of the training population and a minimum number of markers for capturing every QTL information. Missing data up to 40%, if randomly distributed, do not degrade predictive ability once imputed, and up to 80% randomly distributed missing data are still acceptable once imputed with Expectation-Maximization method of package rrBLUP. It is worth noticing that selecting markers that are most associated to the trait do improve predictive ability, compared with the whole set of markers, but only when marker selection is made on the whole population. When marker selection is made only on the sampled training set, this advantage nearly disappeared, since it was clearly due to overfitting. Few differences are observed between the 15 prediction models with this dataset. Although non-parametric methods that are supposed to capture non-additive effects have slightly better predictive accuracy, differences remain small. Finally, the GEBV from the 15 prediction models are all highly correlated to each other. These results are encouraging for an efficient use of genomic selection in applied breeding programmes and BWGS is a simple and powerful toolbox to apply in breeding programmes or training activities.

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Section: Discussionsupporting

confidence: 56%

Section: Discussionmentioning

confidence: 99%

BWGS: a R package for genomic selection and its application to a wheat breeding programme

Charmet

Tran

Auzanneau³

et al. 2019

Preprint

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“…In the long run, the gains achieved using these approaches through increased selection intensity and faster breeding cycles should give additional advantage over the traditional marker or phenotypic selection [15,16]. Furthermore, prediction ability could be improved by optimizing different factors that affect GS accuracy such as the genetic architecture of the trait, heritability, number of markers, genetic and phenotypic correlations among the traits, and the percentage of missing data, either alone or in combination [66][67][68]. Ultimately, the success of genomic predictions in breeding programs does not all depend on the calculated prediction ability [69] but on how breeders will use this information in performing guided decisions on which lines to advance or used as parents.…”

Section: Prediction Ability For Single and Multiple Trait Genomic Selmentioning

confidence: 99%

Accuracy of Single and Multi-Trait Genomic Prediction Models for Grain Yield in US Pacific Northwest Winter Wheat

2019

Crop Breed Genet Genom.

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Incorporating secondary correlated traits collected from high-throughput phenotyping in genomic selection (GS) models for complex traits has been demonstrated to improve accuracy. The prediction ability of different single and multiple trait partial least square (PLS) regression models for grain yield were assessed for winter wheat lines evaluated in US Pacific Northwest environments. Different populations including a diversity panel, F5, and double haploid breeding lines were evaluated in Lind and Pullman, WA between 2015 and 2018 and were genotyped with genotyping by sequencing-derived SNP markers. Prediction ability was assessed under cross-validations and independent predictions. Multi-trait covariate models were advantageous in obtaining optimal predictions for yield, especially when there is less genetic relatedness between the training and test populations. Adding multiple traits in the model improved predictions for environments with low heritability. Cross-validations resulted in the highest prediction ability (0.16) whereas independent predictions using the diversity panel to predict F5 and double haploid winter wheat breeding lines obtained the lowest (0.002). Relatedness between populations, heritability of the secondary traits, and the type of PLS model used were among the principal factors affecting prediction ability. Our results showed the relevance of using multi-trait GS models to achieve increased predictions. Genetic architecture of the target trait and genetic relatedness between populations should be taken into consideration when choosing which type of models to implement in the breeding program. An increased prediction ability for the multi-trait models indicates the potential to attain improved genetic gains for yield in wheat breeding programs through these GS approaches.

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“…The model is eventually used to predict the un-phenotyped validation population and calculate GEBV. The correlation between the observed and predicted values is called prediction accuracy and is affected by several factors, including genetic relatedness between the training and test populations, training population size and composition, number of markers, population structure, and genetic architecture of traits [22][23][24][25].Among the measures of phenotypic stability that have been widely used are the Additive Main Effect and Multiplicative Interaction (AMMI) and Finlay-Wilkinson (FW) regression, where each parameter measures different aspects of trait stability. The AMMI model uses the additive analysis of variance (ANOVA) to partition variation into genotype (G), environment (E), and GEI effects, and the multiplicative principal components analysis (PCA) of the GEI [26,27], where G and G + GE effects represent wide and specific adaptation, respectively [28,29].…”

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confidence: 99%

Insights into the Genetic Architecture of Phenotypic Stability Traits in Winter Wheat

Lozada

Carter

2020

Agronomy

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Examining the architecture of traits through genomics is necessary to gain a better understanding of the genetic loci affecting important traits to facilitate improvement. Genomewide association study (GWAS) and genomic selection (GS) were implemented for grain yield, heading date, and plant height to gain insights into the genetic complexity of phenotypic stability of traits in a diverse population of US Pacific Northwest winter wheat. Analysis of variance using the Additive Main Effect and Multiplicative Interaction (AMMI) approach revealed significant genotype and genotype by environment interactions. GWAS identified 12 SNP markers distributed across 10 chromosomes affecting variation for both trait and phenotypic stability, indicating potential pleiotropic effects and signifying that similar genetic loci could be associated with different aspects of stability. The lack of stable and major effect loci affecting phenotypic variation supports the complexity of stability of traits. Accuracy of GS was low to moderate, between 0.14 and 0.66, indicating that phenotypic stability is under genetic control. The moderate to high correlation between trait and trait stability suggests the potential of simultaneous selection for trait and trait stability. Our results demonstrate the complex genetic architecture of trait stability and show the potential for improving stability in winter wheat using genomic-assisted approaches.Agronomy 2020, 10, 368 2 of 15 the strength of linkage disequilibrium (i.e., the non-random association of alleles at multiple loci) between markers and functional polymorphism across diverse germplasm [10]. In wheat, GWAS has been conducted in different traits such as grain yield, yield components (thousand kernel weight and kernel number), Fusarium head blight resistance, snow mold tolerance, plant height, and heading date [11][12][13][14][15][16]. Compared with biparental mapping, GWAS results in a higher mapping resolution as it exploits the majority of the recombination histories of the individuals belonging to the diverse population used [17,18]. The main drawback of GWAS, nonetheless, is that it might not able to capture the causative and rare variants (loci) with small effects, and therefore would be a special case of missing heritability, i.e., the portion of genetic variance that cannot be explained by all significant loci [19]. In this case, genomic selection (GS) is seen as a complementary approach to GWAS.GS uses genomewide markers to estimate the marker effects and calculate genomic estimated breeding values (GEBV), which represents the genetic "worth" or merit of an individual. A high GEBV for grain yield, for example, would mean that a line is predicted to have better grain yield compared with others. In contrast to GWAS, GS does not test for significance but rather uses genomewide marker data to calculate the GEBV. More genetic variation could be captured in GS compared with marker-assisted selection as all markers are used in estimating breeding values [20]. In GS, a prediction model ...

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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

Cited by 91 publications

References 42 publications

BWGS: a R package for genomic selection and its application to a wheat breeding programme

BWGS: a R package for genomic selection and its application to a wheat breeding programme

Accuracy of Single and Multi-Trait Genomic Prediction Models for Grain Yield in US Pacific Northwest Winter Wheat

Insights into the Genetic Architecture of Phenotypic Stability Traits in Winter Wheat

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