Strategies Using Genomic Selection to Increase Genetic Gain in Breeding Programs for Wheat

Tessema, Biructawit Bekele; Liu, Huiming; Sørensen, Anders Christian; Andersen, Jeppe Reitan; Jensen, Just

doi:10.3389/fgene.2020.578123

Cited by 30 publications

(19 citation statements)

References 23 publications

(39 reference statements)

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“…A significant advantage of GS is the ability to select superior individuals in a population at very early stages in a breeding cycle based on their genotype, versus conducting lengthy/expensive phenotyping trials prior to each selection cycle. For instance, in a simulated wheat breeding program, selection based on GEBVs for grain yield tripled the genetic gain compared with PS [ 75 ]. In alfalfa breeding programs, GS can be implemented in elite large germplasm panels with genotyped individuals to decrease PS efforts, thus reducing overall selection cycle time and accelerating variety development.…”

Section: Discussionmentioning

confidence: 99%

Strategies to Increase Prediction Accuracy in Genomic Selection of Complex Traits in Alfalfa (Medicago sativa L.)

Medina

Kaur

Ray

et al. 2021

Cells

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Agronomic traits such as biomass yield and abiotic stress tolerance are genetically complex and challenging to improve through conventional breeding approaches. Genomic selection (GS) is an alternative approach in which genome-wide markers are used to determine the genomic estimated breeding value (GEBV) of individuals in a population. In alfalfa (Medicago sativa L.), previous results indicated that low to moderate prediction accuracy values (<70%) were obtained in complex traits, such as yield and abiotic stress resistance. There is a need to increase the prediction value in order to employ GS in breeding programs. In this paper we reviewed different statistic models and their applications in polyploid crops, such as alfalfa and potato. Specifically, we used empirical data affiliated with alfalfa yield under salt stress to investigate approaches that use DNA marker importance values derived from machine learning models, and genome-wide association studies (GWAS) of marker-trait association scores based on different GWASpoly models, in weighted GBLUP analyses. This approach increased prediction accuracies from 50% to more than 80% for alfalfa yield under salt stress. Finally, we expended the weighted GBLUP approach to potato and analyzed 13 phenotypic traits and obtained similar results. This is the first report on alfalfa to use variable importance and GWAS-assisted approaches to increase the prediction accuracy of GS, thus helping to select superior alfalfa lines based on their GEBVs.

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Section: Discussionmentioning

confidence: 99%

Strategies to Increase Prediction Accuracy in Genomic Selection of Complex Traits in Alfalfa (Medicago sativa L.)

Medina

Kaur

Ray

et al. 2021

Cells

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show abstract

“…A significant advantage of GS is the ability to select superior individuals in a population at very early stages in a breeding cycle based on their genotype, versus conducting lengthy/expensive phenotyping trials prior to each selection cycle. For instance, in a simulated wheat breeding program, selection based on GEBVs for grain yield tripled the genetic gain compared with PS [71]. In alfalfa breeding programs, GS can be implemented in elite large germplasm panels with genotyped individuals to decrease PS efforts, thus reducing overall selection cycle time and accelerating variety development.…”

Section: Discussionmentioning

confidence: 99%

Strategies to Increase Prediction Accuracy in Genomic Selection of Complex Traits in Alfalfa (<em>Medicago sativa</em> L.)

Medina¹,

Kaur²,

Ray³

et al. 2021

Preprint

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Agronomic traits such as biomass yield and abiotic stress tolerance are genetically complex and challenging to improve by conventional breeding strategies. Genomic selection (GS) is an alternative approach in which genome-wide markers are used to determine the genomic estimated breeding value (GEBV) of individuals in a population. In alfalfa, previous results indicated that low to moderate prediction accuracy values (<70%) were obtained in complex traits such as yield and abiotic stress resistance. There is a need to increase the prediction value in order to employ GS in breeding programs. In this paper we reviewed different statistic models and their applications in polyploid crops including alfalfa. Specifically, we used empirical data affiliated with alfalfa yield under salt stress to investigate approaches which use DNA marker importance values derived from machine learning models, and genome-wide association studies (GWAS) of marker-trait association scores based on different GWASpoly models, in weighted GBLUP analyses. This approach increased prediction accuracies from 50% to more than 80% for alfalfa yield under salt stress. This is the first report in alfalfa to use variable importance and GWAS-assisted approaches to increase the prediction accuracy of GS, thus helping to select superior alfalfa lines based on their GEBVs.

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“…Bread wheat breeding has huge impact on worldwide food security and socio-economic development (Tessema, Liu et al 2020). Therefore, minor improvements in GP methodology leading to overall genetic gain can have high impact.…”

Section: Outperformed Parametric Methods For Predicting Complex Wheat...mentioning

confidence: 99%

Genomic prediction in plants: opportunities for machine learning-based approaches

Farooq

Dijk

Nijveen

et al. 2022

Preprint

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Many studies have demonstrated the utility of machine learning (ML) methods for genomic prediction (GP) of various plant traits, but a clear rationale for choosing ML over conventionally used, often simpler parametric methods is still lacking. Predictive performance of GP models might depend on a plethora of factors including sample size, number of markers, population structure and genetic architecture. Here, we investigate which problem and dataset characteristics are related to good performance of ML methods for genomic prediction. We compare the predictive performance of two frequently used ensemble ML methods (Random Forest and Extreme Gradient Boosting) with parametric methods including genomic best linear unbiased prediction (GBLUP), reproducing kernel Hilbert space regression (RKHS), BayesA and BayesB. To explore problem characteristics, we use simulated and real plant traits under different genetic complexity levels determined by the number of Quantitative Trait Loci (QTLs), heritability (h2 and h2e), population structure and linkage disequilibrium between causal nucleotides and other SNPs. Results show that ML methods are a better choice for nonlinear phenotypes and still comparable to Bayesian methods for linear phenotypes in the case of large effect QTNs. Furthermore, we find that ML methods are susceptible to confounding due to population structure and less sensitive to low linkage disequilibrium than linear parametric methods. Overall, this provides insights into the role of ML in GP as well as guidelines for practitioners.

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Strategies Using Genomic Selection to Increase Genetic Gain in Breeding Programs for Wheat

Cited by 30 publications

References 23 publications

Strategies to Increase Prediction Accuracy in Genomic Selection of Complex Traits in Alfalfa (Medicago sativa L.)

Strategies to Increase Prediction Accuracy in Genomic Selection of Complex Traits in Alfalfa (Medicago sativa L.)

Strategies to Increase Prediction Accuracy in Genomic Selection of Complex Traits in Alfalfa (<em>Medicago sativa</em> L.)

Genomic prediction in plants: opportunities for machine learning-based approaches

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