Genomic Prediction of Breeding Values Using a Subset of SNPs Identified by Three Machine Learning Methods

Li, Bo; Zhang, Nanxi; Wang, You‐Gan; George, Andrew W.; Reverter, Antônio; Li, Yutao

doi:10.3389/fgene.2018.00237

Cited by 151 publications

(143 citation statements)

References 68 publications

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“…The authors stated that "adding non-signal predictors can adversely affect the predictive accuracy of the non-linear regression models". Other studies have shown the interest of machine learning methods for genomic prediction [59][60]. In our study, this was not the case for RKHS, but for SVM and BRNN.…”

Section: Plos Onementioning

confidence: 55%

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

Charmet

Tran

Auzanneau³

et al. 2020

PLoS ONE

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We developed an integrated R library called BWGS to enable easy computation of Genomic Estimates of Breeding values (GEBV) for genomic selection. BWGS, for BreedWheat Genomic selection, was developed in the framework of a cooperative private-public partnership project called Breedwheat (https://breedwheat.fr) and 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: Plos Onementioning

confidence: 55%

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

Charmet

Tran

Auzanneau³

et al. 2020

PLoS ONE

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“…The availability of large amount of genomic resources and high-density genotyping data has facilitated the successful implementation of GS in chickpea (Roorkiwal et al , 2018bLi et al 2018). Phenotyping and genotyping data collected from 320 elite breeding lines were combined with six statistical GS models to evaluate prediction accuracies.…”

Section: Genomic Selection For Selecting Loci With Relatively Small Gmentioning

confidence: 99%

“…Prominent results were evident from the high prediction accuracies (up to 0.91) obtained for diverse yield and its component traits for GS in chickpea breeding . A recent GS study suggested the possibility of increasing prediction accuracies for complex traits like drought by incorporating GWAS results into GS models (Li et al 2018). This study also provided evidence that the application of GS models using a subset of SNPs closely associated with the trait, in contrast to all SNPs, can increase prediction accuracies by several folds for yield-related traits.…”

Section: Genomic Selection For Selecting Loci With Relatively Small Gmentioning

confidence: 99%

Integrating genomics for chickpea improvement: achievements and opportunities

et al. 2020

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Key message Integration of genomic technologies with breeding efforts have been used in recent years for chickpea improvement. Modern breeding along with low cost genotyping platforms have potential to further accelerate chickpea improvement efforts. Abstract The implementation of novel breeding technologies is expected to contribute substantial improvements in crop productivity. While conventional breeding methods have led to development of more than 200 improved chickpea varieties in the past, still there is ample scope to increase productivity. It is predicted that integration of modern genomic resources with conventional breeding efforts will help in the delivery of climate-resilient chickpea varieties in comparatively less time. Recent advances in genomics tools and technologies have facilitated the generation of large-scale sequencing and genotyping data sets in chickpea. Combined analysis of high-resolution phenotypic and genetic data is paving the way for identifying genes and biological pathways associated with breeding-related traits. Genomics technologies have been used to develop diagnostic markers for use in marker-assisted backcrossing programmes, which have yielded several molecular breeding products in chickpea. We anticipate that a sequence-based holistic breeding approach, including the integration of functional omics, parental selection, forward breeding and genome-wide selection, will bring a paradigm shift in development of superior chickpea varieties. There is a need to integrate the knowledge generated by modern genomics technologies with molecular breeding efforts to bridge the genome-to-phenome gap. Here, we review recent advances that have led to new possibilities for developing and screening breeding populations, and provide strategies for enhancing the selection efficiency and accelerating the rate of genetic gain in chickpea.

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“…With FS, it is possible to reduce marker density and build simpler and more comprehensive models 46 , thereby increasing predictive power due to the identification of phenotype-associated polymorphisms. A few previous studies applied ML methods to decrease the number of SNP datasets needed for phenotypic predictions [47][48][49] , achieving high accuracy. The identification of such a subset of putative causal polymorphisms is crucial for improving production in plants 42 and represents a novel strategy for genomic prediction in sugarcane.…”

Section: Introductionmentioning

confidence: 99%

Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance

Aono

Costa

Rody

et al. 2020

Preprint

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Sugarcane is an economically important crop, but its genomic complexity has hindered advances in molecular approaches for genetic breeding. New cultivars are released based on the identification of interesting traits, and for sugarcane, brown rust resistance is a desirable characteristic due to the large economic impact of the disease. Although marker-assisted selection for rust resistance has been successful, the genes involved are still unknown, and the associated regions vary among cultivars, thus restricting methodological generalization. We used genotyping by sequencing of full-sib progeny to relate genomic regions with brown rust phenotypes. We established a pipeline to identify reliable SNPs in complex polyploid data, which were used for phenotypic prediction via machine learning. We identified 14,540 SNPs, which led to a mean prediction accuracy of 50% by using different models. We also tested feature selection algorithms to increase predictive accuracy, resulting in a reduced dataset with more explanatory power for rust phenotypes. Using different feature selection techniques, we achieved accuracy of up to 95% with a dataset of 131 SNPs related to brown rust QTL regions and auxiliary genes. Therefore, our novel strategy has the potential to assist studies of the genomic organization of brown rust resistance in sugarcane. 3/154/15 10/15

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Genomic Prediction of Breeding Values Using a Subset of SNPs Identified by Three Machine Learning Methods

Cited by 151 publications

References 68 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

Integrating genomics for chickpea improvement: achievements and opportunities

Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance

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