Deep‐learning power and perspectives for genomic selection

Montesinos‐López, Osval A.; Montesinos‐López, Abelardo; Hernandez-Suarez, Carlos; Barrón-López, José Alberto; Crossa, J.

doi:10.1002/tpg2.20122

Cited by 14 publications

(8 citation statements)

References 37 publications

(52 reference statements)

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“…To improve on linear models in GS, there has been an increased use of nonlinear methods such as ML and DLmodels to predict plant phenotypes ( Montesinos-López et al, 2021a ). Nonlinear methods have been theorised to be better able to capture small interactions between markers, account for environment interactions and generate predictions with higher accuracy for data with high dimensionality ( Pérez-Rodríguez et al, 2012 ; Cuevas et al, 2016 ).…”

Section: Machine Learning Methods For Genotype To Phenotype Predictionmentioning

confidence: 99%

Plant Genotype to Phenotype Prediction Using Machine Learning

et al. 2022

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Genomic prediction tools support crop breeding based on statistical methods, such as the genomic best linear unbiased prediction (GBLUP). However, these tools are not designed to capture non-linear relationships within multi-dimensional datasets, or deal with high dimension datasets such as imagery collected by unmanned aerial vehicles. Machine learning (ML) algorithms have the potential to surpass the prediction accuracy of current tools used for genotype to phenotype prediction, due to their capacity to autonomously extract data features and represent their relationships at multiple levels of abstraction. This review addresses the challenges of applying statistical and machine learning methods for predicting phenotypic traits based on genetic markers, environment data, and imagery for crop breeding. We present the advantages and disadvantages of explainable model structures, discuss the potential of machine learning models for genotype to phenotype prediction in crop breeding, and the challenges, including the scarcity of high-quality datasets, inconsistent metadata annotation and the requirements of ML models.

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Section: Machine Learning Methods For Genotype To Phenotype Predictionmentioning

confidence: 99%

Plant Genotype to Phenotype Prediction Using Machine Learning

et al. 2022

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“…The implication of computer science that uses algorithms and existing samples to capture the characteristics of well-defined patterns in ML is thus a great advantage in the context of GS in polyploids ( González-Camacho et al., 2018 ). In general, the random forest and the support vector machine are the widely used methods in ML ( Montesinos-López et al., 2021 ). The classic GS approaches use the parametric methods that assume that the inputs are normally distributed, which is not always the case, and the input data must then be arranged, whereas the ML takes into account each type of input and includes the epistatic effects.…”

Section: Perspectives To Succeed Gs On Polyploid Cropsmentioning

confidence: 99%

Perspective for genomic-enabled prediction against black sigatoka disease and drought stress in polyploid species

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et al. 2022

Front. Plant Sci.

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Genomic selection (GS) in plant breeding is explored as a promising tool to solve the problems related to the biotic and abiotic threats. Polyploid plants like bananas (Musa spp.) face the problem of drought and black sigatoka disease (BSD) that restrict their production. The conventional plant breeding is experiencing difficulties, particularly phenotyping costs and long generation interval. To overcome these difficulties, GS in plant breeding is explored as an alternative with a great potential for reducing costs and time in selection process. So far, GS does not have the same success in polyploid plants as with diploid plants because of the complexity of their genome. In this review, we present the main constraints to the application of GS in polyploid plants and the prospects for overcoming these constraints. Particular emphasis is placed on breeding for BSD and drought—two major threats to banana production—used in this review as a model of polyploid plant. It emerges that the difficulty in obtaining markers of good quality in polyploids is the first challenge of GS on polyploid plants, because the main tools used were developed for diploid species. In addition to that, there is a big challenge of mastering genetic interactions such as dominance and epistasis effects as well as the genotype by environment interaction, which are very common in polyploid plants. To get around these challenges, we have presented bioinformatics tools, as well as artificial intelligence approaches, including machine learning. Furthermore, a scheme for applying GS to banana for BSD and drought has been proposed. This review is of paramount impact for breeding programs that seek to reduce the selection cycle of polyploids despite the complexity of their genome.

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“…Machine learning has become the main approach for solving complex, data-based problems and it is being used everywhere from devices and digital services such as smartphones and websites, to scientific research in various fields (Wang et al, 2016;Ott et al, 2020;Shahin et al, 2020;Montesinos-López et al, 2021a). As machine learning research has progressed, so has the supply and demand of software that facilitates its implementation.…”

Section: Introductionmentioning

confidence: 99%

A General-Purpose Machine Learning R Library for Sparse Kernels Methods With an Application for Genome-Based Prediction

et al. 2022

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The adoption of machine learning frameworks in areas beyond computer science have been facilitated by the development of user-friendly software tools that do not require an advanced understanding of computer programming. In this paper, we present a new package (sparse kernel methods, SKM) software developed in R language for implementing six (generalized boosted machines, generalized linear models, support vector machines, random forest, Bayesian regression models and deep neural networks) of the most popular supervised machine learning algorithms with the optional use of sparse kernels. The SKM focuses on user simplicity, as it does not try to include all the available machine learning algorithms, but rather the most important aspects of these six algorithms in an easy-to-understand format. Another relevant contribution of this package is a function for the computation of seven different kernels. These are Linear, Polynomial, Sigmoid, Gaussian, Exponential, Arc-Cosine 1 and Arc-Cosine L (with L = 2, 3, … ) and their sparse versions, which allow users to create kernel machines without modifying the statistical machine learning algorithm. It is important to point out that the main contribution of our package resides in the functionality for the computation of the sparse version of seven basic kernels, which is indispensable for reducing computational resources to implement kernel machine learning methods without a significant loss in prediction performance. Performance of the SKM is evaluated in a genome-based prediction framework using both a maize and wheat data set. As such, the use of this package is not restricted to genome prediction problems, and can be used in many different applications.

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Deep‐learning power and perspectives for genomic selection

Cited by 14 publications

References 37 publications

Plant Genotype to Phenotype Prediction Using Machine Learning

Plant Genotype to Phenotype Prediction Using Machine Learning

Perspective for genomic-enabled prediction against black sigatoka disease and drought stress in polyploid species

A General-Purpose Machine Learning R Library for Sparse Kernels Methods With an Application for Genome-Based Prediction

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