Machine learning approaches for crop improvement: Leveraging phenotypic and genotypic big data

Tong, Hao; Nikoloski, Zoran

doi:10.1016/j.jplph.2020.153354

Cited by 78 publications

(38 citation statements)

References 120 publications

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“…Query population The training population is used to calibrate (d) ML models that aim using genomic information to predict genomic estimated adaptive values (GEAVs, an analogous rank to the polygenic risk score (PGS) and genomic estimated breeding value (GEBV) from the quantitative genomics literature, e.g., [102,136]). The computer screen depicts a hypothetical hidden neural network (HNN) algorithm, which is one among many potential ML tools; the repertoire includes several regressions, classification, and deep learning models, thoughtfully reviewed this year by Sebestyén et al [137] and Tong and Nikoloski [138]. Meanwhile, the testing population is used to compute the (e) unbiased predictive ability of the model by comparing the GEAVs with the recorded environmental (or phenotypic) abiotic stress tolerant/susceptible indices.…”

Section: Geavs Geavsmentioning

confidence: 99%

Harnessing Crop Wild Diversity for Climate Change Adaptation

Cortés

López-Hernández

2021

Genes

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Warming and drought are reducing global crop production with a potential to substantially worsen global malnutrition. As with the green revolution in the last century, plant genetics may offer concrete opportunities to increase yield and crop adaptability. However, the rate at which the threat is happening requires powering new strategies in order to meet the global food demand. In this review, we highlight major recent ‘big data’ developments from both empirical and theoretical genomics that may speed up the identification, conservation, and breeding of exotic and elite crop varieties with the potential to feed humans. We first emphasize the major bottlenecks to capture and utilize novel sources of variation in abiotic stress (i.e., heat and drought) tolerance. We argue that adaptation of crop wild relatives to dry environments could be informative on how plant phenotypes may react to a drier climate because natural selection has already tested more options than humans ever will. Because isolated pockets of cryptic diversity may still persist in remote semi-arid regions, we encourage new habitat-based population-guided collections for genebanks. We continue discussing how to systematically study abiotic stress tolerance in these crop collections of wild and landraces using geo-referencing and extensive environmental data. By uncovering the genes that underlie the tolerance adaptive trait, natural variation has the potential to be introgressed into elite cultivars. However, unlocking adaptive genetic variation hidden in related wild species and early landraces remains a major challenge for complex traits that, as abiotic stress tolerance, are polygenic (i.e., regulated by many low-effect genes). Therefore, we finish prospecting modern analytical approaches that will serve to overcome this issue. Concretely, genomic prediction, machine learning, and multi-trait gene editing, all offer innovative alternatives to speed up more accurate pre- and breeding efforts toward the increase in crop adaptability and yield, while matching future global food demands in the face of increased heat and drought. In order for these ‘big data’ approaches to succeed, we advocate for a trans-disciplinary approach with open-source data and long-term funding. The recent developments and perspectives discussed throughout this review ultimately aim to contribute to increased crop adaptability and yield in the face of heat waves and drought events.

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Section: Geavs Geavsmentioning

confidence: 99%

Harnessing Crop Wild Diversity for Climate Change Adaptation

Cortés

López-Hernández

2021

Genes

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“…Since SNPs represent the most abundant form of allelic variations [2], they represent the predominant factor that induces phenotypic differences among individuals. Usage of SNPs with modern machine-learning approaches have revolutionized molecular plant breeding, both with respect to applied research in prediction of traits and basic research in the mechanisms governing a trait [3][4][5]. Hence, characterising the effects of SNPs on agronomically relevant traits is a key problem in the interlinked fields of plant systems biology, quantitative genetics, and plant breeding.…”

Section: Introductionmentioning

confidence: 99%

Characterization of effects of genetic variants via genome-scale metabolic modelling

Tong

Küken

Razaghi-Moghadam

et al. 2021

Cell. Mol. Life Sci.

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Genome-scale metabolic networks for model plants and crops in combination with approaches from the constraint-based modelling framework have been used to predict metabolic traits and design metabolic engineering strategies for their manipulation. With the advances in technologies to generate large-scale genotyping data from natural diversity panels and other populations, genome-wide association and genomic selection have emerged as statistical approaches to determine genetic variants associated with and predictive of traits. Here, we review recent advances in constraint-based approaches that integrate genetic variants in genome-scale metabolic models to characterize their effects on reaction fluxes. Since some of these approaches have been applied in organisms other than plants, we provide a critical assessment of their applicability particularly in crops. In addition, we further dissect the inferred effects of genetic variants with respect to reaction rate constants, abundances of enzymes, and concentrations of metabolites, as main determinants of reaction fluxes and relate them with their combined effects on complex traits, like growth. Through this systematic review, we also provide a roadmap for future research to increase the predictive power of statistical approaches by coupling them with mechanistic models of metabolism.

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“…Recently [19] addressed a machine learning approach for crop improvement by leveraging phenotypic and genotypic big data to predict agricultural production based on phenotypic traits used in genomic selection in breeding. Furthermore, [20] reviewed the latest studies on machine learning in the field of plant breeding and biotechnology.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Stability and Analytical Solution with Homotopy Perturbation Method of Multicompartment Non-Linear Epidemic Model with Saturated Rate

Chahrazed¹

2021

AJAMS

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In this work, we consider a nonlinear epidemic model with a saturated incidence rate. we consider a population of size N(t) at time t, this population is divided into six subclasses, with N(t)=S(t)+I(t)+I₁(t)+I₂(t)+I₃(t)+Q(t). Where S(t), I(t), I₁(t), I₂(t), I₃(t), and Q(t) denote the sizes of the population susceptible to disease, infectious members, and quarantine members, respectively. We have made the following contributions: 1. The local stabilities of the infection-free equilibrium and endemic equilibrium are; analyzed, respectively. The stability of a disease-free equilibrium and the existence of other nontrivial equilibria can be determined by the ratio called the basic reproductive number. 2. We find the analytical solution of the nonlinear epidemic model by Homotopy perturbation method. 3. Finally the stochastic stabilities. The study of its sections are justified with theorems and demonstrations under certain conditions. In this work, we have used the different references cited in different studies in the three sections already mentioned.

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Machine learning approaches for crop improvement: Leveraging phenotypic and genotypic big data

Cited by 78 publications

References 120 publications

Harnessing Crop Wild Diversity for Climate Change Adaptation

Harnessing Crop Wild Diversity for Climate Change Adaptation

Characterization of effects of genetic variants via genome-scale metabolic modelling

Stochastic Stability and Analytical Solution with Homotopy Perturbation Method of Multicompartment Non-Linear Epidemic Model with Saturated Rate

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