Haploid maize seeds prediction using deep learning and using mock reference genomes for genomic prediction of hybrids

Abstract Background Several conventional genomic Bayesian (or no Bayesian) prediction methods have been proposed including the standard additive genetic effect model for which the variance components are estimated with mixed model equations. In recent years, deep learning (DL) methods have been considered in the context of genomic prediction. The DL methods are nonparametric models providing flexibility to adapt to complicated associations between data and output with the ability to adapt to very complex patterns. Main body We review the applications of deep learning (DL) methods in genomic selection (GS) to obtain a meta-picture of GS performance and highlight how these tools can help solve challenging plant breeding problems. We also provide general guidance for the effective use of DL methods including the fundamentals of DL and the requirements for its appropriate use. We discuss the pros and cons of this technique compared to traditional genomic prediction approaches as well as the current trends in DL applications. Conclusions The main requirement for using DL is the quality and sufficiently large training data. Although, based on current literature GS in plant and animal breeding we did not find clear superiority of DL in terms of prediction power compared to conventional genome based prediction models. Nevertheless, there are clear evidences that DL algorithms capture nonlinear patterns more efficiently than conventional genome based. Deep learning algorithms are able to integrate data from different sources as is usually needed in GS assisted breeding and it shows the ability for improving prediction accuracy for large plant breeding data. It is important to apply DL to large training-testing data sets.

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A Guide for Using Deep Learning for Complex Trait Genomic Prediction

Genes ◽

10.3390/genes10070553 ◽

2019 ◽

Vol 10 (7) ◽

pp. 553 ◽

Cited By ~ 19

Author(s):

Pérez-Enciso ◽

Zingaretti

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Genomic Prediction ◽

Genetic Risk ◽

Complex Trait ◽

Molecular Data ◽

Multilayer Perceptrons ◽

Complex Data ◽

Theoretical Foundations

Deep learning (DL) has emerged as a powerful tool to make accurate predictions from complex data such as image, text, or video. However, its ability to predict phenotypic values from molecular data is less well studied. Here, we describe the theoretical foundations of DL and provide a generic code that can be easily modified to suit specific needs. DL comprises a wide variety of algorithms which depend on numerous hyperparameters. Careful optimization of hyperparameter values is critical to avoid overfitting. Among the DL architectures currently tested in genomic prediction, convolutional neural networks (CNNs) seem more promising than multilayer perceptrons (MLPs). A limitation of DL is in interpreting the results. This may not be relevant for genomic prediction in plant or animal breeding but can be critical when deciding the genetic risk to a disease. Although DL technologies are not ”plug-and-play”, they are easily implemented using Keras and TensorFlow public software. To illustrate the principles described here, we implemented a Keras-based code in GitHub.

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Exploring Deep Learning for Complex Trait Genomic Prediction in Polyploid Outcrossing Species

Frontiers in Plant Science ◽

10.3389/fpls.2020.00025 ◽

2020 ◽

Vol 11 ◽

Cited By ~ 4

Author(s):

Laura M. Zingaretti ◽

Salvador Alejandro Gezan ◽

Luis Felipe V. Ferrão ◽

Luis F. Osorio ◽

Amparo Monfort ◽

...

Keyword(s):

Deep Learning ◽

Genomic Prediction ◽

Complex Trait ◽

Outcrossing Species

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Multi-Trait, Multi-Environment Genomic Prediction of Durum Wheat With Genomic Best Linear Unbiased Predictor and Deep Learning Methods

Frontiers in Plant Science ◽

10.3389/fpls.2019.01311 ◽

2019 ◽

Vol 10 ◽

Cited By ~ 2

Author(s):

Osval A. Montesinos-López ◽

Abelardo Montesinos-López ◽

Roberto Tuberosa ◽

Marco Maccaferri ◽

Giuseppe Sciara ◽

...

Keyword(s):

Deep Learning ◽

Durum Wheat ◽

Genomic Prediction ◽

Best Linear Unbiased Predictor ◽

Learning Methods ◽

Best Linear Unbiased

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Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimes

Genes ◽

10.3390/genes11111270 ◽

2020 ◽

Vol 11 (11) ◽

pp. 1270 ◽

Cited By ~ 1

Author(s):

Jia Guo ◽

Jahangir Khan ◽

Sumit Pradhan ◽

Dipendra Shahi ◽

Naeem Khan ◽

...

Keyword(s):

Deep Learning ◽

Genomic Prediction ◽

Genotyping By Sequencing ◽

Triticum Aestivum L ◽

Yield Component ◽

Soft Wheat ◽

Response To Selection ◽

Best Linear Unbiased ◽

Trait Model ◽

Variable Water

The performance of genomic prediction (GP) on genetically correlated traits can be improved through an interdependence multi-trait model under a multi-environment context. In this study, a panel of 237 soft facultative wheat (Triticum aestivum L.) lines was evaluated to compare single- and multi-trait models for predicting grain yield (GY), harvest index (HI), spike fertility (SF), and thousand grain weight (TGW). The panel was phenotyped in two locations and two years in Florida under drought and moderately drought stress conditions, while the genotyping was performed using 27,957 genotyping-by-sequencing (GBS) single nucleotide polymorphism (SNP) makers. Five predictive models including Multi-environment Genomic Best Linear Unbiased Predictor (MGBLUP), Bayesian Multi-trait Multi-environment (BMTME), Bayesian Multi-output Regressor Stacking (BMORS), Single-trait Multi-environment Deep Learning (SMDL), and Multi-trait Multi-environment Deep Learning (MMDL) were compared. Across environments, the multi-trait statistical model (BMTME) was superior to the multi-trait DL model for prediction accuracy in most scenarios, but the DL models were comparable to the statistical models for response to selection. The multi-trait model also showed 5 to 22% more genetic gain compared to the single-trait model across environment reflected by the response to selection. Overall, these results suggest that multi-trait genomic prediction can be an efficient strategy for economically important yield component related traits in soft wheat.

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98 Using differential evolution to improve predictive accuracy of deep learning models applied to pig production data

Journal of Animal Science ◽

10.1093/jas/skaa054.048 ◽

2020 ◽

Vol 98 (Supplement_3) ◽

pp. 27-27

Author(s):

Junjie Han ◽

Cedric Gondro ◽

Juan Steibel

Keyword(s):

Deep Learning ◽

Differential Evolution ◽

Image Classification ◽

Genomic Prediction ◽

Prediction Accuracy ◽

Predictive Accuracy ◽

Traditional Approach ◽

Exhaustive Search ◽

Pig Production ◽

Hyperparameter Selection

Abstract Deep learning (DL) is being used for prediction in precision livestock farming and in genomic prediction. However, optimizing hyperparameters in DL models is critical for their predictive performance. Grid search is the traditional approach to select hyperparameters in DL, but it requires exhaustive search over the parameter space. We propose hyperparameter selection using differential evolution (DE), which is a heuristic algorithm that does not require exhaustive search. The goal of this study was to design and apply DE to optimize hyperparameters of DL models for genomic prediction and image analysis in pig production systems. One dataset consisted of 910 pigs genotyped with 28,916 SNP markers to predict their post-mortem meat pH. Another dataset consisted of 1,334 images of pigs eating inside a single-spaced feeder classified as: “single pig” or “multiple pigs.” The accuracy of genomic prediction was defined as the correlation between the predicted pH and the observed pH. The image classification prediction accuracy was the proportion of correctly classified images. For genomic prediction, a multilayer perceptron (MLP) was optimized. For image classification, MLP and convolutional neural networks (CNN) were optimized. For genomic prediction, the initial hyperparameter set resulted in an accuracy of 0.032 and for image classification, the initial accuracy was between 0.72 and 0.76. After optimization using DE, the genomic prediction accuracy was 0.3688 compared to 0.334 using GBLUP. The top selected models included one layer, 60 neurons, sigmoid activation and L2 penalty = 0.3. The accuracy of image classification after optimization was between 0.89 and 0.92. Selected models included three layers, adamax optimizer and relu or elu activation for the MLP, and one layer, 64 filters and 5×5 filter size for the CNN. DE can adapt the hyperparameter selection to each problem, dataset and model, and it significantly increased prediction accuracy with minimal user input.

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Using local convolutional neural networks for genomic prediction

10.1101/2020.05.12.090118 ◽

2020 ◽

Author(s):

Torsten Pook ◽

Jan Freudenthal ◽

Arthur Korte ◽

Henner Simianer

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Convolutional Neural Networks ◽

Genomic Prediction ◽

Linear Models ◽

Predictive Ability ◽

High Heritability ◽

Fully Connected

ABSTRACTThe prediction of breeding values and phenotypes is of central importance for both livestock and crop breeding. With increasing computational power and more and more data to potentially utilize, Machine Learning and especially Deep Learning have risen in popularity over the last few years. In this study, we are proposing the use of local convolutional neural networks for genomic prediction, as a region specific filter corresponds much better with our prior genetic knowledge of traits than traditional convolutional neural networks. Model performances are evaluated on a simulated maize data panel (n = 10,000) and real Arabidopsis data (n = 2,039) for a variety of traits with the local convolutional neural network outperforming both multi layer perceptrons and convolutional neural networks for basically all considered traits. Linear models like the genomic best linear unbiased prediction that are often used for genomic prediction are outperformed by up to 24%. Highest gains in predictive ability was obtained in cases of medium trait complexity with high heritability and large training populations. However, for small dataset with 100 or 250 individuals for the training of the models, the local convolutional neural network is performing slightly worse than the linear models. Nonetheless, this is still 15% better than a traditional convolutional neural network, indicating a better performance and robustness of our proposed model architecture for small training populations. In addition to the baseline model, various other architectures with different windows size and stride in the local convolutional layer, as well as different number of nodes in subsequent fully connected layers are compared against each other. Finally, the usefulness of Deep Learning and in particular local convolutional neural networks in practice is critically discussed, in regard to multi dimensional inputs and outputs, computing times and other potential hazards.

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Heuristic hyperparameter optimization of deep learning models for genomic prediction

10.1101/2020.11.25.398800 ◽

2020 ◽

Author(s):

Junjie Han ◽

Cedric Gondro ◽

Kenneth Reid ◽

Juan P. Steibel

Keyword(s):

Deep Learning ◽

Genomic Prediction ◽

Best Practice ◽

Cross Validation ◽

Model Fitting ◽

Combinatorial Problem ◽

Predictive Performance ◽

Search Space ◽

Hyperparameter Optimization ◽

Optimization Search

AbstractThere is a growing interest among quantitative geneticists and animal breeders in the use of deep learning (DL) for genomic prediction. However, the performance of DL is affected by hyperparameters that are typically manually set by users. These hyperparameters do not simply specify the architecture of the model, they are also critical for the efficacy of the optimization and model fitting process. To date, most DL approaches used for genomic prediction have concentrated on identifying suitable hyperparameters by exploring discrete options from a subset of the hyperparameter space. Enlarging the hyperparameter optimization search space with continuous hyperparameters is a daunting combinatorial problem. To deal with this problem, we propose using differential evolution (DE) to perform an efficient search of arbitrarily complex hyperparameter spaces in DL models and we apply this to the specific case of genomic prediction of livestock phenotypes. This approach was evaluated on two pig and cattle datasets with real genotypes and simulated phenotypes (N=7,539 animals and M=48,541 markers) and one real dataset (N=910 individuals and M=28,916 markers). Hyperparameters were evaluated using cross validation. We compared the predictive performance of DL models using hyperparameters optimized by DE against DL models with “best practice” hyperparameters selected from published studies and baseline DL models with randomly specified hyperparameters. Optimized models using DE showed clear improvement in predictive performance across all three datasets.DE optimized hyperparameters also resulted in DL models with less overfitting and less variation in predictive performance over repeated retraining compared to non-optimized DL models.

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GPTransformer: A Transformer-Based Deep Learning Method for Predicting Fusarium Related Traits in Barley

Frontiers in Plant Science ◽

10.3389/fpls.2021.761402 ◽

2021 ◽

Vol 12 ◽

Author(s):

Sheikh Jubair ◽

James R. Tucker ◽

Nathan Henderson ◽

Colin W. Hiebert ◽

Ana Badea ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Molecular Markers ◽

Fusarium Head Blight ◽

High Throughput ◽

Genomic Prediction ◽

Prediction Models ◽

Spring Barley ◽

Pearson Correlation ◽

Head Blight

Fusarium head blight (FHB) incited by Fusarium graminearum Schwabe is a devastating disease of barley and other cereal crops worldwide. Fusarium head blight is associated with trichothecene mycotoxins such as deoxynivalenol (DON), which contaminates grains, making them unfit for malting or animal feed industries. While genetically resistant cultivars offer the best economic and environmentally responsible means to mitigate disease, parent lines with adequate resistance are limited in barley. Resistance breeding based upon quantitative genetic gains has been slow to date, due to intensive labor requirements of disease nurseries. The production of a high-throughput genome-wide molecular marker assembly for barley permits use in development of genomic prediction models for traits of economic importance to this crop. A diverse panel consisting of 400 two-row spring barley lines was assembled to focus on Canadian barley breeding programs. The panel was evaluated for FHB and DON content in three environments and over 2 years. Moreover, it was genotyped using an Illumina Infinium High-Throughput Screening (HTS) iSelect custom beadchip array of single nucleotide polymorphic molecular markers (50 K SNP), where over 23 K molecular markers were polymorphic. Genomic prediction has been demonstrated to successfully reduce FHB and DON content in cereals using various statistical models. Herein, we have studied an alternative method based on machine learning and compare it with a statistical approach. The bi-allelic SNPs represented pairs of alleles and were encoded in two ways: as categorical (–1, 0, 1) or using Hardy-Weinberg probability frequencies. This was followed by selecting essential genomic markers for phenotype prediction. Subsequently, a Transformer-based deep learning algorithm was applied to predict FHB and DON. Apart from the Transformer method, a Residual Fully Connected Neural Network (RFCNN) was also applied. Pearson correlation coefficients were calculated to compare true vs. predicted outputs. Models which included all markers generally showed marginal improvement in prediction. Hardy-Weinberg encoding generally improved correlation for FHB (6.9%) and DON (9.6%) for the Transformer network. This study suggests the potential of the Transformer based method as an alternative to the popular BLUP model for genomic prediction of complex traits such as FHB or DON, having performed equally or better than existing machine learning and statistical methods.

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Deep Learning Algorithms for Complex Traits Genomic Prediction

Hayvan Bilimi ve Ürünleri Dergisi ◽

10.51970/jasp.1039713 ◽

2021 ◽

Author(s):

Hayrettin OKUT

Keyword(s):

Deep Learning ◽

Genomic Prediction ◽

Complex Traits ◽

Learning Algorithms

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