Using Local Convolutional Neural Networks for Genomic Prediction

Pook, Torsten; Freudenthal, Jan A.; Korte, Arthur; Simianer, Henner

doi:10.3389/fgene.2020.561497

Cited by 38 publications

(43 citation statements)

References 55 publications

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“…It is believed that machine and deep learning models should be used on very large training datasets, which is often not possible for end-use quality traits that are evaluated at later stages of the breeding process. However, this and other studies have shown that even small datasets can give equivalent or superior performance to the traditional parametric GS models [21,25,74]. Moreover, Bellot et al [75] have used a training set of 100,000 individuals and showed no advantage of deep learning models over the conventional GS models.…”

Section: Discussionmentioning

confidence: 83%

Genomic Selection for End-Use Quality and Processing Traits in Soft White Winter Wheat Breeding Program with Machine and Deep Learning Models

Sandhu

Aoun

Morris

et al. 2021

Biology

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Breeding for grain yield, biotic and abiotic stress resistance, and end-use quality are important goals of wheat breeding programs. Screening for end-use quality traits is usually secondary to grain yield due to high labor needs, cost of testing, and large seed requirements for phenotyping. Genomic selection provides an alternative to predict performance using genome-wide markers under forward and across location predictions, where a previous year’s dataset can be used to build the models. Due to large datasets in breeding programs, we explored the potential of the machine and deep learning models to predict fourteen end-use quality traits in a winter wheat breeding program. The population used consisted of 666 wheat genotypes screened for five years (2015–19) at two locations (Pullman and Lind, WA, USA). Nine different models, including two machine learning (random forest and support vector machine) and two deep learning models (convolutional neural network and multilayer perceptron) were explored for cross-validation, forward, and across locations predictions. The prediction accuracies for different traits varied from 0.45–0.81, 0.29–0.55, and 0.27–0.50 under cross-validation, forward, and across location predictions. In general, forward prediction accuracies kept increasing over time due to increments in training data size and was more evident for machine and deep learning models. Deep learning models were superior over the traditional ridge regression best linear unbiased prediction (RRBLUP) and Bayesian models under all prediction scenarios. The high accuracy observed for end-use quality traits in this study support predicting them in early generations, leading to the advancement of superior genotypes to more extensive grain yield trails. Furthermore, the superior performance of machine and deep learning models strengthens the idea to include them in large scale breeding programs for predicting complex traits.

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Section: Discussionmentioning

confidence: 83%

Genomic Selection for End-Use Quality and Processing Traits in Soft White Winter Wheat Breeding Program with Machine and Deep Learning Models

Sandhu

Aoun

Morris

et al. 2021

Biology

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“…With computational advancement and algorithms enhancements, artificial neural networks (ANNs) have emerged as an alternative statistical framework and have gained increasing interest in genomic studies [ 1 , 137 , 138 , 139 , 140 ]. This method can be particularly useful when the number of unknown variables is much higher than the number of samples (high-dimensional genomic information), since ANNs have the ability to capture non-linearities, adaptively [ 1 , 141 ].…”

Section: Genomic Selection/prediction An Extension Of Blup Methods To Maximize the Predictive Power Of Traits Of Interestmentioning

confidence: 99%

Achievements and Challenges of Genomics-Assisted Breeding in Forest Trees: From Marker-Assisted Selection to Genome Editing

Ahmar

Ballesta²,

Ali

et al. 2021

IJMS

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Forest tree breeding efforts have focused mainly on improving traits of economic importance, selecting trees suited to new environments or generating trees that are more resilient to biotic and abiotic stressors. This review describes various methods of forest tree selection assisted by genomics and the main technological challenges and achievements in research at the genomic level. Due to the long rotation time of a forest plantation and the resulting long generation times necessary to complete a breeding cycle, the use of advanced techniques with traditional breeding have been necessary, allowing the use of more precise methods for determining the genetic architecture of traits of interest, such as genome-wide association studies (GWASs) and genomic selection (GS). In this sense, main factors that determine the accuracy of genomic prediction models are also addressed. In turn, the introduction of genome editing opens the door to new possibilities in forest trees and especially clustered regularly interspaced short palindromic repeats and CRISPR-associated protein 9 (CRISPR/Cas9). It is a highly efficient and effective genome editing technique that has been used to effectively implement targetable changes at specific places in the genome of a forest tree. In this sense, forest trees still lack a transformation method and an inefficient number of genotypes for CRISPR/Cas9. This challenge could be addressed with the use of the newly developing technique GRF-GIF with speed breeding.

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“…At last, an unknown and unverifiable cause could be the number of training samples, which might not have been enough for modeling linear and nonlinear interactions between markers by the NN (Montesinos-López et al 2020). This is a problem of common occurrence in plant breeding given the usually low number of samples, a large number of markers, and heterogeneity of data (Abdollahi-Arpanahi et al 2020;Pook et al 2020).…”

Section: Regression Analysis -The Standardmentioning

confidence: 99%

Automated Machine Learning: a case study of genomic “image-based” prediction in maize hybrids

Galli

Sabadin

Yassue

et al. 2021

Preprint

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Machine learning methods such as Multilayer perceptrons (MLP) and Convolutional Neural Networks (CNN) have emerged as promising methods for genomic prediction (GP). In this sense, we assess the performance of MLP and CNN on regression and classification tasks in a case study with maize hybrids. The genomic information was provided to the MLP as a relationship matrix and to the CNN as “genomic images”. In the regression task, the machine learning models were compared along with GBLUP. Under the classification task, MLP and CNN were compared. In this case, the traits (plant height and grain yield) were discretized in such a way to create balanced (moderate selection intensity) and unbalanced (extreme selection intensity) datasets for further evaluations. An automatic hyperparameter search for MLP and CNN was performed, and the best models were reported. For both task types, several metrics were calculated under a validation scheme to assess the effect of the prediction method and other variables. Overall, MLP and CNN presented competitive results to GBLUP but improved a little using only the additive genomic layer. It is expected that the average effect of allele substitution is mostly linear. Nevertheless, the methodology’s potential for GP is unprecedented because we can create “multispectral genome images,” including other effects and layers of data, such as dominance, epistasis, g × e, transcriptome, and so on, capturing linear and non-linear effects and boosting prediction accuracies. Hence, we bring new insights on automated machine learning for genomic prediction and its implications to plant breeding.

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Using Local Convolutional Neural Networks for Genomic Prediction

Cited by 38 publications

References 55 publications

Genomic Selection for End-Use Quality and Processing Traits in Soft White Winter Wheat Breeding Program with Machine and Deep Learning Models

Genomic Selection for End-Use Quality and Processing Traits in Soft White Winter Wheat Breeding Program with Machine and Deep Learning Models

Achievements and Challenges of Genomics-Assisted Breeding in Forest Trees: From Marker-Assisted Selection to Genome Editing

Automated Machine Learning: a case study of genomic “image-based” prediction in maize hybrids

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