Multi-environment Genomic Prediction of Plant Traits Using Deep Learners With Dense Architecture

Abstract

Genomic selection is revolutionizing plant breeding and therefore methods that improve prediction accuracy are useful. For this reason, active research is being conducted to build and test methods from other areas and adapt them to the context of genomic selection. In this paper we explore the novel deep learning (DL) methodology in the context of genomic selection. We compared DL methods with densely connected network architecture to one of the most often used genome-enabled prediction models: Genomic Best Linear Unbiased Prediction (GBLUP). We used nine published real genomic data sets to compare a fraction of all possible deep learning models to obtain a “meta picture” of the performance of DL methods with densely connected network architecture. In general, the best predictions were obtained with the GBLUP model when genotype×environment interaction (G×E) was taken into account (8 out of 9 data sets); when the interactions were ignored, the DL method was better than the GBLUP in terms of prediction accuracy in 6 out of the 9 data sets. For this reason, we believe that DL should be added to the data science toolkit of scientists working on animal and plant breeding. This study corroborates the view that there are no universally best prediction machines.

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Cited by papers we hold 8

• A review of deep learning applications for genomic selection · 2021
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• The Modern Plant Breeding Triangle: Optimizing the Use of Genomics, Phenomics, and Enviromics Data · 2021
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• Enhancing Genetic Gain through Genomic Selection: From Livestock to Plants · 2020
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• Exploring Deep Learning for Complex Trait Genomic Prediction in Polyploid Outcrossing Species · 2020
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• Deep learning versus parametric and ensemble methods for genomic prediction of complex phenotypes · 2020
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• Phenotype Prediction and Genome-Wide Association Study Using Deep Convolutional Neural Network of Soybean · 2019
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• A Guide on Deep Learning for Complex Trait Genomic Prediction · 2019
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• Multi-trait, Multi-environment Deep Learning Modeling for Genomic-Enabled Prediction of Plant Traits · 2018
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