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A Guide on Deep Learning for Complex Trait Genomic Prediction

Catalan Institution for Research and Advanced Studies (ICREA), Passeig de Lluís Companys 23, 08010 Barcelona, Spain
Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, 08193 Bellaterra, Spain
Author to whom correspondence should be addressed.
Genes 2019, 10(7), 553;
Received: 12 June 2019 / Revised: 6 July 2019 / Accepted: 18 July 2019 / Published: 20 July 2019
(This article belongs to the Special Issue Genomic Prediction Methods for Sequencing Data)
PDF [2045 KB, uploaded 24 July 2019]


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. View Full-Text
Keywords: deep learning; genomic prediction; machine learning deep learning; genomic prediction; machine learning

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Pérez-Enciso, M.; Zingaretti, L.M. A Guide on Deep Learning for Complex Trait Genomic Prediction. Genes 2019, 10, 553.

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