A New Deep Learning Calibration Method Enhances Genome-Based Prediction of Continuous Crop Traits

Montesinos-López, Osval A. and Montesinos-López, Abelardo and Mosqueda-González, Brandon A. and Bentley, Alison R. and Lillemo, Morten and Varshney, Rajeev K. and Crossa, José (2021) A New Deep Learning Calibration Method Enhances Genome-Based Prediction of Continuous Crop Traits. Frontiers in Genetics, 12. ISSN 1664-8021

[thumbnail of pubmed-zip/versions/1/package-entries/fgene-12-798840/fgene-12-798840.pdf] Text
pubmed-zip/versions/1/package-entries/fgene-12-798840/fgene-12-798840.pdf - Published Version

Download (1MB)

Abstract

Genomic selection (GS) has the potential to revolutionize predictive plant breeding. A reference population is phenotyped and genotyped to train a statistical model that is used to perform genome-enabled predictions of new individuals that were only genotyped. In this vein, deep neural networks, are a type of machine learning model and have been widely adopted for use in GS studies, as they are not parametric methods, making them more adept at capturing nonlinear patterns. However, the training process for deep neural networks is very challenging due to the numerous hyper-parameters that need to be tuned, especially when imperfect tuning can result in biased predictions. In this paper we propose a simple method for calibrating (adjusting) the prediction of continuous response variables resulting from deep learning applications. We evaluated the proposed deep learning calibration method (DL_M2) using four crop breeding data sets and its performance was compared with the standard deep learning method (DL_M1), as well as the standard genomic Best Linear Unbiased Predictor (GBLUP). While the GBLUP was the most accurate model overall, the proposed deep learning calibration method (DL_M2) helped increase the genome-enabled prediction performance in all data sets when compared with the traditional DL method (DL_M1). Taken together, we provide evidence for extending the use of the proposed calibration method to evaluate its potential and consistency for predicting performance in the context of GS applied to plant breeding.

Item Type: Article
Subjects: OA Open Library > Medical Science
Depositing User: Unnamed user with email support@oaopenlibrary.com
Date Deposited: 05 Jan 2023 10:25
Last Modified: 04 Sep 2023 06:59
URI: http://archive.sdpublishers.com/id/eprint/2

Actions (login required)

View Item
View Item