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

Montesinos-López, O A and Montesinos-Lopez, A and Mosqueda-González, B A and Bentley, A R and Lillemo, M and Varshney, R K and Crossa, J (2021) A New Deep Learning Calibration Method Enhances Genome-Based Prediction of Continuous Crop Traits. Frontiers in Genetics (TSI), 12. pp. 1-12. ISSN 1664-8021

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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
Divisions:	Center of Excellence in Genomics and Systems Biology
CRP:	UNSPECIFIED
Uncontrolled Keywords:	genomic selection, genomic prediction, calibration of predictions, deep learning, GBLUP, plant breeding
Subjects:	Others > Plant Breeding Others > Genetics and Genomics
Depositing User:	Mr Nagaraju T
Date Deposited:	17 Apr 2025 03:48
Last Modified:	17 Apr 2025 03:48
URI:	http://oar.icrisat.org/id/eprint/13036
Official URL:	https://www.frontiersin.org/journals/genetics/arti...
Projects:	UNSPECIFIED
Funders:	UNSPECIFIED
Acknowledgement:	We thank all scientists, field workers, and lab assistants from the National Programs who collected the data used in this study.
Links:	Google Scholar

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