Upcycling rice yield trial data using a weather-driven crop growth model.

Efficient plant breeding plays a significant role in increasing crop yields and attaining food security under climate change. Screening new cultivars through yield trials in multi-environments has improved crop yields, but the accumulated data from these trials has not been effectively upcycled. We propose a simple method that quantifies cultivar-specific productivity characteristics using two regression coefficients: yield-ability (ß) and yield-plasticity (α). The recorded yields of each cultivar are expressed as a unique linear regression in response to the theoretical potential yield (Yp) calculated by a weather-driven crop growth model, called as the "YpCGM method". We apply this to 72510 independent datasets from yield trials of rice that used 237 cultivars measured at 110 locations in Japan over 38 years. The YpCGM method can upcycle accumulated yield data for use in genetic-gain analysis and genome-wide-association studies to guide future breeding programs for developing new cultivars suitable for the world's changing climate.

DeepCGP: A Deep Learning Method to Compress Genome-Wide Polymorphisms for Predicting Phenotype of Rice.

Genomic selection (GS) is expected to accelerate plant and animal breeding. During the last decade, genome-wide polymorphism data have increased, which has raised concerns about storage cost and computational time. Several individual studies have attempted to compress the genome data and predict phenotypes. However, compression models lack adequate quality of data after compression, and prediction models are time consuming and use original data to predict the phenotype. Therefore, a combined application of compression and genomic prediction modeling using deep learning could resolve these limitations. A Deep Learning Compression-based Genomic Prediction (DeepCGP) model that can compress genome-wide polymorphism data and predict phenotypes of a target trait from compressed information was proposed. The DeepCGP model contained two parts: (i) an autoencoder model based on deep neural networks to compress genome-wide polymorphism data, and (ii) regression models based on random forests (RF), genomic best linear unbiased prediction (GBLUP), and Bayesian variable selection (BayesB) to predict phenotypes from compressed information. Two datasets with genome-wide marker genotypes and target trait phenotypes in rice were applied. The DeepCGP model obtained up to 99% prediction accuracy to the maximum for a trait after 98% compression. BayesB required extensive computational time among the three methods, and showed the highest accuracy; however, BayesB could only be used with compressed data. Overall, DeepCGP outperformed state-of-the-art methods in terms of both compression and prediction. Our code and data are available at https://github.com/tanzilamohita/DeepCGP.