Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics
Singh, Ravi P
KAUST DepartmentBiological and Environmental Science and Engineering (BESE) Division
Center for Desert Agriculture
Embargo End Date2024-05-23
Permanent link to this recordhttp://hdl.handle.net/10754/692037
MetadataShow full item record
AbstractMotivation: Developing new crop varieties with superior performance is highly important to ensure robust and sustainable global food security. The speed of variety development is limited by long field cycles and advanced generation selections in plant breeding programs. While methods to predict yield from genotype or phenotype data have been proposed, improved performance and integrated models are needed. Results: We propose a machine learning model that leverages both genotype and phenotype measurements by fusing genetic variants with multiple data sources collected by unmanned aerial systems. We use a deep multiple instance learning framework with an attention mechanism that sheds light on the importance given to each input during prediction, enhancing interpretability. Our model reaches 0.754 ± 0.024 Pearson correlation coefficient when predicting yield in similar environmental conditions; a 34.8% improvement over the genotype-only linear baseline (0.559 ± 0.050). We further predict yield on new lines in an unseen environment using only genotypes, obtaining a prediction accuracy of 0.386 ± 0.010, a 13.5% improvement over the linear baseline. Our multi-modal deep learning architecture efficiently accounts for plant health and environment, distilling the genetic contribution and providing excellent predictions. Yield prediction algorithms leveraging phenotypic observations during training therefore promise to improve breeding programs, ultimately speeding up delivery of improved varieties.
CitationTogninalli, M., Wang, X., Kucera, T., Shrestha, S., Juliana, P., Mondal, S., Pinto, F., Govindan, V., Crespo-Herrera, L., Huerta-Espino, J., Singh, R. P., Borgwardt, K., & Poland, J. (2023). Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics. Bioinformatics. https://doi.org/10.1093/bioinformatics/btad336
SponsorsWe sincerely appreciate the support of CIMMYT field staff with assistance in management of the field trials. Byron Evers and Mark Lucas made valuable contributions to data management and organization and Shuangye Wu for genotyping support. This material is based upon work supported by the National Science Foundation under Grant No. (1238187), the Feed the Future Innovation Lab for Applied Wheat Genomics through the U.S. Agency for International Development (Contract No AID-OAA-A-13- 00051) and the U.S. NIFA International Wheat Yield Partnership (grant no. 2017-67007-25933/project accession no. 1011391).
PublisherOxford University Press (OUP)
RelationsIs Supplemented By:
Wang, X., Shrestha, S., Juliana, P., Mondal, S., Poland, J., Espinosa, F. P., Singh, R., Togninalli, M., Kucera, T., & Borgwardt, K. (2021). Supporting data for: Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics (Version 5) [Data set]. Dryad. https://doi.org/10.5061/DRYAD.KPRR4XH5P. DOI: 10.5061/dryad.kprr4xh5p Handle: 10754/693677
Except where otherwise noted, this item's license is described as Archived with thanks to Bioinformatics under a Creative Commons license, details at: https://creativecommons.org/licenses/by/4.0/
- Phenomic selection in wheat breeding: prediction of the genotype-by-environment interaction in multi-environment breeding trials.
- Authors: Robert P, Goudemand E, Auzanneau J, Oury FX, Rolland B, Heumez E, Bouchet S, Caillebotte A, Mary-Huard T, Le Gouis J, Rincent R
- Issue date: 2022 Oct
- Genomic assisted selection for enhancing line breeding: merging genomic and phenotypic selection in winter wheat breeding programs with preliminary yield trials.
- Authors: Michel S, Ametz C, Gungor H, Akgöl B, Epure D, Grausgruber H, Löschenberger F, Buerstmayr H
- Issue date: 2017 Feb
- Multimodal deep learning methods enhance genomic prediction of wheat breeding.
- Authors: Montesinos-López A, Rivera C, Pinto F, Piñera F, Gonzalez D, Reynolds M, Pérez-Rodríguez P, Li H, Montesinos-López OA, Crossa J
- Issue date: 2023 May 2
- Simultaneous improvement of grain yield and protein content in durum wheat by different phenotypic indices and genomic selection.
- Authors: Rapp M, Lein V, Lacoudre F, Lafferty J, Müller E, Vida G, Bozhanova V, Ibraliu A, Thorwarth P, Piepho HP, Leiser WL, Würschum T, Longin CFH
- Issue date: 2018 Jun
- Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimes.
- Authors: Guo J, Khan J, Pradhan S, Shahi D, Khan N, Avci M, Mcbreen J, Harrison S, Brown-Guedira G, Murphy JP, Johnson J, Mergoum M, Esten Mason R, Ibrahim AMH, Sutton R, Griffey C, Babar MA
- Issue date: 2020 Oct 28