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Multimodal deep learning methods enhance genomic prediction of wheat breeding

dc.creatorMontesinos-Lopez, A.
dc.creatorRivera-Amado, C.
dc.creatorPinto Espinosa, F.
dc.creatorPiñera Chavez, F.J.
dc.creatorGonzález-Diéguez, D.
dc.creatorReynolds, M.P.
dc.creatorPerez-Rodriguez, P.
dc.creatorHuihui Li
dc.creatorMontesinos-Lopez, O.A.
dc.creatorCrossa, J.
dc.date2023-05-29T22:43:29Z
dc.date2023-05-29T22:43:29Z
dc.date2023
dc.date.accessioned2023-07-17T20:10:36Z
dc.date.available2023-07-17T20:10:36Z
dc.identifierhttps://hdl.handle.net/10883/22606
dc.identifier10.1093/g3journal/jkad045
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7514349
dc.descriptionWhile several statistical machine learning methods have been developed and studied for assessing the genomic prediction (GP) accuracy of unobserved phenotypes in plant breeding research, few methods have linked genomics and phenomics (imaging). Deep learning (DL) neural networks have been developed to increase the GP accuracy of unobserved phenotypes while simultaneously accounting for the complexity of genotype–environment interaction (GE); however, unlike conventional GP models, DL has not been investigated for when genomics is linked with phenomics. In this study we used 2 wheat data sets (DS1 and DS2) to compare a novel DL method with conventional GP models. Models fitted for DS1 were GBLUP, gradient boosting machine (GBM), support vector regression (SVR) and the DL method. Results indicated that for 1 year, DL provided better GP accuracy than results obtained by the other models. However, GP accuracy obtained for other years indicated that the GBLUP model was slightly superior to the DL. DS2 is comprised only of genomic data from wheat lines tested for 3 years, 2 environments (drought and irrigated) and 2–4 traits. DS2 results showed that when predicting the irrigated environment with the drought environment, DL had higher accuracy than the GBLUP model in all analyzed traits and years. When predicting drought environment with information on the irrigated environment, the DL model and GBLUP model had similar accuracy. The DL method used in this study is novel and presents a strong degree of generalization as several modules can potentially be incorporated and concatenated to produce an output for a multi-input data structure.
dc.formatpdf
dc.languageEnglish
dc.relationhttps://hdl.handle.net/11529/10548885
dc.rightsOpen Access
dc.rightsCIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose.
dc.source5
dc.source13
dc.source2160-1836
dc.sourcejkad045
dc.subjectAGRICULTURAL SCIENCES AND BIOTECHNOLOGY
dc.subjectConventional Methods
dc.subjectGenomic Prediction Accuracy
dc.subjectDeep Learning
dc.subjectNovel Methods
dc.subjectWHEAT
dc.subjectBREEDING
dc.subjectMACHINE LEARNING
dc.subjectMETHODS
dc.subjectMARKER-ASSISTED SELECTION
dc.titleMultimodal deep learning methods enhance genomic prediction of wheat breeding
dc.typeArticle
dc.typePublished Version


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