Evaluation of deep learning for predicting rice traits using structural and single-nucleotide genomic variants
Vourlaki, Ioanna Theoni (Centre de Recerca en Agrigenòmica)
Ramos-Onsins, Sebastian (Centre de Recerca en Agrigenòmica)
Perez-Enciso, Miguel (Universitat Autònoma de Barcelona)
Castanera, Raúl (Centre de Recerca en Agrigenòmica)

Date:	2024
Abstract:	Structural genomic variants (SVs) are prevalent in plant genomes and have played an important role in evolution and domestication, as they constitute a significant source of genomic and phenotypic variability. Nevertheless, most methods in quantitative genetics focusing on crop improvement, such as genomic prediction, consider only Single Nucleotide Polymorphisms (SNPs). Deep Learning (DL) is a promising strategy for genomic prediction, but its performance using SVs and SNPs as genetic markers remains unknown. We used rice to investigate whether combining SVs and SNPs can result in better trait prediction over SNPs alone and examine the potential advantage of Deep Learning (DL) networks over Bayesian Linear models. Specifically, the performances of BayesC (considering additive effects) and a Bayesian Reproducible Kernel Hilbert space (RKHS) regression (considering both additive and non-additive effects) were compared to those of two different DL architectures, the Multilayer Perceptron, and the Convolution Neural Network, to explore their prediction ability by using various marker input strategies. We found that exploiting structural and nucleotide variation slightly improved prediction ability on complex traits in 87% of the cases. DL models outperformed Bayesian models in 75% of the studied cases, considering the four traits and the two validation strategies used. Finally, DL systematically improved prediction ability of binary traits against the Bayesian models. Our study reveals that the use of structural genomic variants can improve trait prediction in rice, independently of the methodology used. Also, our results suggest that Deep Learning (DL) networks can perform better than Bayesian models in the prediction of binary traits, and in quantitative traits when the training and target sets are not closely related. This highlights the potential of DL to enhance crop improvement in specific scenarios and the importance to consider SVs in addition to SNPs in genomic selection.
Grants:	Agencia Estatal de Investigación PID2019-108829RB-I00 Agencia Estatal de Investigación CEX2019-000902-S Agencia Estatal de Investigación IJC2020-045949-I Agencia Estatal de Investigación BES-2017-081139 Agencia Estatal de Investigación RYC2022-037459-I
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Deep learning ; Structural variation ; Genomic prediction ; Rice
Published in:	Plant methods, Vol. 20 (August 2024) , art. 121, ISSN 1746-4811

DOI: 10.1186/s13007-024-01250-y
PMID: 39127715

16 p, 3.4 MB

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Research literature > UAB research groups literature > Research Centres and Groups (research output) > Experimental sciences > CRAG (Centre for Research in Agricultural Genomics)
Articles > Research articles
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