Exploring deep learning for complex trait genomic prediction in polyploid outcrossing species
Zingaretti, Laura M. (Centre de Recerca en Agrigenòmica)
Gezan, Salvador Alejandro (University of Florida. School of Forest Resources and Conservation (USA))
Ferrão, Luis Felipe V. (University of Florida. Horticultural Sciences Department (USA))
Osorio, Luis F. (University of Florida. IFAS Gulf Coast Research and Education Center (USA))
Monfort, Amparo (Centre de Recerca en Agrigenòmica)
Muñoz, Patricio R. (University of Florida. Horticultural Sciences Department (USA))
Whitaker, Vance M. (University of Florida. IFAS Gulf Coast Research and Education Center (USA))
Perez-Enciso, Miguel (Centre de Recerca en Agrigenòmica)

Fecha:	2020
Resumen:	Genomic prediction (GP) is the procedure whereby the genetic merits of untested candidates are predicted using genome wide marker information. Although numerous examples of GP exist in plants and animals, applications to polyploid organisms are still scarce, partly due to limited genome resources and the complexity of this system. Deep learning (DL) techniques comprise a heterogeneous collection of machine learning algorithms that have excelled at many prediction tasks. A potential advantage of DL for GP over standard linear model methods is that DL can potentially take into account all genetic interactions, including dominance and epistasis, which are expected to be of special relevance in most polyploids. In this study, we evaluated the predictive accuracy of linear and DL techniques in two important small fruits or berries: strawberry and blueberry. The two datasets contained a total of 1,358 allopolyploid strawberry (2n=8x=112) and 1,802 autopolyploid blueberry (2n=4x=48) individuals, genotyped for 9,908 and 73,045 single nucleotide polymorphism (SNP) markers, respectively, and phenotyped for five agronomic traits each. DL depends on numerous parameters that influence performance and optimizing hyperparameter values can be a critical step. Here we show that interactions between hyperparameter combinations should be expected and that the number of convolutional filters and regularization in the first layers can have an important effect on model performance. In terms of genomic prediction, we did not find an advantage of DL over linear model methods, except when the epistasis component was important. Linear Bayesian models were better than convolutional neural networks for the full additive architecture, whereas the opposite was observed under strong epistasis. However, by using a parameterization capable of taking into account these non-linear effects, Bayesian linear models can match or exceed the predictive accuracy of DL. A semiautomatic implementation of the DL pipeline is available at https://github. com/lauzingaretti/deepGP/.
Ayudas:	Ministerio de Economía y Competitividad AGL2016-78709-R Ministerio de Economía y Competitividad BFU2016-77236-P Ministerio de Economía y Competitividad SEV-2015-0533
Derechos:	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.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Genomic prediction ; Genomic selection ; Polyploid species ; Deep learning ; Epistasis ; Complex traits ; Strawberry ; Blueberry
Publicado en:	Frontiers in plant science, Vol. 11 (February 2020) , art. 25, ISSN 1664-462X

DOI: 10.3389/fpls.2020.00025
PMID: 32117371

14 p, 1.9 MB

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