Exploring deep learning for complex trait genomic prediction in polyploid outcrossing species
Visualitza/Obre
Autor/a
Zingaretti, Laura M.
Gezan, Salvador Alejandro
Ferrão, Luis Felipe V.
Osorio, Luis F.
Muñoz, Patricio R.
Whitaker, Vance M.
Pérez-Enciso, Miguel
Data de publicació
2020-02-06ISSN
1664-462X
Resum
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/.
Tipus de document
Article
Versió del document
Versió publicada
Llengua
English
Matèries (CDU)
633 - Cultius i produccions
Pàgines
14
Publicat per
Frontiers Media
Publicat a
Frontiers in Plant Science
Citació
Zingaretti, Laura M., Salvador Alejandro Gezan, Luis Felipe V. Ferrão, Luis F. Osorio, Amparo Monfort, Patricio R. Muñoz, Vance M. Whitaker, and Miguel Pérez-Enciso. 2020. "Exploring Deep Learning For Complex Trait Genomic Prediction In Polyploid Outcrossing Species". Frontiers In Plant Science 11. Frontiers Media SA. doi:10.3389/fpls.2020.00025.
Número de l'acord de la subvenció
MINECO/Programa Estatal de I+D+I orientada a los retos de la sociedad/AGL2016-78709-R/ES/UTILIZACION DE SECUENCIAS COMPLETAS PARA LA MEJORA DE ESPECIES DOMESTICAS/
MINECO/Programa Estatal de fomento de la investigación científica y técnica de excelencia/SEV-2015-0533/ES/ /
MINECO-FEDER/Programa Estatal de fomento de la investigación científica y técnica de excelencia/BFU2016-77236-P/ES/FUNCION DEL RELOJ CIRCADIANO EN EL CONTROL DE LAS RESPUESTAS DE LA PLANTA A CAMBIOS MEDIOAMBIENTALES/
Programa
Genòmica i Biotecnologia
Aquest element apareix en la col·lecció o col·leccions següent(s)
- ARTICLES CIENTÍFICS [2850]
Excepte que s'indiqui una altra cosa, la llicència de l'ítem es descriu com http://creativecommons.org/licenses/by/4.0/