A review of deep learning applications for genomic selection.

Journal: BMC genomics
Published Date: Jan 6, 2021

Abstract

BACKGROUND: Several conventional genomic Bayesian (or no Bayesian) prediction methods have been proposed including the standard additive genetic effect model for which the variance components are estimated with mixed model equations. In recent years, deep learning (DL) methods have been considered in the context of genomic prediction. The DL methods are nonparametric models providing flexibility to adapt to complicated associations between data and output with the ability to adapt to very complex patterns.

Authors

  • Osval Antonio Montesinos-López
    Facultad de Telemática, Universidad de Colima, Colima, 28040, México.
  • Abelardo Montesinos-López
    Departamento de Matemáticas, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, 44430, Guadalajara, Jalisco, México.
  • Paulino Pérez-Rodríguez
    Colegio de Postgraduados, Campus Montecillo, Texcoco, México, 056230, México. perpdgo@gmail.com.
  • José Alberto Barrón-López
    Department of Animal Production (DPA), Universidad Nacional Agraria La Molina, Av. La Molina s/n La Molina, 15024, Lima, Peru.
  • Johannes W R Martini
    Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Km 45, CP 52640, Carretera Mexico-Veracruz, Mexico.
  • Silvia Berenice Fajardo-Flores
    Facultad de Telemática, Universidad de Colima, 28040, Colima, Colima, Mexico.
  • Laura S Gaytan-Lugo
    School of Mechanical and Electrical Engineering, Universidad de Colima, 28040, Colima, Colima, Mexico.
  • Pedro C Santana-Mancilla
    Facultad de Telemática, Universidad de Colima, 28040, Colima, Colima, Mexico.
  • José Crossa
    Biometrics and Statistics Unit (BSU), International Maize and Wheat Improvement Center (CIMMYT), Apdo Postal 6-641, México DF, 06600 24105, México. j.crossa@cgiar.org.

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