Sparse kernel models provide optimization of training set design for genomic prediction in multiyear wheat breeding data

Sparse kernel models provide optimization of training set design for genomic prediction in multiyear wheat breeding data

Abstract The success of genomic selection (GS) in breeding schemes relies on its ability to provide accurate predictions of unobserved lines at early stages. Multigeneration data provides opportunities to increase the training data size and thus, the likelihood of extracting useful information from...

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Bibliographic Details
Main Authors: Marco Lopez‐Cruz, Susanne Dreisigacker, Leonardo Crespo‐Herrera, Alison R Bentley, Ravi Singh, Jesse Poland, Sandesh Shrestha, Julio Huerta‐Espino, Velu Govindan, Philomin Juliana, Suchismita Mondal, Paulino Pérez‐Rodríguez, Jose Crossa
Format: Article
Language:English
Published: Wiley 2022-12-01
Series:The Plant Genome
Online Access:https://doi.org/10.1002/tpg2.20254

Internet

https://doi.org/10.1002/tpg2.20254

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