Genomic prediction and training set optimization in a structured Mediterranean oat population

Genomic prediction and training set optimization in a structured Mediterranean oat population

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Trabajo presentado en el IV Symposium on Physiology and Breeding of Cereals, celebrado en Pamplona el 16 y 17 de diciembre de 2021.

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Main Authors: Rio, Simon, Gallego-Sánchez, L., Montilla-Bascón, Gracia, Canales, Francisco José, Isidro-Sánchez, Julio, Prats, Elena
Other Authors: Ministerio de Ciencia e Innovación (España)
Format: comunicación de congreso biblioteca
Published: 2021-12
Online Access:http://hdl.handle.net/10261/268490
http://dx.doi.org/10.13039/501100004837
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spelling dig-inia-es-10261-2684902022-05-04T01:42:08Z Genomic prediction and training set optimization in a structured Mediterranean oat population Rio, Simon Gallego-Sánchez, L. Montilla-Bascón, Gracia Canales, Francisco José Isidro-Sánchez, Julio Prats, Elena Ministerio de Ciencia e Innovación (España) Trabajo presentado en el IV Symposium on Physiology and Breeding of Cereals, celebrado en Pamplona el 16 y 17 de diciembre de 2021. In this study, we investigated the efficiency of genomic prediction and training set optimization in a highly structured population of cultivars and landraces of cultivated oat (Avena sativa) from the Mediterranean basin, including white (subsp. sativa) and red (subsp. byzantina) oats, genotyped using genotype-by- sequencing markers and evaluated for agronomic traits in Southern Spain. For most traits, the predictive abilities were moderate to high with little differences between models, except for biomass for which Bayes-B showed a substantial gain compared to other models. The consistency between the structure of the training population and the population to be predicted was key to the predictive ability of genomic predictions. The predictive ability of inter-subspecies predictions was indeed much lower than that of intra-subspecies predictions for all traits. Regarding training set optimization, the linear mixed model optimization criteria (prediction error variance (PEVmean) and coefficient of determination (CDmean)) performed better than the heuristic approach “partitioning around medoids,” even under high population structure. The superiority of CDmean and PEVmean could be explained by their ability to adapt the representation of each genetic group according to those represented in the population to be predicted. These results represent an important step towards the implementation of genomic prediction in oat breeding programs and address important issues faced by the genomic prediction community regarding population structure and training set optimization. This work was supported by the Spanish Ministry of Science and Innovation [PID2019-104518RB-I00], and regional government through the AGR-253 group. 2022-05-03T11:40:10Z 2022-05-03T11:40:10Z 2021-12 2022-05-03T11:40:10Z comunicación de congreso http://purl.org/coar/resource_type/c_5794 IV Symposium on Physiology and Breeding of Cereals (2021) http://hdl.handle.net/10261/268490 http://dx.doi.org/10.13039/501100004837 #PLACEHOLDER_PARENT_METADATA_VALUE# info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-104518RB-I00/ES/MEJORA DE AVENA RESILIENTE ADAPTADA A AMBIENTES MEDITERRANEOS/ Sí open
institution INIA ES
collection DSpace
country España
countrycode ES
component Bibliográfico
access En linea
databasecode dig-inia-es
tag biblioteca
region Europa del Sur
libraryname Biblioteca del INIA España
description Trabajo presentado en el IV Symposium on Physiology and Breeding of Cereals, celebrado en Pamplona el 16 y 17 de diciembre de 2021.
author2 Ministerio de Ciencia e Innovación (España)
author_facet Ministerio de Ciencia e Innovación (España)
Rio, Simon
Gallego-Sánchez, L.
Montilla-Bascón, Gracia
Canales, Francisco José
Isidro-Sánchez, Julio
Prats, Elena
format comunicación de congreso
author Rio, Simon
Gallego-Sánchez, L.
Montilla-Bascón, Gracia
Canales, Francisco José
Isidro-Sánchez, Julio
Prats, Elena
spellingShingle Rio, Simon
Gallego-Sánchez, L.
Montilla-Bascón, Gracia
Canales, Francisco José
Isidro-Sánchez, Julio
Prats, Elena
Genomic prediction and training set optimization in a structured Mediterranean oat population
author_sort Rio, Simon
title Genomic prediction and training set optimization in a structured Mediterranean oat population
title_short Genomic prediction and training set optimization in a structured Mediterranean oat population
title_full Genomic prediction and training set optimization in a structured Mediterranean oat population
title_fullStr Genomic prediction and training set optimization in a structured Mediterranean oat population
title_full_unstemmed Genomic prediction and training set optimization in a structured Mediterranean oat population
title_sort genomic prediction and training set optimization in a structured mediterranean oat population
publishDate 2021-12
url http://hdl.handle.net/10261/268490
http://dx.doi.org/10.13039/501100004837
work_keys_str_mv AT riosimon genomicpredictionandtrainingsetoptimizationinastructuredmediterraneanoatpopulation
AT gallegosanchezl genomicpredictionandtrainingsetoptimizationinastructuredmediterraneanoatpopulation
AT montillabascongracia genomicpredictionandtrainingsetoptimizationinastructuredmediterraneanoatpopulation
AT canalesfranciscojose genomicpredictionandtrainingsetoptimizationinastructuredmediterraneanoatpopulation
AT isidrosanchezjulio genomicpredictionandtrainingsetoptimizationinastructuredmediterraneanoatpopulation
AT pratselena genomicpredictionandtrainingsetoptimizationinastructuredmediterraneanoatpopulation
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