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

Simon Rio¹, Luis Gallego-Sánchez², Gracia Montilla-Bascón²

¹Centro de Biotecnologia y Genómica de Plantas (CBGP, UPM-INIA), Instituto Nacional de Investigación y Tecnologia Agraria y Alimentaria (INIA), Universidad Politécnica de Madrid (UPM), Campus de Montegancedo-UPM, 28223, Pozuelo de Alarcón, Madrid, Spain. rio.simon56@gmail.com.

TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik

|August 3, 2021

Summary

Genomic prediction in Mediterranean oats shows moderate to high accuracy, influenced by population structure. Optimized training sets using linear mixed models improve prediction performance, crucial for oat breeding programs.

Keywords:

Avena sativa Environmental adaptation Genetic structure Genomic prediction Oat Training set optimization

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Area of Science:

Plant genetics
Agricultural science
Bioinformatics

Background:

Cultivated oat (Avena sativa) exhibits significant genetic structure in Mediterranean populations.
Understanding this structure is vital for effective genomic prediction in breeding.

Purpose of the Study:

Investigate genomic prediction efficiency in a structured Mediterranean oat population.
Evaluate training set optimization methods for improved predictive ability.

Main Methods:

Genotyping using genotype-by-sequencing (GBS) markers.
Phenotypic evaluation of agronomic traits in Southern Spain.
Comparison of genomic prediction models (e.g., Bayes-B) and training set optimization techniques (PEVmean, CDmean, partitioning around medoids).

Main Results:

Moderate to high predictive abilities observed for most traits; Bayes-B excelled for biomass.
Training population structure significantly impacts genomic prediction accuracy.
Inter-subspecies predictions showed lower accuracy than intra-subspecies predictions.
Linear mixed model optimization criteria (PEVmean, CDmean) outperformed heuristic methods.

Conclusions:

Population structure is a key factor influencing genomic prediction in Mediterranean oats.
Optimized training sets enhance genomic prediction performance, supporting implementation in breeding programs.
PEVmean and CDmean are effective for training set optimization in structured populations.