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Optimizing fully-efficient two-stage models for genomic selection using open-source software.

Javier Fernández-González¹, Julio Isidro Y Sánchez²

¹Centro de Biotecnologia y Genómica de Plantas (CBGP, UPM-INIA) - Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnologia Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223, Pozuelo de Alarcón, Madrid, Spain. javier.fgonzalez@upm.es.

|February 5, 2025

Summary

Fully-efficient two-stage genomic selection models offer improved prediction accuracy, especially for augmented designs. Incorporating estimation error covariance into a random effect (Full_R model) enhances genetic gain in breeding programs.

Keywords:

Fully-efficient Genomic prediction Genomic selection Open-source Plant breeding Two-stage models Variance-covariance Weighted regression

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

Quantitative genetics
Plant and animal breeding

Background:

Genomic selection (GS) uses dense genetic markers to predict genomic breeding values (GEBV).
Single-stage models are computationally intensive, while two-stage models offer efficiency but often neglect error correlations.
Unweighted (UNW) two-stage models assume independent errors, potentially reducing prediction accuracy.

Purpose of the Study:

To evaluate the performance of fully-efficient two-stage genomic selection models.
To compare fully-efficient models against unweighted models, particularly for augmented experimental designs.
To investigate the impact of non-additive effects and experimental design on prediction accuracy.

Main Methods:

Developed and simulated fully-efficient two-stage models, including the Full_R model incorporating estimation error covariance.
Compared model performance using simulation studies across various scenarios and experimental designs (randomized complete block and augmented).
Assessed the influence of non-additive genetic effects on prediction accuracy.

Main Results:

Fully-efficient two-stage models performed similarly to UNW models in randomized complete block designs but substantially better in augmented designs.
Incorporating non-additive effects and augmented designs significantly improved prediction accuracy.
The Full_R model, which includes estimation error covariance as a random effect, demonstrated consistent performance and potential for increased genetic gain.

Conclusions:

Fully-efficient two-stage models, especially the Full_R model, are recommended for genomic selection due to improved accuracy and efficiency.
The synergy between experimental design and modeling strategy is crucial for maximizing prediction accuracy.
The study provides theoretical background and open-source R code to promote the adoption of fully-efficient models in breeding programs, potentially increasing genetic gain.