GBLUP Outperforms Quantile Mapping and Outlier Detection for Enhanced Genomic Prediction

Abstract

Genomic selection (GS) accelerates plant breeding by predicting complex traits using genomic data. This study compares genomic best linear unbiased prediction (GBLUP), quantile mapping (QM)-an adjustment to GBLUP predictions-and four outlier detection methods. Using 14 real datasets, predictive accuracy was evaluated with Pearson's correlation (COR) and normalized root mean square error (NRMSE). GBLUP consistently outperformed all other methods, achieving an average COR of 0.65 and an NRMSE reduction of up to 10% compared to alternative approaches. The proportion of detected outliers was low (

Keywords

quantile mapping; GBLUP; outlier detection methods; plant breeding; genomic prediction

Published in

International Journal of Molecular Sciences
2025, volume: 26, number: 8, article number: 3620
Publisher: MDPI

SLU Authors

• Chawade, Aakash

  • Department of Plant Breeding, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Botany
Molecular Biology

Publication identifier

• DOI: https://doi.org/10.3390/ijms26083620

Permanent link to this page (URI)

https://res.slu.se/id/publ/141782