GBLUP Outperforms Quantile Mapping and Outlier Detection for Enhanced Genomic Prediction

Sammanfattning

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 (

Nyckelord

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

Publicerad i

International Journal of Molecular Sciences
2025, volym: 26, nummer: 8, artikelnummer: 3620
Utgivare: MDPI

SLU författare

• Chawade, Aakash

  • Institutionen för växtförädling, Sveriges lantbruksuniversitet
    

UKÄ forskningsämne

Botanik
Molekylärbiologi

Publikationens identifierare

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

Permanent länk till denna sida (URI)

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