GBLUP Outperforms Quantile Mapping and Outlier Detection for Enhanced Genomic Prediction

TL;DR

This study shows that GBLUP is more effective than quantile mapping and outlier detection for genomic prediction in plant breeding.

Contribution

The study demonstrates GBLUP's consistent superiority over alternative methods in genomic prediction.

Findings

GBLUP achieved an average Pearson’s correlation of 0.65 across 14 datasets.

GBLUP reduced normalized root mean square error by up to 10% compared to other methods.

Outlier removal had minimal impact on GBLUP's performance, with outliers comprising less than 7% of data.

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 (<7%), and their removal had minimal impact on GBLUP’s predictive performance. QM provided slight improvements in datasets with skewed distributions but showed no significant advantage in well-distributed data. These findings confirm GBLUP’s robustness and reliability, suggesting limited utility for QM when data deviations are minimal.

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