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Updated: May 12, 2025

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GBLUP Outperforms Quantile Mapping and Outlier Detection for Enhanced Genomic Prediction.

Osval Antonio Montesinos-López1, José Crossa2,3, Paolo Vitale2

  • 1Facultad de Telemática, Universidad de Colima, Colima 28040, CL, Mexico.

International Journal of Molecular Sciences
|May 7, 2025
PubMed
Summary

Genomic selection (GS) using genomic best linear unbiased prediction (GBLUP) is a robust method for predicting plant traits. While quantile mapping (QM) offers minor benefits in skewed data, GBLUP remains the most reliable approach for genomic prediction.

Keywords:
GBLUPgenomic predictionoutlier detection methodsplant breedingquantile mapping

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Area of Science:

  • Plant breeding
  • Genomics
  • Quantitative genetics

Background:

  • Genomic selection (GS) is crucial for accelerating crop improvement by predicting complex traits.
  • Accurate prediction models are essential for efficient breeding programs.

Purpose of the Study:

  • To compare the predictive accuracy of genomic best linear unbiased prediction (GBLUP) against quantile mapping (QM) and outlier detection methods.
  • To evaluate the impact of data adjustments on genomic prediction performance.

Main Methods:

  • Utilized 14 real plant genetics datasets.
  • Assessed predictive accuracy using Pearson's correlation (COR) and normalized root mean square error (NRMSE).
  • Compared GBLUP, QM-adjusted GBLUP, and four outlier detection techniques.

Main Results:

  • GBLUP consistently demonstrated superior predictive accuracy, achieving an average COR of 0.65.
  • GBLUP provided up to a 10% reduction in NRMSE compared to alternative methods.
  • Quantile mapping showed marginal benefits only in datasets with skewed distributions.

Conclusions:

  • Genomic best linear unbiased prediction (GBLUP) is a robust and reliable method for genomic prediction in plant breeding.
  • The utility of quantile mapping is limited to datasets with significant distributional deviations.
  • Outlier detection had minimal impact on GBLUP's predictive performance.