Montesinos-López Osval Antonio, Crossa José, Vitale Paolo, Gerard Guillermo, Crespo-Herrera Leonardo, Dreisigacker Susanne, Saint Pierre Carolina, Posadas Luis G, Agbona Afolabi, Buenrostro-Mariscal Raymundo, Montesinos-López Abelardo, Chawade Aakash
Facultad de Telemática, Universidad de Colima, Colima 28040, CL, Mexico.
International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera México-Veracruz, Texcoco 52640, EM, Mexico.
Int J Mol Sci. 2025 Apr 11;26(8):3620. doi: 10.3390/ijms26083620.
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.
基因组选择（GS）通过利用基因组数据预测复杂性状来加速植物育种。本研究比较了基因组最佳线性无偏预测（GBLUP）、分位数映射（QM）——对GBLUP预测的一种调整——以及四种异常值检测方法。使用14个真实数据集，通过皮尔逊相关系数（COR）和归一化均方根误差（NRMSE）评估预测准确性。GBLUP始终优于所有其他方法，与其他方法相比，平均COR达到0.65，NRMSE降低高达10%。检测到的异常值比例较低（<7%），去除这些异常值对GBLUP的预测性能影响最小。QM在分布偏斜的数据集中略有改进，但在分布良好的数据中没有显示出显著优势。这些发现证实了GBLUP的稳健性和可靠性，表明当数据偏差最小时，QM的效用有限。
