Department of Agronomy, Centro Universitário UNIDEAU, Getúlio Vargas, RS 99900-000, Brazil.
Department of Agronomy, Federal University of Viçosa, Viçosa, MG 36570-900, Brazil.
Bioinformatics. 2021 Jun 16;37(10):1383-1389. doi: 10.1093/bioinformatics/btaa981.
Multivariate data are common in biological experiments and using the information on multiple traits is crucial to make better decisions for treatment recommendations or genotype selection. However, identifying genotypes/treatments that combine high performance across many traits has been a challenger task. Classical linear multi-trait selection indexes are available, but the presence of multicollinearity and the arbitrary choosing of weighting coefficients may erode the genetic gains.
We propose a novel approach for genotype selection and treatment recommendation based on multiple traits that overcome the fragility of classical linear indexes. Here, we use the distance between the genotypes/treatment with an ideotype defined a priori as a multi-trait genotype-ideotype distance index (MGIDI) to provide a selection process that is unique, easy-to-interpret, free from weighting coefficients and multicollinearity issues. The performance of the MGIDI index is assessed through a Monte Carlo simulation study where the percentage of success in selecting traits with desired gains is compared with classical and modern indexes under different scenarios. Two real plant datasets are used to illustrate the application of the index from breeders and agronomists' points of view. Our experimental results indicate that MGIDI can effectively select superior treatments/genotypes based on multi-trait data, outperforming state-of-the-art methods, and helping practitioners to make better strategic decisions toward an effective multivariate selection in biological experiments.
The source code is available in the R package metan (https://github.com/TiagoOlivoto/metan) under the function mgidi().
Supplementary data are available at Bioinformatics online.
多变量数据在生物实验中很常见，利用多个性状的信息对于做出更好的治疗建议或基因型选择决策至关重要。然而，确定在许多性状上表现出色的基因型/治疗方法一直是一项具有挑战性的任务。现有的经典线性多性状选择指数，但由于存在多重共线性和任意选择加权系数，可能会削弱遗传增益。
我们提出了一种基于多性状的新型基因型选择和治疗建议方法，克服了经典线性指数的脆弱性。在这里，我们使用与先验定义的理想型之间的基因型/治疗距离作为多性状基因型-理想型距离指数（MGIDI）来提供一种独特的、易于解释的选择过程，无需加权系数和多重共线性问题。通过蒙特卡罗模拟研究评估了 MGIDI 指数的性能，比较了在不同情况下选择具有期望增益的性状的成功率，与经典和现代指数的比较。使用两个真实的植物数据集从育种家和农学家的角度说明了该指数的应用。我们的实验结果表明，MGIDI 可以有效地根据多性状数据选择优秀的处理/基因型，优于最先进的方法，并帮助从业者在生物实验中做出更好的多变量选择策略决策。
源代码可在 R 包 metan（https://github.com/TiagoOlivoto/metan）中的函数 mgidi()中获得。
补充数据可在生物信息学在线获得。
