MGIDI: toward an effective multivariate selection in biological experiments

Abstract

Motivation 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. Results 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 towards an effective multivariate selection in biological experiments. Availability and implementation The source code is available in the R package metan (https://github.com/TiagoOlivoto/metan) under the function mgidi(). Supplementary information Supplementary data are available at Bioinformatics online.

Multivariate analysis of strawberry experiments: where are we now and where can we go?

“…The MGIDI index (Olivoto and Nardino, 2020) was used to rank the treatments based on the desired values of studied trait. First, a factor analysis was computed with (rX ij ) to account for the correlation structure and dimensionality reduction of the data.…”

“…Eigenvalues greater than one were retained. Then, an Euclidean distance between the scores of treatments and the ideal treatment was computed as follows (Olivoto and Nardino, 2020):…”

“…In this context, Olivoto and Nardino (2020) have proposed a selection index for selecting genotypes and/or recommending treatments based on information of multiple traits. The multi-trait genotype-ideotype distance index (MGIDI) allows a more ecient and accurate treatment recommendation based on desired or undesired characteristics for the crop studied (Olivoto and Nardino, 2020).…”

“…In this context, Olivoto and Nardino (2020) have proposed a selection index for selecting genotypes and/or recommending treatments based on information of multiple traits. The multi-trait genotype-ideotype distance index (MGIDI) allows a more ecient and accurate treatment recommendation based on desired or undesired characteristics for the crop studied (Olivoto and Nardino, 2020). Thus, the objective of this study was to evaluate and select the combination of the best factor combinations (cultivar, transplant origin, and substrate mixture) in strawberry production where most of the characteristics show favorable gains using the multi-trait genotype-ideotype distance index.…”

MGIDI and WAASB indices: The useful approaches for selection of salt-tolerant barley genotype at the early growth and maturity stages

“…The multi-trait genotype-ideotype distance index (MGIDI) was used to rank the genotypes based on information of multiple traits as proposed by Olivoto and Nardino (2020). In the first step, each trait (rX ij ) were rescaled using following equation: (Olivoto and Nardino 2020). In the next step, a factor analysis (FA) was performed to account for the dimensionality reduction of the data and relationships structure.…”