Improving predictive ability in sparse testing designs in soybean populations

Abstract

The availability of high-dimensional genomic data and advancements in genome-based prediction models (GP) have revolutionized and contributed to accelerated genetic gains in soybean breeding programs. GP-based sparse testing is a promising concept that allows increasing the testing capacity of genotypes in environments, of genotypes or environments at a fixed cost, or a substantial reduction of costs at a fixed testing capacity. This study represents the first attempt to implement GP-based sparse testing in soybeans by evaluating different training set compositions going from non-overlapped RILs until almost the other extreme of having same set of genotypes observed across environments for different training set sizes. A total of 1,755 recombinant inbred lines (RILs) tested in nine environments were used in this study. RILs were derived from 39 bi-parental populations of the Soybean Nested Association Mapping (NAM) project. The predictive abilities of various models and training set sizes and compositions were investigated. Training compositions included a range of ratios of overlapping (O-RILs) and non-overlapping (NO-RILs) RILs across environments, as well as a methodology to maximize or minimize the genetic diversity in a fixed-size sample. Reducing the training set size compromised predictive ability in most training set compositions. Overall, maximizing the genetic diversity within the training set and the inclusion of O-RILs increased prediction accuracy given a fixed training set size; however, the most complex model was less affected by these factors. More testing environments in the early stages of the breeding pipeline can provide a more comprehensive assessment of genotype stability and adaptation which are fundamental for the precise selection of superior genotypes adapted to a wide range of environments.

Utilizing genomic prediction to boost hybrid performance in a sweet corn breeding program

“…Furthermore, aiming to optimize the testcross phases, sparse testing designs should be considered ( Jarquin et al., 2020 ; Crespo-Herrera et al., 2021 ; Persa et al., 2023 ). These authors showed that this methodology has the potential to substantially save resources by optimizing the genotypes and environments explored in trials, by accounting for the G×E interaction.…”

“…e ., a matrix that connects the phenotypes with environments), β is the fixed effect of environment, and for the RKHS model: and , being and , Σ AE and Σ DE is the variance-covariance matrix for AE and DE interaction effects across traits, and for the GBLUP model: and , and , being the Hadamard product ( Jarquín et al., 2014 ). This model accounts for the main effects of genotypes, the main effects of environments, and the interactions between genotypes and environments ( Jarquín et al., 2014 ; Costa-Neto et al., 2021 ; Persa et al., 2023 ).…”

Sparse Testing Designs for Optimizing Predictive Ability in Sugarcane Populations

“…This raises critical questions about the optimal design of sparse testing in METs, such as the balance between testing a few genotypes across multiple environments versus many genotypes in fewer environments; and the trade-off between prediction accuracy and selection intensity. Empirical evidence from crops like maize (Jarquin et al, 2020; Atanda et al, 2021; Montesinos-Lopez et al, 2023), wheat (Crespo-Herrera et al, 2021; Atanda et al, 2022) and soybean (Persa et al, 2023) has provided insights into optimizing sparse testing designs. Research by Jarquin et al (2020) and Crespo-Herrera et al (2021) revealed that GS model incorporating G×E interactions can maintain robust predictive ability even with reduced training sets.…”

Editorial: Enriching genomic breeding with environmental covariates, crop models, and high-throughput phenotyping

“…The six articles Tolley et al ; López et al ; Jackson et al ; Boatwright et al ; Cooper et al ; Persa et al (2023) comprising this issue address key aspects of modern plant breeding, bringing together insights from genomics, enviromics, and phenomics.…”

“…Wrapping up this special edition, Persa et al (2023) address the challenges posed by sparse testing designs in soybean populations. Their work focuses on improving predictive ability, offering practical solutions to enhance the robustness of breeding programs in the face of limited resources and data constraints.…”