Incorporation of Soil-Derived Covariates in Progeny Testing and Line Selection to Enhance Genomic Prediction Accuracy in Soybean Breeding

Abstract

The availability of high-dimensional molecular markers has allowed plant breeding programs to maximize their efficiency through the genomic prediction of a phenotype of interest. Yield is a complex quantitative trait whose expression is sensitive to environmental stimuli. In this research, we investigated the potential of incorporating soil texture information and its interaction with molecular markers via covariance structures for enhancing predictive ability across breeding scenarios. A total of 797 soybean lines derived from 367 unique bi-parental populations were genotyped using the Illumina BARCSoySNP6K and tested for yield during 5 years in Tiptonville silt loam, Sharkey clay, and Malden fine sand environments. Four statistical models were considered, including the GBLUP model (M1), the reaction norm model (M2) including the interaction between molecular markers and the environment (G×E), an extended version of M2 that also includes soil type (S), and the interaction between soil type and molecular markers (G×S) (M3), and a parsimonious version of M3 which discards the G×E term (M4). Four cross-validation scenarios simulating progeny testing and line selection of tested–untested genotypes (TG, UG) in observed–unobserved environments [OE, UE] were implemented (CV2 [TG, OE], CV1 [UG, OE], CV0 [TG, UE], and CV00 [UG, UE]). Across environments, the addition of G×S interaction in M3 decreased the amount of variability captured by the environment (−30.4%) and residual (−39.2%) terms as compared to M1. Within environments, the G×S term in M3 reduced the variability captured by the residual term by 60 and 30% when compared to M1 and M2, respectively. M3 outperformed all the other models in CV2 (0.577), CV1 (0.480), and CV0 (0.488). In addition to the Pearson correlation, other measures were considered to assess predictive ability and these showed that the addition of soil texture seems to structure/dissect the environmental term revealing its components that could enhance or hinder the predictability of a model, especially in the most complex prediction scenario (CV00). Hence, the availability of soil texture information before the growing season could be used to optimize the efficiency of a breeding program by allowing the reconsideration of field experimental design, allocation of resources, reduction of preliminary trials, and shortening of the breeding cycle.

SoilType: An R package to interplay soil characterization in plant science

“…Similar results were described by Canella Vieira et al. (2022), showing that soil texture can explain a great proportion of the phenotypic variation in annual crops. Furthermore, using Gaussian kernels, it was possible to identify the soil covariates across locations (Figure 1) and the relationship (SRM) between them (Figure 2), group the locations via a k ‐means clustering approach (Figure 3), and visualize how the multi‐environment trials as distributed in the Mississippi Delta (Figure 4).…”

“…However, the success of such studies relies heavily on the quality and quantity of data used. One factor that can significantly impact the accuracy of plant breeding studies is the incorporation of soil‐derived covariates (Canella Vieira et al., 2022). These covariates, which include soil nutrient content, moisture levels, and pH, can greatly affect plant growth and development (Poggio et al., 2021).…”

Phenomic and genomic prediction of yield on multiple locations in winter wheat

“…The soil conductivity measurements were expected to be a proxy for local variation in water holding capacity, which could have affected yield in a dry season. It was anticipated that these covariates would account for a portion of the environmentally-induced variation in observed yields, as found by Vieira et al (2022) . However, despite variation in soil conductivity across the fields in each trial, no detectable relationship to the yield variation was found.…”

Using machine learning to integrate genetic and environmental data to model genotype-by-environment interactions

“…The high availability of environmental data in testing locations has resulted in a thorough characterization of environmental effects over the observed phenotype. In addition to the traits commonly measured by plant breeders, data from weather stations, soil surveys, and public repositories has been recently integrated into genomic prediction (GP) models (Malosetti et al 2016; Monteverde et al 2019; Canella Vieira et al 2022).…”