Missing comparability: When genomic selection faces field variability. A case study in soybeans.

Abstract

The processing of phenotypic information prior to training genomic selection (GS) models is a key factor that is frequently overlooked. Several approaches have been proposed to isolate the genetic signal from the field variability. However, in most cases, the estimated genetic signal still carries the field variability print. In addition, the statistical metrics are not conclusive about the model that isolates the signal the best since the breeding values are unknown. In this study, we evaluate the effects of different spatial models for separating the genetic from the field variability components, and their repercussions implementing GS models. A real soybean (Glycine max L. Merr.) data and a simulation study under controlled conditions were analyzed. Three standard models were implemented accounting for different field variability components (M1: block, M2: block + row + column, and M3: block + row + column + row × column). Results derived from the real dataset showed that accounting for field variability reduces predictive ability of the isolated genetic signals. In the simulated data, however, it was found that field variability corrections improved the predictive ability of breeding values. We conclude that training GS models with isolated genetic signals improves the predictability of breeding values and that the current benchmarks, relying on the correlation between predicted and observed values, can be misleading due to the lack of comparability between phenotypes and breeding values.