Salinity stress tolerance prediction for biomass‐related traits in maize (Zea mays L.) using genome‐wide markers

Maize (Zea mays L.) is the third most important cereal crop after rice (Oryza sativa) and wheat (Triticum aestivum). Salinity stress significantly affects vegetative biomass and grain yield and, therefore, reduces the food and silage productivity of maize. Selecting salt‐tolerant genotypes is a cumbersome and time‐consuming process that requires meticulous phenotyping. To predict salt tolerance in maize, we estimated breeding values for four biomass‐related traits, including shoot length, shoot weight, root length, and root weight under salt‐stressed and controlled conditions. A five‐fold cross‐validation method was used to select the best model among genomic best linear unbiased prediction (GBLUP), ridge‐regression BLUP (rrBLUP), extended GBLUP, Bayesian Lasso, Bayesian ridge regression, BayesA, BayesB, and BayesC. Examination of the effect of different marker densities on prediction accuracy revealed that a set of low‐density single nucleotide polymorphisms obtained through filtering based on a combination of analysis of variance and linkage disequilibrium provided the best prediction accuracy for all the traits. The average prediction accuracy in cross‐validations ranged from 0.46 to 0.77 across the four derived traits. The GBLUP, rrBLUP, and all Bayesian models except BayesB demonstrated comparable levels of prediction accuracy that were superior to the other modeling approaches. These findings provide a roadmap for the deployment and optimization of genomic selection in breeding for salt tolerance in maize. Core Ideas Genomic selection was evaluated for salt tolerance in maize to have a general idea of expected prediction accuracy. Genomic best linear unbiased prediction (GBLUP), ridge‐regression BLUP, Bayesian ridge regression, BayesA, and BayesB showed similar performance for all the studied traits when only trait associated markers were used. Bayesian models showed slightly higher accuracies when higher density of randomly selected markers were used. A reduced marker set selected based on analysis of variance + linkage disequilibrium showed improved prediction accuracy in cross‐validation.