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

Abstract

Maize (Zea mays L.) is the third most important cereal crop after rice (Oryza sativa)and wheat (Triticum aestivum). Salinity stress significantly affects vegetative biomassand grain yield and, therefore, reduces the food and silage productivity of maize.Selecting salt-tolerant genotypes is a cumbersome and time-consuming process thatrequires meticulous phenotyping. To predict salt tolerance in maize, we estimatedbreeding values for four biomass-related traits, including shoot length, shoot weight,root length, and root weight under salt-stressed and controlled conditions. A five-foldcross-validation method was used to select the best model among genomic best linearunbiased prediction (GBLUP), ridge-regression BLUP (rrBLUP), extended GBLUP,Bayesian Lasso, Bayesian ridge regression, BayesA, BayesB, and BayesC. Exam-ination of the effect of different marker densities on prediction accuracy revealedthat a set of low-density single nucleotide polymorphisms obtained through filteringbased on a combination of analysis of variance and linkage disequilibrium providedthe best prediction accuracy for all the traits. The average prediction accuracy incross-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 ofprediction accuracy that were superior to the other modeling approaches. These find-ings provide a roadmap for the deployment and optimization of genomic selection inbreeding for salt tolerance in maize.