Interpreting genotype × environment interaction in tropical maize using linked molecular markers and environmental covariables uri icon

abstract

  • An understanding of the genetic and environmental basis of genotypexenvironment interaction (GEI) is of fundamental importance in plant breeding. In mapping quantitative trait loci (QTLs), suitable genetic populations are grown in different environments causing QTLsxenvironment interaction (QEI). The main objective of the present study is to show how Partial Least Squares (PLS) regression and Factorial Regression (FR) models using genetic markers and environmental covariables can be used for studying QEI related to GEI. Biomass data were analyzed from a multi-environment trial consisting of 161 lines from a F-3:4 maize segregating population originally created with the purpose of mapping QTLs loci and investigating adaptation differences between highland and lowland tropical maize. PLS and FR methods detected 30 genetic markers (out of 86) that explained a sizeable proportion of the interaction of maize lines over four contrasting environments involving two low-altitude sites, one intermediate-altitude site, and one high-altitude site for biomass production. Based on a previous study, most of the 30 markers were associated with QTLs for biomass and exhibited significant QEI. It was found that marker loci in lines with positive GEI for the highland environments contained more highland alleles, whereas marker loci in lines with positive GEI for intermediate and lowland environments contained more lowland alleles. In addition, PLS and FR models identifled maximum temperature as the most-important environmental covariable for GEI. Using a stepwise variable selection procedure, a FR model was constructed for GEI and QEI that exclusively included cross products between genetic markers and environmental covariables. Higher maximum temperature in low- and intermediate-altitude sites affected the expression of some QTLs, while minimum temperature affected the expression of other QTLs.
  • An understanding of the genetic and environmentalbasis of genotype´environment interaction (GEI)is of fundamental importance in plant breeding. In mappingquantitative trait loci (QTLs), suitable genetic populationsare grown in different environments causingQTLs´environment interaction (QEI). The main objectiveof the present study is to show how Partial LeastSquares (PLS) regression and Factorial Regression (FR)models using genetic markers and environmental covariablescan be used for studying QEI related to GEI. Biomassdata were analyzed from a multi-environment trialconsisting of 161 lines from a F3:4 maize segregatingpopulation originally created with the purpose of mappingQTLs loci and investigating adaptation differencesbetween highland and lowland tropical maize. PLS andFR methods detected 30 genetic markers (out of 86) thatexplained a sizeable proportion of the interaction ofmaize lines over four contrasting environments involvingtwo low-altitude sites, one intermediate-altitude site, andone high-altitude site for biomass production. Based on aprevious study, most of the 30 markers were associatedwith QTLs for biomass and exhibited significant QEI. Itwas found that marker loci in lines with positive GEI forthe highland environments contained more highland alleles,whereas marker loci in lines with positive GEI forintermediate and lowland environments contained morelowland alleles. In addition, PLS and FR models identified maximum temperature as the most-important environmentalcovariable for GEI. Using a stepwise variableselection procedure, a FR model was constructed forGEI and QEI that exclusively included cross productsbetween genetic markers and environmental covariables.Higher maximum temperature in low- and intermediatealtitudesites affected the expression of some QTLs,while minimum temperature affected the expression ofother QTLs

publication date

  • 1999
  • 1999
  • 1999