Estimating Genotypic Correlations and Their Standard Errors Using Multivariate Restricted Maximum Likelihood Estimation with SAS Proc MIXED

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The Rht13 dwarfing gene reduces peduncle length and plant height to increase grain number and yield (2011, methodology): “Genotypic correlations and their standard errors were estimated after Holland (2006). Unless otherwise stated statistical significancewas at P=0.”
Drought Tolerance in Maize: Indirect Selection through Secondary Traits versus Genomewide Selection (2013, methodology): “Genetic correlations were estimated in two ways: first, from genetic variances and covariances using multivariate maximum likelihood estimation in SAS PROC MIXED (SAS Institute, 2009) as outlined by Holland (2006) and second, as the correlation between genomewide marker effects for each trait.”
Genetic Analysis of Corn Kernel Chemical Composition in the Random Mated 7 Generation of the Cross of Generations 70 of IHP × ILP (2006, methodology): “Estimates of phenotypic and genotypic correlations were used to measure relationships among traits A method described by Holland (2006) was used to calculate both phenotypic and genotypic correlations and standard errors.”
Markers associated with a QTL for grain yield in wheat under drought (2007, methodology): “The genetic correlations and their standard errors were estimated using multivariate restricted maximum likelihood (REML)(Holland 2006).”