The computational methodology of Genome Wide Association Studies(GWAS) currently has several limitations: 1) the number of observations (rows) on a quantitative trait tends to be smaller than the number of single nucleotidepolymrophisms (SNPs) (columns) in the design matrix; 2) each SNP is usually modeled separately, failing to acknowledge interaction between each other; 3) there is implicit linkage disequilibrium (LD) between neighboring SNPs. To overcome these issues, we developed a tool that uses ensemble methods to fit mixed linear models into GWAS, and these ensemble methods include the development of a new experimental design approach in GWAS which uses the resultant models and data to select the next informative experiment over time. This new adaptive approach for GWAS experimental design was developed and tested in a 3 year adaptive model-guided discovery experiment.