Quantification of the Genomic Contribution towards Food and Energy-related Crop Traits

Statistical approaches for genome-wide association studies (GWASs) and genomic selection (GS) have enabled the identification of genomic loci associated with agronomically important traits while controlling for false positives and the use of genome-wide marker data to accurately predict trait values. Using these developments as starting points, the Lipka Lab at the University of Illinois is exploring the optimization of GWAS and GS approaches and their implementation into freely available software packages. In this presentation, three different examples of research projects from the Lipka Lab are presented. Collectively, these highlight the impact of genomic properties underlying a trait and species on the performance of statistical approaches for GWAS and GS.