The search for quantitative trait loci (QTL) that explain complex traits has been ongoing in many species.Here we discuss the results of using a MAGIC population of maize lines created from 16 diverse founders to perform QTL mapping for agronomic and gene expression traits. We compare three models that assume bi-allelic, founder, and ancestral haplotype allelic states for QTL and show they have differing power to detect QTL for a variety of complex traits. Although the founder approach finds the most QTL, all methods are able to find unique QTL, suggesting that each model has advantages for traits with different genetic architectures. A closer look at a well-characterized flowering time QTL, qDTA8, which contains vgt1, highlights the strengths and weaknesses of each method and suggests a potential allelic series. Overall, our results reinforce the importance of considering different approaches to analyzing genotypic datasets, and shows the limitations of binary SNP data for identifying multi-allelic QTL.

Gene expression is a crucial intermediary between genetic variation and complex traits, and many studies have sought to incorporate gene expression in QTL mapping and GWAS to determine the underlying mechanisms of QTL for complex traits.However, the success of these integrative eQTL-QTL studies in identifying candidate genes and explaining genetic variation in complex traits has been mixed. We tested for association between effect sizes of overlapping local-eQTL and QTL and identified 5 promising candidate genes for two flowering time QTL. Generally, we found that local-eQTL for single genes tended to explain QTL for complex traits better than eQTL for factors of correlated genes, although we did identify a factor trans-eQTL moderately associated with a QTL for grain moisture. We show evidence of both historical and recent stabilizing selection acting on gene expression traits and find a significant relationship between the correlation of local-eQTL and complex traits and the significance of the local-eQTL. These findings provide support for the strong impact of stabilizing selection on gene expression traits, particularly for genes associated with complex traits.