The availability of cost-effective, high-throughput genotyping technologies has generated a tremendous amount of interest in genetic association studies. This interest has led to the belief that one could possibly test thousands to millions of representative polymorphic sites on the genome for association with a trait or disease in order to identify the few sites that may be of relevance to the expression of that trait or disease. The choice of which polymorphic sites are "representative" and to be interrogated in such studies is problematic and has involved considerations of the putative functional significance of the sites as well as the linkage disequilibrium relationships between variations at those sites and other neighboring sites. We consider an obvious alternative to genotyping-based strategies and settings for association studies for which decisions about which variations to interrogate are obviated. Essentially, we anticipate a time when cost-effective, high-throughput DNA sequencing technologies are available and researchers will have actual sequence information on the individuals under study rather than information about what variations they possess at a few well-chosen polymorphic genomic sites. We consider Multivariate Distance Matrix Regression analysis to evaluate associations between DNA sequence information and quantitative traits such as blood pressure and cholesterol level. We evaluate the potential of the method in a few (albeit contrived) settings via simulation studies. Ultimately, we show that the procedure has promise and argue that consideration of DNA sequence-based association data should usher in a new era in genetic association study designs and methodologies.