Recent developments in association mapping methods along with improvements in sequencing technology have made it possible to link locations along the genome (single nucleotide polymorphisms, or SNPs) with quantitative traits in genome-wide association study (GWAS) data. Although this goal is central in the biological sciences, progress has been limited by the inability of existing methods to consider complex, but relevant, scenarios such as the simultaneous influence of genetic and external effects on quantitative trait(s) under study. Traditional statistical modeling (e.g., regression-based methods) are computationally feasible and perform well in detecting external effects but may miss weaker genetic signals since they fail to consider uneven evolutionary relatedness among samples. For this reason, it is difficult to detect and localize SNPs associated with quantitative traits in GWAS using classical statistics. This talk will include a coalescent approach to search for SNPs associated with quantitative traits in GWAS data by considering the evolutionary history among SNPs using phylogenetic analysis, through both mixed model and Bayesian approaches. Performance of phylogenetic and classical methods will be evaluated using simulation data. An application to a deer mouse data set example to identify SNPs associated with tail stripe color variation will also be presented to demonstrate utility of phylogenetic analysis methods in practice.