The key to complex trait dissection is to bridge molecular diversity with functional diversity found in the germplasm under study. Linkage analysis and association studies are two complementary tools to achieve this goal. While results from linkage analyses can only be interpreted within a narrow germplasm, association studies have been constrained by spurious significances caused by population structure. First, we review the current status of linkage analysis and association mapping in general. Second, we present a newly developed mixed-model method that accounts for multiple levels of relatedness, detected by random genetic markers, in a sample. The superiority of this association mapping method is shown with both maize SNP data for QTL and human SNP data for expression QTL (eQTL) mapping. Third, we present simulation results of a large-scale linkage analysis consisting twenty-five recombinant inbred line (RIL) populations. The SNP haplotype data of 26 diverse maize inbred lines are used to initiate the simulation. Finally, we discuss the possibility of genome-wide association study and high-density linkage analysis in maize with SNP.