Classical epidemiologic studies have made seminal contributions to identifying the etiology of most common cancers. Molecular epidemiology was conceived of as an extension of traditional epidemiology to incorporate biomarkers with questionnaire data to further our understanding of the mechanisms of carcinogenesis. Early molecular epidemiologic studies employed functional assays. These studies were hampered by the need for sequential and/or prediagnostic samples, viable lymphocytes and the uncertainty of how well these functional data (derived from surrogate lymphocytic tissue) reflected events in the target tissue. The completion of the Human Genome Project and Hapmap Project, together with the unparalleled advances in high-throughput genotyping revolutionized the practice of molecular epidemiology. Early studies had been constrained by existing technology to use the hypothesis-driven candidate gene approach, with disappointing results. Pathway analysis addressed some of the concerns, although the study of interacting and overlapping gene networks remained a challenge. Whole-genome scanning approaches were designed as agnostic studies using a dense set of markers to capture much of the common genome variation to study germ-line genetic variation as risk factors for common complex diseases. It should be possible to exploit the wealth of these data for pharmacogenetic studies to realize the promise of personalized therapy. Going forward, the temptation for epidemiologists to be lured by high-tech 'omics' will be immense. Systems Epidemiology, the observational prototype of systems biology, is an extension of classical epidemiology to include powerful new platforms such as the transcriptome, proteome and metabolome. However, there will always be the need for impeccably designed and well-powered epidemiologic studies with rigorous quality control of data, specimen acquisition and statistical analysis.