The search for DNA alterations that cause human disease has been an area of active research for more than 50 years, since the time that the genetic code was first solved. In the absence of data implicating chromosomal aberrations, researchers historically have performed whole genome linkage analysis or candidate gene association analysis to develop hypotheses about the genes that most likely cause a specific phenotype or disease. Whereas whole genome linkage analysis examines all chromosomal locations without a priori predictions regarding what genes underlie susceptibility, candidate gene association studies require a researcher to know in advance the genes that he or she wishes to test (based on their knowledge of a disease). To date, very few whole genome linkage studies and candidate gene studies have produced results that lead to generalizable findings about common diseases. One factor contributing to this lack of results has certainly been the previously limited resolution of the techniques. Recent technological advances, however, have made it possible to perform highly informative whole genome linkage and association analyses, as well as whole genome transcription (transcriptome) analysis. In addition, for the first time we can detect structural DNA aberrations throughout the genome on a fine scale. Each of these four approaches has its own strengths and weaknesses, but taken together, the results from an integrated analysis can implicate highly promising novel candidate genes. Here, we provide an overview of the integrated methodology that we have used to combine high-throughput genetic and functional genomic data with bioinformatics data that have produced new insights into the potential biological basis for schizophrenia. We believe that the potential of this combined approach is greater than that of a single mode of discovery, particularly for complex genetic diseases.