Two trends have dramatically altered the landscape of training in biomedical research. The first is increasing emphasis on multidisciplinary research, requiring investigators to have both depth in their own particular area of expertise, as well as the breadth of skills required to form new liaisons and collaborations with other scientists in diverse fields. The second is the increasing use of sophisticated statistical and computational data analyses, especially of genomic data. Mathematical and computational scientists must be thoroughly engaged in the biological issues of the problems they are working on if they are to be able to communicate with their colleagues and make important contributions. The proposed program will offer new pre-and postdoctoral training in environmental genetics that spans the disciplines of genetic, molecular and environmental epidemiology, statistical genetics, bioinformatics and computational molecular biology. The investigators propose to build upon the historical strength of the (University of Southern California) USC Department of Preventive Medicine in cancer and environmental epidemiology to focus their training on dissecting complex biological pathways involving gene-environment and gene-gene interactions, issues that are central to the NIEHS Environmental Genome Project. The investigators also propose to integrate these training programs based at the Keck School of Medicine more closely with those in the Program in Molecular and Computational Biology located at the University Park Campus, in order to provide training in modern genomics and bioinformatics techniques that are relevant to the NHGRI Human Genome Project and International Haplotype Mapping ("HapMap") Project. The specific aims of this proposal are to provide multidisciplinary training for three pre-doctoral scientists and two postdoctoral scientists in specialized training at the interface between environmental health and genomics. Extensive research resources are available in support of studying gene-environment and gene/gene interactions in cancer and other diseases.