What is the Japanese Consortium of Genetic Epidemiology studies (J-CGE)?

The Japanese Consortium of Genetic Epidemiology studies (J-CGE) includes five molecular epidemiology research groups with genomic information, namely the Japan Public Health Center-based Prospective Study (JPHC Study, National Cancer Center), Aichi Cancer Center Hospital Epidemiology Research (HERPACC, Aichi Cancer Center), Japan Multicenter Cohort Study (J-MICC Study, Nagoya University), Tohoku Medical Megabank Project (TMM, Tohoku University and Iwate Medical University), and Tsuruoka Metabolomics Cohort Study (TMCS, Keio University). These groups have established a joint research consortium called J-CGE to promote research that investigates the genetic basis of the causes of cancer and other diseases in Japanese. The consortium is funded by the National Cancer Center Research and Development Fund, and others.

Background of Study

Environmental factors such as smoking and diet play an important role in the development of cancer, as well as of cardiovascular and many other diseases. In recent years, the rapid development of analysis technology has lead to a focus on omics analysis, which comprehensively analyzes in vivo molecular information. For example, genome-wide association studies (GWAS) have reported that many single nucleotide polymorphisms (SNPs) are associated with the risk of diseases such as cancer and other diseases, and are also associated with risk factors for diseases, such as drinking behavior. Establishing the foundation of molecular epidemiology research in Japan is a necessary step in strengthening the evidence base for disease prevention.

Purpose of the Study

The J-CGE will implement GWAS for the risk factors of cancer and other diseases in Japanese populations. In addition, we will perform Mendelian randomization analyses using GWAS to identify potential risk factors and establish cancer and other diseases as study outcomes. Mendelian randomization analysis has attracted attention in recent years. By taking advantage of the random distribution of genotypes, a phenomenon explained under Mendel's laws, this analysis mimics a pseudo-randomized controlled trial situation in observational studies, making it less susceptible to confounding and reverse causation. We aim to use these new methods to provide stronger evidence for prevention in cancer and other diseases.