Type 2 Diabetes (T2D) and prostate cancer (PCa) are leading health problems both globally with differential risk across racial/ethnic groups. Genome-wide association studies (GWAS) have made important contributions towards understanding genetic susceptibility and biological mechanisms in T2D and PCa. However, there is growing recognition of the inadequate representation of diverse racial/ethnic populations in genetic studies, with concern regarding the translational impact of genetic findings on populations of non-European ancestry globally. Genetic data from ethnically diverse populations is crucial to powering genome-phenome association studies. Likewise, the lack of representation of diverse populations in genetic research will exacerbate global health disparities that exist for many diseases.
PAGE (Population Architecture using Genomics and Epidemiology) was specifically developed to conduct genetic epidemiologic research in diverse populations particularly Hispanic/Latinos, African Americans, Asians, Native Hawaiians and Native Americans. Further, in collaboration with Illumina and others, PAGE has designed the Multi-Ethnic Genotyping Array (MEGA) which includes a GWAS scaffold to tag common and low frequency variants among global populations. Among 49,839 individuals of non-European ancestry from multiple cohorts and biobanks we are examining genetic risk factors that contribute to T2D disease risk via a multi-ethnic GWAS, fine mapping of significant regions, and meta-analysis with European populations.
PRACTICAL (Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome) is a growing consortium of 123 diverse study groups from Europe, Australia, Malaysia, China, Japan, India, Africa, Canada and the United States, committed to identifying variants that may be related to PCa risk. With the proposed research, we aim to compare ethnic specific findings to the European population of UK Biobank to understand why ethnic group disease rates differ by population for T2D and PCa, expand our current data to better understand the role of genetic contributions to T2D and PCa risk, and develop predictive tools for both diseases to inform clinical decisions across disparate populations.