Genome-wide association studies (GWASs) have been performed extensively in diverse populations to identify single nucleotide polymorphisms (SNPs) associated with complex diseases or traits. However, to date, the SNPs identified fail to explain a large proportion of the variance of the traits/diseases. GWASs on type 2 diabetes (T2D) and obesity are generally focused on individual traits independently, and genetic intercommunity (common genetic contributions or the product of over correlated phenotypic world) between them are largely unknown, despite extensive data showing that these two phenotypes share both genetic and environmental risk factors. Here, we applied a recently developed genetic pleiotropic conditional false discovery rate (cFDR) approach to discover novel loci associated with BMI and T2D by incorporating the summary statistics from existing GWASs of these two traits. Conditional Q-Q and fold enrichment plots were used to visually demonstrate the strength of pleiotropic enrichment. Adopting a cFDR nominal significance level of 0.05, 287 loci were identified for BMI and 75 loci for T2D, 23 of which for both traits. By incorporating related traits into a conditional analysis framework, we observed significant pleiotropic enrichment between obesity and T2D. These findings may provide novel insights into the etiology of obesity and T2D, individually and jointly.