The product of a hundred years of technological and methodological innovation in genetic mapping, Genome-wide association studies (GWAS) have brought about an unprecedented era of genomic discovery identifying tens of thousands of genomic regions harboring variants that influence common disease risk. Yet, translating these insights into new therapies has proven difficult. Though associated regions are each expected to carry a small number of causal variants, there are usually many possible candidates for those variants, and they are inherited together. Furthermore, the associated variants are often found outside coding sequences making it challenging to identify the mechanisms and genes by which they exert their effects. In this thesis, I provide an in-depth review of the technical developments that led to the advent of GWAS and the issues slowing efforts to map causal variants and genes at GWAS loci. I also present two studies that leverage statistics, bioinformatics, and functional genomics to pinpoint these effector variants and genes. The first of these utilized the ancestrally-diverse Million Veteran Program and the Sum of Single Effects (SuSiE) framework to fine-map 57,601 independent causal signals across 936 traits, identifying 6,318 causal variants with high confidence. These signals implicate 15,596 gene-trait connections with notable examples such as the connection between SLC22A18/SLC22A18AS and keloid scarring that could only be found using the diverse ancestral groups represented in the biobank. The second study used a single-cell CRISPRi screen targeted to non-coding GWAS variants to map 23 likely effector genes for bone mineral density (BMD). Four of these showed consistent effects on osteoblast biology upon knockdown (ARID5B, CC2D1B, EIF4G2, and NCOA3) indicating a likely mechanism for these genes in mediating BMD-related disease. Computational analyses supporting the screen also strongly suggested that multiple tissues beyond bone are involved in determining BMD and that the causes for BMD-related disease are more multifactorial than previously appreciated. Both overall and in the specific context of BMD, this thesis greatly expands the known list of causal variants and genes for common diseases while providing important insights into the value of diversity in genetic studies and the inter-relatedness of biological systems.