Mining insights from GWAS
Genetic association data from genome-wide association studies (GWAS) are foundational for our understanding of complex diseases and traits. But in order to apply these results to diagnosis, drug development, and treatment, we need to identify the effector genes that explain those genetic associations. This is rarely straightforward: most SNPs associated with disease are located outside of coding regions of the genome, so that their impact on genes is not obvious; and even a variant located in a protein-coding gene may actually affect a different gene. And to complicate things further, a variant that is strongly associated with disease may not have a direct impact on a gene, but may rather be "along for the ride" with a tightly linked causal variant. To help bridge the gap between genetic association results and the effector genes that are directly involved in disease, we are aggregating additional data types—for example, transcriptional regulation, tissue specificity, curat