In the past decade, more than 2500 genome-wide association studies (GWAS) have identified thousands of genetic loci for hundreds of traits1. The past 3 years have seen an explosive increase in GWAS sample sizes2–4, and these are expected to increase even further to 0.5–1 million in the next year and beyond5. These well-powered GWAS will not only lead to more reliable results but also to an increase in the number of detected disease-associated genetic loci. To benefit from these results, it is crucial to translate genetic loci into actionable variants that can guide functional genomics experimentation and drug target testing6. However, since the majority of GWAS hits are located in non-coding or intergenic regions7, direct inference from significantly associated single-nucleotide polymorphisms (SNPs) rarely yields functional variants. More commonly, GWAS hits span a genomic region (“GWAS risk loci”) that is characterized by multiple correlated SNPs, and may cover multiple closely located genes. Some of these genes may be relevant to the disease, while others are not, yet due to the correlated nature of closely located genetic variants, distinguishing relevant from non-relevant
