Abstract
Genome-wide association studies have identified thousands of intergenic variants associated with disease, most of which are presumed to act by affecting gene regulation. Standard expression quantitative trait locus (eQTL) studies were able to link many disease-associated loci to changes in gene expression. Yet, many disease-associated loci show no detectable regulatory effects in baseline bulk gene expression datasets from adult tissues. Recent work shows that, overall, standard eQTLs differ systematically from disease-associated loci, pointing to regulatory effects not captured under baseline conditions. We review emerging evidence that context-specific eQTLs, revealed under environmental perturbations, stress, or developmental transitions, resemble disease loci more closely. We highlight new in vitro systems and machine learning approaches that promise systematic identification of these context-dependent effects.