The article reviews the role of regulatory variation in complex traits and diseases, focusing on expression quantitative trait loci (eQTLs) and their impact on gene expression and subsequent physiological changes. eQTLs are genomic regions that influence gene expression levels, and their identification has been a key tool in linking DNA sequence variation to phenotypes. The authors discuss recent advances in understanding the molecular nature of eQTLs, including the identification of causal variants and their modes of action. They highlight the importance of both local and distant eQTLs, with local eQTLs acting in cis or trans and distant eQTLs affecting expression levels over longer genomic distances. The article also explores the relationship between eQTLs and higher-order organismal phenotypes, such as physiology and disease, and presents examples of complete chains of causality linking individual polymorphisms to changes in gene expression and ultimately to disease risk. Additionally, the authors discuss the challenges and opportunities in using eQTL data to prioritize causal variants in genome-wide association studies (GWAS) and the potential for integrating eQTL data with other genomic and functional annotations to improve understanding and prediction of disease risk.The article reviews the role of regulatory variation in complex traits and diseases, focusing on expression quantitative trait loci (eQTLs) and their impact on gene expression and subsequent physiological changes. eQTLs are genomic regions that influence gene expression levels, and their identification has been a key tool in linking DNA sequence variation to phenotypes. The authors discuss recent advances in understanding the molecular nature of eQTLs, including the identification of causal variants and their modes of action. They highlight the importance of both local and distant eQTLs, with local eQTLs acting in cis or trans and distant eQTLs affecting expression levels over longer genomic distances. The article also explores the relationship between eQTLs and higher-order organismal phenotypes, such as physiology and disease, and presents examples of complete chains of causality linking individual polymorphisms to changes in gene expression and ultimately to disease risk. Additionally, the authors discuss the challenges and opportunities in using eQTL data to prioritize causal variants in genome-wide association studies (GWAS) and the potential for integrating eQTL data with other genomic and functional annotations to improve understanding and prediction of disease risk.