Abstract Regulatory variation plays a central role in the genetics of complex traits; however, it remains challenging to determine which genes and regulatory mechanisms are affected. While large international collaborations have prioritized understanding how enhancer and promoter regions control gene expression, there remains a gap in our understanding of how genetic elements outside enhancers and promoters impact gene regulation. We propose to use a panel of 70 unrelated HapMap Yoruba lymphoblastoid cell lines (LCLs) to study the mechanisms by which genetic variation impact gene regulation independent of enhancers and promoters. We will further use the iPSCs (induced pluripotent stem cells) and derived cardiomyocytes that we have recently established from these 70 lines to study the effects of the same variants in multiple different cell-types. Recently, we found a class of expression quantitative trait loci (QTLs) variants that affect gene expression as measured by RNA-seq, but that do not show any signal of affecting enhancer or promoter function. We named these variants post-transcription initiation expression QTLs (piQTLs), reflecting our belief that piQTLs function independently of enhancers and promoters. Our data suggest that piQTLs may contribute up to 1/3 of all eQTLs, implying that they explain a considerable fraction of the genetic effects on gene expression levels. Our data also imply that the vast majority of genetic variants that affect RNA splicing (sQTLs) function independently of enhancers and promoters. Because sQTLs are a major link between genetic variation and complex traits, we propose that genetic variants outside enhancers and promoters may contribute substantially to complex traits and disease. In the project, we propose four distinct co-transcriptional mechanisms that may drive piQTLs and/or sQTLs, and we propose a series of analyses to quantify the relative contributions of these mechanisms. We will evaluate the role of each of these proposed mechanisms in LCLs, iPSCs and cardiomyocytes. We will use genome-wide association study (GWAS) data to quantify the contribution of these mechanisms to complex traits and disease. We will focus on cardiac traits for which our data in cardiomyocytes can help us tease putative causal molecular mechanisms apart. At the conclusion of this project we will have thoroughly characterized a class of variants that contribute to the genetic of complex traits independently of enhancers and promoters.