The amount of transcription at genomic loci may be determined by two classes of mechanisms: Those that act on single genes, for example hormone receptors acting as transcription factors, and those that act on multiple genes within a genomic neighbourhood, influenced by local three-dimensional chromatin architectures. How much of the transcriptional output is caused by each of these two classes of mechanisms is hard to determine. Previously, we have inferred the magnitude of these two components along the genome, using a Bayesian hierarchical model on transcriptional data. On CAGE (Cap Analysis of Gene Expression) data, this transcriptional decomposition approach accurately revealed chromatin compartments and boundaries of active topologically associating domains (Rennie et al., 2018). In addition, it was shown to lead to better prediction of enhancer- promoter interactions than correlation-based methods. Our decomposition approach can also successfully be used on RNA-Seq data, shown by the high similarity in the positionally dependent (PD) component derived from CAGE and RNA-Seq data. Here, we applied transcriptional decomposition to the GEUVADIS data set (Lappalainen et al., 2013), which contains RNA-Seq data of 456 individuals from the 1000 genomes project, to determine if differences in the PD component can be used to infer individual variation in chromatin architecture. We identify regions showing distinct component differences across different populations and link them to genetic variants observed in these regions. Overall, our work provides yet unobserved insights into the link between transcription, three-dimensional architectures and genetic variation across large groups of individuals.