Recent advances in the cost-efficiency of sequencing technologies enabled the combined DNA- and RNA-sequencing of human individuals at the population-scale, making genome-wide investigations of the inter-individual genetic impact on gene expression viable. Employing mRNA-sequencing data from the Geuvadis Project and genome sequencing data from the 1000 Genomes Project we show that the computational analysis of DNA sequences around splice sites and poly-A signals is able to explain several observations in the phenotype data. In contrast to widespread assessments of statistically significant associations between DNA polymorphisms and quantitative traits, we developed a computational tool to pinpoint the molecular mechanisms by which genetic markers drive variation in RNA-processing, cataloguing and classifying alleles that change the affinity of core RNA elements to their recognizing factors. The in silico models we employ further suggest RNA editing can moonlight as a splicing-modulator, albeit less frequently than genomic sequence diversity. Beyond existing annotations, we demonstrate that the ultra-high resolution of RNA-Seq combined from 462 individuals also provides evidence for thousands of bona fide novel elements of RNA processing—alternative splice sites, introns, and cleavage sites—which are often rare and lowly expressed but in other characteristics similar to their annotated counterparts.

This research leading to these results has received funding from the European Commission 7th Framework Program, Project N. 261123 (GEUVADIS). PGF received funding by POPH - QREN Type 4.2, European Social Fund and Portuguese Ministry of Science and Technology (MCTES), Contrato Programa no âmbito do Programa Investigador FCT, 2014, IF/01127/2014. MO received funding by the National Counsel of Technological and Scientific Development (CNPq) grant 310132/2015-0, and MS received funding by the Research Support Foundation of the State of Rio de Janeiro (FAPERJ) E_06/2015, and by CNPq grant 401626/2015-6.