Using eSNPs to infer regulatory networks structure
In the last decade many genetic variants (eSNPs) have been reported as associated with expression of transcripts, holding the promise for functional dissection of regulatory structure of human transcription. In this talk I will present four projects: the first and second are published work, the third is a work in progress and the last one is in initial stages.
In the first project  we develop a computational approach, which aims at elucidating the joint relationships between modules of transcripts and single-nucleotide polymorphisms (SNPs). We find that modules are significantly more, larger and denser than found in permuted data. Topological analysis of each module identifies novel insights regarding the flow of causality between the main SNP and transcripts. We observe similar annotations of modules from two sources of information: the enrichment of a module in gene subsets and locus annotation of the genetic variants. This and further phenotypic analysis provide a validation for our methodology.
In the second project  we follow two kinds of usual suspects: SNPs that alter coding regions or transcription factors. We show these interpretable genomic regions are enriched for eSNP association signals, thereby naturally defining source-target gene pairs. We map these pairs onto a protein-protein interaction (PPI) network and study their topological properties. Our results suggest two modes of trans regulation: transcription factor variation frequently acts via a modular regulation mechanism, with multiple targets that share a function with the transcription factor source. Notwithstanding, exon variation often acts by a local cis effect, delineating shorter paths of interacting proteins across functional clusters of the PPI network.
In the third project we devise a computational framework for examining pairs of source eSNPs that are both associated with the same pair of target transcripts. We characterize such quartets through their genomic, topological and functional properties. We establish that this regulatory structure of quartets is frequent in real data, but is rarely observed in permutations. eSNP sources are mostly located on different chromosomes and away from their targets. In the majority of quartets, SNPs affect the expression of the two gene targets independently of one another, suggesting a mutually independent rather than a directionally dependent effect. Furthermore, the directions in which the minor allele count of the SNP affects gene expression within quartets are consistent, so that the two source eSNPs either both have the same effect on the target transcripts or both affect one transcript in the opposite direction to the other. Same-effect eSNPs are observed more often than expected by chance. Overall, our analysis offers insights concerning the fine motif structure of human regulatory networks.
In the fourth project we aim to compare the modular structure between two different tissue types.