Meet the speaker
If you want to meet Dr. Bart Deplancke on Thursday March 31st in Forskningsparken, please book a time slot at doodle. Please book maximum 1 hour and send an email to anthony.mathelier@ncmm.uio.no to confirm the time.
Abstract
It is by now well-established that the vast majority of complex trait- or disease-associated genetic variants are located outside gene coding regions or even promoters. Yet, how these “regulatory variants” contribute to phenotypic variation or disease remains poorly understood. In my talk, I will describe how recent studies by our group and others revealed that such non-coding variants can have wide-spread effects that go beyond modulating expression of nearby genes, resulting in coordinated changes in transcription factor (TF) binding and chromatin mark enrichment over regions that span more than 100 kb. We and others have previously named these regions “cis-regulatory domains (CRDs)” or “variable chromatin modules” (VCMs) (Waszak et al., Cell, 2015; Grubert et al., Cell, 2015; Delaneau et al., Science, 2019), terms that provide a conceptual framework of how regulatory variation might shape complex traits. Yet, VCMs have up to now stayed a statistical concept. This is because it has so far remained elusive how VCMs are molecularly formed and thus what the gene regulatory forces are that trigger their appearance. In the second part of my talk, I will show how we were recently able to dissect the molecular mechanisms underlying one regulatory variant that modulates the activity of the VCM linked to AXIN2 through long-range TF nucleation and chromatin compaction. Moreover, in doing so, we found that this variant is capable of modulating overall susceptibility to chronic lymphocytic leukemia and lymphoma, illustrating the conceptual value of VCMs for studying the role of regulatory variants in complex traits and disease.
Junior talk
Dr. Daniel Osorio, postdoc at the Department of Oncology, Livestrong Cancer Institutes, University of Texas, Austin, USA, will present his work on "Drug Combination Predition for Cancer Treatment using Disease-Specific Drug Response Profiles and Single-Cell Transcriptional Signatures."