dc.creator | Benton, Mary Lauren | |
dc.date.accessioned | 2020-08-22T20:50:49Z | |
dc.date.available | 2018-08-28 | |
dc.date.issued | 2018-08-28 | |
dc.identifier.uri | https://etd.library.vanderbilt.edu/etd-08212018-210357 | |
dc.identifier.uri | http://hdl.handle.net/1803/13969 | |
dc.description.abstract | Non-coding gene regulatory enhancers are essential to transcription in mammalian cells. As a result, a large variety of experimental and computational strategies have been developed to identify cis-regulatory enhancer sequences. In practice, most studies consider enhancers identified by only a single method, and the concordance of enhancers identified by different methods has not been comprehensively evaluated. We assessed the similarities of enhancer sets identified by representative strategies in four biological contexts and evaluated the robustness of downstream conclusions to the choice of identification strategy. We demonstrate significant dissimilarity between enhancer sets in their genomic characteristics, evolutionary conservation, and association with functional loci within each context. The disagreement is sufficient to influence interpretation of GWAS SNPs and eQTL, and to lead to disparate conclusions about enhancer biology and disease mechanisms. We also find only limited evidence that regions identified by multiple enhancer identification methods are better candidates than those identified by a single method. As a result, we cannot recommend the use of any enhancer identification strategy in isolation. While these findings highlight a major challenge to mapping the genetic architecture of complex disease and interpreting regulatory variants found in patient genomes, a systematic understanding of similarities and differences in enhancer identification methodology will ultimately enable robust inferences about gene regulatory sequences. | |
dc.format.mimetype | application/pdf | |
dc.subject | gene regulation | |
dc.title | Genome-wide Enhancer Maps Differ Significantly in their Genomic Distribution, Evolution, and Function | |
dc.type | thesis | |
dc.contributor.committeeMember | Emily Hodges | |
dc.contributor.committeeMember | Jacob J Hughey | |
dc.type.material | text | |
thesis.degree.name | MS | |
thesis.degree.level | thesis | |
thesis.degree.discipline | Biomedical Informatics | |
thesis.degree.grantor | Vanderbilt University | |
local.embargo.terms | 2018-08-28 | |
local.embargo.lift | 2018-08-28 | |
dc.contributor.committeeChair | John A Capra | |