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Massachusetts Institute of Technology

Deciphering genetic associations using genome-wide epigenomics approaches

Abstract

dc:description.abstract

Genetic mapping of the drivers of complex human phenotypes and disease through the genome-wide association study (GWAS) has identified thousands of causal genetic loci in the human population. However, genetic mapping approaches can often only reveal a particular causal locus, not the molecular mechanism through which it acts. Biological interpretation of these genetic results is thus a bottleneck for turning results from GWAS into meaningful biological insights for human biology. Genetic mapping of complex human traits has revealed that most common variants influencing human phenotypes have weak effect sizes and reside outside protein-coding regions, complicating biological interpretation of their function. In this thesis we use computational and experimental approaches to study the non-coding genome. In particular, we focus on using epigenomic signatures to characterize non-coding transcriptional regulatory elements and predict regulatory segments of DNA disrupted by genetic variants. In Chapter 2, we describe how genome-wide maps of epigenomic modifications can be used to characterize and discover new GWAS loci. In Chapter 3, we outline an experimental method for the high-throughput assessment of putative transcriptional regulatory elements. In summary, our research highlights the value of interpreting human genetics information through an epigenomic lens, and provides a glimpse into the possible biological insights that manifest from the intersection of these two areas of research.

Degree

thesis:*
Department dc:contributor.department
Massachusetts Institute of Technology. Department of Biology.
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2017

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Wang, Xinchen, Ph. D. Massachusetts Institute of Technology
Advisor dc:contributor.advisor
  • Manolis Kellis and Laurie A. Boyer.

Subjects

dc:subject × 1

Rights

dc:rights
Statement dc:rights
  • MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
Language dc:language.iso
eng

Identifiers

dc:identifier.*
Handle dc:identifier.uri
http://hdl.handle.net/1721.1/111239
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/111239

Chain of custody

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MIT
Base URL
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Last updated
2026-07-22
Source record
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related terms
citation

Wang, Xinchen, Ph. D. Massachusetts Institute of Technology. Deciphering genetic associations using genome-wide epigenomics approaches. Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111239