Dissecting the gene-regulatory circuitry of disease-associated genetic variants

This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.

Bibliographic Details
Main Author: Herr, Taylor(Taylor J.)
Other Authors: Manolis Kellis.
Format: Thesis
Language:eng
Published: Massachusetts Institute of Technology 2020
Subjects:
Online Access:https://hdl.handle.net/1721.1/124573
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author Herr, Taylor(Taylor J.)
author2 Manolis Kellis.
author_facet Manolis Kellis.
Herr, Taylor(Taylor J.)
author_sort Herr, Taylor(Taylor J.)
collection MIT
description This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
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spelling mit-1721.1/1245732020-04-14T03:02:37Z Dissecting the gene-regulatory circuitry of disease-associated genetic variants Herr, Taylor(Taylor J.) Manolis Kellis. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Electrical Engineering and Computer Science. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019 Cataloged from student-submitted PDF version of thesis. "June 2019." Includes bibliographical references (pages 89-91). Disease-associated nucleotides lie primarily in non-coding regions, increasing the urgency of understanding how gene-regulatory circuitry impacts human disease. Here, we use the increasing availability of functional genomics datasets and models elucidating how regulatory proteins control genes, to evaluate the impact of genetic variants on the activity of diverse regulators. First, we generate a comprehensive compendium of predicted binding intensities across the entire genome for over 500 transcription factors. Second, we create a novel dataset to connect how these binding intensities change in the context of disease datasets. Third, we develop a statistical framework to integrate these two datasets using dimensionality reduction, latent cluster discovery, and topic modeling. We use these techniques to show that regulatory proteins with analogous biological functions share similar global changes in binding due to genome-wide genetic variation. We also use our framework to discover a latent set of topics behind all genomic locations in chromosome 1, to link the locations in each of the topic clusters with a class of related diseases, and to show that relevant biological processes are statistically enriched in the genomic locations most related to each cluster. by Taylor Herr. M. Eng. M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 2020-04-13T18:22:23Z 2020-04-13T18:22:23Z 2017 2019 Thesis https://hdl.handle.net/1721.1/124573 1149038812 eng 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. http://dspace.mit.edu/handle/1721.1/7582 91 pages application/pdf Massachusetts Institute of Technology
spellingShingle Electrical Engineering and Computer Science.
Herr, Taylor(Taylor J.)
Dissecting the gene-regulatory circuitry of disease-associated genetic variants
title Dissecting the gene-regulatory circuitry of disease-associated genetic variants
title_full Dissecting the gene-regulatory circuitry of disease-associated genetic variants
title_fullStr Dissecting the gene-regulatory circuitry of disease-associated genetic variants
title_full_unstemmed Dissecting the gene-regulatory circuitry of disease-associated genetic variants
title_short Dissecting the gene-regulatory circuitry of disease-associated genetic variants
title_sort dissecting the gene regulatory circuitry of disease associated genetic variants
topic Electrical Engineering and Computer Science.
url https://hdl.handle.net/1721.1/124573
work_keys_str_mv AT herrtaylortaylorj dissectingthegeneregulatorycircuitryofdiseaseassociatedgeneticvariants