Learning the effects of genetic variation on gene regulation across human tissues

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Genome-wide studies have identified tens of thousands of associations between genetic variants and human phenotypes, but few trait associations have been linked to causal mechanisms. Many associated variants reside outside coding regions, suggesting that an appreciable proportion of associations may be due to altered gene expression. Most work on gene regulation has been in single tissues, but this limited setting does not provide a complete picture because gene regulation varies between tissues. In this dissertation, I present my work developing and applying methods to learn how genetic variation alters gene expression across human tissues. First, I analyzed the small RNA of cells and exosomes from large family to answer questions about the impact of genetic variation beyond individual cell types. Exosomes are small extracellular vesicles that can shuttle material, including small RNA, between cells. To understand how cells can influence their neighbors, I characterized the regulation of small RNA cargo in exosomes. I conducted the first association study of microRNA expression in exosomes and detected evidence that genetically controlled expression variation within a cell can carry forward to the exosomes it releases. Second, I evaluated a simple metric, the median Z-score, for identifying multi- tissue gene expression outliers. These are individuals with extreme expression of a particular gene across tissues compared with the rest of the population. Using v6 data from the Genotype-Tissue Expression Project (GTEx), I have shown that expression outliers enable us to discover rare variants with large effects on expression and have characterized the functional properties of these rare variants. Third, I describe a new method for discovering multi-tissue gene expression outliers while modeling the correlation structure between tissues. I present findings from applying this method to v7 GTEx data, which include over 400 more individuals with whole genome sequencing than v6, in addition to the multi-tissue RNA-seq measurements. This new approach identifies expression outliers with complementary properties to those found using the median Z-score.


Type of resource text
Form electronic; electronic resource; remote
Extent 1 online resource.
Publication date 2017
Issuance monographic
Language English


Associated with Tsang, Emily K
Associated with Stanford University, Program in Biomedical Informatics.
Primary advisor Montgomery, Stephen, 1979-
Thesis advisor Montgomery, Stephen, 1979-
Thesis advisor Bustamante, Carlos
Thesis advisor Kundaje, Anshul, 1980-
Advisor Bustamante, Carlos
Advisor Kundaje, Anshul, 1980-


Genre Theses

Bibliographic information

Statement of responsibility Emily K. Tsang.
Note Submitted to the Program in Biomedical Informatics.
Thesis Thesis (Ph.D.)--Stanford University, 2017.
Location electronic resource

Access conditions

© 2017 by Emily Katherine Tsang
This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).

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