Integrating statistical and multiplex functional genomics to interpret non-coding human genetic variation
Abstract/Contents
- Abstract
- Human genetics connects natural genetic variation with molecular and organism phenotypes, yet the space of human genetic variation is vast and almost entirely non-coding with unknown function. Statistical and multiplexed molecular genetics provide distinct and complementary approaches to identify and interpret pervasive genetic complexity, particularly with respect to molecular phenotypes like gene expression. Chapter 2 describes the design and implementation of a massively parallel reporter assay to identify causal genetic variants within tightly linked genome regions that are statistically associated with gene expression. It describes a wide range of molecular properties of regulatory variants, and demonstrates that multiple linked causal variants underlie many human genetic associations. Chapter 3 reports the generation and analysis of high-resolution allele-specific expression data gathered from samples in the Genotype-Tissue Expression Consortium and a set of ovarian cancers, and shows how allele-specific expression is connected to proximal genetic cis-regulation as well as cancer progression. Finally, Chapter 4 uses statistical and molecular genetics to dissect two biological systems, the RNA content of extracellular exosomes and the role of genetic variation in regulating the retinal pigment epithelium. These studies represent substantial new data describing human genetic variation and its molecular or phenotypic effects, as well as extensive statistical and computational interpretation thereof, across a range of cellular contexts.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Abell, Nathan Samuel |
|---|---|
| Degree supervisor | Montgomery, Stephen, 1979- |
| Thesis advisor | Montgomery, Stephen, 1979- |
| Thesis advisor | Bassik, Michael |
| Thesis advisor | He, Zihuai, (Researcher in biostatistics) |
| Thesis advisor | Sidow, Arend |
| Degree committee member | Bassik, Michael |
| Degree committee member | He, Zihuai, (Researcher in biostatistics) |
| Degree committee member | Sidow, Arend |
| Associated with | Stanford University, Department of Genetics |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Nathan S. Abell. |
|---|---|
| Note | Submitted to the Department of Genetics. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/jk227nv8042 |
Access conditions
- Copyright
- © 2021 by Nathan Samuel Abell
- License
- This work is licensed under a Creative Commons Attribution 3.0 Unported license (CC BY).
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