Rare-variant approaches to complex traits across population biobanks
Abstract/Contents
- Abstract
- Complex diseases are a significant global burden, accounting for 70% of deaths in the U.S. annually. For example, 70,000 new cases of inflammatory bowel disease are diagnosed every year. Many such diseases have underlying genetic etiologies responsible for their pathology. Understanding their genetic basis could lead to more timely diagnosis and improved prognosis. Furthermore, human genetics presents an opportunity to identify new therapeutic targets. However, while much of the disease-causative common variation is well-documented, our understanding of rare, disease-contributory variation is sparse, largely due to the lack of power (limited sample size) and ascertainment to detect such variation with accuracy. While the above goal of understanding rare variation is not achievable with small cohort (n < 100,000) studies, population-level biobanks (n ~ 500,000) offer the ability to study this type of genetic variation. These datasets present a unique opportunity for rapid discovery of robustly-validated disease-causative variants because they possess large sample sizes, better population-level annotations, and next-generation sequencing technologies like whole-exome and -genome sequencing. This dissertation contains six Chapters. In Chapter 1, I introduce key terms and methodologies and then outline my contributions to the human genetics space that are a) key and b) ancillary to the dissertation. Chapter 2 explores the human leukocyte antigen (HLA) region in the UK Biobank, cataloging rare variants that explain additional heritability of complex diseases. In Chapter 3, I introduce Multiple Rare-variants and Phenotypes (MRP), a novel, flexible, Bayesian framework for rare-variant signal aggregation across variants, studies, and phenotypes. Chapter 4 details a corollary method to MRP, the Multiple Rare-variants and Phenotypes Mixture Model (MRPMM), which clusters rare variants into groups based on their effects on a multivariate phenotype. In Chapter 5, I, along with collaborators from the International Inflammatory Bowel Disease Genetics Consortium, identify rare coding variants newly associated with Crohn's disease using a mixed-model approach with a software called SAIGE. I conclude in Chapter 6 with a summary of the value in studying rare variation in the genome, the takeaways from my research, and the areas in which future research should go.
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 | Venkataraman, Guhan Ram |
|---|---|
| Degree supervisor | Montgomery, Stephen, 1979- |
| Degree supervisor | Rivas, Manuel |
| Thesis advisor | Montgomery, Stephen, 1979- |
| Thesis advisor | Rivas, Manuel |
| Thesis advisor | Bustamante, Carlos |
| Thesis advisor | Hastie, Trevor |
| Thesis advisor | Kundaje, Anshul, 1980- |
| Degree committee member | Bustamante, Carlos |
| Degree committee member | Hastie, Trevor |
| Degree committee member | Kundaje, Anshul, 1980- |
| Associated with | Stanford University, Program in Biomedical Informatics |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Guhan Venkataraman. |
|---|---|
| Note | Submitted to the Program in Biomedical Informatics. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/kx853gk2003 |
Access conditions
- Copyright
- © 2021 by Guhan Ram Venkataraman
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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