Integrative statistical approaches to gain biological insights from genome-wide association studies
dc.contributor.advisor | Li, Bingshan | |
dc.creator | Ji, Ying | |
dc.date.accessioned | 2021-07-09T03:51:25Z | |
dc.date.created | 2021-06 | |
dc.date.issued | 2021-06-11 | |
dc.date.submitted | June 2021 | |
dc.identifier.uri | http://hdl.handle.net/1803/16730 | |
dc.description.abstract | This paper | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | |
dc.subject | Genome wide association studies (GWAS) | |
dc.subject | gene-level association test | |
dc.subject | splicing | |
dc.subject | false discovery rate | |
dc.subject | hypothesis weighting | |
dc.subject | polygenic risk score | |
dc.title | Integrative statistical approaches to gain biological insights from genome-wide association studies | |
dc.type | Thesis | |
dc.date.updated | 2021-07-09T03:51:25Z | |
dc.type.material | text | |
thesis.degree.name | PhD | |
thesis.degree.level | Doctoral | |
thesis.degree.discipline | Human Genetics | |
thesis.degree.grantor | Vanderbilt University Graduate School | |
local.embargo.terms | 2021-12-01 | |
local.embargo.lift | 2021-12-01 | |
dc.creator.orcid | 0000-0001-5691-1303 | |
dc.contributor.committeeChair | Sutcliffe, James S |
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