Integrative statistical approaches to gain biological insights from genome-wide association studies

dc.contributor.advisorLi, Bingshan
dc.contributor.committeeChairSutcliffe, James S
dc.creatorJi, Ying
dc.creator.orcid0000-0001-5691-1303
dc.date.accessioned2021-07-09T03:51:25Z
dc.date.created2021-06
dc.date.issued2021-06-11
dc.date.submittedJune 2021
dc.date.updated2021-07-09T03:51:25Z
dc.description.abstractThis paper
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1803/16730
dc.language.isoen
dc.subjectGenome wide association studies (GWAS)
dc.subjectgene-level association test
dc.subjectsplicing
dc.subjectfalse discovery rate
dc.subjecthypothesis weighting
dc.subjectpolygenic risk score
dc.titleIntegrative statistical approaches to gain biological insights from genome-wide association studies
dc.typeThesis
dc.type.materialtext
local.embargo.lift2021-12-01
local.embargo.terms2021-12-01
thesis.degree.disciplineHuman Genetics
thesis.degree.grantorVanderbilt University Graduate School
thesis.degree.levelDoctoral
thesis.degree.namePhD

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