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dc.contributor.advisorLin, Xihong
dc.contributor.advisorAryee, Martin
dc.contributor.advisorMiller, Jeffrey
dc.contributor.authorMcCaw, Zachary Ryan
dc.date.accessioned2019-12-12T08:26:40Z
dc.date.created2019-05
dc.date.issued2019-05-17
dc.date.submitted2019
dc.identifier.citationMcCaw, Zachary Ryan. 2019. Transformation and Multivariate Methods for Improving Power in Genome-Wide Association Studies. Doctoral dissertation, Harvard University, Graduate School of Arts & Sciences.
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:42029562*
dc.description.abstractThis dissertation proposes methods for improving power while maintaining valid inference in genome-wide association studies (GWAS) of common, complex, quantitative traits. Chapter 1 proposes association tests that incorporate the rank-based inverse normal transformation (INT). In the direct approach, the phenotype itself is transformed, whereas in the indirect approach, phenotypic residuals are transformed. Since neither test is uniformly most powerful, these approaches are combined into an adaptive INT-based omnibus test (O-INT). Chapter 2 proposes Surrogate Phenotype Regression Analysis (Spray) for leveraging information from surrogate outcome to improve inference on a partially missing target outcome. The target and surrogate outcomes are jointly modeled within a bivariate normal regression framework. Estimation in the presence of bilateral missingness is performed using the expectation conditional maximization algorithm, and a Wald test for the total effect of genotype on the target outcome is derived. Chapter 3 proposes Synthetic Surrogate Analysis (SSA) for extending Spray to the setting of multiple candidate surrogates. Rather than directly modeling the target outcome together with multiple surrogates, the candidate surrogates are instead combined into a univariate summary measure, termed the synthetic surrogate, which is jointly analyzed with the target outcome using the bivariate framework. Combining multiple surrogates to predict the target outcome improves power while maintaining computational tractability. All proposed estimation and inference procedures have been implemented in publicly available R packages.
dc.description.sponsorshipBiostatistics
dc.format.mimetypeapplication/pdf
dc.language.isoen
dash.licenseLAA
dc.subjectGenome-wide Association Studies
dc.subjectInverse Normal Transformation
dc.subjectMissing Data
dc.subjectMultivariate Analysis
dc.subjectPower
dc.subjectStatistical Genetics
dc.subjectType I Error
dc.titleTransformation and Multivariate Methods for Improving Power in Genome-Wide Association Studies
dc.typeThesis or Dissertation
dash.depositing.authorMcCaw, Zachary Ryan
dc.date.available2019-12-12T08:26:40Z
thesis.degree.date2019
thesis.degree.grantorGraduate School of Arts & Sciences
thesis.degree.grantorGraduate School of Arts & Sciences
thesis.degree.levelDoctoral
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy
thesis.degree.nameDoctor of Philosophy
dc.type.materialtext
thesis.degree.departmentBiostatistics
thesis.degree.departmentBiostatistics
dash.identifier.vireo
dc.identifier.orcid0000-0002-2006-9828
dash.author.emailzrmacc@gmail.com


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