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Title page for ETD etd-11212016-090319

Type of Document Dissertation
Author Levinson, Rebecca Terrall
URN etd-11212016-090319
Title The Phenotypic Consequences of Distinct Genetic Variation in an Electronic Medical Record
Degree PhD
Department Human Genetics
Advisory Committee
Advisor Name Title
Melinda C Aldrich Committee Chair
Bingshan Li Committee Member
David C Samuels Committee Member
Douglas P Mortlock Committee Member
Joshua C Denny Committee Member
  • PheWAS
Date of Defense 2016-11-09
Availability restricted
While genetic association studies have been able to elucidate the importance of genetics in human disease outcomes, these studies are limited by the necessity of collecting specifically tailored cohorts and that they frequently only test a single outcome. This focus on a single disease at a time ignores the interconnected nature of both biological pathways and disease phenotypes. My dissertation uses phenome-wide association scans (PheWAS), a method of testing one predictor for association with many disease outcomes, to expand our knowledge of multiple genetic variants and types of genetic variation. We used BioVU, a biobank linked to de-identified electronic medical records (EMRs), to explored a variety of applications for PheWAS. Each chapter presents a project where PheWAS was implemented as a starting point due to a specific hypothesis, before follow-up analyses based on the PheWAS outcome and out existing knowledge of the gene, protein, or variant were performed. The projects presented here begin with the most straight-forward scenario, directly genotyped single SNPs, and progress to imputed deletions before exploring ways to use PheWAS in multi-dimensional studies. In conclusion, I used PheWAS to uncover novel genotype-phenotype associations, and further explored these associations using other data types in the EMR. While PheWAS can be a useful tool for discovering unexpected disease consequences of genetic predictors, using it successfully requires sufficient knowledge of the genetic variation tested to evaluate the biological relevance of association signals
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