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Leveraging Data from Large Biorepositories to Study the Genetic Basis of Metabolic Syndrome

  • Author(s): Cao, Steven Yiran
  • Advisor(s): Salem, Rany
  • Briggs, Steven
  • et al.
No data is associated with this publication.
Abstract

Metabolic syndrome (MetS) is an emerging global epidemic of public health importance. MetS is a syndrome characterized by having three of the following conditions: abdominal obesity, hypertension, high blood sugars, abnormal levels of triglycerides and high-density lipoprotein (HDL). It is well-established that environmental factors play a major role in the development of MetS, but a full understanding of the genetic variants that are involved in the disease pathogenesis is incomplete. To identify these genetic variants, we have conducted several large- scale Genome Wide Association Studies (GWAS) on samples retrieved from the Database for Genotype and Phenotypes (dbGaP), a large scale biomedical repository for individual level genotype and phenotype data . Prior GWAS of MetS have largely utilized modest sample sizes that only focus on a single component of MetS. In this analysis, three different studies with a total of 10,000 MetS cases were used to discover novel genetic variants associated with MetS. For each study, an extensive quality control was performed to filter and harmonize these datasets. Variable harmonization for uniformity and genetic variant imputation for maximizing the number of Single Nucleotide Polymorphisms (SNPs) were also completed prior to the GWAS analysis. After quality control and imputation, GWAS was performed separately on each dataset, and results were combined via meta-analysis. In this combined analysis, we identified four genome-wide significant variants (rs287, rs964184, rs11076176, rs247616) in the European ancestry subset and two genome-wide significant variants in the African ancestry subset (rs117729532, rs115553887).

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This item is under embargo until September 11, 2021.