Jihoon Kim

Project Personnel, University of California, San Diego

5 active projects

Mani-Lab

Factors that underlie the clustering of metabolic syndrome traits are not fully known. We performed whole-exome sequence analysis in kindreds with extreme phenotypes of early-onset atherosclerosis and metabolic syndrome, and identified novel loss-of-function mutations in the gene encoding the pancreatic…

Scientific Questions Being Studied

Factors that underlie the clustering of metabolic syndrome traits are not fully known. We performed whole-exome sequence analysis in kindreds with extreme phenotypes of early-onset atherosclerosis and metabolic syndrome, and identified novel loss-of-function mutations in the gene encoding the pancreatic elastase chymotrypsin-like elastase family member 2A (CELA2A). We would like to perform replication study of previously identified variants in association with CAD in AoU.

Project Purpose(s)

  • Disease Focused Research (coronary artery disease)

Scientific Approaches

Will build a cohort for three phenotypes: 1) Early-onset CAD: a first-time angiographic diagnosis of CAD at or before the age of 30 years in men and 35 years in women, with only modest hyperlipidemia and without familial hypercholesterolemia, 2) HTN: hypertensive if they had blood pressures greater than 140/90 mmHg or were receiving antihypertensive medications and 3) Diabetic: diabetic or to have impaired glucose tolerance if they were taking glucose-lowering medications or had a fasting blood sugar greater than 126 mg/dl or 100 mg/dl, respectively. Then perform association study of each of the phenotype and variants.

Anticipated Findings

We anticipate to find the p-values with similar level of significance and the odds ratio with same direction and similar magnitude for the association of clinical traits above.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Jihoon Kim - Project Personnel, University of California, San Diego

Collaborators:

  • Youwen Liu - Project Personnel, Yale University
  • Michael Yen - Project Personnel, Yale University

a genetic association test in the AoU Workspace

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Scientific Questions Being Studied

a genetic association test in the AoU Workspacea genetic association test in the AoU Workspacea genetic association test in the AoU Workspacea genetic association test in the AoU Workspacea genetic association test in the AoU Workspacea genetic association test in the AoU Workspace

Project Purpose(s)

  • Control Set

Scientific Approaches

a genetic association test in the AoU Workspacea genetic association test in the AoU Workspacea genetic association test in the AoU Workspacea genetic association test in the AoU Workspacea genetic association test in the AoU Workspace

Anticipated Findings

a genetic association test in the AoU Workspacea genetic association test in the AoU Workspacea genetic association test in the AoU Workspacea genetic association test in the AoU Workspacea genetic association test in the AoU Workspacea genetic association test in the AoU Workspace

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Youwen Liu - Project Personnel, Yale University
  • xueting wang - Graduate Trainee, Yale University
  • Michael Yen - Project Personnel, Yale University
  • Jihoon Kim - Project Personnel, University of California, San Diego

GA

Race value correspondence between self-reported values in the OMOP and the inferred values using genotype ancestry methods

Scientific Questions Being Studied

Race value correspondence between self-reported values in the OMOP and the inferred values using genotype ancestry methods

Project Purpose(s)

  • Methods Development
  • Ancestry

Scientific Approaches

Compare, contrast, and develop new methods for genetic ancestry inference methods. This will foster accurate variant discovery by correct use of ancestry information

Anticipated Findings

Methods of global and local genetic ancestry inference and their impact on downstream genetic association study

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Jihoon Kim - Project Personnel, University of California, San Diego

Collaborators:

  • Youwen Liu - Project Personnel, Yale University
  • Michael Yen - Project Personnel, Yale University

YSM-AoU

Teach AoU workbench to Yale investigators. This will cover how to access AoU workbench, AoU datasets, python programming, OMOP, and GWAS.

Scientific Questions Being Studied

Teach AoU workbench to Yale investigators. This will cover how to access AoU workbench, AoU datasets, python programming, OMOP, and GWAS.

Project Purpose(s)

  • Population Health
  • Educational
  • Ancestry

Scientific Approaches

We will teach basic programming skills and demonstrate the replication studies in AoU workbench. A model based association test will be conducted with known traits on the eligible cohort.

Anticipated Findings

After this training, the trainees should be able to create a cohort meeting inclusion criteria, perform data wrangling, and conduct genome-wide association study (GWAS) on the trait of interest to identify the variants in association with statistical significance.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Jihoon Kim - Project Personnel, University of California, San Diego

CAST

Want to develop and apply federated learning algorithms to conduct genome-wide association studies (GWAS) without sharing individual-level data.

Scientific Questions Being Studied

Want to develop and apply federated learning algorithms to conduct genome-wide association studies (GWAS) without sharing individual-level data.

Project Purpose(s)

  • Methods Development

Scientific Approaches

Will apply federated learning algorithms to conduct genome-wide association studies (GWAS) across multiple biobanks including All of Us and Million Veterans Program.

Anticipated Findings

Will demonstrated that it is feasible to perform federated learning algorithms for genome-wide association studies (GWAS) across multiple biobanks without sharing individual-level data.

Demographic Categories of Interest

This study will not center on underrepresented populations.

Data Set Used

Controlled Tier

Research Team

Owner:

  • Jihoon Kim - Project Personnel, University of California, San Diego
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