Bio

Hyunkyung Claire Kim is a Postdoctoral Research Scholar in the Translational Genomics of Diabetes Lab led by Dr. Anna Gloyn. She received her PhD in Genetics from the University of Chicago, where she developed a statistical method to disentangle shared and trait-specific genetic architecture across complex diseases using large-scale biobank data. Prior to her doctoral training, she worked at Massachusetts General Hospital, studying the genetic subtypes and heterogeneity of type 2 diabetes through data-driven clustering approaches.

She is interested in the translational genomics of diabetes, including integrating human genetics with clinical data to uncover disease mechanisms and advance precision medicine. Her long-term research interests include developing computational methods to understand how genetic, molecular, and environmental factors jointly shape metabolic disease risk, disease heterogeneity, and progression.

Professional Education

  • Doctor of Philosophy, University of Chicago (2026)
  • Master of Science, Harvard University (2019)
  • Bachelor of Science, Yonsei University, Computer Science (2016)

Stanford Advisors

Contact

  • Academic
    hkkim02@stanford.edu
    University - Scholar Department: Pediatrics Position: Postdoctoral Scholar

Additional Info

Lab Affiliations

All Publications

  • Multi-Trait Genetic Analysis Reveals Clinically Interpretable Hypertension Subtypes CIRCULATION-GENOMIC AND PRECISION MEDICINE Vaura, F., Kim, H., Udler, M. S., FinnGen, Salomaa, V., Lahti, L., Niiranen, T. 2022; 15 (4): 317-326

    Abstract

    Hypertension comprises a heterogeneous range of phenotypes. We asked whether underlying genetic structure could explain a part of this heterogeneity.Our study sample comprised N=198 148 FinnGen participants (56% women, mean age 58 years) and N=21 168 well-phenotyped FINRISK participants (53% women, mean age 50 years). First, we identified genetic hypertension components with an unsupervised Bayesian non-negative matrix factorization algorithm using public genome-wide association data for 144 genetic hypertension variants and 16 clinical traits. For these components, we computed their (1) cross-sectional associations with clinical traits in FINRISK using linear regression and (2) longitudinal associations with incident adverse outcomes in FinnGen using Cox regression.We observed 4 genetic hypertension components corresponding to recognizable clinical phenotypes: obesity (high body mass index), dyslipidemia (low high-density lipoprotein cholesterol and high triglycerides), hypolipidemia (low low-density lipoprotein cholesterol and low total cholesterol), and short stature. In FINRISK, all hypertension components had robust associations with their respective clinical characteristics. In FinnGen, the Obesity component was associated with increased diabetes risk (hazard ratio per 1 SD increase 1.08 [Bonferroni corrected CI, 1.05-1.10]) and the Hypolipidemia component with increased autoimmune disease risk (hazard ratio per 1 SD increase 1.05 [Bonferroni corrected CI, 1.03-1.07]). In addition, all hypertension components were related to both hypertension and cardiovascular disease.Our unsupervised analysis demonstrates that the genetic basis of hypertension can be understood as a mixture of 4 broad, clinically interpretable components capturing disease heterogeneity. These components could be used to stratify individuals into specific genetic subtypes and, therefore, to benefit personalized health care and pharmaceutical research.

    View details for DOI 10.1161/CIRCGEN.121.003583

    View details for Web of Science ID 000840881000009

    View details for PubMedID 35604428

    View details for PubMedCentralID PMC9558213

https://orcid.org/0000-0001-7964-3073