The Ohio State University · Biomedical Informatics

Wang Lab

We develop and apply computational and statistical methods that leverage large-scale biobank data to study disease trajectories, improve disease risk and progression prediction, and advance equitable, globally generalizable models for precision health.

Led by Ying Wang, MD, PhD, Assistant Professor, Department of Biomedical Informatics, College of Medicine.

Our research Join the lab

Research

Our work lies at the intersection of statistical genetics, biobank-scale data science, and precision health. We develop and apply computational and statistical methods to characterize the genetic architecture of disease onset, progression, and trajectories by integrating genomic variation, socio-environmental factors, longitudinal electronic health records, and multi-omic data. A central focus of our research is the development of scalable and rigorous frameworks for multi-ancestry analysis, cross-biobank harmonization, rare and common variant association, and equitable risk prediction across diverse populations and healthcare systems. Our long-term goal is to translate these advances into robust, interpretable, and globally generalizable models for understanding disease progression and advancing precision health.

Disease trajectories & progression

Developing time-to-event and longitudinal methods for biobank and electronic health record data to characterize disease onset, progression, and transitions, enabling prediction across the entire disease course.

Population & statistical genetics

Characterizing the genetic architecture of complex diseases across diverse populations through multi-ancestry, common and rare variant, and gene-environment analyses.

Biobank-scale data science

Developing scalable computational methods for harmonizing and integrating global biobanks and heterogeneous health data to enable reproducible genomic discovery.

Precision risk prediction

Building equitable, globally generalizable prediction models by integrating genomics, clinical records, socio-environmental exposures, and other multi-modal data.

People

Ying Wang, Principal Investigator of the Wang Lab

Ying Wang, MD, PhD

Principal Investigator · Assistant Professor

Department of Biomedical Informatics, College of Medicine
Member, The Ohio State University Comprehensive Cancer Center (OSUCCC – James)

Ying is a statistical geneticist who develops and applies integrative modeling frameworks to combine biobank-scale genomic data, socio-environmental determinants, and longitudinal health records to better understand complex disease trajectories. Her research advances multi-ancestry genetic inference and cross-biobank harmonization, with the goal of building equitable and globally generalizable models for population-scale precision health. She is a recipient of the NIH-NHGRI K99/R00 Pathway to Independence Award. Before joining Ohio State, she was an Instructor in the Analytical and Translational Genetics Unit at Massachusetts General Hospital and the Broad Institute of MIT and Harvard, where she also completed her postdoctoral training. She earned her PhD in statistical genetics from the University of Queensland.

Google Scholar

We are recruiting. PhD students, postdocs, and rotation students who are excited about statistical genetics, biobank-scale data, AI and machine learning for genetics, and precision health are encouraged to get in touch. See Join Us.

Publications

Selected publications below. For the complete and up-to-date list, see Google Scholar. (Y. Wang = lab PI)

  1. K. Tsuo, Z. Shi, T. Ge, R. Mandla, K. Hou, Y. Ding, B. Pasaniuc, Y. Wang, A. R. Martin. All of Us diversity and scale improve polygenic prediction contextually with greatest improvements for underrepresented populations. bioRxiv, 2024. Accepted at Nature Genetics. Y. Wang: co-senior/co-corresponding author. bioRxiv
  2. Y. Wang, Y. He, Y. Shi, D. C. Qian, K. J. Gray, R. Winn, A. R. Martin. Aspiring toward equitable benefits from genomic advances to individuals of ancestrally diverse backgrounds. The American Journal of Human Genetics, 111(5):809–824, 2024. Y. Wang: co-first author. DOI
  3. Y. Wang, M. Kanai, T. Tan, M. Kamariza, K. Tsuo, K. Yuan, W. Zhou, Y. Okada, H. Huang, P. Turley, E. G. Atkinson, A. R. Martin. Polygenic prediction across populations is influenced by ancestry, genetic architecture, and methodology. Cell Genomics, 3(10):100408, 2023. DOI
  4. Y. Wang, S. Namba, E. Lopera, S. Kerminen, K. Tsuo, K. Läll, M. Kanai, et al., A. R. Martin, J. Hirbo. Global Biobank analyses provide lessons for developing polygenic risk scores across diverse cohorts. Cell Genomics, 3(1):100241, 2023. DOI
  5. Y. Wang, K. Tsuo, M. Kanai, B. M. Neale, A. R. Martin. Challenges and opportunities for developing more generalizable polygenic risk scores. Annual Review of Biomedical Data Science, 5:293–320, 2022. DOI
  6. Y. Wang, J. Guo, G. Ni, J. Yang, P. M. Visscher, L. Yengo. Theoretical and empirical quantification of the accuracy of polygenic scores in ancestry divergent populations. Nature Communications, 11(1):3865, 2020. DOI

News

  • Aug 2026 Our study showing that All of Us diversity and scale improve polygenic prediction, with the greatest gains for underrepresented populations, was accepted at Nature Genetics.
  • Jul 2026 The Wang Lab launches at The Ohio State University College of Medicine. Ying joins the Department of Biomedical Informatics as a tenure-track Assistant Professor. We are recruiting. See Join Us.
  • Oct 2025 Ying gave a contributed talk on GATE-GENE at the ASHG Annual Meeting in Boston and was named a Postdoctoral Semifinalist for the ASHG Trainee Research Excellence Award.
  • Jul 2025 Ying was awarded the NIH-NHGRI K99/R00 Pathway to Independence Award for Integrating Genetics and Socio-Environmental Factors to Predict Disease Progression.
  • May 2024 Our perspective on aspiring toward equitable benefits from genomic advances for ancestrally diverse populations was published in The American Journal of Human Genetics.

Join Us

We are building the lab and are actively recruiting people who are excited about statistical genetics, biobank-scale data, and precision health.

Prospective PhD students

Students in relevant OSU graduate programs (biomedical informatics, biostatistics, and related quantitative fields) are encouraged to reach out and mention the lab in their applications.

Postdoctoral researchers

If you have (or are completing) a PhD in statistical genetics, computational biology, biostatistics, computer science, machine learning/AI, or a related field, we would love to hear from you.

Rotation & current OSU students

Email to chat about projects and rotations in the lab.

Please send a short note about your background and interests, along with your CV, to the email address listed under Contact.

Contact

Wang Lab
Department of Biomedical Informatics
College of Medicine, The Ohio State University
Lincoln Tower 250, 1800 Cannon Dr
Columbus, OH 43210, USA

Google Scholar