Jack Flanagan

Affiliation

Department of Statistics
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About

Dr. Flanagan joined the Department of Statistics in 2025 with a joint appointment in the Department of Molecular Biosciences. His research focuses on statistical genetics, genomics, and the application of large-scale sequencing data to population health studies. Prior to joining RERF, he completed a postdoctoral fellowship in the Department of Data Science at Seoul National University, where he investigated rare-variant associations in the UK Biobank whole-genome sequencing cohort, with a particular focus on non-coding regions of the genome. Previously, at the University of Liverpool, he studied the impact of European ancestry bias in large public genomic datasets and its effects on genotype imputation across diverse populations. At RIKEN, he contributed to the development of a Japanese genotype imputation reference panel, integrating Japanese whole-genome sequencing data to improve imputation accuracy and support genetic studies in Japanese cohorts. At RERF, his work focuses on genome-wide association studies in the Adult Health Study and the analysis of somatic mutations and clonal haematopoiesis using whole-genome sequencing data.

Education

2022
Doctor of Philosophy, Statistical Genetics, University of Liverpool, Liverpool, UK
2016
Master of Science, Biochemistry, University of Liverpool, Liverpool, UK

Experience

Radiation Effects Research Foundation
  • 2025–

    Research Scientist, Department of Statistics and Department of Molecular Biosciences

Graduate School of Data Science, Seoul National University (Seoul, Korea)
  • 2022–2025

    Post Doctoral researcher—Lee lab for statistical genetics and metabolism

RIKEN (Yokohama, Japan)
  • 2018–2021

    IPA Student—Laboratory for genomics of diabetes and metabolism

Selected publications

Flanagan, Jack, et al. Population-specific reference panel improves imputation quality for genome-wide association studies conducted on the Japanese population. Communications biology. 2024; 1665.
Mahajan, Anubha, et al. Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation. Nature genetics. 2022; 560-572.

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