Leqi Xu

Leqi Xu

Biostatistics PhD student

I am a fourth-year Ph.D. student in Biostatistics at Yale School of Public Health, advised by Dr. Hongyu Zhao. My research interests focus on both methodological development and applied analysis involving polygenic risk scores (PRS) and electronic health records (EHR). I develop statistical methods to integrate genetic and clinical data, aiming to enhance disease prediction, risk stratification, and personalized healthcare.

📖 Education

  • Ph.D. in Biostatisitcs, Yale University (2021 - Present).
  • B.S. in Statistics, Fudan University (2017 - 2021).

📝 Publications

(∗ indicates co-first authorship; † indicates co-corresponding authorship)

Lead/Co-Lead

  1. Xu, L., Zheng, W., Hu, J., Lin, Y., Zhao, J., Wang, G., Liu, T. and Zhao, H., 2025. Improving polygenic risk prediction performance through integrating electronic health records by phenotype embedding. bioRxiv. [preprint][software][analysis]

  2. Xu, L., Dong, Y., Zeng, X., Bian, Z., Zhou, G., Guan, L., and Zhao, H., 2025. Almost Free Enhancement of Multi-Population PRS: From Data-Fission to Pseudo-GWAS Subsampling. bioRxiv. [preprint][software][analysis]

  3. Xu, L., Zhou, G., Jiang, W., Zhang, H., Dong, Y., Guan, L.†, and Zhao, H.†, 2025. JointPRS: A data-adaptive framework for multi-population genetic risk prediction incorporating genetic correlation. Nature Communications, 16(1), 3841. [paper] [software] [analysis]
    • Reviewers’ Choice Award, American Society of Human Genetics Annual Meeting (2023)
  4. Ye, Y.∗, Xu, L.∗, and Zhao, H., 2024. Leveraging Functional Annotations Improves Cross-Population Genetic Risk Prediction. In: Statistics in Precision Health: Theory, Methods and Applications (pp. 453-471). Cham: Springer International Publishing. [paper]

Collaborations

  1. Lin, C., Lin, Y., Li, W., Xu, L., Zhang, X., and Zhao, H., 2025. Leveraging cell-type specificity and similarity improves single-cell eQTL fine-mapping. bioRxiv. [preprint]

  2. Jiang, J., Xu, L., Zhang, Y., and Zhao, H., 2024. The Method of Limits and Its Application to the Analysis of Count Data in Genome-wide Association Studies. Statistica Sinica. [paper]

  3. Gygi, J.P.∗, Maguire, C.∗, Patel, R.K.∗, Shinde, P., Konstorum, A., Shannon, C.P., Xu, L., Hoch, A., Jayavelu, N.D., Haddad, E.K., and Reed, E.F., 2024. Integrated longitudinal multiomics study identifies immune programs associated with acute COVID-19 severity and mortality. The Journal of Clinical Investigation, 134(9). [paper]