Jin Jin, PhD
Assistant Professor of Biostatistics and Epidemiology
Department: Biostatistics and Epidemiology
Graduate Group Affiliations
Contact information
University of Pennsylvania
Perelman School of Medicine
Department of Biostatistics, Epidemiology and Informatics
203 Blockley Hall
423 Guardian Drive
Philadelphia, PA 19104-6021
Perelman School of Medicine
Department of Biostatistics, Epidemiology and Informatics
203 Blockley Hall
423 Guardian Drive
Philadelphia, PA 19104-6021
Education:
BS (Statistics)
School for the Gifted Young, University of Science and Technology of China, Hefei, Anhui, China, 2014.
MS (Biostatistics)
University of Minnesota School of Public Health, Minneapolis, MN, 2016.
PhD (Biostatistics)
University of Minnesota School of Public Health, Minneapolis, MN, 2019.
Permanent linkBS (Statistics)
School for the Gifted Young, University of Science and Technology of China, Hefei, Anhui, China, 2014.
MS (Biostatistics)
University of Minnesota School of Public Health, Minneapolis, MN, 2016.
PhD (Biostatistics)
University of Minnesota School of Public Health, Minneapolis, MN, 2019.
Description of Research Expertise
Statistical genetics, Bayesian statistics, predictive modeling, Mendelian randomization.Selected Publications
Dun, Y., Chatterjee, N., Jin, J. and Nishimura, A.: A Robust Bayesian Method for Building Polygenic Risk Scores using Projected Summary Statistics and Bridge Prior. arXiv Jan 2024 Notes: doi.org/10.48550/arXiv.2401.15014.Jin, J., Zhan, J., Zhang, J., Zhao, R., O’Connell, J., Jiang, Y., Buyske, S., Gignoux, C., Haiman, C., Kenny, E.E. and Kooperberg, C: MUSSEL: enhanced Bayesian polygenic risk prediction leveraging information across multiple ancestry groups. BioRxiv 2024 Notes: To appear in Cell Genomics; doi: 10.1101/2023.04.12.536510.
Zhang, J., Zhan, J., Jin, J., Ma, C., Zhao, R., O’Connell, J., Jiang, Y., Koelsch, B.L., Zhang, H., Chatterjee, N. and 23andMe Research Team: An ensemble penalized regression method for multi-ancestry polygenic risk prediction. BioRxiv 2024 Notes: To appear in Nature Communications; doi: 10.1101/2023.03.15.532652.
Zhang, H., Zhan, J., Jin, J., Zhang, J., Lu, W., Zhao, R., Ahearn, T.U., Yu, Z., O’Connell, J., Jiang, Y. and Chen, T.: A new method for multiancestry polygenic prediction improves performance across diverse populations. Nature genetics 55(10): 1757-1768, Oct 2023.
Dzaye, O., Razavi, A.C., Dardari, Z.A.,Wang, F., Honda, Y., Nasir, K., Coresh, J., Howard-Claudio, C. M., Jin, J., Yu, B., de Vries, P., Wagenknecht, L., Folsom, A., Blankstein, B., Kelly, T.N., Whelton, S.P., Mortensen, M.B., Chatterjee, N., Matsushita, K., Blaha, M.J.: Polygenic Score and Extreme Coronary Artery Calcium Phenotypes (CAC=0 and CAC>1000) in Adults>75 Years Old: The Atherosclerosis Risk in Communities Study. Journal of Cardiovascular Computed Tomography 17(4): S66-S67, Jul 2023.
Hou, X., Guo, P., Wang, P., Liu, P., Lin, D., Fan, H., Li, Y., Wei, L., Lin, Z., Jiang, D., Jin, J., Pillai, J.J., Huang, J., Pinho, M.C., Thomas, B.P., Welch, B.G., Park, D.C., Hillis, A.E., Patel, V.M., and Lu, H.: Deep-learning-enabled brain hemodynamic mapping using resting-state fMRI. npj Digital Medicine 6(1): 116, Jun 2023.
Jin J, Qi, G., Yu, Z., Chatterjee, N.: Mendelian Randomization Analysis Using Multiple Biomarkers of an Underlying Common Exposure. Biostatistics Feb 2023 Notes: doi: https://doi.org/10.1101/2021.02.05.429979.
Steinbrenner, I., Yu, Z., Jin, J., Schultheiss, U.T., Kotsis, F., Grams, M., Coresh, J., Wuttke, M., Eckardt, K.U., Chatterjee, N., Sekula, P., Kotgen, A., on behalf of the GCKD investigators: A Polygenic Score for Reduced Kidney Function and Adverse Outcomes in a Chronic Kidney Disease Cohort. Kidney International 103(2): 421-424, Feb 2023.
Rabinowitz JA, Jin J, Kuo SI, Campos AI, Rentería ME, Huhn AS, Thrul J, Reboussin BA, Benke K, Domingue B, Ialongo NS, Maher BS, Kertes D, Troiani V, Uhl G: Positive associations between cannabis and alcohol use polygenic risk scores and phenotypic opioid misuse among African-Americans. PLoS One 17(4): e0266384, Apr 2022.
Jin J, Zhang L, Leng E, Metzger GJ, Koopmeiners JS: Bayesian spatial models for Voxel-wise prostate cancer classification using multi-parametric magnetic resonance imaging data. Statistics in Medicine 41(3): 483-499, Feb 2022.