Research

Selected Publications

  1. Gyawali, P.K., Liu, X., Zou, J. and He, Z. (2022). Ensembling improves stability and power of feature selection for deep learning models. arXiv preprint arXiv:2210.00604.

  2. He, Z., Liu, L., Belloy, M.E., Le Guen, Y., Sossin, A., Liu, X., Qi, X., Ma, S., Wyss-Coray, T., Tang, H., Sabatti, C., Candes, E., Greicius, M.D., Ionita-Laza, I. (2022). Summary statistics knockoff inference empowers identification of putative causal variants in genome-wide association studies. https://www.biorxiv.org/content/10.1101/2021.12.06.471440v1. Nature Communications, accepted.

  3. Lu, F., Sossin, A., Abell, N., Montgomery, S. B., He, Z. (2022). Deep learning-assisted genome-wide characterization of massively parallel reporter assays. Nucleic Acid Research, in press.

  4. Kassani, P.H., Lu, F., Guen, Y.L. and He, Z. (2022). Deep neural networks with controlled variable selection for the identification of putative causal genetic variants. Nature Machine Intelligence, 4(9), pp.761-771.

  5. Abell, N.S., DeGorter, M.K., Gloudemans, M., Greenwald, E., Smith, K.S., He, Z., Montgomery, S.B. (2022). Multiple Causal Variants Underlie Genetic Associations in Humans. Science, 375 (6586), pp.1247-1254.

  6. Ma, S., Dalgleish, J., Lee, J., Wang, C., Liu, L., Gill, R., Buxbaum, J.D., Chung, W.K., Aschard, H., Silverman, E.K., Cho, M.H., He, Z., Ionita-Laza, I. (2021). Powerful gene-based testing by integrating long-range chromatin interactions and knockoff genotypes. Proceedings of the National Academy of Sciences, 118(47).

  7. He, Z., Liu, L., Wang, C., Le Guen, Y., Lee, J., Gogarten, S., Lu, Fred., Montgomery, S., Tang, H., Silverman, E., Cho, M.H., Greicius, M.D., Ionita-Laza, I. (2021). Identification of putative causal loci in whole-genome sequencing data via knockoff statistics. Nature Communications, 12(1), pp.1-18.

  8. He, Z., Le Guen, Y., Liu, L., Lee, J., Ma, S., Yang, A.C., Liu, X., Rutledge, J., Losada, P.M., Song, B., Belloy, M.E., Butler III, R.R., Longo, F.M., Tang, H., Mormino, E.C., Wyss-Coray, T., Greicius, M.D., Ionita-Laza, I. (2021). Genome-wide analysis of common and rare variants via multiple knockoffs at biobank scale, with an application to Alzheimer disease genetics. The American Journal of Human Genetics, 108(12), pp.2336-2353.

  9. Le Guen, Y., Belloy, M.E., Napolioni, V., Eger, S.J., Kennedy, G., Tao, R., He, Z., Greicius, M. (2021) A novel age-informed approach for genetic association analysis in Alzheimer’s disease. Alzheimer's research & therapy, 13(1), pp.1-14.

  10. He, Z., Xu, B., Buxbaum, J., Ionita-Laza, I. (2019) A genome-wide scan statistic framework for whole-genome sequence data analysis. Nature Communications, 10(1), 3018.

  11. He, Z., Liu, L., Wang, K., Ionita-Laza, I. (2018). A semi-supervised approach for predicting cell type specific functional consequences of non-coding variation using MPRAs. Nature Communications, 9(1), 5199.

  12. He, Z., Xu, B., Lee, S., Ionita-Laza, I. (2017). Unified sequence-based association tests allowing for multiple functional annotations, and meta-analysis of noncoding variation in Metabochip data. The American Journal of Human Genetics, 101(3), 340-352.

  13. He, Z., Zhang, M., Lee, S., Smith, J.A., Kardia, S.L.R., Diez Roux, A.V. and Mukherjee, B. (2017). Set-based tests for gene-environment interaction in longitudinal studies. Journal of the American Statistical Association, 112(519), 966-978.

  14. He, Z., Zhang, M., Lee, S., Smith, J.A., Guo, X., Palmas, W., Kardia, S.L.R., Diez Roux, A.V., and Mukherjee, B. (2015). Set-based tests for genetic association in longitudinal studies. Biometrics, 71(3), 606-615.

  15. He, Z., Zhang, M., Zhan, X., and Lu, Q. (2014). Modeling and testing for joint association using a genetic random field model. Biometrics, 70 (3), 471-479.