Zihuai He, Ph.D.
Assistant Professor

Dr. He received his PhD from the University of Michigan in 2016. Following a postdoctoral training in biostatistics at Columbia University, he joined QSU in November 2018. 

 

 

His research is concentrated in the area of statistical genetics and integrative analysis of omics data, attempting to develop new statistical methodologies that aid with the identification and interpretation of complex biological pathways involved in human diseases, particularly neurological disorders. His methodology interest includes high-dimensional data analysis, correlated (longitudinal, familial) data analysis and machine learning algorithms.   

Selected Publications:

*co-first author; **co-corresponding author.

  1. He, Z., Liu, L., Wang, K.  and Ionita-Laza, I. (2018). A semi-supervised approach for predicting cell-type specific functional consequences of non-coding variation using MPRAs. Nature Communications, to appear.
  2. Backenroth, D., He, Z., Kiryluk, K., Boeva, V., Pethukova, L., Khurana, E., Christiano, A., Buxbaum, J., Ionita-Laza, I. (2018). FUN-LDA: A Latent Dirichlet Allocation model for predicting tissue-specific functional effects of noncoding variation: methods and applications. The American Journal of Human Genetics, 102(5) 920-942.
  3. Li, M.*, He, Z.*, Tong, X., Witte, J.S. and Lu, Q. (2018). Detecting rare mutations with heterogeneous effects using a family-based genetic random field method. Genetics, genetics-301266.
  4. He, Z., Xu, B., Lee, S., Ionita-Laza, I. (2017). Unified sequence-based association tests allowing for multiple functional annotations, and applications to meta-analysis of noncoding variation in Metabochip data. The American Journal of Human Genetics, 101(3), 340-352. 
  5. 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.
  6. He, Z., Lee, S., Zhang, M., Smith, J.A., Guo, X., Palmas, W., Kardia, S.L.R., Ionita-Laza, I., and Mukherjee, B. (2017). Rare-variant association tests in longitudinal studies, with an application to the Multi-Ethnic Study of Atherosclerosis (MESA). Genetic Epidemiology, 41(8), 801-810.
  7. Mukherjee, B., Chen, Y., Ko, Y., He, Z., Lee, S., Zhang, M., and Park, S.K. (2016). Statistical strategies for modeling gene-environment interactions in longitudinal cohort studies. Statistical Approaches to Gene-Environment Interactions for Complex Phenotypes, Cambridge, MA: MIT Press, 2016.
  8. 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.
  9. He, Z., Payne, E.K., Mukherjee, B., Lee, S., Smith, J.A., Ware, E.B., Sánchez, B.N., Seeman, T.E., Kardia, S.L.R., and Diez Roux, A.V. (2015). Association between stress response genes and features of diurnal cortisol curves in the Multi-Ethnic Study of Atherosclerosis. PLOS ONE, e0126637.
  10. 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.