Research

Publications

* Joint first author, **Joint 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, 9(1), 5199.
  2. 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.
  3. 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, bioRxiv, FUN-LDA webpage
  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. FST R package on CRAN
  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. LGEWIS R package on CRAN
  6. Zhao, W., Ware, E.B., He, Z., Kardia, S.L., Faul, J.D. and Smith, J.A., (2017). Interaction between Social/Psychosocial Factors and Genetic Variants on Body Mass Index: A Gene-Environment Interaction Analysis in a Longitudinal Setting. International Journal of Environmental Research and Public Health, 14(10), E1153.
  7. 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. LGEWIS R package on CRAN
  8. Li, M., Li, J., He, Z., Lu, Q., Witte, J.S., Macleod, S.L., Hobbs, C.A., Cleves, M.A., and the National Birth Defect Prevention Study (2016). Testing allele transmission for a SNP-set with a family-based generalized genetic random field method. Genetic Epidemiology, 40(4), 341-351.
  9. Wen, Y., He, Z., Li, M., and Lu, Q. (2016). Risk prediction modeling of sequencing data using a forward random field method. Scientific Reports, 6.
  10. 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.
  11. 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. LGRF R package on CRAN
  12. Li, M.*, He, Z.*, Schaid D.J., Cleves M.A., Nick T.G., and Lu Q. (2015). A powerful non-parametric statistical framework for family-based association analyses. Genetics, 200 (1), 69-78.
  13. 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.
  14. Li, M.*, He, Z.*, Zhang, M., Zhan, X., Wei, C., Elston, R.C., and Lu, Q. (2014). A generalized genetic random field method for the genetic association analysis of sequencing data. Genetic Epidemiology, 38 (3), 242-253.
  15. Wei C., Li, M., He, Z., Vsevolozhskaya O., Schaid, D.J., and Lu, Q. (2014). A weighted U-statistic for genetic association analyses of sequencing data. Genetic Epidemiology, 38 (8), 699-708.
  16. 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.

Preprints

17. He, Z., Xu, B., Buxbaum, J. and Ionita-Laza, I.. GenoScan: a genome-wide scan statistic framework for whole-genome sequence data analysis with applications to data from the Simons Simplex Collection.

18. Zhang, M., Wang, S., He, Z. and Mukherjee, B.. Interaction analysis under misspecification of main effects: some common mistakes and simple solutions.