M. Chen, J. Cho, H. Zhao (2011) Incorporating biological pathways via a Markov random field model in genome-wide association studies. PLoS Genetics, 7: e1001353.

L. Hou, M. Chen, C. K. Zhang, J. Cho, H. Zhao (2014) Guilt by Rewiring: Gene prioritization through network rewiring in genome wide association studies. Human Molecular Genetics, 23: 2780-2790.

D. Chung, C. Yang, C. Li, J. Gelernter, H. Zhao (2014) GPA: A statistical approach to prioritizing GWAS results by integrating pleiotropy and annotation. PLOS Genetics, 10: e1004787.

J. Jiang, C. Li, D. Paul, C. Yang, H. Zhao (2016) On high-dimensional misspecified mixed model analysis in genome-wide association study. Annals of Statistics, 44: 2127–2160.

Q. Lu, R. Powles, Q. Wang, J. He, H. Zhao (2016) Integrative tissue-specific functional annotations in the human genome provide novel insights on many complex traits and improve signal prioritization in genome wide association studies. PLOS Genetics, 12: e1005947.

D. Chung, H. J. Kim, H. Zhao (2017) graph-GPA: A graphical model for prioritizing GWAS results and investigating pleiotropic architecture. PLOS Computational Biology, 13: e1005388.

Q. Lu, R. L. Powles, S. Abdallah, D. Ou, Q. Wang, Y. Hu, Y. Lu, W. Liu, B. Li, S. Mukherjee, P. K. Crane, H. Zhao (2017) Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset Alzheimer’s disease. PLOS Genetics, 13: e1006933.

Q. Lu, B. Li, D. Ou, M. Erlendsdottir, R. L. Powles, T. Jiang, Y. Hu, D. Chang, C. Jin, W. Dai, Q. He, Z. Liu, S. Mukherjee, P. K. Crane, H. Zhao (2017) A powerful approach to estimating annotation-stratified genetic covariance using GWAS summary statistics. American Journal of Human Genetics, 101: 939-964.

Y. Hu, M. Li, Q. Lu, H. Weng, J. Wang, S. M. Zekavat, Z. Yu, B. Li, J. Gu, S. Muchnik, Y. Shi, B. W. Kunkle, S. Mukherjee, P. Natarajan, A. Naj, A. Kuzma, Y. Zhao, P. K. Crane, Alzheimer’s Disease Genetics Consortium, H. Lu, H. Zhao (2019) A statistical framework for cross-tissue transcriptome-wide association analysis. Nature Genetics, 51: 568-576.

C. Li, C. Yang, J. Gelernter, H. Zhao (2014) Improving genetic risk prediction by leveraging pleiotropy. Human Genetics, 133: 639-650.

Y. Hu, Q. Lu, R. Powles, X. Yao, C. Yang, F. Fang, X. Xu, H. Zhao (2017) Leveraging functional annotations in genetic risk prediction for human complex diseases. PLOS Computational Biology, 13: e1005589.

Y. Hu, Q. Lu, W. Liu, Y. Zhang, M. Li, H. Zhao (2017) Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction. PLOS Genetics, 13: e1006836.

S. Song, W. Jiang, L. Hou, H. Zhao (2020) Leveraging effect size distributions to improve polygenic risk scores derived from summary statistics of genome-wide association studies. PLOS Computational Biology, 16: e1007565.

S. Zaidi, M. Choi, H. Wakimoto, L. Ma, J. Jiang, J. D. Overton, A. Romano-Adesman, R. D. Bjornson, R. E. Breitbart, K. K. Brown, N. J. Carriero, Y. H. Cheung, J. Deanfield, S. DePalma, K. A. Fakhro, J. Glessner, H. Hakonarson, M. J. Italia, J. R. Kaltman, J. Kaski, R. Kim, J. K. Kline, T. Lee, J. Leipzig, A. Lopez, S. M. Mane, L. E. Mitchell, J. W. Newburger, M. Parfenov, I. Pe’er, G. Porter, A. E. Roberts, R. Sachidanandam, S. J. Sanders, H. S. Seiden, M. W. State, S. Subramanian, I. R. Tikhonova, W. Wang, D. Warburton, P. S. White, I. A. Williams, H. Zhao, J. G. Seidman, M. Brueckner, W. K. Chung, B. D. Gelb, E. Goldmuntz, C. E. Seidman, R. P. Lifton (2013) De novo mutations in histone-modifying genes in congenital heart disease. Nature, 498: 220-223.

S. C. Jin, J. Homsy, S. Zaidi, Q. Lu, S. Morton, S. R. DePalma, X. Zeng, H. Qi, W. Chang, W.-C. Hung, M. C. Sierant, S. Haider, J. Zhang, J. Knight, R. D. Bjornson, C. Castaldi, I. R. Tikhonoa, K. Bilguvar, S. M. Mane, S. J. Sanders, S. Mital, M. Russell, W. Gaynor, J. Deanfield, A. Giardini, G. A. Porter Jr., D. Srivastava, C. W. Lo, Y. Shen, W. S. Watkins, M. Yandell, H. J. Yost, M. Tristani-Firouzi, J. W. Newburger, A. E. Roberts, R. Kim, H. Zhao, J. R. Kaltman, E. Goldmuntz, W. K. Chung, J. G. Seidman, B. D. Gelb, C. E. Seidman2, R. P. Lifton, M. Brueckner (2017)

Contribution of rare inherited and de novo variants among 2,871 congenital heart disease probands. Nature Genetics, 49: 1593-1601.

Y. Zhang, Z. Ouyang, H. Zhao (2017) A statistical framework for data integration through graphical models with application to cancer genomics. Annals of Applied Statistics, 11: 161-184.

L. Zeng, J. L. Warren, H. Zhao (2019) Phylogeny-based tumor subclone identification using a Bayesian feature allocation model. Annals of Applied Statistics, 13: 1212–1241.

D. Tang, S. Park, H. Zhao (2019) NITUMID: Nonnegative Matrix Factorization-based Immune-TUmor MIcroenvironment Deconvolution. Bioinformatics, 36: 1344-1350.

S. Park, H. Xu, H. Zhao (2020) Integrating multidimensional data for clustering analysis with applications to cancer patient data. Journal of American Statistical Association, in press.

Z. Lin, S. Sanders, M. Li, N. Sestan, M. State, H. Zhao (2015) A Markov random field-based approach to characterizing human brain development using spatial-temporal transcriptome data. Annals of Applied Statistics, 9: 429–451.

Y. Zhu, A. M. M. Sousa, T. Gao, M. Skarica, M. Li, G. Santpere, P. Esteller-Cucala, D. Juan, L. Ferrández-Peral, F. O. Gulden, M. Yang, D. J. Miller, T. Marques-Bonet, Y. Imamura Kawasawa, H. Zhao, N. Sestan (2018) Spatiotemporal transcriptomic divergence across human and macaque brain development. Science 362(6420).

S. Park, H. Zhao (2018) Spectral clustering based on learning similarity matrix. Bioinformatics, 34: 2069-2076.

Y. Liu, J. Warren, H. Zhao (2019) A hierarchical Bayesian model for single-cell clustering using RNA-sequencing data. Annals of Applied Statistics, Volume 13: 1733-1752.

T. Wang, H. Zhao (2017) Structured subcomposition selection in regression and its application to microbiome data analysis. Annals of Applied Statistics, 11: 771-791.

T. Wang, H. Zhao (2017) A Dirichlet-tree multinomial regression model for associating dietary nutrients with gut microorganisms. Biometrics, 73: 792-801.

T. Wang, H. Zhao (2017) Constructing predictive microbial signatures at multiple taxonomic levels. Journal of the American Statistical Association, 112: 1022-1031.

T. Wang, C. Yang, H. Zhao (2019) Prediction analysis for microbiome sequencing data. Biometrics, 75: 875-884.

B. Li, H. Chun, H. Zhao (2012) Sparse estimation of conditional graphical models with application to gene networks. Journal of American Statistical Association, 107: 152-167.

B. Li, H. Chun, H. Zhao (2014) On an additive semi-graphoid model for statistical networks with application to pathway analysis. Journal of American Statistical Association, 109: 1188-1204.

H. Chun, X. Zhang, H. Zhao (2015) Gene regulation network inference with joint sparse Gaussian graphical models. Journal of Computational and Graphical Statistics, 24: 954–974.

Y. Hu, H. Zhao (2016) CCor: a whole genome network-based similarity measure between two genes. Biometrics, 72: 1216-1225.

Z. Lin, T. Wang, C. Yang, H. Zhao (2017) On joint estimation of Gaussian graphical models for spatial and temporal data. Biometrics, 73: 769-779.

K. Lee, B. Li, H. Zhao (2016) On an additive partial correlation operator and nonparametric estimation of graphical models. Biometrika, 103: 513-530.

K-Y Lee, T. Liu, B. Li, H. Zhao (2020) Learning causal networks via additive faithfulness. Journal of Machine Learning Research, in press.