Representative Papers:

[1] Z. Dong, W. Jiang, H. Li, A. T. DeWan, H. Zhao (2024) LDER-GE estimates phenotypic variance component of gene-environment interactions in human complex traits accurately with GE interaction summary statistics and full LD information. Briefings in Bioinformatics, 25: bbae335.

[2] X. Zhang, W. Jiang, H. Zhao (2024) Integration of expression QTLs with fine mapping via SuSiE. PLOS Genetics, 20: e1010929.

[3] Liu W, Deng W, Chen M, Dong Z, Zhu B, Yu Z, Tang D, Sauler M, Lin C, Wain LV, Cho MH, Kaminski N, Zhao H (2023) A statistical framework to identify cell types whose genetically regulated proportions are associated with complex diseases. PLOS Genetics, 19: e1010825.

[4] J. Jiang, W. Jiang, D. Paul, Y. Zhang, H. Zhao (2023) High dimensional asymptotic behavior of inference based on GWAS summary statistics. Statistica Sinica, in press.

[5] S. Song, W. Jiang, Y. Zhang, L. Hou, H. Zhao (2022) Leveraging LD eigenvalue regression to improve the estimation of SNP heritability and confounding inflations. American Journal of Human Genetics, 109: 802-811.

[6] X. Hu, J. Zhao, Z. Lin, Y. Wang, H. Peng, H. Zhao, X. Wan, C. Yang (2022) Mendelian Randomization for causal inference accounting for pleiotropy and sample structure using genome-wide summary statistics. PNAS, 119: e2106858119.

[7] Y. Zhang, Q. Lu, Y. Ye, K. Huang, W. Liu, Y. Wu, X. Zhong, B. Li, Z. Yu, B. Travers, D. Werling, J. Li, H. Zhao (2021) SUPERGNOVA: Local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits. Genome Biology, 22: 262.

[8] W. Liu, M. Li, W. Zhang, G. Zhou, X. Wu, J. Wang, Q. Lu, H. Zhao (2020) Leveraging functional annotation to identify genes associated with complex diseases. PLOS Computational Biology, 16: e1008315.

[9] 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.

[10] 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.

[11] 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.

[12] 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.

[13] 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.

[14] 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.

[15] 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.

[16] 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.