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Hongyu Zhao PhD

Ira V. Hiscock Professor of Public Health (Biostatistics) and Professor of Genetics and of Statistics

Research Interests

Statistical genomics; Computational biology; Genetic epidemiology; Genome wide association studies; Complex diseases; Risk prediction; High dimensional data; Dimension reduction; Network models; Graphical models; Bioinformatics; Next generation sequencing analysis; Cancer genomics; Epigenetics; Herbal medicine; Cicrobiome; Proteomics

Current Projects

  • Genome Wide Associatio Studies: We are developing statisticla methods to integrate diverse data types and prior biological knowledge to identify genes for common diseases and risk prediction models. The diseases we work on include Crohn's disease, substance abuse, schizophrenia, bipolar, obesity, aneurysm, and autism.
  • Network Modeling: We are developing statistical methods to model biological networks under the general framework of Gaussian graphical models. Specific networks we are working on include gene expression regulatory networks, signaling networks, and eQTL networks.
  • Cancer Genomics: We are developing statistical and computational methods to analyze cancer genomics data, e.g. microarrays and next generation sequencing, to identify cancer subtypes, driver mutations, and appropriate treatments for cancer patients.
  • Microbiome: We are developing modeling and analysis approaches for microbiome data generated from next generation sequencing data.
  • Proteomics: Our current focus is on targeted proteomics, such as Multiple Reaction Monitoring.
  • Herbal Medicine: Through systems biology approach, we are identifying tissue-specific target pathways of herbal medicine.

Research Summary

Our research is driven by the need to analyze and interpret large and complex data sets in biomedical research. For example, in genome wide association studies involving thousands of individuals, millions of DNA variants are collected for each person. Such data offer people the opportunity to identify variants affecting disease susceptibility and develop risk prediction models to facilitate disease prevention and treatment. There are many statistical challenges arising from the analysis of such data, including the very high dimensionality, the relatively weak signals, and the need to incorporate prior knowledge and other data sets in analysis. Another example is the analysis of next generation sequence data which present even greater statistical and computational challenges. Our group has been developing statistical methods to address these challenges, such as empirical Bayes methods to borrow information across different data sets, different generalizations of Gaussian graphical models for network inference, Markov random field models for spatial and temporal modeling, and general machine learning methods for high dimensional data.


Selected Publications

  • C. Yang, L. Wang, S. Zhang, H. Zhao (2013) Accounting for non-genetic factors by low-rank representation and sparse regression for eQTL mapping. Bioinformatics, 29: 1026-1034.
  • H. Zhu, F. Hu, H. Zhao (2013) Adaptive clinical trial designs to detect interaction of treatment and a dichotomous biomarker. Canadian Journal of Statistics, in press.
  • L. Wang, W. Zheng, H. Zhao, M. Deng (2013) Statistical analysis reveals co-expression patterns of many pairs of genes in yeast are jointly regulated by interacting loci. PLoS Genetics, 9: e1003414.
  • X. Qi, H. Zhao (2013) Sparse principal component analysis by choice of norm. Journal of Multivariate Analysis, 114: 127-160.
  • J. Ferguson, C. Yang, J. Cho, H. Zhao (2013) Empirical Bayes correction for the winner's curse in genetic association studies. Genetic Epidemiology, 37: 60-68.
  • 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.
  • H. Ma, H. Zhao (2012) iFad: an integrative factor analysis model for drug-pathway association inference. Bioinformatics, 28: 1911-1918.
  • R. Luo, H. Zhao (2011) Bayesian hierarchical modeling for signaling pathway inference from single cell interventional data. Annals of Applied Statistics, 5: 725–745.
  • 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.

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