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Hongyu Zhao, Ph.D |
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Ira V. Hiscock Associate Professor of Public Health
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| The research interests in our group are to develop mathematical, statistical, computational, and visualization tools to address
scientific problems in molecular biology and genetics. Currently, we are focusing the following areas:
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| (1) Genetic Linkage and
Association Studies: Essentially all human diseases are the result of genetic mutations at the cellular level. Therefore, any
effective prevention, diagnosis, and treatment of a certain disease must start with a good understanding of (a) which genes cause
the disease; (b) how genes interact with each other to affect the phenotype; and (c) how genes and environmental factors interact
with each other to affect the disease risk. Linkage and association studies represent two types of research designs that geneticists
and epidemiologists commonly use to answer these questions. We are developing statistical methods and computer programs for these
studies, with a special focus on using densely spaced genetic markers in such studies.
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| (2) Population Genetics: A good understanding
of population genetics is essential for genetic linkage analysis. We are collaborating with Dr. Kidd's laboratory in the Department
of Genetics at Yale to study linkage disequilibrium (LD) patterns and developing statistical tools to uncover patterns in the LD data
to investigate the implications of the observed patterns on our strategies to identify DNA variants underlying complex diseases.
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(3) Computational Biology:
Recent advances in large-scale RNA expression measurements and proteomics technologies have opened the opportunity for massively
parallel biological data acquisition and thus have shifted our attention towards an integrated understanding of the genetic networks
underlying complex biological phenotypes. We are focusing on developing statistical and computational methods to integrate different
types of genomics and proteomics data to dissect biological pathways, including metabolic pathways, transcriptional regulatory pathways,
and signal transduction pathways.
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| (4) Proteomics: We are focusing on biomarker discovery using mass spectrometry based approaches to
identifying peptides and proteins whose relative level of expressions can be used to classify individuals into either normal or
different disease categories. In addition, we are developing statistical methods to better identify peptides and proteins from mass
spectrometry data.
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| Representative Publications: | |
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Y. Liu, N. Liu, H. Zhao (2005)
Inferring protein-protein interactions through high-throughput interaction data from diverse organisms
Bioinformatics, 21: 3279-3285.
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L. Chen, H. Zhao (2005)
Gene expression analysis reveals that histone deacetylation sites may serve as partitions of chromatin gene expression domains
BMC Genomics, 6: 44.
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M. Holford, N. Li, P. Nadkarni, H. Zhao (2005)
VitaPad: visualization tools for the analysis of pathway data
Bioinformatics, 21: 1596-1602.
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Z. Guan, H. Zhao (2005)
A semiparametric approach for marker gene selection based on gene expression data
Bioinformatics, 21: 529-536.
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K. Zhang, F. Sun, H. Zhao. (2005)
HAPLORE: A program for haplotype reconstruction in general pedigrees without recombination.
Bioinformatics, 21: 90-103.
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F. H. Wilson, A. Hariri, A. Farhi, H. Zhao, K. F. Petersen, H. R. Toka, C. Nelson-Williams, K. M. Raja, M. Kashgarian, G. I. Shulman, S. J. Scheinman, R. P. Lifton (2004)
A cluster of metabolic defects caused by mutation in a mitochondrial tRNA
Science, 306: 1190-1194.
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N. Liu, S. L. Sawyer, N. Mukherjee, A. J. Pakstis, J. R. Kidd, K. K. Kidd, A. J.Brookes, H. Zhao (2004)
Haplotype block structures show significant variation among populations
Genetic Epidemiology, 27: 385-400.
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Y. Lai, B. Wu, L. Chen, H. Zhao (2004)
Statistical method for identifying differential gene-gene coexpression patterns
Bioinformatics, 20: 3146-3155.
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| Contact Information: | | |
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