Gene transcription is a complex and tightly regulated process. Accumulating evidence has indicated that it was concertedly regulated by regulatory proteins, mainly transcription factors (TF), and epigenetic modifications. The role of TFs in the regulation of gene transcription has been extensively studied, but much less understood is the role of epigenetic modification. DNA methylation has been newly discovered as key controller in gene transcription too. Aberrant DNA methylation changes can cause a number of human diseases such as developmental diseases (ICF syndrome, Prader-Willi and Angelman syndromes etc), aging related diseases (i.e. Alzheimer's disease), heart disease, diabetes, and autoimmune diseases. Moreover, large amount of evidence implicated that DNA methylation is a key player in cancer development. The overarching goal of this project is to develop a set of novel statistical tools to identify the differentially expressed DNA methylation patterns and understand the roles of DNA methylation in gene transcriptional regulation. In particular, we intends to achieve three scientific goals: 1) improving the sensitivity and specificity in identifyng the single nucleotide based DNA methylation change; 2) bridging the research gap in DNA methylation analysis and gene expression analysis by using the sufficient dimension reduction model; 3) developing a new statistical framework to overcome the grand challenges in epigenetic analysis and build the mathematical underpinning. With the rapid development of next generation sequencing technique in the past decades, where large amount of epigenetic and genomic data are routinely collected, processed and stored, we believe our efforts will not only extend our understanding of the regulatory mechanism in gene transcription but also lead to (1) fundamental advances in DNA methylation analysis, (2) development and refinement of technology for the rapid and continuous identification of gene regulation related DNA methylation cites, (3) prototyping of the epigenetic chip for human intervention of certain disease.

Public Health Relevance

In this proposal, we will develop a suite of statistical models to broaden our understanding of how methylation patterns are established and maintained during normal development and under different environmental conditions. The results from this proposal may provide potential epigenetic remedy for certain disease.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM113242-02
Application #
8916806
Study Section
Special Emphasis Panel (ZGM1-BBCB-5 (BM))
Program Officer
Brazhnik, Paul
Project Start
2014-08-15
Project End
2018-07-31
Budget Start
2015-08-01
Budget End
2016-07-31
Support Year
2
Fiscal Year
2015
Total Cost
$363,452
Indirect Cost
$60,049
Name
University of Georgia
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
004315578
City
Athens
State
GA
Country
United States
Zip Code
30602
Wang, HaiYing; Zhu, Rong; Ma, Ping (2018) Optimal Subsampling for Large Sample Logistic Regression. J Am Stat Assoc 113:829-844
Deng, Libin; Zhang, Cheng; Miao, Duling et al. (2017) Mediator complex components are frequent targets for genetic alterations in various types of human cancer. J Genet Genomics 44:587-591
Noonepalle, Satish K; Gu, Franklin; Lee, Eun-Joon et al. (2017) Promoter Methylation Modulates Indoleamine 2,3-Dioxygenase 1 Induction by Activated T Cells in Human Breast Cancers. Cancer Immunol Res 5:330-344
Zhang, Liyun; Zhang, Xinlian; Zhang, Gaonan et al. (2017) Expression profiling of the retina of pde6c, a zebrafish model of retinal degeneration. Sci Data 4:170182
Xing, Xin; Liu, Jun S; Zhong, Wenxuan (2017) MetaGen: reference-free learning with multiple metagenomic samples. Genome Biol 18:187
Liu, Yiwen; Ma, Ping; Cassidy, Paige A et al. (2017) Statistical Analysis of Zebrafish Locomotor Behaviour by Generalized Linear Mixed Models. Sci Rep 7:2937
Akay, Alper; Di Domenico, Tomas; Suen, Kin M et al. (2017) The Helicase Aquarius/EMB-4 Is Required to Overcome Intronic Barriers to Allow Nuclear RNAi Pathways to Heritably Silence Transcription. Dev Cell 42:241-255.e6
Li, Mulin Jun; Li, Miaoxin; Liu, Zipeng et al. (2017) cepip: context-dependent epigenomic weighting for prioritization of regulatory variants and disease-associated genes. Genome Biol 18:52
Wu, Jiazhu; Shi, Huidong (2017) Unlocking the epigenetic code of T cell exhaustion. Transl Cancer Res 6:S384-S387
Sun, Xiaoxiao; Dalpiaz, David; Wu, Di et al. (2016) Statistical inference for time course RNA-Seq data using a negative binomial mixed-effect model. BMC Bioinformatics 17:324

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