Statistical Methods for DNA Methylation Data Abstract The overall objective of this study is to develop novel statistical methods and software to study how DNA methylation profiles associated with cancers. DNA methylation is a molecular modification of DNA that is important for normal organism development. Genes that are rich in CpG dinucleotides are usually not methylated in normal tissues, but are frequently hypermethylated in cancer. This is often associated with gene silencing and is an important mechanism for the inactivation of tumor suppressor genes. Studies have also suggested that methylation profiles differ between cancers arising in different organs and between different cancer histologies from the same organ. For example, different DNA methylation profiles are found in different subtypes of leukemia and lung cancer. With the rapid development in array technologies, high-throughput arrays with DNA mathylation measures on the genome-wide level have become widely available. There is a great need for development of novel statistical models to evaluate complex DNA methylation data generated with high-throughput platforms. The specific objectives of this project are: (1) to develop novel models for the distribution of methylation proportions to select differentially methylated loci between cancer and normal subjects;(2) to propose a new classification method that differentiates tumor subtypes using DNA methylation profiles;(3) to develop computer software packages that implement methods developed in specific aims 1-2. The proposed methods will be applied to an existing data with tumor samples/normal tissue samples and an ongoing methylation study the PI is collaborating. We believe the proposed methods will significantly improve current and future efforts in understanding the significance of DNA methylation profiles in cancers.

Public Health Relevance

Abstract narrative To develop a series of novel and powerful statistical methods to study DNA methylation profiles. The proposed methods will significantly improve current and future efforts in understanding the significance of DNA methylation profiles in cancers.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Small Research Grants (R03)
Project #
1R03CA150140-01
Application #
7898457
Study Section
Special Emphasis Panel (ZCA1-SRLB-D (J1))
Program Officer
Divi, Rao L
Project Start
2010-04-01
Project End
2012-03-31
Budget Start
2010-04-01
Budget End
2011-03-31
Support Year
1
Fiscal Year
2010
Total Cost
$80,413
Indirect Cost
Name
Columbia University (N.Y.)
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
621889815
City
New York
State
NY
Country
United States
Zip Code
10032
Chen, Yong; Ning, Yang; Hong, Chuan et al. (2014) Semiparametric tests for identifying differentially methylated loci with case-control designs using Illumina arrays. Genet Epidemiol 38:42-50
Herbstman, Julie B; Wang, Shuang; Perera, Frederica P et al. (2013) Predictors and consequences of global DNA methylation in cord blood and at three years. PLoS One 8:e72824
Shen, Jing; Wang, Shuang; Zhang, Yu-Jing et al. (2013) Exploring genome-wide DNA methylation profiles altered in hepatocellular carcinoma using Infinium HumanMethylation 450 BeadChips. Epigenetics 8:34-43
Sun, Hokeun; Wang, Shuang (2013) Network-based regularization for matched case-control analysis of high-dimensional DNA methylation data. Stat Med 32:2127-39
Shen, Jing; Wang, Shuang; Zhang, Yu-Jing et al. (2012) Genome-wide DNA methylation profiles in hepatocellular carcinoma. Hepatology 55:1799-808
Shen, Jing; Wang, Shuang; Zhang, Yu-Jing et al. (2012) Genome-wide aberrant DNA methylation of microRNA host genes in hepatocellular carcinoma. Epigenetics 7:1230-7
Sun, Hokeun; Wang, Shuang (2012) Penalized logistic regression for high-dimensional DNA methylation data with case-control studies. Bioinformatics 28:1368-75
Wang, Shuang; Yu, Zhaoxia; Miller, Rachel L et al. (2011) Methods for detecting interactions between imprinted genes and environmental exposures using birth cohort designs with mother-offspring pairs. Hum Hered 71:196-208
Wang, Shuang (2011) Method to detect differentially methylated loci with case-control designs using Illumina arrays. Genet Epidemiol 35:686-94