Epigenetic distance-based methods
We developed a weighted epigenetic distance-based method characterizing (dis)similarity in methylation measures at multiple CpGs in a gene or a genetic region between pairwise samples, with weights to up-weight signal CpGs and down-weight noise CpGs. Using distance-based approaches, weak signals that might be filtered out in a CpG site-level analysis could be accumulated and therefore boost the overall study power. In constructing epigenetic distances, we considered both differential methylation (DM) and differential variability (DV) signals. We demonstrated the superior performance of the proposed weighted epigenetic distance-based method over non-weighted versions and site-level EWAS (epigenome-wide association studies) methods in simulation studies and application to breast cancer methylation data to identify epigenetic field defects, i.e., early pre-cancer DNA methylation changes.
Many studies have demonstrated associations between DNA methylation and environmental factors with evidence also supporting the idea that DNA methylation may modify the risk of environmental factors on health outcomes. Here we constructed cross-product terms between CpGs and environmental factors that are able to capture their interactions and developed a weighted distance-based method. We demonstrated great performance of the proposed method in simulated datasets and an application to the Mothers and Newborns birth cohort of the Columbia Center for Children’s Environmental Health to determine whether prenatal polycyclic aromatic hydrocarbons interacts with DNA methylation in association with Attention Deficit Hyperactivity Disorder and Mental Development Index at age 3.
Date and Time
Location
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Registration
- Date: 17 Mar 2021
- Time: 02:00 PM to 03:00 PM
- All times are (GMT-05:00) US/Eastern
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- 1000 River Road
- Teaneck , New Jersey
- United States 07666
- Building: Muscarelle Center, M105,
- Room Number: M105
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- Co-sponsored by North Jersey Section, Computer Chapter, Signal Processing Chapter, AP/MTT
- Starts 22 January 2021 11:56 AM
- Ends 17 March 2021 02:00 PM
- All times are (GMT-05:00) US/Eastern
- No Admission Charge
Speakers
Prof. Shuang Wang of Columbia University, NY
Epigenetic distance-based methods
Biography:
Dr. Shuang Wang is a Professor of Biostatistics in the Department of Biostatistics at Columbia University, NY. She received her Ph.D. from Yale University and BS from the University of Science and Technology of China. Her research focuses on methodology development to analyze genetic and epigenetic data and integration of multi-platform omics data.
Email:
Address:722 West 168th St. Room 622, , New York , United States, 10027
Agenda
We developed a weighted epigenetic distance-based method characterizing (dis)similarity in methylation measures at multiple CpGs in a gene or a genetic region between pairwise samples, with weights to up-weight signal CpGs and down-weight noise CpGs. Using distance-based approaches, weak signals that might be filtered out in a CpG site-level analysis could be accumulated and therefore boost the overall study power. In constructing epigenetic distances, we considered both differential methylation (DM) and differential variability (DV) signals. We demonstrated the superior performance of the proposed weighted epigenetic distance-based method over non-weighted versions and site-level EWAS (epigenome-wide association studies) methods in simulation studies and application to breast cancer methylation data to identify epigenetic field defects, i.e., early pre-cancer DNA methylation changes.
Many studies have demonstrated associations between DNA methylation and environmental factors with evidence also supporting the idea that DNA methylation may modify the risk of environmental factors on health outcomes. Here we constructed cross-product terms between CpGs and environmental factors that are able to capture their interactions and developed a weighted distance-based method. We demonstrated great performance of the proposed method in simulated datasets and an application to the Mothers and Newborns birth cohort of the Columbia Center for Children’s Environmental Health to determine whether prenatal polycyclic aromatic hydrocarbons interacts with DNA methylation in association with Attention Deficit Hyperactivity Disorder and Mental Development Index at age 3.