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Abstract Details
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Activity Number:
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4
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Type:
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Invited
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Date/Time:
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Sunday, July 31, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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ENAR
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| Abstract - #300492 |
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Title:
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Statistical Methods for Analysis of Genome-Wide DNA Methylation Data
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Author(s):
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Karen Conneely*+
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Companies:
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Emory University
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Address:
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, , ,
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Keywords:
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genetics ;
epigenetics ;
methylation ;
epigenomics
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Abstract:
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DNA methylation provides a potential link between gene, environment, and phenotype, in that it can change in response to environmental triggers and can influence phenotype by altering gene expression. The recent availability of high-density methylation arrays has sparked a growing interest in the relationship between methylation and a variety of disease phenotypes and environmental predictors. DNA samples collected for genetic association studies can easily be utilized in methylation studies, but new statistical and experimental design issues arise that are specific to methylation data. I will discuss the challenges involved in analysis of genome-wide methylation data and will present my current work on statistical methods to deal with these challenges.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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