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Activity Number: 571
Type: Contributed
Date/Time: Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #305538
Title: Statistical Power for Detecting Several Epigenetic Factors Using RNA-Seq Data
Author(s): Dao-Peng Chen*+ and Shili Lin
Companies: The Ohio State University and The Ohio State University
Address: 80 W Lane AVE APT 3C, Columbus, OH, 43201, United States
Keywords: RNA-seq ; genomic imprinting ; allelic expression imbalance

RNA-seq has become an important tool for genome-wide quantification of RNA abundance, and helps to determine the transcriptional structure of genes and splicing patterns. Two of the epigenetic phenomena that we attempt to address through RNA-seq are genomic imprinting and allelic expression imbalance. Our study emphasizes on whether current sequencing technology (e.g., read length and sequencing depth) can lead to sufficient statistical power for detecting these two epigenetic phenomena. To study genomic imprinting in mice, Wang et al. (PLoS ONE, 3, e3839, 2008) performed RNA-seq experiments from reciprocal crosses of two mouse strains. Through this reciprocal cross design and mathematical modeling, we discuss how several key parameters in RNA-seq may affect the power of some simple statistical tests to detect these phenomena.

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