Statistical power for RNA-seq data to detect two epigenetic phenomena

<p>Epigenetics is the study of heritable changes in gene expression or cellular phenotype caused by mechanisms other than changing the underlying DNA sequence. Two epigenetic phenomena, genomic imprinting and Allelic Expression Imbalance (AEI), are discussed in this dissertation. RNA-seq is a powerful new technology for mapping and quantifying transcriptomes using ultra high throughput next generation sequencing technologies. Using RNA-seq, a genome-wide study can investigate genome-wide genomic imprinting and AEI without prior knowledge of genes or coding regions. Moreover, RNA-seq shows many benefits compared with traditional microarray hybridization-based and sequence-based methods.</p><p>In this dissertation, we focus on how investigating sequencing parameters may affect power of tests for detecting imprinting and/or AEI, and whether the current technology can provide sufficient power for mouse and human data. Since existing methods in the literatures are not amenable for detecting such effects and since these two effects may be confounded with one another, we propose a joint test for simultaneous detection of imprinting and AEI. For mouse data, the reciprocal cross design for mouse, and two definitions of informative reads based on binomial distribution are used throughout this dissertation. The joint test and the two-chi-squares test in the literature are used for power calculation and simulation study, and their results are compared and contrasted. The results show that the joint test is not only applicable for simultaneous detection of the two epigenetic effects, but it is also more powerful compared to the two-chi-squares test. </p><p>We provide theoretical power under some combinations of sequencing depth (E(T), read length(l), and sequencing divergence (d). If an informative read is defined as covering at least one SNP for mouse reciprocal cross design, increasing sequence depth, not read length, is the key to improve power. If an informative read is defined as covering a particular SNP, then E(T) at least 130 and l at least 250 is necessary to achieve sufficient power under d=2% , even when imprinting and AEI effects are strong.</p><p>As for human data, we discuss which trio structures are informative to detect imprinting and AEI. In the theoretical power calculation, except for the effects of E(T), l and d, the number of families (N) in a random sample is also considered. If an informative read is defined as covering at least one SNP, increasing sequence depth is still the key to improve power under N=50. As for N, under E(T)=4, d=1% and l=100, even N=20 leads to a sufficient power for detecting strong imprinting and AEI effects. However, a larger N is necessary for more moderate effects. If an informative read is defined as covering a particular SNP, E(T) of at least 10 and l of at least 250 is necessary to achieve sufficient power under N=100 and d=2%, even when imprinting and/or AEI effects are strong. As for the effect of N, under E(T)=10, d=2% and l=250, N=200 is necessary to achieve sufficient power.</p>

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Statistics

Grantor dc:publisher

The Ohio State University

Year dc:date

2013

Author dc:creator

• Chen, Dao-Peng

Contributors dc:contributor

• Lin, Shili

• Biostatistics
• RNA-seq
• NGS
• sequencing parameter
• power

Statement dc:rights

• unrestricted
• This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.

Language dc:language

English

Repository record dc:identifier

http://rave.ohiolink.edu/etdc/view?acc_num=osu1357248975

OAI identifier oai:identifier

oai:etd.ohiolink.edu:osu1357248975