A Bayesian Framework To Detect Differentially Methylated Loci in Both Mean And Variability with Next Generation Sequencing

Hdl Handle:
http://hdl.handle.net/10675.2/579943
Title:
A Bayesian Framework To Detect Differentially Methylated Loci in Both Mean And Variability with Next Generation Sequencing
Authors:
Li, Shuang
Abstract:
DNA methylation at CpG loci is the best known epigenetic process involved in many complex diseases including cancer. In recent years, next-generation sequencing (NGS) has been widely used to generate genome-wide DNA methylation data. Although substantial evidence indicates that di erence in mean methylation proportion between normal and disease is meaningful, it has recently been proposed that it may be important to consider DNA methylation variability underlying common complex disease and cancer. We introduce a robust hierarchical Bayesian framework with a Latent Gaussian model which incorporates both mean and variance to detect di erentially methylated loci for NGS data. To identify methylation loci which are associated with disease, we consider Bayesian statistical hypotheses testing for methylation mean and methylation variance using a twodimensional highest posterior density region. To improve computational e ciency, we use Integrated Nested Laplace Approximation (INLA), which combines Laplace approximations and numerical integration in a very e cient manner for deriving marginal posterior distributions. We performed simulations to compare our proposed method to other alternative methods. The simulation results illustrate that our proposed approach is more powerful in that it detects less false positives and it has true positive rate comparable to the other methods.
Affiliation:
Department of Biostatistics and Epidemiology
Issue Date:
Jul-2015
URI:
http://hdl.handle.net/10675.2/579943
Type:
Dissertation
Appears in Collections:
Theses and Dissertations; Department of Biostatistics and Epidemiology Theses and Dissertations

Full metadata record

DC FieldValue Language
dc.contributor.authorLi, Shuangen
dc.date.accessioned2015-10-20T20:19:32Zen
dc.date.available2015-10-20T20:19:32Zen
dc.date.issued2015-07en
dc.identifier.urihttp://hdl.handle.net/10675.2/579943en
dc.description.abstractDNA methylation at CpG loci is the best known epigenetic process involved in many complex diseases including cancer. In recent years, next-generation sequencing (NGS) has been widely used to generate genome-wide DNA methylation data. Although substantial evidence indicates that di erence in mean methylation proportion between normal and disease is meaningful, it has recently been proposed that it may be important to consider DNA methylation variability underlying common complex disease and cancer. We introduce a robust hierarchical Bayesian framework with a Latent Gaussian model which incorporates both mean and variance to detect di erentially methylated loci for NGS data. To identify methylation loci which are associated with disease, we consider Bayesian statistical hypotheses testing for methylation mean and methylation variance using a twodimensional highest posterior density region. To improve computational e ciency, we use Integrated Nested Laplace Approximation (INLA), which combines Laplace approximations and numerical integration in a very e cient manner for deriving marginal posterior distributions. We performed simulations to compare our proposed method to other alternative methods. The simulation results illustrate that our proposed approach is more powerful in that it detects less false positives and it has true positive rate comparable to the other methods.en
dc.rightsCopyright protected. Unauthorized reproduction or use beyond the exceptions granted by the Fair Use clause of U.S. Copyright law may violate federal law.en
dc.subjectDNA Methylationen
dc.subjectnext-generation sequencingen
dc.subjectBayes Theoremen
dc.subjectLatent Gaussian modelen
dc.titleA Bayesian Framework To Detect Differentially Methylated Loci in Both Mean And Variability with Next Generation Sequencingen
dc.typeDissertationen
dc.contributor.departmentDepartment of Biostatistics and Epidemiologyen
dc.description.advisorXu,Hongyanen
dc.description.committeeGeorge, Varghese; Ryu, Duchwan; Podolsky, Robert; Wang, Xiaoling; Shi, Huidongen
dc.description.degreeDoctor of Philosophy with a Major in Biostatisticsen
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