Model-based clustering of DNA methylation array data: a recursive-partitioning algorithm for high-dimensional data arising as a mixture of beta distributions

E Andres Houseman,Brock C Christensen,Ru-Fang Yeh,Carmen J Marsit,Heather H Nelson,Margaret Wrensch,Margaret R Karagas,Shichun Zheng,Joseph Wiemels,John K Wiencke,Karl T Kelsey

doi:10.1186/1471-2105-9-365

Abstract

BackgroundEpigenetics is the study of heritable changes in gene function that cannot be explained by changes in DNA sequence. One of the most commonly studied epigenetic alterations is cytosine methylation, which is a well recognized mechanism of epigenetic gene silencing and often occurs at tumor suppressor gene loci in human cancer. Arrays are now being used to study DNA methylation at a large number of loci; for example, the Illumina GoldenGate platform assesses DNA methylation at 1505 loci associated with over 800 cancer-related genes. Model-based cluster analysis is often used to identify DNA methylation subgroups in data, but it is unclear how to cluster DNA methylation data from arrays in a scalable and reliable manner.ResultsWe propose a novel model-based recursive-partitioning algorithm to navigate clusters in a beta mixture model. We present simulations that show that the method is more reliable than competing nonparametric clustering approaches, and is at least as reliable as conventional mixture model methods. We also show that our proposed method is more computationally efficient than conventional mixture model approaches. We demonstrate our method on the normal tissue samples and show that the clusters are associated with tissue type as well as age.ConclusionOur proposed recursively-partitioned mixture model is an effective and computationally efficient method for clustering DNA methylation data.

Highlights

Epigenetics is the study of heritable changes in gene function that cannot be explained by changes in DNA sequence [1]
DNA methylation associated gene silencing is a well recognized epigenetic mechanism that often occurs at tumor suppressor gene loci in human cancer
There is a basic need for epigenetic profiling of normal tissues to better understand the contribution of these profiles to tissue specificity, especially in the context of phenotypically important CpGs, where deregulation is associated with human diseases such as cancer

Summary

Introduction

Epigenetics is the study of heritable changes in gene function that cannot be explained by changes in DNA sequence. One of the most commonly studied epigenetic alterations is cytosine methylation, which is a well recognized mechanism of epigenetic gene silencing and often occurs at tumor suppressor gene loci in human cancer. DNA methylation associated gene silencing is a well recognized epigenetic mechanism that often occurs at tumor suppressor gene loci in human cancer. There is a basic need for epigenetic profiling of normal tissues to better understand the contribution of these profiles to tissue specificity, especially in the context of phenotypically important CpGs, where deregulation is associated with human diseases such as cancer. Most CpGs or CpG regions have been found to have a bimodal distribution of methylation profiles, either hypo- or hypermethylated Another theme is the disproportionate location of cell or tissue type dependent differentially methylated regions in non-CpG island sites [4,8]. An approach which characterizes the basal epigenetic state is best suited to foster the discovery of epigenetic profiles that are associated with particular disease states, patient characteristics, exposures or other covariates that may contribute to pathogenesis

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Conclusion