Dataset Information

Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies.

ABSTRACT:

Background

During the past 5 years, high-throughput technologies have been successfully used by epidemiology studies, but almost all have focused on sequence variation through genome-wide association studies (GWAS). Today, the study of other genomic events is becoming more common in large-scale epidemiological studies. Many of these, unlike the single-nucleotide polymorphism studied in GWAS, are continuous measures. In this context, the exercise of searching for regions of interest for disease is akin to the problems described in the statistical 'bump hunting' literature.

Methods

New statistical challenges arise when the measurements are continuous rather than categorical, when they are measured with uncertainty, and when both biological signal, and measurement errors are characterized by spatial correlation along the genome. Perhaps the most challenging complication is that continuous genomic data from large studies are measured throughout long periods, making them susceptible to 'batch effects'. An example that combines all three characteristics is genome-wide DNA methylation measurements. Here, we present a data analysis pipeline that effectively models measurement error, removes batch effects, detects regions of interest and attaches statistical uncertainty to identified regions.

Results

We illustrate the usefulness of our approach by detecting genomic regions of DNA methylation associated with a continuous trait in a well-characterized population of newborns. Additionally, we show that addressing unexplained heterogeneity like batch effects reduces the number of false-positive regions.

Conclusions

Our framework offers a comprehensive yet flexible approach for identifying genomic regions of biological interest in large epidemiological studies using quantitative high-throughput methods.

SUBMITTER: Jaffe AE

PROVIDER: S-EPMC3304533 | biostudies-literature | 2012 Feb

REPOSITORIES: biostudies-literature

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Publications

Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies.

Jaffe Andrew E AE Murakami Peter P Lee Hwajin H Leek Jeffrey T JT Fallin M Daniele MD Feinberg Andrew P AP Irizarry Rafael A RA

International journal of epidemiology 20120201 1

<h4>Background</h4>During the past 5 years, high-throughput technologies have been successfully used by epidemiology studies, but almost all have focused on sequence variation through genome-wide association studies (GWAS). Today, the study of other genomic events is becoming more common in large-scale epidemiological studies. Many of these, unlike the single-nucleotide polymorphism studied in GWAS, are continuous measures. In this context, the exercise of searching for regions of interest for d ...[more]

PMID: 22422453

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Project description:Phenotypic variability among inbred littermates reared in controlled environments remains poorly understood. Metastable epialleles refer to loci that intrinsically behave in this way and a few examples have been described. They display differential methylation in association with differential expression. For example, inbred mice carrying the agouti viable yellow (A (vy) ) allele show a range of coat colours associated with different DNA methylation states at the locus. The availability of next-generation sequencing, in particular whole genome sequencing of bisulphite converted DNA, allows us, for the first time, to search for metastable epialleles at base pair resolution.Using whole genome bisulphite sequencing of DNA from the livers of five mice from the A (vy) colony, we searched for sites at which DNA methylation differed among the mice. A small number of loci, 356, were detected and we call these inter-individual Differentially Methylated Regions, iiDMRs, 55 of which overlap with endogenous retroviral elements (ERVs). Whole genome resequencing of two mice from the colony identified very few differences and these did not occur at or near the iiDMRs. Further work suggested that the majority of ERV iiDMRs are metastable epialleles; the level of methylation was maintained in tissue from other germ layers and the level of mRNA from the neighbouring gene inversely correlated with methylation state. Most iiDMRs that were not overlapping ERV insertions occurred at tissue-specific DMRs and it cannot be ruled out that these are driven by changes in the ratio of cell types in the tissues analysed.Using the most thorough genome-wide profiling technologies for differentially methylated regions, we find very few intrinsically epigenetically variable regions that we term iiDMRs. The most robust of these are at retroviral elements and appear to be metastable epialleles. The non-ERV iiDMRs cannot be described as metastable epialleles at this stage but provide a novel class of variably methylated elements for further study.

Dataset Information

Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies.

Background

Methods

Results

Conclusions

Publications

Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies.

Similar Datasets

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