title: 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.
authors.affiliation: Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
authors.forename: Andrew E
authors.initials: AE
authors.lastname: Jaffe
authors.forename: Peter
authors.initials: P
authors.lastname: Murakami
authors.forename: Hwajin
authors.initials: H
authors.lastname: Lee
authors.forename: Jeffrey T
authors.initials: JT
authors.lastname: Leek
authors.forename: M Daniele
authors.initials: MD
authors.lastname: Fallin
authors.forename: Andrew P
authors.initials: AP
authors.lastname: Feinberg
authors.forename: Rafael A
authors.initials: RA
authors.lastname: Irizarry
mesh_terms: D019175:DNA Methylation / Q000235:genetics*; D044127:Epigenesis, Genetic; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D015233:Models, Statistical