Bump Hunting to Identify Differentially Methylated Regions in Epigenetic Epidemiology Studies

• Activity Number: 668
• Type: Contributed
• Date/Time: Thursday, August 2, 2012 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Andrew Jaffe*+
• Companies: The Lieber Institute
• Keywords: Epigenetic Epidemiology ; DNA Methylation ; Bump hunting ; Batch effects ; Smoothing ; Genome-wide analysis

Abstract

High-throughput technologies have been successfully used by epidemiology studies, but most have focused on sequence variation through GWAS. New statistical challenges arise when other measurements are continuous rather than categorical, when they are measured with uncertainty, and when both biological signal and measurement error 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. 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. We illustrate the usefulness of our approach by detecting genomic regions of DNA methylation associated with gestational age in a population of newborns. Our framework offers a comprehensive yet flexible approach for identifying regions of biological interest in epidemiological studies using quantitative high-throughput methods.