title: Simultaneous detection and estimation of trait associations with genomic phenotypes.
abstract: Genomic phenotypes, such as DNA methylation and chromatin accessibility, can be used to characterize the transcriptional and regulatory activity of DNA within a cell. Recent technological advances have made it possible to measure such phenotypes very densely. This density often results in spatial structure, in the sense that measurements at nearby sites are very similar. In this article, we consider the task of comparing genomic phenotypes across experimental conditions, cell types, or disease subgroups. We propose a new method, Joint Adaptive Differential Estimation (JADE), which leverages the spatial structure inherent to genomic phenotypes. JADE simultaneously estimates smooth underlying group average genomic phenotype profiles and detects regions in which the average profile differs between groups. We evaluate JADE's performance in several biologically plausible simulation settings. We also consider an application to the detection of regions with differential methylation between mature skeletal muscle cells, myotubes, and myoblasts.
authors: Department of Biostatistics, University of Washington, Seattle, WA 98195, USA jeanm5@uw.edu.
authors: Jean
authors: J
authors: Morrison
authors: Department of Biostatistics, University of Washington, Seattle, WA 98195, USA jeanm5@uw.edu.
authors: Noah
authors: N
authors: Simon
authors: Department of Statistics, University of Washington, Seattle, WA, 98195, USA and Department of Biostatistics, University of Washington, Seattle, WA 98195, USA.
authors: Daniela
authors: D
authors: Witten
mesh_terms: D019175:DNA Methylation / Q000235:genetics*; D016678:Genome / Q000235:genetics*; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D018485:Muscle Fibers, Skeletal / Q000378:metabolism; D032448:Myoblasts, Skeletal / Q000378:metabolism; D010641:Phenotype
keywords: Bioinformatics; Computer intensive methods; Functional data; Statistical genetics