arXiv subjects

Barbara E Engelhardt

Publications and source records attributed to Barbara E Engelhardt.

20 records · Page 2Linked to original sources

arXiv2013

Predicting genome-wide DNA methylation using methylation marks, genomic position, and DNA regulatory elements

Background: Recent assays for individual-specific genome-wide DNA methylation profiles have enabled epigenome-wide association studies to identify specific CpG sites associated with a phenotype. Computational prediction of CpG site-specific methylation levels is important, but current approaches tackle average methylation within a genomic locus and are often limited to specific genomic regions. Results: We characterize genome-wide DNA methylation patterns, and show that correlation among CpG sites decays rapidly, making predictions solely based on neighboring sites challenging. We built a random forest classifier to predict CpG site methylation levels using as features neighboring CpG site methylation levels and genomic distance, and co-localization with coding regions, CGIs, and regulatory elements from the ENCODE project, among others. Our approach achieves 91% -- 94% prediction accuracy of genome-wide methylation levels at single CpG site precision. The accuracy increases to 98% when restricted to CpG sites within CGIs. Our classifier outperforms state-of-the-art methylation classifiers and identifies features that contribute to prediction accuracy: neighboring CpG site methylation status, CpG island status, co-localized DNase I hypersensitive sites, and specific transcription factor binding sites were found to be most predictive of methylation levels. Conclusions: Our observations of DNA methylation patterns led us to develop a classifier to predict site-specific methylation levels that achieves the best DNA methylation predictive accuracy to date. Furthermore, our method identified genomic features that interact with DNA methylation, elucidating mechanisms involved in DNA methylation modification and regulation, and linking different epigenetic processes.

q-bio.GN↗

arXiv2012

Integrative modeling of eQTLs and cis-regulatory elements suggest mechanisms underlying cell type specificity of eQTLs

Genetic variants in cis-regulatory elements or trans-acting regulators commonly influence the quantity and spatiotemporal distribution of gene transcription. Recent interest in expression quantitative trait locus (eQTL) mapping has paralleled the adoption of genome-wide association studies (GWAS) for the analysis of complex traits and disease in humans. Under the hypothesis that many GWAS associations tag non-coding SNPs with small effects, and that these SNPs exert phenotypic control by modifying gene expression, it has become common to interpret GWAS associations using eQTL data. To exploit the mechanistic interpretability of eQTL-GWAS comparisons, an improved understanding of the genetic architecture and cell type specificity of eQTLs is required. We address this need by performing an eQTL analysis in four parts: first we identified eQTLs from eleven studies on seven cell types; next we quantified cell type specific eQTLs across the studies; then we integrated eQTL data with cis-regulatory element (CRE) data sets from the ENCODE project; finally we built a classifier to predict cell type specific eQTLs. Consistent with prior studies, we demonstrate that allelic heterogeneity is pervasive at cis-eQTLs and that cis-eQTLs are often cell type specific. Within and between cell type eQTL replication is associated with eQTL SNP overlap with hundreds of cell type specific CRE element classes, including enhancer, promoter, and repressive chromatin marks, regions of open chromatin, and many classes of DNA binding proteins. Using a random forest classifier including 526 CRE data sets as features, we successfully predict the cell type specificity of eQTL SNPs in the absence of gene expression data from the cell type of interest. We anticipate that such integrative, predictive modeling will improve our ability to understand the mechanistic basis of human complex phenotypic variation.

q-bio.GN↗