Ellen M. Schmidt

dblp:166/8724 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2015
0000-0003-1198-7536ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
epigenomics
0.212015
GREGOR: evaluating global enrichment of trait-associated variants in epigenomic features using a systematic, data-driven approach · Bioinform. 2015
Bioinformatics and computational biology › genomics
genome-wide association study
0.212015
GREGOR: evaluating global enrichment of trait-associated variants in epigenomic features using a systematic, data-driven approach · Bioinform. 2015
Bioinformatics and computational biology › gene regulation › regulatory element
regulatory element annotation
0.212015
GREGOR: evaluating global enrichment of trait-associated variants in epigenomic features using a systematic, data-driven approach · Bioinform. 2015
YearPublicationVenuePosition
2015 GREGOR: evaluating global enrichment of trait-associated variants in epigenomic features using a systematic, data-driven approach
abstract
MOTIVATION: The majority of variation identified by genome wide association studies falls in non-coding genomic regions and is hypothesized to impact regulatory elements that modulate gene expression. Here we present a statistically rigorous software tool GREGOR (Genomic Regulatory Elements and Gwas Overlap algoRithm) for evaluating enrichment of any set of genetic variants with any set of regulatory features. Using variants from five phenotypes, we describe a data-driven approach to determine the tissue and cell types most relevant to a trait of interest and to identify the subset of regulatory features likely impacted by these variants. Last, we experimentally evaluate six predicted functional variants at six lipid-associated loci and demonstrate significant evidence for allele-specific impact on expression levels. GREGOR systematically evaluates enrichment of genetic variation with the vast collection of regulatory data available to explore novel biological mechanisms of disease and guide us toward the functional variant at trait-associated loci. AVAILABILITY AND IMPLEMENTATION: GREGOR, including source code, documentation, examples, and executables, is available at http://genome.sph.umich.edu/wiki/GREGOR. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Ellen M. Schmidt, Karen L. Mohlke, Y. Eugene Chen, Cristen J. Willer
Bioinform.1