Author

Eric S. Torstenson

Broad InstituteORCID 0000-0002-8434-7647Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Organism:human· 2 Approach:statistical model· 2 Research Focus:database / resource· 1 Research Focus:differential expression· 1 Assay:electronic health records· 1 Approach:experimental assay· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

Ira M. HalliD· 2 shared Alessandro CulottiiD· 1 shared Alexander Baumann· 1 shared Alexander OstrovskyiD· 1 shared Alexandra J. ScottiD· 1 shared Alexandru MahmoudiD· 1 shared Alexis J. BattleiD· 1 shared Allie Hajian· 1 shared

Activity by Year

2018-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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2 shared papersBioinformatics

Ira M. HallORCID

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Author profile →

3 papers

Recent·Most cited

Inverting the model of genomics data sharing with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL)

Michael C. Schatz, Anthony Philippakis, Enis Afgan, Eric Banks, Vincent J. Carey, Robert J. Carroll, Alessandro Culotti, Kyle Ellrott, Jeremy Goecks, Robert L. Grossman, Ira M. Hall, Kasper D. Hansen, Jonathan Lawson, Jeffrey T. Leek, Anne O’Donnell‐Luria, , Jason Walker

The paper describes AnVIL, a cloud-based platform that enables genomic data analysis without data transfer, improving security, scalability, and collaboration.

Cell Genomics · Jan 1, 2022Peer-reviewedBioinformatics

A Clinical Phenotyping Algorithm to Identify Cases of Chronic Obstructive Pulmonary Disease in Electronic Health Records

Victoria L. Martucci, Nancy Liu, Vern Eric Kerchberger, Travis John Osterman, Eric S. Torstenson, Bradley W. Richmond, Melinda C. Aldrich

This paper develops a clinical phenotyping algorithm to identify COPD cases in EHRs using ICD codes, achieving high sensitivity and negative predictive value.

bioRxiv · Jul 28, 2019 · PreprintBioinformatics

Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics

Alvaro Barbeira, Scott P. Dickinson, Rodrigo Bonazzola, Jiamao Zheng, Heather E. Wheeler, Jason M. Torres, Eric S. Torstenson, Kaanan P. Shah, Tzintzuni Garcia, Todd L. Edwards, Eli Stahl, Laura M. Huckins, François Aguet, Kristin Ardlie, Beryl B. Cummings, , Benjamin J. Strober

The paper develops S-PrediXcan to compute gene expression associations from GWAS summary data and applies it to 44 GTEx tissues and 100+ phenotypes, finding tissue-specific effects and colocalized associations.

Nature Communications · May 2, 2018Peer-reviewedGenomics