Author

David L. Thomas

Johns Hopkins UniversityORCID 0000-0002-0749-925XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Genetic & Genomic Medicine· 1 Genomics· 1

Signature Assays, Organisms & Methods

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Approach:graph neural network· 1 Research Focus:GWAS / association mapping· 1 Assay:GWAS summary statistics· 1 Organism:human· 1 Approach:statistical model· 1 Research Focus:variant calling· 1 Organism:virus· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

Candelaria VergaraiD· 2 shared Chloe L. ThioiD· 2 shared Dylan DucheniD· 2 shared Genevieve L. WojcikiD· 2 shared Priya DuggaliD· 2 shared Nilanjan ChatterjeeiD· 1 shared Prosenjit KunduiD· 1 shared Steven J. ClipmaniD· 1 shared

Activity by Year

2022-2024 · peak 1/yr

Co-Author Collaboration Network

8 top collaborators · 26 mutual co-author links

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2 shared papersGenomics

Candelaria VergaraORCID

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2 papers

Recent·Most cited

A hepatitis B virus (HBV) sequence variation graph improves sequence alignment and sample-specific consensus sequence construction for genetic analysis of HBV

Dylan Duchen, Steven J. Clipman, Candelaria Vergara, Chloe L. Thio, David L. Thomas, Priya Duggal, Genevieve L. Wojcik

This paper shows that using a genome graph instead of a linear reference improves HBV sequence alignment and sample-specific consensus sequence construction.

PLoS ONE · Apr 26, 2024Peer-reviewedGenomics

Pathogen exposure misclassification can bias association signals in GWAS of infectious diseases when using population-based common controls

Dylan Duchen, Candelaria Vergara, Chloe L. Thio, Prosenjit Kundu, Nilanjan Chatterjee, David L. Thomas, Genevieve L. Wojcik, Priya Duggal

This study shows that using population-based common controls in GWAS of infectious diseases can lead to biased effect estimates and spurious associations due to unaccounted pathogen exposure.

medRxiv · Jul 14, 2022 · PreprintGenetic & Genomic Medicine