Author

David Bushek

Rutgers, The State University of New JerseyORCID 0000-0002-0701-4267Unclaimed 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:GWAS / association mapping· 1 Assay:GWAS summary statistics· 1 Approach:machine learning (classical)· 1 Organism:non-human primate· 1 Approach:statistical model· 1 Research Focus:variant effect prediction· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

Henry Sun· 2 shared Jérôme F. La PeyreiD· 2 shared Mason L. Williams· 2 shared Paul Coÿne· 2 shared Scott Rikard· 2 shared Ximing GuoiD· 2 shared Zhenwei WangiD· 2 shared Juliet M. WongiD· 1 shared

Activity by Year

2026-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 43 mutual co-author links

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2 shared papersGenomics

Henry Sun

Mutual co-authors in cluster

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2 papers

Recent·Most cited

Improved two-stage GWAS identifies rare genetic variants associated with dermo susceptibility in the eastern oyster

Paul Coÿne, Henry Sun, Zhenwei Wang, Sandra Casa, Jérôme F. La Peyre, Mason L. Williams, Scott Rikard, David Bushek, Ximing Guo

This study uses a two-stage GWAS to identify rare genetic variants linked to dermo resistance in Eastern oysters, finding associations with immune response and energy metabolism genes.

bioRxiv · Sep 14, 2026 · PreprintGenomics

Multigenerational machine learning-based genomic prediction for dermo resistance in eastern oyster Crassostrea virginica

Henry Sun, Paul Coÿne, Zhenwei Wang, Sandra M. Casas, Jérôme F. La Peyre, Mason L. Williams, Scott Rikard, David Bushek, Juliet M. Wong, Ximing Guo

The paper improves genomic prediction for dermo resistance in oysters using multigenerational machine learning, achieving higher accuracy with rare variant inclusion.

bioRxiv · Sep 14, 2026 · PreprintGenomics