Author

Sunyoung Shin

The University of Texas at DallasPrimary ORCID profile (0000-0002-9270-0738 · 1 paper)Unclaimed ORCID profile · follow to get notified of new preprints

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Same name, identified by ORCID

Sunyoung ShiniD1 paper · Pohang University of Science and Technology

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Research Focus:GWAS / association mapping· 2 Approach:statistical model· 2 Assay:ATAC-seq· 1 Assay:bulk RNA-seq· 1 Assay:ChIP-seq· 1 Assay:DNA methylation· 1 Research Focus:fine-mapping· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

Alan AttieiD· 1 shared Chenyang DongiD· 1 shared Donnie S. StapletoniD· 1 shared Fulai JiniD· 1 shared Gary A. ChurchilliD· 1 shared Kathryn L. SchueleriD· 1 shared Leina LuiD· 1 shared Mark P. KelleriD· 1 shared

Activity by Year

2016-2021 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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1 shared paperBioinformatics

Alan AttieORCID

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2 papers

Recent·Most cited

INFIMA leverages multi-omics model organism data to identify effector genes of human GWAS variants

Chenyang Dong, Shane P. Simonett, Sunyoung Shin, Donnie S. Stapleton, Kathryn L. Schueler, Gary A. Churchill, Leina Lu, Xiaoxiao Liu, Fulai Jin, Yan Li, Alan Attie, Mark P. Keller, Sündüz Keleş

INFIMA identifies effector genes of GWAS variants by integrating multi-omics data from mice and applying it to diabetes-associated variants.

Genome biology · Aug 23, 2021Peer-reviewedBioinformatics

Annotation Regression for Genome-Wide Association Studies with an Application to Psychiatric Genomic Consortium Data

Sunyoung Shin, Sunduz Keles

This paper introduces ARoG, a regression framework for GWAS that identifies causal variants by integrating functional annotations, applied to psychiatric disorders data.

bioRxiv · Jul 14, 2016Bioinformatics