Author

Seokho Jeong

Seoul National UniversityORCID 0000-0002-1864-9962Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:human· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Research Focus:fine-mapping· 1 Research Focus:GWAS / association mapping· 1 Assay:proteomics· 1 Assay:whole-exome sequencing· 1

Frequent Co-Authors

Seunggeun LeeiD· 2 shared Allison R. GreenplateiD· 1 shared Anurag VermaiD· 1 shared Bingxin ZhaoiD· 1 shared Bogdan PaşaniuciD· 1 shared Damián MasedaiD· 1 shared Daniel James RaderiD· 1 shared Dokyoon KimiD· 1 shared

Activity by Year

2024-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 37 mutual co-author links

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2 shared papersGenetic & Genomic Medicine

Seunggeun LeeORCID

Mutual co-authors in cluster

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2 papers

Recent·Most cited

Large-scale evaluation of proteomic and polygenic risk scores reveals complementary contributions to incident disease prediction

Jakob Woerner, Thomas M. Westbrook, Jaehyun Joo, Manu Shivakumar, Rasika Venkatesh, Tess Cherlin, Sang‐Hyuk Jung, Seokho Jeong, Damián Maseda, Michelle L. McKeague, Shwetank, Matei Ionita, Joost Wagenaar, Sarah A. Abramowitz, Anurag Verma, , Dokyoon Kim

The paper evaluates proteomic and polygenic risk scores for disease prediction, finding proteomic scores outperform polygenic scores in most traits and show temporal responsiveness.

medRxiv · Jul 10, 2025 · PreprintGenetic & Genomic Medicine

Meta-SAIGE: Scalable and Accurate Meta-Analysis for Rare Variants

Eunjae Park, Kisung Nam, Seokho Jeong, Karl Keat, Dokyoon Kim, Vikas Bansal, Wei Zhou, Seunggeun Lee

Meta-SAIGE improves rare variant meta-analysis by controlling type I error and enhancing computational efficiency, identifying 237 associations across 83 disease phenotypes.

medRxiv · Sep 17, 2024 · PreprintGenetic & Genomic Medicine