Author

Jong Wha J. Joo

Dongguk UniversityORCID 0000-0002-1863-4664Unclaimed 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· 3 Research Focus:fine-mapping· 3 Assay:GWAS summary statistics· 3 Organism:human· 3 Approach:statistical model· 3 Research Focus:GWAS / association mapping· 2 Approach:benchmark study· 1 Assay:bulk RNA-seq· 1

Frequent Co-Authors

Eleazar EskiniD· 3 shared Farhad HormozdiariiD· 3 shared Junghyun JungiD· 2 shared Abdullah Al NahidiD· 1 shared Alex ZelikovskyiD· 1 shared Anthony Zhu· 1 shared Austin NguyeniD· 1 shared Ayellet V. SegrèiD· 1 shared

Activity by Year

2017-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

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3 shared papersGenetics

Eleazar EskinORCID

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

A rigorous benchmarking of alignment-based HLA callers for RNA-seq data

Dottie Yu, Ram Ayyala, Sarah Hany Sadek, Likhitha Chittampalli, Hafsa Farooq, Junghyun Jung, Abdullah Al Nahid, Grigore Boldirev, Mina Jung, Sung‐Min Park, Austin Nguyen, Alex Zelikovsky, Nicholas Mancuso, Jong Wha J. Joo, Reid F. Thompson, , Serghei Mangul

This study benchmarks 12 HLA callers on RNA-seq data across 682 samples to evaluate accuracy, computational cost, and performance differences by ancestry.

bioRxiv · May 22, 2023 · PreprintBioinformatics

Leveraging allelic heterogeneity to increase power of association testing

Farhad Hormozdiari, Junghyun Jung, Eleazar Eskin, Jong Wha J. Joo

This paper introduces MARS, a statistical method that improves GWAS power by considering multiple causal variants, and shows it identifies more eGenes and association loci than existing methods.

bioRxiv · Dec 17, 2018 · PreprintGenetics

The Multivariate Normal Distribution Framework for Analyzing Association Studies

Jose Antonio Lozano, Farhad Hormozdiari, Jong Wha J. Joo, Buhm Han, Eleazar Eskin

This paper reviews how the multivariate normal distribution framework can be used to analyze GWAS by accounting for variant correlations, performing association testing, and enabling fine mapping and imputation.

bioRxiv · Oct 28, 2017 · PreprintGenetics

Widespread allelic heterogeneity in complex traits

Farhad Hormozdiari, Anthony Zhu, Gleb Kichaev, Chelsea J.‐T. Ju, Ayellet V. Segrè, Jong Wha J. Joo, Hyejung Won, Sriram Sankararaman, Bogdan Paşaniuc, Sagiv Shifman, Eleazar Eskin

The paper develops a method to infer allelic heterogeneity in GWAS and eQTL datasets, identifying 4152 loci with strong evidence of AH across different traits.

The American Journal of Human Genetics · May 1, 2017Peer-reviewedGenetics