Junghyun Jung

Pacific Design Center

700 N. San Vicente Blvd.

West Hollywood, CA 90069

My name is Junghyun Jung. I’m currently a Research data scientist in Dr. Kyoung Jae Won’s lab in the Department of Computational Biomedicine at Cedars-Sinai Medical Center.

My research focuses on

developing novel computational tools to analyze spatial omics and single-cell eQTL data.
investigating multi-omics data to gain insights into the underlying etiology of complex traits and disease.

I received my Ph.D. in Life Science from Dongguk University in Korea, where I studied multi-omics integrative analysis and method development for functional genomic data. While earning my Ph.D., I was advised by Dr. Jong Wha (Joanne) Joo and Dr. Wonhee Jang.

I also was a postdoctoral scholar in Dr. Nicholas Mancuso’s lab at Keck School of Medicine USC and Dr. Serghei Mangul’s lab at USC School of Pharmacy. My work aimed to perform integrative analysis using Transcriptome-wide association study (TWAS) and analyze GWAS and WES data related to COVID-19.

Please feel free to contact me through email if you have any questions.

news

Feb 10, 2024	Finally, the Large-scale integrative analysis of juvenile idiopathic arthritis for new insight into its pathogenesis have been published in Arthritis Research & Therapy!
Dec 22, 2023	WOWOW!! The Nature Reviews Neurology features our stroke multiome-wide association study paper! Please check News & Views in Nature Reviews Neurology
Oct 13, 2023	The Novel insight into the etiology of ischemic stroke gained by integrative multiome-wide association study has been accepted for publication in Human Molecular Genetics!
Jul 10, 2023	I have recently become a member of the Department of Computational Biomedicine at Cedars-Sinai Medical Center!!
Apr 30, 2021	Finally, the MARS: leveraging allelic heterogeneity to increase power of association testing has been published!

selected publications

Analysis (Corresponding)

Novel insight into the etiology of ischemic stroke gained by integrative multiome-wide association study

Junghyun Jung*, Zeyun Lu, Adam Smith, and 1 more author

Human Molecular Genetics 2023

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Stroke, characterized by sudden neurological deficits, is the second leading cause of death worldwide. Although genome-wide association studies (GWAS) have successfully identified many genomic regions associated with ischemic stroke (IS), the genes underlying risk and their regulatory mechanisms remain elusive. Here, we integrate a large-scale GWAS (N = 1 296 908) for IS together with molecular QTLs data, including mRNA, splicing, enhancer RNA (eRNA), and protein expression data from up to 50 tissues (total N = 11 588). We identify 136 genes/eRNA/proteins associated with IS risk across 60 independent genomic regions and find IS risk is most enriched for eQTLs in arterial and brain-related tissues. Focusing on IS-relevant tissues, we prioritize 9 genes/proteins using probabilistic fine-mapping TWAS analyses. In addition, we discover that blood cell traits, particularly reticulocyte cells, have shared genetic contributions with IS using TWAS-based pheWAS and genetic correlation analysis. Lastly, we integrate our findings with a large-scale pharmacological database and identify a secondary bile acid, deoxycholic acid, as a potential therapeutic component. Our work highlights IS risk genes/splicing-sites/enhancer activity/proteins with their phenotypic consequences using relevant tissues as well as identify potential therapeutic candidates for IS.
Method (First)

MARS: leveraging allelic heterogeneity to increase power of association testing

Farhad Hormozdiari†, Junghyun Jung†, Eleazar Eskin, and 1 more author

Genome biology Apr 2021

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In standard genome-wide association studies (GWAS), the standard association test is underpowered to detect associations between loci with multiple causal variants with small effect sizes. We propose a statistical method, Model-based Association test Reflecting causal Status (MARS), that finds associations between variants in risk loci and a phenotype, considering the causal status of variants, only requiring the existing summary statistics to detect associated risk loci. Utilizing extensive simulated data and real data, we show that MARS increases the power of detecting true associated risk loci compared to previous approaches that consider multiple variants, while controlling the type I error.