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1. MR-GGI: accurate inference of gene–gene interactions using Mendelian randomization

Author

Wonseok Oh, Junghyun Jung, and Jong Wha J. Joo

Subjects
Gene–gene interactions, Mendelian randomization, Gene regulatory network, Yeast GRN, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Background Researchers have long studied the regulatory processes of genes to uncover their functions. Gene regulatory network analysis is one of the popular approaches for understanding these processes, requiring accurate identification of interactions among the genes to establish the gene regulatory network. Advances in genome-wide association studies and expression quantitative trait loci studies have led to a wealth of genomic data, facilitating more accurate inference of gene–gene interactions. However, unknown confounding factors may influence these interactions, making their interpretation complicated. Mendelian randomization (MR) has emerged as a valuable tool for causal inference in genetics, addressing confounding effects by estimating causal relationships using instrumental variables. In this paper, we propose a new statistical method, MR-GGI, for accurately inferring gene–gene interactions using Mendelian randomization. Results MR-GGI applies one gene as the exposure and another as the outcome, using causal cis-single-nucleotide polymorphisms as instrumental variables in the inverse-variance weighted MR model. Through simulations, we have demonstrated MR-GGI's ability to control type 1 error and maintain statistical power despite confounding effects. MR-GGI performed the best when compared to other methods using the F1 score on the DREAM5 dataset. Additionally, when applied to yeast genomic data, MR-GGI successfully identified six clusters. Through gene ontology analysis, we have confirmed that each cluster in our study performs distinct functional roles by gathering genes with specific functions. Conclusion These findings demonstrate that MR-GGI accurately inferences gene–gene interactions despite the confounding effects in real biological environments.

Published
2024
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2. Elucidating immunological characteristics of the adenoma-carcinoma sequence in colorectal cancer patients in South Korea using a bioinformatics approach

Author

Jaeseung Song, Daeun Kim, Junghyun Jung, Eunyoung Choi, Yubin Lee, Yeonbin Jeong, Byungjo Lee, Sora Lee, Yujeong Shim, Youngtae Won, Hyeki Cho, Dong Kee Jang, Hyoun Woo Kang, Jong Wha J. Joo, and Wonhee Jang

Subjects
Colorectal cancer, Advanced colorectal adenoma, Adenoma-carcinoma sequence, Immune repertoire, Medicine, Science
Abstract

Abstract Colorectal cancer (CRC) is one of the top five most common and life-threatening malignancies worldwide. Most CRC develops from advanced colorectal adenoma (ACA), a precancerous stage, through the adenoma-carcinoma sequence. However, its underlying mechanisms, including how the tumor microenvironment changes, remain elusive. Therefore, we conducted an integrative analysis comparing RNA-seq data collected from 40 ACA patients who visited Dongguk University Ilsan Hospital with normal adjacent colons and tumor samples from 18 CRC patients collected from a public database. Differential expression analysis identified 21 and 79 sequentially up- or down-regulated genes across the continuum, respectively. The functional centrality of the continuum genes was assessed through network analysis, identifying 11 up- and 13 down-regulated hub-genes. Subsequently, we validated the prognostic effects of hub-genes using the Kaplan–Meier survival analysis. To estimate the immunological transition of the adenoma-carcinoma sequence, single-cell deconvolution and immune repertoire analyses were conducted. Significant composition changes for innate immunity cells and decreased plasma B-cells with immunoglobulin diversity were observed, along with distinctive immunoglobulin recombination patterns. Taken together, we believe our findings suggest underlying transcriptional and immunological changes during the adenoma-carcinoma sequence, contributing to the further development of pre-diagnostic markers for CRC.

Published
2024
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3. Large-scale integrative analysis of juvenile idiopathic arthritis for new insight into its pathogenesis

Author

Daeun Kim, Jaeseung Song, Nicholas Mancuso, Serghei Mangul, Junghyun Jung, and Wonhee Jang

Subjects
Juvenile idiopathic arthritis, Transcriptome-wide and proteome-wide association studies, T cell receptor (TCR) repertoire, Multi-ethnicity RNA typing, Diseases of the musculoskeletal system, RC925-935
Abstract

Abstract Background Juvenile idiopathic arthritis (JIA) is one of the most prevalent rheumatic disorders in children and is classified as an autoimmune disease (AID). While a robust genetic contribution to JIA etiology has been established, the exact pathogenesis remains unclear. Methods To prioritize biologically interpretable susceptibility genes and proteins for JIA, we conducted transcriptome-wide and proteome-wide association studies (TWAS/PWAS). Then, to understand the genetic architecture of JIA, we systematically analyzed single-nucleotide polymorphism (SNP)-based heritability, a signature of natural selection, and polygenicity. Next, we conducted HLA typing using multi-ethnicity RNA sequencing data. Additionally, we examined the T cell receptor (TCR) repertoire at a single-cell level to explore the potential links between immunity and JIA risk. Results We have identified 19 TWAS genes and two PWAS proteins associated with JIA risks. Furthermore, we observe that the heritability and cell type enrichment analysis of JIA are enriched in T lymphocytes and HLA regions and that JIA shows higher polygenicity compared to other AIDs. In multi-ancestry HLA typing, B*45:01 is more prevalent in African JIA patients than in European JIA patients, whereas DQA1*01:01, DQA1*03:01, and DRB1*04:01 exhibit a higher frequency in European JIA patients. Using single-cell immune repertoire analysis, we identify clonally expanded T cell subpopulations in JIA patients, including CXCL13 + BHLHE40 + TH cells which are significantly associated with JIA risks. Conclusion Our findings shed new light on the pathogenesis of JIA and provide a strong foundation for future mechanistic studies aimed at uncovering the molecular drivers of JIA.

Published
2024
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5. Exome-wide association study to identify rare variants influencing COVID-19 outcomes: Results from the Host Genetics Initiative.

Author

Guillaume Butler-Laporte, Gundula Povysil, Jack A Kosmicki, Elizabeth T Cirulli, Theodore Drivas, Simone Furini, Chadi Saad, Axel Schmidt, Pawel Olszewski, Urszula Korotko, Mathieu Quinodoz, Elifnaz Çelik, Kousik Kundu, Klaudia Walter, Junghyun Jung, Amy D Stockwell, Laura G Sloofman, Daniel M Jordan, Ryan C Thompson, Diane Del Valle, Nicole Simons, Esther Cheng, Robert Sebra, Eric E Schadt, Seunghee Kim-Schulze, Sacha Gnjatic, Miriam Merad, Joseph D Buxbaum, Noam D Beckmann, Alexander W Charney, Bartlomiej Przychodzen, Timothy Chang, Tess D Pottinger, Ning Shang, Fabian Brand, Francesca Fava, Francesca Mari, Karolina Chwialkowska, Magdalena Niemira, Szymon Pula, J Kenneth Baillie, Alex Stuckey, Antonio Salas, Xabier Bello, Jacobo Pardo-Seco, Alberto Gómez-Carballa, Irene Rivero-Calle, Federico Martinón-Torres, Andrea Ganna, Konrad J Karczewski, Kumar Veerapen, Mathieu Bourgey, Guillaume Bourque, Robert Jm Eveleigh, Vincenzo Forgetta, David Morrison, David Langlais, Mark Lathrop, Vincent Mooser, Tomoko Nakanishi, Robert Frithiof, Michael Hultström, Miklos Lipcsey, Yanara Marincevic-Zuniga, Jessica Nordlund, Kelly M Schiabor Barrett, William Lee, Alexandre Bolze, Simon White, Stephen Riffle, Francisco Tanudjaja, Efren Sandoval, Iva Neveux, Shaun Dabe, Nicolas Casadei, Susanne Motameny, Manal Alaamery, Salam Massadeh, Nora Aljawini, Mansour S Almutairi, Yaseen M Arabi, Saleh A Alqahtani, Fawz S Al Harthi, Amal Almutairi, Fatima Alqubaishi, Sarah Alotaibi, Albandari Binowayn, Ebtehal A Alsolm, Hadeel El Bardisy, Mohammad Fawzy, Fang Cai, Nicole Soranzo, Adam Butterworth, COVID-19 Host Genetics Initiative, DeCOI Host Genetics Group, GEN-COVID Multicenter Study (Italy), Mount Sinai Clinical Intelligence Center, GEN-COVID consortium (Spain), GenOMICC Consortium, Japan COVID-19 Task Force, Regeneron Genetics Center, Daniel H Geschwind, Stephanie Arteaga, Alexis Stephens, Manish J Butte, Paul C Boutros, Takafumi N Yamaguchi, Shu Tao, Stefan Eng, Timothy Sanders, Paul J Tung, Michael E Broudy, Yu Pan, Alfredo Gonzalez, Nikhil Chavan, Ruth Johnson, Bogdan Pasaniuc, Brian Yaspan, Sandra Smieszek, Carlo Rivolta, Stephanie Bibert, Pierre-Yves Bochud, Maciej Dabrowski, Pawel Zawadzki, Mateusz Sypniewski, Elżbieta Kaja, Pajaree Chariyavilaskul, Voraphoj Nilaratanakul, Nattiya Hirankarn, Vorasuk Shotelersuk, Monnat Pongpanich, Chureerat Phokaew, Wanna Chetruengchai, Katsushi Tokunaga, Masaya Sugiyama, Yosuke Kawai, Takanori Hasegawa, Tatsuhiko Naito, Ho Namkoong, Ryuya Edahiro, Akinori Kimura, Seishi Ogawa, Takanori Kanai, Koichi Fukunaga, Yukinori Okada, Seiya Imoto, Satoru Miyano, Serghei Mangul, Malak S Abedalthagafi, Hugo Zeberg, Joseph J Grzymski, Nicole L Washington, Stephan Ossowski, Kerstin U Ludwig, Eva C Schulte, Olaf Riess, Marcin Moniuszko, Miroslaw Kwasniewski, Hamdi Mbarek, Said I Ismail, Anurag Verma, David B Goldstein, Krzysztof Kiryluk, Alessandra Renieri, Manuel A R Ferreira, and J Brent Richards

Subjects
Genetics, QH426-470
Abstract

Host genetics is a key determinant of COVID-19 outcomes. Previously, the COVID-19 Host Genetics Initiative genome-wide association study used common variants to identify multiple loci associated with COVID-19 outcomes. However, variants with the largest impact on COVID-19 outcomes are expected to be rare in the population. Hence, studying rare variants may provide additional insights into disease susceptibility and pathogenesis, thereby informing therapeutics development. Here, we combined whole-exome and whole-genome sequencing from 21 cohorts across 12 countries and performed rare variant exome-wide burden analyses for COVID-19 outcomes. In an analysis of 5,085 severe disease cases and 571,737 controls, we observed that carrying a rare deleterious variant in the SARS-CoV-2 sensor toll-like receptor TLR7 (on chromosome X) was associated with a 5.3-fold increase in severe disease (95% CI: 2.75-10.05, p = 5.41x10-7). This association was consistent across sexes. These results further support TLR7 as a genetic determinant of severe disease and suggest that larger studies on rare variants influencing COVID-19 outcomes could provide additional insights.

Published
2022
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6. An in-silico approach to studying a very rare neurodegenerative disease using a disease with higher prevalence with shared pathways and genes: Cerebral adrenoleukodystrophy and Alzheimer’s disease

Author

Yu Jeong Shim, Min Kyoung Shin, Junghyun Jung, Bongseong Koo, and Wonhee Jang

Subjects
adrenoleukodystrophy, Alzheimer’s disease, shared pathway, meta-analysis, neurodegenerative disease, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Cerebral adrenoleukodystrophy (cALD) is a rare neurodegenerative disease characterized by inflammatory demyelination in the central nervous system. Another neurodegenerative disease with a high prevalence, Alzheimer’s disease (AD), shares many common features with cALD such as cognitive impairment and the alleviation of symptoms by erucic acid. We investigated cALD and AD in parallel to study the shared pathological pathways between a rare disease and a more common disease. The approach may expand the biological understandings and reveal novel therapeutic targets. Gene set enrichment analysis (GSEA) and weighted gene correlation network analysis (WGCNA) were conducted to identify both the resemblance in gene expression patterns and genes that are pathologically relevant in the two diseases. Within differentially expressed genes (DEGs), GSEA identified 266 common genes with similar up- or down-regulation patterns in cALD and AD. Among the interconnected genes in AD data, two gene sets containing 1,486 genes preserved in cALD data were selected by WGCNA that may significantly affect the development and progression of cALD. WGCNA results filtered by functional correlation via protein–protein interaction analysis overlapping with GSEA revealed four genes (annexin A5, beta-2-microglobulin, CD44 molecule, and fibroblast growth factor 2) that showed robust associations with the pathogeneses of cALD and AD, where they were highly involved in inflammation, apoptosis, and the mitogen-activated protein kinase pathway. This study provided an integrated strategy to provide new insights into a rare disease with scant publicly available data (cALD) using a more prevalent disorder with some pathological association (AD), which suggests novel druggable targets and drug candidates.

Published
2022
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7. MARS: leveraging allelic heterogeneity to increase power of association testing

Author

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

Subjects
Association studies, Causal variants, Set-based association analysis, Biology (General), QH301-705.5, Genetics, QH426-470
Abstract

Abstract 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.

Published
2021
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9. Fully automated web-based tool for identifying regulatory hotspots

Author

Ju Hun Choi, Taegun Kim, Junghyun Jung, and Jong Wha J. Joo

Subjects
Regulatory hotspot, Parallel processing, Web-based tool, Google cloud compute engine (GCE), PLINK, VCF (variant call format), Biotechnology, TP248.13-248.65, Genetics, QH426-470
Abstract

Abstract Background Regulatory hotspots are genetic variations that may regulate the expression levels of many genes. It has been of great interest to find those hotspots utilizing expression quantitative trait locus (eQTL) analysis. However, it has been reported that many of the findings are spurious hotspots induced by various unknown confounding factors. Recently, methods utilizing complicated statistical models have been developed that successfully identify genuine hotspots. Next-generation Intersample Correlation Emended (NICE) is one of the methods that show high sensitivity and low false-discovery rate in finding regulatory hotspots. Even though the methods successfully find genuine hotspots, they have not been widely used due to their non-user-friendly interfaces and complex running processes. Furthermore, most of the methods are impractical due to their prohibitively high computational complexity. Results To overcome the limitations of existing methods, we developed a fully automated web-based tool, referred to as NICER (NICE Renew), which is based on NICE program. First, we dramatically reduced running and installing burden of NICE. Second, we significantly reduced running time by incorporating multi-processing. Third, besides our web-based NICER, users can use NICER on Google Compute Engine and can readily install and run the NICER web service on their local computers. Finally, we provide different input formats and visualizations tools to show results. Utilizing a yeast dataset, we show that NICER can be successfully used in an eQTL analysis to identify many genuine regulatory hotspots, for which more than half of the hotspots were previously reported elsewhere. Conclusions Even though many hotspot analysis tools have been proposed, they have not been widely used for many practical reasons. NICER is a fully-automated web-based solution for eQTL mapping and regulatory hotspots analysis. NICER provides a user-friendly interface and has made hotspot analysis more viable by reducing the running time significantly. We believe that NICER will become the method of choice for increasing power of eQTL hotspot analysis.

Published
2020
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10. Integrative genomic and transcriptomic analysis of genetic markers in Dupuytren’s disease

Author

Junghyun Jung, Go Woon Kim, Byungjo Lee, Jong Wha J. Joo, and Wonhee Jang

Subjects
Dupuytren’s disease, Unfolded protein response (UPR), Endoplasmic reticulum (ER) stress, Multiple-phenotype analysis, trans-regulatory hotspots, ZFP57 zinc finger protein, Internal medicine, RC31-1245, Genetics, QH426-470
Abstract

Abstract Background Dupuytren’s disease (DD) is a fibroproliferative disorder characterized by thickening and contracting palmar fascia. The exact pathogenesis of DD remains unknown. Results In this study, we identified co-expressed gene set (DD signature) consisting of 753 genes via weighted gene co-expression network analysis. To confirm the robustness of DD signature, module enrichment analysis and meta-analysis were performed. Moreover, this signature effectively classified DD disease samples. The DD signature were significantly enriched in unfolded protein response (UPR) related to endoplasmic reticulum (ER) stress. Next, we conducted multiple-phenotype regression analysis to identify trans-regulatory hotspots regulating expression levels of DD signature using Genotype-Tissue Expression data. Finally, 10 trans-regulatory hotspots and 16 eGenes genes that are significantly associated with at least one cis-eQTL were identified. Conclusions Among these eGenes, major histocompatibility complex class II genes and ZFP57 zinc finger protein were closely related to ER stress and UPR, suggesting that these genetic markers might be potential therapeutic targets for DD.

Published
2019
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11. Molecular origin of AuNPs-induced cytotoxicity and mechanistic study

Author

Euiyeon Lee, Hyunjin Jeon, Minhyeong Lee, Jeahee Ryu, Chungwon Kang, Soyoun Kim, Junghyun Jung, and Youngeun Kwon

Subjects
Medicine, Science
Abstract

Abstract Gold nanoparticles (AuNPs) with diverse physicochemical properties are reported to affect biological systems differently, but the relationship between the physicochemical properties of AuNPs and their biological effects is not clearly understood. Here, we aimed to elucidate the molecular origins of AuNP-induced cytotoxicity and their mechanisms, focusing on the surface charge and structural properties of modified AuNPs. We prepared a library of well-tailored AuNPs exhibiting various functional groups and surface charges. Through this work, we revealed that the direction or the magnitude of surface charge is not an exclusive factor that determines the cytotoxicity of AuNPs. We, instead, suggested that toxic AuNPs share a common structural characteristics of a hydrophobic moiety neighbouring the positive charge, which can induce lytic interaction with plasma membrane. Mechanistic study showed that the toxic AuNPs interfered with the formation of cytoskeletal structure to slow cell migration, inhibited DNA replication and caused DNA damage via oxidative stress to hinder cell proliferation. Gene expression analysis showed that the toxic AuNPs down-regulated genes associated with cell cycle processes. We discovered structural characteristics that define the cytotoxic AuNPs and suggested the mechanisms of their cytotoxicity. These findings will help us to understand and to predict the biological effects of modified AuNPs based on their physicochemical properties.

Published
2019
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12. A Deep Learning Approach with Data Augmentation to Predict Novel Spider Neurotoxic Peptides

Author

Byungjo Lee, Min Kyoung Shin, In-Wook Hwang, Junghyun Jung, Yu Jeong Shim, Go Woon Kim, Seung Tae Kim, Wonhee Jang, and Jung-Suk Sung

Subjects
deep learning, data augmentation, convolutional neural network, neurotoxic peptide prediction, spider transcriptome, Biology (General), QH301-705.5, Chemistry, QD1-999
Abstract

As major components of spider venoms, neurotoxic peptides exhibit structural diversity, target specificity, and have great pharmaceutical potential. Deep learning may be an alternative to the laborious and time-consuming methods for identifying these peptides. However, the major hurdle in developing a deep learning model is the limited data on neurotoxic peptides. Here, we present a peptide data augmentation method that improves the recognition of neurotoxic peptides via a convolutional neural network model. The neurotoxic peptides were augmented with the known neurotoxic peptides from UniProt database, and the models were trained using a training set with or without the generated sequences to verify the augmented data. The model trained with the augmented dataset outperformed the one with the unaugmented dataset, achieving accuracy of 0.9953, precision of 0.9922, recall of 0.9984, and F1 score of 0.9953 in simulation dataset. From the set of all RNA transcripts of Callobius koreanus spider, we discovered neurotoxic peptides via the model, resulting in 275 putative peptides of which 252 novel sequences and only 23 sequences showing homology with the known peptides by Basic Local Alignment Search Tool. Among these 275 peptides, four were selected and shown to have neuromodulatory effects on the human neuroblastoma cell line SH-SY5Y. The augmentation method presented here may be applied to the identification of other functional peptides from biological resources with insufficient data.

Published
2021
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13. An Integrative Transcriptomic Analysis of Systemic Juvenile Idiopathic Arthritis for Identifying Potential Genetic Markers and Drug Candidates

Author

Daeun Kim, Jaeseung Song, Sora Lee, Junghyun Jung, and Wonhee Jang

Subjects
systemic juvenile idiopathic arthritis, meta-analysis, weighted gene co-expression network analysis (WGCNA), drug repositioning, Biology (General), QH301-705.5, Chemistry, QD1-999
Abstract

Systemic juvenile idiopathic arthritis (sJIA) is a rare subtype of juvenile idiopathic arthritis, whose clinical features are systemic fever and rash accompanied by painful joints and inflammation. Even though sJIA has been reported to be an autoinflammatory disorder, its exact pathogenesis remains unclear. In this study, we integrated a meta-analysis with a weighted gene co-expression network analysis (WGCNA) using 5 microarray datasets and an RNA sequencing dataset to understand the interconnection of susceptibility genes for sJIA. Using the integrative analysis, we identified a robust sJIA signature that consisted of 2 co-expressed gene sets comprising 103 up-regulated genes and 25 down-regulated genes in sJIA patients compared with healthy controls. Among the 128 sJIA signature genes, we identified an up-regulated cluster of 11 genes and a down-regulated cluster of 4 genes, which may play key roles in the pathogenesis of sJIA. We then detected 10 bioactive molecules targeting the significant gene clusters as potential novel drug candidates for sJIA using an in silico drug repositioning analysis. These findings suggest that the gene clusters may be potential genetic markers of sJIA and 10 drug candidates can contribute to the development of new therapeutic options for sJIA.

Published
2021
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15. Large-scale Integrative Analysis of Juvenile Idiopathic Arthritis for New Insight into Its Pathogenesis

Author

Daeun Kim, Jaeseung Song, Nicholas Mancuso, Serghei Mangul, Chul Woo Ahn, Junghyun Jung, and Wonhee Jang

Abstract

ObjectivesJuvenile idiopathic arthritis (JIA) is one of the most prevalent rheumatic disorders in children and is classified as an autoimmune disease (AID). While a robust genetic contribution to JIA etiology has been established, the exact pathogenesis remains unclear. We conducted a comprehensive integrative analysis to gain new insights into the etiology of JIA.MethodsTo prioritize biologically interpretable susceptibility genes and proteins for JIA, we conducted transcriptome-wide and proteome-wide association studies (TWAS/PWAS). Then, to understand genetic architecture JIA, we systematically analyzed single nucleotide polymorphism (SNP)-based heritability, a signature of natural selection, and polygenicity. Finally, we performed HLA typing using multi-ancestry RNA sequencing data and analyzed the T cell receptor (TCR) repertoire at a single-cell level to investigate the associations between immunity and JIA risk.ResultsWe have identified 19 TWAS genes and two PWAS proteins that are associated with JIA risks. Furthermore, we observe that the heritability and cell type enrichment analysis of JIA are enriched in T lymphocytes and HLA regions, and that JIA shows higher polygenicity compared to other AIDs. In multi-ancestry HLA typing, B*45:01 is more prevalent in African JIA patients than in European JIA patients, whereas DQA1*01:01, DQA1*03:01, and DRB1*04:01 exhibit a higher frequency in European JIA patients. Using single-cell immune repertoire analysis, we identify clonally expanded T cell subpopulations in JIA patients, includingCXCL13+BHLHE40+THcells which are significantly associated with JIA risks.ConclusionsOur findings shed new light on the pathogenesis of JIA and provide a strong foundation for future mechanistic studies aimed at uncovering the molecular drivers of JIA

Published
2023

16. Novel insight into the etiology of ischemic stroke gained by integrative transcriptome-wide association study

Author

Junghyun Jung, Zeyun Lu, Adam de Smith, and Nicholas Mancuso

Subjects
Article
Abstract

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 mRNA, splicing, enhancer RNA (eRNA) and protein expression data (N=11,588) from 50 tissues. We identify 136 genes/eRNA/proteins associated with IS risk across 54 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.

Published
2023

17. A scalable variational approach to characterize pleiotropic components across thousands of human diseases and complex traits using GWAS summary statistics

Author

Zixuan Zhang, Junghyun Jung, Artem Kim, Noah Suboc, Steven Gazal, and Nicholas Mancuso

Subjects
Article
Abstract

Genome-wide association studies (GWAS) across thousands of traits have revealed the pervasive pleiotropy of trait-associated genetic variants. While methods have been proposed to characterize pleiotropic components across groups of phenotypes, scaling these approaches to ultra large-scale biobanks has been challenging. Here, we propose FactorGo, a scalable variational factor analysis model to identify and characterize pleiotropic components using biobank GWAS summary data. In extensive simulations, we observe that FactorGo outperforms the state-of-the-art (model-free) approach tSVD in capturing latent pleiotropic factors across phenotypes, while maintaining a similar computational cost. We apply FactorGo to estimate 100 latent pleiotropic factors from GWAS summary data of 2,483 phenotypes measured in European-ancestry Pan-UK BioBank individuals (N=420,531). Next, we find that factors from FactorGo are more enriched with relevant tissue-specific annotations than those identified by tSVD (P=2.58E-10), and validate our approach by recapitulating brain-specific enrichment for BMI and the height-related connection between reproductive system and muscular-skeletal growth. Finally, our analyses suggest novel shared etiologies between rheumatoid arthritis and periodontal condition, in addition to alkaline phosphatase as a candidate prognostic biomarker for prostate cancer. Overall, FactorGo improves our biological understanding of shared etiologies across thousands of GWAS.

Published
2023

18. A Study on the Reusing Korean Old Characters

Author

Wonsic Mun and Junghyun Jung

Subjects
Computer science, business.industry, Completeness (order theory), General Medicine, Artificial intelligence, Foreign word, Reuse, computer.software_genre, business, computer, Natural language processing
Published
2020

19. A Fully Automated Parallel-Processing R Package for High-Dimensional Multiple-Phenotype Analysis Considering Population Structure

Author

Sungmin Park, Gi Ju Lee, Junghyun Jung, and Jong Wha J. Joo

Subjects
R package, Computational Theory and Mathematics, Fully automated, Parallel processing (DSP implementation), Artificial Intelligence, Logic, Computer science, Signal Processing, Population structure, High dimensional, Phenotype, Computer Science Applications, Computational science
Published
2020

20. Rigorous Benchmarking of HLA Callers for RNA Sequencing Data

Author

Ram Ayyala, Dottie Yu, Junghyun Jung, Sergey Knyazev, and SERGHEI MANGUL

Abstract

Although precise identification of the human leukocyte antigen (HLA) allele is crucial for various clinical and research applications, HLA typing remains challenging due to high polymorphism of the HLA loci. However, with Next-Generation Sequencing (NGS) data becoming widely accessible, many computational tools have been developed to predict HLA types from RNA sequencing (RNA-seq) data. However, there is a lack of comprehensive and systematic benchmarking of RNA-seq HLA callers using large-scale and realist gold standards. In order to address this limitation, we rigorously compared the performance of 12 HLA callers over 50,000 HLA tasks including searching 30 pairwise combinations of HLA callers and reference in over 1,500 samples. In each case, we produced evaluation metrics of accuracy that is the percentage of correctly predicted alleles (two and four-digit resolution) based on six gold standard datasets spanning 650 RNA-seq samples. To determine the influence of the relationship of the read length over the HLA region on prediction quality using each tool, we explored the read length effect by considering read length in the range 37-126 bp, which was available in our gold standard datasets. Moreover, using the Genotype-Tissue Expression (GTEx) v8 data, we carried out evaluation metrics by calculating the concordance of the same HLA type across different tissues from the same individual to evaluate how well the HLA callers can maintain consistent results across various tissues of the same individual. This study offers crucial information for researchers regarding appropriate choices of methods for an HLA analysis.

Published
2021

21. A Deep Learning Approach with Data Augmentation to Predict Novel Spider Neurotoxic Peptides

Author

Go Woon Kim, Jung-Suk Sung, In-Wook Hwang, Yu Jeong Shim, Byungjo Lee, Seung Tae Kim, Junghyun Jung, Min Kyoung Shin, and Wonhee Jang

Subjects
spider transcriptome, Spider Venoms, Computer science, QH301-705.5, Neurotoxins, convolutional neural network, Peptide, Computational biology, Convolutional neural network, Catalysis, Article, Inorganic Chemistry, Animals, Physical and Theoretical Chemistry, Biology (General), Databases, Protein, Molecular Biology, QD1-999, Spectroscopy, chemistry.chemical_classification, Spider, neurotoxic peptide prediction, Training set, business.industry, Deep learning, Organic Chemistry, deep learning, Spiders, General Medicine, Computer Science Applications, Chemistry, chemistry, Artificial intelligence, UniProt, business, F1 score, Peptides, data augmentation
Abstract

As major components of spider venoms, neurotoxic peptides exhibit structural diversity, target specificity, and have great pharmaceutical potential. Deep learning may be an alternative to the laborious and time-consuming methods for identifying these peptides. However, the major hurdle in developing a deep learning model is the limited data on neurotoxic peptides. Here, we present a peptide data augmentation method that improves the recognition of neurotoxic peptides via a convolutional neural network model. The neurotoxic peptides were augmented with the known neurotoxic peptides from UniProt database, and the models were trained using a training set with or without the generated sequences to verify the augmented data. The model trained with the augmented dataset outperformed the one with the unaugmented dataset, achieving accuracy of 0.9953, precision of 0.9922, recall of 0.9984, and F1 score of 0.9953 in simulation dataset. From the set of all RNA transcripts of Callobius koreanus spider, we discovered neurotoxic peptides via the model, resulting in 275 putative peptides of which 252 novel sequences and only 23 sequences showing homology with the known peptides by Basic Local Alignment Search Tool. Among these 275 peptides, four were selected and shown to have neuromodulatory effects on the human neuroblastoma cell line SH-SY5Y. The augmentation method presented here may be applied to the identification of other functional peptides from biological resources with insufficient data.

Published
2021
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22. Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

Author

Fallerini, Chiara, Picchiotti, Nicola, Baldassarri, Margherita, Zguro, Kristina, Daga, Sergio, Fava, Francesca, Benetti, Elisa, Amitrano, Sara, Bruttini, Mirella, Palmieri, Maria, Croci, Susanna, Lista, Mirjam, Beligni, Giada, Valentino, Floriana, Meloni, Ilaria, Tanfoni, Marco, Minnai, Francesca, Colombo, Francesca, Cabri, Enrico, Fratelli, Maddalena, Gabbi, Chiara, Mantovani, Stefania, Frullanti, Elisa, Gori, Marco, Crawley, Francis P, Butler-Laporte, Guillaume, Richards, Brent, Zeberg, Hugo, Lipcsey, Miklos, Hultström, Michael, Ludwig, Kerstin U, Schulte, Eva C, Pairo-Castineira, Erola, Baillie, John Kenneth, Schmidt, Axel, Frithiof, Robert, Mari, Francesca, Renieri, Alessandra, Furini, Simone Simone Furini, Francesca, Montagnani, Mario, Tumbarello, Ilaria, Rancan, Massimiliano, Fabbiani, Barbara, Rossetti, Laura, Bergantini, Miriana, D'Alessandro, Paolo, Cameli, David, Bennett, Federico, Anedda, Simona, Marcantonio, Sabino, Scolletta, Federico, Franchi, Maria Antonietta Mazzei, Susanna, Guerrini, Edoardo, Conticini, Luca, Cantarini, Bruno, Frediani, Danilo, Tacconi, Chiara Spertilli Raffaelli, Marco, Feri, Alice, Donati, Raffaele, Scala, Luca, Guidelli, Genni, Spargi, Marta, Corridi, Cesira, Nencioni, Leonardo, Croci, Gian Piero Caldarelli, Maurizio, Spagnesi, Davide, Romani, Paolo, Piacentini, Maria, Bandini, Elena, Desanctis, Silvia, Cappelli, Anna, Canaccini, Agnese, Verzuri, Valentina, Anemoli, Manola, Pisani, Agostino, Ognibene, Alessandro, Pancrazzi, Maria, Lorubbio, Massimo, Vaghi, Antonella, D 'Arminio Monforte, Federica Gaia Miraglia, Mario, U Mondelli, Massimo, Girardis, Sophie, Venturelli, Stefano, Busani, Andrea, Cossarizza, Andrea, Antinori, Alessandra, Vergori, Arianna, Emiliozzi, Stefano, Rusconi, Matteo, Siano, Arianna, Gabrieli, Agostino, Riva, Daniela, Francisci, Elisabetta, Schiaroli, Francesco, Paciosi, Andrea, Tommasi, Pier Giorgio Scotton, Francesca, Andretta, Sandro, Panese, Stefano, Baratti, Renzo, Scaggiante, Francesca, Gatti, Saverio Giuseppe Parisi, Francesco, Castelli, Eugenia, Quiros-Roldan, Melania Degli Antoni, Isabella, Zanella, Matteo Della Monica, Carmelo, Piscopo, Mario, Capasso, Roberta, Russo, Immacolata, Andolfo, Achille, Iolascon, Giuseppe, Fiorentino, Massimo, Carella, Marco, Castori, Filippo, Aucella, Pamela, Raggi, Rita, Perna, Matteo, Bassetti, Antonio Di Biagio, Maurizio, Sanguinetti, Luca, Masucci, Alessandra, Guarnaccia, Serafina, Valente, Oreste De Vivo, Gabriella, Doddato, Rossella, Tita, Annarita, Giliberti, Maria Antonietta Mencarelli, Caterina Lo Rizzo, Anna Maria Pinto, Valentina, Perticaroli, Francesca, Ariani, Miriam Lucia Carriero, Laura Di Sarno, Diana, Alaverdian, Elena, Bargagli, Marco, Mandalà, Alessia, Giorli, Lorenzo, Salerni, Patrizia, Zucchi, Pierpaolo, Parravicini, Elisabetta, Menatti, Tullio, Trotta, Ferdinando, Giannattasio, Gabriella, Coiro, Fabio, Lena, Leonardo Gianluca Lacerenza, Domenico, A Coviello, Cristina, Mussini, Enrico, Martinelli, Sandro, Mancarella, Luisa, Tavecchia, Mary Ann Belli, Lia, Crotti, Gianfranco, Parati, Maurizio, Sanarico, Francesco, Raimondi, Filippo, Biscarini, Alessandra, Stella, Marco, Rizzi, Franco, Maggiolo, Diego, Ripamonti, Claudia, Suardi, Tiziana, Bachetti, Maria Teresa La Rovere, Simona, Sarzi-Braga, Maurizio, Bussotti, Katia, Capitani, Simona, Dei, Sabrina, Ravaglia, Rosangela, Artuso, Elena, Andreucci, Giulia, Gori, Angelica, Pagliazzi, Erika, Fiorentini, Antonio, Perrella, Francesco, Bianchi, Paola, Bergomi, Emanuele, Catena, Riccardo, Colombo, Sauro, Luchi, Giovanna, Morelli, Paola, Petrocelli, Sarah, Iacopini, Sara, Modica, Silvia, Baroni, Francesco Vladimiro Segala, Francesco, Menichetti, Marco, Falcone, Giusy, Tiseo, Chiara, Barbieri, Tommaso, Matucci, Grassi, Davide, Ferri, Claudio, Marinangeli, Franco, Brancati, Francesco, Antonella, Vincenti, Valentina, Borgo, Lombardi, Stefania, Mirco, Lenzi, Massimo Antonio Di Pietro, Francesca, Vichi, Benedetta, Romanin, Letizia, Attala, Cecilia, Costa, Andrea, Gabbuti, Menè, Roberto, Umberto, Zuccon, Lucia, Vietri, Stefano, Ceri, Pietro, Pinoli, Patrizia, Casprini, Giuseppe, Merla, Gabriella Maria Squeo, Marcello, Maffezzoni, Raffaele, Bruno, Marco, Vecchia, Marta, Colaneri, Serena, Ludovisi, Yanara, Marincevic-Zuniga, Jessica, Nordlund, Tomas, Luther, Anders, Larsson, Katja Hanslin Anna Gradin, Sarah, Galien, Sara Bulow Anderberg, Jacob, Rosén, Sten, Rubertsson, Hugo, Zeberg, Robert, Frithiof, Miklós, Lipcsey, Michael, Hultström, Sara Clohisey Peter Horby, Johnny, Millar, Julian, Knight, Hugh, Montgomery, David, Maslove, Lowell, Ling, Alistair, Nichol, Charlotte, Summers, Tim, Walsh, Charles, Hinds, Malcolm, G Semple, Peter J, M Openshaw, Manu, Shankar-Hari, Antonia, Ho, Danny, Mcauley, Chris, Ponting, Kathy, Rowan, J Kenneth Baillie, Fiona, Griffiths, Wilna, Oosthuyzen, Jen, Meikle, Paul, Finernan, James, Furniss, Ellie, Mcmaster, Andy, Law, Sara, Clohisey, Trevor, Paterson, Tony, Wackett, Ruth, Armstrong, Lee, Murphy, Angie, Fawkes, Richard, Clark, Audrey, Coutts, Lorna, Donnelly, Tammy, Gilchrist, Katarzyna, Hafezi, Louise, Macgillivray, Alan, Maclean, Sarah, Mccafferty, Kirstie, Morrice, Jane, Weaver, Ceilia, Boz, Ailsa, Golightly, Mari, Ward, Hanning, Mal, Helen, Szoor-McElhinney, Adam, Brown, Ross, Hendry, Andrew, Stenhouse, Louise, Cullum, Dawn, Law, Sarah, Law, Rachel, Law, Max Head Fourman, Maaike, Swets, Nicky, Day, Filip, Taneski, Esther, Duncan, Marie, Zechner, Nicholas, Parkinson, Erola, Pairo-Castineira, Lucija, Klaric, Andrew, D Bretherick, Konrad, Rawlik, Dorota, Pasko, Susan, Walker, Nick, Parkinson, Clark, D Russell, Anne, Richmond, Elvina, Gountouna, David, Harrison, Wang, Bo, Yang, Wu, Alison, Meynert, Athanasios, Kousathanas, Loukas, Moutsianas, Zhijian, Yang, Ranran, Zhai, Chenqing, Zheng, Graeme, Grimes, Jonathan, Millar, Barbara, Shih, Jian, Yang, Xia, Shen, Chris, P Ponting, Albert, Tenesa, Andrew, Law, Veronique, Vitart, James, F Wilson, Collier, D, Wood, S, Zak, A, Borra, C, Matharu, M, May, P, Alldis, Z, Mitchelmore, O, Bowles, R, Easthorpe, A, Bibi, F, Lancoma-Malcolm, I, Gurasashvili, J, Pheby, J, Shiel, J, Bolton, M, Patel, M, Taylor, M, Zongo, O, Ebano, P, Harding, P, Astin-Chamberlain, R, Choudhury, Y, Cox, A, Kallon, D, Burton, M, Hall, R, Blowes, S, Prime, Z, Biddle, J, Prysyazhna, O, Newman, T, Tierney, C, Kassam, J, Shankar-Hari, M, Ostermann, M, Campos, S, Bociek, A, Lim, R, Grau, N, O Jones, T, Whitton, C, Marotti, M, Arbane, G, Bonner, S, Hugill, K, Reid, J, Welters, I, Waugh, V, Williams, K, Shaw, D, J Fernandez Roman, M Lopez Martinez, Johnson, E, Waite, A, Johnson, B, Hamilton, O, Mulla, S, Mcphail, M, Smith, J, K Baillie, J, Barclay, L, Hope, D, Mcculloch, C, Mcquillan, L, Clark, S, Singleton, J, Priestley, K, Rea, N, Callaghan, M, Campbell, R, Andrew, G, Marshall, L, Mckechnie, S, Hutton, P, Bashyal, A, Davidson, N, Summers, C, Polgarova, P, Stroud, K, Pathan, N, Elston, K, Agrawal, S, Battle, C, Newey, L, Rees, T, Harford, R, Brinkworth, E, Williams, M, Murphy, C, White, I, Croft, M, Bandla, N, Gellamucho, M, Tomlinson, J, Turner, H, Davies, M, Quinn, A, Hussain, I, Thompson, C, Parker, H, Bradley, R, Griffiths, R, Scriven, J, Gill, J, Puxty, A, Cathcart, S, Salutous, D, Turner, L, Duffy, K, Puxty, K, Joseph, A, Herdman-Grant, R, Simms, R, Swain, A, Naranjo, A, Crowe, R, Sollesta, K, Loveridge, A, Baptista, D, Morino, E, Davey, M, Golden, D, Jones, J, J Moreno Cuesta, Haldeos, A, Bakthavatsalam, D, Vincent, R, Elhassan, M, Xavier, K, Ganesan, A, Purohit, D, Abdelrazik, M, Morgan, J, Akeroyd, L, Bano, S, Warren, D, Bromley, M, Sellick, K, Gurr, L, Wilkinson, B, Nagarajan, V, Szedlak, P, Cupitt, J, Stoddard, E, Benham, L, Preston, S, Slawson, N, Bradshaw, Z, Brown, J, Caswell, M, Smelling, Bamford, P, Faulkner, M, Cawley, K, Jeffrey, H, London, E, Sainsbury, H, Nagra, I, Nasir, F, Dunmore, Ce, Jones, R, Abraheem, A, Al-Moasseb, M, Girach, R, Brantwood, C, Alexander, P, Bradley-Potts, J, Allen, S, Felton, T, Manna, S, Farnell-Ward, S, Leaver, S, Queiroz, J, Maccacari, E, Dawson, D, C Castro Delgado, R Pepermans Saluzzio, Ezeobu, O, Ding, L, Sicat, C, Kanu, R, Durrant, G, Texeira, J, Harrison, A, Samakomva, T, Willis, H, Hopkins, B, Thrasyvoulou, L, Jackson, M, Zaki, A, Tibke, C, Bennett, S, Woodyatt, W, Kent, A, Goodwin, E, Brandwood, C, Clark, R, Smith, L, Rooney, K, Thomson, N, Rodden, N, Hughes, E, Mcglynn, D, Clark, C, Clark, P, Abel, L, Sundaram, R, Gemmell, L, Brett, M, Hornsby, J, Macgoey, P, Price, R, Digby, B, O'Neil, P, Mcconnell, P, Henderson, P, Henderson, S, Sim, M, Kennedy-Hay, S, Mcparland, C, Rooney, L, Baxter, N, Pogson, D, Rose, S, Daly, Z, Brimfield, L, K Phull, M, Hussain, M, Pogreban, T, Rosaroso, L, E Salciute, L Grauslyte, Brealey, D, Wraith, E, Maccallum, N, Bercades, G, Hass, I, Smyth, D, Reyes, A, Martir, G, D Clement, I, Webster, K, Hays, C, Gulati, A, Hodgson, L, Margarson, M, Gomez, R, Baird, Y, Thirlwall, Y, Folkes, L, Butler, A, Meadows, E, Moore, S, Raynard, D, Fox, H, Riddles, L, King, K, Kimber, S, Hobden, G, Mccarthy, A, Cannons, V, Balagosa, I, Chadbourn, I, Gardner, A, Horner, D, Mclaughlanv, D, Charles, B, Proudfoot, N, Marsden, T, L Mc Morrow, Blackledge, B, Pendlebury, J, Harvey, A, Apetri, E, Basikolo, C, Catlow, L, Doonan, R, Knowles, K, Lee, S, Lomas, D, Lyons, C, Perez, J, Poulaka, M, Slaughter, M, Slevin, K, Thomas, V, Walker, D, Harris, J, Drummond, A, Tully, R, Dearden, J, Philbin, J, Munt, S, Rishton, C, O'Connor, G, Mulcahy, M, Dobson, E, Cuttler, J, Edward, M, Rose, A, Sloan, B, Buckley, S, Brooke, H, Smithson, E, Charlesworth, R, Sandu, R, Thirumaran, M, Wagstaff, V, J Cebrian Suarez, Kaliappan, A, Vertue, M, Nicholson, A, Riches, J, Solesbury, A, Kittridge, L, Forsey, M, Maloney, G, Cole, J, Davies, R, Hill, H, Thomas, E, Williams, A, Duffin, D, Player, B, Radhakrishnan, J, Gibson, S, Lyle, A, Mcneela, F, Patel, B, Gummadi, M, Sloane, G, Dormand, N, Salmi, S, Farzad, Z, Cristiano, D, Liyanage, K, Thwaites, V, Varghese, M, Meredith, M, Mills, G, Willson, J, Harrington, K, Lenagh, B, Cawthron, K, Masuko, S, Raithatha, A, Bauchmuller, K, Ahmad, N, Barker, J, Jackson, Y, Kibutu, F, Bird, S, Watson, G, Martin, J, Bevan, E, C Wrey Brown, Trodd, D, English, K, Bell, G, Wilcox, L, Katary, A, Gopal, S, Lake, V, Harris, N, Metherell, S, Radford, E, Moore, F, Bancroft, H, Daglish, J, Sangombe, M, Carmody, M, Rhodes, J, Bellamy, M, Garg, A, Kuravi, A, Virgilio, E, Ranga, P, Butler, J, Botfield, L, Dexter, C, Fletcher, J, Shanmugasundaram, P, Hambrook, G, Burn, I, Manso, K, Thornton, D, Tebbutt, J, Penn, R, Hulme, J, Hussain, S, Maqsood, Z, Joseph, S, Colley, J, Hayes, A, Ahmed, C, Haque, R, Clamp, S, Kumar, R, Purewal, M, Baines, B, Frise, M, Jacques, N, Coles, H, Caterson, J, S Gurung Rai, Brunton, M, Tilney, E, Keating, L, Walden, A, Antcliffe, D, Gordon, A, Templeton, M, Rojo, R, Banach, D, S Sousa Arias, Fernandez, Z, Coghlan, P, Williams, D, Jardine, C, Bewley, J, Sweet, K, Grimmer, L, Johnson, R, Garland, Z, Gumbrill, B, Phillips, C, L Ortiz-Ruiz de Gordoa, Peasgood, E, Tridente, A, K Shuker, S Greer, Lynch, C, Pothecary, C, Roche, L, Deacon, B, Turner, K, Singh, J, G Sera Howe, Paul, P, Gill, M, Wynter, I, Ratnam, V, Shelton, S, Naisbitt, J, Melville, J, Baruah, R, Morrison, S, Mcgregor, A, Parris, V, Mpelembue, M, Srikaran, S, Dennis, C, Sukha, A, Verlande, M, Holding, K, Riches, K, Downes, C, Swan, C, Rostron, A, Roy, A, Woods, L, Cornell, S, Wakinshaw, F, Creagh-Brown, B, Blackman, H, Salberg, A, Smith, E, Donlon, S, Mtuwa, S, Michalak-Glinska, N, Stone, S, Beazley, C, Pristopan, V, Nikitas, N, Lankester, L, Wells, C, S Raj, A, Fletcher, K, Khade, R, Tsinaslanidis, G, Mcmahon, M, Fowler, S, Coventry, T, Stewart, R, Wren, L, Mwaura, E, Mew, L, Scaletta, D, Williams, F, Inweregbu, K, Lancaster, N, Cunningham, M, Daniels, A, Harrison, L, Hope, S, Jones, S, Crew, A, Wray, G, Matthews, J, Crawley, R, Carter, J, Birkinshaw, I, Ingham, J, Scott, Z, Howard, K, Joy, R, Roche, S, Clark, M, Purvis, S, Morrison, A, Strachan, D, Clements, S, Black, K, Parmar, C, Altabaibeh, A, Simpson, K, Mostoles, L, Gilbert, K, L, Ma, Alvaro, A, Thomas, M, Faulkner, B, Worner, R, Hayes, K, Gendall, E, Blakemore, H, Borislavova, B, Goff, E, Vuylsteke, A, Mwaura, L, Zamikula, J, Garner, L, Mitchell, A, Mepham, S, Cagova, L, Fofano, A, Holcombe, H, Praman, K, Szakmany, T, E Heron, A, Cherian, S, Cutler, S, Roynon-Reed, A, Randell, G, Convery, K, K Stammers, D Fottrell-Gould, Hudig, L, Keshet-Price, J, Peters, M, O'Neill, L, Ray, S, Belfield, H, Mchugh, T, Jones, G, Akinkugbe, O, Tomas, A, Abaleke, E, Beech, E, Meghari, H, Yussuf, S, Bamford, A, Hairsine, B, Dooks, E, Farquhar, F, Packham, S, Bates, H, Armstrong, L, Kaye, C, Allan, A, Medhora, J, Liew, J, Botello, A, Anderson, F, Cusack, R, Golding, H, Prager, K, Williams, T, Leggett, S, Golder, K, Male, M, Jones, O, Criste, K, Marani, M, Anumakonda, V, Amin, V, Karthik, K, Kausar, R, Anastasescu, E, Reid, K, Jacqui, M, Hormis, A, Walker, R, Duncan, T, Uriel, A, Ustianowski, A, T-Michael, H, Bruce, M, Connolly, K, Smith, K, Partridge, R, Griffin, D, Mcdonald, M, Muchenje, N, Martin, D, Filipe, H, Eastgate, C, Jackson, C, Gratrix, A, Foster, L, Martinson, V, Stones, E, Caroline, Abernathy, Parkinson, P, Reed, A, Prendergast, C, Rogers, P, Woodruff, M, Shokkar, R, Kaul, S, Barron, A, Collins, C, Beavis, S, Whileman, A, Dale, K, Hawes, J, Pritchard, K, Gascoyne, R, Stevenson, L, Jha, R, Lim, L, Krishnamurthy, V, Parker, R, Turner-Bone, I, Wilding, L, Reddy, A, Whiteley, S, Wilby, E, Howcroft, C, Aspinwall, A, Charlton, S, Ogg, B, Menzies, D, Pugh, R, Allan, E, Lean, R, Davies, F, Easton, J, Qiu, X, Kumar, S, Darlington, K, Houston, G, O'Brien, P, Geary, T, Allan, J, Meikle, A, Hughes, G, Balasubramaniam, M, Latham, S, Mckenna, E, Flanagan, R, Sathe, S, Davies, E, Chablani, M, Kirkby, A, Netherton, K, Archer, S, Yates, B, Ashbrook-Raby, C, Cole, S, Casey, M, Cabrelli, L, Chapman, S, Austin, P, Hutcheon, A, Whyte, C, Almaden-Boyle, C, Pattison, N, Cruz, C, Vochin, A, Kent, H, Thomas, A, Murdoch, S, David, B, Penacerrada, M, Lubimbi, G, Bastion, V, Wulandari, R, Valentine, J, Clarke, D, Serrano-Ruiz, A, Hierons, S, Ramos, L, Demetriou, C, Mitchard, S, White, K, White, N, Pitts, S, Branney, D, Frankham, J, Watters, M, Langton, H, Prout, R, Page, V, Varghes, T, Cowton, A, Kay, A, Potts, K, Birt, M, Kent, M, Wilkinson, A, Jude, E, Turner, V, Savill, H, Mccormick, J, Coulding, M, Siddiqui, S, Mercer, O, Rehman, H, Potla, D, Capps, N, Donaldson, D, Button, H, Martin, T, Hard, K, Agasou, A, Tonks, L, Arden, T, Boyle, P, Carnahan, M, Strickley, J, Adams, C, Childs, D, Rikunenko, R, Leigh, M, Breekes, M, Wilcox, R, Bowes, A, Tiveran, H, Hurford, F, Summers, J, Carter, A, Hussain, Y, Ting, L, Javaid, A, Motherwell, N, Moore, H, Millward, H, Jose, S, Schunki, N, Noakes, A, Clulow, C, Sadera, G, Jacob, R, Jones, C, Blunt, M, Coton, Z, Curgenven, H, S Mohamed Ally, Beaumont, K, Elsaadany, M, Fernandes, K, I Ali Mohamed Ali, Rangarajan, H, Sarathy, V, Selvanayagam, S, Vedage, D, White, M, Smith, M, Truman, N, Chukkambotla, S, Keith, S, Cockerill-Taylor, J, Ryan-Smith, J, Bolton, R, Springle, P, Dykes, J, Thomas, J, Khan, M, T Hijazi, M, Massey, E, Croston, G, Reschreiter, H, Camsooksai, J, Patch, S, Jenkins, S, Humphrey, C, Wadams, B, Bhatia, N, Msiska, M, Adanini, O, Attwood, B, Parsons, P, Tatham, K, Jhanji, S, Black, E, A Dela Rosa, Howle, R, Thomas, B, Bemand, T, Raobaikady, R, Saha, R, Staines, N, Daniel, A, Finn, J, Hutter, J, Doble, P, Shovelton, C, Pawley, C, Kannan, T, Hill, M, Combes, E, Monnery, S, Joefield, T, Popescu, M, Thankachen, M, Oblak, M, Little, J, Mcivor, S, Brady, A, Whittle, H, Prady, H, Chan, R, Ahmed, A, Morris, A, Gibson, C, Gordon, E, Keenan, S, Quinn, H, Benyon, S, Marriott, S, Zitter, L, Park, L, Baines, K, Lyons, M, Holland, M, Keenan, N, Young, M, Garrioch, S, Dawson, J, Tolson, M, Scholefield, B, R, Bi, Richardson, N, Schumacher, N, Cosier, T, Millen, G, Higham, A, Turki, S, Allen, L, Crisp, N, Hazleton, T, Knight, A, Deery, J, Price, C, Turney, S, Tilbey, S, Beranova, E, Wright, D, Georg, L, Twiss, S, Wadd, S, Postlethwaite, K, Gondo, P, Masunda, B, Kayani, A, Hadebe, B, Whiteside, J, Clarke, N, Donnison, P, Trim, F, Leadbitter, I, Butcher, D, O'Sullivan, S, Purewal, B, Bell, S, Rivers', V, O'Leary, R, Birch, J, Collins, E, Anderson, S, Hammerton, K, Andrews, E, Burns, K, Edmond, I, Todd, A, Donnachie, J, Turner, P, Prentice, L, Symon, L, Runciman, N, Auld, F, Halkes, M, Mercer, P, Thornton, L, Debreceni, G, Wilkins, J, Brown, A, Crickmore, V, Subramanian, G, Marshall, R, Jennings, C, Latif, M, Bunni, L, Spivey, M, Bean, S, Burt, K, Linnett, V, Ritzema, J, Sanderson, A, Mccormick, W, Bokhari, M, Kapoor, R, Loader, D, Ayers, A, Harrison, W, North, J, Belagodu, Z, Parasomthy, R, Olufuwa, O, Gherman, A, Fuller, B, Stuart, C, Kelsall, O, Davis, C, Wild, L, Wood, H, Thrush, J, Durie, A, Austin', K, Archer, K, Anderson, P, Vigurs, C, Thorpe, C, Knights, E, Boyle, N, Price, A, Kubisz-Pudelko, A, Wood, D, Lewis, A, Board, S, Pippard, L, Perry, J, Beesley, K, Rattray, A, Lee, E, Lennon, L, Douglas, K, Bell, D, Boyle, R, Glass, L, M Nauman Akhtar, Dent, K, Potoczna, D, Pearson, S, Horsley, E, Spencer, S, Mullan, D, Skinner, D, Gaylard, J, Ortiz-Ruizdegordoa, L, Barber, R, Hewitt, C, Hilldrith, A, Shepardson, S, Wills, M, Jackson-Lawrence, K, Gupta, A, Easthope, A, Timlick, E, Gorman, C, Otaha, I, Gales, A, Coetzee, S, Raj, M, Peiu, M, Quaid, S, Watson, E, Elliott, K, Mallinson, J, Chandler, B, Turnbull, A, Finch, C, Holl, C, Cooper, J, Evans, A, Khaliq, W, Collins, A, E Treus Gude, Love, N, L van Koutrik, Hunt, J, Kaye, D, Fisher, E, Brayne, A, Tuckey, V, Jackson, P, Parkin, J, Raith, E, Tariq, A, Houlden, H, Tucci, A, Hardy, J, Moncur, E, Highgate, J, Cowley, A, Mitra, A, Stead, R, Behan, T, Burnett, C, Newton, M, Heeney, E, Pollard, R, Hatton, J, Patel, A, Kasipandian, V, Allibone, S, M Genetu, R, Otahal, I, O'Brien, L, Omar, Z, Perkins, E, Davies, K, Tetla, D, Shelley, B, Irvine, V, Williams, S, Williams, P, Goodsell, J, Tutton, R, Bough, L, Winter-Goodwin, B, Kitson, R, Pinnell, J, Wilson, A, Nortcliffe, T, Wood, T, Home, M, Holdroyd, K, Robinson, M, Shaw, R, Greig, J, Brady, M, Haigh, A, Matupe, L, Usher, M, Mellor, S, Dale, S, Gledhill, L, Shaw, L, Turner, G, Kelly, D, Anwar, B, Riley, H, Sturgeon, H, Ali, A, Thomis, L, Melia, D, Dance, A, Hanson, K, Humphreys, S, Frost, I, Gopal, V, Godden, J, Holden, A, Swann, S, Smith, T, Clapham, M, Poultney, U, Harper, R, Rice, P, Reece-Anthony, R, Gurung, B, Moultrie, S, Odam, M, Mayer, A, Bellini, A, Pickard, A, Bryant, J, Roe, N, Sowter, J, Lang, K, Taylor, J, Barry, P, Hobrok, M, Tench, H, Wolf-Roberts, R, Mcguinness, H, Loosley, R, Hawcutt, D, Rad, L, O'Malley, L, Saunderson, P, Seddon, G, Anderson, T, Rogers, N, Ruddy, J, Harkins, M, Beith, C, Mcalpine, A, Ferguson, L, Grant, P, Macfadyen, S, Mclaughlin, M, Baird, T, Rundell, S, Welsh, B, Hamill, R, Fisher, F, Gregory, J, Axel, Schmidt, Kerstin, U Ludwig, Selina, Rolker, Markus, M Nöthen, Julia, Fazaal, Verena, Keitel, Björn, Jensen, Torsten, Feldt, Lisa, Knopp, Julia, Schröder, Carlo, Maj, Fabian, Brand, Marc, M Berger, Thorsten, Brenner, Anke, Hinney, Oliver, Witzke, Robert, Bals, Christian, Herr, Nicole, Ludwig, Jörn, Walter, Jochen, Schneider, Johanna, Erber, Christoph, D Spinner, Clemens, M Wendtner, Christof, Winter, Ulrike, Protzer, Nicolas, Casadei, Stephan, Ossowski, Olaf, H Riess, Eva, C Schulte, J Brent Richards, Guillaume, Butler-Laporte, Mirosław, Kwasniewski, Urszula, Korotko, Karolina, Chwialkowska, Magdalena, Niemira, Jerzy, Jaroszewicz, Barbara, Sobala-Szczygiel, Beata, Puzanowska, Anna, Parfieniuk-Kowerda, Diana, Martonik, Anna, Moniuszko-Malinowska, Sławomir, Pancewicz, Dorota, Zarębska-Michaluk, Krzysztof, Simon, Monika, Pazgan-Simon, Iwona, Mozer-Lisewska, Maciej, Bura, Agnieszka, Adamek, Krzysztof Tomasiewicz Małgorzata Pawłowska, Anna, Piekarska, Aleksandra, Berkan-Kawinska, Andrzej, Horban, Justyna, Kowalska, Regina, Podlasin, Piotr, Wasilewski, Arsalin, Azzadin, Miroslaw, Czuczwar, Slawomir, Czaban, Paweł, Olszewski, Jacek, Bogocz, Magdalena, Ochab, Anna, Kruk, Sandra, Uszok, Agnieszka, Bielska, Anna, Szałkowska, Justyna, Raczkowska, Gabriela, Sokołowska, Joanna, Chorostowska-Wynimko, Aleksandra, Jezela-Stanek, Adriana, Roży, Urszula, Lechowicz, Urszula, Polowianiuk, Kamil, Grubczak, Aleksandra, Starosz, Andrzej, Eljaszewicz, Wiktoria, Izdebska, Adam, Krętowski, Robert, Flisiak, Marcin Moniuszko Malak Abedalthagafi Manal Alaamery, Salam, Massadeh, Mohamed, Fawzy, Hadeel, Albardis, Nora, Aljawini, Moneera, Alsuwailm, Faisal, Almalki, Serghei, Mangul, Junghyun, Jung, Hamdi, Mbarek, Chadi, Saad, Yaser, Al-Sarraj, Wadha, Al-Muftah, Radja, Badji, Asma Al Thani, Said, I Ismail, HGI, WES/WGS Working Group Within the, Consortium, GenOMICC, Study, GEN-COVID Multicenter, Renieri, Alessandra [0000-0002-0846-9220], Apollo - University of Cambridge Repository, NIHR, Fallerini, C, Picchiotti, N, Baldassarri, M, Zguro, K, Daga, S, Fava, F, Benetti, E, Amitrano, S, Bruttini, M, Palmieri, M, Croci, S, Lista, M, Beligni, G, Valentino, F, Meloni, I, Tanfoni, M, Minnai, F, Colombo, F, Cabri, E, Fratelli, M, Gabbi, C, Mantovani, S, Frullanti, E, Gori, M, Crawley, F, Butler-Laporte, G, Richards, B, Zeberg, H, Lipcsey, M, Hultström, M, Ludwig, K, Schulte, E, Pairo-Castineira, E, Baillie, J, Schmidt, A, Frithiof, R, Mari, F, Renieri, A, Furini, S, and Crotti, L

Subjects
Male, Medicin och hälsovetenskap, Linkage disequilibrium, Medizin, severity, Genome-wide association study, Disease, WES/WGS Working Group Within the HGI, Logistic regression, Severity of Illness Index, Medical and Health Sciences, Whole Exome Sequencing, Cohort Studies, 0302 clinical medicine, Lasso (statistics), GEN-COVID Multicenter Study, Germany, 80 and over, Genetics (clinical), Exome sequencing, Original Investigation, Genetics & Heredity, Aged, 80 and over, 0303 health sciences, Adult, Aged, COVID-19, Female, Humans, Italy, Middle Aged, Polymorphism, Single Nucleotide, Quebec, SARS-CoV-2, Sweden, United Kingdom, Genetic Predisposition to Disease, Phenotype, Single Nucleotide, covid-19, 1104 Complementary and Alternative Medicine, GenOMICC Consortium, Human, coding variants, Computational biology, Biology, 03 medical and health sciences, Exome Sequencing, Genetics, Polymorphism, Gene, 030304 developmental biology, 0604 Genetics, 1114 Paediatrics and Reproductive Medicine, Cohort Studie, 030217 neurology & neurosurgery, Coding (social sciences)
Abstract

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. Supplementary Information The online version contains supplementary material available at 10.1007/s00439-021-02397-7.

Published
2021

23. Integrative transcriptome-wide analysis of atopic dermatitis for drug repositioning

Author

Jaeseung Song, Daeun Kim, Sora Lee, Junghyun Jung, Jong Wha J. Joo, and Wonhee Jang

Subjects
Drug Repositioning, Quality of Life, Medicine (miscellaneous), Humans, General Agricultural and Biological Sciences, Transcriptome, General Biochemistry, Genetics and Molecular Biology, Dermatitis, Atopic, Genome-Wide Association Study
Abstract

Atopic dermatitis (AD) is one of the most common inflammatory skin diseases, which significantly impact the quality of life. Transcriptome-wide association study (TWAS) was conducted to estimate both transcriptomic and genomic features of AD and detected significant associations between 31 expression quantitative loci and 25 genes. Our results replicated well-known genetic markers for AD, as well as 4 novel associated genes. Next, transcriptome meta-analysis was conducted with 5 studies retrieved from public databases and identified 5 additional novel susceptibility genes for AD. Applying the connectivity map to the results from TWAS and meta-analysis, robustly enriched perturbations were identified and their chemical or functional properties were analyzed. Here, we report the first research on integrative approaches for an AD, combining TWAS and transcriptome meta-analysis. Together, our findings could provide a comprehensive understanding of the pathophysiologic mechanisms of AD and suggest potential drug candidates as alternative treatment options.

Published
2021

24. Rare loss-of-function variants in type I IFN immunity genes are not associated with severe COVID-19

Author

Mohamed Fawzy, David Goldstein, Mansour S Almutairi, Nora Aljawini, Naveed Aziz, Serghei Mangul, Krzysztof Kiryluk, J. Brent Richards, Sirui Zhou, Hani M. Al-Afghani, Vincent Mooser, Bartha Maria Knoppers, May Alrashed, Nour Albes, Saleh A. Alqahtani, Radja Badji, Lisa J. Strug, Atlas Khan, Manal Alaamery, Yaseen M. Arabi, Mathieu Bourgey, Leen Abu-Safieh, Vincenzo Forgetta, Guillaume Bourque, Bader Alghamdi, Steven J.M. Jones, Chadi Saad, Salam Massadeh, Guillaume Butler-Laporte, Jiannis Ragoussis, Abdulraheem Alshareef, Wadha Al-Muftah, Gundula Povysil, Chen Wang, Stephen W. Scherer, Robert Eveleigh, Ebrahim Mahmoud, Said I. Ismail, Pierre Lepage, Hamdi Mbarek, Abdulaziz Almalik, Mark Lathrop, Tomoko Nakanishi, Bandar A. Suliman, Junghyun Jung, Ali G. Gharavi, Jahad Alghamdi, Ning Shang, Malak Abedalthagafi, Hadeel El Bardisy, Asma Al Thani, and Fawz S. Al Harthi

Subjects
0301 basic medicine, Candidate gene, education.field_of_study, Mutation, Population, Case-control study, macromolecular substances, General Medicine, Biology, medicine.disease_cause, 03 medical and health sciences, 030104 developmental biology, 0302 clinical medicine, 030220 oncology & carcinogenesis, Genetic variation, Immunology, medicine, education, Gene, Loss function, Exome sequencing
Abstract

A recent report found that rare predicted loss-of-function (pLOF) variants across 13 candidate genes in TLR3- and IRF7-dependent type I IFN pathways explain up to 3.5% of severe COVID-19 cases. We performed whole-exome or whole-genome sequencing of 1,864 COVID-19 cases (713 with severe and 1,151 with mild disease) and 15,033 ancestry-matched population controls across 4 independent COVID-19 biobanks. We tested whether rare pLOF variants in these 13 genes were associated with severe COVID-19. We identified only 1 rare pLOF mutation across these genes among 713 cases with severe COVID-19 and observed no enrichment of pLOFs in severe cases compared to population controls or mild COVID-19 cases. We found no evidence of association of rare LOF variants in the 13 candidate genes with severe COVID-19 outcomes.

Published
2021

25. Molecular origin of AuNPs-induced cytotoxicity and mechanistic study

Author

Jeahee Ryu, Junghyun Jung, Minhyeong Lee, Youngeun Kwon, Hyunjin Jeon, Soyoun Kim, Euiyeon Lee, and Chungwon Kang

Subjects
0301 basic medicine, DNA damage, Science, Metal Nanoparticles, Cell Cycle Proteins, Article, 03 medical and health sciences, 0302 clinical medicine, Cell Movement, Humans, Surface charge, Cytoskeleton, Cytotoxicity, Cells, Cultured, Cell Proliferation, Multidisciplinary, Chemistry, Cell growth, DNA replication, Cell cycle, Fibroblasts, Gene Expression Regulation, Neoplastic, Oxidative Stress, 030104 developmental biology, Colloidal gold, Biophysics, Medicine, Gold, Hydrophobic and Hydrophilic Interactions, 030217 neurology & neurosurgery, HeLa Cells
Abstract

Gold nanoparticles (AuNPs) with diverse physicochemical properties are reported to affect biological systems differently, but the relationship between the physicochemical properties of AuNPs and their biological effects is not clearly understood. Here, we aimed to elucidate the molecular origins of AuNP-induced cytotoxicity and their mechanisms, focusing on the surface charge and structural properties of modified AuNPs. We prepared a library of well-tailored AuNPs exhibiting various functional groups and surface charges. Through this work, we revealed that the direction or the magnitude of surface charge is not an exclusive factor that determines the cytotoxicity of AuNPs. We, instead, suggested that toxic AuNPs share a common structural characteristics of a hydrophobic moiety neighbouring the positive charge, which can induce lytic interaction with plasma membrane. Mechanistic study showed that the toxic AuNPs interfered with the formation of cytoskeletal structure to slow cell migration, inhibited DNA replication and caused DNA damage via oxidative stress to hinder cell proliferation. Gene expression analysis showed that the toxic AuNPs down-regulated genes associated with cell cycle processes. We discovered structural characteristics that define the cytotoxic AuNPs and suggested the mechanisms of their cytotoxicity. These findings will help us to understand and to predict the biological effects of modified AuNPs based on their physicochemical properties.

Published
2019

26. SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Author

Matteo D’Antonio, Jennifer P. Nguyen, Timothy D. Arthur, Hiroko Matsui, Agnieszka D’Antonio-Chronowska, Kelly A. Frazer, Benjamin M. Neale, Mark Daly, Andrea Ganna, Christine Stevens, Gita A. Pathak, Shea J. Andrews, Masahiro Kanai, Mattia Cordioli, Juha Karjalainen, Renato Polimanti, Matti Pirinen, Nadia Harerimana, Kumar Veerapen, Brooke Wolford, Huy Nguyen, Matthew Solomonson, Rachel G. Liao, Karolina Chwialkowska, Amy Trankiem, Mary K. Balaconis, Caroline Hayward, Anne Richmond, Archie Campbell, Marcela Morris, Chloe Fawns-Ritchie, Joseph T. Glessner, Douglas M. Shaw, Xiao Chang, Hannah Polikowski, Petty E. Lauren, Hung-Hsin Chen, Zhu Wanying, Hakon Hakonarson, David J. Porteous, Jennifer Below, Kari North, Joseph B. McCormick, Paul R.H.J. Timmers, James F. Wilson, Albert Tenesa, Kenton D’Mellow, Shona M. Kerr, Mari E.K. Niemi, Lindokuhle Nkambul, Kathrin Aprile von Hohenstaufen, Ali Sobh, Madonna M. Eltoukhy, Amr M. Yassen, Mohamed A.F. Hegazy, Kamal Okasha, Mohammed A. Eid, Hanteera S. Moahmed, Doaa Shahin, Yasser M. El-Sherbiny, Tamer A. Elhadidy, Mohamed S. Abd Elghafar, Jehan J. El-Jawhari, Attia A.S. Mohamed, Marwa H. Elnagdy, Amr Samir, Mahmoud Abdel-Aziz, Walid T. Khafaga, Walaa M. El-Lawaty, Mohamed S. Torky, Mohamed R. El-shanshory, Chiara Batini, Paul H. Lee, Nick Shrine, Alexander T. Williams, Martin D. Tobin, Anna L. Guyatt, Catherine John, Richard J. Packer, Altaf Ali, Robert C. Free, Xueyang Wang, Louise V. Wain, Edward J. Hollox, Laura D. Venn, Catherine E. Bee, Emma L. Adams, Ahmadreza Niavarani, Bahareh Sharififard, Rasoul Aliannejad, Ali Amirsavadkouhi, Zeinab Naderpour, Hengameh Ansari Tadi, Afshar Etemadi Aleagha, Saeideh Ahmadi, Seyed Behrooz Mohseni Moghaddam, Alireza Adamsara, Morteza Saeedi, Hamed Abdollahi, Abdolmajid Hosseini, Pajaree Chariyavilaskul, Monpat Chamnanphon, Thitima B. Suttichet, Vorasuk Shotelersuk, Monnat Pongpanich, Chureerat Phokaew, Wanna Chetruengchai, Watsamon Jantarabenjakul, Opass Putchareon, Pattama Torvorapanit, Thanyawee Puthanakit, Pintip Suchartlikitwong, Nattiya Hirankarn, Voraphoj Nilaratanakul, Pimpayao Sodsai, Ben M. Brumpton, Kristian Hveem, Cristen Willer, Wei Zhou, Tormod Rogne, Erik Solligard, Bjørn Olav Åsvold, Malak Abedalthagafi, Manal Alaamery, Saleh Alqahtani, Dona Baraka, Fawz Al Harthi, Ebtehal Alsolm, Leen Abu Safieh, Albandary M. Alowayn, Fatimah Alqubaishi, Amal Al Mutairi, Serghei Mangul, Abdulraheem Alshareef, Mona Sawaji, Mansour Almutairi, Nora Aljawini, Nour Albesher, Yaseen M. Arabi, Ebrahim S. Mahmoud, Amin K. Khattab, Roaa T. Halawani, Ziab Z. Alahmadey, Jehad K. Albakri, Walaa A. Felemban, Bandar A. Suliman, Rana Hasanato, Laila Al-Awdah, Jahad Alghamdi, Deema AlZahrani, Sameera AlJohani, Hani Al-Afghani, May Alrashed, Nouf AlDhawi, Hadeel AlBardis, Sarah Alkwai, Moneera Alswailm, Faisal Almalki, Maha Albeladi, Iman Almohammed, Eman Barhoush, Anoud Albader, Salam Massadeh, Abdulaziz AlMalik, Sara Alotaibi, Bader Alghamdi, Junghyun Jung, Mohammad S. Fawzy, Yunsung Lee, Per Magnus, Lill-Iren S. Trogstad, Øyvind Helgeland, Jennifer R. Harris, Massimo Mangino, Tim D. Spector, Duncan Emma, Sandra P. Smieszek, Bartlomiej P. Przychodzen, Christos Polymeropoulos, Vasilios Polymeropoulos, Mihael H. Polymeropoulos, Israel Fernandez-Cadenas, Jordi Perez-Tur, Laia Llucià-Carol, Natalia Cullell, Elena Muiño, Jara Cárcel-Márquez, Marta L. DeDiego, Lara Lloret Iglesias, Anna M. Planas, Alex Soriano, Veronica Rico, Daiana Agüero, Josep L. Bedini, Francisco Lozano, Carlos Domingo, Veronica Robles, Francisca Ruiz-Jaén, Leonardo Márquez, Juan Gomez, Eliecer Coto, Guillermo M. Albaiceta, Marta García-Clemente, David Dalmau, Maria J. Arranz, Beatriz Dietl, Alex Serra-Llovich, Pere Soler, Roger Colobrán, Andrea Martín-Nalda, Alba Parra Martínez, David Bernardo, Silvia Rojo, Aida Fiz-López, Elisa Arribas, Paloma de la Cal-Sabater, Tomás Segura, Esther González-Villa, Gemma Serrano-Heras, Joan Martí-Fàbregas, Elena Jiménez-Xarrié, Alicia de Felipe Mimbrera, Jaime Masjuan, Sebastian García-Madrona, Anna Domínguez-Mayoral, Joan Montaner Villalonga, Paloma Menéndez-Valladares, Daniel I. Chasman, Julie E. Buring, Paul M. Ridker, Giulianini Franco, Howard D. Sesso, JoAnn E. Manson, Joseph R. Glessner, Carolina Medina-Gomez, Andre G. Uitterlinden, M. Arfan Ikram, Kati Kristiansson, Sami Koskelainen, Markus Perola, Kati Donner, Katja Kivinen, Aarno Palotie, Samuli Ripatti, Sanni Ruotsalainen, Mari Kaunisto, null FinnGen, Tomoko Nakanishi, Guillaume Butler-Laporte, Vincenzo Forgetta, David R. Morrison, Biswarup Ghosh, Laetitia Laurent, Alexandre Belisle, Danielle Henry, Tala Abdullah, Olumide Adeleye, Noor Mamlouk, Nofar Kimchi, Zaman Afrasiabi, Nardin Rezk Branka Vulesevic, Meriem Bouab, Charlotte Guzman, Louis Petitjean, Chris Tselios, Xiaoqing Xue, Erwin Schurr, Jonathan Afilalo, Marc Afilalo, Maureen Oliveira, Bluma Brenner, Pierre Lepage, Jiannis Ragoussis, Daniel Auld, Nathalie Brassard, Madeleine Durand, Michaël Chassé, Daniel E. Kaufmann, G. Mark Lathrop, Vincent Mooser, J. Brent Richards, Rui Li, Darin Adra, Souad Rahmouni, Michel Georges, Michel Moutschen, Benoit Misset, Gilles Darcis, Julien Guiot, Julien Guntz, Samira Azarzar, Stéphanie Gofflot, Yves Beguin, Sabine Claassen, Olivier Malaise, Pascale Huynen, Christelle Meuris, Marie Thys, Jessica Jacques, Philippe Léonard, Frederic Frippiat, Jean-Baptiste Giot, Anne-Sophie Sauvage, Christian Von Frenckell, Yasmine Belhaj, Bernard Lambermont, Sara Pigazzini, Lindokuhle Nkambule, Michelle Daya, Jonathan Shortt, Nicholas Rafaels, Stephen J. Wicks, Kristy Crooks, Kathleen C. Barnes, Christopher R. Gignoux, Sameer Chavan, Triin Laisk, Kristi Läll, Maarja Lepamets, Reedik Mägi, Tõnu Esko, Ene Reimann, Lili Milani, Helene Alavere, Kristjan Metsalu, Mairo Puusepp, Andres Metspalu, Paul Naaber, Edward Laane, Jaana Pesukova, Pärt Peterson, Kai Kisand, Jekaterina Tabri, Raili Allos, Kati Hensen, Joel Starkopf, Inge Ringmets, Anu Tamm, Anne Kallaste, Pierre-Yves Bochud, Carlo Rivolta, Stéphanie Bibert, Mathieu Quinodoz, Dhryata Kamdar, Noémie Boillat, Semira Gonseth Nussle, Werner Albrich, Noémie Suh, Dionysios Neofytos, Véronique Erard, Cathy Voide, null FHoGID, null RegCOVID, null P-PredictUs, null SeroCOVID, null CRiPSI, Rafael de Cid, Iván Galván-Femenía, Natalia Blay, Anna Carreras, Beatriz Cortés, Xavier Farré, Lauro Sumoy, Victor Moreno, Josep Maria Mercader, Marta Guindo-Martinez, David Torrents, Manolis Kogevinas, Judith Garcia-Aymerich, Gemma Castaño-Vinyals, Carlota Dobaño, Alessandra Renieri, Francesca Mari, Chiara Fallerini, Sergio Daga, Elisa Benetti, Margherita Baldassarri, Francesca Fava, Elisa Frullanti, Floriana Valentino, Gabriella Doddato, Annarita Giliberti, Rossella Tita, Sara Amitrano, Mirella Bruttini, Susanna Croci, Ilaria Meloni, Maria Antonietta Mencarelli, Caterina Lo Rizzo, Anna Maria Pinto, Giada Beligni, Andrea Tommasi, Laura Di Sarno, Maria Palmieri, Miriam Lucia Carriero, Diana Alaverdian, Stefano Busani, Raffaele Bruno, Marco Vecchia, Mary Ann Belli, Nicola Picchiotti, Maurizio Sanarico, Marco Gori, Simone Furini, Stefania Mantovani, Serena Ludovisi, Mario Umberto Mondelli, Francesco Castelli, Eugenia Quiros-Roldan, Melania Degli Antoni, Isabella Zanella, Massimo Vaghi, Stefano Rusconi, Matteo Siano, Francesca Montagnani, Arianna Emiliozzi, Massimiliano Fabbiani, Barbara Rossetti, Elena Bargagli, Laura Bergantini, Miriana D’Alessandro, Paolo Cameli, David Bennett, Federico Anedda, Simona Marcantonio, Sabino Scolletta, Federico Franchi, Maria Antonietta Mazzei, Susanna Guerrini, Edoardo Conticini, Luca Cantarini, Bruno Frediani, Danilo Tacconi, Chiara Spertilli, Marco Feri, Alice Donati, Raffaele Scala, Luca Guidelli, Genni Spargi, Marta Corridi, Cesira Nencioni, Leonardo Croci, Maria Bandini, Gian Piero Caldarelli, Paolo Piacentini, Elena Desanctis, Silvia Cappelli, Anna Canaccini, Agnese Verzuri, Valentina Anemoli, Agostino Ognibene, Alessandro Pancrazzi, Maria Lorubbio, Antonella D’Arminio Monforte, Federica Gaia Miraglia, Massimo Girardis, Sophie Venturelli, Andrea Cossarizza, Andrea Antinori, Alessandra Vergori, Arianna Gabrieli, Agostino Riva, Daniela Francisci, Elisabetta Schiaroli, Francesco Paciosi, Pier Giorgio Scotton, Francesca Andretta, Sandro Panese, Renzo Scaggiante, Francesca Gatti, Saverio Giuseppe Parisi, Stefano Baratti, Matteo Della Monica, Carmelo Piscopo, Mario Capasso, Roberta Russo, Immacolata Andolfo, Achille Iolascon, Giuseppe Fiorentino, Massimo Carella, Marco Castori, Giuseppe Merla, Gabriella Maria Squeo, Filippo Aucella, Pamela Raggi, Carmen Marciano, Rita Perna, Matteo Bassetti, Antonio Di Biagio, Maurizio Sanguinetti, Luca Masucci, Serafina Valente, Marco Mandalà, Alessia Giorli, Lorenzo Salerni, Patrizia Zucchi, Pierpaolo Parravicini, Elisabetta Menatti, Tullio Trotta, Ferdinando Giannattasio, Gabriella Coiro, Fabio Lena, Domenico A. Coviello, Cristina Mussini, Enrico Martinelli, Sandro Mancarella, Luisa Tavecchia, Lia Crotti, Chiara Gabbi, Marco Rizzi, Franco Maggiolo, Diego Ripamonti, Tiziana Bachetti, Maria Teresa La Rovere, Simona Sarzi-Braga, Maurizio Bussotti, Stefano Ceri, Pietro Pinoli, Francesco Raimondi, Filippo Biscarini, Alessandra Stella, Kristina Zguro, Katia Capitani, Claudia Suardi, Simona Dei, Gianfranco Parati, Sabrina Ravaglia, Rosangela Artuso, Giordano Bottà, Paolo Di Domenico, Ilaria Rancan, Antonio Perrella Francesco Bianchi, Davide Romani, Paola Bergomi, Emanuele Catena, Riccardo Colombo, Marco Tanfoni, Antonella Vincenti, Claudio Ferri, Davide Grassi, Gloria Pessina, Mario Tumbarello, Massimo Di Pietro, Ravaglia Sabrina, Sauro Luchi, Chiara Barbieri, Donatella Acquilini, Elena Andreucci, Francesco Vladimiro Segala, Giusy Tiseo, Marco Falcone, Mirjam Lista, Monica Poscente, Oreste De Vivo, Paola Petrocelli, Alessandra Guarnaccia, Silvia Baroni, Albert V. Smith, Andrew P. Boughton, Kevin W. Li, Jonathon LeFaive, Aubrey Annis, Anne E. Justice, Tooraj Mirshahi, Geetha Chittoor, Navya Shilpa Josyula, Jack A. Kosmicki, Manuel A.R. Ferreira, Joseph B. Leader, Dave J. Carey, Matthew C. Gass, Julie E. Horowitz, Michael N. Cantor, Ashish Yadav, Aris Baras, Goncalo R. Abecasis, David A. van Heel, Karen A. Hunt, Dan Mason, Qin Qin Huang, Sarah Finer, null Genes & Health Research Team, Bhavi Trivedi, Christopher J. Griffiths, Hilary C. Martin, John Wright, Richard C. Trembath, Nicole Soranzo, Jing Hua Zhao, Adam S. Butterworth, John Danesh, Emanuele Di Angelantonio, Lude Franke Marike Boezen, Patrick Deelen, Annique Claringbould, Esteban Lopera, Robert Warmerdam, Judith.M. Vonk, Irene van Blokland, Pauline Lanting, Anil P.S. Ori, Brooke Wolford Sebastian Zöllner, Jiongming Wang, Andrew Beck, Gina Peloso, Yuk-Lam Ho, Yan V. Sun, Jennifer E. Huffman, Christopher J. O’Donnell, Kelly Cho, Phil Tsao, J. Michael Gaziano, Michel (M.G.) Nivard, Eco (E.J.C.) de geus, Meike Bartels, Jouke Jan Hottenga, Scott T. Weiss, Elizabeth W. Karlson, Jordan W. Smoller, Robert C. Green, Yen-Chen Anne Feng, Josep Mercader, Shawn N. Murphy, James B. Meigs, Ann E. Woolley, Emma F. Perez, Daniel Rader, Anurag Verma, Marylyn D. Ritchie, Binglan Li, Shefali S. Verma, Anastasia Lucas, Yuki Bradford, Hugo Zeberg, Robert Frithiof, Michael Hultström, Miklos Lipcsey, Lindo Nkambul, Nicolas Tardif, Olav Rooyackers, Jonathan Grip, Tomislav Maricic, Konrad J. Karczewski, Elizabeth G. Atkinson, Kristin Tsuo, Nikolas Baya, Patrick Turley, Rahul Gupta, Shawneequa Callier, Raymond K. Walters, Duncan S. Palmer, Gopal Sarma, Nathan Cheng, Wenhan Lu, Sam Bryant, Claire Churchhouse, Caroline Cusick, Jacqueline I. Goldstein, Daniel King, Cotton Seed, Hilary Finucane, Alicia R. Martin, F. Kyle Satterstrom, Daniel J. Wilson, Jacob Armstrong, Justine K. Rudkin, Gavin Band, Sarah G. Earle, Shang-Kuan Lin, Nicolas Arning, Derrick W. Crook, David H. Wyllie, Anne Marie O’Connell, Chris C.A. Spencer, Nils Koelling, Mark J. Caulfield, Richard H. Scott, Tom Fowler, Loukas Moutsianas, Athanasios Kousathanas, Dorota Pasko, Susan Walker, Augusto Rendon, Alex Stuckey, Christopher A. Odhams, Daniel Rhodes, Georgia Chan, Prabhu Arumugam, Catherine A. Ball, Eurie L. Hong, Kristin Rand, Ahna Girshick, Harendra Guturu, Asher Haug Baltzell, Genevieve Roberts, Danny Park, Marie Coignet, Shannon McCurdy, Spencer Knight, Raghavendran Partha, Brooke Rhead, Miao Zhang, Nathan Berkowitz, Michael Gaddis, Keith Noto, Luong Ruiz, Milos Pavlovic, Laura G. Sloofman, Alexander W. Charney, Noam D. Beckmann, Eric E. Schadt, Daniel M. Jordan, Ryan C. Thompson, Kyle Gettler, Noura S. Abul-Husn, Steven Ascolillo, Joseph D. Buxbaum, Kumardeep Chaudhary, Judy H. Cho, Yuval Itan, Eimear E. Kenny, Gillian M. Belbin, Stuart C. Sealfon, Robert P. Sebra, Irene Salib, Brett L. Collins, Tess Levy, Bari Britvan, Katherine Keller, Lara Tang, Michael Peruggia, Liam L. Hiester, Kristi Niblo, Alexandra Aksentijevich, Alexander Labkowsky, Avromie Karp, Menachem Zlatopolsky, Michael Preuss, Ruth J.F. Loos, Girish N. Nadkarni, Ron Do, Clive Hoggart, Sam Choi, Slayton J. Underwood, Paul O’Reilly, Laura M. Huckins, Marissa Zyndorf, AII - Infectious diseases, Amsterdam Neuroscience - Neuroinfection & -inflammation, and Neurology

Subjects
Medical Physiology, Gene Expression, Genome-wide association study, Genome, Severity of Illness Index, colocalization, Gene expression, Databases, Genetic, Ethnicity, 2.1 Biological and endogenous factors, GWAS, Aetiology, Biology (General), Lung, Genetics, Chromosome Mapping, Single Nucleotide, Organ Specificity, Biotechnology, Cell type, QH301-705.5, Quantitative Trait Loci, Single-nucleotide polymorphism, Biology, eQTL, Polymorphism, Single Nucleotide, Article, General Biochemistry, Genetics and Molecular Biology, Databases, Genetic, SNP, Humans, Genetic Predisposition to Disease, COVID-19 Host Genetics Initiative, Polymorphism, Gene, COVID-19, SARS-CoV-2, Gene Expression Profiling, Prevention, Human Genome, Computational Biology, Genetic Variation, Good Health and Well Being, Expression quantitative trait loci, Biochemistry and Cell Biology, Transcriptome, Genome-Wide Association Study
Abstract

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types., Graphical abstract, D’Antonio et al. characterize associations between GWAS signals for COVID-19 disease and eQTLs in 69 human tissues to identify causal variants and their underlying molecular mechanisms. They show that diverse symptoms and disease severity of COVID-19 are associated with variants affecting gene expression in a wide variety of tissues.

Published
2022

28. Additional file 1 of MARS: leveraging allelic heterogeneity to increase power of association testing

Author

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

Abstract

Additional file 1 Fig. S2— MARS detects more eGenes in GTEx Whole Blood data. Fig. S3— Venn diagrams comparing eGenes identified by MARS using GTEx v6, eGenes reported by GTEx v6, and eGenes reported by GTEx v7 for all the tissues. Fig. S4— Compare p-values estimated from the standard sampling and those estimated from the importance sampling. Fig. S5— Venn diagrams comparing set-based association identified by MARS and the univariate test for traits in NFBC data. Fig. S6— Number of loci found by previous studies among the loci identified by MARS but not by the univariate test. Fig. S7—Power of MARS on the choice of priors on gamma. Fig. S8— Using only top 50 SNPs with bigger summary statistics well approximates the result using all the SNPs in MARS.

Published
2021
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29. Integrative Meta-analysis of Transcriptome Data for Unmasking Biological Mechanism of Idiopathic pulmonary fibrosis

Author

Sungmin Park, Jong Wha J. Joo, and Junghyun Jung

Subjects
0209 industrial biotechnology, Lung, Microarray, 02 engineering and technology, Disease, Computational biology, medicine.disease, Transcriptome, Pathogenesis, Idiopathic pulmonary fibrosis, 020901 industrial engineering & automation, medicine.anatomical_structure, Gene expression, 0202 electrical engineering, electronic engineering, information engineering, medicine, 020201 artificial intelligence & image processing, Gene
Abstract

Idiopathic pulmonary fibrosis is one of the chronic and fatal interstitial lung diseases. IPF generally shows poor prognosis, and their exact pathogenesis and casualties are not clearly revealed yet. RNA sequencing and microarray experiments enable the determination of genes whose expression levels are significantly different in IPF disease group compared with the healthy control group. Total 749 genes were identified as differentially expressed genes in both two data sets via (P-value < 0.05) via oligo, limma, and DESeq R packages. Among total DEGs, 453 genes were significantly up-regulated genes and 250 genes were down-regulated genes. In order to confirm the systemic functions of the obtained DEGs, we performed gene set enrichment analysis and functional annotation by database for Annotation, Visualization, and Integrated Discovery.

Published
2020

30. Fully automated web-based tool for identifying regulatory hotspots

Author

Junghyun Jung, Taegun Kim, Ju Hun Choi, and Jong Wha J. Joo

Subjects
lcsh:QH426-470, Computational complexity theory, lcsh:Biotechnology, Quantitative Trait Loci, Saccharomyces cerevisiae, Biology, eQTL, computer.software_genre, 03 medical and health sciences, 0302 clinical medicine, lcsh:TP248.13-248.65, Hotspot (geology), Parallel processing, Genetics, PLINK, Web application, Web-based tool, Spurious relationship, 030304 developmental biology, VCF (variant call format), Internet, 0303 health sciences, Models, Statistical, Google cloud compute engine (GCE), business.industry, Methodology, Chromosome Mapping, Statistical model, Running time, lcsh:Genetics, Fully automated, Data mining, Web service, business, Regulatory hotspot, computer, 030217 neurology & neurosurgery, Biotechnology
Abstract

Background Regulatory hotspots are genetic variations that may regulate the expression levels of many genes. It has been of great interest to find those hotspots utilizing expression quantitative trait locus (eQTL) analysis. However, it has been reported that many of the findings are spurious hotspots induced by various unknown confounding factors. Recently, methods utilizing complicated statistical models have been developed that successfully identify genuine hotspots. Next-generation Intersample Correlation Emended (NICE) is one of the methods that show high sensitivity and low false-discovery rate in finding regulatory hotspots. Even though the methods successfully find genuine hotspots, they have not been widely used due to their non-user-friendly interfaces and complex running processes. Furthermore, most of the methods are impractical due to their prohibitively high computational complexity. Results To overcome the limitations of existing methods, we developed a fully automated web-based tool, referred to as NICER (NICE Renew), which is based on NICE program. First, we dramatically reduced running and installing burden of NICE. Second, we significantly reduced running time by incorporating multi-processing. Third, besides our web-based NICER, users can use NICER on Google Compute Engine and can readily install and run the NICER web service on their local computers. Finally, we provide different input formats and visualizations tools to show results. Utilizing a yeast dataset, we show that NICER can be successfully used in an eQTL analysis to identify many genuine regulatory hotspots, for which more than half of the hotspots were previously reported elsewhere. Conclusions Even though many hotspot analysis tools have been proposed, they have not been widely used for many practical reasons. NICER is a fully-automated web-based solution for eQTL mapping and regulatory hotspots analysis. NICER provides a user-friendly interface and has made hotspot analysis more viable by reducing the running time significantly. We believe that NICER will become the method of choice for increasing power of eQTL hotspot analysis.

Published
2020

31. Modelling APOE ɛ3/4 allele-associated sporadic Alzheimer’s disease in an induced neuron

Author

Byung Soo Kim, Yujung Chang, Wonhee Jang, Dong-Gyu Jo, Jongpil Kim, Junghyun Jung, Janghwan Kim, Hongwon Kim, Junsang Yoo, Jaein Shin, and Christopher J. Lengner

Subjects
0301 basic medicine, Apolipoprotein E, Amyloid, Apolipoprotein E4, Models, Neurological, BACE1-AS, Apolipoprotein E3, Hyperphosphorylation, tau Proteins, Neuroprotection, Amyloid beta-Protein Precursor, Mice, 03 medical and health sciences, Alzheimer Disease, medicine, Amyloid precursor protein, Animals, Humans, Cellular Reprogramming Techniques, Phosphorylation, Alleles, Cells, Cultured, Neurons, Amyloid beta-Peptides, Desmoglein 2, biology, Fibroblasts, medicine.disease, Peptide Fragments, 030104 developmental biology, medicine.anatomical_structure, Gene Knockdown Techniques, biology.protein, Neurology (clinical), Neuron, Alzheimer's disease, Neuroscience
Abstract

The recent generation of induced neurons by direct lineage conversion holds promise for in vitro modelling of sporadic Alzheimer's disease. Here, we report the generation of induced neuron-based model of sporadic Alzheimer's disease in mice and humans, and used this system to explore the pathogenic mechanisms resulting from the sporadic Alzheimer's disease risk factor apolipoprotein E (APOE) ɛ3/4 allele. We show that mouse and human induced neurons overexpressing mutant amyloid precursor protein in the background of APOE ɛ3/4 allele exhibit altered amyloid precursor protein (APP) processing, abnormally increased production of amyloid-β42 and hyperphosphorylation of tau. Importantly, we demonstrate that APOE ɛ3/4 patient induced neuron culture models can faithfully recapitulate molecular signatures seen in APOE ɛ3/4-associated sporadic Alzheimer's disease patients. Moreover, analysis of the gene network derived from APOE ɛ3/4 patient induced neurons reveals a strong interaction between APOE ɛ3/4 and another Alzheimer's disease risk factor, desmoglein 2 (DSG2). Knockdown of DSG2 in APOE ɛ3/4 induced neurons effectively rescued defective APP processing, demonstrating the functional importance of this interaction. These data provide a direct connection between APOE ɛ3/4 and another Alzheimer's disease susceptibility gene and demonstrate in proof of principle the utility of induced neuron-based modelling of Alzheimer's disease for therapeutic discovery.

Published
2017

32. Meta-analysis of microarray and RNA-Seq gene expression datasets for carcinogenic risk: An assessment of Bisphenol A

Author

Woosuk Lee, Wonhee Jang, Changsoo Mok, and Junghyun Jung

Subjects
0301 basic medicine, Genetics, Microarray, Health, Toxicology and Mutagenesis, Public Health, Environmental and Occupational Health, Cancer, RNA-Seq, Biology, Toxicology, medicine.disease, Pathology and Forensic Medicine, 03 medical and health sciences, 030104 developmental biology, 0302 clinical medicine, Breast cancer, Disease Ontology, Genetic marker, 030220 oncology & carcinogenesis, Gene expression, medicine, General Pharmacology, Toxicology and Pharmaceutics, Gene
Abstract

Bisphenol A (BPA) is an endocrine-disrupting chemical that is related to many diseases, including heart attacks and diabetes. Recently, several studies have reported the carcinogenic potential of BPA in rodents, yet carcinogenic effects of BPA in humans remains unclear. In this study, meta-analysis was applied to independent GEO datasets, based on 158 Affymetrix microarrays and 8 Illumina RNA-Seqs. Additionally, we performed functional enrichment analysis, disease similarity analysis based on Disease Ontology (DO) analysis, and network analysis. 1,993 (1,457 up-, 536 down-regulated) differentially expressed genes (DEGs) were identified from five GEO datasets by adjusting for batch effects. Using disease similarity analysis, we demonstrated that results of DO analysis of the top 20 diseases were highly related to breast cancer. Moreover, we showed that the DEGs were significantly enriched in gene expression datasets on human breast cancer tissue via gene set enrichment analysis. By performing network analysis, we finally identified 85 (68 up- and 17 down-regulated) DEGs, and some of their expression levels were validated by quantitative PCR. The identified DEGs were regarded as genetic markers for carcinogenic risks, indicating that BPA may be a potential carcinogenic chemical contributing to the cause of breast cancer in humans.

Published
2017

33. Meta-analysis of microarray datasets for the risk assessment of coplanar polychlorinated biphenyl 77 (PCB77) on human health

Author

Junghyun Jung, Woosuk Lee, Wonhee Jang, and Kyoungyoung Hah

Subjects
0301 basic medicine, False discovery rate, Microarray, Health, Toxicology and Mutagenesis, Polychlorinated biphenyl, Computational biology, 010501 environmental sciences, Biology, Toxicology, Bioinformatics, 01 natural sciences, 03 medical and health sciences, chemistry.chemical_compound, 030104 developmental biology, chemistry, Genetic marker, Meta-analysis, Risk assessment, Gene, 0105 earth and related environmental sciences, Rank product
Abstract

Polychlorinated biphenyls (PCBs) are persistent organic compounds that have been banned since 1970s, but continue to contaminate the environment. PCBs are categorized into two structural groups: coplanar and non-coplanar PCBs. The coplanar PCBs are dioxin-like potent toxic compounds. To evaluate their effects on humans, we chose a coplanar PCB77 for data analysis. We performed meta- analysis by integrating datasets via the Rank Product method, and identified 375 up- and 66 down- regulated differentially expressed genes (DEGs). Notably, up-regulated genes were significantly associated with liver and kidney diseases. Using gene ontology enrichment, we found that the up-regulated DEGs were significantly enriched in the apoptotic process (false discovery rate, FDR=1.62e-10) and response to unfolded protein (FDR=7.65e-10). Protein-protein interaction networks identified the hub proteins containing HSP90AB1 and HSPA5. These findings suggest that our DEGs may provide a robust set of genetic markers for PCB77.

Published
2017

34. Additional file 1: of Integrative genomic and transcriptomic analysis of genetic markers in Dupuytrenâ s disease

Author

Junghyun Jung, Kim, Go, Byungjo Lee, Joo, Jong, and Wonhee Jang

Abstract

Identification of soft-thresholding power for co-expression network construction. (A) An analysis of scale free topology for picking an appropriate soft-thresholding power. (B) An analysis of the mean connectivity for picking an appropriate soft-thresholding power. (PPTX 277 kb)

Published
2019
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35. Additional file 2: of Integrative genomic and transcriptomic analysis of genetic markers in Dupuytrenâ s disease

Author

Junghyun Jung, Kim, Go, Byungjo Lee, Joo, Jong, and Wonhee Jang

Subjects
genetic structures, mental disorders, sense organs, Astrophysics::Earth and Planetary Astrophysics, behavioral disciplines and activities, psychological phenomena and processes, Astrophysics::Galaxy Astrophysics
Abstract

Point range plots showing the results of the enrichment of DEGs about each module via Fisherâ s exact test. (A) Odds ratios for the enrichment of DEGs compared with non-DEGs by one-color meta-analysis with 95% confidence intervals of odds. Because tan and midnight blue modules have no DEGs, the odds ratio is zero. (B) Odds ratios for the enrichment of DEGs compared with non-DEGs by two-color microarray analysis and 95% confidence intervals of the odds. (PPTX 242 kb)

Published
2019
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37. Leveraging allelic heterogeneity to increase power of association testing

Author

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

Subjects
Locus (genetics), Allelic heterogeneity, Genome-wide association study, Computational biology, Mars Exploration Program, Northern finland, Biology, Statistical power, Type I and type II errors, Genetic association
Abstract

The standard genome-wide association studies (GWAS) detects an association between a single variant and a phenotype of interest. Recently, several studies reported that at many risk loci, there may exist multiple causal variants. For a locus with multiple causal variants with small effect sizes, the standard association test is underpowered to detect the associations. Alternatively, an approach considering effects of multiple variants simultaneously may increase statistical power by leveraging effects of multiple causal variants. In this paper, we propose a new statistical method, Model-based Association test Reflecting causal Status (MARS), that tries to find an association between variants in risk loci and a phenotype, considering the causal status of the variants. One of the main advantages of MARS is that it only requires the existing summary statistics to detect associated risk loci. Thus, MARS is applicable to any association study with summary statistics, even though individual level data is not available for the study. Utilizing extensive simulated data sets, we show that MARS increases the power of detecting true associated risk loci compared to previous approaches that consider multiple variants, while robustly controls the type I error. Applied to data of 44 tissues provided by the Genotype-Tissue Expression (GTEx) consortium, we show that MARS identifies more eGenes compared to previous approaches in most of the tissues; e.g. MARS identified 16% more eGenes than the ones reported by the GTEx consortium. Moreover, applied to Northern Finland Birth Cohort (NFBC) data, we demonstrate that MARS effectively identifies association loci with improved power (56% of more loci found by MARS) inGWAS studies compared to the standard association test.

Published
2018

38. Galectin-3 supports stemness in ovarian cancer stem cells by activation of the Notch1 intracellular domain

Author

Junghyun Jung, Seok-Jun Kim, Da-Hyun Kim, Yunhee Cho, Kyung-Hee Chun, Hyeok Gu Kang, and Wonhee Jang

Subjects
cancer stem cells, 0301 basic medicine, endocrine system, Programmed cell death, animal structures, endocrine system diseases, Galectin 3, Galectins, Transplantation, Heterologous, Notch signaling pathway, Mice, Nude, Apoptosis, Biology, 03 medical and health sciences, Cell Movement, Cancer stem cell, Cell Line, Tumor, Spheroids, Cellular, galectin-3, otorhinolaryngologic diseases, medicine, Animals, Humans, Gene silencing, Receptor, Notch1, HES1, Cell Proliferation, Ovarian Neoplasms, Notch1, Blood Proteins, medicine.disease, Gene Expression Regulation, Neoplastic, stomatognathic diseases, ovarian cancer, 030104 developmental biology, Oncology, Galectin-3, Immunology, Neoplastic Stem Cells, Cancer research, Female, RNA Interference, Stem cell, Ovarian cancer, Research Paper
Abstract

Ovarian cancer is the most lethal gynecologic disease because usually, it is lately sensed, easily acquires chemoresistance, and has a high recurrence rate. Recent studies suggest that ovarian cancer stem cells (CSCs) are involved in these malignancies. Here, we demonstrated that galectin-3 maintains ovarian CSCs by activating the Notch1 intracellular domain (NICD1). The number and size of ovarian CSCs decreased in the absence of galectin-3, and overexpression of galectin-3 increased them. Overexpression of galectin-3 increased the resistance for cisplatin and paclitaxel-induced cell death. Silencing of galectin-3 decreased the migration and invasion of ovarian cancer cells, and overexpression of galectin-3 reversed these effects. The Notch signaling pathway was strongly activated by galectin-3 overexpression in A2780 cells. Silencing of galectin-3 reduced the levels of cleaved NICD1 and expression of the Notch target genes, Hes1 and Hey1. Overexpression of galectin-3 induced NICD1 cleavage and increased expression of Hes1 and Hey1. Moreover, overexpression of galectin-3 increased the nuclear translocation of NICD1. Interestingly, the carbohydrate recognition domain of galectin-3 interacted with NICD1. Overexpression of galectin-3 increased tumor burden in A2780 ovarian cancer xenografted mice. Increased expression of galectin-3 was detected in advanced stages, compared to stage 1 or 2 in ovarian cancer patients, suggesting that galectin-3 supports stemness of these cells. Based on these results, we suggest that targeting galectin-3 may be a potent approach for improving ovarian cancer therapy.

Published
2016

39. Meta- and cross-species analyses of insulin resistance based on gene expression datasets in human white adipose tissues

Author

Sung Hyun Chung, Wonhee Jang, Changsoo Mok, Woosuk Lee, Junghyun Jung, and Go Woon Kim

Subjects
0301 basic medicine, Microarray, Adipose Tissue, White, Gene regulatory network, lcsh:Medicine, Adipose tissue, White adipose tissue, Computational biology, Biology, Article, Transcriptome, 03 medical and health sciences, Species Specificity, Databases, Genetic, Drug Discovery, Gene expression, Humans, Gene Regulatory Networks, Obesity, lcsh:Science, Gene, Multidisciplinary, Gene Expression Profiling, lcsh:R, Computational Biology, Gene expression profiling, Gene Ontology, 030104 developmental biology, Gene Expression Regulation, lcsh:Q, Insulin Resistance
Abstract

Ample evidence indicates that insulin resistance (IR) is closely related to white adipose tissue (WAT), but the underlying mechanisms of IR pathogenesis are still unclear. Using 352 microarray datasets from seven independent studies, we identified a meta-signature which comprised of 1,413 genes. Our meta-signature was also enriched in overall WAT in in vitro and in vivo IR models. Only 12 core enrichment genes were consistently enriched across all IR models. Among the meta-signature, we identified a drug signature made up of 211 genes with expression levels that were co-regulated by thiazolidinediones and metformin using cross-species analysis. To confirm the clinical relevance of our drug signature, we found that the expression levels of 195 genes in the drug signature were significantly correlated with both homeostasis model assessment 2-IR score and body mass index. Finally, 18 genes from the drug signature were identified by protein-protein interaction network cluster. Four core enrichment genes were included in 18 genes and the expression levels of selected 8 genes were validated by quantitative PCR. These findings suggest that our signatures provide a robust set of genetic markers which can be used to provide a starting point for developing potential therapeutic targets in improving IR in WAT.

Published
2018

40. Inhibitory Effects of Novel SphK2 Inhibitors on Migration of Cancer Cells

Author

Deokho Jung, Youngeun Kwon, Junghyun Jung, Chang Seo Park, Hyunjin Jeon, Euiyeon Lee, Wonhee Jang, and Chang Soo Mok

Subjects
0301 basic medicine, Cancer Research, Motility, Biology, HeLa, 03 medical and health sciences, chemistry.chemical_compound, Cell Movement, Sphingosine, Humans, Enzyme Inhibitors, Cytoskeleton, Cell Proliferation, Pharmacology, Reverse Transcriptase Polymerase Chain Reaction, Cell growth, Cell migration, biology.organism_classification, Cell biology, Phosphotransferases (Alcohol Group Acceptor), SPHK2, 030104 developmental biology, chemistry, Cancer cell, Molecular Medicine, Signal transduction, HeLa Cells
Abstract

Background Cell migration is an essential process for survival and differentiation of mammalian cells. Numerous diseases are induced or influenced by inappropriate regulation of cell migration, which plays a key role in cancer cell metastasis. In fact, very few anti-metastasis drugs are available on the market. SphKs are enzymes that convert sphingosine to sphingosine-1-phosphate (S1P) and are known to control various cellular functions, including migration of cells. In human, SphK2 is known to promote apoptosis, suppresses cell growth, and controls cell migration; in addition, the specific ablation of SphK2 activity was reported to inhibit cancer cell metastasis. Objective The previously identified SG12 and SG14 are synthetic analogs of sphingoid and can specifically inhibit the functions of SphK2. We investigated the effects of the SphK2 specific inhibitors on the migratory behavior of cells. Method We investigated how SG12 and SG14 affect cell migration by monitoring both cumulative and individual cell migration behavior using HeLa cells. Results SG12 and SG14 mutually showed stronger inhibitory effects with less cytotoxicity compared with a general SphK inhibitor, N,N-dimethylsphingosine (DMS). The mechanistic aspects of specific SphK2 inhibition were studied by examining actin filamentation and the expression levels of motility-related genes. Conclusion The data revealed that SG12 and SG14 resemble DMS in decreasing overall cell motility, but differ in that they differentially affect motility parameters and motility-related signal transduction pathways and therefore actin polymerization, which are not altered by DMS. Our findings show that SphK2 inhibitors are putative candidates for anti-metastatic drugs.

Published
2018

41. Meta-Analysis of Polymyositis and Dermatomyositis Microarray Data Reveals Novel Genetic Biomarkers

Author

Go Woon Kim, Daeun Kim, Jaeseung Song, Sejin Park, Juyeon Hong, Junghyun Jung, Hee Jung Park, Jong Wha J. Joo, and Wonhee Jang

Subjects
Genetic Markers, 0301 basic medicine, lcsh:QH426-470, dermatomyositis, Quantitative Trait Loci, Gene Expression, Single-nucleotide polymorphism, Disease, Biology, Polymorphism, Single Nucleotide, Polymyositis, Article, Pathogenesis, 03 medical and health sciences, 0302 clinical medicine, Databases, Genetic, Genetics, medicine, Humans, Genetic Predisposition to Disease, Gene, Genetics (clinical), multiple-phenotype analysis, Myositis, Microarray analysis techniques, Gene Expression Profiling, Dermatomyositis, medicine.disease, meta-analysis, lcsh:Genetics, 030104 developmental biology, Case-Control Studies, 030220 oncology & carcinogenesis, Expression quantitative trait loci, Unfolded Protein Response, Interferons, Biomarkers
Abstract

Polymyositis (PM) and dermatomyositis (DM) are both classified as idiopathic inflammatory myopathies. They share a few common characteristics such as inflammation and muscle weakness. Previous studies have indicated that these diseases present aspects of an auto-immune disorder, however, their exact pathogenesis is still unclear. In this study, three gene expression datasets (PM: 7, DM: 50, Control: 13) available in public databases were used to conduct meta-analysis. We then conducted expression quantitative trait loci analysis to detect the variant sites that may contribute to the pathogenesis of PM and DM. Six-hundred differentially expressed genes were identified in the meta-analysis (false discovery rate (FDR) &lt, 0.01), among which 317 genes were up-regulated and 283 were down-regulated in the disease group compared with those in the healthy control group. The up-regulated genes were significantly enriched in interferon-signaling pathways in protein secretion, and/or in unfolded-protein response. We detected 10 single nucleotide polymorphisms (SNPs) which could potentially play key roles in driving the PM and DM. Along with previously reported genes, we identified 4 novel genes and 10 SNP-variant regions which could be used as candidates for potential drug targets or biomarkers for PM and DM.

Published
2019

42. Electromagnetized gold nanoparticles mediate direct lineage reprogramming into induced dopamine neurons in vivo for Parkinson's disease therapy

Author

Junsang Yoo, Jaein Shin, Soochan Kim, Wonhee Jang, Christopher J. Lengner, Hongwon Kim, Wonwoong Lee, Jongki Hong, Hee Young Kim, Junghyun Jung, Won Jun, Soonbong Baek, Euiyeon Lee, Youngeun Kwon, Sang Hyun Moh, Yujung Chang, Hi-Joon Park, Dong-ho Youn, and Jongpil Kim

Subjects
0301 basic medicine, Male, Parkinson's disease, Lineage (genetic), Chromosomal Proteins, Non-Histone, Biomedical Engineering, Metal Nanoparticles, Bioengineering, 02 engineering and technology, Cell Line, Histones, 03 medical and health sciences, Mice, Electromagnetic Fields, In vivo, Dopamine, medicine, Animals, General Materials Science, Electrical and Electronic Engineering, Therapeutic strategy, Dopaminergic Neurons, fungi, food and beverages, MPTP Poisoning, Acetylation, 021001 nanoscience & nanotechnology, Condensed Matter Physics, medicine.disease, Cellular Reprogramming, Atomic and Molecular Physics, and Optics, Cell biology, Enzyme Activation, 030104 developmental biology, Colloidal gold, Gold, 0210 nano-technology, Reprogramming, medicine.drug, Transcription Factors
Abstract

Electromagnetic fields (EMF) are physical energy fields generated by electrically charged objects, and specific ranges of EMF can influence numerous biological processes, which include the control of cell fate and plasticity. In this study, we show that electromagnetized gold nanoparticles (AuNPs) in the presence of specific EMF conditions facilitate an efficient direct lineage reprogramming to induced dopamine neurons in vitro and in vivo. Remarkably, electromagnetic stimulation leads to a specific activation of the histone acetyltransferase Brd2, which results in histone H3K27 acetylation and a robust activation of neuron-specific genes. In vivo dopaminergic neuron reprogramming by EMF stimulation of AuNPs efficiently and non-invasively alleviated symptoms in mouse Parkinson's disease models. This study provides a proof of principle for EMF-based in vivo lineage conversion as a potentially viable and safe therapeutic strategy for the treatment of neurodegenerative disorders.

Published
2016

43. Modelling APOE ɛ3/4 allele-associated sporadic Alzheimer's disease in an induced neuron.

Author

Hongwon Kim, Junsang Yoo, Jaein Shin, Yujung Chang, Junghyun Jung, Dong-Gyu Jo, Janghwan Kim, Wonhee Jang, Lengner, Christopher J., Byung-Soo Kim, Jongpil Kim, Kim, Hongwon, Yoo, Junsang, Shin, Jaein, Chang, Yujung, Jung, Junghyun, Jo, Dong-Gyu, Kim, Janghwan, Jang, Wonhee, and Kim, Byung-Soo

Subjects
ALZHEIMER'S disease, APOLIPOPROTEIN E, DESMOGLEINS, NEURONS, ALLELES
Abstract

The recent generation of induced neurons by direct lineage conversion holds promise for in vitro modelling of sporadic Alzheimer's disease. Here, we report the generation of induced neuron-based model of sporadic Alzheimer's disease in mice and humans, and used this system to explore the pathogenic mechanisms resulting from the sporadic Alzheimer's disease risk factor apolipoprotein E (APOE) ɛ3/4 allele. We show that mouse and human induced neurons overexpressing mutant amyloid precursor protein in the background of APOE ɛ3/4 allele exhibit altered amyloid precursor protein (APP) processing, abnormally increased production of amyloid-β42 and hyperphosphorylation of tau. Importantly, we demonstrate that APOE ɛ3/4 patient induced neuron culture models can faithfully recapitulate molecular signatures seen in APOE ɛ3/4-associated sporadic Alzheimer's disease patients. Moreover, analysis of the gene network derived from APOE ɛ3/4 patient induced neurons reveals a strong interaction between APOE ɛ3/4 and another Alzheimer's disease risk factor, desmoglein 2 (DSG2). Knockdown of DSG2 in APOE ɛ3/4 induced neurons effectively rescued defective APP processing, demonstrating the functional importance of this interaction. These data provide a direct connection between APOE ɛ3/4 and another Alzheimer's disease susceptibility gene and demonstrate in proof of principle the utility of induced neuron-based modelling of Alzheimer's disease for therapeutic discovery. [ABSTRACT FROM AUTHOR]

Published
2017
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