101
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Ba Q, Hei Y, Dighe A, Li W, Maziarz J, Pak I, Wang S, Wagner GP, Liu Y. Proteotype coevolution and quantitative diversity across 11 mammalian species. SCIENCE ADVANCES 2022; 8:eabn0756. [PMID: 36083897 PMCID: PMC9462687 DOI: 10.1126/sciadv.abn0756] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 07/25/2022] [Indexed: 06/15/2023]
Abstract
Evolutionary profiling has been largely limited to the nucleotide level. Using consistent proteomic methods, we quantified proteomic and phosphoproteomic layers in fibroblasts from 11 common mammalian species, with transcriptomes as reference. Covariation analysis indicates that transcript and protein expression levels and variabilities across mammals remarkably follow functional role, with extracellular matrix-associated expression being the most variable, demonstrating strong transcriptome-proteome coevolution. The biological variability of gene expression is universal at both interindividual and interspecies scales but to a different extent. RNA metabolic processes particularly show higher interspecies versus interindividual variation. Our results further indicate that while the ubiquitin-proteasome system is strongly conserved in mammals, lysosome-mediated protein degradation exhibits remarkable variation between mammalian lineages. In addition, the phosphosite profiles reveal a phosphorylation coevolution network independent of protein abundance.
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Affiliation(s)
- Qian Ba
- Yale Cancer Biology Institute, West Haven, CT 06516, USA
| | - Yuanyuan Hei
- Yale Cancer Biology Institute, West Haven, CT 06516, USA
| | - Anasuya Dighe
- Yale Systems Biology Institute, West Haven, CT 06516, USA
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520, USA
| | - Wenxue Li
- Yale Cancer Biology Institute, West Haven, CT 06516, USA
| | - Jamie Maziarz
- Yale Systems Biology Institute, West Haven, CT 06516, USA
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520, USA
| | - Irene Pak
- Yale Systems Biology Institute, West Haven, CT 06516, USA
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520, USA
| | - Shisheng Wang
- West China-Washington Mitochondria and Metabolism Research Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Günter P. Wagner
- Yale Systems Biology Institute, West Haven, CT 06516, USA
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520, USA
- Department of Obstetrics, Gynecology and Reproductive Sciences, Yale University School of Medicine, New Haven, CT 06510, USA
- Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI 48202, USA
| | - Yansheng Liu
- Yale Cancer Biology Institute, West Haven, CT 06516, USA
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06510, USA
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102
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Fernández-Torras A, Duran-Frigola M, Bertoni M, Locatelli M, Aloy P. Integrating and formatting biomedical data as pre-calculated knowledge graph embeddings in the Bioteque. Nat Commun 2022; 13:5304. [PMID: 36085310 PMCID: PMC9463154 DOI: 10.1038/s41467-022-33026-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 08/30/2022] [Indexed: 12/25/2022] Open
Abstract
Biomedical data is accumulating at a fast pace and integrating it into a unified framework is a major challenge, so that multiple views of a given biological event can be considered simultaneously. Here we present the Bioteque, a resource of unprecedented size and scope that contains pre-calculated biomedical descriptors derived from a gigantic knowledge graph, displaying more than 450 thousand biological entities and 30 million relationships between them. The Bioteque integrates, harmonizes, and formats data collected from over 150 data sources, including 12 biological entities (e.g., genes, diseases, drugs) linked by 67 types of associations (e.g., 'drug treats disease', 'gene interacts with gene'). We show how Bioteque descriptors facilitate the assessment of high-throughput protein-protein interactome data, the prediction of drug response and new repurposing opportunities, and demonstrate that they can be used off-the-shelf in downstream machine learning tasks without loss of performance with respect to using original data. The Bioteque thus offers a thoroughly processed, tractable, and highly optimized assembly of the biomedical knowledge available in the public domain.
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Affiliation(s)
- Adrià Fernández-Torras
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Miquel Duran-Frigola
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
- Ersilia Open Source Initiative, Cambridge, UK
| | - Martino Bertoni
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Martina Locatelli
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Patrick Aloy
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain.
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain.
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103
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Zhou YH, Gallins PJ, Etheridge AS, Jima D, Scholl E, Wright FA, Innocenti F. A resource for integrated genomic analysis of the human liver. Sci Rep 2022; 12:15151. [PMID: 36071064 PMCID: PMC9452507 DOI: 10.1038/s41598-022-18506-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 08/08/2022] [Indexed: 11/18/2022] Open
Abstract
In this study, we generated whole-transcriptome RNA-Seq from n = 192 genotyped liver samples and used these data with existing data from the GTEx Project (RNA-Seq) and previous liver eQTL (microarray) studies to create an enhanced transcriptomic sequence resource in the human liver. Analyses of genotype-expression associations show pronounced enrichment of associations with genes of drug response. The associations are primarily consistent across the two RNA-Seq datasets, with some modest variation, indicating the importance of obtaining multiple datasets to produce a robust resource. We further used an empirical Bayesian model to compare eQTL patterns in liver and an additional 20 GTEx tissues, finding that MHC genes, and especially class II genes, are enriched for liver-specific eQTL patterns. To illustrate the utility of the resource to augment GWAS analysis with small sample sizes, we developed a novel meta-analysis technique to combine several liver eQTL data sources. We also illustrate its application using a transcriptome-enhanced re-analysis of a study of neutropenia in pancreatic cancer patients. The associations of genotype with liver expression, including splice variation and its genetic associations, are made available in a searchable genome browser.
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Affiliation(s)
- Yi-Hui Zhou
- Department of Biological Sciences, North Carolina State University, Raleigh NC State University, Raleigh, NC, 27695, USA.
- Bioinformatics Research Center, North Carolina State University, Raleigh NC State University, Raleigh, NC, 27695, USA.
| | - Paul J Gallins
- Bioinformatics Research Center, North Carolina State University, Raleigh NC State University, Raleigh, NC, 27695, USA
| | - Amy S Etheridge
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Dereje Jima
- Bioinformatics Research Center, North Carolina State University, Raleigh NC State University, Raleigh, NC, 27695, USA
| | - Elizabeth Scholl
- Bioinformatics Research Center, North Carolina State University, Raleigh NC State University, Raleigh, NC, 27695, USA
| | - Fred A Wright
- Department of Biological Sciences, North Carolina State University, Raleigh NC State University, Raleigh, NC, 27695, USA
- Bioinformatics Research Center, North Carolina State University, Raleigh NC State University, Raleigh, NC, 27695, USA
- Department of Statistics, North Carolina State University, Raleigh NC State University, Raleigh, NC, 27695, USA
| | - Federico Innocenti
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA.
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104
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Liu S, Gao Y, Canela-Xandri O, Wang S, Yu Y, Cai W, Li B, Xiang R, Chamberlain AJ, Pairo-Castineira E, D’Mellow K, Rawlik K, Xia C, Yao Y, Navarro P, Rocha D, Li X, Yan Z, Li C, Rosen BD, Van Tassell CP, Vanraden PM, Zhang S, Ma L, Cole JB, Liu GE, Tenesa A, Fang L. A multi-tissue atlas of regulatory variants in cattle. Nat Genet 2022; 54:1438-1447. [PMID: 35953587 PMCID: PMC7613894 DOI: 10.1038/s41588-022-01153-5] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 07/07/2022] [Indexed: 12/12/2022]
Abstract
Characterization of genetic regulatory variants acting on livestock gene expression is essential for interpreting the molecular mechanisms underlying traits of economic value and for increasing the rate of genetic gain through artificial selection. Here we build a Cattle Genotype-Tissue Expression atlas (CattleGTEx) as part of the pilot phase of the Farm animal GTEx (FarmGTEx) project for the research community based on 7,180 publicly available RNA-sequencing (RNA-seq) samples. We describe the transcriptomic landscape of more than 100 tissues/cell types and report hundreds of thousands of genetic associations with gene expression and alternative splicing for 23 distinct tissues. We evaluate the tissue-sharing patterns of these genetic regulatory effects, and functionally annotate them using multiomics data. Finally, we link gene expression in different tissues to 43 economically important traits using both transcriptome-wide association and colocalization analyses to decipher the molecular regulatory mechanisms underpinning such agronomic traits in cattle.
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Affiliation(s)
- Shuli Liu
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705, USA
- College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
| | - Yahui Gao
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705, USA
- Department of Animal and Avian Sciences, University of Maryland, College Park, Maryland 20742, USA
| | - Oriol Canela-Xandri
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Sheng Wang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China
| | - Ying Yu
- College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Wentao Cai
- Institute of Animal Science, Chinese Academy of Agricultural Science, Beijing 100193, China
| | - Bingjie Li
- Scotland’s Rural College (SRUC), Roslin Institute Building, Midlothian EH25 9RG, UK
| | - Ruidong Xiang
- Faculty of Veterinary & Agricultural Science, The University of Melbourne, Parkville 3052, Victoria, Australia
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, Victoria 3083, Australia
| | - Amanda J. Chamberlain
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, Victoria 3083, Australia
| | - Erola Pairo-Castineira
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian EH25 9RG, UK
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Kenton D’Mellow
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Konrad Rawlik
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian EH25 9RG, UK
| | - Charley Xia
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian EH25 9RG, UK
| | - Yuelin Yao
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Pau Navarro
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Dominique Rocha
- INRAE, AgroParisTech, GABI, Université Paris-Saclay, Jouy-en-Josas, F-78350, France
| | - Xiujin Li
- Guangdong Provincial Key Laboratory of Waterfowl Healthy Breeding, College of Animal Science & Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, Guangdong 510225, China
| | - Ze Yan
- College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Congjun Li
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705, USA
| | - Benjamin D. Rosen
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705, USA
| | - Curtis P. Van Tassell
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705, USA
| | - Paul M. Vanraden
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705, USA
| | - Shengli Zhang
- College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Li Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park, Maryland 20742, USA
| | - John B. Cole
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705, USA
| | - George E. Liu
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705, USA
| | - Albert Tenesa
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian EH25 9RG, UK
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Lingzhao Fang
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service, USDA, Beltsville, Maryland 20705, USA
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh EH4 2XU, UK
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105
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Shao Z, Wang T, Qiao J, Zhang Y, Huang S, Zeng P. A comprehensive comparison of multilocus association methods with summary statistics in genome-wide association studies. BMC Bioinformatics 2022; 23:359. [PMID: 36042399 PMCID: PMC9429742 DOI: 10.1186/s12859-022-04897-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 08/22/2022] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Multilocus analysis on a set of single nucleotide polymorphisms (SNPs) pre-assigned within a gene constitutes a valuable complement to single-marker analysis by aggregating data on complex traits in a biologically meaningful way. However, despite the existence of a wide variety of SNP-set methods, few comprehensive comparison studies have been previously performed to evaluate the effectiveness of these methods. RESULTS We herein sought to fill this knowledge gap by conducting a comprehensive empirical comparison for 22 commonly-used summary-statistics based SNP-set methods. We showed that only seven methods could effectively control the type I error, and that these well-calibrated approaches had varying power performance under the simulation scenarios. Overall, we confirmed that the burden test was generally underpowered and score-based variance component tests (e.g., sequence kernel association test) were much powerful under the polygenic genetic architecture in both common and rare variant association analyses. We further revealed that two linkage-disequilibrium-free P value combination methods (e.g., harmonic mean P value method and aggregated Cauchy association test) behaved very well under the sparse genetic architecture in simulations and real-data applications to common and rare variant association analyses as well as in expression quantitative trait loci weighted integrative analysis. We also assessed the scalability of these approaches by recording computational time and found that all these methods can be scalable to biobank-scale data although some might be relatively slow. CONCLUSION In conclusion, we hope that our findings can offer an important guidance on how to choose appropriate multilocus association analysis methods in post-GWAS era. All the SNP-set methods are implemented in the R package called MCA, which is freely available at https://github.com/biostatpzeng/ .
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Affiliation(s)
- Zhonghe Shao
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Ting Wang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Jiahao Qiao
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Yuchen Zhang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Shuiping Huang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
- Key Laboratory of Environment and Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
- Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Key Laboratory of Environment and Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
- Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
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106
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Splicing QTL analysis focusing on coding sequences reveals mechanisms for disease susceptibility loci. Nat Commun 2022; 13:4659. [PMID: 36002455 PMCID: PMC9402578 DOI: 10.1038/s41467-022-32358-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 07/26/2022] [Indexed: 12/26/2022] Open
Abstract
Splicing quantitative trait loci (sQTLs) are one of the major causal mechanisms in genome-wide association study (GWAS) loci, but their role in disease pathogenesis is poorly understood. One reason is the complexity of alternative splicing events producing many unknown isoforms. Here, we propose two approaches, namely integration and selection, for this complexity by focusing on protein-structure of isoforms. First, we integrate isoforms with the same coding sequence (CDS) and identify 369-601 integrated-isoform ratio QTLs (i2-rQTLs), which altered protein-structure, in six immune subsets. Second, we select CDS incomplete isoforms annotated in GENCODE and identify 175-337 isoform-ratio QTL (i-rQTL). By comprehensive long-read capture RNA-sequencing among these incomplete isoforms, we reveal 29 full-length isoforms with unannotated CDSs associated with GWAS traits. Furthermore, we show that disease-causal sQTL genes can be identified by evaluating their trans-eQTL effects. Our approaches highlight the understudied role of protein-altering sQTLs and are broadly applicable to other tissues and diseases. Splicing QTL (sQTL), genetic variants regulating alternative splicing, can be biologically important, but complex to detect and interpret. Here, the authors identify sQTL by focusing on protein coding sequences, as an alternative to junction-based approaches.
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107
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Wang QS, Edahiro R, Namkoong H, Hasegawa T, Shirai Y, Sonehara K, Tanaka H, Lee H, Saiki R, Hyugaji T, Shimizu E, Katayama K, Kanai M, Naito T, Sasa N, Yamamoto K, Kato Y, Morita T, Takahashi K, Harada N, Naito T, Hiki M, Matsushita Y, Takagi H, Ichikawa M, Nakamura A, Harada S, Sandhu Y, Kabata H, Masaki K, Kamata H, Ikemura S, Chubachi S, Okamori S, Terai H, Morita A, Asakura T, Sasaki J, Morisaki H, Uwamino Y, Nanki K, Uchida S, Uno S, Nishimura T, Ishiguro T, Isono T, Shibata S, Matsui Y, Hosoda C, Takano K, Nishida T, Kobayashi Y, Takaku Y, Takayanagi N, Ueda S, Tada A, Miyawaki M, Yamamoto M, Yoshida E, Hayashi R, Nagasaka T, Arai S, Kaneko Y, Sasaki K, Tagaya E, Kawana M, Arimura K, Takahashi K, Anzai T, Ito S, Endo A, Uchimura Y, Miyazaki Y, Honda T, Tateishi T, Tohda S, Ichimura N, Sonobe K, Sassa CT, Nakajima J, Nakano Y, Nakajima Y, Anan R, Arai R, Kurihara Y, Harada Y, Nishio K, Ueda T, Azuma M, Saito R, Sado T, Miyazaki Y, Sato R, Haruta Y, Nagasaki T, Yasui Y, Hasegawa Y, Mutoh Y, Kimura T, Sato T, Takei R, Hagimoto S, Noguchi Y, Yamano Y, Sasano H, Ota S, Nakamori Y, Yoshiya K, Saito F, Yoshihara T, Wada D, Iwamura H, Kanayama S, Maruyama S, Yoshiyama T, Ohta K, Kokuto H, Ogata H, Tanaka Y, Arakawa K, Shimoda M, Osawa T, Tateno H, Hase I, Yoshida S, Suzuki S, Kawada M, Horinouchi H, Saito F, Mitamura K, Hagihara M, Ochi J, Uchida T, Baba R, Arai D, Ogura T, Takahashi H, Hagiwara S, Nagao G, Konishi S, Nakachi I, Murakami K, Yamada M, Sugiura H, Sano H, Matsumoto S, Kimura N, Ono Y, Baba H, Suzuki Y, Nakayama S, Masuzawa K, Namba S, Shiroyama T, Noda Y, Niitsu T, Adachi Y, Enomoto T, Amiya S, Hara R, Yamaguchi Y, Murakami T, Kuge T, Matsumoto K, Yamamoto Y, Yamamoto M, Yoneda M, Tomono K, Kato K, Hirata H, Takeda Y, Koh H, Manabe T, Funatsu Y, Ito F, Fukui T, Shinozuka K, Kohashi S, Miyazaki M, Shoko T, Kojima M, Adachi T, Ishikawa M, Takahashi K, Inoue T, Hirano T, Kobayashi K, Takaoka H, Watanabe K, Miyazawa N, Kimura Y, Sado R, Sugimoto H, Kamiya A, Kuwahara N, Fujiwara A, Matsunaga T, Sato Y, Okada T, Hirai Y, Kawashima H, Narita A, Niwa K, Sekikawa Y, Nishi K, Nishitsuji M, Tani M, Suzuki J, Nakatsumi H, Ogura T, Kitamura H, Hagiwara E, Murohashi K, Okabayashi H, Mochimaru T, Nukaga S, Satomi R, Oyamada Y, Mori N, Baba T, Fukui Y, Odate M, Mashimo S, Makino Y, Yagi K, Hashiguchi M, Kagyo J, Shiomi T, Fuke S, Saito H, Tsuchida T, Fujitani S, Takita M, Morikawa D, Yoshida T, Izumo T, Inomata M, Kuse N, Awano N, Tone M, Ito A, Nakamura Y, Hoshino K, Maruyama J, Ishikura H, Takata T, Odani T, Amishima M, Hattori T, Shichinohe Y, Kagaya T, Kita T, Ohta K, Sakagami S, Koshida K, Hayashi K, Shimizu T, Kozu Y, Hiranuma H, Gon Y, Izumi N, Nagata K, Ueda K, Taki R, Hanada S, Kawamura K, Ichikado K, Nishiyama K, Muranaka H, Nakamura K, Hashimoto N, Wakahara K, Koji S, Omote N, Ando A, Kodama N, Kaneyama Y, Maeda S, Kuraki T, Matsumoto T, Yokote K, Nakada TA, Abe R, Oshima T, Shimada T, Harada M, Takahashi T, Ono H, Sakurai T, Shibusawa T, Kimizuka Y, Kawana A, Sano T, Watanabe C, Suematsu R, Sageshima H, Yoshifuji A, Ito K, Takahashi S, Ishioka K, Nakamura M, Masuda M, Wakabayashi A, Watanabe H, Ueda S, Nishikawa M, Chihara Y, Takeuchi M, Onoi K, Shinozuka J, Sueyoshi A, Nagasaki Y, Okamoto M, Ishihara S, Shimo M, Tokunaga Y, Kusaka Y, Ohba T, Isogai S, Ogawa A, Inoue T, Fukuyama S, Eriguchi Y, Yonekawa A, Kan-O K, Matsumoto K, Kanaoka K, Ihara S, Komuta K, Inoue Y, Chiba S, Yamagata K, Hiramatsu Y, Kai H, Asano K, Oguma T, Ito Y, Hashimoto S, Yamasaki M, Kasamatsu Y, Komase Y, Hida N, Tsuburai T, Oyama B, Takada M, Kanda H, Kitagawa Y, Fukuta T, Miyake T, Yoshida S, Ogura S, Abe S, Kono Y, Togashi Y, Takoi H, Kikuchi R, Ogawa S, Ogata T, Ishihara S, Kanehiro A, Ozaki S, Fuchimoto Y, Wada S, Fujimoto N, Nishiyama K, Terashima M, Beppu S, Yoshida K, Narumoto O, Nagai H, Ooshima N, Motegi M, Umeda A, Miyagawa K, Shimada H, Endo M, Ohira Y, Watanabe M, Inoue S, Igarashi A, Sato M, Sagara H, Tanaka A, Ohta S, Kimura T, Shibata Y, Tanino Y, Nikaido T, Minemura H, Sato Y, Yamada Y, Hashino T, Shinoki M, Iwagoe H, Takahashi H, Fujii K, Kishi H, Kanai M, Imamura T, Yamashita T, Yatomi M, Maeno T, Hayashi S, Takahashi M, Kuramochi M, Kamimaki I, Tominaga Y, Ishii T, Utsugi M, Ono A, Tanaka T, Kashiwada T, Fujita K, Saito Y, Seike M, Watanabe H, Matsuse H, Kodaka N, Nakano C, Oshio T, Hirouchi T, Makino S, Egi M, Omae Y, Nannya Y, Ueno T, Takano T, Katayama K, Ai M, Kumanogoh A, Sato T, Hasegawa N, Tokunaga K, Ishii M, Koike R, Kitagawa Y, Kimura A, Imoto S, Miyano S, Ogawa S, Kanai T, Fukunaga K, Okada Y. The whole blood transcriptional regulation landscape in 465 COVID-19 infected samples from Japan COVID-19 Task Force. Nat Commun 2022; 13:4830. [PMID: 35995775 PMCID: PMC9395416 DOI: 10.1038/s41467-022-32276-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 07/25/2022] [Indexed: 11/12/2022] Open
Abstract
Coronavirus disease 2019 (COVID-19) is a recently-emerged infectious disease that has caused millions of deaths, where comprehensive understanding of disease mechanisms is still unestablished. In particular, studies of gene expression dynamics and regulation landscape in COVID-19 infected individuals are limited. Here, we report on a thorough analysis of whole blood RNA-seq data from 465 genotyped samples from the Japan COVID-19 Task Force, including 359 severe and 106 non-severe COVID-19 cases. We discover 1169 putative causal expression quantitative trait loci (eQTLs) including 34 possible colocalizations with biobank fine-mapping results of hematopoietic traits in a Japanese population, 1549 putative causal splice QTLs (sQTLs; e.g. two independent sQTLs at TOR1AIP1), as well as biologically interpretable trans-eQTL examples (e.g., REST and STING1), all fine-mapped at single variant resolution. We perform differential gene expression analysis to elucidate 198 genes with increased expression in severe COVID-19 cases and enriched for innate immune-related functions. Finally, we evaluate the limited but non-zero effect of COVID-19 phenotype on eQTL discovery, and highlight the presence of COVID-19 severity-interaction eQTLs (ieQTLs; e.g., CLEC4C and MYBL2). Our study provides a comprehensive catalog of whole blood regulatory variants in Japanese, as well as a reference for transcriptional landscapes in response to COVID-19 infection.
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Affiliation(s)
- Qingbo S Wang
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Ryuya Edahiro
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Ho Namkoong
- Department of Infectious Diseases, Keio University School of Medicine, Tokyo, Japan
| | - Takanori Hasegawa
- M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yuya Shirai
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Kyuto Sonehara
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
| | - Hiromu Tanaka
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Ho Lee
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Ryunosuke Saiki
- Department of Pathology and Tumor Biology, Kyoto University, Kyoto, Japan
| | - Takayoshi Hyugaji
- Division of Health Medical Intelligence, Human Genome Center, the Institute of Medical Science, the University of Tokyo, Tokyo, Japan
| | - Eigo Shimizu
- Division of Health Medical Intelligence, Human Genome Center, the Institute of Medical Science, the University of Tokyo, Tokyo, Japan
| | - Kotoe Katayama
- Division of Health Medical Intelligence, Human Genome Center, the Institute of Medical Science, the University of Tokyo, Tokyo, Japan
| | - Masahiro Kanai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Tatsuhiko Naito
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Noah Sasa
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Otorhinolaryngology-Head and Neck Surgery, Osaka University Graduate School of Medicine, Suita, Japan
| | - Kenichi Yamamoto
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Department of Pediatrics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Yasuhiro Kato
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Immunopathology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Takayoshi Morita
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Immunopathology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Kazuhisa Takahashi
- Department of Respiratory Medicine, Juntendo University Faculty of Medicine and Graduate School of Medicine, Tokyo, Japan
| | - Norihiro Harada
- Department of Respiratory Medicine, Juntendo University Faculty of Medicine and Graduate School of Medicine, Tokyo, Japan
| | - Toshio Naito
- Department of General Medicine, Juntendo University Faculty of Medicine and Graduate School of Medicine, Tokyo, Japan
| | - Makoto Hiki
- Department of Emergency and Disaster Medicine, Juntendo University Faculty of Medicine and Graduate School of Medicine, Tokyo, Japan
- Department of Cardiovascular Biology and Medicine, Juntendo University Faculty of Medicine and Graduate School of Medicine, Tokyo, Japan
| | - Yasushi Matsushita
- Department of Internal Medicine and Rheumatology, Juntendo University Faculty of Medicine and Graduate School of Medicine, Tokyo, Japan
| | - Haruhi Takagi
- Department of Respiratory Medicine, Juntendo University Faculty of Medicine and Graduate School of Medicine, Tokyo, Japan
| | - Masako Ichikawa
- Department of Respiratory Medicine, Juntendo University Faculty of Medicine and Graduate School of Medicine, Tokyo, Japan
| | - Ai Nakamura
- Department of Respiratory Medicine, Juntendo University Faculty of Medicine and Graduate School of Medicine, Tokyo, Japan
| | - Sonoko Harada
- Department of Respiratory Medicine, Juntendo University Faculty of Medicine and Graduate School of Medicine, Tokyo, Japan
- Atopy (Allergy) Research Center, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yuuki Sandhu
- Department of Respiratory Medicine, Juntendo University Faculty of Medicine and Graduate School of Medicine, Tokyo, Japan
| | - Hiroki Kabata
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Katsunori Masaki
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Hirofumi Kamata
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Shinnosuke Ikemura
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Shotaro Chubachi
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Satoshi Okamori
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Hideki Terai
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Atsuho Morita
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Takanori Asakura
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Junichi Sasaki
- Department of Emergency and Critical Care Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Hiroshi Morisaki
- Department of Anesthesiology, Keio University School of Medicine, Tokyo, Japan
| | - Yoshifumi Uwamino
- Department of Laboratory Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Kosaku Nanki
- Division of Gastroenterology and Hepatology, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Sho Uchida
- Department of Infectious Diseases, Keio University School of Medicine, Tokyo, Japan
| | - Shunsuke Uno
- Department of Infectious Diseases, Keio University School of Medicine, Tokyo, Japan
| | - Tomoyasu Nishimura
- Department of Infectious Diseases, Keio University School of Medicine, Tokyo, Japan
- Keio University Health Center, Tokyo, Japan
| | - Takashri Ishiguro
- Department of Respiratory Medicine, Saitama Cardiovascular and Respiratory Center, Kumagaya, Japan
| | - Taisuke Isono
- Department of Respiratory Medicine, Saitama Cardiovascular and Respiratory Center, Kumagaya, Japan
| | - Shun Shibata
- Department of Respiratory Medicine, Saitama Cardiovascular and Respiratory Center, Kumagaya, Japan
| | - Yuma Matsui
- Department of Respiratory Medicine, Saitama Cardiovascular and Respiratory Center, Kumagaya, Japan
| | - Chiaki Hosoda
- Department of Respiratory Medicine, Saitama Cardiovascular and Respiratory Center, Kumagaya, Japan
| | - Kenji Takano
- Department of Respiratory Medicine, Saitama Cardiovascular and Respiratory Center, Kumagaya, Japan
| | - Takashi Nishida
- Department of Respiratory Medicine, Saitama Cardiovascular and Respiratory Center, Kumagaya, Japan
| | - Yoichi Kobayashi
- Department of Respiratory Medicine, Saitama Cardiovascular and Respiratory Center, Kumagaya, Japan
| | - Yotaro Takaku
- Department of Respiratory Medicine, Saitama Cardiovascular and Respiratory Center, Kumagaya, Japan
| | - Noboru Takayanagi
- Department of Respiratory Medicine, Saitama Cardiovascular and Respiratory Center, Kumagaya, Japan
| | - Soichiro Ueda
- JCHO (Japan Community Health care Organization) Saitama Medical Center, Internal Medicine, Saitama, Japan
| | - Ai Tada
- JCHO (Japan Community Health care Organization) Saitama Medical Center, Internal Medicine, Saitama, Japan
| | - Masayoshi Miyawaki
- JCHO (Japan Community Health care Organization) Saitama Medical Center, Internal Medicine, Saitama, Japan
| | - Masaomi Yamamoto
- JCHO (Japan Community Health care Organization) Saitama Medical Center, Internal Medicine, Saitama, Japan
| | - Eriko Yoshida
- JCHO (Japan Community Health care Organization) Saitama Medical Center, Internal Medicine, Saitama, Japan
| | - Reina Hayashi
- JCHO (Japan Community Health care Organization) Saitama Medical Center, Internal Medicine, Saitama, Japan
| | - Tomoki Nagasaka
- JCHO (Japan Community Health care Organization) Saitama Medical Center, Internal Medicine, Saitama, Japan
| | - Sawako Arai
- JCHO (Japan Community Health care Organization) Saitama Medical Center, Internal Medicine, Saitama, Japan
| | - Yutaro Kaneko
- JCHO (Japan Community Health care Organization) Saitama Medical Center, Internal Medicine, Saitama, Japan
| | - Kana Sasaki
- JCHO (Japan Community Health care Organization) Saitama Medical Center, Internal Medicine, Saitama, Japan
| | - Etsuko Tagaya
- Department of Respiratory Medicine, Tokyo Women's Medical University, Tokyo, Japan
| | - Masatoshi Kawana
- Department of General Medicine, Tokyo Women's Medical University, Tokyo, Japan
| | - Ken Arimura
- Department of Respiratory Medicine, Tokyo Women's Medical University, Tokyo, Japan
| | - Kunihiko Takahashi
- M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Tatsuhiko Anzai
- M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Satoshi Ito
- M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Akifumi Endo
- Clinical Research Center, Tokyo Medical and Dental University Hospital of Medicine, Tokyo, Japan
| | - Yuji Uchimura
- Department of Medical Informatics, Tokyo Medical and Dental University Hospital of Medicine, Tokyo, Japan
| | - Yasunari Miyazaki
- Respiratory Medicine, Tokyo Medical and Dental University, Tokyo, Japan
| | - Takayuki Honda
- Respiratory Medicine, Tokyo Medical and Dental University, Tokyo, Japan
| | - Tomoya Tateishi
- Respiratory Medicine, Tokyo Medical and Dental University, Tokyo, Japan
| | - Shuji Tohda
- Clinical Laboratory, Tokyo Medical and Dental University Hospital of Medicine, Tokyo, Japan
| | - Naoya Ichimura
- Clinical Laboratory, Tokyo Medical and Dental University Hospital of Medicine, Tokyo, Japan
| | - Kazunari Sonobe
- Clinical Laboratory, Tokyo Medical and Dental University Hospital of Medicine, Tokyo, Japan
| | - Chihiro Tani Sassa
- Clinical Laboratory, Tokyo Medical and Dental University Hospital of Medicine, Tokyo, Japan
| | - Jun Nakajima
- Clinical Laboratory, Tokyo Medical and Dental University Hospital of Medicine, Tokyo, Japan
| | - Yasushi Nakano
- Kawasaki Municipal Ida Hospital, Department of Internal Medicine, Kawasaki, Japan
| | - Yukiko Nakajima
- Kawasaki Municipal Ida Hospital, Department of Internal Medicine, Kawasaki, Japan
| | - Ryusuke Anan
- Kawasaki Municipal Ida Hospital, Department of Internal Medicine, Kawasaki, Japan
| | - Ryosuke Arai
- Kawasaki Municipal Ida Hospital, Department of Internal Medicine, Kawasaki, Japan
| | - Yuko Kurihara
- Kawasaki Municipal Ida Hospital, Department of Internal Medicine, Kawasaki, Japan
| | - Yuko Harada
- Kawasaki Municipal Ida Hospital, Department of Internal Medicine, Kawasaki, Japan
| | - Kazumi Nishio
- Kawasaki Municipal Ida Hospital, Department of Internal Medicine, Kawasaki, Japan
| | - Tetsuya Ueda
- Department of Respiratory Medicine, Osaka Saiseikai Nakatsu Hospital, Osaka, Japan
| | - Masanori Azuma
- Department of Respiratory Medicine, Osaka Saiseikai Nakatsu Hospital, Osaka, Japan
| | - Ryuichi Saito
- Department of Respiratory Medicine, Osaka Saiseikai Nakatsu Hospital, Osaka, Japan
| | - Toshikatsu Sado
- Department of Respiratory Medicine, Osaka Saiseikai Nakatsu Hospital, Osaka, Japan
| | - Yoshimune Miyazaki
- Department of Respiratory Medicine, Osaka Saiseikai Nakatsu Hospital, Osaka, Japan
| | - Ryuichi Sato
- Department of Respiratory Medicine, Osaka Saiseikai Nakatsu Hospital, Osaka, Japan
| | - Yuki Haruta
- Department of Respiratory Medicine, Osaka Saiseikai Nakatsu Hospital, Osaka, Japan
| | - Tadao Nagasaki
- Department of Respiratory Medicine, Osaka Saiseikai Nakatsu Hospital, Osaka, Japan
| | - Yoshinori Yasui
- Department of Infection Control, Osaka Saiseikai Nakatsu Hospital, Osaka, Japan
| | - Yoshinori Hasegawa
- Department of Respiratory Medicine, Osaka Saiseikai Nakatsu Hospital, Osaka, Japan
| | - Yoshikazu Mutoh
- Department of Infectious Diseases, Tosei General Hospital, Seto, Japan
| | - Tomoki Kimura
- Department of Respiratory, Allergic Diseases Internal Medicine, Tosei General Hospital, Seto, Japan
| | - Tomonori Sato
- Department of Respiratory, Allergic Diseases Internal Medicine, Tosei General Hospital, Seto, Japan
| | - Reoto Takei
- Department of Respiratory, Allergic Diseases Internal Medicine, Tosei General Hospital, Seto, Japan
| | - Satoshi Hagimoto
- Department of Respiratory, Allergic Diseases Internal Medicine, Tosei General Hospital, Seto, Japan
| | - Yoichiro Noguchi
- Department of Respiratory, Allergic Diseases Internal Medicine, Tosei General Hospital, Seto, Japan
| | - Yasuhiko Yamano
- Department of Respiratory, Allergic Diseases Internal Medicine, Tosei General Hospital, Seto, Japan
| | - Hajime Sasano
- Department of Respiratory, Allergic Diseases Internal Medicine, Tosei General Hospital, Seto, Japan
| | - Sho Ota
- Department of Respiratory, Allergic Diseases Internal Medicine, Tosei General Hospital, Seto, Japan
| | - Yasushi Nakamori
- Department of Emergency and Critical Care Medicine, Kansai Medical University General Medical Center, Moriguchi, Japan
| | - Kazuhisa Yoshiya
- Department of Emergency and Critical Care Medicine, Kansai Medical University General Medical Center, Moriguchi, Japan
| | - Fukuki Saito
- Department of Emergency and Critical Care Medicine, Kansai Medical University General Medical Center, Moriguchi, Japan
| | - Tomoyuki Yoshihara
- Department of Emergency and Critical Care Medicine, Kansai Medical University General Medical Center, Moriguchi, Japan
| | - Daiki Wada
- Department of Emergency and Critical Care Medicine, Kansai Medical University General Medical Center, Moriguchi, Japan
| | - Hiromu Iwamura
- Department of Emergency and Critical Care Medicine, Kansai Medical University General Medical Center, Moriguchi, Japan
| | - Syuji Kanayama
- Department of Emergency and Critical Care Medicine, Kansai Medical University General Medical Center, Moriguchi, Japan
| | - Shuhei Maruyama
- Department of Emergency and Critical Care Medicine, Kansai Medical University General Medical Center, Moriguchi, Japan
| | - Takashi Yoshiyama
- Japan Anti-Tuberculosis Association (JATA) Fukujuji Hospital, Kiyose, Japan
| | - Ken Ohta
- Japan Anti-Tuberculosis Association (JATA) Fukujuji Hospital, Kiyose, Japan
| | - Hiroyuki Kokuto
- Japan Anti-Tuberculosis Association (JATA) Fukujuji Hospital, Kiyose, Japan
| | - Hideo Ogata
- Japan Anti-Tuberculosis Association (JATA) Fukujuji Hospital, Kiyose, Japan
| | - Yoshiaki Tanaka
- Japan Anti-Tuberculosis Association (JATA) Fukujuji Hospital, Kiyose, Japan
| | - Kenichi Arakawa
- Japan Anti-Tuberculosis Association (JATA) Fukujuji Hospital, Kiyose, Japan
| | - Masafumi Shimoda
- Japan Anti-Tuberculosis Association (JATA) Fukujuji Hospital, Kiyose, Japan
| | - Takeshi Osawa
- Japan Anti-Tuberculosis Association (JATA) Fukujuji Hospital, Kiyose, Japan
| | - Hiroki Tateno
- Department of Pulmonary Medicine, Saitama City Hospital, Saitama, Japan
| | - Isano Hase
- Department of Pulmonary Medicine, Saitama City Hospital, Saitama, Japan
| | - Shuichi Yoshida
- Department of Pulmonary Medicine, Saitama City Hospital, Saitama, Japan
| | - Shoji Suzuki
- Department of Pulmonary Medicine, Saitama City Hospital, Saitama, Japan
| | - Miki Kawada
- Department of Infectious Diseases, Saitama City Hospital, Saitama, Japan
| | - Hirohisa Horinouchi
- Department of General Thoracic Surgery, Saitama City Hospital, Saitama, Japan
| | - Fumitake Saito
- Department of Pulmonary Medicine, Eiju General Hospital, Tokyo, Japan
| | - Keiko Mitamura
- Division of Infection Control, Eiju General Hospital, Tokyo, Japan
| | - Masao Hagihara
- Department of Hematology, Eiju General Hospital, Tokyo, Japan
| | - Junichi Ochi
- Department of Pulmonary Medicine, Eiju General Hospital, Tokyo, Japan
| | - Tomoyuki Uchida
- Department of Hematology, Eiju General Hospital, Tokyo, Japan
| | - Rie Baba
- Saiseikai Utsunomiya Hospital, Utsunomiya, Japan
| | - Daisuke Arai
- Saiseikai Utsunomiya Hospital, Utsunomiya, Japan
| | | | | | | | - Genta Nagao
- Saiseikai Utsunomiya Hospital, Utsunomiya, Japan
| | | | | | - Koji Murakami
- Department of Respiratory Medicine, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Mitsuhiro Yamada
- Department of Respiratory Medicine, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Hisatoshi Sugiura
- Department of Respiratory Medicine, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Hirohito Sano
- Department of Respiratory Medicine, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Shuichiro Matsumoto
- Department of Respiratory Medicine, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Nozomu Kimura
- Department of Respiratory Medicine, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Yoshinao Ono
- Department of Respiratory Medicine, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Hiroaki Baba
- Department of Infectious Diseases, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Yusuke Suzuki
- Department of Respiratory Medicine, Kitasato University Kitasato Institute Hospital, Tokyo, Japan
| | - Sohei Nakayama
- Department of Respiratory Medicine, Kitasato University Kitasato Institute Hospital, Tokyo, Japan
| | - Keita Masuzawa
- Department of Respiratory Medicine, Kitasato University Kitasato Institute Hospital, Tokyo, Japan
| | - Shinichi Namba
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Takayuki Shiroyama
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Yoshimi Noda
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Takayuki Niitsu
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Yuichi Adachi
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Takatoshi Enomoto
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Saori Amiya
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Reina Hara
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Yuta Yamaguchi
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Immunopathology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Teruaki Murakami
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Immunopathology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Tomoki Kuge
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Kinnosuke Matsumoto
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Yuji Yamamoto
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Makoto Yamamoto
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Midori Yoneda
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Kazunori Tomono
- Division of Infection Control and Prevention, Osaka University Hospital, Suita, Japan
| | - Kazuto Kato
- Department of Biomedical Ethics and Public Policy, Osaka University Graduate School of Medicine, Suita, Japan
| | - Haruhiko Hirata
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Yoshito Takeda
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
| | | | | | | | | | | | | | | | | | - Tomohisa Shoko
- Department of Emergency and Critical Care Medicine, Tokyo Women's Medical University Medical Center East, Tokyo, Japan
| | - Mitsuaki Kojima
- Department of Emergency and Critical Care Medicine, Tokyo Women's Medical University Medical Center East, Tokyo, Japan
| | - Tomohiro Adachi
- Department of Emergency and Critical Care Medicine, Tokyo Women's Medical University Medical Center East, Tokyo, Japan
| | - Motonao Ishikawa
- Department of Medicine, Tokyo Women's Medical University Medical Center East, Tokyo, Japan
| | - Kenichiro Takahashi
- Department of Pediatrics, Tokyo Women's Medical University Medical Center East, Tokyo, Japan
| | - Takashi Inoue
- Internal Medicine, Sano Kosei General Hospital, Sano, Japan
| | | | | | | | - Kazuyoshi Watanabe
- Japan Community Health care Organization Kanazawa Hospital, Kanazawa, Japan
| | - Naoki Miyazawa
- Department of Respiratory Medicine, Saiseikai Yokohamashi Nanbu Hospital, Yokohama, Japan
| | - Yasuhiro Kimura
- Department of Respiratory Medicine, Saiseikai Yokohamashi Nanbu Hospital, Yokohama, Japan
| | - Reiko Sado
- Department of Respiratory Medicine, Saiseikai Yokohamashi Nanbu Hospital, Yokohama, Japan
| | - Hideyasu Sugimoto
- Department of Respiratory Medicine, Saiseikai Yokohamashi Nanbu Hospital, Yokohama, Japan
| | - Akane Kamiya
- Department of Clinical Laboratory, Saiseikai Yokohamashi Nanbu Hospital, Yokohama, Japan
| | - Naota Kuwahara
- Internal Medicine, Internal Medicine Center, Showa University Koto Toyosu Hospital, Tokyo, Japan
| | - Akiko Fujiwara
- Internal Medicine, Internal Medicine Center, Showa University Koto Toyosu Hospital, Tokyo, Japan
| | - Tomohiro Matsunaga
- Internal Medicine, Internal Medicine Center, Showa University Koto Toyosu Hospital, Tokyo, Japan
| | - Yoko Sato
- Internal Medicine, Internal Medicine Center, Showa University Koto Toyosu Hospital, Tokyo, Japan
| | - Takenori Okada
- Internal Medicine, Internal Medicine Center, Showa University Koto Toyosu Hospital, Tokyo, Japan
| | - Yoshihiro Hirai
- Department of Respiratory Medicine, Japan Organization of Occupational Health and Safety, Kanto Rosai Hospital, Kawasaki, Japan
| | - Hidetoshi Kawashima
- Department of Respiratory Medicine, Japan Organization of Occupational Health and Safety, Kanto Rosai Hospital, Kawasaki, Japan
| | - Atsuya Narita
- Department of Respiratory Medicine, Japan Organization of Occupational Health and Safety, Kanto Rosai Hospital, Kawasaki, Japan
| | - Kazuki Niwa
- Department of General Internal Medicine, Japan Organization of Occupational Health and Safety, Kanto Rosai Hospital, Kawasaki, Japan
| | - Yoshiyuki Sekikawa
- Department of General Internal Medicine, Japan Organization of Occupational Health and Safety, Kanto Rosai Hospital, Kawasaki, Japan
| | - Koichi Nishi
- Ishikawa Prefectural Central Hospital, Kanazawa, Japan
| | | | - Mayuko Tani
- Ishikawa Prefectural Central Hospital, Kanazawa, Japan
| | - Junya Suzuki
- Ishikawa Prefectural Central Hospital, Kanazawa, Japan
| | | | - Takashi Ogura
- Kanagawa Cardiovascular and Respiratory Center, Yokohama, Japan
| | - Hideya Kitamura
- Kanagawa Cardiovascular and Respiratory Center, Yokohama, Japan
| | - Eri Hagiwara
- Kanagawa Cardiovascular and Respiratory Center, Yokohama, Japan
| | - Kota Murohashi
- Kanagawa Cardiovascular and Respiratory Center, Yokohama, Japan
| | | | - Takao Mochimaru
- Department of Respiratory Medicine, National Hospital Organization Tokyo Medical Center, Tokyo, Japan
- Department of Allergy, National Hospital Organization Tokyo Medical Center, Tokyo, Japan
| | - Shigenari Nukaga
- Department of Respiratory Medicine, National Hospital Organization Tokyo Medical Center, Tokyo, Japan
| | - Ryosuke Satomi
- Department of Respiratory Medicine, National Hospital Organization Tokyo Medical Center, Tokyo, Japan
| | - Yoshitaka Oyamada
- Department of Respiratory Medicine, National Hospital Organization Tokyo Medical Center, Tokyo, Japan
- Department of Allergy, National Hospital Organization Tokyo Medical Center, Tokyo, Japan
| | - Nobuaki Mori
- Department of General Internal Medicine and Infectious Diseases, National Hospital Organization Tokyo Medical Center, Tokyo, Japan
| | - Tomoya Baba
- Department of Respiratory Medicine, Toyohashi Municipal Hospital, Toyohashi, Japan
| | - Yasutaka Fukui
- Department of Respiratory Medicine, Toyohashi Municipal Hospital, Toyohashi, Japan
| | - Mitsuru Odate
- Department of Respiratory Medicine, Toyohashi Municipal Hospital, Toyohashi, Japan
| | - Shuko Mashimo
- Department of Respiratory Medicine, Toyohashi Municipal Hospital, Toyohashi, Japan
| | - Yasushi Makino
- Department of Respiratory Medicine, Toyohashi Municipal Hospital, Toyohashi, Japan
| | | | | | | | | | - Satoshi Fuke
- KKR Sapporo Medical Center, Department of respiratory medicine, Sapporo, Japan
| | - Hiroshi Saito
- KKR Sapporo Medical Center, Department of respiratory medicine, Sapporo, Japan
| | - Tomoya Tsuchida
- Division of General Internal Medicine, Department of Internal Medicine, St. Marianna University School of Medicine, Kawasaki, Japan
| | - Shigeki Fujitani
- Department of Emergency and Critical Care Medicine, St.Marianna University School of Medicine, Kawasaki, Japan
| | - Mumon Takita
- Department of Emergency and Critical Care Medicine, St.Marianna University School of Medicine, Kawasaki, Japan
| | - Daiki Morikawa
- Department of Emergency and Critical Care Medicine, St.Marianna University School of Medicine, Kawasaki, Japan
| | - Toru Yoshida
- Department of Emergency and Critical Care Medicine, St.Marianna University School of Medicine, Kawasaki, Japan
| | | | | | | | | | - Mari Tone
- Japanese Red Cross Medical Center, Tokyo, Japan
| | | | - Yoshihiko Nakamura
- Department of Emergency and Critical Care Medicine, Faculty of Medicine, Fukuoka University, Fukuoka, Japan
| | - Kota Hoshino
- Department of Emergency and Critical Care Medicine, Faculty of Medicine, Fukuoka University, Fukuoka, Japan
| | - Junichi Maruyama
- Department of Emergency and Critical Care Medicine, Faculty of Medicine, Fukuoka University, Fukuoka, Japan
| | - Hiroyasu Ishikura
- Department of Emergency and Critical Care Medicine, Faculty of Medicine, Fukuoka University, Fukuoka, Japan
| | - Tohru Takata
- Department of Infection Control, Fukuoka University Hospital, Fukuoka, Japan
| | - Toshio Odani
- Department of Rheumatology, National Hospital Organization Hokkaido Medical Center, Sapporo, Japan
| | - Masaru Amishima
- Department of Respiratory Medicine, National Hospital Organization Hokkaido Medical Center, Sapporo, Japan
| | - Takeshi Hattori
- Department of Respiratory Medicine, National Hospital Organization Hokkaido Medical Center, Sapporo, Japan
| | - Yasuo Shichinohe
- Department of Emergency and Critical Care Medicine, National Hospital Organization Hokkaido Medical Center, Sapporo, Japan
| | - Takashi Kagaya
- National Hospital Organization Kanazawa Medical Center, Kanazawa, Japan
| | - Toshiyuki Kita
- National Hospital Organization Kanazawa Medical Center, Kanazawa, Japan
| | - Kazuhide Ohta
- National Hospital Organization Kanazawa Medical Center, Kanazawa, Japan
| | - Satoru Sakagami
- National Hospital Organization Kanazawa Medical Center, Kanazawa, Japan
| | - Kiyoshi Koshida
- National Hospital Organization Kanazawa Medical Center, Kanazawa, Japan
| | - Kentaro Hayashi
- Nihon University School of Medicine, Department of Internal Medicine, Division of Respiratory Medicine, Tokyo, Japan
| | - Tetsuo Shimizu
- Nihon University School of Medicine, Department of Internal Medicine, Division of Respiratory Medicine, Tokyo, Japan
| | - Yutaka Kozu
- Nihon University School of Medicine, Department of Internal Medicine, Division of Respiratory Medicine, Tokyo, Japan
| | - Hisato Hiranuma
- Nihon University School of Medicine, Department of Internal Medicine, Division of Respiratory Medicine, Tokyo, Japan
| | - Yasuhiro Gon
- Nihon University School of Medicine, Department of Internal Medicine, Division of Respiratory Medicine, Tokyo, Japan
| | | | | | - Ken Ueda
- Musashino Red Cross Hospital, Musashino, Japan
| | - Reiko Taki
- Musashino Red Cross Hospital, Musashino, Japan
| | | | - Kodai Kawamura
- Division of Respiratory Medicine, Social Welfare Organization Saiseikai Imperial Gift Foundation, Inc., Saiseikai Kumamoto Hospital, Kumamoto, Japan
| | - Kazuya Ichikado
- Division of Respiratory Medicine, Social Welfare Organization Saiseikai Imperial Gift Foundation, Inc., Saiseikai Kumamoto Hospital, Kumamoto, Japan
| | - Kenta Nishiyama
- Division of Respiratory Medicine, Social Welfare Organization Saiseikai Imperial Gift Foundation, Inc., Saiseikai Kumamoto Hospital, Kumamoto, Japan
| | - Hiroyuki Muranaka
- Division of Respiratory Medicine, Social Welfare Organization Saiseikai Imperial Gift Foundation, Inc., Saiseikai Kumamoto Hospital, Kumamoto, Japan
| | - Kazunori Nakamura
- Division of Respiratory Medicine, Social Welfare Organization Saiseikai Imperial Gift Foundation, Inc., Saiseikai Kumamoto Hospital, Kumamoto, Japan
| | - Naozumi Hashimoto
- Department of Respiratory Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Keiko Wakahara
- Department of Respiratory Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Sakamoto Koji
- Department of Respiratory Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Norihito Omote
- Department of Respiratory Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Akira Ando
- Department of Respiratory Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Nobuhiro Kodama
- Fukuoka Tokushukai Hospital, Department of Internal Medicine, Kasuga, Japan
| | - Yasunari Kaneyama
- Fukuoka Tokushukai Hospital, Department of Internal Medicine, Kasuga, Japan
| | - Shunsuke Maeda
- Fukuoka Tokushukai Hospital, Department of Internal Medicine, Kasuga, Japan
| | - Takashige Kuraki
- Fukuoka Tokushukai Hospital, Respiratory Medicine, Kasuga, Japan
| | | | - Koutaro Yokote
- Department of Endocrinology, Hematology and Gerontology, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Taka-Aki Nakada
- Department of Emergency and Critical Care Medicine, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Ryuzo Abe
- Department of Emergency and Critical Care Medicine, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Taku Oshima
- Department of Emergency and Critical Care Medicine, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Tadanaga Shimada
- Department of Emergency and Critical Care Medicine, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Masahiro Harada
- National Hospital Organization Kumamoto Medical Center, Kumamoto, Japan
| | - Takeshi Takahashi
- National Hospital Organization Kumamoto Medical Center, Kumamoto, Japan
| | - Hiroshi Ono
- National Hospital Organization Kumamoto Medical Center, Kumamoto, Japan
| | - Toshihiro Sakurai
- National Hospital Organization Kumamoto Medical Center, Kumamoto, Japan
| | | | - Yoshifumi Kimizuka
- Division of Infectious Diseases and Respiratory Medicine, Department of Internal Medicine, National Defense Medical College, Tokorozawa, Japan
| | - Akihiko Kawana
- Division of Infectious Diseases and Respiratory Medicine, Department of Internal Medicine, National Defense Medical College, Tokorozawa, Japan
| | - Tomoya Sano
- Division of Infectious Diseases and Respiratory Medicine, Department of Internal Medicine, National Defense Medical College, Tokorozawa, Japan
| | - Chie Watanabe
- Division of Infectious Diseases and Respiratory Medicine, Department of Internal Medicine, National Defense Medical College, Tokorozawa, Japan
| | - Ryohei Suematsu
- Division of Infectious Diseases and Respiratory Medicine, Department of Internal Medicine, National Defense Medical College, Tokorozawa, Japan
| | | | - Ayumi Yoshifuji
- Department of Internal Medicine, Tokyo Saiseikai Central Hospital, Tokyo, Japan
| | - Kazuto Ito
- Department of Internal Medicine, Tokyo Saiseikai Central Hospital, Tokyo, Japan
| | - Saeko Takahashi
- Department of Pulmonary Medicine, Tokyo Saiseikai Central Hospital, Tokyo, Japan
| | - Kota Ishioka
- Department of Pulmonary Medicine, Tokyo Saiseikai Central Hospital, Tokyo, Japan
| | - Morio Nakamura
- Department of Pulmonary Medicine, Tokyo Saiseikai Central Hospital, Tokyo, Japan
| | - Makoto Masuda
- Department of Respiratory Medicine, Fujisawa City Hospital, Fujisawa, Japan
| | - Aya Wakabayashi
- Department of Respiratory Medicine, Fujisawa City Hospital, Fujisawa, Japan
| | - Hiroki Watanabe
- Department of Respiratory Medicine, Fujisawa City Hospital, Fujisawa, Japan
| | - Suguru Ueda
- Department of Respiratory Medicine, Fujisawa City Hospital, Fujisawa, Japan
| | - Masanori Nishikawa
- Department of Respiratory Medicine, Fujisawa City Hospital, Fujisawa, Japan
| | | | | | | | | | | | - Yoji Nagasaki
- Department of Infectious Disease and Clinical Research Institute, National Hospital Organization Kyushu Medical Center, Fukuoka, Japan
| | - Masaki Okamoto
- Department of Respirology, National Hospital Organization Kyushu Medical Center, Fukuoka, Japan
- Division of Respirology, Rheumatology, and Neurology, Department of Internal Medicine, Kurume University School of Medicine, Kurume, Japan
| | - Sayoko Ishihara
- Department of Infectious Disease, National Hospital Organization Kyushu Medical Center, Fukuoka, Japan
| | - Masatoshi Shimo
- Department of Infectious Disease, National Hospital Organization Kyushu Medical Center, Fukuoka, Japan
| | - Yoshihisa Tokunaga
- Department of Respirology, National Hospital Organization Kyushu Medical Center, Fukuoka, Japan
- Division of Respirology, Rheumatology, and Neurology, Department of Internal Medicine, Kurume University School of Medicine, Kurume, Japan
| | - Yu Kusaka
- Ome Municipal General Hospital, Ome, Japan
| | | | | | - Aki Ogawa
- Ome Municipal General Hospital, Ome, Japan
| | | | - Satoru Fukuyama
- Research Institute for Diseases of the Chest, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yoshihiro Eriguchi
- Department of Medicine and Biosystemic Science, Kyushu University Graduate School of Medical Sciences, Fukuoka, Japan
| | - Akiko Yonekawa
- Department of Medicine and Biosystemic Science, Kyushu University Graduate School of Medical Sciences, Fukuoka, Japan
| | - Keiko Kan-O
- Research Institute for Diseases of the Chest, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Koichiro Matsumoto
- Research Institute for Diseases of the Chest, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | | | | | | | - Yoshiaki Inoue
- Department of Emergency and Critical Care Medicine, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan
| | - Shigeru Chiba
- Department of Hematology, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan
| | - Kunihiro Yamagata
- Department of Nephrology, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan
| | - Yuji Hiramatsu
- Department of Cardiovascular Surgery, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan
| | - Hirayasu Kai
- Department of Nephrology, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan
| | - Koichiro Asano
- Division of Pulmonary Medicine, Department of Medicine, Tokai University School of Medicine, Isehara, Japan
| | - Tsuyoshi Oguma
- Division of Pulmonary Medicine, Department of Medicine, Tokai University School of Medicine, Isehara, Japan
| | - Yoko Ito
- Division of Pulmonary Medicine, Department of Medicine, Tokai University School of Medicine, Isehara, Japan
| | - Satoru Hashimoto
- Department of Anesthesiology and Intensive Care Medicine, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Masaki Yamasaki
- Department of Anesthesiology and Intensive Care Medicine, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Yu Kasamatsu
- Department of Infection Control and Laboratory Medicine, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Yuko Komase
- Department of Respiratory Internal Medicine, St Marianna University School of Medicine, Yokohama-City Seibu Hospital, Yokohama, Japan
| | - Naoya Hida
- Department of Respiratory Internal Medicine, St Marianna University School of Medicine, Yokohama-City Seibu Hospital, Yokohama, Japan
| | - Takahiro Tsuburai
- Department of Respiratory Internal Medicine, St Marianna University School of Medicine, Yokohama-City Seibu Hospital, Yokohama, Japan
| | - Baku Oyama
- Department of Respiratory Internal Medicine, St Marianna University School of Medicine, Yokohama-City Seibu Hospital, Yokohama, Japan
| | | | | | - Yuichiro Kitagawa
- Gifu University School of Medicine Graduate School of Medicine, Emergency and Disaster Medicine, Gifu, Japan
| | - Tetsuya Fukuta
- Gifu University School of Medicine Graduate School of Medicine, Emergency and Disaster Medicine, Gifu, Japan
| | - Takahito Miyake
- Gifu University School of Medicine Graduate School of Medicine, Emergency and Disaster Medicine, Gifu, Japan
| | - Shozo Yoshida
- Gifu University School of Medicine Graduate School of Medicine, Emergency and Disaster Medicine, Gifu, Japan
| | - Shinji Ogura
- Gifu University School of Medicine Graduate School of Medicine, Emergency and Disaster Medicine, Gifu, Japan
| | - Shinji Abe
- Department of Respiratory Medicine, Tokyo Medical University Hospital, Tokyo, Japan
| | - Yuta Kono
- Department of Respiratory Medicine, Tokyo Medical University Hospital, Tokyo, Japan
| | - Yuki Togashi
- Department of Respiratory Medicine, Tokyo Medical University Hospital, Tokyo, Japan
| | - Hiroyuki Takoi
- Department of Respiratory Medicine, Tokyo Medical University Hospital, Tokyo, Japan
| | - Ryota Kikuchi
- Department of Respiratory Medicine, Tokyo Medical University Hospital, Tokyo, Japan
| | | | | | | | - Arihiko Kanehiro
- Okayama Rosai Hospital, Okayama, Japan
- Himeji St. Mary's Hospital, Himeji, Japan
| | | | | | - Sae Wada
- Okayama Rosai Hospital, Okayama, Japan
| | | | - Kei Nishiyama
- Emergency & Critical Care, Niigata University, Niigata, Japan
| | - Mariko Terashima
- Emergency & Critical Care Center, National Hospital Organization Kyoto Medical Center, Kyoto, Japan
| | - Satoru Beppu
- Emergency & Critical Care Center, National Hospital Organization Kyoto Medical Center, Kyoto, Japan
| | - Kosuke Yoshida
- Emergency & Critical Care Center, National Hospital Organization Kyoto Medical Center, Kyoto, Japan
| | - Osamu Narumoto
- National Hospital Organization Tokyo Hospital Hospital, Kiyose, Japan
| | - Hideaki Nagai
- National Hospital Organization Tokyo Hospital Hospital, Kiyose, Japan
| | - Nobuharu Ooshima
- National Hospital Organization Tokyo Hospital Hospital, Kiyose, Japan
| | | | - Akira Umeda
- Department of General Medicine, School of Medicine, International University of Health and Welfare Shioya Hospital, Ohtawara, Japan
| | - Kazuya Miyagawa
- Department of Pharmacology, School of Pharmacy, International University of Health and Welfare Shioya Hospital, Ohtawara, Japan
| | - Hisato Shimada
- Department of Respiratory Medicine, International University of Health and Welfare Shioya Hospital, Ohtawara, Japan
| | - Mayu Endo
- Department of Clinical Laboratory, International University of Health and Welfare Shioya Hospital, Ohtawara, Japan
| | - Yoshiyuki Ohira
- Department of General Medicine, School of Medicine, International University of Health and Welfare Shioya Hospital, Ohtawara, Japan
| | - Masafumi Watanabe
- Department of Cardiology, Pulmonology, and Nephrology, Yamagata University Faculty of Medicine, Yamagata, Japan
| | - Sumito Inoue
- Department of Cardiology, Pulmonology, and Nephrology, Yamagata University Faculty of Medicine, Yamagata, Japan
| | - Akira Igarashi
- Department of Cardiology, Pulmonology, and Nephrology, Yamagata University Faculty of Medicine, Yamagata, Japan
| | - Masamichi Sato
- Department of Cardiology, Pulmonology, and Nephrology, Yamagata University Faculty of Medicine, Yamagata, Japan
| | - Hironori Sagara
- Division of Respiratory Medicine and Allergology, Department of Medicine, School of Medicine, Showa University, Tokyo, Japan
| | - Akihiko Tanaka
- Division of Respiratory Medicine and Allergology, Department of Medicine, School of Medicine, Showa University, Tokyo, Japan
| | - Shin Ohta
- Division of Respiratory Medicine and Allergology, Department of Medicine, School of Medicine, Showa University, Tokyo, Japan
| | - Tomoyuki Kimura
- Division of Respiratory Medicine and Allergology, Department of Medicine, School of Medicine, Showa University, Tokyo, Japan
| | - Yoko Shibata
- Department of Pulmonary Medicine, Fukushima Medical University, Fukushima, Japan
| | - Yoshinori Tanino
- Department of Pulmonary Medicine, Fukushima Medical University, Fukushima, Japan
| | - Takefumi Nikaido
- Department of Pulmonary Medicine, Fukushima Medical University, Fukushima, Japan
| | - Hiroyuki Minemura
- Department of Pulmonary Medicine, Fukushima Medical University, Fukushima, Japan
| | - Yuki Sato
- Department of Pulmonary Medicine, Fukushima Medical University, Fukushima, Japan
| | | | | | | | - Hajime Iwagoe
- Division of Infectious Diseases, Kumamoto City Hospital, Kumamoto, Japan
| | - Hiroshi Takahashi
- Department of Respiratory Medicine, Kumamoto City Hospital, Kumamoto, Japan
| | - Kazuhiko Fujii
- Department of Respiratory Medicine, Kumamoto City Hospital, Kumamoto, Japan
| | - Hiroto Kishi
- Department of Respiratory Medicine, Kumamoto City Hospital, Kumamoto, Japan
| | - Masayuki Kanai
- Department of Emergency and Critical Care Medicine, Tokyo Metropolitan Police Hospital, Tokyo, Japan
| | - Tomonori Imamura
- Department of Emergency and Critical Care Medicine, Tokyo Metropolitan Police Hospital, Tokyo, Japan
| | - Tatsuya Yamashita
- Department of Emergency and Critical Care Medicine, Tokyo Metropolitan Police Hospital, Tokyo, Japan
| | - Masakiyo Yatomi
- Department of Respiratory Medicine, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Toshitaka Maeno
- Department of Respiratory Medicine, Gunma University Graduate School of Medicine, Maebashi, Japan
| | | | - Mai Takahashi
- National hospital organization Saitama Hospital, Wako, Japan
| | | | - Isamu Kamimaki
- National hospital organization Saitama Hospital, Wako, Japan
| | | | - Tomoo Ishii
- Tokyo Medical University Ibaraki Medical Center, Inashiki, Japan
| | - Mitsuyoshi Utsugi
- Department of Internal Medicine, Kiryu Kosei General Hospital, Kiryu, Japan
| | - Akihiro Ono
- Department of Internal Medicine, Kiryu Kosei General Hospital, Kiryu, Japan
| | - Toru Tanaka
- Department of Pulmonary Medicine and Oncology, Graduate School of Medicine, Nippon Medical School, Tokyo, Japan
| | - Takeru Kashiwada
- Department of Pulmonary Medicine and Oncology, Graduate School of Medicine, Nippon Medical School, Tokyo, Japan
| | - Kazue Fujita
- Department of Pulmonary Medicine and Oncology, Graduate School of Medicine, Nippon Medical School, Tokyo, Japan
| | - Yoshinobu Saito
- Department of Pulmonary Medicine and Oncology, Graduate School of Medicine, Nippon Medical School, Tokyo, Japan
| | - Masahiro Seike
- Department of Pulmonary Medicine and Oncology, Graduate School of Medicine, Nippon Medical School, Tokyo, Japan
| | - Hiroko Watanabe
- Division of Respiratory Medicine, Tsukuba Kinen General Hospital, Tsukuba, Japan
| | - Hiroto Matsuse
- Division of Respiratory Medicine, Department of Internal Medicine, Toho University Ohashi Medical Center, Tokyo, Japan
| | - Norio Kodaka
- Division of Respiratory Medicine, Department of Internal Medicine, Toho University Ohashi Medical Center, Tokyo, Japan
| | - Chihiro Nakano
- Division of Respiratory Medicine, Department of Internal Medicine, Toho University Ohashi Medical Center, Tokyo, Japan
| | - Takeshi Oshio
- Division of Respiratory Medicine, Department of Internal Medicine, Toho University Ohashi Medical Center, Tokyo, Japan
| | - Takatomo Hirouchi
- Division of Respiratory Medicine, Department of Internal Medicine, Toho University Ohashi Medical Center, Tokyo, Japan
| | - Shohei Makino
- Division of Anesthesiology, Department of Surgery Related, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Moritoki Egi
- Division of Anesthesiology, Department of Surgery Related, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Yosuke Omae
- Genome Medical Science Project (Toyama), National Center for Global Health and Medicine, Tokyo, Japan
| | - Yasuhito Nannya
- Department of Pathology and Tumor Biology, Kyoto University, Kyoto, Japan
| | - Takafumi Ueno
- Department of Biomolecular Engineering, Graduate School of Tokyo Institute of Technology, Tokyo, Japan
| | - Tomomi Takano
- Laboratory of Veterinary Infectious Disease, School of Veterinary Medicine, Kitasato University, Aomori, Japan
| | - Kazuhiko Katayama
- Laboratory of Viral Infection, Department of Infection Control and Immunology, Ōmura Satoshi Memorial Institute & Graduate School of Infection Control Sciences, Kitasato University, Tokyo, Japan
| | - Masumi Ai
- Department of Insured Medical Care Management, Tokyo Medical and Dental University Hospital of Medicine, Tokyo, Japan
| | - Atsushi Kumanogoh
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Suita, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
- Department of Immunopathology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita, Japan
| | - Toshiro Sato
- Department of Organoid Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Naoki Hasegawa
- Department of Infectious Diseases, Keio University School of Medicine, Tokyo, Japan
| | - Katsushi Tokunaga
- Genome Medical Science Project (Toyama), National Center for Global Health and Medicine, Tokyo, Japan
| | - Makoto Ishii
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Ryuji Koike
- Medical Innovation Promotion Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yuko Kitagawa
- Department of Surgery, Keio University School of Medicine, Tokyo, Japan
| | - Akinori Kimura
- Institute of Research, Tokyo Medical and Dental University, Tokyo, Japan
| | - Seiya Imoto
- Division of Health Medical Intelligence, Human Genome Center, the Institute of Medical Science, the University of Tokyo, Tokyo, Japan
| | - Satoru Miyano
- M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Seishi Ogawa
- Department of Pathology and Tumor Biology, Kyoto University, Kyoto, Japan
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan
- Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institute, Stockholm, Sweden
| | - Takanori Kanai
- Keio University Health Center, Tokyo, Japan
- AMED-CREST, Japan Agency for Medical Research and Development, Tokyo, Japan
| | - Koichi Fukunaga
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan.
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan.
- Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita, Japan.
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan.
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108
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Perron G, Jandaghi P, Moslemi E, Nishimura T, Rajaee M, Alkallas R, Lu T, Riazalhosseini Y, Najafabadi HS. Pan-cancer analysis of mRNA stability for decoding tumour post-transcriptional programs. Commun Biol 2022; 5:851. [PMID: 35987939 PMCID: PMC9392771 DOI: 10.1038/s42003-022-03796-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 08/04/2022] [Indexed: 11/25/2022] Open
Abstract
Measuring mRNA decay in tumours is a prohibitive challenge, limiting our ability to map the post-transcriptional programs of cancer. Here, using a statistical framework to decouple transcriptional and post-transcriptional effects in RNA-seq data, we uncover the mRNA stability changes that accompany tumour development and progression. Analysis of 7760 samples across 18 cancer types suggests that mRNA stability changes are ~30% as frequent as transcriptional events, highlighting their widespread role in shaping the tumour transcriptome. Dysregulation of programs associated with >80 RNA-binding proteins (RBPs) and microRNAs (miRNAs) drive these changes, including multi-cancer inactivation of RBFOX and miR-29 families. Phenotypic activation or inhibition of RBFOX1 highlights its role in calcium signaling dysregulation, while modulation of miR-29 shows its impact on extracellular matrix organization and stemness genes. Overall, our study underlines the integral role of mRNA stability in shaping the cancer transcriptome, and provides a resource for systematic interrogation of cancer-associated stability pathways. The role of mRNA stability in shaping the cancer transcriptome is revealed using a statistical analysis of transcriptomic data.
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109
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Kosoy R, Fullard JF, Zeng B, Bendl J, Dong P, Rahman S, Kleopoulos SP, Shao Z, Girdhar K, Humphrey J, de Paiva Lopes K, Charney AW, Kopell BH, Raj T, Bennett D, Kellner CP, Haroutunian V, Hoffman GE, Roussos P. Genetics of the human microglia regulome refines Alzheimer's disease risk loci. Nat Genet 2022; 54:1145-1154. [PMID: 35931864 PMCID: PMC9388367 DOI: 10.1038/s41588-022-01149-1] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 06/08/2022] [Indexed: 02/07/2023]
Abstract
Microglia are brain myeloid cells that play a critical role in neuroimmunity and the etiology of Alzheimer's disease (AD), yet our understanding of how the genetic regulatory landscape controls microglial function and contributes to AD is limited. Here, we performed transcriptome and chromatin accessibility profiling in primary human microglia from 150 donors to identify genetically driven variation and cell-specific enhancer-promoter (E-P) interactions. Integrative fine-mapping analysis identified putative regulatory mechanisms for 21 AD risk loci, of which 18 were refined to a single gene, including 3 new candidate risk genes (KCNN4, FIBP and LRRC25). Transcription factor regulatory networks captured AD risk variation and identified SPI1 as a key putative regulator of microglia expression and AD risk. This comprehensive resource capturing variation in the human microglia regulome provides insights into the etiology of neurodegenerative disease.
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Affiliation(s)
- Roman Kosoy
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, USA.
- Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, USA.
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, USA.
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, USA.
| | - John F Fullard
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Biao Zeng
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Pengfei Dong
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Samir Rahman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Steven P Kleopoulos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Zhiping Shao
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Kiran Girdhar
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Jack Humphrey
- Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Katia de Paiva Lopes
- Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA
| | - Alexander W Charney
- Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Brian H Kopell
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Towfique Raj
- Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - David Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | | | - Vahram Haroutunian
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, USA
- Mental Illness Research Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY, USA
| | - Gabriel E Hoffman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, USA.
- Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, USA.
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, USA.
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, USA.
| | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, USA.
- Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, USA.
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, USA.
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, USA.
- Mental Illness Research Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY, USA.
- Center for Dementia Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
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Pressler MP, Horvath A, Entcheva E. Sex-dependent transcription of cardiac electrophysiology and links to acetylation modifiers based on the GTEx database. Front Cardiovasc Med 2022; 9:941890. [PMID: 35935618 PMCID: PMC9354462 DOI: 10.3389/fcvm.2022.941890] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 06/29/2022] [Indexed: 11/30/2022] Open
Abstract
Development of safer drugs based on epigenetic modifiers, e.g., histone deacetylase inhibitors (HDACi), requires better understanding of their effects on cardiac electrophysiology. Using RNAseq data from the genotype-tissue-expression database (GTEx), we created models that link the abundance of acetylation enzymes (HDAC/SIRT/HATs), and the gene expression of ion channels (IC) via select cardiac transcription factors (TFs) in male and female adult human hearts (left ventricle, LV). Gene expression data (transcripts per million, TPM) from GTEx donors (21–70 y.o.) were filtered, normalized and transformed to Euclidian space to allow quantitative comparisons in 84 female and 158 male LVs. Sex-specific partial least-square (PLS) regression models, linking gene expression data for HDAC/SIRT/HATs to TFs and to ICs gene expression, revealed tight co-regulation of cardiac ion channels by HDAC/SIRT/HATs, with stronger clustering in the male LV. Co-regulation of genes encoding excitatory and inhibitory processes in cardiac tissue by the acetylation modifiers may help explain their predominantly net-neutral effects on cardiac electrophysiology. ATP1A1, encoding for the Na/K pump, represented an outlier—with orthogonal regulation by the acetylation modifiers to most of the ICs. The HDAC/SIRT/HAT effects were mediated by strong (+) TF regulators of ICs, e.g., MEF2A and TBX5, in both sexes. Furthermore, for male hearts, PLS models revealed a stronger (+/-) mediatory role on ICs for NKX25 and TGF1B/KLF4, respectively, while RUNX1 exhibited larger (-) TF effects on ICs in females. Male-trained PLS models of HDAC/SIRT/HAT effects on ICs underestimated the effects on some ICs in females. Insights from the GTEx dataset about the co-expression and transcriptional co-regulation of acetylation-modifying enzymes, transcription factors and key cardiac ion channels in a sex-specific manner can help inform safer drug design.
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Affiliation(s)
- Michael P. Pressler
- Department of Biomedical Engineering, George Washington University, Washington, DC, United States
| | - Anelia Horvath
- Department of Biochemistry and Molecular Medicine, McCormick Genomics and Proteomics Center, School of Medicine and Health Sciences, The George Washington University, Washington, DC, United States
| | - Emilia Entcheva
- Department of Biomedical Engineering, George Washington University, Washington, DC, United States
- *Correspondence: Emilia Entcheva,
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111
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Mauracher AA, Henrickson SE. Leveraging Systems Immunology to Optimize Diagnosis and Treatment of Inborn Errors of Immunity. FRONTIERS IN SYSTEMS BIOLOGY 2022; 2:910243. [PMID: 37670772 PMCID: PMC10477056 DOI: 10.3389/fsysb.2022.910243] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/07/2023]
Abstract
Inborn errors of immunity (IEI) are monogenic disorders that can cause diverse symptoms, including recurrent infections, autoimmunity and malignancy. While many factors have contributed, the increased availability of next-generation sequencing has been central in the remarkable increase in identification of novel monogenic IEI over the past years. Throughout this phase of disease discovery, it has also become evident that a given gene variant does not always yield a consistent phenotype, while variants in seemingly disparate genes can lead to similar clinical presentations. Thus, it is increasingly clear that the clinical phenotype of an IEI patient is not defined by genetics alone, but is also impacted by a myriad of factors. Accordingly, we need methods to amplify our current diagnostic algorithms to better understand mechanisms underlying the variability in our patients and to optimize treatment. In this review, we will explore how systems immunology can contribute to optimizing both diagnosis and treatment of IEI patients by focusing on identifying and quantifying key dysregulated pathways. To improve mechanistic understanding in IEI we must deeply evaluate our rare IEI patients using multimodal strategies, allowing both the quantification of altered immune cell subsets and their functional evaluation. By studying representative controls and patients, we can identify causative pathways underlying immune cell dysfunction and move towards functional diagnosis. Attaining this deeper understanding of IEI will require a stepwise strategy. First, we need to broadly apply these methods to IEI patients to identify patterns of dysfunction. Next, using multimodal data analysis, we can identify key dysregulated pathways. Then, we must develop a core group of simple, effective functional tests that target those pathways to increase efficiency of initial diagnostic investigations, provide evidence for therapeutic selection and contribute to the mechanistic evaluation of genetic results. This core group of simple, effective functional tests, targeting key pathways, can then be equitably provided to our rare patients. Systems biology is thus poised to reframe IEI diagnosis and therapy, fostering research today that will provide streamlined diagnosis and treatment choices for our rare and complex patients in the future, as well as providing a better understanding of basic immunology.
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Affiliation(s)
- Andrea A. Mauracher
- Division of Allergy and Immunology, Department of Pediatrics, Children’s Hospital of Philadelphia, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Sarah E. Henrickson
- Division of Allergy and Immunology, Department of Pediatrics, Children’s Hospital of Philadelphia, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Institute for Immunology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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112
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Kalita CA, Gusev A. DeCAF: a novel method to identify cell-type specific regulatory variants and their role in cancer risk. Genome Biol 2022; 23:152. [PMID: 35804456 PMCID: PMC9264694 DOI: 10.1186/s13059-022-02708-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 06/15/2022] [Indexed: 01/09/2023] Open
Abstract
Here, we propose DeCAF (DEconvoluted cell type Allele specific Function), a new method to identify cell-fraction (cf) QTLs in tumors by leveraging both allelic and total expression information. Applying DeCAF to RNA-seq data from TCGA, we identify 3664 genes with cfQTLs (at 10% FDR) in 14 cell types, a 5.63× increase in discovery over conventional interaction-eQTL mapping. cfQTLs replicated in external cell-type-specific eQTL data are more enriched for cancer risk than conventional eQTLs. Our new method, DeCAF, empowers the discovery of biologically meaningful cfQTLs from bulk RNA-seq data in moderately sized studies.
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Affiliation(s)
- Cynthia A. Kalita
- grid.38142.3c000000041936754XDivision of Population Sciences, Dana–Farber Cancer Institute & Harvard Medical School, Boston, USA
| | - Alexander Gusev
- grid.38142.3c000000041936754XDivision of Population Sciences, Dana–Farber Cancer Institute & Harvard Medical School, Boston, USA ,grid.66859.340000 0004 0546 1623The Broad Institute, Boston, USA ,grid.62560.370000 0004 0378 8294Division of Genetics, Brigham & Women’s Hospital, Boston, USA
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113
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Pereira B, Labrot E, Durand E, Korn JM, Kauffmann A, Campbell CD. Contribution and clinical relevance of germline variation to the cancer transcriptome. BMC Cancer 2022; 22:675. [PMID: 35725412 PMCID: PMC9208227 DOI: 10.1186/s12885-022-09757-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 06/10/2022] [Indexed: 11/20/2022] Open
Abstract
Background Somatic alterations in the cancer genome, some of which are associated with changes in gene expression, have been characterized in multiple studies across diverse cancer types. However, less is known about germline variants that influence tumor biology by shaping the cancer transcriptome. Methods We performed expression quantitative trait loci (eQTL) analyses using multi-dimensional data from The Cancer Genome Atlas to explore the role of germline variation in mediating the cancer transcriptome. After accounting for associations between somatic alterations and gene expression, we determined the contribution of inherited variants to the cancer transcriptome relative to that of somatic variants. Finally, we performed an interaction analysis using estimates of tumor cellularity to identify cell type-restricted eQTLs. Results The proportion of genes with at least one eQTL varied between cancer types, ranging between 0.8% in melanoma to 28.5% in thyroid cancer and was correlated more strongly with intratumor heterogeneity than with somatic alteration rates. Although contributions to variance in gene expression was low for most genes, some eQTLs accounted for more than 30% of expression of proximal genes. We identified cell type-restricted eQTLs in genes known to be cancer drivers including LPP and EZH2 that were associated with disease-specific mortality in TCGA but not associated with disease risk in published GWAS. Together, our results highlight the need to consider germline variation in interpreting cancer biology beyond risk prediction. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-022-09757-0.
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Affiliation(s)
- Bernard Pereira
- Novartis Institutes for Biomedical Research, 250 Massachusetts Avenue, Cambridge, MA, 02139, USA
| | - Emma Labrot
- Novartis Institutes for Biomedical Research, 250 Massachusetts Avenue, Cambridge, MA, 02139, USA
| | - Eric Durand
- Novartis Institutes for Biomedical Research, Novartis Campus, Fabrikstrasse 2, CH-4056, Basel, Switzerland
| | - Joshua M Korn
- Novartis Institutes for Biomedical Research, 250 Massachusetts Avenue, Cambridge, MA, 02139, USA
| | - Audrey Kauffmann
- Novartis Institutes for Biomedical Research, Novartis Campus, Fabrikstrasse 2, CH-4056, Basel, Switzerland
| | - Catarina D Campbell
- Novartis Institutes for Biomedical Research, 250 Massachusetts Avenue, Cambridge, MA, 02139, USA.
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Nathan A, Asgari S, Ishigaki K, Valencia C, Amariuta T, Luo Y, Beynor JI, Baglaenko Y, Suliman S, Price AL, Lecca L, Murray MB, Moody DB, Raychaudhuri S. Single-cell eQTL models reveal dynamic T cell state dependence of disease loci. Nature 2022; 606:120-128. [PMID: 35545678 PMCID: PMC9842455 DOI: 10.1038/s41586-022-04713-1] [Citation(s) in RCA: 72] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 03/31/2022] [Indexed: 02/02/2023]
Abstract
Non-coding genetic variants may cause disease by modulating gene expression. However, identifying these expression quantitative trait loci (eQTLs) is complicated by differences in gene regulation across fluid functional cell states within cell types. These states-for example, neurotransmitter-driven programs in astrocytes or perivascular fibroblast differentiation-are obscured in eQTL studies that aggregate cells1,2. Here we modelled eQTLs at single-cell resolution in one complex cell type: memory T cells. Using more than 500,000 unstimulated memory T cells from 259 Peruvian individuals, we show that around one-third of 6,511 cis-eQTLs had effects that were mediated by continuous multimodally defined cell states, such as cytotoxicity and regulatory capacity. In some loci, independent eQTL variants had opposing cell-state relationships. Autoimmune variants were enriched in cell-state-dependent eQTLs, including risk variants for rheumatoid arthritis near ORMDL3 and CTLA4; this indicates that cell-state context is crucial to understanding potential eQTL pathogenicity. Moreover, continuous cell states explained more variation in eQTLs than did conventional discrete categories, such as CD4+ versus CD8+, suggesting that modelling eQTLs and cell states at single-cell resolution can expand insight into gene regulation in functionally heterogeneous cell types.
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Affiliation(s)
- Aparna Nathan
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Samira Asgari
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Kazuyoshi Ishigaki
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Cristian Valencia
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Tiffany Amariuta
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Yang Luo
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Jessica I Beynor
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Yuriy Baglaenko
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Sara Suliman
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Alkes L Price
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Leonid Lecca
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA
- Socios En Salud Sucursal Peru, Lima, Peru
| | - Megan B Murray
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA
- Division of Global Health Equity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - D Branch Moody
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Centre for Genetics and Genomics Versus Arthritis, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK.
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Hecker M, Fitzner B, Putscher E, Schwartz M, Winkelmann A, Meister S, Dudesek A, Koczan D, Lorenz P, Boxberger N, Zettl UK. Implication of genetic variants in primary microRNA processing sites in the risk of multiple sclerosis. EBioMedicine 2022; 80:104052. [PMID: 35561450 PMCID: PMC9111935 DOI: 10.1016/j.ebiom.2022.104052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 04/19/2022] [Accepted: 04/25/2022] [Indexed: 12/01/2022] Open
Abstract
Background Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system with a well-established genetic contribution to susceptibility. Over 200 genetic regions have been linked to the inherited risk of developing MS, but the disease-causing variants and their functional effects at the molecular level are still largely unresolved. We hypothesised that MS-associated single-nucleotide polymorphisms (SNPs) affect the recognition and enzymatic cleavage of primary microRNAs (pri-miRNAs). Methods Our study focused on 11 pri-miRNAs (9 primate-specific) that are encoded in genetic risk loci for MS. The levels of mature miRNAs and potential isoforms (isomiRs) produced from those pri-miRNAs were measured in B cells obtained from the peripheral blood of 63 MS patients and 28 healthy controls. We tested for associations between SNP genotypes and miRNA expression in cis using quantitative trait locus (cis-miR-eQTL) analyses. Genetic effects on miRNA stem-loop processing efficiency were verified using luciferase reporter assays. Potential direct miRNA target genes were identified by transcriptome profiling and computational binding site assessment. Findings Mature miRNAs and isomiRs from hsa-mir-26a-2, hsa-mir-199a-1, hsa-mir-4304, hsa-mir-4423, hsa-mir-4464 and hsa-mir-4492 could be detected in all B-cell samples. When MS patient subgroups were compared with healthy controls, a significant differential expression was observed for miRNAs from the 5’ and 3’ strands of hsa-mir-26a-2 and hsa-mir-199a-1. The cis-miR-eQTL analyses and reporter assays pointed to a slightly more efficient Drosha-mediated processing of hsa-mir-199a-1 when the MS risk allele T of SNP rs1005039 is present. On the other hand, the MS risk allele A of SNP rs817478, which substitutes the first C in a CNNC sequence motif, was found to cause a markedly lower efficiency in the processing of hsa-mir-4423. Overexpression of hsa-mir-199a-1 inhibited the expression of 60 protein-coding genes, including IRAK2, MIF, TNFRSF12A and TRAF1. The only target gene identified for hsa-mir-4423 was TMEM47. Interpretation We found that MS-associated SNPs in sequence determinants of pri-miRNA processing can affect the expression of mature miRNAs. Our findings complement the existing literature on the dysregulation of miRNAs in MS. Further studies on the maturation and function of miRNAs in different cell types and tissues may help to gain a more detailed functional understanding of the genetic basis of MS. Funding This study was funded by the Rostock University Medical Center (FORUN program, grant: 889002), Sanofi Genzyme (grant: GZ-2016-11560) and Merck Serono GmbH (Darmstadt, Germany, an affiliate of Merck KGaA, CrossRef Funder ID: 10.13039/100009945, grant: 4501860307). NB was supported by the Stiftung der Deutschen Wirtschaft (sdw) and the FAZIT foundation. EP was supported by the Landesgraduiertenförderung Mecklenburg-Vorpommern.
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Guintivano J, Aberg KA, Clark SL, Rubinow DR, Sullivan PF, Meltzer-Brody S, van den Oord EJCG. Transcriptome-wide association study for postpartum depression implicates altered B-cell activation and insulin resistance. Mol Psychiatry 2022; 27:2858-2867. [PMID: 35365803 PMCID: PMC9156403 DOI: 10.1038/s41380-022-01525-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 02/08/2022] [Accepted: 03/09/2022] [Indexed: 12/12/2022]
Abstract
Postpartum depression (PPD) affects 1 in 7 women and has negative mental health consequences for both mother and child. However, the precise biological mechanisms behind the disorder are unknown. Therefore, we performed the largest transcriptome-wide association study (TWAS) for PPD (482 cases, 859 controls) to date using RNA-sequencing in whole blood and deconvoluted cell types. No transcriptional changes were observed in whole blood. B-cells showed a majority of transcriptome-wide significant results (891 transcripts representing 789 genes) with pathway analyses implicating altered B-cell activation and insulin resistance. Integration of other data types revealed cell type-specific DNA methylation loci and disease-associated eQTLs (deQTLs), but not hormones/neuropeptides (estradiol, progesterone, oxytocin, BDNF), serve as regulators for part of the transcriptional differences between cases and controls. Further, deQTLs were enriched for several brain region-specific eQTLs, but no overlap with MDD risk loci was observed. Altogether, our results constitute a convergence of evidence for pathways most affected in PPD with data across different biological mechanisms.
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Affiliation(s)
- Jerry Guintivano
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Karolina A Aberg
- Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - Shaunna L Clark
- Department of Psychiatry & Behavioral Sciences, Texas A&M University, College Station, TX, USA
| | - David R Rubinow
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Patrick F Sullivan
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Samantha Meltzer-Brody
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Edwin J C G van den Oord
- Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, Richmond, VA, USA
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Immune disease risk variants regulate gene expression dynamics during CD4 + T cell activation. Nat Genet 2022; 54:817-826. [PMID: 35618845 PMCID: PMC9197762 DOI: 10.1038/s41588-022-01066-3] [Citation(s) in RCA: 52] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 03/30/2022] [Indexed: 12/22/2022]
Abstract
During activation, T cells undergo extensive gene expression changes that shape the properties of cells to exert their effector function. Understanding the regulation of this process could help explain how genetic variants predispose to immune diseases. Here, we mapped genetic effects on gene expression (expression quantitative trait loci (eQTLs)) using single-cell transcriptomics. We profiled 655,349 CD4+ T cells, capturing transcriptional states of unstimulated cells and three time points of cell activation in 119 healthy individuals. This identified 38 cell clusters, including transient clusters that were only present at individual time points of activation. We found 6,407 genes whose expression was correlated with genetic variation, of which 2,265 (35%) were dynamically regulated during activation. Furthermore, 127 genes were regulated by variants associated with immune-mediated diseases, with significant enrichment for dynamic effects. Our results emphasize the importance of studying context-specific gene expression regulation and provide insights into the mechanisms underlying genetic susceptibility to immune-mediated diseases.
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118
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Long E, García-Closas M, Chanock SJ, Camargo MC, Banovich NE, Choi J. The case for increasing diversity in tissue-based functional genomics datasets to understand human disease susceptibility. Nat Commun 2022; 13:2907. [PMID: 35614099 PMCID: PMC9133089 DOI: 10.1038/s41467-022-30650-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Accepted: 05/11/2022] [Indexed: 11/16/2022] Open
Affiliation(s)
- Erping Long
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Montserrat García-Closas
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - M Constanza Camargo
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Jiyeon Choi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
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Eraslan G, Drokhlyansky E, Anand S, Fiskin E, Subramanian A, Slyper M, Wang J, Van Wittenberghe N, Rouhana JM, Waldman J, Ashenberg O, Lek M, Dionne D, Win TS, Cuoco MS, Kuksenko O, Tsankov AM, Branton PA, Marshall JL, Greka A, Getz G, Segrè AV, Aguet F, Rozenblatt-Rosen O, Ardlie KG, Regev A. Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function. Science 2022; 376:eabl4290. [PMID: 35549429 PMCID: PMC9383269 DOI: 10.1126/science.abl4290] [Citation(s) in RCA: 157] [Impact Index Per Article: 78.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Understanding gene function and regulation in homeostasis and disease requires knowledge of the cellular and tissue contexts in which genes are expressed. Here, we applied four single-nucleus RNA sequencing methods to eight diverse, archived, frozen tissue types from 16 donors and 25 samples, generating a cross-tissue atlas of 209,126 nuclei profiles, which we integrated across tissues, donors, and laboratory methods with a conditional variational autoencoder. Using the resulting cross-tissue atlas, we highlight shared and tissue-specific features of tissue-resident cell populations; identify cell types that might contribute to neuromuscular, metabolic, and immune components of monogenic diseases and the biological processes involved in their pathology; and determine cell types and gene modules that might underlie disease mechanisms for complex traits analyzed by genome-wide association studies.
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Affiliation(s)
- Gökcen Eraslan
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Eugene Drokhlyansky
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Shankara Anand
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Evgenij Fiskin
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Ayshwarya Subramanian
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Michal Slyper
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Jiali Wang
- Department of Ophthalmology, Harvard Medical School, Boston, MA 02115, USA
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA 02114, USA
- Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - John M. Rouhana
- Department of Ophthalmology, Harvard Medical School, Boston, MA 02115, USA
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA 02114, USA
- Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Julia Waldman
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Orr Ashenberg
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Monkol Lek
- Department of Genetics, Yale School of Medicine, New Haven, CT 06510, USA
| | - Danielle Dionne
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Thet Su Win
- Department of Dermatology, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Michael S. Cuoco
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Olena Kuksenko
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Philip A. Branton
- The Joint Pathology Center Gynecologic/Breast Pathology, Silver Spring, MD 20910, USA
| | | | - Anna Greka
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Gad Getz
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Center for Cancer Research and Department of Pathology, Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Ayellet V. Segrè
- Department of Ophthalmology, Harvard Medical School, Boston, MA 02115, USA
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA 02114, USA
- Medical and Population Genetics Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - François Aguet
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Orit Rozenblatt-Rosen
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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120
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Björkegren JLM, Lusis AJ. Atherosclerosis: Recent developments. Cell 2022; 185:1630-1645. [PMID: 35504280 PMCID: PMC9119695 DOI: 10.1016/j.cell.2022.04.004] [Citation(s) in RCA: 370] [Impact Index Per Article: 185.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 03/30/2022] [Accepted: 04/01/2022] [Indexed: 12/13/2022]
Abstract
Atherosclerosis is an inflammatory disease of the large arteries that is the major cause of cardiovascular disease (CVD) and stroke. Here, we review the current understanding of the molecular, cellular, genetic, and environmental contributions to atherosclerosis, from both individual pathway and systems perspectives. We place an emphasis on recent developments, some of which have yielded unexpected biology, including previously unknown heterogeneity of inflammatory and smooth muscle cells in atherosclerotic lesions, roles for senescence and clonal hematopoiesis, and links to the gut microbiome.
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Affiliation(s)
- Johan L M Björkegren
- Department of Genetics and Genomic Sciences, Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Medicine, Huddinge, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden.
| | - Aldons J Lusis
- Department of Medicine/Division of Cardiology, Department of Microbiology, Immunology and Molecular Genetics, Department of Human Genetics, A2-237 Center for the Health Sciences, University of California, Los Angeles, Los Angeles, CA USA.
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121
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Olayinka OA, O'Neill NK, Farrer LA, Wang G, Zhang X. Molecular Quantitative Trait Locus Mapping in Human Complex Diseases. Curr Protoc 2022; 2:e426. [PMID: 35587224 PMCID: PMC9186089 DOI: 10.1002/cpz1.426] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Mapping quantitative trait loci (QTLs) for molecular traits from chromatin to metabolites (i.e., xQTLs) provides insight into the locations and effect modes of genetic variants that influence these molecular phenotypes and the propagation of functional consequences of each variant. xQTL studies indirectly interrogate the functional landscape of the molecular basis of complex diseases, including the impact of non-coding regulatory variants, the tissue specificity of regulatory elements, and their contribution to disease by integrating with genome-wide association studies (GWAS). We summarize a variety of molecular xQTL studies in human tissues and cells. In addition, using the Alzheimer's Disease Sequencing Project (ADSP) as an example, we describe the ADSP xQTL project, a collaborative effort across the ADSP Functional Genomics Consortium (ADSP-FGC). The project's ultimate goal is a reference map of Alzheimer's-related QTLs using existing datasets from multiple omics layers to help us study the consequences of genetic variants identified in the ADSP. xQTL studies enable the identification of the causal genes and pathways in GWAS loci, which will likely aid in the discovery of novel biomarkers and therapeutic targets for complex diseases. © 2022 Wiley Periodicals LLC.
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Affiliation(s)
- Oluwatosin A Olayinka
- Bioinformatics Program, Boston University, Boston, Massachusetts
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, Massachusetts
| | - Nicholas K O'Neill
- Bioinformatics Program, Boston University, Boston, Massachusetts
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, Massachusetts
| | - Lindsay A Farrer
- Bioinformatics Program, Boston University, Boston, Massachusetts
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, Massachusetts
- Department of Neurology, Boston University School of Medicine, Boston, Massachusetts
- Department of Ophthalmology, Boston University School of Medicine, Boston, Massachusetts
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts
| | - Gao Wang
- Department of Neurology, Columbia University, New York, New York
- Gertrude H. Sergievsky Center, Columbia University, New York, New York
| | - Xiaoling Zhang
- Bioinformatics Program, Boston University, Boston, Massachusetts
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, Massachusetts
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
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122
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Flynn E, Lappalainen T. Functional Characterization of Genetic Variant Effects on Expression. Annu Rev Biomed Data Sci 2022; 5:119-139. [PMID: 35483347 DOI: 10.1146/annurev-biodatasci-122120-010010] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Thousands of common genetic variants in the human population have been associated with disease risk and phenotypic variation by genome-wide association studies (GWAS). However, the majority of GWAS variants fall into noncoding regions of the genome, complicating our understanding of their regulatory functions, and few molecular mechanisms of GWAS variant effects have been clearly elucidated. Here, we set out to review genetic variant effects, focusing on expression quantitative trait loci (eQTLs), including their utility in interpreting GWAS variant mechanisms. We discuss the interrelated challenges and opportunities for eQTL analysis, covering determining causal variants, elucidating molecular mechanisms of action, and understanding context variability. Addressing these questions can enable better functional characterization of disease-associated loci and provide insights into fundamental biological questions of the noncoding genetic regulatory code and its control of gene expression. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Elise Flynn
- New York Genome Center, New York, NY, USA; , .,Department of Systems Biology, Columbia University, New York, NY, USA
| | - Tuuli Lappalainen
- New York Genome Center, New York, NY, USA; , .,Department of Systems Biology, Columbia University, New York, NY, USA.,Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
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123
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Stikker BS, Stik G, van Ouwerkerk AF, Trap L, Spicuglia S, Hendriks RW, Stadhouders R. Severe COVID-19-associated variants linked to chemokine receptor gene control in monocytes and macrophages. Genome Biol 2022; 23:96. [PMID: 35421995 PMCID: PMC9009160 DOI: 10.1186/s13059-022-02669-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 04/06/2022] [Indexed: 12/11/2022] Open
Abstract
AbstractGenome-wide association studies have identified 3p21.31 as the main risk locus for severe COVID-19, although underlying mechanisms remain elusive. We perform an epigenomic dissection of 3p21.31, identifying a CTCF-dependent tissue-specific 3D regulatory chromatin hub that controls the activity of several chemokine receptor genes. Risk SNPs colocalize with regulatory elements and are linked to increased expression of CCR1, CCR2 and CCR5 in monocytes and macrophages. As excessive organ infiltration of inflammatory monocytes and macrophages is a hallmark of severe COVID-19, our findings provide a rationale for the genetic association of 3p21.31 variants with elevated risk of hospitalization upon SARS-CoV-2 infection.
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124
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Yazar S, Alquicira-Hernandez J, Wing K, Senabouth A, Gordon MG, Andersen S, Lu Q, Rowson A, Taylor TRP, Clarke L, Maccora K, Chen C, Cook AL, Ye CJ, Fairfax KA, Hewitt AW, Powell JE. Single-cell eQTL mapping identifies cell type-specific genetic control of autoimmune disease. Science 2022; 376:eabf3041. [PMID: 35389779 DOI: 10.1126/science.abf3041] [Citation(s) in RCA: 171] [Impact Index Per Article: 85.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The human immune system displays substantial variation between individuals, leading to differences in susceptibility to autoimmune disease. We present single-cell RNA sequencing (scRNA-seq) data from 1,267,758 peripheral blood mononuclear cells from 982 healthy human subjects. For 14 cell types, we identified 26,597 independent cis-expression quantitative trait loci (eQTLs) and 990 trans-eQTLs, with most showing cell type-specific effects on gene expression. We subsequently show how eQTLs have dynamic allelic effects in B cells that are transitioning from naïve to memory states and demonstrate how commonly segregating alleles lead to interindividual variation in immune function. Finally, using a Mendelian randomization approach, we identify the causal route by which 305 risk loci contribute to autoimmune disease at the cellular level. This work brings together genetic epidemiology with scRNA-seq to uncover drivers of interindividual variation in the immune system.
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Affiliation(s)
- Seyhan Yazar
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, Australia
| | - Jose Alquicira-Hernandez
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, Australia.,Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Kristof Wing
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia.,Department of Ophthalmology, Royal Hobart Hospital, Hobart, TAS, Australia
| | - Anne Senabouth
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, Australia
| | - M Grace Gordon
- Division of Rheumatology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.,Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.,Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | - Stacey Andersen
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Qinyi Lu
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia
| | - Antonia Rowson
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia.,Department of Surgery, School of Clinical Science at Monash Health, Monash University, VIC, Australia
| | - Thomas R P Taylor
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia
| | - Linda Clarke
- Centre for Eye Research Australia, University of Melbourne, East Melbourne, VIC, Australia
| | - Katia Maccora
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia.,Department of Surgery, School of Clinical Science at Monash Health, Monash University, VIC, Australia
| | - Christine Chen
- Department of Surgery, School of Clinical Science at Monash Health, Monash University, VIC, Australia
| | - Anthony L Cook
- Wicking Dementia Research and Education Centre, University of Tasmania, Hobart, TAS, Australia
| | - Chun Jimmie Ye
- Division of Rheumatology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.,Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.,Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA.,Institute of Computational Health Sciences, University of California, San Francisco, San Francisco, CA, USA.,Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA.,Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Kirsten A Fairfax
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia
| | - Alex W Hewitt
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia.,Department of Ophthalmology, Royal Hobart Hospital, Hobart, TAS, Australia.,Centre for Eye Research Australia, University of Melbourne, East Melbourne, VIC, Australia
| | - Joseph E Powell
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, Australia.,UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, NSW, Australia
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125
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Fisher JL, Jones EF, Flanary VL, Williams AS, Ramsey EJ, Lasseigne BN. Considerations and challenges for sex-aware drug repurposing. Biol Sex Differ 2022; 13:13. [PMID: 35337371 PMCID: PMC8949654 DOI: 10.1186/s13293-022-00420-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 03/06/2022] [Indexed: 01/09/2023] Open
Abstract
Sex differences are essential factors in disease etiology and manifestation in many diseases such as cardiovascular disease, cancer, and neurodegeneration [33]. The biological influence of sex differences (including genomic, epigenetic, hormonal, immunological, and metabolic differences between males and females) and the lack of biomedical studies considering sex differences in their study design has led to several policies. For example, the National Institute of Health's (NIH) sex as a biological variable (SABV) and Sex and Gender Equity in Research (SAGER) policies to motivate researchers to consider sex differences [204]. However, drug repurposing, a promising alternative to traditional drug discovery by identifying novel uses for FDA-approved drugs, lacks sex-aware methods that can improve the identification of drugs that have sex-specific responses [7, 11, 14, 33]. Sex-aware drug repurposing methods either select drug candidates that are more efficacious in one sex or deprioritize drug candidates based on if they are predicted to cause a sex-bias adverse event (SBAE), unintended therapeutic effects that are more likely to occur in one sex. Computational drug repurposing methods are encouraging approaches to develop for sex-aware drug repurposing because they can prioritize sex-specific drug candidates or SBAEs at lower cost and time than traditional drug discovery. Sex-aware methods currently exist for clinical, genomic, and transcriptomic information [1, 7, 155]. They have not expanded to other data types, such as DNA variation, which has been beneficial in other drug repurposing methods that do not consider sex [114]. Additionally, some sex-aware methods suffer from poorer performance because a disproportionate number of male and female samples are available to train computational methods [7]. However, there is development potential for several different categories (i.e., data mining, ligand binding predictions, molecular associations, and networks). Low-dimensional representations of molecular association and network approaches are also especially promising candidates for future sex-aware drug repurposing methodologies because they reduce the multiple hypothesis testing burden and capture sex-specific variation better than the other methods [151, 159]. Here we review how sex influences drug response, the current state of drug repurposing including with respect to sex-bias drug response, and how model organism study design choices influence drug repurposing validation.
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Affiliation(s)
- Jennifer L. Fisher
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Emma F. Jones
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Victoria L. Flanary
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Avery S. Williams
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Elizabeth J. Ramsey
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
| | - Brittany N. Lasseigne
- Department of Cell, Developmental and Integrative Biology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294 USA
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126
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Weighill D, Ben Guebila M, Glass K, Quackenbush J, Platig J. Predicting genotype-specific gene regulatory networks. Genome Res 2022; 32:524-533. [PMID: 35193937 PMCID: PMC8896459 DOI: 10.1101/gr.275107.120] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 01/11/2022] [Indexed: 11/25/2022]
Abstract
Understanding how each person's unique genotype influences their individual patterns of gene regulation has the potential to improve our understanding of human health and development, and to refine genotype-specific disease risk assessments and treatments. However, the effects of genetic variants are not typically considered when constructing gene regulatory networks, despite the fact that many disease-associated genetic variants are thought to have regulatory effects, including the disruption of transcription factor (TF) binding. We developed EGRET (Estimating the Genetic Regulatory Effect on TFs), which infers a genotype-specific gene regulatory network for each individual in a study population. EGRET begins by constructing a genotype-informed TF-gene prior network derived using TF motif predictions, expression quantitative trait locus (eQTL) data, individual genotypes, and the predicted effects of genetic variants on TF binding. It then uses a technique known as message passing to integrate this prior network with gene expression and TF protein–protein interaction data to produce a refined, genotype-specific regulatory network. We used EGRET to infer gene regulatory networks for two blood-derived cell lines and identified genotype-associated, cell line–specific regulatory differences that we subsequently validated using allele-specific expression, chromatin accessibility QTLs, and differential ChIP-seq TF binding. We also inferred EGRET networks for three cell types from each of 119 individuals and identified cell type–specific regulatory differences associated with diseases related to those cell types. EGRET is, to our knowledge, the first method that infers networks reflective of individual genetic variation in a way that provides insight into the genetic regulatory associations driving complex phenotypes.
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Affiliation(s)
- Deborah Weighill
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA
| | | | - Kimberly Glass
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA.,Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA.,Harvard Medical School, Boston, Massachusetts 02115, USA
| | - John Quackenbush
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA.,Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA
| | - John Platig
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA.,Harvard Medical School, Boston, Massachusetts 02115, USA
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127
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White RJ, Mackay E, Wilson SW, Busch-Nentwich EM. Allele-specific gene expression can underlie altered transcript abundance in zebrafish mutants. eLife 2022; 11:72825. [PMID: 35175196 PMCID: PMC8884726 DOI: 10.7554/elife.72825] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 02/16/2022] [Indexed: 11/13/2022] Open
Abstract
In model organisms, RNA-sequencing (RNA-seq) is frequently used to assess the effect of genetic mutations on cellular and developmental processes. Typically, animals heterozygous for a mutation are crossed to produce offspring with different genotypes. Resultant embryos are grouped by genotype to compare homozygous mutant embryos to heterozygous and wild-type siblings. Genes that are differentially expressed between the groups are assumed to reveal insights into the pathways affected by the mutation. Here we show that in zebrafish, differentially expressed genes are often over-represented on the same chromosome as the mutation due to different levels of expression of alleles from different genetic backgrounds. Using an incross of haplotype-resolved wild-type fish, we found evidence of widespread allele-specific expression, which appears as differential expression when comparing embryos homozygous for a region of the genome to their siblings. When analysing mutant transcriptomes, this means that the differential expression of genes on the same chromosome as a mutation of interest may not be caused by that mutation. Typically, the genomic location of a differentially expressed gene is not considered when interpreting its importance with respect to the phenotype. This could lead to pathways being erroneously implicated or overlooked due to the noise of spurious differentially expressed genes on the same chromosome as the mutation. These observations have implications for the interpretation of RNA-seq experiments involving outbred animals and non-inbred model organisms.
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Affiliation(s)
- Richard J White
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Department of Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Eirinn Mackay
- Department of Cell and Developmental Biology, University College London, London, United Kingdom
| | - Stephen W Wilson
- Department of Cell and Developmental Biology, University College London, London, United Kingdom
| | - Elisabeth M Busch-Nentwich
- Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Department of Medicine, University of Cambridge, Cambridge, United Kingdom.,School of Biological and Behavioural Sciences, Faculty of Science and Engineering, Queen Mary University of London, London, United Kingdom
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128
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Remmel MC, Coyle SM, Eshoo MW, Sweeney TE, Rawling DC. Diagnostic Host Gene Expression Analysis by Quantitative Reverse Transcription Loop-Mediated Isothermal Amplification to Discriminate between Bacterial and Viral Infections. Clin Chem 2022; 68:550-560. [PMID: 35134876 DOI: 10.1093/clinchem/hvab275] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 11/30/2021] [Indexed: 12/23/2022]
Abstract
BACKGROUND Early and accurate diagnosis of acute infections can help minimize the overprescription of antibiotics and improve patient outcomes. Discrimination between bacterial and viral etiologies in acute infection based on changes in host gene expression has been described. Unfortunately, established technologies used for gene expression profiling are typically expensive and slow, confounding integration into clinical workflows. Here we report the development of an ultra-rapid test system for host gene expression profiling from blood based on quantitative reverse transcription followed by loop-mediated isothermal amplification (qRT-LAMP). METHODS We developed 10 messenger ribonucleic acid-specific assays based on qRT-LAMP targeting 7 informative biomarkers to discriminate viral from bacterial infections and 3 housekeeping reference genes. We optimized qRT-LAMP formulations to achieve a turnaround time of 12 min without sacrificing specificity or precision. The accuracy of the test system was verified utilizing blood samples from 57 patients and comparing qRT-LAMP results to profiles obtained using an orthogonal reference technology. RESULTS We observed a Pearson coefficient of 0.90 between bacterial/viral metascores generated by qRT-LAMP and the reference technology. CONCLUSIONS qRT-LAMP assays can provide sufficiently accurate gene expression profiling data to enable discrimination between bacterial and viral etiologies using an established set of biomarkers and a classification algorithm.
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129
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Zeng B, Bendl J, Kosoy R, Fullard JF, Hoffman GE, Roussos P. Multi-ancestry eQTL meta-analysis of human brain identifies candidate causal variants for brain-related traits. Nat Genet 2022; 54:161-169. [PMID: 35058635 PMCID: PMC8852232 DOI: 10.1038/s41588-021-00987-9] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 11/17/2021] [Indexed: 12/11/2022]
Abstract
While large-scale, genome-wide association studies (GWAS) have identified hundreds of loci associated with brain-related traits, identification of the variants, genes and molecular mechanisms underlying these traits remains challenging. Integration of GWAS with expression quantitative trait loci (eQTLs) and identification of shared genetic architecture have been widely adopted to nominate genes and candidate causal variants. However, this approach is limited by sample size, statistical power and linkage disequilibrium. We developed the multivariate multiple QTL approach and performed a large-scale, multi-ancestry eQTL meta-analysis to increase power and fine-mapping resolution. Analysis of 3,983 RNA-sequenced samples from 2,119 donors, including 474 non-European individuals, yielded an effective sample size of 3,154. Joint statistical fine-mapping of eQTL and GWAS identified 329 variant-trait pairs for 24 brain-related traits driven by 204 unique candidate causal variants for 189 unique genes. This integrative analysis identifies candidate causal variants and elucidates potential regulatory mechanisms for genes underlying schizophrenia, bipolar disorder and Alzheimer's disease.
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Affiliation(s)
- Biao Zeng
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Roman Kosoy
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - John F Fullard
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Gabriel E Hoffman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Mental Illness Research, Education and Clinical Centers, James J. Peters VA Medical Center, Bronx, NY, USA.
- Center for Dementia Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
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Orozco G. Fine mapping with epigenetic information and 3D structure. Semin Immunopathol 2022; 44:115-125. [PMID: 35022890 PMCID: PMC8837508 DOI: 10.1007/s00281-021-00906-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 12/13/2021] [Indexed: 12/12/2022]
Abstract
Since 2005, thousands of genome-wide association studies (GWAS) have been published, identifying hundreds of thousands of genetic variants that increase risk of complex traits such as autoimmune diseases. This wealth of data has the potential to improve patient care, through personalized medicine and the identification of novel drug targets. However, the potential of GWAS for clinical translation has not been fully achieved yet, due to the fact that the functional interpretation of risk variants and the identification of causal variants and genes are challenging. The past decade has seen the development of great advances that are facilitating the overcoming of these limitations, by utilizing a plethora of genomics and epigenomics tools to map and characterize regulatory elements and chromatin interactions, which can be used to fine map GWAS loci, and advance our understanding of the biological mechanisms that cause disease.
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Affiliation(s)
- Gisela Orozco
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, AV Hill Building, Oxford Road, Manchester, M13 9LJ, UK. .,NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK.
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131
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Ahearn TU, Zhang H, Michailidou K, Milne RL, Bolla MK, Dennis J, Dunning AM, Lush M, Wang Q, Andrulis IL, Anton-Culver H, Arndt V, Aronson KJ, Auer PL, Augustinsson A, Baten A, Becher H, Behrens S, Benitez J, Bermisheva M, Blomqvist C, Bojesen SE, Bonanni B, Børresen-Dale AL, Brauch H, Brenner H, Brooks-Wilson A, Brüning T, Burwinkel B, Buys SS, Canzian F, Castelao JE, Chang-Claude J, Chanock SJ, Chenevix-Trench G, Clarke CL, Collée JM, Cox A, Cross SS, Czene K, Daly MB, Devilee P, Dörk T, Dwek M, Eccles DM, Evans DG, Fasching PA, Figueroa J, Floris G, Gago-Dominguez M, Gapstur SM, García-Sáenz JA, Gaudet MM, Giles GG, Goldberg MS, González-Neira A, Alnæs GIG, Grip M, Guénel P, Haiman CA, Hall P, Hamann U, Harkness EF, Heemskerk-Gerritsen BAM, Holleczek B, Hollestelle A, Hooning MJ, Hoover RN, Hopper JL, Howell A, Jakimovska M, Jakubowska A, John EM, Jones ME, Jung A, Kaaks R, Kauppila S, Keeman R, Khusnutdinova E, Kitahara CM, Ko YD, Koutros S, Kristensen VN, Krüger U, Kubelka-Sabit K, Kurian AW, Kyriacou K, Lambrechts D, Lee DG, Lindblom A, Linet M, Lissowska J, Llaneza A, Lo WY, MacInnis RJ, Mannermaa A, Manoochehri M, Margolin S, Martinez ME, McLean C, Meindl A, Menon U, Nevanlinna H, Newman WG, Nodora J, Offit K, Olsson H, Orr N, Park-Simon TW, Patel AV, Peto J, Pita G, Plaseska-Karanfilska D, Prentice R, Punie K, Pylkäs K, Radice P, Rennert G, Romero A, Rüdiger T, Saloustros E, Sampson S, Sandler DP, Sawyer EJ, Schmutzler RK, Schoemaker MJ, Schöttker B, Sherman ME, Shu XO, Smichkoska S, Southey MC, Spinelli JJ, Swerdlow AJ, Tamimi RM, Tapper WJ, Taylor JA, Teras LR, Terry MB, Torres D, Troester MA, Vachon CM, van Deurzen CHM, van Veen EM, Wagner P, Weinberg CR, Wendt C, Wesseling J, Winqvist R, Wolk A, Yang XR, Zheng W, Couch FJ, Simard J, Kraft P, Easton DF, Pharoah PDP, Schmidt MK, García-Closas M, Chatterjee N. Common variants in breast cancer risk loci predispose to distinct tumor subtypes. Breast Cancer Res 2022; 24:2. [PMID: 34983606 PMCID: PMC8725568 DOI: 10.1186/s13058-021-01484-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 11/02/2021] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Genome-wide association studies (GWAS) have identified multiple common breast cancer susceptibility variants. Many of these variants have differential associations by estrogen receptor (ER) status, but how these variants relate with other tumor features and intrinsic molecular subtypes is unclear. METHODS Among 106,571 invasive breast cancer cases and 95,762 controls of European ancestry with data on 173 breast cancer variants identified in previous GWAS, we used novel two-stage polytomous logistic regression models to evaluate variants in relation to multiple tumor features (ER, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and grade) adjusting for each other, and to intrinsic-like subtypes. RESULTS Eighty-five of 173 variants were associated with at least one tumor feature (false discovery rate < 5%), most commonly ER and grade, followed by PR and HER2. Models for intrinsic-like subtypes found nearly all of these variants (83 of 85) associated at p < 0.05 with risk for at least one luminal-like subtype, and approximately half (41 of 85) of the variants were associated with risk of at least one non-luminal subtype, including 32 variants associated with triple-negative (TN) disease. Ten variants were associated with risk of all subtypes in different magnitude. Five variants were associated with risk of luminal A-like and TN subtypes in opposite directions. CONCLUSION This report demonstrates a high level of complexity in the etiology heterogeneity of breast cancer susceptibility variants and can inform investigations of subtype-specific risk prediction.
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Affiliation(s)
- Thomas U Ahearn
- Division of Cancer Epidemiology and GeneticsDepartment of Health and Human Services, Medical Center Drive, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Haoyu Zhang
- Division of Cancer Epidemiology and GeneticsDepartment of Health and Human Services, Medical Center Drive, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Kyriaki Michailidou
- Institute of Neurology & Genetics, Biostatistics Unit, Nicosia, Cyprus
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Cyprus School of Molecular Medicine, Institute of Neurology & Genetics, Nicosia, Cyprus
| | - Roger L Milne
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Manjeet K Bolla
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Alison M Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Michael Lush
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Qin Wang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Irene L Andrulis
- Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Hoda Anton-Culver
- Department of Medicine, Genetic Epidemiology Research Institute, University of California Irvine, Irvine, CA, USA
| | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Kristan J Aronson
- Department of Public Health Sciences, and Cancer Research Institute, Queen's University, Kingston, ON, Canada
| | - Paul L Auer
- Cancer Prevention Program, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI, USA
| | - Annelie Augustinsson
- Department of Cancer Epidemiology, Clinical Sciences, Lund University, Lund, Sweden
| | - Adinda Baten
- Leuven Multidisciplinary Breast Center, Department of Oncology, Leuven Cancer Institute, University Hospitals Leuven, Leuven, Belgium
| | - Heiko Becher
- Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Sabine Behrens
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Javier Benitez
- Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
- Biomedical Network On Rare Diseases (CIBERER), Madrid, Spain
| | - Marina Bermisheva
- Institute of Biochemistry and Genetics, Ufa Federal Research Centre of the Russian Academy of Sciences, Ufa, Russia
- Saint Petersburg State University, Saint-Petersburg, Russia
| | - Carl Blomqvist
- Department of Oncology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
- Department of Oncology, Örebro University Hospital, Örebro, Sweden
| | - Stig E Bojesen
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
- Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
| | - Bernardo Bonanni
- Division of Cancer Prevention and Genetics, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Anne-Lise Børresen-Dale
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital-Radiumhospitalet, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Hiltrud Brauch
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany
- iFIT-Cluster of Excellence, University of Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Tübingen, Tübingen, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ), National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Angela Brooks-Wilson
- Genome Sciences Centre, BC Cancer Agency, Vancouver, BC, Canada
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada
| | - Thomas Brüning
- Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute, Ruhr University Bochum (IPA), Bochum, Germany
| | - Barbara Burwinkel
- Molecular Epidemiology Group, German Cancer Research Center (DKFZ), C080, Heidelberg, Germany
- Molecular Biology of Breast Cancer, University Womens Clinic Heidelberg, University of Heidelberg, Heidelberg, Germany
| | - Saundra S Buys
- Department of Medicine, Huntsman Cancer Institute, Salt Lake City, UT, USA
| | - Federico Canzian
- Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jose E Castelao
- Oncology and Genetics Unit, Instituto de Investigacion Sanitaria Galicia Sur (IISGS), Xerencia de Xestion Integrada de Vigo-SERGAS, Vigo, Spain
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Stephen J Chanock
- Division of Cancer Epidemiology and GeneticsDepartment of Health and Human Services, Medical Center Drive, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Georgia Chenevix-Trench
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Christine L Clarke
- Westmead Institute for Medical Research, University of Sydney, Sydney, NSW, Australia
| | - J Margriet Collée
- Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Angela Cox
- Department of Oncology and Metabolism, Sheffield Institute for Nucleic Acids (SInFoNiA), University of Sheffield, Sheffield, UK
| | - Simon S Cross
- Department of Neuroscience, Academic Unit of Pathology, University of Sheffield, Sheffield, UK
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Mary B Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Peter Devilee
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
| | - Thilo Dörk
- Gynaecology Research Unit, Hannover Medical School, Hannover, Germany
| | - Miriam Dwek
- School of Life Sciences, University of Westminster, London, UK
| | - Diana M Eccles
- Faculty of Medicine, University of Southampton, Southampton, UK
| | - D Gareth Evans
- North West Genomics Laboratory Hub, Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Peter A Fasching
- Department of Gynecology and Obstetrics Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, University Hospital Erlangen, Erlangen, Germany
| | - Jonine Figueroa
- Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, UK
- Cancer Research UK Edinburgh Centre, The University of Edinburgh, Edinburgh, UK
| | - Giuseppe Floris
- Leuven Multidisciplinary Breast Center, Department of Oncology, Leuven Cancer Institute, University Hospitals Leuven, Leuven, Belgium
| | - Manuela Gago-Dominguez
- Fundación Pública Galega de Medicina Xenómica, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Complejo Hospitalario Universitario de Santiago, SERGAS, Santiago de Compostela, Spain
- Moores Cancer Center, University of California San Diego, La Jolla, CA, USA
| | - Susan M Gapstur
- Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, GA, USA
| | - José A García-Sáenz
- Medical Oncology Department, Centro Investigación Biomédica en Red de Cáncer (CIBERONC), Hospital Clínico San Carlos, Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, Spain
| | - Mia M Gaudet
- Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, GA, USA
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Mark S Goldberg
- Division of Clinical Epidemiology, Royal Victoria Hospital, McGill University, Montréal, QC, Canada
- Department of Medicine, McGill University, Montréal, QC, Canada
| | - Anna González-Neira
- Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Grethe I Grenaker Alnæs
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital-Radiumhospitalet, Oslo, Norway
| | - Mervi Grip
- Department of Surgery, Oulu University Hospital, University of Oulu, Oulu, Finland
| | - Pascal Guénel
- Center for Research in Epidemiology and Population Health (CESP), Team Exposome and Heredity, INSERM, University Paris-Saclay, Villejuif, France
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Ute Hamann
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Elaine F Harkness
- Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- Nightingale & Genesis Prevention Centre, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester, UK
- NIHR Manchester Biomedical Research Unit, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | | | | | | | - Maartje J Hooning
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Robert N Hoover
- Division of Cancer Epidemiology and GeneticsDepartment of Health and Human Services, Medical Center Drive, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Anthony Howell
- Division of Cancer Sciences, University of Manchester, Manchester, UK
| | - Milena Jakimovska
- Research Centre for Genetic Engineering and Biotechnology "Georgi D. Efremov", MASA, Skopje, Republic of North Macedonia
| | - Anna Jakubowska
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
- Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian Medical University, Szczecin, Poland
| | - Esther M John
- Department of Epidemiology & Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Michael E Jones
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - Audrey Jung
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Saila Kauppila
- Department of Pathology, Oulu University Hospital, University of Oulu, Oulu, Finland
| | - Renske Keeman
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Elza Khusnutdinova
- Institute of Biochemistry and Genetics, Ufa Federal Research Centre of the Russian Academy of Sciences, Ufa, Russia
- Department of Genetics and Fundamental Medicine, Bashkir State University, Ufa, Russia
| | - Cari M Kitahara
- Radiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Yon-Dschun Ko
- Department of Internal Medicine, Johanniter Kliniken Bonn, Johanniter Krankenhaus, Bonn, Germany
| | - Stella Koutros
- Division of Cancer Epidemiology and GeneticsDepartment of Health and Human Services, Medical Center Drive, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Vessela N Kristensen
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Department of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Ute Krüger
- Department of Cancer Epidemiology, Clinical Sciences, Lund University, Lund, Sweden
| | - Katerina Kubelka-Sabit
- Department of Histopathology and Cytology, Clinical Hospital Acibadem Sistina, Skopje, Republic of North Macedonia
| | - Allison W Kurian
- Department of Epidemiology & Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Kyriacos Kyriacou
- Cyprus School of Molecular Medicine, Institute of Neurology & Genetics, Nicosia, Cyprus
- Cancer Genetics, Therapeutics and Ultrastructural Pathology, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Diether Lambrechts
- Laboratory for Translational Genetics, Department of Human Genetics, University of Leuven, Leuven, Belgium
- VIB Center for Cancer Biology, Leuven, Belgium
| | - Derrick G Lee
- Cancer Control Research, BC Cancer, Vancouver, BC, Canada
- Department of Mathematics and Statistics, St. Francis Xavier University, Antigonish, NS, Canada
| | - Annika Lindblom
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
| | - Martha Linet
- Radiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Jolanta Lissowska
- Department of Cancer Epidemiology and Prevention, M. Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland
| | - Ana Llaneza
- General and Gastroenterology Surgery Service, Hospital Universitario Central de Asturias, Oviedo, Spain
| | - Wing-Yee Lo
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany
- University of Tübingen, Tübingen, Germany
| | - Robert J MacInnis
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Arto Mannermaa
- Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, Kuopio, Finland
- Translational Cancer Research Area, University of Eastern Finland, Kuopio, Finland
- Biobank of Eastern Finland, Kuopio University Hospital, Kuopio, Finland
| | - Mehdi Manoochehri
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Sara Margolin
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
- Department of Clinical Science and Education, Karolinska Institutet, Södersjukhuset Stockholm, Sweden
| | | | - Catriona McLean
- Anatomical Pathology, The Alfred Hospital, Melbourne, VIC, Australia
| | - Alfons Meindl
- Department of Gynecology and Obstetrics, University of Munich, Campus Großhadern, Munich, Germany
| | - Usha Menon
- Institute of Clinical Trials & Methodology, University College London, London, UK
| | - Heli Nevanlinna
- Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
| | - William G Newman
- North West Genomics Laboratory Hub, Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Jesse Nodora
- Moores Cancer Center, University of California San Diego, La Jolla, CA, USA
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Kenneth Offit
- Clinical Genetics Research Lab, Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Håkan Olsson
- Department of Cancer Epidemiology, Clinical Sciences, Lund University, Lund, Sweden
| | - Nick Orr
- Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, Ireland, UK
| | | | - Alpa V Patel
- Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, GA, USA
| | - Julian Peto
- Department of Non-Communicable Disease Epidemiology, School of Hygiene and Tropical Medicine, London, UK
| | - Guillermo Pita
- Human Genotyping-CEGEN Unit, Human Cancer Genetic Program, Spanish National Cancer Research Centre, Madrid, Spain
| | - Dijana Plaseska-Karanfilska
- Research Centre for Genetic Engineering and Biotechnology "Georgi D. Efremov", MASA, Skopje, Republic of North Macedonia
| | - Ross Prentice
- Cancer Prevention Program, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Kevin Punie
- Department of General Medical Oncology and Multidisciplinary Breast Center, Leuven Cancer Institute, University Hospitals Leuven, Leuven, Belgium
| | - Katri Pylkäs
- Laboratory of Cancer Genetics and Tumor Biology, Cancer and Translational Medicine Research Unit, University of Oulu, Biocenter Oulu, Oulu, Finland
- Laboratory of Cancer Genetics and Tumor Biology, Northern Finland Laboratory Centre Oulu, Oulu, Finland
| | - Paolo Radice
- Unit of Molecular Bases of Genetic Risk and Genetic Testing, Department of Research, Fondazione IRCCS Istituto Nazionale Dei Tumori (INT), Milan, Italy
| | - Gad Rennert
- Technion Faculty of Medicine, Clalit National Cancer Control Center, Carmel Medical Center, Haifa, Israel
| | - Atocha Romero
- Medical Oncology Department, Hospital Universitario Puerta de Hierro, Madrid, Spain
| | - Thomas Rüdiger
- Institute of Pathology, Staedtisches Klinikum Karlsruhe, Karlsruhe, Germany
| | | | - Sarah Sampson
- Prevent Breast Cancer Centre and Nightingale Breast Screening Centre, Manchester University NHS Foundation Trust, Manchester, UK
| | - Dale P Sandler
- Epidemiology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA
| | - Elinor J Sawyer
- School of Cancer & Pharmaceutical Sciences, Comprehensive Cancer Centre, Guy's Campus, King's College London, London, UK
| | - Rita K Schmutzler
- Center for Integrated Oncology (CIO), Faculty of Medicine, University Hospital Cologne, University of Cologne, Cologne, Germany
- Center for Molecular Medicine Cologne (CMMC), Faculty of Medicine, University Hospital Cologne, University of Cologne, Cologne, Germany
- Center for Familial Breast and Ovarian Cancer, Faculty of Medicine, University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Minouk J Schoemaker
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - Ben Schöttker
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Network Aging Research, University of Heidelberg, Heidelberg, Germany
| | - Mark E Sherman
- Department of Health Sciences Research, Mayo Clinic College of Medicine, Jacksonville, FL, USA
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Snezhana Smichkoska
- Medical Faculty, Ss. Cyril and Methodius University in Skopje, University Clinic of Radiotherapy and Oncology, Skopje, Republic of North Macedonia
| | - Melissa C Southey
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
- Department of Clinical Pathology, The University of Melbourne, Melbourne, VIC, Australia
| | - John J Spinelli
- Population Oncology, BC Cancer, Vancouver, BC, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | - Anthony J Swerdlow
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
- Division of Breast Cancer Research, The Institute of Cancer Research, London, UK
| | - Rulla M Tamimi
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | | | - Jack A Taylor
- Epidemiology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA
- Epigenetic and Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA
| | - Lauren R Teras
- Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, GA, USA
| | - Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Diana Torres
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Institute of Human Genetics, Pontificia Universidad Javeriana, Bogota, Colombia
| | - Melissa A Troester
- Department of Epidemiology, Gillings School of Global Public Health and UNC Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Celine M Vachon
- Department of Health Science Research, Division of Epidemiology, Mayo Clinic, Rochester, MN, USA
| | | | - Elke M van Veen
- North West Genomics Laboratory Hub, Manchester Centre for Genomic Medicine, St Mary's Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Philippe Wagner
- Department of Cancer Epidemiology, Clinical Sciences, Lund University, Lund, Sweden
| | - Clarice R Weinberg
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA
| | - Camilla Wendt
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
- Department of Clinical Science and Education, Karolinska Institutet, Södersjukhuset Stockholm, Sweden
| | - Jelle Wesseling
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands
- Department of Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Robert Winqvist
- Laboratory of Cancer Genetics and Tumor Biology, Cancer and Translational Medicine Research Unit, University of Oulu, Biocenter Oulu, Oulu, Finland
- Laboratory of Cancer Genetics and Tumor Biology, Northern Finland Laboratory Centre Oulu, Oulu, Finland
| | - Alicja Wolk
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Xiaohong R Yang
- Division of Cancer Epidemiology and GeneticsDepartment of Health and Human Services, Medical Center Drive, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Fergus J Couch
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Jacques Simard
- Genomics Center, Department of Molecular Medicine, Centre Hospitalier Universitaire de Québec, Université Laval Research Center, Université Laval, Québec City, QC, Canada
| | - Peter Kraft
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Paul D P Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Marjanka K Schmidt
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Montserrat García-Closas
- Division of Cancer Epidemiology and GeneticsDepartment of Health and Human Services, Medical Center Drive, National Cancer Institute, National Institutes of Health, Rockville, MD, USA.
| | - Nilanjan Chatterjee
- Department of Biostatistics, Bloomberg School of Public Health, John Hopkins University, Baltimore, MD, USA
- Department of Oncology, School of Medicine, John Hopkins University, Baltimore, MD, USA
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132
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Mortlock S, McKinnon B, Montgomery GW. Genetic Regulation of Transcription in the Endometrium in Health and Disease. FRONTIERS IN REPRODUCTIVE HEALTH 2022; 3:795464. [PMID: 36304015 PMCID: PMC9580733 DOI: 10.3389/frph.2021.795464] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 12/06/2021] [Indexed: 11/25/2023] Open
Abstract
The endometrium is a complex and dynamic tissue essential for fertility and implicated in many reproductive disorders. The tissue consists of glandular epithelium and vascularised stroma and is unique because it is constantly shed and regrown with each menstrual cycle, generating up to 10 mm of new mucosa. Consequently, there are marked changes in cell composition and gene expression across the menstrual cycle. Recent evidence shows expression of many genes is influenced by genetic variation between individuals. We and others have reported evidence for genetic effects on hundreds of genes in endometrium. The genetic factors influencing endometrial gene expression are highly correlated with the genetic effects on expression in other reproductive (e.g., in uterus and ovary) and digestive tissues (e.g., salivary gland and stomach), supporting a shared genetic regulation of gene expression in biologically similar tissues. There is also increasing evidence for cell specific genetic effects for some genes. Sample size for studies in endometrium are modest and results from the larger studies of gene expression in blood report genetic effects for a much higher proportion of genes than currently reported for endometrium. There is also emerging evidence for the importance of genetic variation on RNA splicing. Gene mapping studies for common disease, including diseases associated with endometrium, show most variation maps to intergenic regulatory regions. It is likely that genetic risk factors for disease function through modifying the program of cell specific gene expression. The emerging evidence from our gene mapping studies coupled with tissue specific studies, and the GTEx, eQTLGen and EpiMap projects, show we need to expand our understanding of the complex regulation of gene expression. These data also help to link disease genetic risk factors to specific target genes. Combining our data on genetic regulation of gene expression in endometrium, and cell types within the endometrium with gene mapping data for endometriosis and related diseases is beginning to uncover the specific genes and pathways responsible for increased risk of these diseases.
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Affiliation(s)
| | | | - Grant W. Montgomery
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
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133
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Elorbany R, Popp JM, Rhodes K, Strober BJ, Barr K, Qi G, Gilad Y, Battle A. Single-cell sequencing reveals lineage-specific dynamic genetic regulation of gene expression during human cardiomyocyte differentiation. PLoS Genet 2022; 18:e1009666. [PMID: 35061661 PMCID: PMC8809621 DOI: 10.1371/journal.pgen.1009666] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 02/02/2022] [Accepted: 12/21/2021] [Indexed: 12/13/2022] Open
Abstract
Dynamic and temporally specific gene regulatory changes may underlie unexplained genetic associations with complex disease. During a dynamic process such as cellular differentiation, the overall cell type composition of a tissue (or an in vitro culture) and the gene regulatory profile of each cell can both experience significant changes over time. To identify these dynamic effects in high resolution, we collected single-cell RNA-sequencing data over a differentiation time course from induced pluripotent stem cells to cardiomyocytes, sampled at 7 unique time points in 19 human cell lines. We employed a flexible approach to map dynamic eQTLs whose effects vary significantly over the course of bifurcating differentiation trajectories, including many whose effects are specific to one of these two lineages. Our study design allowed us to distinguish true dynamic eQTLs affecting a specific cell lineage from expression changes driven by potentially non-genetic differences between cell lines such as cell composition. Additionally, we used the cell type profiles learned from single-cell data to deconvolve and re-analyze data from matched bulk RNA-seq samples. Using this approach, we were able to identify a large number of novel dynamic eQTLs in single cell data while also attributing dynamic effects in bulk to a particular lineage. Overall, we found that using single cell data to uncover dynamic eQTLs can provide new insight into the gene regulatory changes that occur among heterogeneous cell types during cardiomyocyte differentiation.
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Affiliation(s)
- Reem Elorbany
- Interdisciplinary Scientist Training Program, University of Chicago, Chicago, Illinois, United States of America
| | - Joshua M. Popp
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Katherine Rhodes
- Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
| | - Benjamin J. Strober
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Kenneth Barr
- Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
| | - Guanghao Qi
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Yoav Gilad
- Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
- Department of Medicine, University of Chicago, Chicago, Illinois, United States of America
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
- Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, United States of America
- Department of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America
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134
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Flynn ED, Tsu AL, Kasela S, Kim-Hellmuth S, Aguet F, Ardlie KG, Bussemaker HJ, Mohammadi P, Lappalainen T. Transcription factor regulation of eQTL activity across individuals and tissues. PLoS Genet 2022; 18:e1009719. [PMID: 35100260 PMCID: PMC8830792 DOI: 10.1371/journal.pgen.1009719] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 02/10/2022] [Accepted: 01/06/2022] [Indexed: 11/18/2022] Open
Abstract
Tens of thousands of genetic variants associated with gene expression (cis-eQTLs) have been discovered in the human population. These eQTLs are active in various tissues and contexts, but the molecular mechanisms of eQTL variability are poorly understood, hindering our understanding of genetic regulation across biological contexts. Since many eQTLs are believed to act by altering transcription factor (TF) binding affinity, we hypothesized that analyzing eQTL effect size as a function of TF level may allow discovery of mechanisms of eQTL variability. Using GTEx Consortium eQTL data from 49 tissues, we analyzed the interaction between eQTL effect size and TF level across tissues and across individuals within specific tissues and generated a list of 10,098 TF-eQTL interactions across 2,136 genes that are supported by at least two lines of evidence. These TF-eQTLs were enriched for various TF binding measures, supporting with orthogonal evidence that these eQTLs are regulated by the implicated TFs. We also found that our TF-eQTLs tend to overlap genes with gene-by-environment regulatory effects and to colocalize with GWAS loci, implying that our approach can help to elucidate mechanisms of context-specificity and trait associations. Finally, we highlight an interesting example of IKZF1 TF regulation of an APBB1IP gene eQTL that colocalizes with a GWAS signal for blood cell traits. Together, our findings provide candidate TF mechanisms for a large number of eQTLs and offer a generalizable approach for researchers to discover TF regulators of genetic variant effects in additional QTL datasets.
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Affiliation(s)
- Elise D. Flynn
- Department of Systems Biology, Columbia University, New York, New York, United States of America
- New York Genome Center, New York, New York, United States of America
| | - Athena L. Tsu
- New York Genome Center, New York, New York, United States of America
- Department of Biomedical Engineering, Columbia University, New York, New York, United States of America
| | - Silva Kasela
- Department of Systems Biology, Columbia University, New York, New York, United States of America
- New York Genome Center, New York, New York, United States of America
| | - Sarah Kim-Hellmuth
- Department of Systems Biology, Columbia University, New York, New York, United States of America
- New York Genome Center, New York, New York, United States of America
- Department of Pediatrics, Dr. von Hauner Children’s Hospital, University Hospital, LMU Munich, Munich, Germany
| | - Francois Aguet
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Kristin G. Ardlie
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Harmen J. Bussemaker
- Department of Systems Biology, Columbia University, New York, New York, United States of America
- Department of Biological Sciences, Columbia University, New York, New York, United States of America
| | - Pejman Mohammadi
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, United States of America
- Scripps Translational Science Institute, The Scripps Research Institute, La Jolla, California, United States of America
- * E-mail: (PM); (TL)
| | - Tuuli Lappalainen
- Department of Systems Biology, Columbia University, New York, New York, United States of America
- New York Genome Center, New York, New York, United States of America
- KTH Royal Institute of Technology, Stockholm, Sweden
- * E-mail: (PM); (TL)
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135
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Ma S, Dalgleish J, Lee J, Wang C, Liu L, Gill R, Buxbaum JD, Chung WK, Aschard H, Silverman EK, Cho MH, He Z, Ionita-Laza I. Powerful gene-based testing by integrating long-range chromatin interactions and knockoff genotypes. Proc Natl Acad Sci U S A 2021; 118:e2105191118. [PMID: 34799441 PMCID: PMC8617518 DOI: 10.1073/pnas.2105191118] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/07/2021] [Indexed: 02/03/2023] Open
Abstract
Gene-based tests are valuable techniques for identifying genetic factors in complex traits. Here, we propose a gene-based testing framework that incorporates data on long-range chromatin interactions, several recent technical advances for region-based tests, and leverages the knockoff framework for synthetic genotype generation for improved gene discovery. Through simulations and applications to genome-wide association studies (GWAS) and whole-genome sequencing data for multiple diseases and traits, we show that the proposed test increases the power over state-of-the-art gene-based tests in the literature, identifies genes that replicate in larger studies, and can provide a more narrow focus on the possible causal genes at a locus by reducing the confounding effect of linkage disequilibrium. Furthermore, our results show that incorporating genetic variation in distal regulatory elements tends to improve power over conventional tests. Results for UK Biobank and BioBank Japan traits are also available in a publicly accessible database that allows researchers to query gene-based results in an easy fashion.
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Affiliation(s)
- Shiyang Ma
- Department of Biostatistics, Columbia University, New York, NY 10032
| | - James Dalgleish
- Department of Biostatistics, Columbia University, New York, NY 10032
| | - Justin Lee
- Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, CA 94305
| | - Chen Wang
- Department of Biostatistics, Columbia University, New York, NY 10032
| | - Linxi Liu
- Department of Statistics, University of Pittsburgh, Pittsburgh, PA 15260
| | - Richard Gill
- Department of Human Genetics, Genentech, South San Francisco, CA 94080
- Department of Epidemiology, Columbia University, New York, NY 10032
| | - Joseph D Buxbaum
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Wendy K Chung
- Department of Pediatrics, Columbia University, New York, NY 10032
- Department of Medicine, Columbia University, New York, NY 10032
| | - Hugues Aschard
- Department of Computational Biology, Institut Pasteur, 75015 Paris, France
| | - Edwin K Silverman
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115
| | - Michael H Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115
| | - Zihuai He
- Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, CA 94305
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305
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136
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Kondratyev NV, Alfimova MV, Golov AK, Golimbet VE. Bench Research Informed by GWAS Results. Cells 2021; 10:3184. [PMID: 34831407 PMCID: PMC8623533 DOI: 10.3390/cells10113184] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 11/11/2021] [Accepted: 11/11/2021] [Indexed: 12/15/2022] Open
Abstract
Scientifically interesting as well as practically important phenotypes often belong to the realm of complex traits. To the extent that these traits are hereditary, they are usually 'highly polygenic'. The study of such traits presents a challenge for researchers, as the complex genetic architecture of such traits makes it nearly impossible to utilise many of the usual methods of reverse genetics, which often focus on specific genes. In recent years, thousands of genome-wide association studies (GWAS) were undertaken to explore the relationships between complex traits and a large number of genetic factors, most of which are characterised by tiny effects. In this review, we aim to familiarise 'wet biologists' with approaches for the interpretation of GWAS results, to clarify some issues that may seem counterintuitive and to assess the possibility of using GWAS results in experiments on various complex traits.
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Affiliation(s)
| | | | - Arkadiy K. Golov
- Mental Health Research Center, 115522 Moscow, Russia; (M.V.A.); (A.K.G.); (V.E.G.)
- Institute of Gene Biology, Russian Academy of Sciences, 119334 Moscow, Russia
| | - Vera E. Golimbet
- Mental Health Research Center, 115522 Moscow, Russia; (M.V.A.); (A.K.G.); (V.E.G.)
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137
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A Combined mRNA- and miRNA-Sequencing Approach Reveals miRNAs as Potential Regulators of the Small Intestinal Transcriptome in Celiac Disease. Int J Mol Sci 2021; 22:ijms222111382. [PMID: 34768815 PMCID: PMC8583991 DOI: 10.3390/ijms222111382] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 10/14/2021] [Accepted: 10/17/2021] [Indexed: 12/14/2022] Open
Abstract
Celiac disease (CeD) is triggered by gluten and results in inflammation and villous atrophy of the small intestine. We aimed to explore the role of miRNA-mediated deregulation of the transcriptome in CeD. Duodenal biopsies of CeD patients (n = 33) and control subjects (n = 10) were available for miRNA-sequencing, with RNA-sequencing also available for controls (n = 5) and CeD (n = 6). Differential expression analysis was performed to select CeD-associated miRNAs and genes. MiRNA‒target transcript pairs selected from public databases that also displayed a strong negative expression correlation in the current dataset (R < -0.7) were used to construct a CeD miRNA‒target transcript interaction network. The network includes 2030 miRNA‒target transcript interactions, including 423 experimentally validated pairs. Pathway analysis found that interactions are involved in immune-related pathways (e.g., interferon signaling) and metabolic pathways (e.g., lipid metabolism). The network includes 13 genes previously prioritized to be causally deregulated by CeD-associated genomic variants, including STAT1. CeD-associated miRNAs might play a role in promoting inflammation and decreasing lipid metabolism in the small intestine, thereby contributing unbalanced cell turnover in the intestinal crypt. Some CeD-associated miRNAs deregulate genes that are also affected by genomic CeD-risk variants, adding an additional layer of complexity to the deregulated transcriptome in CeD.
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138
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Auerbach BJ, Hu J, Reilly MP, Li M. Applications of single-cell genomics and computational strategies to study common disease and population-level variation. Genome Res 2021; 31:1728-1741. [PMID: 34599006 PMCID: PMC8494214 DOI: 10.1101/gr.275430.121] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The advent and rapid development of single-cell technologies have made it possible to study cellular heterogeneity at an unprecedented resolution and scale. Cellular heterogeneity underlies phenotypic differences among individuals, and studying cellular heterogeneity is an important step toward our understanding of the disease molecular mechanism. Single-cell technologies offer opportunities to characterize cellular heterogeneity from different angles, but how to link cellular heterogeneity with disease phenotypes requires careful computational analysis. In this article, we will review the current applications of single-cell methods in human disease studies and describe what we have learned so far from existing studies about human genetic variation. As single-cell technologies are becoming widely applicable in human disease studies, population-level studies have become a reality. We will describe how we should go about pursuing and designing these studies, particularly how to select study subjects, how to determine the number of cells to sequence per subject, and the needed sequencing depth per cell. We also discuss computational strategies for the analysis of single-cell data and describe how single-cell data can be integrated with bulk tissue data and data generated from genome-wide association studies. Finally, we point out open problems and future research directions.
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Affiliation(s)
- Benjamin J Auerbach
- Graduate Group in Genomics and Computational Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania 19104, USA
| | - Jian Hu
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania 19104, USA
| | - Muredach P Reilly
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, New York 10032, USA
| | - Mingyao Li
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania 19104, USA
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139
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Scherer M, Gasparoni G, Rahmouni S, Shashkova T, Arnoux M, Louis E, Nostaeva A, Avalos D, Dermitzakis ET, Aulchenko YS, Lengauer T, Lyons PA, Georges M, Walter J. Identification of tissue-specific and common methylation quantitative trait loci in healthy individuals using MAGAR. Epigenetics Chromatin 2021; 14:44. [PMID: 34530905 PMCID: PMC8444396 DOI: 10.1186/s13072-021-00415-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 08/02/2021] [Indexed: 12/18/2022] Open
Abstract
Background Understanding the influence of genetic variants on DNA methylation is fundamental for the interpretation of epigenomic data in the context of disease. There is a need for systematic approaches not only for determining methylation quantitative trait loci (methQTL), but also for discriminating general from cell type-specific effects. Results Here, we present a two-step computational framework MAGAR (https://bioconductor.org/packages/MAGAR), which fully supports the identification of methQTLs from matched genotyping and DNA methylation data, and additionally allows for illuminating cell type-specific methQTL effects. In a pilot analysis, we apply MAGAR on data in four tissues (ileum, rectum, T cells, B cells) from healthy individuals and demonstrate the discrimination of common from cell type-specific methQTLs. We experimentally validate both types of methQTLs in an independent data set comprising additional cell types and tissues. Finally, we validate selected methQTLs located in the PON1, ZNF155, and NRG2 genes by ultra-deep local sequencing. In line with previous reports, we find cell type-specific methQTLs to be preferentially located in enhancer elements. Conclusions Our analysis demonstrates that a systematic analysis of methQTLs provides important new insights on the influences of genetic variants to cell type-specific epigenomic variation. Supplementary Information The online version contains supplementary material available at 10.1186/s13072-021-00415-6.
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Affiliation(s)
- Michael Scherer
- Department of Genetics/Epigenetics, Saarland University, Saarbrücken, Germany.,Computational Biology, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany.,Graduate School of Computer Science, Saarland Informatics Campus, Saarbrücken, Germany.,Department of Bioinformatics and Genomics, Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Gilles Gasparoni
- Department of Genetics/Epigenetics, Saarland University, Saarbrücken, Germany
| | - Souad Rahmouni
- Unit of Animal Genomics, GIGA-Institute & Faculty of Veterinary Medicine, University of Liège, Liège, Belgium
| | - Tatiana Shashkova
- Kurchatov Genomics Center of the Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia.,Research and Training Center on Bioinformatics, A.A. Kharkevich Institute for Information Transmission Problems, Moscow, Russia
| | - Marion Arnoux
- Department of Genetics/Epigenetics, Saarland University, Saarbrücken, Germany
| | - Edouard Louis
- Department of Gastroenterology, Liège University Hospital, CHU Liège, Liège, Belgium
| | | | - Diana Avalos
- Department of Genetic Medicine and Development, University of Geneva, Geneva, Switzerland.,Swiss Institute of Bioinformatics (SIB), University of Geneva, Geneva, Switzerland.,Institute of Genetics and Genomics in Geneva, University of Geneva, Geneva, Switzerland
| | - Emmanouil T Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva, Geneva, Switzerland.,Swiss Institute of Bioinformatics (SIB), University of Geneva, Geneva, Switzerland.,Institute of Genetics and Genomics in Geneva, University of Geneva, Geneva, Switzerland
| | - Yurii S Aulchenko
- Kurchatov Genomics Center of the Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia.,Novosibirsk State University, Novosibirsk, Russia.,Moscow Institute of Physics and Technology (State University), Moscow, Russia.,PolyKnomics BV, 's-Hertogenbosch, The Netherlands
| | - Thomas Lengauer
- Computational Biology, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany
| | - Paul A Lyons
- Department of Medicine, University of Cambridge School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.,Cambridge Institute for Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, Cambridge, CB2 0AW, UK
| | - Michel Georges
- Unit of Animal Genomics, GIGA-Institute & Faculty of Veterinary Medicine, University of Liège, Liège, Belgium
| | - Jörn Walter
- Department of Genetics/Epigenetics, Saarland University, Saarbrücken, Germany.
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140
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Strunz T, Kellner M, Kiel C, Weber BHF. Assigning Co-Regulated Human Genes and Regulatory Gene Clusters. Cells 2021; 10:2395. [PMID: 34572044 PMCID: PMC8470523 DOI: 10.3390/cells10092395] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 08/30/2021] [Accepted: 09/10/2021] [Indexed: 12/12/2022] Open
Abstract
Elucidating the role of genetic variation in the regulation of gene expression is key to understanding the pathobiology of complex diseases which, in consequence, is crucial in devising targeted treatment options. Expression quantitative trait locus (eQTL) analysis correlates a genetic variant with the strength of gene expression, thus defining thousands of regulated genes in a multitude of human cell types and tissues. Some eQTL may not act independently of each other but instead may be regulated in a coordinated fashion by seemingly independent genetic variants. To address this issue, we combined the approaches of eQTL analysis and colocalization studies. Gene expression was determined in datasets comprising 49 tissues from the Genotype-Tissue Expression (GTEx) project. From about 33,000 regulated genes, over 14,000 were found to be co-regulated in pairs and were assembled across all tissues to almost 15,000 unique clusters containing up to nine regulated genes affected by the same eQTL signal. The distance of co-regulated eGenes was, on average, 112 kilobase pairs. Of 713 genes known to express clinical symptoms upon haploinsufficiency, 231 (32.4%) are part of at least one of the identified clusters. This calls for caution should treatment approaches aim at an upregulation of a haploinsufficient gene. In conclusion, we present an unbiased approach to identifying co-regulated genes in and across multiple tissues. Knowledge of such common effects is crucial to appreciate implications on biological pathways involved, specifically when a treatment option targets a co-regulated disease gene.
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Affiliation(s)
- Tobias Strunz
- Institute of Human Genetics, University of Regensburg, 93053 Regensburg, Germany; (T.S.); (M.K.); (C.K.)
| | - Martin Kellner
- Institute of Human Genetics, University of Regensburg, 93053 Regensburg, Germany; (T.S.); (M.K.); (C.K.)
| | - Christina Kiel
- Institute of Human Genetics, University of Regensburg, 93053 Regensburg, Germany; (T.S.); (M.K.); (C.K.)
| | - Bernhard H. F. Weber
- Institute of Human Genetics, University of Regensburg, 93053 Regensburg, Germany; (T.S.); (M.K.); (C.K.)
- Institute of Clinical Human Genetics, University Hospital Regensburg, 93053 Regensburg, Germany
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141
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Ruiz-Cantos M, Hutchison CE, Shoulders CC. Musings from the Tribbles Research and Innovation Network. Cancers (Basel) 2021; 13:cancers13184517. [PMID: 34572744 PMCID: PMC8467127 DOI: 10.3390/cancers13184517] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Revised: 09/02/2021] [Accepted: 09/04/2021] [Indexed: 11/16/2022] Open
Abstract
This commentary integrates historical and modern findings that underpin our understanding of the cell-specific functions of the Tribbles (TRIB) proteins that bear on tumorigenesis. We touch on the initial discovery of roles played by mammalian TRIB proteins in a diverse range of cell-types and pathologies, for example, TRIB1 in regulatory T-cells, TRIB2 in acute myeloid leukaemia and TRIB3 in gliomas; the origins and diversity of TRIB1 transcripts; microRNA-mediated (miRNA) regulation of TRIB1 transcript decay and translation; the substantial conformational changes that ensue on binding of TRIB1 to the transcription factor C/EBPα; and the unique pocket formed by TRIB1 to sequester its C-terminal motif bearing a binding site for the E3 ubiquitin ligase COP1. Unashamedly, the narrative is relayed through the perspective of the Tribbles Research and Innovation Network, and its establishment, progress and future ambitions: the growth of TRIB and COP1 research to hasten discovery of their cell-specific contributions to health and obesity-related cancers.
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142
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Zhang T, Choi J, Dilshat R, Einarsdóttir BÓ, Kovacs MA, Xu M, Malasky M, Chowdhury S, Jones K, Bishop DT, Goldstein AM, Iles MM, Landi MT, Law MH, Shi J, Steingrímsson E, Brown KM. Cell-type-specific meQTLs extend melanoma GWAS annotation beyond eQTLs and inform melanocyte gene-regulatory mechanisms. Am J Hum Genet 2021; 108:1631-1646. [PMID: 34293285 PMCID: PMC8456160 DOI: 10.1016/j.ajhg.2021.06.018] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 06/23/2021] [Indexed: 01/09/2023] Open
Abstract
Although expression quantitative trait loci (eQTLs) have been powerful in identifying susceptibility genes from genome-wide association study (GWAS) findings, most trait-associated loci are not explained by eQTLs alone. Alternative QTLs, including DNA methylation QTLs (meQTLs), are emerging, but cell-type-specific meQTLs using cells of disease origin have been lacking. Here, we established an meQTL dataset by using primary melanocytes from 106 individuals and identified 1,497,502 significant cis-meQTLs. Multi-QTL colocalization with meQTLs, eQTLs, and mRNA splice-junction QTLs from the same individuals together with imputed methylome-wide and transcriptome-wide association studies identified candidate susceptibility genes at 63% of melanoma GWAS loci. Among the three molecular QTLs, meQTLs were the single largest contributor. To compare melanocyte meQTLs with those from malignant melanomas, we performed meQTL analysis on skin cutaneous melanomas from The Cancer Genome Atlas (n = 444). A substantial proportion of meQTL probes (45.9%) in primary melanocytes is preserved in melanomas, while a smaller fraction of eQTL genes is preserved (12.7%). Integration of melanocyte multi-QTLs and melanoma meQTLs identified candidate susceptibility genes at 72% of melanoma GWAS loci. Beyond GWAS annotation, meQTL-eQTL colocalization in melanocytes suggested that 841 unique genes potentially share a causal variant with a nearby methylation probe in melanocytes. Finally, melanocyte trans-meQTLs identified a hotspot for rs12203592, a cis-eQTL of a transcription factor, IRF4, with 131 candidate target CpGs. Motif enrichment and IRF4 ChIP-seq analysis demonstrated that these target CpGs are enriched in IRF4 binding sites, suggesting an IRF4-mediated regulatory network. Our study highlights the utility of cell-type-specific meQTLs.
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Affiliation(s)
- Tongwu Zhang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
| | - Jiyeon Choi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
| | - Ramile Dilshat
- Department of Biochemistry and Molecular Biology, BioMedical Center, Faculty of Medicine, University of Iceland, Sturlugata 8, 101 Reykjavik, Iceland
| | - Berglind Ósk Einarsdóttir
- Department of Biochemistry and Molecular Biology, BioMedical Center, Faculty of Medicine, University of Iceland, Sturlugata 8, 101 Reykjavik, Iceland
| | - Michael A Kovacs
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
| | - Mai Xu
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
| | - Michael Malasky
- Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
| | - Salma Chowdhury
- Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
| | - Kristine Jones
- Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
| | - D Timothy Bishop
- Leeds Institute for Data Analytics, School of Medicine, University of Leeds, Leeds LS9 7TF, UK
| | - Alisa M Goldstein
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
| | - Mark M Iles
- Leeds Institute for Data Analytics, School of Medicine, University of Leeds, Leeds LS9 7TF, UK
| | - Maria Teresa Landi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
| | - Matthew H Law
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia; School of Biomedical Sciences, Faculty of Health, and Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, QLD 4059, Australia
| | - Jianxin Shi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
| | - Eiríkur Steingrímsson
- Department of Biochemistry and Molecular Biology, BioMedical Center, Faculty of Medicine, University of Iceland, Sturlugata 8, 101 Reykjavik, Iceland
| | - Kevin M Brown
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA.
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143
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Sheng X, Guan Y, Ma Z, Wu J, Liu H, Qiu C, Vitale S, Miao Z, Seasock MJ, Palmer M, Shin MK, Duffin KL, Pullen SS, Edwards TL, Hellwege JN, Hung AM, Li M, Voight BF, Coffman TM, Brown CD, Susztak K. Mapping the genetic architecture of human traits to cell types in the kidney identifies mechanisms of disease and potential treatments. Nat Genet 2021; 53:1322-1333. [PMID: 34385711 PMCID: PMC9338440 DOI: 10.1038/s41588-021-00909-9] [Citation(s) in RCA: 79] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 06/30/2021] [Indexed: 02/07/2023]
Abstract
The functional interpretation of genome-wide association studies (GWAS) is challenging due to the cell-type-dependent influences of genetic variants. Here, we generated comprehensive maps of expression quantitative trait loci (eQTLs) for 659 microdissected human kidney samples and identified cell-type-eQTLs by mapping interactions between cell type abundances and genotypes. By partitioning heritability using stratified linkage disequilibrium score regression to integrate GWAS with single-cell RNA sequencing and single-nucleus assay for transposase-accessible chromatin with high-throughput sequencing data, we prioritized proximal tubules for kidney function and endothelial cells and distal tubule segments for blood pressure pathogenesis. Bayesian colocalization analysis nominated more than 200 genes for kidney function and hypertension. Our study clarifies the mechanism of commonly used antihypertensive and renal-protective drugs and identifies drug repurposing opportunities for kidney disease.
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Affiliation(s)
- Xin Sheng
- Department of Medicine, Renal Electrolyte and Hypertension Division, University of Pennsylvania, Philadelphia, PA, USA
- Institute of Diabetes, Obesity and Metabolism, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Yuting Guan
- Department of Medicine, Renal Electrolyte and Hypertension Division, University of Pennsylvania, Philadelphia, PA, USA
- Institute of Diabetes, Obesity and Metabolism, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Ziyuan Ma
- Department of Medicine, Renal Electrolyte and Hypertension Division, University of Pennsylvania, Philadelphia, PA, USA
- Institute of Diabetes, Obesity and Metabolism, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Junnan Wu
- Department of Medicine, Renal Electrolyte and Hypertension Division, University of Pennsylvania, Philadelphia, PA, USA
- Institute of Diabetes, Obesity and Metabolism, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Hongbo Liu
- Department of Medicine, Renal Electrolyte and Hypertension Division, University of Pennsylvania, Philadelphia, PA, USA
- Institute of Diabetes, Obesity and Metabolism, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Chengxiang Qiu
- Department of Medicine, Renal Electrolyte and Hypertension Division, University of Pennsylvania, Philadelphia, PA, USA
- Institute of Diabetes, Obesity and Metabolism, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Steven Vitale
- Department of Medicine, Renal Electrolyte and Hypertension Division, University of Pennsylvania, Philadelphia, PA, USA
- Institute of Diabetes, Obesity and Metabolism, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Zhen Miao
- Department of Medicine, Renal Electrolyte and Hypertension Division, University of Pennsylvania, Philadelphia, PA, USA
- Institute of Diabetes, Obesity and Metabolism, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Matthew J Seasock
- Department of Medicine, Renal Electrolyte and Hypertension Division, University of Pennsylvania, Philadelphia, PA, USA
- Institute of Diabetes, Obesity and Metabolism, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Matthew Palmer
- Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | | | - Kevin L Duffin
- Eli Lilly and Company Lilly Corporate Center, Indianapolis, IN, USA
| | - Steven S Pullen
- Department of Cardiometabolic Diseases Research, Boehringer Ingelheim Pharmaceuticals, Ridgefield, CT, USA
| | - Todd L Edwards
- Division of Epidemiology, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jacklyn N Hellwege
- Division of Epidemiology, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Adriana M Hung
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Mingyao Li
- Department of Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Benjamin F Voight
- Institute of Diabetes, Obesity and Metabolism, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
- Institute of Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - Thomas M Coffman
- Cardiovascular and Metabolic Disorders Program, Duke-National University of Singapore Medical School, Singapore, Singapore
| | | | - Katalin Susztak
- Department of Medicine, Renal Electrolyte and Hypertension Division, University of Pennsylvania, Philadelphia, PA, USA.
- Institute of Diabetes, Obesity and Metabolism, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA.
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144
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Min JL, Hemani G, Hannon E, Dekkers KF, Castillo-Fernandez J, Luijk R, Carnero-Montoro E, Lawson DJ, Burrows K, Suderman M, Bretherick AD, Richardson TG, Klughammer J, Iotchkova V, Sharp G, Al Khleifat A, Shatunov A, Iacoangeli A, McArdle WL, Ho KM, Kumar A, Söderhäll C, Soriano-Tárraga C, Giralt-Steinhauer E, Kazmi N, Mason D, McRae AF, Corcoran DL, Sugden K, Kasela S, Cardona A, Day FR, Cugliari G, Viberti C, Guarrera S, Lerro M, Gupta R, Bollepalli S, Mandaviya P, Zeng Y, Clarke TK, Walker RM, Schmoll V, Czamara D, Ruiz-Arenas C, Rezwan FI, Marioni RE, Lin T, Awaloff Y, Germain M, Aïssi D, Zwamborn R, van Eijk K, Dekker A, van Dongen J, Hottenga JJ, Willemsen G, Xu CJ, Barturen G, Català-Moll F, Kerick M, Wang C, Melton P, Elliott HR, Shin J, Bernard M, Yet I, Smart M, Gorrie-Stone T, Shaw C, Al Chalabi A, Ring SM, Pershagen G, Melén E, Jiménez-Conde J, Roquer J, Lawlor DA, Wright J, Martin NG, Montgomery GW, Moffitt TE, Poulton R, Esko T, Milani L, Metspalu A, Perry JRB, Ong KK, Wareham NJ, Matullo G, Sacerdote C, Panico S, Caspi A, Arseneault L, Gagnon F, Ollikainen M, Kaprio J, Felix JF, Rivadeneira F, Tiemeier H, van IJzendoorn MH, Uitterlinden AG, Jaddoe VWV, Haley C, McIntosh AM, Evans KL, Murray A, Räikkönen K, Lahti J, Nohr EA, Sørensen TIA, Hansen T, Morgen CS, Binder EB, Lucae S, Gonzalez JR, Bustamante M, Sunyer J, Holloway JW, Karmaus W, Zhang H, Deary IJ, Wray NR, Starr JM, Beekman M, van Heemst D, Slagboom PE, Morange PE, Trégouët DA, Veldink JH, Davies GE, de Geus EJC, Boomsma DI, Vonk JM, Brunekreef B, Koppelman GH, Alarcón-Riquelme ME, Huang RC, Pennell CE, van Meurs J, Ikram MA, Hughes AD, Tillin T, Chaturvedi N, Pausova Z, Paus T, Spector TD, Kumari M, Schalkwyk LC, Visscher PM, Davey Smith G, Bock C, Gaunt TR, Bell JT, Heijmans BT, Mill J, Relton CL. Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation. Nat Genet 2021; 53:1311-1321. [PMID: 34493871 PMCID: PMC7612069 DOI: 10.1038/s41588-021-00923-x] [Citation(s) in RCA: 202] [Impact Index Per Article: 67.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 07/12/2021] [Indexed: 12/25/2022]
Abstract
Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. In the present study, we describe results of DNAm quantitative trait locus (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTLs, of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We show that the genetic architecture of DNAm levels is highly polygenic. Using shared genetic control between distal DNAm sites, we constructed networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic variants are associated with both DNAm levels and complex diseases, but only in a minority of cases do these associations reflect causal relationships from DNAm to trait or vice versa, indicating a more complex genotype-phenotype map than previously anticipated.
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Affiliation(s)
- Josine L Min
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Gibran Hemani
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Eilis Hannon
- University of Exeter Medical School, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Koen F Dekkers
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | | | - René Luijk
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Elena Carnero-Montoro
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- Pfizer-University of Granada-Andalusian Government Center for Genomics and Oncological Research, Granada, Spain
| | - Daniel J Lawson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Kimberley Burrows
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Matthew Suderman
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Andrew D Bretherick
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Tom G Richardson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Johanna Klughammer
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | | | - Gemma Sharp
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Ahmad Al Khleifat
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London, UK
| | - Aleksey Shatunov
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London, UK
| | - Alfredo Iacoangeli
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London, UK
- Department of Biostatistics and Health Informatics, King's College London, London, UK
| | - Wendy L McArdle
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Karen M Ho
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Ashish Kumar
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Chronic Disease Epidemiology unit, Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Cilla Söderhäll
- Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
| | - Carolina Soriano-Tárraga
- Neurology Department, Hospital del Mar, Institut Hospital del Mar d'Investigacions Mèdiques, Barcelona, Spain
| | - Eva Giralt-Steinhauer
- Neurology Department, Hospital del Mar, Institut Hospital del Mar d'Investigacions Mèdiques, Barcelona, Spain
| | - Nabila Kazmi
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Dan Mason
- Bradford Institute for Health Research, Bradford, UK
| | - Allan F McRae
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - David L Corcoran
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
| | - Karen Sugden
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
| | - Silva Kasela
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Alexia Cardona
- MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Department of Genetics, University of Cambridge, Cambridge, UK
| | - Felix R Day
- MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Giovanni Cugliari
- Department of Medical Sciences, University of Turin, Turin, Italy
- Italian Institute for Genomic Medicine, Turin, Italy
| | - Clara Viberti
- Department of Medical Sciences, University of Turin, Turin, Italy
- Italian Institute for Genomic Medicine, Turin, Italy
| | - Simonetta Guarrera
- Department of Medical Sciences, University of Turin, Turin, Italy
- Italian Institute for Genomic Medicine, Turin, Italy
| | - Michael Lerro
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Richa Gupta
- Institute for Molecular Medicine, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Sailalitha Bollepalli
- Institute for Molecular Medicine, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Pooja Mandaviya
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Yanni Zeng
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China
- Guangdong Province Key Laboratory of Brain Function and Disease, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China
| | - Toni-Kim Clarke
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Rosie M Walker
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, Western General Hospital, University of Edinburgh, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Vanessa Schmoll
- Department of Translational Research in Psychiatry, Max-Planck-Institute of Psychiatry, Munich, Germany
| | - Darina Czamara
- Department of Translational Research in Psychiatry, Max-Planck-Institute of Psychiatry, Munich, Germany
| | - Carlos Ruiz-Arenas
- ISGlobal, Barcelona Global Health Institute, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Faisal I Rezwan
- Department of Computer Science, Aberystwyth University, Aberystwyth, UK
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, Western General Hospital, University of Edinburgh, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Tian Lin
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Yvonne Awaloff
- Department of Translational Research in Psychiatry, Max-Planck-Institute of Psychiatry, Munich, Germany
| | - Marine Germain
- INSERM UMR_S 1219, Bordeaux Population Health Center, University of Bordeaux, Bordeaux, France
| | - Dylan Aïssi
- Department of General and Interventional Cardiology, University Heart Center Hamburg, Hamburg, Germany
| | - Ramona Zwamborn
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Kristel van Eijk
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Annelot Dekker
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Jenny van Dongen
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Jouke-Jan Hottenga
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Gonneke Willemsen
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Cheng-Jian Xu
- University of Groningen, University Medical Center Groningen, Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children's Hospital, GRIAC Research Institute Groningen, Groningen, the Netherlands
- CiiM and TWINCORE, Hannover Medical School and Helmholtz Centre for Infection Research, Hannover, Germany
| | - Guillermo Barturen
- Pfizer-University of Granada-Andalusian Government Center for Genomics and Oncological Research, Granada, Spain
| | - Francesc Català-Moll
- Chromatin and Disease Group, Cancer Epigenetics and Biology Programme, Bellvitge Biomedical Research Institute, Barcelona, Spain
| | - Martin Kerick
- Instituto de Parasitología y Biomedicina López Neyra, CSIC, Granada, Spain
| | - Carol Wang
- School of Medicine and Public Health, College of Health, Medicine and Wellbeing, University of Newcastle, Newcastle, Australia
| | - Phillip Melton
- Menzies Institute for Medical Research, College of Health and Medicine, University of Tasmania, Hobart, Australia
- School of Global Population Health, Faculty of Health and Medical Sciences, University of Western Australia, Perth, Australia
- School of Pharmacy and Biomedical Sciences, Faculty of Health Sciences, Curtin University, Perth, Australia
| | - Hannah R Elliott
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jean Shin
- The Hospital for Sick Children, University of Toronto, Toronto, Canada
| | - Manon Bernard
- The Hospital for Sick Children, University of Toronto, Toronto, Canada
| | - Idil Yet
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
- Department of Bioinformatics, Institute of Health Sciences, Hacettepe University, Ankara, Turkey
| | - Melissa Smart
- Institute for Social and Economic Research, University of Essex, Colchester, UK
| | | | | | - Chris Shaw
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London, UK
- Department of Neurology, King's College Hospital, London, UK
| | - Ammar Al Chalabi
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London, UK
- Department of Neurology, King's College Hospital, London, UK
- United Kingdom Dementia Research Institute, King's College London, London, UK
| | - Susan M Ring
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Göran Pershagen
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Erik Melén
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden
| | - Jordi Jiménez-Conde
- Neurology Department, Hospital del Mar, Institut Hospital del Mar d'Investigacions Mèdiques, Barcelona, Spain
| | - Jaume Roquer
- Neurology Department, Hospital del Mar, Institut Hospital del Mar d'Investigacions Mèdiques, Barcelona, Spain
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - John Wright
- Bradford Institute for Health Research, Bradford, UK
| | | | - Grant W Montgomery
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Terrie E Moffitt
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
- Department of Psychiatry and Behavioral Sciences, Duke University Medical School, Durham, NC, USA
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Richie Poulton
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, Dunedin, New Zealand
| | - Tõnu Esko
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Lili Milani
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Andres Metspalu
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - John R B Perry
- MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Ken K Ong
- MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Nicholas J Wareham
- MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Giuseppe Matullo
- Department of Medical Sciences, University of Turin, Turin, Italy
- Italian Institute for Genomic Medicine, Turin, Italy
| | - Carlotta Sacerdote
- Italian Institute for Genomic Medicine, Turin, Italy
- Piemonte Centre for Cancer Prevention, Turin, Italy
| | - Salvatore Panico
- Dipartimento Di Medicina Clinica E Chirurgia, Federico II University, Naples, Italy
| | - Avshalom Caspi
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
- Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
- Department of Psychiatry and Behavioral Sciences, Duke University Medical School, Durham, NC, USA
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Louise Arseneault
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - France Gagnon
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Miina Ollikainen
- Institute for Molecular Medicine, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Jaakko Kaprio
- Institute for Molecular Medicine, University of Helsinki, Helsinki, Finland
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Janine F Felix
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Fernando Rivadeneira
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Henning Tiemeier
- Department of Child and Adolescent Psychiatry, Erasmus Medical Center, Rotterdam, the Netherlands
- Department of Social and Behavioral Science, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Marinus H van IJzendoorn
- Department of Psychology, Education and Child Studies, Erasmus University Rotterdam, Rotterdam, the Netherlands
- Department of Clinical, Educational and Health Psychology, Division on Psychology and Language Sciences, Faculty of Brain Sciences, University College London, London, UK
| | - André G Uitterlinden
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Vincent W V Jaddoe
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Chris Haley
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Andrew M McIntosh
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Kathryn L Evans
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, Western General Hospital, University of Edinburgh, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Alison Murray
- Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
| | - Katri Räikkönen
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Jari Lahti
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Ellen A Nohr
- Research Unit for Gynaecology and Obstetrics, Institute of Clinical research, University of Southern Denmark, Odense, Denmark
- Centre of Women's, Family and Child Health, University of South-Eastern Norway, Kongsberg, Norway
| | - Thorkild I A Sørensen
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Public Health (Section of Epidemiology), Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Torben Hansen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Camilla S Morgen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The National Institute of Public Health, University of Southern Denmark, Copenhagen, Denmark
| | - Elisabeth B Binder
- Department of Translational Research in Psychiatry, Max-Planck-Institute of Psychiatry, Munich, Germany
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Susanne Lucae
- Department of Translational Research in Psychiatry, Max-Planck-Institute of Psychiatry, Munich, Germany
| | - Juan Ramon Gonzalez
- ISGlobal, Barcelona Global Health Institute, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Mariona Bustamante
- ISGlobal, Barcelona Global Health Institute, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
- Center for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Jordi Sunyer
- ISGlobal, Barcelona Global Health Institute, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
- Hospital del Mar Medical Research Institute, Barcelona, Spain
| | - John W Holloway
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Wilfried Karmaus
- Division of Epidemiology, Biostatistics, and Environmental Health Sciences, School of Public Health, University of Memphis, Memphis, TN, USA
| | - Hongmei Zhang
- Division of Epidemiology, Biostatistics, and Environmental Health Sciences, School of Public Health, University of Memphis, Memphis, TN, USA
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Naomi R Wray
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
- Queensland Brain Institute, University of Queensland, Brisbane, Australia
| | - John M Starr
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, UK
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, UK
| | - Marian Beekman
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Diana van Heemst
- Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
| | - P Eline Slagboom
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | | | | | - Jan H Veldink
- Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | | | - Eco J C de Geus
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Judith M Vonk
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, GRIAC Research Institute Groningen, Groningen, the Netherlands
| | - Bert Brunekreef
- Institute for Risk Assessment Sciences, Universiteit Utrecht, Utrecht, the Netherlands
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Gerard H Koppelman
- University of Groningen, University Medical Center Groningen, Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children's Hospital, GRIAC Research Institute Groningen, Groningen, the Netherlands
| | - Marta E Alarcón-Riquelme
- Pfizer-University of Granada-Andalusian Government Center for Genomics and Oncological Research, Granada, Spain
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Rae-Chi Huang
- Telethon Kids Institute, University of Western Australia, Perth, Australia
| | - Craig E Pennell
- School of Medicine and Public Health, College of Health, Medicine and Wellbeing, University of Newcastle, Newcastle, Australia
| | - Joyce van Meurs
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | | | | | | | - Zdenka Pausova
- The Hospital for Sick Children, University of Toronto, Toronto, Canada
| | - Tomas Paus
- Departments of Psychology and Psychiatry, University of Toronto, Toronto, Canada
| | - Timothy D Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Meena Kumari
- Institute for Social and Economic Research, University of Essex, Colchester, UK
| | | | - Peter M Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
- Queensland Brain Institute, University of Queensland, Brisbane, Australia
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Christoph Bock
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
- Institute of Artificial Intelligence and Decision Support, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jordana T Bell
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Bastiaan T Heijmans
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Jonathan Mill
- University of Exeter Medical School, College of Medicine and Health, University of Exeter, Exeter, UK
| | - Caroline L Relton
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
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145
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Pathak GA, Singh K, Miller-Fleming TW, Wendt FR, Ehsan N, Hou K, Johnson R, Lu Z, Gopalan S, Yengo L, Mohammadi P, Pasaniuc B, Polimanti R, Davis LK, Mancuso N. Integrative genomic analyses identify susceptibility genes underlying COVID-19 hospitalization. Nat Commun 2021; 12:4569. [PMID: 34315903 PMCID: PMC8316582 DOI: 10.1038/s41467-021-24824-z] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 07/07/2021] [Indexed: 12/11/2022] Open
Abstract
Despite rapid progress in characterizing the role of host genetics in SARS-Cov-2 infection, there is limited understanding of genes and pathways that contribute to COVID-19. Here, we integrate a genome-wide association study of COVID-19 hospitalization (7,885 cases and 961,804 controls from COVID-19 Host Genetics Initiative) with mRNA expression, splicing, and protein levels (n = 18,502). We identify 27 genes related to inflammation and coagulation pathways whose genetically predicted expression was associated with COVID-19 hospitalization. We functionally characterize the 27 genes using phenome- and laboratory-wide association scans in Vanderbilt Biobank (n = 85,460) and identified coagulation-related clinical symptoms, immunologic, and blood-cell-related biomarkers. We replicate these findings across trans-ethnic studies and observed consistent effects in individuals of diverse ancestral backgrounds in Vanderbilt Biobank, pan-UK Biobank, and Biobank Japan. Our study highlights and reconfirms putative causal genes impacting COVID-19 severity and symptomology through the host inflammatory response.
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Affiliation(s)
- Gita A Pathak
- Yale School of Medicine, Department of Psychiatry, Division of Human Genetics, New Haven, CT, USA
- Veteran Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Kritika Singh
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Tyne W Miller-Fleming
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Frank R Wendt
- Yale School of Medicine, Department of Psychiatry, Division of Human Genetics, New Haven, CT, USA
- Veteran Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Nava Ehsan
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Kangcheng Hou
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Ruth Johnson
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA
| | - Zeyun Lu
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Shyamalika Gopalan
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Loic Yengo
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Pejman Mohammadi
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
- Scripps Translational Science Institute, The Scripps Research Institute, La Jolla, CA, USA
| | - Bogdan Pasaniuc
- Departments of Computational Medicine, Human Genetics, Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Renato Polimanti
- Yale School of Medicine, Department of Psychiatry, Division of Human Genetics, New Haven, CT, USA
- Veteran Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Lea K Davis
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nicholas Mancuso
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
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146
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Resztak JA, Farrell AK, Mair-Meijers H, Alazizi A, Wen X, Wildman DE, Zilioli S, Slatcher RB, Pique-Regi R, Luca F. Psychosocial experiences modulate asthma-associated genes through gene-environment interactions. eLife 2021; 10:e63852. [PMID: 34142656 PMCID: PMC8282343 DOI: 10.7554/elife.63852] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 06/16/2021] [Indexed: 01/04/2023] Open
Abstract
Social interactions and the overall psychosocial environment have a demonstrated impact on health, particularly for people living in disadvantaged urban areas. Here, we investigated the effect of psychosocial experiences on gene expression in peripheral blood immune cells of children with asthma in Metro Detroit. Using RNA-sequencing and a new machine learning approach, we identified transcriptional signatures of 19 variables including psychosocial factors, blood cell composition, and asthma symptoms. Importantly, we found 169 genes associated with asthma or allergic disease that are regulated by psychosocial factors and 344 significant gene-environment interactions for gene expression levels. These results demonstrate that immune gene expression mediates the link between negative psychosocial experiences and asthma risk.
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Affiliation(s)
- Justyna A Resztak
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
| | | | | | - Adnan Alazizi
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
| | - Xiaoquan Wen
- Department of Biostatistics, University of MichiganAnn ArborUnited States
| | - Derek E Wildman
- College of Public Health, University of South FloridaTampaUnited States
| | - Samuele Zilioli
- Department of Psychology, Wayne State UniversityDetroitUnited States
- Department of Family Medicine and Public Health Sciences, Wayne State UniversityDetroitUnited States
| | | | - Roger Pique-Regi
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
- Department of Obstetrics and Gynecology, Wayne State UniversityDetroitUnited States
| | - Francesca Luca
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
- Department of Obstetrics and Gynecology, Wayne State UniversityDetroitUnited States
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147
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Stikker B, Stik G, Hendriks RW, Stadhouders R. Severe COVID-19 associated variants linked to chemokine receptor gene control in monocytes and macrophages. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2021:2021.01.22.427813. [PMID: 33501435 PMCID: PMC7836105 DOI: 10.1101/2021.01.22.427813] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Genome-wide association studies have identified 3p21.31 as the main risk locus for severe disease in COVID-19 patients, although underlying biological mechanisms remain elusive. We performed a comprehensive epigenomic dissection of the 3p21.31 locus, identifying a CTCF-dependent tissue-specific 3D regulatory chromatin hub that controls the activity of several tissue-homing chemokine receptor (CCR) genes in monocytes and macrophages. Risk SNPs colocalized with regulatory elements and were linked to increased expression of CCR1, CCR2 and CCR5 in monocytes and macrophages. As excessive organ infiltration of inflammatory monocytes and macrophages is a hallmark of severe COVID-19, our findings provide a rationale for the genetic association of 3p21.31 variants with elevated risk of hospitalization upon SARS-CoV-2 infection.
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Affiliation(s)
- Bernard Stikker
- Department of Pulmonary Medicine, Erasmus MC, University Medical Center Rotterdam, the Netherlands
| | - Grégoire Stik
- Centre for Genomic Regulation (CRG) and Institute of Science and Technology (BIST), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Rudi W Hendriks
- Department of Pulmonary Medicine, Erasmus MC, University Medical Center Rotterdam, the Netherlands
| | - Ralph Stadhouders
- Department of Pulmonary Medicine, Erasmus MC, University Medical Center Rotterdam, the Netherlands
- Department of Cell Biology, Erasmus MC, Rotterdam, the Netherlands
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148
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Stein-O'Brien GL, Ainsile MC, Fertig EJ. Forecasting cellular states: from descriptive to predictive biology via single-cell multiomics. CURRENT OPINION IN SYSTEMS BIOLOGY 2021; 26:24-32. [PMID: 34660940 PMCID: PMC8516130 DOI: 10.1016/j.coisb.2021.03.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
As the single cell field races to characterize each cell type, state, and behavior, the complexity of the computational analysis approaches the complexity of the biological systems. Single cell and imaging technologies now enable unprecedented measurements of state transitions in biological systems, providing high-throughput data that capture tens-of-thousands of measurements on hundreds-of-thousands of samples. Thus, the definition of cell type and state is evolving to encompass the broad range of biological questions now attainable. To answer these questions requires the development of computational tools for integrated multi-omics analysis. Merged with mathematical models, these algorithms will be able to forecast future states of biological systems, going from statistical inferences of phenotypes to time course predictions of the biological systems with dynamic maps analogous to weather systems. Thus, systems biology for forecasting biological system dynamics from multi-omic data represents the future of cell biology empowering a new generation of technology-driven predictive medicine.
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Affiliation(s)
- Genevieve L Stein-O'Brien
- Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD
- Convergence Institute, Johns Hopkins University, Baltimore, MD
| | - Michaela C Ainsile
- Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD
| | - Elana J Fertig
- Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD
- Convergence Institute, Johns Hopkins University, Baltimore, MD
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
- Department of Applied Mathematics & Statistics, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD
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149
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A retrospective analysis of cardiovascular adverse events associated with immune checkpoint inhibitors. CARDIO-ONCOLOGY 2021; 7:19. [PMID: 34049595 PMCID: PMC8161966 DOI: 10.1186/s40959-021-00106-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 05/04/2021] [Indexed: 12/11/2022]
Abstract
Background Modern therapies in oncology have increased cancer survivorship, as well as the incidence of cardiovascular adverse events. While immune checkpoint inhibitors have shown significant clinical impact in several cancer types, the incidence of immune-related cardiovascular (CV) adverse events poses an additional health concern and has been reported. Methods We performed a retrospective analysis of the FDA Adverse Event Reporting System data of suspect product reports for immunotherapy and classical chemotherapy from January 2010–March 2020. We identified 90,740 total adverse event reports related to immune checkpoint inhibitors and classical chemotherapy. Results We found that myocarditis was significantly associated with patients receiving anti-program cell death protein 1 (PD-1) or anti-program death ligand 1 (PD-L1), odds ratio (OR) = 23.86 (95% confidence interval [CI] 11.76–48.42, (adjusted p-value) q < 0.001), and combination immunotherapy, OR = 7.29 (95% CI 1.03–51.89, q = 0.047). Heart failure was significantly associated in chemotherapy compared to PD-(L)1, OR = 0.50 (95% CI 0.37–0.69, q < 0.001), CTLA4, OR = 0.08 (95% CI 0.03–0.20, q < 0.001), and combination immunotherapy, OR = 0.25 (95% CI 0.13–0.48, q < 0.001). Additionally, we observe a sex-specificity towards males in cardiac adverse reports for arrhythmias, OR = 0.81 (95% CI 0.75–0.87, q < 0.001), coronary artery disease, 0.63 (95% CI 0.53–0.76, q < 0.001), myocardial infarction, OR = 0.60 (95% CI 0.53–0.67, q < 0.001), myocarditis, OR = 0.59 (95% CI 0.47–0.75, q < 0.001) and pericarditis, OR = 0.5 (95% CI 0.35–0.73, q < 0.001). Conclusion Our study provides the current risk estimates of cardiac adverse events in patients treated with immunotherapy compared to conventional chemotherapy. Understanding the clinical risk factors that predispose immunotherapy-treated cancer patients to often fatal CV adverse events will be crucial in Cardio-Oncology management. Supplementary Information The online version contains supplementary material available at 10.1186/s40959-021-00106-x.
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150
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Jin T, Rehani P, Ying M, Huang J, Liu S, Roussos P, Wang D. scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks. Genome Med 2021; 13:95. [PMID: 34044854 PMCID: PMC8161957 DOI: 10.1186/s13073-021-00908-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Accepted: 05/13/2021] [Indexed: 02/06/2023] Open
Abstract
Understanding cell-type-specific gene regulatory mechanisms from genetic variants to diseases remains challenging. To address this, we developed a computational pipeline, scGRNom (single-cell Gene Regulatory Network prediction from multi-omics), to predict cell-type disease genes and regulatory networks including transcription factors and regulatory elements. With applications to schizophrenia and Alzheimer's disease, we predicted disease genes and regulatory networks for excitatory and inhibitory neurons, microglia, and oligodendrocytes. Further enrichment analyses revealed cross-disease and disease-specific functions and pathways at the cell-type level. Our machine learning analysis also found that cell-type disease genes improved clinical phenotype predictions. scGRNom is a general-purpose tool available at https://github.com/daifengwanglab/scGRNom .
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Affiliation(s)
- Ting Jin
- Department of Biostatistics and Medical Informatics, University of Wisconsin - Madison, Madison, WI, 53706, USA
- Waisman Center, University of Wisconsin - Madison, Madison, WI, 53705, USA
| | - Peter Rehani
- Waisman Center, University of Wisconsin - Madison, Madison, WI, 53705, USA
- Department of Integrative Biology, University of Wisconsin - Madison, Madison, WI, 53706, USA
- Present address: Morgridge Institute for Research, Madison, WI, 53715, USA
| | - Mufang Ying
- Department of Statistics, University of Wisconsin - Madison, Madison, WI, 53706, USA
- Present address: Department of Statistics, Rutgers University, Piscataway, NJ, 08854, USA
| | - Jiawei Huang
- Department of Statistics, University of Wisconsin - Madison, Madison, WI, 53706, USA
| | - Shuang Liu
- Waisman Center, University of Wisconsin - Madison, Madison, WI, 53705, USA
| | - Panagiotis Roussos
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Daifeng Wang
- Department of Biostatistics and Medical Informatics, University of Wisconsin - Madison, Madison, WI, 53706, USA.
- Waisman Center, University of Wisconsin - Madison, Madison, WI, 53705, USA.
- Department of Computer Sciences, University of Wisconsin - Madison, Madison, WI, 53706, USA.
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