1
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He J, Perera D, Wen W, Ping J, Li Q, Lyu L, Chen Z, Shu X, Long J, Cai Q, Shu XO, Zheng W, Long Q, Guo X. Enhancing Disease Risk Gene Discovery by Integrating Transcription Factor-Linked Trans-located Variants into Transcriptome-Wide Association Analyses. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.10.10.23295443. [PMID: 37873299 PMCID: PMC10593059 DOI: 10.1101/2023.10.10.23295443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Transcriptome-wide association studies (TWAS) have been successful in identifying disease susceptibility genes by integrating cis-variants predicted gene expression with genome-wide association studies (GWAS) data. However, trans-located variants for predicting gene expression remain largely unexplored. Here, we introduce transTF-TWAS, which incorporates transcription factor (TF)-linked trans-located variants to enhance model building. Using data from the Genotype-Tissue Expression project, we predict gene expression and alternative splicing and applied these models to large GWAS datasets for breast, prostate, and lung cancers. We demonstrate that transTF-TWAS outperforms other existing TWAS approaches in both constructing gene prediction models and identifying disease-associated genes, as evidenced by simulations and real data analysis. Our transTF-TWAS approach significantly contributes to the discovery of disease risk genes. Findings from this study have shed new light on several genetically driven key regulators and their associated regulatory networks underlying disease susceptibility.
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2
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Sumida TS, Cheru NT, Hafler DA. The regulation and differentiation of regulatory T cells and their dysfunction in autoimmune diseases. Nat Rev Immunol 2024; 24:503-517. [PMID: 38374298 PMCID: PMC11216899 DOI: 10.1038/s41577-024-00994-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/15/2024] [Indexed: 02/21/2024]
Abstract
The discovery of FOXP3+ regulatory T (Treg) cells as a distinct cell lineage with a central role in regulating immune responses provided a deeper understanding of self-tolerance. The transcription factor FOXP3 serves a key role in Treg cell lineage determination and maintenance, but is not sufficient to enable the full potential of Treg cell suppression, indicating that other factors orchestrate the fine-tuning of Treg cell function. Moreover, FOXP3-independent mechanisms have recently been shown to contribute to Treg cell dysfunction. FOXP3 mutations in humans cause lethal fulminant systemic autoinflammation (IPEX syndrome). However, it remains unclear to what degree Treg cell dysfunction is contributing to the pathophysiology of common autoimmune diseases. In this Review, we discuss the origins of Treg cells in the periphery and the multilayered mechanisms by which Treg cells are induced, as well as the FOXP3-dependent and FOXP3-independent cellular programmes that maintain the suppressive function of Treg cells in humans and mice. Further, we examine evidence for Treg cell dysfunction in the context of common autoimmune diseases such as multiple sclerosis, inflammatory bowel disease, systemic lupus erythematosus and rheumatoid arthritis.
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Affiliation(s)
- Tomokazu S Sumida
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA.
| | - Nardos T Cheru
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
| | - David A Hafler
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA.
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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3
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Baumgarten N, Rumpf L, Kessler T, Schulz MH. A statistical approach for identifying single nucleotide variants that affect transcription factor binding. iScience 2024; 27:109765. [PMID: 38736546 PMCID: PMC11088338 DOI: 10.1016/j.isci.2024.109765] [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: 07/14/2023] [Revised: 01/30/2024] [Accepted: 04/15/2024] [Indexed: 05/14/2024] Open
Abstract
Non-coding variants located within regulatory elements may alter gene expression by modifying transcription factor (TF) binding sites, thereby leading to functional consequences. Different TF models are being used to assess the effect of DNA sequence variants, such as single nucleotide variants (SNVs). Often existing methods are slow and do not assess statistical significance of results. We investigated the distribution of absolute maximal differential TF binding scores for general computational models that affect TF binding. We find that a modified Laplace distribution can adequately approximate the empirical distributions. A benchmark on in vitro and in vivo datasets showed that our approach improves upon an existing method in terms of performance and speed. Applications on eQTLs and on a genome-wide association study illustrate the usefulness of our statistics by highlighting cell type-specific regulators and target genes. An implementation of our approach is freely available on GitHub and as bioconda package.
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Affiliation(s)
- Nina Baumgarten
- Institute of Cardiovascular Regeneration, Goethe University, 60590 Frankfurt am Main, Germany
- Institute for Computational Genomic Medicine, Goethe University, 60590 Frankfurt am Main, Germany
- Institute for Computer Science, Goethe University, 60590 Frankfurt am Main, Germany
- German Center for Cardiovascular Research, Partner Site Rhein-Main, 60590 Frankfurt am Main, Germany
| | - Laura Rumpf
- Institute of Cardiovascular Regeneration, Goethe University, 60590 Frankfurt am Main, Germany
- Institute for Computational Genomic Medicine, Goethe University, 60590 Frankfurt am Main, Germany
- Institute for Computer Science, Goethe University, 60590 Frankfurt am Main, Germany
- German Center for Cardiovascular Research, Partner Site Rhein-Main, 60590 Frankfurt am Main, Germany
| | - Thorsten Kessler
- German Heart Centre Munich, Department of Cardiology, School of Medicine and Health, Technical University of Munich, 80636 Munich, Germany
- German Centre for Cardiovascular Research, Partner Site Munich Heart Alliance, 80636 Munich, Germany
| | - Marcel H. Schulz
- Institute of Cardiovascular Regeneration, Goethe University, 60590 Frankfurt am Main, Germany
- Institute for Computational Genomic Medicine, Goethe University, 60590 Frankfurt am Main, Germany
- Institute for Computer Science, Goethe University, 60590 Frankfurt am Main, Germany
- German Center for Cardiovascular Research, Partner Site Rhein-Main, 60590 Frankfurt am Main, Germany
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4
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Lagattuta KA, Park HL, Rumker L, Ishigaki K, Nathan A, Raychaudhuri S. The genetic basis of autoimmunity seen through the lens of T cell functional traits. Nat Commun 2024; 15:1204. [PMID: 38331990 PMCID: PMC10853555 DOI: 10.1038/s41467-024-45170-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 01/15/2024] [Indexed: 02/10/2024] Open
Abstract
Autoimmune disease heritability is enriched in T cell-specific regulatory regions of the genome. Modern-day T cell datasets now enable association studies between single nucleotide polymorphisms (SNPs) and a myriad of molecular phenotypes, including chromatin accessibility, gene expression, transcriptional programs, T cell antigen receptor (TCR) amino acid usage, and cell state abundances. Such studies have identified hundreds of quantitative trait loci (QTLs) in T cells that colocalize with genetic risk for autoimmune disease. The key challenge facing immunologists today lies in synthesizing these results toward a unified understanding of the autoimmune T cell: which genes, cell states, and antigens drive tissue destruction?
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Affiliation(s)
- Kaitlyn A Lagattuta
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, 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
- 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
| | - Hannah L Park
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, 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
- 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
| | - Laurie Rumker
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, 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
- 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, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, 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
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Laboratory for Human Immunogenetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Aparna Nathan
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Division of Genetics, Department of Medicine, 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.
- 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.
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Division of Genetics, Department of Medicine, 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.
- 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.
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5
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Yamamoto K, Ishigaki K, Okada Y. How can genetics analysis allow for early detection of rheumatoid arthritis. Semin Arthritis Rheum 2024; 64S:152323. [PMID: 38151400 DOI: 10.1016/j.semarthrit.2023.152323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 09/23/2023] [Indexed: 12/29/2023]
Abstract
Early detection and diagnosis of rheumatoid arthritis (RA) is crucial for initiating appropriate therapy promptly, thereby leading to increase the likelihood of achieving remission and eventual cure. In this article, we will discuss the contribution of genetic information in predicting and detecting RA.
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Affiliation(s)
| | | | - Yukinori Okada
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan; The University of Tokyo, Tokyo, Japan; Osaka University, Osaka, Japan
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6
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Ishikawa Y, Tanaka N, Asano Y, Kodera M, Shirai Y, Akahoshi M, Hasegawa M, Matsushita T, Saito K, Motegi SI, Yoshifuji H, Yoshizaki A, Kohmoto T, Takagi K, Oka A, Kanda M, Tanaka Y, Ito Y, Nakano K, Kasamatsu H, Utsunomiya A, Sekiguchi A, Niiro H, Jinnin M, Makino K, Makino T, Ihn H, Yamamoto M, Suzuki C, Takahashi H, Nishida E, Morita A, Yamamoto T, Fujimoto M, Kondo Y, Goto D, Sumida T, Ayuzawa N, Yanagida H, Horita T, Atsumi T, Endo H, Shima Y, Kumanogoh A, Hirata J, Otomo N, Suetsugu H, Koike Y, Tomizuka K, Yoshino S, Liu X, Ito S, Hikino K, Suzuki A, Momozawa Y, Ikegawa S, Tanaka Y, Ishikawa O, Takehara K, Torii T, Sato S, Okada Y, Mimori T, Matsuda F, Matsuda K, Amariuta T, Imoto I, Matsuo K, Kuwana M, Kawaguchi Y, Ohmura K, Terao C. GWAS for systemic sclerosis identifies six novel susceptibility loci including one in the Fcγ receptor region. Nat Commun 2024; 15:319. [PMID: 38296975 PMCID: PMC10830486 DOI: 10.1038/s41467-023-44541-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 12/18/2023] [Indexed: 02/02/2024] Open
Abstract
Here we report the largest Asian genome-wide association study (GWAS) for systemic sclerosis performed to date, based on data from Japanese subjects and comprising of 1428 cases and 112,599 controls. The lead SNP is in the FCGR/FCRL region, which shows a penetrating association in the Asian population, while a complete linkage disequilibrium SNP, rs10917688, is found in a cis-regulatory element for IRF8. IRF8 is also a significant locus in European GWAS for systemic sclerosis, but rs10917688 only shows an association in the presence of the risk allele of IRF8 in the Japanese population. Further analysis shows that rs10917688 is marked with H3K4me1 in primary B cells. A meta-analysis with a European GWAS detects 30 additional significant loci. Polygenic risk scores constructed with the effect sizes of the meta-analysis suggest the potential portability of genetic associations beyond populations. Prioritizing the top 5% of SNPs of IRF8 binding sites in B cells improves the fitting of the polygenic risk scores, underscoring the roles of B cells and IRF8 in the development of systemic sclerosis. The results also suggest that systemic sclerosis shares a common genetic architecture across populations.
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Affiliation(s)
- Yuki Ishikawa
- RIKEN Center for Integrative Medical Sciences, The Laboratory for Statistical and Translational Genetics, Yokohama, Japan
| | - Nao Tanaka
- RIKEN Center for Integrative Medical Sciences, The Laboratory for Statistical and Translational Genetics, Yokohama, Japan
- Department of Rheumatology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yoshihide Asano
- Department of Dermatology, Tohoku University Graduate School of Medicine, Sendai, Japan
- Department of Dermatology, The University of Tokyo, Tokyo, Japan
| | - Masanari Kodera
- Department of Dermatology, Chukyo Hospital, Japan Community Health Care Organization, Nagoya, Japan
| | - Yuichiro Shirai
- Department of Allergy and Rheumatology, Nippon Medical School Graduate School of Medicine, Tokyo, Japan
| | - Mitsuteru Akahoshi
- Department of Medicine and Biosystemic Science, Kyushu University Graduate School of Medical Sciences, Fukuoka, Japan
- Department of Rheumatology, Saga University Hospital, Saga, Japan
| | - Minoru Hasegawa
- Faculty of Medical Sciences, Department of Dermatology, University of Fukui, Fukui, Japan
| | - Takashi Matsushita
- Department of Dermatology, Faculty of Medicine, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Kanazawa, Japan
| | - Kazuyoshi Saito
- The First Department of Internal Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan
| | - Sei-Ichiro Motegi
- Department of Dermatology, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Hajime Yoshifuji
- Department of Rheumatology and Clinical Immunology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Ayumi Yoshizaki
- Department of Dermatology, The University of Tokyo, Tokyo, Japan
| | - Tomohiro Kohmoto
- Aichi Cancer Center Research Institute, Division of Molecular Genetics, Nagoya, Japan
| | - Kae Takagi
- Tokyo Women's Medical University, Adachi Medical Center, Tokyo, Japan
| | - Akira Oka
- Department of Molecular Life Sciences, Division of Basic Medical Science and Molecular Medicine, Tokai University School of Medicine, Isehara, Japan
| | - Miho Kanda
- Department of Dermatology, Chukyo Hospital, Japan Community Health Care Organization, Nagoya, Japan
| | - Yoshihito Tanaka
- Department of Dermatology, Chukyo Hospital, Japan Community Health Care Organization, Nagoya, Japan
| | - Yumi Ito
- Department of Dermatology, Chukyo Hospital, Japan Community Health Care Organization, Nagoya, Japan
| | - Kazuhisa Nakano
- The First Department of Internal Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan
| | - Hiroshi Kasamatsu
- Faculty of Medical Sciences, Department of Dermatology, University of Fukui, Fukui, Japan
| | - Akira Utsunomiya
- Faculty of Medical Sciences, Department of Dermatology, University of Fukui, Fukui, Japan
| | - Akiko Sekiguchi
- Department of Dermatology, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Hiroaki Niiro
- Department of Medicine and Biosystemic Science, Kyushu University Graduate School of Medical Sciences, Fukuoka, Japan
| | - Masatoshi Jinnin
- Department of Dermatology, Wakayama Medical University Graduate School of Medicine, Wakayama, Japan
| | - Katsunari Makino
- Department of Dermatology and Plastic Surgery, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Takamitsu Makino
- Department of Dermatology and Plastic Surgery, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Hironobu Ihn
- Department of Dermatology and Plastic Surgery, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Motohisa Yamamoto
- Department of Rheumatology and Allergy, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Chisako Suzuki
- Department of Rheumatology and Clinical Immunology, Sapporo Medical University School of Medicine, Sapporo, Japan
| | - Hiroki Takahashi
- Department of Rheumatology and Clinical Immunology, Sapporo Medical University School of Medicine, Sapporo, Japan
| | - Emi Nishida
- Department of Geriatric and Environmental Dermatology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
- Department of Dermatology, Okazaki City Hospital, Okazaki, Japan
| | - Akimichi Morita
- Department of Geriatric and Environmental Dermatology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
| | - Toshiyuki Yamamoto
- Department of Dermatology, Fukushima Medical University, School of Medicine, Fukushima, Japan
| | - Manabu Fujimoto
- Department of Dermatology, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Yuya Kondo
- Department of Rheumatology, Institute of Medicine, University of Tsukuba, Tsukuba, Japan
| | - Daisuke Goto
- Department of Rheumatology, Institute of Medicine, University of Tsukuba, Tsukuba, Japan
| | - Takayuki Sumida
- Department of Rheumatology, Institute of Medicine, University of Tsukuba, Tsukuba, Japan
| | - Naho Ayuzawa
- Department of Clinical Immunology, National Hospital Organization, Utano National Hospital, Kyoto, Japan
| | - Hidetoshi Yanagida
- Department of Clinical Immunology, National Hospital Organization, Utano National Hospital, Kyoto, Japan
| | - Tetsuya Horita
- Faculty of Medicine and Graduate School of Medicine, Department of Rheumatology, Endocrinology and Nephrology, Hokkaido University, Sapporo, Japan
| | - Tatsuya Atsumi
- Faculty of Medicine and Graduate School of Medicine, Department of Rheumatology, Endocrinology and Nephrology, Hokkaido University, Sapporo, Japan
| | - Hirahito Endo
- Omori Medical Center, Toho University, Rheumatic Disease Center, Tokyo, Japan
| | - Yoshihito Shima
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Atsushi Kumanogoh
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Jun Hirata
- Immunology Frontier Center, Osaka University, Statistical Immunology, Osaka, Japan
| | - Nao Otomo
- RIKEN Center for Integrative Medical Sciences, The Laboratory for Statistical and Translational Genetics, Yokohama, Japan
| | - Hiroyuki Suetsugu
- RIKEN Center for Integrative Medical Sciences, The Laboratory for Statistical and Translational Genetics, Yokohama, Japan
| | - Yoshinao Koike
- RIKEN Center for Integrative Medical Sciences, The Laboratory for Statistical and Translational Genetics, Yokohama, Japan
| | - Kohei Tomizuka
- RIKEN Center for Integrative Medical Sciences, The Laboratory for Statistical and Translational Genetics, Yokohama, Japan
| | - Soichiro Yoshino
- RIKEN Center for Integrative Medical Sciences, The Laboratory for Statistical and Translational Genetics, Yokohama, Japan
| | - Xiaoxi Liu
- RIKEN Center for Integrative Medical Sciences, The Laboratory for Statistical and Translational Genetics, Yokohama, Japan
| | - Shuji Ito
- RIKEN Center for Integrative Medical Sciences, The Laboratory for Statistical and Translational Genetics, Yokohama, Japan
| | - Keiko Hikino
- RIKEN Center for Integrative Medical Sciences, The Laboratory for Pharmacogenomics, Yokohama, Japan
| | - Akari Suzuki
- RIKEN Center for Integrative Medical Sciences, The Laboratory for Autoimmune Diseases, Yokohama, Japan
| | - Yukihide Momozawa
- RIKEN Center for Integrative Medical Sciences, The Laboratory for Genotyping Development, Yokohama, Japan
| | - Shiro Ikegawa
- RIKEN Center for Integrative Medical Sciences, The Laboratory for Bone and Joint Diseases, Yokohama, Japan
| | - Yoshiya Tanaka
- The First Department of Internal Medicine, University of Occupational and Environmental Health, Japan, Kitakyushu, Japan
| | - Osamu Ishikawa
- Department of Dermatology, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Kazuhiko Takehara
- Department of Dermatology, Faculty of Medicine, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Kanazawa, Japan
| | | | - Shinichi Sato
- Department of Dermatology, The University of Tokyo, Tokyo, Japan
| | - Yukinori Okada
- Immunology Frontier Center, Osaka University, Statistical Immunology, Osaka, Japan
| | - Tsuneyo Mimori
- Department of Rheumatology and Clinical Immunology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Ijinkai Takeada General Hospital, Kyoto, Japan
| | - Fumihiko Matsuda
- Graduate School of Medicine, Kyoto University, Center for Genomic Medicine, Kyoto, Japan
| | - Koichi Matsuda
- Institute of Medical Science, The University of Tokyo, Laboratory of Genome Technology, Human Genome Center, Tokyo, Japan
- Department of Computational Biology and Medical Sciences, Laboratory of Clinical Genome Sequencing, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Tiffany Amariuta
- Center for Data Sciences, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, 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
- Graduate School of Arts and Sciences, Harvard University, Cambridge, MA, USA
| | - Issei Imoto
- Aichi Cancer Center Research Institute, Nagoya, Japan
| | - Keitaro Matsuo
- Aichi Cancer Center Research Institute, Division of Cancer Epidemiology and Prevention, Nagoya, Japan
| | - Masataka Kuwana
- Department of Allergy and Rheumatology, Nippon Medical School Graduate School of Medicine, Tokyo, Japan
| | - Yasushi Kawaguchi
- Tokyo Women's Medical University, Division of Rheumatology, Department of Internal Medicine, Tokyo, Japan
| | - Koichiro Ohmura
- Department of Rheumatology and Clinical Immunology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Chikashi Terao
- RIKEN Center for Integrative Medical Sciences, The Laboratory for Statistical and Translational Genetics, Yokohama, Japan.
- Shizuoka General Hospital, The Clinical Research Center, Shizuoka, Japan.
- The Department of Applied Genetics, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan.
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7
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Gupta A, Weinand K, Nathan A, Sakaue S, Zhang MJ, Donlin L, Wei K, Price AL, Amariuta T, Raychaudhuri S. Dynamic regulatory elements in single-cell multimodal data implicate key immune cell states enriched for autoimmune disease heritability. Nat Genet 2023; 55:2200-2210. [PMID: 38036783 PMCID: PMC10787644 DOI: 10.1038/s41588-023-01577-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 10/18/2023] [Indexed: 12/02/2023]
Abstract
In autoimmune diseases such as rheumatoid arthritis, the immune system attacks the body's own cells. Developing a precise understanding of the cell states where noncoding autoimmune risk variants impart causal mechanisms is critical to developing curative therapies. Here, to identify noncoding regions with accessible chromatin that associate with cell-state-defining gene expression patterns, we leveraged multimodal single-nucleus RNA and assay for transposase-accessible chromatin (ATAC) sequencing data across 28,674 cells from the inflamed synovial tissue of 12 donors. Specifically, we used a multivariate Poisson model to predict peak accessibility from single-nucleus RNA sequencing principal components. For 14 autoimmune diseases, we discovered that cell-state-dependent ('dynamic') chromatin accessibility peaks in immune cell types were enriched for heritability, compared with cell-state-invariant ('cs-invariant') peaks. These dynamic peaks marked regulatory elements associated with T peripheral helper, regulatory T, dendritic and STAT1+CXCL10+ myeloid cell states. We argue that dynamic regulatory elements can help identify precise cell states enriched for disease-critical genetic variation.
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Affiliation(s)
- Anika Gupta
- 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
| | - Kathryn Weinand
- 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
| | - 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
| | - Saori Sakaue
- 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
| | - Martin Jinye Zhang
- 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
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Laura Donlin
- Weill Cornell Medicine, New York, NY, USA
- Hospital for Special Surgery, New York, NY, USA
| | - Kevin Wei
- 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
| | - 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.
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA.
- Department of Medicine, University of California San Diego, La Jolla, CA, 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.
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8
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Amariuta T, Siewert-Rocks K, Price AL. Modeling tissue co-regulation estimates tissue-specific contributions to disease. Nat Genet 2023; 55:1503-1511. [PMID: 37580597 PMCID: PMC10904330 DOI: 10.1038/s41588-023-01474-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 07/13/2023] [Indexed: 08/16/2023]
Abstract
Integrative analyses of genome-wide association studies and gene expression data have implicated many disease-critical tissues. However, co-regulation of genetic effects on gene expression across tissues impedes distinguishing biologically causal tissues from tagging tissues. In the present study, we introduce tissue co-regulation score regression (TCSC), which disentangles causal tissues from tagging tissues by regressing gene-disease association statistics (from transcriptome-wide association studies) on tissue co-regulation scores, reflecting correlations of predicted gene expression across genes and tissues. We applied TCSC to 78 diseases/traits (average n = 302,000) and gene expression prediction models for 48 GTEx tissues. TCSC identified 21 causal tissue-trait pairs at a 5% false discovery rate (FDR), including well-established findings, biologically plausible new findings (for example, aorta artery and glaucoma) and increased specificity of known tissue-trait associations (for example, subcutaneous adipose, but not visceral adipose, and high-density lipoprotein). TCSC also identified 17 causal tissue-trait covariance pairs at 5% FDR. In conclusion, TCSC is a precise method for distinguishing causal tissues from tagging tissues.
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Affiliation(s)
- Tiffany Amariuta
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA.
- Department of Medicine, University of California San Diego, La Jolla, CA, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Katherine Siewert-Rocks
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alkes L Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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9
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Chen M, Zhang Y, Adams T, Ji D, Jiang W, Wain LV, Cho M, Kaminski N, Zhao H. Integrative analyses for the identification of idiopathic pulmonary fibrosis-associated genes and shared loci with other diseases. Thorax 2023; 78:792-798. [PMID: 36216496 PMCID: PMC10083187 DOI: 10.1136/thorax-2021-217703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 09/16/2022] [Indexed: 01/18/2023]
Abstract
BACKGROUND Although genome-wide association studies (GWAS) have identified many genomic regions associated with idiopathic pulmonary fibrosis (IPF), the causal genes and functions remain largely unknown. Many single-cell expression data have become available for IPF, and there is increasing evidence suggesting a shared genetic basis between IPF and other diseases. METHODS We conducted integrative analyses to improve the power of GWAS. First, we calculated global and local genetic correlations to identify IPF genetically associated traits and local regions. Then, we prioritised candidate genes contributing to local genetic correlation. Second, we performed transcriptome-wide association analysis (TWAS) of 44 tissues to identify candidate genes whose genetically predicted expression level is associated with IPF. To replicate our findings and investigate the regulatory role of the transcription factors (TF) in identified candidate genes, we first conducted the heritability enrichment analysis in TF binding sites. Then, we examined the enrichment of the TF target genes in cell-type-specific differentially expressed genes (DEGs) identified from single-cell expression data of IPF and healthy lung samples. FINDINGS We identified 12 candidate genes across 13 genomic regions using local genetic correlation, including the POT1 locus (p value=0.00041), which contained variants with protective effects on lung cancer but increasing IPF risk. We identified another 13 novel genes using TWAS. Two TFs, MAFK and SMAD2, showed significant enrichment in both partitioned heritability and cell-type-specific DEGs. INTERPRETATION Our integrative analysis identified new genes for IPF susceptibility and expanded the understanding of the complex genetic architecture and disease mechanism of IPF.
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Affiliation(s)
- Ming Chen
- Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
| | - Yiliang Zhang
- Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
| | - Taylor Adams
- Section of Pulmonary, Critical Care and Sleep Medicine, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Dingjue Ji
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, USA
| | - Wei Jiang
- Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
| | - Louise V Wain
- National Institute for Health Research, Leicester Respiratory Biomedical Research Centre, Glenfield Hospital, Leicester, UK
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - Michael Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Naftali Kaminski
- Section of Pulmonary, Critical Care and Sleep Medicine, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Hongyu Zhao
- Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, USA
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10
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Jeong R, Bulyk ML. Blood cell traits' GWAS loci colocalization with variation in PU.1 genomic occupancy prioritizes causal noncoding regulatory variants. CELL GENOMICS 2023; 3:100327. [PMID: 37492098 PMCID: PMC10363807 DOI: 10.1016/j.xgen.2023.100327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 02/10/2023] [Accepted: 04/25/2023] [Indexed: 07/27/2023]
Abstract
Genome-wide association studies (GWASs) have uncovered numerous trait-associated loci across the human genome, most of which are located in noncoding regions, making interpretation difficult. Moreover, causal variants are hard to statistically fine-map at many loci because of widespread linkage disequilibrium. To address this challenge, we present a strategy utilizing transcription factor (TF) binding quantitative trait loci (bQTLs) for colocalization analysis to identify trait associations likely mediated by TF occupancy variation and to pinpoint likely causal variants using motif scores. We applied this approach to PU.1 bQTLs in lymphoblastoid cell lines and blood cell trait GWAS data. Colocalization analysis revealed 69 blood cell trait GWAS loci putatively driven by PU.1 occupancy variation. We nominate PU.1 motif-altering variants as the likely shared causal variants at 51 loci. Such integration of TF bQTL data with other GWAS data may reveal transcriptional regulatory mechanisms and causal noncoding variants underlying additional complex traits.
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Affiliation(s)
- Raehoon Jeong
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
- Bioinformatics and Integrative Genomics Graduate Program, Harvard University, Cambridge, MA 02138, USA
| | - Martha L. Bulyk
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
- Bioinformatics and Integrative Genomics Graduate Program, Harvard University, Cambridge, MA 02138, USA
- Department of Pathology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
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11
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Bartell E, Lin K, Tsuo K, Gan W, Vedantam S, Cole JB, Baronas JM, Yengo L, Marouli E, Amariuta T, Chen Z, Li L, Renthal NE, Jacobsen CM, Salem RM, Walters RG, Hirschhorn JN. Genetics of skeletal proportions in two different populations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.22.541772. [PMID: 37292977 PMCID: PMC10245876 DOI: 10.1101/2023.05.22.541772] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Human height can be divided into sitting height and leg length, reflecting growth of different parts of the skeleton whose relative proportions are captured by the ratio of sitting to total height (as sitting height ratio, SHR). Height is a highly heritable trait, and its genetic basis has been well-studied. However, the genetic determinants of skeletal proportion are much less well-characterized. Expanding substantially on past work, we performed a genome-wide association study (GWAS) of SHR in ∼450,000 individuals with European ancestry and ∼100,000 individuals with East Asian ancestry from the UK and China Kadoorie Biobanks. We identified 565 loci independently associated with SHR, including all genomic regions implicated in prior GWAS in these ancestries. While SHR loci largely overlap height-associated loci (P < 0.001), the fine-mapped SHR signals were often distinct from height. We additionally used fine-mapped signals to identify 36 credible sets with heterogeneous effects across ancestries. Lastly, we used SHR, sitting height, and leg length to identify genetic variation acting on specific body regions rather than on overall human height.
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12
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Jeong R, Bulyk ML. Colocalization of blood cell traits GWAS associations and variation in PU.1 genomic occupancy prioritizes causal noncoding regulatory variants. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.29.534582. [PMID: 37034747 PMCID: PMC10081269 DOI: 10.1101/2023.03.29.534582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Genome-wide association studies (GWAS) have uncovered numerous trait-associated loci across the human genome, most of which are located in noncoding regions, making interpretations difficult. Moreover, causal variants are hard to statistically fine-map at many loci because of widespread linkage disequilibrium. To address this challenge, we present a strategy utilizing transcription factor (TF) binding quantitative trait loci (bQTLs) for colocalization analysis to identify trait associations likely mediated by TF occupancy variation and to pinpoint likely causal variants using motif scores. We applied this approach to PU.1 bQTLs in lymphoblastoid cell lines and blood cell traits GWAS data. Colocalization analysis revealed 69 blood cell trait GWAS loci putatively driven by PU.1 occupancy variation. We nominate PU.1 motif-altering variants as the likely shared causal variants at 51 loci. Such integration of TF bQTL data with other GWAS data may reveal transcriptional regulatory mechanisms and causal noncoding variants underlying additional complex traits.
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Affiliation(s)
- Raehoon Jeong
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
- Bioinformatics and Integrative Genomics Graduate Program, Harvard University, Cambridge, MA 02138, USA
| | - Martha L. Bulyk
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
- Bioinformatics and Integrative Genomics Graduate Program, Harvard University, Cambridge, MA 02138, USA
- Department of Pathology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
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13
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Nagafuchi Y, Ota M, Hatano H, Inoue M, Kobayashi S, Okubo M, Sugimori Y, Nakano M, Yamada S, Yoshida R, Tsuchida Y, Iwasaki Y, Shoda H, Okada Y, Yamamoto K, Ishigaki K, Okamura T, Fujio K. Control of naive and effector CD4 T cell receptor repertoires by rheumatoid-arthritis-risk HLA alleles. J Autoimmun 2022; 133:102907. [PMID: 36126366 DOI: 10.1016/j.jaut.2022.102907] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 08/30/2022] [Accepted: 08/31/2022] [Indexed: 12/13/2022]
Abstract
OBJECTIVE Human Leukocyte Antigen (HLA) alleles regulate susceptibility to rheumatoid arthritis (RA) and immune-mediated diseases. This study aims to elucidate the impact of HLA alleles to T cell subsets. METHODS We performed genome-wide and HLA allele association analysis for T cell receptor (TCR) beta chain repertoire in 13 purified T cell subsets from the ImmuNexUT database, consisting of 407 donors with ten immune-mediated diseases and healthy controls. RESULTS HLA class II alleles were associated with TRBV gene usage and the public clones of CD4 T cells, while HLA class I alleles were associated with CD8 T cells. RA-risk and immune-mediated diseases-risk HLA alleles were associated with TRBV gene usage of naive and effector CD4 T cell subsets and public clones accumulating in Th17. Clonal diversity was independent of HLA alleles and was correlated with transcriptome changes that reflect TCR signaling. CONCLUSION This study revealed in vivo evidence that both HLA alleles and environmental factors shape naive and effector TCR repertoires in RA and immune-mediated diseases patients.
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Affiliation(s)
- Yasuo Nagafuchi
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; Department of Functional Genomics and Immunological Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
| | - Mineto Ota
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; Department of Functional Genomics and Immunological Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Hiroaki Hatano
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; Laboratory for Human Immunogenetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Mariko Inoue
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Satomi Kobayashi
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Mai Okubo
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yusuke Sugimori
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Masahiro Nakano
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Saeko Yamada
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Ryochi Yoshida
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yumi Tsuchida
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yukiko Iwasaki
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Hirofumi Shoda
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan; Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Kazuhiko Yamamoto
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Kazuyoshi Ishigaki
- Laboratory for Human Immunogenetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Tomohisa Okamura
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; Department of Functional Genomics and Immunological Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Keishi Fujio
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
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14
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He J, Wen W, Beeghly A, Chen Z, Cao C, Shu XO, Zheng W, Long Q, Guo X. Integrating transcription factor occupancy with transcriptome-wide association analysis identifies susceptibility genes in human cancers. Nat Commun 2022; 13:7118. [PMID: 36402776 PMCID: PMC9675749 DOI: 10.1038/s41467-022-34888-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 11/10/2022] [Indexed: 11/21/2022] Open
Abstract
Transcriptome-wide association studies (TWAS) have successfully discovered many putative disease susceptibility genes. However, TWAS may suffer from inaccuracy of gene expression predictions due to inclusion of non-regulatory variants. By integrating prior knowledge of susceptible transcription factor occupied elements, we develop sTF-TWAS and demonstrate that it outperforms existing TWAS approaches in both simulation and real data analyses. Under the sTF-TWAS framework, we build genetic models to predict alternative splicing and gene expression in normal breast, prostate and lung tissues from the Genotype-Tissue Expression project and apply these models to data from large genome-wide association studies (GWAS) conducted among European-ancestry populations. At Bonferroni-corrected P < 0.05, we identify 354 putative susceptibility genes for these cancers, including 189 previously unreported in GWAS loci and 45 in loci unreported by GWAS. These findings provide additional insight into the genetic susceptibility of human cancers. Additionally, we show the generalizability of the sTF-TWAS on non-cancer diseases.
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Affiliation(s)
- Jingni He
- grid.22072.350000 0004 1936 7697Department of Biochemistry & Molecular Biology, University of Calgary, Calgary, Canada ,grid.452223.00000 0004 1757 7615Department of Oncology, Xiangya Hospital, Central South University, Changsha, Hunan China
| | - Wanqing Wen
- grid.152326.10000 0001 2264 7217Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN USA
| | - Alicia Beeghly
- grid.152326.10000 0001 2264 7217Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN USA
| | - Zhishan Chen
- grid.152326.10000 0001 2264 7217Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN USA
| | - Chen Cao
- grid.22072.350000 0004 1936 7697Department of Biochemistry & Molecular Biology, University of Calgary, Calgary, Canada
| | - Xiao-Ou Shu
- grid.152326.10000 0001 2264 7217Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN USA
| | - Wei Zheng
- grid.152326.10000 0001 2264 7217Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN USA
| | - Quan Long
- grid.22072.350000 0004 1936 7697Department of Biochemistry & Molecular Biology, University of Calgary, Calgary, Canada ,grid.22072.350000 0004 1936 7697Department of Medical Genetics, University of Calgary, Calgary, Canada ,grid.22072.350000 0004 1936 7697Department of Mathematics & Statistics, University of Calgary, Calgary, Canada ,grid.22072.350000 0004 1936 7697Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, Canada ,grid.22072.350000 0004 1936 7697Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
| | - Xingyi Guo
- grid.152326.10000 0001 2264 7217Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN USA ,grid.152326.10000 0001 2264 7217Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN USA
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15
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Lan AY, Corces MR. Deep learning approaches for noncoding variant prioritization in neurodegenerative diseases. Front Aging Neurosci 2022; 14:1027224. [PMID: 36466610 PMCID: PMC9716280 DOI: 10.3389/fnagi.2022.1027224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 10/24/2022] [Indexed: 11/19/2022] Open
Abstract
Determining how noncoding genetic variants contribute to neurodegenerative dementias is fundamental to understanding disease pathogenesis, improving patient prognostication, and developing new clinical treatments. Next generation sequencing technologies have produced vast amounts of genomic data on cell type-specific transcription factor binding, gene expression, and three-dimensional chromatin interactions, with the promise of providing key insights into the biological mechanisms underlying disease. However, this data is highly complex, making it challenging for researchers to interpret, assimilate, and dissect. To this end, deep learning has emerged as a powerful tool for genome analysis that can capture the intricate patterns and dependencies within these large datasets. In this review, we organize and discuss the many unique model architectures, development philosophies, and interpretation methods that have emerged in the last few years with a focus on using deep learning to predict the impact of genetic variants on disease pathogenesis. We highlight both broadly-applicable genomic deep learning methods that can be fine-tuned to disease-specific contexts as well as existing neurodegenerative disease research, with an emphasis on Alzheimer's-specific literature. We conclude with an overview of the future of the field at the intersection of neurodegeneration, genomics, and deep learning.
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Affiliation(s)
- Alexander Y. Lan
- Gladstone Institute of Neurological Disease, San Francisco, CA, United States
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, United States
- Department of Neurology, University of California San Francisco, San Francisco, CA, United States
| | - M. Ryan Corces
- Gladstone Institute of Neurological Disease, San Francisco, CA, United States
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, United States
- Department of Neurology, University of California San Francisco, San Francisco, CA, United States
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16
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Ishigaki K, Sakaue S, Terao C, Luo Y, Sonehara K, Yamaguchi K, Amariuta T, Too CL, Laufer VA, Scott IC, Viatte S, Takahashi M, Ohmura K, Murasawa A, Hashimoto M, Ito H, Hammoudeh M, Emadi SA, Masri BK, Halabi H, Badsha H, Uthman IW, Wu X, Lin L, Li T, Plant D, Barton A, Orozco G, Verstappen SMM, Bowes J, MacGregor AJ, Honda S, Koido M, Tomizuka K, Kamatani Y, Tanaka H, Tanaka E, Suzuki A, Maeda Y, Yamamoto K, Miyawaki S, Xie G, Zhang J, Amos CI, Keystone E, Wolbink G, van der Horst-Bruinsma I, Cui J, Liao KP, Carroll RJ, Lee HS, Bang SY, Siminovitch KA, de Vries N, Alfredsson L, Rantapää-Dahlqvist S, Karlson EW, Bae SC, Kimberly RP, Edberg JC, Mariette X, Huizinga T, Dieudé P, Schneider M, Kerick M, Denny JC, Matsuda K, Matsuo K, Mimori T, Matsuda F, Fujio K, Tanaka Y, Kumanogoh A, Traylor M, Lewis CM, Eyre S, Xu H, Saxena R, Arayssi T, Kochi Y, Ikari K, Harigai M, Gregersen PK, Yamamoto K, Louis Bridges S, Padyukov L, Martin J, Klareskog L, Okada Y, Raychaudhuri S. Multi-ancestry genome-wide association analyses identify novel genetic mechanisms in rheumatoid arthritis. Nat Genet 2022; 54:1640-1651. [PMID: 36333501 PMCID: PMC10165422 DOI: 10.1038/s41588-022-01213-w] [Citation(s) in RCA: 92] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 09/26/2022] [Indexed: 11/06/2022]
Abstract
Rheumatoid arthritis (RA) is a highly heritable complex disease with unknown etiology. Multi-ancestry genetic research of RA promises to improve power to detect genetic signals, fine-mapping resolution and performances of polygenic risk scores (PRS). Here, we present a large-scale genome-wide association study (GWAS) of RA, which includes 276,020 samples from five ancestral groups. We conducted a multi-ancestry meta-analysis and identified 124 loci (P < 5 × 10-8), of which 34 are novel. Candidate genes at the novel loci suggest essential roles of the immune system (for example, TNIP2 and TNFRSF11A) and joint tissues (for example, WISP1) in RA etiology. Multi-ancestry fine-mapping identified putatively causal variants with biological insights (for example, LEF1). Moreover, PRS based on multi-ancestry GWAS outperformed PRS based on single-ancestry GWAS and had comparable performance between populations of European and East Asian ancestries. Our study provides several insights into the etiology of RA and improves the genetic predictability of RA.
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Affiliation(s)
- Kazuyoshi Ishigaki
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Laboratory for Human Immunogenetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Saori Sakaue
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan
- The Department of Applied Genetics, The School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Yang Luo
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - 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
| | - Kensuke Yamaguchi
- Department of Genomic Function and Diversity, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
- Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Tiffany Amariuta
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Chun Lai Too
- Immunogenetics Unit, Allergy and Immunology Research Center, Institute for Medical Research, National Institutes of Health Complex, Ministry of Health, Kuala Lumpur, Malaysia
- Department of Medicine, Division of Rheumatology, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Vincent A Laufer
- Department of Clinical Immunology and Rheumatology, University of Alabama at Birmingham School of Medicine, Birmingham, AL, USA
- Department of Pathology, Michigan Medicine, Ann Arbor, MI, USA
| | - Ian C Scott
- Haywood Academic Rheumatology Centre, Haywood Hospital, Midlands Partnership NHS Foundation Trust, Burslem, UK
- Primary Care Centre Versus Arthritis, School of Medicine, Keele University, Keele, UK
| | - Sebastien Viatte
- 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, Manchester Academic Health Science Centre, Manchester, UK
- Lydia Becker Institute of Immunology and Inflammation, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- NIHR Manchester Biomedical Research Centre, Manchester University Foundation Trust, Manchester, UK
| | - Meiko Takahashi
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Koichiro Ohmura
- Department of Rheumatology and Clinical immunology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Akira Murasawa
- Department of Rheumatology, Niigata Rheumatic Center, Niigata, Japan
| | - Motomu Hashimoto
- Department of Advanced Medicine for Rheumatic Diseases, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Department of Clinical Immunology, Graduate School of Medicine, Osaka City University, Osaka, Japan
| | - Hiromu Ito
- Department of Advanced Medicine for Rheumatic Diseases, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Department of Orthopaedic Surgery, Kurashiki Central Hospital, Kurashiki, Japan
| | - Mohammed Hammoudeh
- Rheumatology Division, Department of Internal Medicine, Hamad Medical Corporation, Doha, Qatar
| | - Samar Al Emadi
- Rheumatology Division, Department of Internal Medicine, Hamad Medical Corporation, Doha, Qatar
| | - Basel K Masri
- Department of Internal Medicine, Jordan Hospital, Amman, Jordan
| | - Hussein Halabi
- Section of Rheumatology, Department of Internal Medicine, King Faisal Specialist Hospital and Research Center, Jeddah, Saudi Arabia
| | - Humeira Badsha
- Dr. Humeira Badsha Medical Center, Emirates Hospital, Dubai, United Arab Emirates
| | - Imad W Uthman
- Department of Rheumatology, American University of Beirut, Beirut, Lebanon
| | - Xin Wu
- Department of Rheumatology and Immunology, Shanghai Changzeng Hospital, The Second Military Medical University, Shanghai, China
| | - Li Lin
- Department of Rheumatology and Immunology, Shanghai Changzeng Hospital, The Second Military Medical University, Shanghai, China
| | - Ting Li
- Department of Rheumatology and Immunology, Shanghai Changzeng Hospital, The Second Military Medical University, Shanghai, China
| | - Darren Plant
- 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, Manchester Academic Health Science Centre, Manchester, UK
| | - Anne Barton
- 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, Manchester Academic Health Science Centre, Manchester, UK
- NIHR Manchester Biomedical Research Centre, Manchester University Foundation Trust, Manchester, UK
| | - 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, Manchester Academic Health Science Centre, Manchester, UK
- NIHR Manchester Biomedical Research Centre, Manchester University Foundation Trust, Manchester, UK
| | - Suzanne M M Verstappen
- NIHR Manchester Biomedical Research Centre, Manchester University Foundation Trust, Manchester, UK
- Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, The University of Manchester, Manchester, UK
| | - John Bowes
- 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, Manchester Academic Health Science Centre, Manchester, UK
- NIHR Manchester Biomedical Research Centre, Manchester University Foundation Trust, Manchester, UK
| | | | - Suguru Honda
- Institute of Rheumatology, Tokyo Women's Medical University Hospital, Tokyo, Japan
- Department of Rheumatology, Tokyo Women's Medical University School of Medicine, Tokyo, Japan
| | - Masaru Koido
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Kohei Tomizuka
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yoichiro Kamatani
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Hiroaki Tanaka
- The First Department of Internal Medicine, School of Medicine, University of Occupational and Environmental Health Japan, Kitakyushu, Japan
| | - Eiichi Tanaka
- Institute of Rheumatology, Tokyo Women's Medical University Hospital, Tokyo, Japan
- Department of Rheumatology, Tokyo Women's Medical University School of Medicine, Tokyo, Japan
| | - Akari Suzuki
- Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yuichi Maeda
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
- 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
| | - 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
| | - Satoru Miyawaki
- Department of Neurosurgery, Faculty of Medicine, the University of Tokyo, Tokyo, Japan
| | - Gang Xie
- Lunenfeld-Tanenbaum Research Institute, Toronto, Ontario, Canada
| | - Jinyi Zhang
- Lunenfeld-Tanenbaum Research Institute, Toronto, Ontario, Canada
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | | | | | - Gertjan Wolbink
- Department of Rheumatology, Amsterdam Rheumatology and Immunology Center (ARC), Reade, Amsterdam, the Netherlands
| | - Irene van der Horst-Bruinsma
- Department of Rheumatology & Clinical Immunology/ARC, Amsterdam Institute for Infection and Immunity, Amsterdam UMC location Vrije Universiteit, Amsterdam, the Netherlands
| | - Jing Cui
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Katherine P Liao
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, USA
| | - Robert J Carroll
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Hye-Soon Lee
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Korea
- Hanyang University Institute for Rheumatology Research, Seoul, Korea
| | - So-Young Bang
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Korea
- Hanyang University Institute for Rheumatology Research, Seoul, Korea
| | - Katherine A Siminovitch
- Lunenfeld-Tanenbaum Research Institute, Toronto, Ontario, Canada
- Departments of Medicine and Immunology, University of Toronto, Toronto, Ontario, Canada
| | - Niek de Vries
- Department of Rheumatology & Clinical Immunology/ARC, Amsterdam Institute for Infection and Immunity, Amsterdam UMC location AMC/University of Amsterdam, Amsterdam, the Netherlands
| | - Lars Alfredsson
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | | | - Elizabeth W Karlson
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Sang-Cheol Bae
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Korea
- Hanyang University Institute for Rheumatology Research, Seoul, Korea
| | - Robert P Kimberly
- Center for Clinical and Translational Science, Division of Clinical Immunology and Rheumatology, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jeffrey C Edberg
- Center for Clinical and Translational Science, Division of Clinical Immunology and Rheumatology, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Xavier Mariette
- Department of Rheumatology, Université Paris-Saclay, Assistance Pubique - Hôpitaux de Paris, Hôpital Bicêtre, INSERM UMR1184, Le Kremlin Bicêtre, France
| | - Tom Huizinga
- Leiden University Medical Center, Leiden, the Netherlands
| | - Philippe Dieudé
- University of Paris Cité, Inserm, PHERE, F-75018, Paris, France
- Department of Rheumatology, Hôpital Bichat, APHP, Paris, France
| | - Matthias Schneider
- Department of Rheumatology & Hiller Research Unit Rheumatology, UKD, Heinrich-Heine University, Düsseldorf, Germany
| | - Martin Kerick
- Institute of Parasitology and Biomedicine Lopez-Neyra, CSIC, Granada, Spain
| | - Joshua C Denny
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA
- All of Us Research Program, Office of the Director, National Institutes of Health, Bethesda, MD, USA
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Koichi Matsuda
- Laboratory of Genome Technology, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Keitaro Matsuo
- Division of Cancer Epidemiology and Prevention, Department of Preventive Medicine, Aichi Cancer Center Research Institute, Nagoya, Japan
- Department of Cancer Epidemiology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Tsuneyo Mimori
- Department of Rheumatology and Clinical immunology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Keishi Fujio
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yoshiya Tanaka
- The First Department of Internal Medicine, School of Medicine, University of Occupational and Environmental Health Japan, Kitakyushu, Japan
| | - Atsushi Kumanogoh
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
- 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, Manchester Academic Health Science Centre, Manchester, UK
- 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
| | - Matthew Traylor
- Department of Medical & Molecular Genetics, King's College London, London, UK
- Department of Genetics, Novo Nordisk Research Centre Oxford, Oxford, UK
- Clinical Pharmacology, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Cathryn M Lewis
- Department of Medical & Molecular Genetics, King's College London, London, UK
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, UK
| | - Stephen Eyre
- 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, Manchester Academic Health Science Centre, Manchester, UK
- NIHR Manchester Biomedical Research Centre, Manchester University Foundation Trust, Manchester, UK
| | - Huji Xu
- Department of Rheumatology and Immunology, Shanghai Changzeng Hospital, The Second Military Medical University, Shanghai, China
- School of Clinical Medicine Tsinghua University, Beijing, China
- Peking-Tsinghua Center for Life Sciences, Tsinghua University, Beijing, China
| | - Richa Saxena
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Thurayya Arayssi
- Department of Internal Medicine, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar
| | - Yuta Kochi
- Department of Genomic Function and Diversity, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
- Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Katsunori Ikari
- Institute of Rheumatology, Tokyo Women's Medical University Hospital, Tokyo, Japan
- Department of Orthopedic Surgery, Tokyo Women's Medical University School of Medicine, Tokyo, Japan
- Division of Multidisciplinary Management of Rheumatic Diseases, Tokyo Women's Medical University, Tokyo, Japan
| | - Masayoshi Harigai
- Institute of Rheumatology, Tokyo Women's Medical University Hospital, Tokyo, Japan
- Division of Rheumatology, Department of Internal Medicine, Tokyo Women's Medical University School of Medicine, Tokyo, Japan
| | - Peter K Gregersen
- Robert S. Boas Center for Genomics and Human Genetics, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Kazuhiko Yamamoto
- Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - S Louis Bridges
- Department of Medicine, Hospital for Special Surgery, New York, NY, USA
- Division of Rheumatology, Weill Cornell Medicine, New York, NY, USA
| | - Leonid Padyukov
- Department of Medicine, Division of Rheumatology, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Javier Martin
- Institute of Parasitology and Biomedicine Lopez-Neyra, CSIC, Granada, Spain
| | - Lars Klareskog
- Department of Medicine, Division of Rheumatology, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Yukinori Okada
- 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.
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan.
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
- Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita, Japan.
- Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- 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, Manchester Academic Health Science Centre, Manchester, UK.
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17
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Kerdidani D, Papaioannou NE, Nakou E, Alissafi T. Rebooting Regulatory T Cell and Dendritic Cell Function in Immune-Mediated Inflammatory Diseases: Biomarker and Therapy Discovery under a Multi-Omics Lens. Biomedicines 2022; 10:2140. [PMID: 36140240 PMCID: PMC9495698 DOI: 10.3390/biomedicines10092140] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 08/29/2022] [Accepted: 08/29/2022] [Indexed: 12/24/2022] Open
Abstract
Immune-mediated inflammatory diseases (IMIDs) are a group of autoimmune and chronic inflammatory disorders with constantly increasing prevalence in the modern world. The vast majority of IMIDs develop as a consequence of complex mechanisms dependent on genetic, epigenetic, molecular, cellular, and environmental elements, that lead to defects in immune regulatory guardians of tolerance, such as dendritic (DCs) and regulatory T (Tregs) cells. As a result of this dysfunction, immune tolerance collapses and pathogenesis emerges. Deeper understanding of such disease driving mechanisms remains a major challenge for the prevention of inflammatory disorders. The recent renaissance in high throughput technologies has enabled the increase in the amount of data collected through multiple omics layers, while additionally narrowing the resolution down to the single cell level. In light of the aforementioned, this review focuses on DCs and Tregs and discusses how multi-omics approaches can be harnessed to create robust cell-based IMID biomarkers in hope of leading to more efficient and patient-tailored therapeutic interventions.
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Affiliation(s)
- Dimitra Kerdidani
- Immune Regulation Laboratory, Center of Basic Research, Biomedical Research Foundation Academy of Athens, 11527 Athens, Greece
- Laboratory of Biology, Medical School, National and Kapodistrian University of Athens, 12462 Athens, Greece
| | - Nikos E. Papaioannou
- Immune Regulation Laboratory, Center of Basic Research, Biomedical Research Foundation Academy of Athens, 11527 Athens, Greece
- Laboratory of Biology, Medical School, National and Kapodistrian University of Athens, 12462 Athens, Greece
| | - Evangelia Nakou
- Immune Regulation Laboratory, Center of Basic Research, Biomedical Research Foundation Academy of Athens, 11527 Athens, Greece
- Laboratory of Biology, Medical School, National and Kapodistrian University of Athens, 12462 Athens, Greece
| | - Themis Alissafi
- Immune Regulation Laboratory, Center of Basic Research, Biomedical Research Foundation Academy of Athens, 11527 Athens, Greece
- Laboratory of Biology, Medical School, National and Kapodistrian University of Athens, 12462 Athens, Greece
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18
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Karimzadeh M, Hoffman MM. Virtual ChIP-seq: predicting transcription factor binding by learning from the transcriptome. Genome Biol 2022; 23:126. [PMID: 35681170 PMCID: PMC9185870 DOI: 10.1186/s13059-022-02690-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 05/16/2022] [Indexed: 11/29/2022] Open
Abstract
Existing methods for computational prediction of transcription factor (TF) binding sites evaluate genomic regions with similarity to known TF sequence preferences. Most TF binding sites, however, do not resemble known TF sequence motifs, and many TFs are not sequence-specific. We developed Virtual ChIP-seq, which predicts binding of individual TFs in new cell types, integrating learned associations with gene expression and binding, TF binding sites from other cell types, and chromatin accessibility data in the new cell type. This approach outperforms methods that predict TF binding solely based on sequence preference, predicting binding for 36 TFs (MCC>0.3).
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Affiliation(s)
- Mehran Karimzadeh
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.,Princess Margaret Cancer Centre, Toronto, ON, Canada.,Vector Institute, Toronto, ON, Canada
| | - Michael M Hoffman
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada. .,Princess Margaret Cancer Centre, Toronto, ON, Canada. .,Vector Institute, Toronto, ON, Canada. .,Department of Computer Science, University of Toronto, Toronto, ON, Canada.
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19
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Guo MH, Sama P, LaBarre BA, Lokhande H, Balibalos J, Chu C, Du X, Kheradpour P, Kim CC, Oniskey T, Snyder T, Soghoian DZ, Weiner HL, Chitnis T, Patsopoulos NA. Dissection of multiple sclerosis genetics identifies B and CD4+ T cells as driver cell subsets. Genome Biol 2022; 23:127. [PMID: 35672799 PMCID: PMC9175345 DOI: 10.1186/s13059-022-02694-y] [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: 08/23/2021] [Accepted: 05/16/2022] [Indexed: 11/10/2022] Open
Abstract
Background Multiple sclerosis (MS) is an autoimmune condition of the central nervous system with a well-characterized genetic background. Prior analyses of MS genetics have identified broad enrichments across peripheral immune cells, yet the driver immune subsets are unclear. Results We utilize chromatin accessibility data across hematopoietic cells to identify cell type-specific enrichments of MS genetic signals. We find that CD4 T and B cells are independently enriched for MS genetics and further refine the driver subsets to Th17 and memory B cells, respectively. We replicate our findings in data from untreated and treated MS patients and find that immunomodulatory treatments suppress chromatin accessibility at driver cell types. Integration of statistical fine-mapping and chromatin interactions nominate numerous putative causal genes, illustrating complex interplay between shared and cell-specific genes. Conclusions Overall, our study finds that open chromatin regions in CD4 T cells and B cells independently drive MS genetic signals. Our study highlights how careful integration of genetics and epigenetics can provide fine-scale insights into causal cell types and nominate new genes and pathways for disease. Supplementary Information The online version contains supplementary material available at 10.1186/s13059-022-02694-y.
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20
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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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21
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Bossini-Castillo L, Glinos DA, Kunowska N, Golda G, Lamikanra AA, Spitzer M, Soskic B, Cano-Gamez E, Smyth DJ, Cattermole C, Alasoo K, Mann A, Kundu K, Lorenc A, Soranzo N, Dunham I, Roberts DJ, Trynka G. Immune disease variants modulate gene expression in regulatory CD4 + T cells. CELL GENOMICS 2022; 2:None. [PMID: 35591976 PMCID: PMC9010307 DOI: 10.1016/j.xgen.2022.100117] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 11/02/2021] [Accepted: 03/15/2022] [Indexed: 12/30/2022]
Abstract
Identifying cellular functions dysregulated by disease-associated variants could implicate novel pathways for drug targeting or modulation in cell therapies. However, follow-up studies can be challenging if disease-relevant cell types are difficult to sample. Variants associated with immune diseases point toward the role of CD4+ regulatory T cells (Treg cells). We mapped genetic regulation (quantitative trait loci [QTL]) of gene expression and chromatin activity in Treg cells, and we identified 133 colocalizing loci with immune disease variants. Colocalizations of immune disease genome-wide association study (GWAS) variants with expression QTLs (eQTLs) controlling the expression of CD28 and STAT5A, involved in Treg cell activation and interleukin-2 (IL-2) signaling, support the contribution of Treg cells to the pathobiology of immune diseases. Finally, we identified seven known drug targets suitable for drug repurposing and suggested 63 targets with drug tractability evidence among the GWAS signals that colocalized with Treg cell QTLs. Our study is the first in-depth characterization of immune disease variant effects on Treg cell gene expression modulation and dysregulation of Treg cell function.
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Affiliation(s)
| | - Dafni A. Glinos
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- New York Genome Center, New York, NY, USA
| | - Natalia Kunowska
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Gosia Golda
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Abigail A. Lamikanra
- NHS Blood and Transplant, Oxford, UK
- BRC Haematology Theme, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Michaela Spitzer
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
- Open Targets, Wellcome Genome Campus, Cambridge, UK
| | - Blagoje Soskic
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- Open Targets, Wellcome Genome Campus, Cambridge, UK
| | - Eddie Cano-Gamez
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- Open Targets, Wellcome Genome Campus, Cambridge, UK
| | - Deborah J. Smyth
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- Open Targets, Wellcome Genome Campus, Cambridge, UK
| | | | - Kaur Alasoo
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Alice Mann
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Kousik Kundu
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Anna Lorenc
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Nicole Soranzo
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Ian Dunham
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
- Open Targets, Wellcome Genome Campus, Cambridge, UK
| | - David J. Roberts
- NHS Blood and Transplant, Oxford, UK
- BRC Haematology Theme, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Gosia Trynka
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- Open Targets, Wellcome Genome Campus, Cambridge, UK
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22
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Schilder BM, Raj T. Fine-mapping of Parkinson's disease susceptibility loci identifies putative causal variants. Hum Mol Genet 2022; 31:888-900. [PMID: 34617105 PMCID: PMC8947317 DOI: 10.1093/hmg/ddab294] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Revised: 09/05/2021] [Accepted: 09/11/2021] [Indexed: 12/13/2022] Open
Abstract
Recent genome-wide association studies have identified 78 loci associated with Parkinson's disease susceptibility but the underlying mechanisms remain largely unclear. To identify likely causal variants for disease risk, we fine-mapped these Parkinson's-associated loci using four different fine-mapping methods. We then integrated multi-assay cell type-specific epigenomic profiles to pinpoint the likely mechanism of action of each variant, allowing us to identify Consensus single nucleotide polymorphism (SNPs) that disrupt LRRK2 and FCGR2A regulatory elements in microglia, an MBNL2 enhancer in oligodendrocytes, and a DYRK1A enhancer in neurons. This genome-wide functional fine-mapping investigation of Parkinson's disease substantially advances our understanding of the causal mechanisms underlying this complex disease while avoiding focus on spurious, non-causal mechanisms. Together, these results provide a robust, comprehensive list of the likely causal variants, genes and cell-types underlying Parkinson's disease risk as demonstrated by consistently greater enrichment of our fine-mapped SNPs relative to lead GWAS SNPs across independent functional impact annotations. In addition, our approach prioritized an average of 3/85 variants per locus as putatively causal, making downstream experimental studies both more tractable and more likely to yield disease-relevant, actionable results. Large-scale studies comparing individuals with Parkinson's disease to age-matched controls have identified many regions of the genome associated with the disease. However, there is widespread correlation between different parts of the genome, making it difficult to tell which genetic variants cause Parkinson's and which are simply co-inherited with causal variants. We therefore applied a suite of statistical models to identify the most likely causal genetic variants (i.e. fine-mapping). We then linked these genetic variants with epigenomic and gene expression signatures across a wide variety of tissues and cell types to identify how these variants cause disease. Therefore, this study provides a comprehensive and robust list of cellular and molecular mechanisms that may serve as targets in the development of more effective Parkinson's therapeutics.
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Affiliation(s)
- Brian M Schilder
- Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Ronald M. Loeb Center for Alzheimer’s disease, 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
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Towfique Raj
- Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Ronald M. Loeb Center for Alzheimer’s disease, 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
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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23
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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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24
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Schilder BM, Humphrey J, Raj T. echolocatoR: an automated end-to-end statistical and functional genomic fine-mapping pipeline. Bioinformatics 2022; 38:536-539. [PMID: 34529038 PMCID: PMC10060715 DOI: 10.1093/bioinformatics/btab658] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Revised: 09/06/2021] [Accepted: 09/13/2021] [Indexed: 02/03/2023] Open
Abstract
SUMMARY echolocatoR integrates a diverse suite of statistical and functional fine-mapping tools to identify, test enrichment in, and visualize high-confidence causal consensus variants in any phenotype. It requires minimal input from users (a summary statistics file), can be run in a single R function, and provides extensive access to relevant datasets (e.g. reference linkage disequilibrium panels, quantitative trait loci, genome-wide annotations, cell-type-specific epigenomics), thereby enabling rapid, robust and scalable end-to-end fine-mapping investigations. AVAILABILITY AND IMPLEMENTATION echolocatoR is an open-source R package available through GitHub under the GNU General Public License (Version 3) license: https://github.com/RajLabMSSM/echolocatoR. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Brian M Schilder
- Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA
- Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA
| | - Jack Humphrey
- Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA
- Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA
| | - Towfique Raj
- Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA
- Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA
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25
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Hosoya T, Saito T, Baba H, Tanaka N, Noda S, Komiya Y, Tagawa Y, Yamamoto A, Mizoguchi F, Kawahata K, Miyasaka N, Kohsaka H, Yasuda S. Chondroprotective effects of CDK4/6 inhibition via enhanced ubiquitin-dependent degradation of JUN in synovial fibroblasts. Rheumatology (Oxford) 2021; 61:3427-3438. [PMID: 34849618 PMCID: PMC9348617 DOI: 10.1093/rheumatology/keab874] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 11/13/2021] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVE Targeting synovial fibroblasts (SF) using a cyclin-dependent kinase (CDK) 4/6 inhibitor (CDKI) could be a potent therapy for rheumatoid arthritis (RA) via inhibition of proliferation and MMP-3 production. This study was designed to elucidate the mechanism of chondroprotective effects on SFs by CDK 4/6 inhibition. METHODS CDK4/6 activity was inhibited using CDKI treatment or enhanced by adenoviral gene transduction. Chondroprotective effects were evaluated using a collagen induced arthritis model (CIA). Gene and protein expression were evaluated with quantitative PCR, ELISA, and Western blotting. The binding of nuclear extracts to DNA was assessed with an electrophoresis mobility shift assay. RNA-Seq was performed to identify gene sets affected by CDKI treatment. RESULTS CDKI attenuated cartilage destruction and MMP-3 production in CIA. In RASFs, CDKI impaired the binding of AP-1 components to DNA and inhibited the production of MMP-1 and MMP-3, which contain the AP-1 binding sequence in their promoter. CDK4/6 protected JUN from proteasome-dependent degradation by inhibiting ubiquitination. The RNA-Seq analysis identified CDKI-sensitive inflammatory genes, which were associated with the pathway of RA-associated genes, cytokine-cytokine receptor interaction, and IL-17 signalling. Notably, the AP-1 motif was enriched in these genes. CONCLUSION The mechanism of chondroprotective effects by CDK4/6 inhibition was achieved by the attenuation of AP-1 transcriptional activity via the impaired stability of JUN. Since the pharmacologic inhibition of CDK4/6 has been established as tolerable in cancer treatment, it could also be beneficial in patients with RA due to its chondroprotective and anti-inflammatory effects.
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Affiliation(s)
- Tadashi Hosoya
- Department of Rheumatology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
| | - Tetsuya Saito
- Department of Rheumatology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
| | - Hiroyuki Baba
- Department of Rheumatology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
| | - Nao Tanaka
- Department of Rheumatology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
| | - Seiji Noda
- Department of Rheumatology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
| | - Youji Komiya
- Department of Rheumatology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
| | - Yasuhiro Tagawa
- Department of Rheumatology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
| | - Akio Yamamoto
- Department of Rheumatology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
| | - Fumitaka Mizoguchi
- Department of Rheumatology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
| | - Kimito Kawahata
- Department of Rheumatology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
| | - Nobuyuki Miyasaka
- Department of Rheumatology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
| | - Hitoshi Kohsaka
- Department of Rheumatology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
| | - Shinsuke Yasuda
- Department of Rheumatology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
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26
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Wang YF, Wei W, Tangtanatakul P, Zheng L, Lei Y, Lin Z, Qian C, Qin X, Hou F, Zhang X, Shao L, Satproedprai N, Mahasirimongkol S, Pisitkun P, Song Q, Lau YL, Zhang Y, Hirankarn N, Yang W. Multi-ancestral GWAS identifies shared and Asian-specific loci for SLE and links type III interferon signaling and lysosomal function to the disease. Arthritis Rheumatol 2021; 74:840-848. [PMID: 34783190 DOI: 10.1002/art.42021] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 09/21/2021] [Accepted: 11/09/2021] [Indexed: 11/07/2022]
Abstract
OBJECTIVES Systemic lupus erythematosus (SLE) is a prototype autoimmune disease with differences in prevalence and severity among ancestral groups. This study aims to identify novel genetic components either shared or distinct between Asian and European populations. METHODS Both trans-ancestral and ancestry-specific meta-analyses of genome-wide association studies (GWAS) for SLE were performed, involving 30,604 participants of European, Chinese or Thai origin. Using public epigenomic data and expression quantitative trait loci, fine-mapping analyses were conducted to identify putative causal variants and genes for the newly identified loci. Performance of polygenic risk scores (PRS) for the Thai cohort was evaluated comparing different training data. RESULTS We identified ten novel SLE susceptibility loci, four of which were found by Asian-specific meta-analyses. A 1bp deletion upstream of IFNLR1 was found associated with SLE, with the risk allele correlated with increased expression of IFNLR1. This gene encodes interferon lambda receptor 1, pointing to the role of type III interferon signaling in SLE. An intronic variant in SLC29A3 was found associated with SLE only in Asians. The putative risk variant may modulate SLC29A3 expression in a monocyte-specific manner. SLC29A3 encodes a lysosomal nucleoside transporter, and subsequent analyses suggest reduced lysosomal function and phagocytosis might be the mechanism underlying this association. In addition, trans-ancestral meta-analysis was proved to be valuable in risk prediction for individuals without ancestry-matched data. CONCLUSION Multi-ancestral GWAS identified both shared and Asian-specific loci for SLE, and functional annotation pointed to the involvement of increased type III interferon signaling and reduced lysosomal function in SLE.
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Affiliation(s)
- Yong-Fei Wang
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong, China.,Shenzhen Futian hospital for rheumatic diseases, Shenzhen, China
| | - Wei Wei
- Medical Research Center, Affiliated Hospital of Jining Medical University, Shandong, China.,Collaborative Innovation Center for Birth Defect Research and Transformation of Shandong Province, Jining Medical University, Shandong, China
| | - Pattarin Tangtanatakul
- Department of Transfusion Sciences and Clinical Microbiology, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand
| | - Lichuan Zheng
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong, China
| | - Yao Lei
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong, China
| | - Zhiming Lin
- Department of Rheumatology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Chengmin Qian
- School of Biomedical Sciences, The University of Hong Kong, Hong Kong, China
| | - Xiao Qin
- Collaborative Innovation Center for Birth Defect Research and Transformation of Shandong Province, Jining Medical University, Shandong, China
| | - Fei Hou
- Collaborative Innovation Center for Birth Defect Research and Transformation of Shandong Province, Jining Medical University, Shandong, China
| | - Xinyu Zhang
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong, China.,Department of Rheumatology and Lupus Research Institute, The Affiliated Hospital of Jining Medical University, Shandong, China
| | - Li Shao
- Department of Rheumatology and Lupus Research Institute, The Affiliated Hospital of Jining Medical University, Shandong, China
| | - Nusara Satproedprai
- Department of Medical Sciences, Ministry of Public Health, Nonthaburi, Thailand
| | | | - Prapaporn Pisitkun
- Division of Allergy, Immunology, and Rheumatology, Department of Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Qin Song
- Department of Rheumatology and Lupus Research Institute, The Affiliated Hospital of Jining Medical University, Shandong, China
| | - Yu Lung Lau
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong, China
| | - Yan Zhang
- Department of Pediatric Surgery, Guangzhou Institute of Pediatrics, Guangdong Provincial Key Laboratory of Research in Structural Birth Defect Disease, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Nattiya Hirankarn
- Centre of Excellent in Immunology and Immune-Mediated Diseases, Department of Microbiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Wanling Yang
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Hong Kong, China
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27
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Ishigaki K. Beyond GWAS: from simple associations to functional insights. Semin Immunopathol 2021; 44:3-14. [PMID: 34605948 DOI: 10.1007/s00281-021-00894-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 09/08/2021] [Indexed: 12/31/2022]
Abstract
Each human, when born, has slightly different DNA sequences, which make each of us unique. The variations in DNA sequences are called genetic variants. The primary aim of genome-wide association study (GWAS) is to detect associations between genetic variants and human phenotypes. Since GWAS focuses on germ-line variants, there is no reverse causation. Therefore, GWAS is one of the few tools that can assess the causality of human diseases. In the past 10 years, many large-scale GWAS have been conducted. Although the primary outputs of GWAS are just a series of statistics, its downstream analyses provided many insights beyond simple associations: the causal mechanisms for autoimmune diseases and shared etiology between diseases. Moreover, GWAS downstream analyses generated scores potentially helpful in predicting clinical outcomes of each patient. This review focuses on GWAS for autoimmune diseases and introduces significant achievements of its downstream analyses. We also provide future directions that potentially overcome current limitations. We restrict our discussion to common autoimmune diseases (e.g., rheumatoid arthritis) since rare Mendelian diseases possess distinct genetic etiologies and are not tested by GWAS.
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Affiliation(s)
- Kazuyoshi Ishigaki
- Laboratory for Human Immunogenetics, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama City, Kanagawa, 230-0045, Japan.
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28
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Yao Q, Ferragina P, Reshef Y, Lettre G, Bauer DE, Pinello L. Motif-Raptor: a cell type-specific and transcription factor centric approach for post-GWAS prioritization of causal regulators. Bioinformatics 2021; 37:2103-2111. [PMID: 33532840 PMCID: PMC11025460 DOI: 10.1093/bioinformatics/btab072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 11/30/2020] [Accepted: 01/28/2021] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Genome-wide association studies (GWASs) have identified thousands of common trait-associated genetic variants but interpretation of their function remains challenging. These genetic variants can overlap the binding sites of transcription factors (TFs) and therefore could alter gene expression. However, we currently lack a systematic understanding on how this mechanism contributes to phenotype. RESULTS We present Motif-Raptor, a TF-centric computational tool that integrates sequence-based predictive models, chromatin accessibility, gene expression datasets and GWAS summary statistics to systematically investigate how TF function is affected by genetic variants. Given trait-associated non-coding variants, Motif-Raptor can recover relevant cell types and critical TFs to drive hypotheses regarding their mechanism of action. We tested Motif-Raptor on complex traits such as rheumatoid arthritis and red blood cell count and demonstrated its ability to prioritize relevant cell types, potential regulatory TFs and non-coding SNPs which have been previously characterized and validated. AVAILABILITY AND IMPLEMENTATION Motif-Raptor is freely available as a Python package at: https://github.com/pinellolab/MotifRaptor. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Qiuming Yao
- Department of Pathology, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Division of Hematology/Oncology, Boston Children’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Paolo Ferragina
- Department of Computer Science, University of Pisa, Pisa 56128, Italy
| | - Yakir Reshef
- Department of Computer Science, Harvard University, Cambridge, MA 02138, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Guillaume Lettre
- Faculty of Medicine, Université de Montréal, Montreal, Quebec H3C3J7, Canada
- Montreal Heart Institute, Montreal, Quebec H1T1C8, Canada
| | - Daniel E Bauer
- Division of Hematology/Oncology, Boston Children’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Luca Pinello
- Department of Pathology, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Harvard Medical School, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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29
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Baglaenko Y, Macfarlane D, Marson A, Nigrovic PA, Raychaudhuri S. Genome editing to define the function of risk loci and variants in rheumatic disease. Nat Rev Rheumatol 2021; 17:462-474. [PMID: 34188205 PMCID: PMC10782829 DOI: 10.1038/s41584-021-00637-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/20/2021] [Indexed: 02/06/2023]
Abstract
Discoveries in human genetic studies have revolutionized our understanding of complex rheumatic and autoimmune diseases, including the identification of hundreds of genetic loci and single nucleotide polymorphisms that potentially predispose individuals to disease. However, in most cases, the exact disease-causing variants and their mechanisms of action remain unresolved. Functional follow-up of these findings is most challenging for genomic variants that are in non-coding genomic regions, where the large majority of common disease-associated variants are located, and/or that probably affect disease progression via cell type-specific gene regulation. To deliver on the therapeutic promise of human genetic studies, defining the mechanisms of action of these alleles is essential. Genome editing technology, such as CRISPR-Cas, has created a vast toolbox for targeted genetic and epigenetic modifications that presents unprecedented opportunities to decipher disease-causing loci, genes and variants in autoimmunity. In this Review, we discuss the past 5-10 years of progress in resolving the mechanisms underlying rheumatic disease-associated alleles, with an emphasis on how genomic editing techniques can enable targeted dissection and mechanistic studies of causal autoimmune risk variants.
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Affiliation(s)
- Yuriy Baglaenko
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Dana Macfarlane
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Alexander Marson
- Gladstone Institutes, San Francisco, CA, USA
- Department of Medicine, University of California, San Francisco, CA, USA
- Department of Microbiology and Immunology, University of California, San Francisco, CA, USA
- Innovative Genomics Institute, University of California, Berkeley, CA, USA
- Rosalind Russell/Ephraim P. Engleman Rheumatology Research Center, 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
| | - Peter A Nigrovic
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Division of Immunology, Boston Children's Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Faculty of Medical and Human Sciences, University of Manchester, Manchester, UK.
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30
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Zheng A, Lamkin M, Zhao H, Wu C, Su H, Gymrek M. Deep neural networks identify sequence context features predictive of transcription factor binding. NAT MACH INTELL 2021; 3:172-180. [PMID: 33796819 PMCID: PMC8009085 DOI: 10.1038/s42256-020-00282-y] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Accepted: 12/10/2020] [Indexed: 12/11/2022]
Abstract
Transcription factors (TFs) bind DNA by recognizing specific sequence motifs, typically of length 6-12bp. A motif can occur many thousands of times in the human genome, but only a subset of those sites are actually bound. Here we present a machine learning framework leveraging existing convolutional neural network architectures and model interpretation techniques to identify and interpret sequence context features most important for predicting whether a particular motif instance will be bound. We apply our framework to predict binding at motifs for 38 TFs in a lymphoblastoid cell line, score the importance of context sequences at base-pair resolution, and characterize context features most predictive of binding. We find that the choice of training data heavily influences classification accuracy and the relative importance of features such as open chromatin. Overall, our framework enables novel insights into features predictive of TF binding and is likely to inform future deep learning applications to interpret non-coding genetic variants.
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Affiliation(s)
- An Zheng
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA USA
| | - Michael Lamkin
- Department of Bioengineering, University of California San Diego, La Jolla, CA USA
| | - Hanqing Zhao
- Department of Biology, University of California San Diego, La Jolla, CA, USA
| | - Cynthia Wu
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA USA
| | - Hao Su
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA USA
| | - Melissa Gymrek
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA USA
- Department of Medicine, University of California San Diego, La Jolla, CA USA
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31
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Amariuta T, Ishigaki K, Sugishita H, Ohta T, Koido M, Dey KK, Matsuda K, Murakami Y, Price AL, Kawakami E, Terao C, Raychaudhuri S. Improving the trans-ancestry portability of polygenic risk scores by prioritizing variants in predicted cell-type-specific regulatory elements. Nat Genet 2020; 52:1346-1354. [PMID: 33257898 PMCID: PMC8049522 DOI: 10.1038/s41588-020-00740-8] [Citation(s) in RCA: 94] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Accepted: 10/19/2020] [Indexed: 12/15/2022]
Abstract
Poor trans-ancestry portability of polygenic risk scores is a consequence of Eurocentric genetic studies and limited knowledge of shared causal variants. Leveraging regulatory annotations may improve portability by prioritizing functional over tagging variants. We constructed a resource of 707 cell-type-specific IMPACT regulatory annotations by aggregating 5,345 epigenetic datasets to predict binding patterns of 142 transcription factors across 245 cell types. We then partitioned the common SNP heritability of 111 genome-wide association study summary statistics of European (average n ≈ 189,000) and East Asian (average n ≈ 157,000) origin. IMPACT annotations captured consistent SNP heritability between populations, suggesting prioritization of shared functional variants. Variant prioritization using IMPACT resulted in increased trans-ancestry portability of polygenic risk scores from Europeans to East Asians across all 21 phenotypes analyzed (49.9% mean relative increase in R2). Our study identifies a crucial role for functional annotations such as IMPACT to improve the trans-ancestry portability of genetic data.
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Affiliation(s)
- Tiffany Amariuta
- Center for Data Sciences, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, 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
- Graduate School of Arts and Sciences, Harvard University, Cambridge, MA, USA
| | - Kazuyoshi Ishigaki
- Center for Data Sciences, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Hiroki Sugishita
- Laboratory for Developmental Genetics, RIKEN Center for Integrative Medical Sciences (IMS), Kanagawa, Japan
| | - Tazro Ohta
- Medical Sciences Innovation Hub Program, RIKEN, Kanagawa, Japan
- Database Center for Life Science, Joint Support-Center for Data Science Research, Research Organization of Information and Systems, Shizuoka, Japan
| | - Masaru Koido
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
- Division of Molecular Pathology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Kushal K Dey
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Koichi Matsuda
- Laboratory of Genome Technology, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Yoshinori Murakami
- Division of Molecular Pathology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - 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
| | - Eiryo Kawakami
- Medical Sciences Innovation Hub Program, RIKEN, Kanagawa, Japan
- Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
- Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan
- Department of Applied Genetics, The School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Soumya Raychaudhuri
- Center for Data Sciences, Harvard Medical School, Boston, MA, USA.
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, 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, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK.
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32
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Ding J, Frantzeskos A, Orozco G. Functional genomics in autoimmune diseases. Hum Mol Genet 2020; 29:R59-R65. [PMID: 32420598 PMCID: PMC7530520 DOI: 10.1093/hmg/ddaa097] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 05/13/2020] [Accepted: 05/14/2020] [Indexed: 12/11/2022] Open
Abstract
Associations between genetic loci and increased susceptibility to autoimmune disease have been well characterized, however, translating this knowledge into mechanistic insight and patient benefit remains a challenge. While improvements in the precision, completeness and accuracy of our genetic understanding of autoimmune diseases will undoubtedly be helpful, meeting this challenge will require two interlinked problems to be addressed: first which of the highly correlated variants at an individual locus is responsible for increased disease risk, and second what are the downstream effects of this variant. Given that the majority of loci are thought to affect non-coding regulatory elements, the second question is often reframed as what are the target gene(s) and pathways affected by causal variants. Currently, these questions are being addressed using a wide variety of novel techniques and datasets. In many cases, these approaches are complementary and it is likely that the most accurate picture will be generated by consolidating information relating to transcription, regulatory activity, chromatin accessibility, chromatin conformation and readouts from functional experiments, such as genome editing and reporter assays. It is clear that it will be necessary to gather this information from disease relevant cell types and conditions and that by doing so our understanding of disease etiology will be improved. This review is focused on the field of autoimmune disease functional genomics with a particular focus on the most exciting and significant research to be published within the last couple of years.
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Affiliation(s)
- James Ding
- 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, Manchester M13 9LJ, UK
| | - Antonios Frantzeskos
- 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, Manchester M13 9LJ, UK
| | - 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, Manchester M13 9LJ, UK
- NIHR Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9WL, UK
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33
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Amariuta T, Luo Y, Knevel R, Okada Y, Raychaudhuri S. Advances in genetics toward identifying pathogenic cell states of rheumatoid arthritis. Immunol Rev 2019; 294:188-204. [PMID: 31782165 DOI: 10.1111/imr.12827] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 11/07/2019] [Indexed: 12/11/2022]
Abstract
Rheumatoid arthritis (RA) risk has a large genetic component (~60%) that is still not fully understood. This has hampered the design of effective treatments that could promise lifelong remission. RA is a polygenic disease with 106 known genome-wide significant associated loci and thousands of small effect causal variants. Our current understanding of RA risk has suggested cell-type-specific contexts for causal variants, implicating CD4 + effector memory T cells, as well as monocytes, B cells and stromal fibroblasts. While these cellular states and categories are still mechanistically broad, future studies may identify causal cell subpopulations. These efforts are propelled by advances in single cell profiling. Identification of causal cell subpopulations may accelerate therapeutic intervention to achieve lifelong remission.
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Affiliation(s)
- Tiffany Amariuta
- Center for Data Sciences, Harvard Medical School, Boston, MA, USA.,Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, 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.,Graduate School of Arts and Sciences, Harvard University, Boston, MA, USA
| | - Yang Luo
- Center for Data Sciences, Harvard Medical School, Boston, MA, USA.,Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, 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
| | - Rachel Knevel
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Rheumatology, Leiden University Medical Centre, Leiden, The Netherlands
| | - Yukinori Okada
- Division of Medicine, Osaka University, Osaka, Japan.,Osaka University Graduate School of Medicine, Osaka, Japan
| | - Soumya Raychaudhuri
- Center for Data Sciences, Harvard Medical School, Boston, MA, USA.,Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, 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.,Arthritis Research UK Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
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34
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Luo Y, Suliman S, Asgari S, Amariuta T, Baglaenko Y, Martínez-Bonet M, Ishigaki K, Gutierrez-Arcelus M, Calderon R, Lecca L, León SR, Jimenez J, Yataco R, Contreras C, Galea JT, Becerra M, Nejentsev S, Nigrovic PA, Moody DB, Murray MB, Raychaudhuri S. Early progression to active tuberculosis is a highly heritable trait driven by 3q23 in Peruvians. Nat Commun 2019; 10:3765. [PMID: 31434886 PMCID: PMC6704092 DOI: 10.1038/s41467-019-11664-1] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Accepted: 07/24/2019] [Indexed: 12/13/2022] Open
Abstract
Of the 1.8 billion people worldwide infected with Mycobacterium tuberculosis, 5-15% will develop active tuberculosis (TB). Approximately half will progress to active TB within the first 18 months after infection, presumably because they fail to mount an effective initial immune response. Here, in a genome-wide genetic study of early TB progression, we genotype 4002 active TB cases and their household contacts in Peru. We quantify genetic heritability ([Formula: see text]) of early TB progression to be 21.2% (standard error 0.08). This suggests TB progression has a strong genetic basis, and is comparable to traits with well-established genetic bases. We identify a novel association between early TB progression and variants located in a putative enhancer region on chromosome 3q23 (rs73226617, OR = 1.18; P = 3.93 × 10-8). With in silico and in vitro analyses we identify rs73226617 or rs148722713 as the likely functional variant and ATP1B3 as a potential causal target gene with monocyte specific function.
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Affiliation(s)
- Yang Luo
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Sara Suliman
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Samira Asgari
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Tiffany Amariuta
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Graduate School of Arts and Sciences, Harvard University, Cambridge, MA, 02138, USA
| | - Yuriy Baglaenko
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Marta Martínez-Bonet
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kazuyoshi Ishigaki
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Maria Gutierrez-Arcelus
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | | | | | | | | | | | - Jerome T Galea
- School of Social Work, University of South Florida, Tampa, FL, USA
| | - Mercedes Becerra
- Department of Global Health and Social Medicine, and Division of Global Health Equity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Sergey Nejentsev
- Department of Medicine, University of Cambridge, Cambridge, UK
- Department of Molecular Cell Biology and Immunology, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | - Peter A Nigrovic
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Immunology, Boston Children's Hospital, Boston, MA, USA
| | - D Branch Moody
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Megan B Murray
- Department of Global Health and Social Medicine, and Division of Global Health Equity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Soumya Raychaudhuri
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Arthritis Research UK Centre for Genetics and Genomics, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK.
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