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Hematopoietic mosaic chromosomal alterations increase the risk for diverse types of infection

Nature Medicine volume 27, pages 1012–1024 (2021)Cite this article

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Abstract

Age is the dominant risk factor for infectious diseases, but the mechanisms linking age to infectious disease risk are incompletely understood. Age-related mosaic chromosomal alterations (mCAs) detected from genotyping of blood-derived DNA, are structural somatic variants indicative of clonal hematopoiesis, and are associated with aberrant leukocyte cell counts, hematological malignancy, and mortality. Here, we show that mCAs predispose to diverse types of infections. We analyzed mCAs from 768,762 individuals without hematological cancer at the time of DNA acquisition across five biobanks. Expanded autosomal mCAs were associated with diverse incident infections (hazard ratio (HR) 1.25; 95% confidence interval (CI) = 1.15–1.36; P = 1.8 × 10−7), including sepsis (HR 2.68; 95% CI = 2.25–3.19; P = 3.1 × 10−28), pneumonia (HR 1.76; 95% CI = 1.53–2.03; P = 2.3 × 10−15), digestive system infections (HR 1.51; 95% CI = 1.32–1.73; P = 2.2 × 10−9) and genitourinary infections (HR 1.25; 95% CI = 1.11–1.41; P = 3.7 × 10−4). A genome-wide association study of expanded mCAs identified 63 loci, which were enriched at transcriptional regulatory sites for immune cells. These results suggest that mCAs are a marker of impaired immunity and confer increased predisposition to infections.

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Fig. 1: Schematic diagram of the study flow, and distribution of mCAs with age and sex.
Fig. 2: Associations of mCAs with hematologic traits.
Fig. 3: Association of expanded mCAs with incident infections.
Fig. 4: Association of expanded autosomal mCAs and incident infections, stratified by antecedent cancer history.
Fig. 5: Association of expanded mCAs with COVID-19 severity.
Fig. 6: Inherited risk factors for expanded mCAs: GWAS, TWAS and cell-type enrichment.

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Data availability

UKB individual-level data are available by request via application (https://www.ukbiobank.ac.uk). The mCA call set was previously returned to the UKB (return 2062) to enable individual-level linkage to approved UKB applications. Individual-level MGBB data are available from https://personalizedmedicine.partners.org/Biobank/Default.aspx, but restrictions apply to the availability of these data, which were used under institutional review board (IRB) approval for the current study, and so are not publicly available. The BBJ genotype data are available from the Japanese Genotype-phenotype Archive (JGA; http://trace.ddbj.nig.ac.jp/jga/index_e.html) under accession code JGAD00000000123. Individual-level linkage of mosaic events can be provided by the BBJ project upon request (https://biobankjp.org/english/index.html). FinnGen data may be accessed through Finnish Biobanks’ FinnBB portal (www.finbb.fi). Individual-level CUB COVID-19 data, including the mCA call set, are available by application from https://www.ps.columbia.edu/research/core-and-shared-facilities/core-facilities-category/columbia-university-biobank, but consent-related restrictions apply to the availability of these data, and data access requires separate IRB approval for the proposed data use. Aggregate data are also available upon reasonable request. Additionally, the full expanded mCA genome-wide association summary statistics have been uploaded onto the LocusZoom website (https://my.locuszoom.org/gwas/525823/). The present article includes all other data generated or analyzed during this study.

Code availability

A standalone software implementation (MoChA) of the algorithm used to call mCAs is available at https://github.com/freeseek/mocha. A pipeline to execute the whole workflow from raw files all the way to final mCA calls is available in WDL format for the Cromwell execution engine as part of MoChA. Code for all other computations is available upon request from the corresponding authors.

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Acknowledgements

The authors thank C. Whelan, C. Llanwarne, J. Cerrato, K. Vernest and K. Shakir, and many other members of the Terra/Cromwell team, for their help and advice in the development of the MoChA pipeline. The authors also thank P. Danecek for implementing critical features needed in BCFtools, S. Chanock for critical input and comments, E. Loftfield for assistance with the 25-level smoking adjustment variable, and the participants and staff of the UKB, MGBB, and BBJ. The UKB analyses were conducted using applications 7089 and 21552. The authors thank all of the study participants and their families for contributing to the CUB COVID-19 cohort. The genotyping was made possible by the CUB and its COVID-19 Genomics Workgroup members, including A. Califano, W. Chung, C. K. Garcia, D. B. Goldstein, I. Ionita-Laza, K. Kiryluk, R. Mayeux, S. M. O’Byrne, D. Pendrick, M. P. Reilly, S. Sengupta, P. Sims and A.-C. Uhlemann. The authors acknowledge the COVID-19 Host Genetics Initiative consortium for providing infrastructure for collaboration (the members are listed in the Supplementary Information). P.N. is supported by a Hassenfeld Scholar Award from the Massachusetts General Hospital, and grants from the National Heart, Lung, and Blood Institute (R01HL1427, R01HL148565 and R01HL148050). P.N. and B.L.E. are supported by a grant from Fondation Leducq (TNE-18CVD04). S.M.Z. is supported by the NIH National Heart, Lung, and Blood Institute (1F30HL149180-01) and the NIH Medical Scientist Training Program Training Grant (T32GM136651). A.G.B. is supported by a Burroughs Wellcome Fund Career Award for Medical Scientists. G.G. is supported by NIH grant R01 HG006855, NIH grant R01 MH104964, and the Stanley Center for Psychiatric Research. J.P.P. is supported by a John S. LaDue Memorial Fellowship. K.P. is supported by NIH grant 5-T32HL007208-43. P.T.E. is supported by supported grants from the National Institutes of Health (1RO1HL092577, R01HL128914, K24HL105780), the American Heart Association (18SFRN34110082), and by the Foundation Leducq (14CVD01). P.-R.L. is supported by NIH grant DP2 ES030554 and a Burroughs Wellcome Fund Career Award at the Scientific Interfaces. This work was supported by the Intramural Research Program of the National Cancer Institute, National Institutes of Health, extramural grants from the National Heart, Lung, and Blood Institute, and Fondation Leducq. The opinions expressed by the authors are their own and this material should not be interpreted as representing the official viewpoint of the US Department of Health and Human Services, the National Institutes of Health, or the National Cancer Institute. The CUB is supported by the Vagelos College of Physicians and Surgeons as well as the Precision Medicine Resource and Biomedical Informatics Resource of Irving Institute for Clinical and Translational Research, home of the Columbia University’s Clinical and Translational Science Award (CTSA), funded by the National Center for Advancing Translational Sciences, National Institutes of Health, through grant number UL1TR001873.

Author information

Author notes

  1. These authors contributed equally: Seyedeh M. Zekavat, Shu-Hong Lin.

  2. These authors jointly supervised this work: Mitchell J. Machiela, Giulio Genovese, Pradeep Natarajan.

Authors and Affiliations

  1. Computational Biology and Bioinformatics Program, Yale University, New Haven, CT, USA

    Seyedeh M. Zekavat, Yixuan Ye, Zhaolong Yu & Hongyu Zhao

  2. Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA, USA

    Seyedeh M. Zekavat, Alexander G. Bick, Kaavya Paruchuri, Md Mesbah Uddin, James P. Pirruccello, Akhil Pampana, Po-Ru Loh, Robert Green, Patrick T. Ellinor, Benjamin L. Ebert, Andrea Ganna, Giulio Genovese & Pradeep Natarajan

  3. Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA

    Seyedeh M. Zekavat, Kaavya Paruchuri, Md Mesbah Uddin, Romit Bhattacharya, James P. Pirruccello, Akhil Pampana, Patrick T. Ellinor & Pradeep Natarajan

  4. Integrative Tumor Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA

    Shu-Hong Lin, Derek W. Brown & Mitchell J. Machiela

  5. Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

    Alexander G. Bick

  6. Institute for Molecular Medicine Finland, Helsinki, Finland

    Aoxing Liu, Mark J. Daly & Andrea Ganna

  7. Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Kaavya Paruchuri, James P. Pirruccello, Po-Ru Loh, Robert Green, Elizabeth W. Karlson, Patrick T. Ellinor, Scott T. Weiss & Pradeep Natarajan

  8. Department of Biostatistics, Mailman School of Public Health, Columbia University, New York City, NY, USA

    Chen Wang & Iuliana Ionita-Laza

  9. Division of Nephrology, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA

    Chen Wang, Krzysztof Kiryluk, Masahiro Kanai, Mutaamba Maasha & Wei Zhou

  10. Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, RIKEN, Yokohama, Japan

    Xiaoxi Liu, Yoichiro Kamatani & Chikashi Terao

  11. Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Puja Kohli

  12. Vertex Pharmaceuticals, Boston, MA, USA

    Puja Kohli

  13. Stanley Center, Broad Institute of Harvard and MIT, Cambridge, MA, USA

    Steven A. McCarroll, Benjamin Neale, Jordan W. Smoller & Giulio Genovese

  14. Department of Genetics, Harvard Medical School, Boston, MA, USA

    Steven A. McCarroll, Robert Green & Giulio Genovese

  15. Irving Institute for Clinical and Translational Research, Columbia University, New York City, NY, USA

    Krzysztof Kiryluk & Muredach P. Reilly

  16. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA

    Benjamin Neale & Andrea Ganna

  17. Infections and Immunoepidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA

    Eric A. Engels

  18. Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA

    Jordan W. Smoller

  19. Department of Psychiatry, Harvard Medical School, Boston, MA, USA

    Jordan W. Smoller

  20. Division of Rheumatology, Inflammation and Immunity, Brigham and Women’s Hospital, Boston, MA, USA

    Elizabeth W. Karlson

  21. Department of Pathology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Matthew Lebo

  22. Laboratory for Molecular Medicine, Partners Healthcare, Cambridge, MA, USA

    Matthew Lebo

  23. Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, USA

    Scott T. Weiss

  24. Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan

    Chikashi Terao

  25. The Department of Applied Genetics, The School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan

    Chikashi Terao

  26. Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA

    Hongyu Zhao

  27. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Benjamin L. Ebert

  28. Howard Hughes Medical Institute, Boston, MA, USA

    Benjamin L. Ebert

  29. Division of Cardiology, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, USA

    Muredach P. Reilly

  30. Laboratory for Cardiovascular Genomics and Informatics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Satoshi Koyama & Kaoru Ito

  31. Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Yukihide Momozawa

  32. Laboratory of Genome Technology, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan

    Koichi Matsuda

  33. Laboratory of Clinical Genome Sequencing, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan

    Koichi Matsuda

  34. Division of Genetics, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan

    Yuji Yamanashi

  35. Division of Clinical Genome Research, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan

    Yoichi Furukawa

  36. Division of Molecular Pathology IMSUT Hospital, Department of Internal Medicine Project Division of Genomic Medicine and Disease Prevention, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan

    Takayuki Morisaki

  37. Department of Cancer Biology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan

    Yoshinori Murakami

  38. Department of Public Policy, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan

    Kaori Muto & Akiko Nagai

  39. Department of Urology, Iwate Medical University, Iwate, Japan

    Wataru Obara

  40. Department of Internal Medicine and Rheumatology, Juntendo University Graduate School of Medicine, Tokyo, Japan

    Ken Yamaji

  41. Department of Respiratory Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan

    Kazuhisa Takahashi

  42. Division of Pharmacology, Department of Biomedical Science, Nihon University School of Medicine, Tokyo, Japan

    Satoshi Asai

  43. Division of Genomic Epidemiology and Clinical Trials, Clinical Trials Research Center, Nihon University School of Medicine, Tokyo, Japan

    Yasuo Takahashi

  44. Tokushukai Group, Tokyo, Japan

    Takao Suzuki & Nobuaki Sinozaki

  45. Departmentof Hematology, Nippon Medical School, Tokyo, Japan

    Hiroki Yamaguchi

  46. Department of Bioregulation, Nippon Medical School, Kawasaki, Japan

    Shiro Minami

  47. Tokyo Metropolitan Geriatric Hospital and Institute of Gerontology, Tokyo, Japan

    Shigeo Murayama

  48. Japan Anti-Tuberculosis Association, Fukujuji Hospital, Tokyo, Japan

    Kozo Yoshimori

  49. The Cancer Institute Hospital of the Japanese Foundation for Cancer Research, Tokyo, Japan

    Satoshi Nagayama

  50. Center for Clinical Research and Advanced Medicine, Shiga University of Medical Science, Shiga, Japan

    Daisuke Obata

  51. Department of General Thoracic Surgery, Osaka International Cancer Institute, Osaka, Japan

    Masahiko Higashiyama

  52. Iizuka Hospital, Fukuoka, Japan

    Akihide Masumoto

  53. National Hospital Organization Osaka National Hospital, Osaka, Japan

    Yukihiro Koretsune

  54. Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland

    Aarno Palotie, Aki Havulinna, Amanda Elliott, Anastasia Shcherban, Andrea Ganna, Anu Jalanko, Arto Lehisto, Awaisa Ghazal, Elina Kilpeläinen, Elisabeth Widen, Esa Pitkänen, Georg Brein, Hannele Laivuori, Henrike Heyne, Huei-Yi Shen, Jaakko Kaprio, Jiwoo Lee, Joel Rämö, Juha Karjalainen, Juha Mehtonen, Juulia Partanen, Kalle Pärn, Kati Donner, Katja Kivinen, Kristin Tsuo, L. Elisa Lahtela, Liu Aoxing, Mari Kaunisto, Mari E. Niemi, Mark J. Daly, Mary Pat Reeve, Matti Peura, Mervi Aavikko, Mitja Kurki, Nina Mars, Oluwaseun A. Dada, Pietro Della Briotta Parolo, Priit Palta, Risto Kajanne, Samuli Ripatti, Sanni Ruotsalainen, Shabbeer Hassan, Shuang Luo, Sina Rüeger, Susanna Lemmelä, Taru Tukiainen, Tero Jyrhämä, Timo P. Sipilä, Tuomo Kiiskinen, Vincent Llorens & Vishal Sinha

  55. AbbVie, Chicago, IL, USA

    Adam Ziemann, Ali Abbasi, Anne Lehtonen, Apinya Lertratanakul, Bridget Riley-Gillis, Danjuma Quarless, Fedik Rahimov, Graham Heap, Howard Jacob, Jeffrey Waring, Justin Wade, Mengzhen Liu, Nizar Smaoui, Relja Popovic & Sahar Esmaeeli

  56. Biogen, Cambridge, MA, USA

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  57. Merck, Kenilworth, NJ, USA

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  58. Hospital District of Helsinki and Uusimaa, Helsinki, Finland

    Aino Salminen, Daniel Gordin, David Rice, Heikki Joensuu, Ilkka Kalliala, Johanna Mattson, Joni A. Turunen, Juha Sinisalo, Jukka Koskela, Kari Eklund, Katariina Hannula-Jouppi, Lauri Aaltonen, Marja-Riitta Taskinen, Martti Färkkilä, Minna Raivio, Oskari Heikinheimo, Paula Kauppi, Pekka Nieminen, Pentti Tienari, Pirkko Pussinen, Sampsa Pikkarainen, Sanna Seitsonen, Terhi Ollila, Tiinamaija Tuomi, Tuomo Meretoja, Tuula Salo & Ulla Palotie

  59. Pirkanmaa Hospital District, Tampere, Finland

    Airi Jussila, Annika Auranen, Hannele Uusitalo-Järvinen, Hannu Kankaanranta, Hannu Uusitalo, Jukka Peltola, Kari Nieminen, Katri Kaukinen, Laura Kotaniemi-Talonen, Mika Kähönen, Pia Isomäki, Teea Salmi & Venla Kurra

  60. AstraZeneca, Cambridge, UK

    Alex Mackay, Benjamin Challis, David Close, Dirk Paul, Eleonor Wigmore, Glenda Lassi, Ioanna Tachmazidou & Slavé Petrovski

  61. Broad Institute, Cambridge, MA, USA

    Amanda Elliott, Jiwoo Lee, Juha Karjalainen, Kristin Tsuo & Mitja Kurki

  62. Novartis, Basel, Switzerland

    Amy Cole, Aviv Madar, Huilei Xu, Jaison Jacob, Jonas Zierer, Jonathan Chung, Katrina de Lange, Maen Obeidat, Nicole Renaud & Richard Siegel

  63. Pfizer, New York, NY, USA

    Anders Mälarstig, Åsa Hedman, Heli Lehtonen, Jaakko Parkkinen, Kari Linden, Kirsi Kalpala, Melissa Miller, Nan Bing, Russell Miller, Stefan McDonough, Xing Chen, Xinli Hu & Ying Wu

  64. Helsinki Biobank, Helsinki University and Hospital District of Helsinki and Uusimaa, Helsinki, Finland

    Anne Pitkäranta, Anu Loukola, Eero Punkka, Miika Koskinen & Olli Carpén

  65. Northern Ostrobothnia Hospital District, Oulu, Finland

    Anne Remes, Johanna Huhtakangas, Kaisa Tasanen, Kirsi Sipilä, Laura Huilaja, Laure Morin-Papunen, Marja Luodonpää, Marja Vääräsmäki, Nina Hautala, Outi Uimari, Peeter Karihtala, Saila Kauppila, Terttu Harju, Timo Blomster & Vuokko Anttonen

  66. Auria Biobank, University of Turku, Hospital District of Southwest Finland, Turku, Finland

    Antti Hakanen, Johanna Schleutker, Lila Kallio, Nina Pitkänen & Petri Virolainen

  67. Hospital District of Southwest Finland, Turku, Finland

    Antti Palomäki, Eeva Ekholm, Juha Rinne, Kaarin Mäkikallio, Kaj Metsärinne, Klaus Elenius, Laura Pirilä, Leena Koulu, Markku Voutilainen, Markus Juonala, Päivi Polo, Riitta Lahesmaa, Roosa Kallionpää, Sirkku Peltonen, Ulvi Gursoy & Vesa Aaltonen

  68. Biobank of Eastern Finland, University of Eastern Finland, Northern Savo Hospital District, Kuopio, Finland

    Arto Mannermaa & Veli-Matti Kosma

  69. GlaxoSmithKline, Brentford, UK

    Audrey Chu, David Pulford, Diptee Kulkarni, Fanli Xu, Janet Kumar, Jimmy Liu, Joanna Betts, John Eicher, Jorge Esparza Gordillo, Karen S. King, Kirsi Auro, Laura Addis, Linda McCarthy, Meg Ehm, Padhraig Gormley, Rajashree Mishra & Soumitra Ghosh

  70. Maze Therapeutics, San Francisco, CA, USA

    Beryl Cummings, Matt Brauer, Robert Graham & Tim Behrens

  71. Sanofi, Paris, France

    Clarence Wang, Clement Chatelain, Deepak Rajpal, Dongyu Liu, Ethan Xu, Franck Auge, Katherine Call, Katherine Klinger, Marika Crohns, Matthias Gossel, Sauli Vuoti & Tai-He Xia

  72. Genentech, San Francisco, CA, USA

    Danny Oh, David Choy, Diana Chang, Edmond Teng, Erich Strauss, Hao Chen, Hubert Chen, Jennifer Schutzman, Julie Hunkapiller, Mark McCarthy, Natalie Bowers, Sarah Pendergrass, Tim Lu & Tushar Bhangale

  73. Janssen Biotech, Beerse, Belgium

    Dawn Waterworth, Robert Yang & Wilco Fleuren

  74. University of Turku, Turku, Finland

    Dhanaprakash Jambulingam, Samuel Heron & Venkat Subramaniam Rathinakannan

  75. Finnish Clinical Biobank Tampere, University of Tampere, Pirkanmaa Hospital District, Tampere, Finland

    Eeva Kangasniemi, Johanna Mäkelä & Tarja Laitinen

  76. University of Jyväskylä, Jyväskylä, Finland

    Eija Laakkonen

  77. THL Biobank, The National Institute of Health and Welfare Helsinki, Helsinki, Finland

    Elina Järvensivu, Essi Kaiharju, Hannele Mattsson, Kati Kristiansson, Lotta Männikkö, Markku Laukkanen, Markus Perola, Päivi Laiho, Regis Wong, Sami Koskelainen, Sini Lähteenmäki, Sirpa Soini, Teemu Paajanen, Terhi Kilpi, Tero Hiekkalinna & Tuuli Sistonen

  78. Celgene, Summit, NJ, USA

    Elmutaz Mohammed, Erika Kvikstad, Janet van Adelsberg, Joseph Maranville, Keith Usiskin, Marla Hochfeld, Minal Caliskan, Robert Plenge, Samir Wadhawan, Shameek Biswas & Steven Greenberg

  79. Bristol Myers Squibb, New York, NY, USA

    Erika Kvikstad, Janet van Adelsberg, Joseph Maranville, Keith Usiskin, Marla Hochfeld, Minal Caliskan, Robert Plenge, Samir Wadhawan, Shameek Biswas & Steven Greenberg

  80. The National Institute of Health and Welfare Helsinki, Helsinki, Finland

    Felix Vaura, Teemu Niiranen & Veikko Salomaa

  81. University of Tampere, Tampere, Finland

    Harri Siirtola, Iida Vähätalo, Javier Garcia-Tabuenca, Jussi Pihlajamäki, Mika Helminen & Tiina Luukkaala

  82. Northern Savo Hospital District, Kuopio, Finland

    Hilkka Soininen, Kai Kaarniranta, Liisa Suominen, Margit Pelkonen, Maria Siponen, Marjo Tuppurainen, Markku Laakso, Mikko Hiltunen, Mikko Kiviniemi, Oili Kaipiainen-Seppänen, Päivi Auvinen, Päivi Mäntylä, Reetta Kälviäinen & Valtteri Julkunen

  83. Central Finland Biobank, University of Jyväskylä, Central Finland Health Care District, Jyväskylä, Finland

    Jari Laukkanen, Teijo Kuopio & Urho Kujala

  84. Finnish Red Cross Blood Service, Helsinki, Finland

    Jarmo Ritari & Kati Hyvärinen

  85. Northern Finland Biobank Borealis, University of Oulu, Northern Ostrobothnia Hospital District, Oulu, Finland

    Johannes Kettunen, Juhani Junttila, Raisa Serpi, Reetta Hinttala & Tuomo Mantere

  86. Finnish Hematology Registry and Clinical Biobank, Finnish Red Cross Blood Service, Helsinki, Finland

    Jukka Partanen & Mikko Arvas

  87. University of Oulu, Oulu, Finland

    Katri Pylkäs, Marita Kalaoja & Minna Karjalainen

  88. University of Helsinki, Helsinki, Finland

    Kimmo Palin & Niko Välimäki

  89. Finnish Biobank Cooperative, Turku, Finland

    Laura Mustaniemi, Marco Hautalahti, Mirkka Koivusalo, Sarah Smith & Tom Southerington

  90. University of Stanford, Stanford, CA, USA

    Manuel Rivas

  91. Business Finland, Helsinki, Finland

    Outi Tuovila & Raimo Pakkanen

  92. University of Eastern Finland, Kuopio, Finland

    Sami Heikkinen

  93. HiLIFE, University of Helsinki, Finland, Finland

    Tomi Mäkelä

Authors
  1. Seyedeh M. Zekavat
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  2. Shu-Hong Lin
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  3. Alexander G. Bick
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  4. Aoxing Liu
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  5. Kaavya Paruchuri
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  6. Chen Wang
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  7. Md Mesbah Uddin
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  8. Yixuan Ye
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  9. Zhaolong Yu
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  10. Xiaoxi Liu
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  11. Yoichiro Kamatani
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  12. Romit Bhattacharya
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  13. James P. Pirruccello
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  14. Akhil Pampana
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  15. Po-Ru Loh
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  16. Puja Kohli
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  17. Steven A. McCarroll
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  18. Krzysztof Kiryluk
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  19. Benjamin Neale
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  20. Iuliana Ionita-Laza
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  21. Eric A. Engels
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  22. Derek W. Brown
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  23. Jordan W. Smoller
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  24. Robert Green
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  25. Elizabeth W. Karlson
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  26. Matthew Lebo
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  27. Patrick T. Ellinor
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  28. Scott T. Weiss
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  29. Mark J. Daly
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  30. Chikashi Terao
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  31. Hongyu Zhao
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  32. Benjamin L. Ebert
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  33. Muredach P. Reilly
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  34. Andrea Ganna
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  35. Mitchell J. Machiela
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  36. Giulio Genovese
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  37. Pradeep Natarajan
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Consortia

The Biobank Japan Project

  • Satoshi Koyama
  • , Kaoru Ito
  • , Yukihide Momozawa
  • , Koichi Matsuda
  • , Yuji Yamanashi
  • , Yoichi Furukawa
  • , Takayuki Morisaki
  • , Yoshinori Murakami
  • , Kaori Muto
  • , Akiko Nagai
  • , Wataru Obara
  • , Ken Yamaji
  • , Kazuhisa Takahashi
  • , Satoshi Asai
  • , Yasuo Takahashi
  • , Takao Suzuki
  • , Nobuaki Sinozaki
  • , Hiroki Yamaguchi
  • , Shiro Minami
  • , Shigeo Murayama
  • , Kozo Yoshimori
  • , Satoshi Nagayama
  • , Daisuke Obata
  • , Masahiko Higashiyama
  • , Akihide Masumoto
  •  & Yukihiro Koretsune

FinnGen Consortium

  • Aarno Palotie
  • , Adam Ziemann
  • , Adele Mitchell
  • , Adriana Huertas-Vazquez
  • , Aino Salminen
  • , Airi Jussila
  • , Aki Havulinna
  • , Alex Mackay
  • , Ali Abbasi
  • , Amanda Elliott
  • , Amy Cole
  • , Anastasia Shcherban
  • , Anders Mälarstig
  • , Andrea Ganna
  • , Andrey Loboda
  • , Anna Podgornaia
  • , Anne Lehtonen
  • , Anne Pitkäranta
  • , Anne Remes
  • , Annika Auranen
  • , Antti Hakanen
  • , Antti Palomäki
  • , Anu Jalanko
  • , Anu Loukola
  • , Aparna Chhibber
  • , Apinya Lertratanakul
  • , Arto Lehisto
  • , Arto Mannermaa
  • , Åsa Hedman
  • , Audrey Chu
  • , Aviv Madar
  • , Awaisa Ghazal
  • , Benjamin Challis
  • , Benjamin Sun
  • , Beryl Cummings
  • , Bridget Riley-Gillis
  • , Caroline Fox
  • , Chia-Yen Chen
  • , Clarence Wang
  • , Clement Chatelain
  • , Daniel Gordin
  • , Danjuma Quarless
  • , Danny Oh
  • , David Choy
  • , David Close
  • , David Pulford
  • , David Rice
  • , Dawn Waterworth
  • , Deepak Rajpal
  • , Denis Baird
  • , Dhanaprakash Jambulingam
  • , Diana Chang
  • , Diptee Kulkarni
  • , Dirk Paul
  • , Dongyu Liu
  • , Edmond Teng
  • , Eero Punkka
  • , Eeva Ekholm
  • , Eeva Kangasniemi
  • , Eija Laakkonen
  • , Eleonor Wigmore
  • , Elina Järvensivu
  • , Elina Kilpeläinen
  • , Elisabeth Widen
  • , Ellen Tsai
  • , Elmutaz Mohammed
  • , Erich Strauss
  • , Erika Kvikstad
  • , Esa Pitkänen
  • , Essi Kaiharju
  • , Ethan Xu
  • , Fanli Xu
  • , Fedik Rahimov
  • , Felix Vaura
  • , Franck Auge
  • , Georg Brein
  • , Glenda Lassi
  • , Graham Heap
  • , Hannele Laivuori
  • , Hannele Mattsson
  • , Hannele Uusitalo-Järvinen
  • , Hannu Kankaanranta
  • , Hannu Uusitalo
  • , Hao Chen
  • , Harri Siirtola
  • , Heikki Joensuu
  • , Heiko Runz
  • , Heli Lehtonen
  • , Henrike Heyne
  • , Hilkka Soininen
  • , Howard Jacob
  • , Hubert Chen
  • , Huei-Yi Shen
  • , Huilei Xu
  • , Iida Vähätalo
  • , Ilkka Kalliala
  • , Ioanna Tachmazidou
  • , Jaakko Kaprio
  • , Jaakko Parkkinen
  • , Jaison Jacob
  • , Janet Kumar
  • , Janet van Adelsberg
  • , Jari Laukkanen
  • , Jarmo Ritari
  • , Javier Garcia-Tabuenca
  • , Jeffrey Waring
  • , Jennifer Schutzman
  • , Jimmy Liu
  • , Jiwoo Lee
  • , Joanna Betts
  • , Joel Rämö
  • , Johanna Huhtakangas
  • , Johanna Mäkelä
  • , Johanna Mattson
  • , Johanna Schleutker
  • , Johannes Kettunen
  • , John Eicher
  • , Jonas Zierer
  • , Jonathan Chung
  • , Joni A. Turunen
  • , Jorge Esparza Gordillo
  • , Joseph Maranville
  • , Juha Karjalainen
  • , Juha Mehtonen
  • , Juha Rinne
  • , Juha Sinisalo
  • , Juhani Junttila
  • , Jukka Koskela
  • , Jukka Partanen
  • , Jukka Peltola
  • , Julie Hunkapiller
  • , Jussi Pihlajamäki
  • , Justin Wade
  • , Juulia Partanen
  • , Kaarin Mäkikallio
  • , Kai Kaarniranta
  • , Kaisa Tasanen
  • , Kaj Metsärinne
  • , Kalle Pärn
  • , Karen S. King
  • , Kari Eklund
  • , Kari Linden
  • , Kari Nieminen
  • , Katariina Hannula-Jouppi
  • , Katherine Call
  • , Katherine Klinger
  • , Kati Donner
  • , Kati Hyvärinen
  • , Kati Kristiansson
  • , Katja Kivinen
  • , Katri Kaukinen
  • , Katri Pylkäs
  • , Katrina de Lange
  • , Keith Usiskin
  • , Kimmo Palin
  • , Kirill Shkura
  • , Kirsi Auro
  • , Kirsi Kalpala
  • , Kirsi Sipilä
  • , Klaus Elenius
  • , Kristin Tsuo
  • , L. Elisa Lahtela
  • , Laura Addis
  • , Laura Huilaja
  • , Laura Kotaniemi-Talonen
  • , Laura Mustaniemi
  • , Laura Pirilä
  • , Laure Morin-Papunen
  • , Lauri Aaltonen
  • , Leena Koulu
  • , Liisa Suominen
  • , Lila Kallio
  • , Linda McCarthy
  • , Liu Aoxing
  • , Lotta Männikkö
  • , Maen Obeidat
  • , Manuel Rivas
  • , Marco Hautalahti
  • , Margit Pelkonen
  • , Mari Kaunisto
  • , Mari E. Niemi
  • , Maria Siponen
  • , Marika Crohns
  • , Marita Kalaoja
  • , Marja Luodonpää
  • , Marja Vääräsmäki
  • , Marja-Riitta Taskinen
  • , Marjo Tuppurainen
  • , Mark J. Daly
  • , Mark McCarthy
  • , Markku Laakso
  • , Markku Laukkanen
  • , Markku Voutilainen
  • , Markus Juonala
  • , Markus Perola
  • , Marla Hochfeld
  • , Martti Färkkilä
  • , Mary Pat Reeve
  • , Masahiro Kanai
  • , Matt Brauer
  • , Matthias Gossel
  • , Matti Peura
  • , Meg Ehm
  • , Melissa Miller
  • , Mengzhen Liu
  • , Mervi Aavikko
  • , Miika Koskinen
  • , Mika Helminen
  • , Mika Kähönen
  • , Mikko Arvas
  • , Mikko Hiltunen
  • , Mikko Kiviniemi
  • , Minal Caliskan
  • , Minna Karjalainen
  • , Minna Raivio
  • , Mirkka Koivusalo
  • , Mitja Kurki
  • , Mutaamba Maasha
  • , Nan Bing
  • , Natalie Bowers
  • , Neha Raghavan
  • , Nicole Renaud
  • , Niko Välimäki
  • , Nina Hautala
  • , Nina Mars
  • , Nina Pitkänen
  • , Nizar Smaoui
  • , Oili Kaipiainen-Seppänen
  • , Olli Carpén
  • , Oluwaseun A. Dada
  • , Onuralp Soylemez
  • , Oskari Heikinheimo
  • , Outi Tuovila
  • , Outi Uimari
  • , Padhraig Gormley
  • , Päivi Auvinen
  • , Päivi Laiho
  • , Päivi Mäntylä
  • , Päivi Polo
  • , Paola Bronson
  • , Paula Kauppi
  • , Peeter Karihtala
  • , Pekka Nieminen
  • , Pentti Tienari
  • , Petri Virolainen
  • , Pia Isomäki
  • , Pietro Della Briotta Parolo
  • , Pirkko Pussinen
  • , Priit Palta
  • , Raimo Pakkanen
  • , Raisa Serpi
  • , Rajashree Mishra
  • , Reetta Hinttala
  • , Reetta Kälviäinen
  • , Regis Wong
  • , Relja Popovic
  • , Richard Siegel
  • , Riitta Lahesmaa
  • , Risto Kajanne
  • , Robert Graham
  • , Robert Plenge
  • , Robert Yang
  • , Roosa Kallionpää
  • , Ruoyu Tian
  • , Russell Miller
  • , Sahar Esmaeeli
  • , Saila Kauppila
  • , Sally John
  • , Sami Heikkinen
  • , Sami Koskelainen
  • , Samir Wadhawan
  • , Sampsa Pikkarainen
  • , Samuel Heron
  • , Samuli Ripatti
  • , Sanna Seitsonen
  • , Sanni Lahdenperä
  • , Sanni Ruotsalainen
  • , Sarah Pendergrass
  • , Sarah Smith
  • , Sauli Vuoti
  • , Shabbeer Hassan
  • , Shameek Biswas
  • , Shuang Luo
  • , Sina Rüeger
  • , Sini Lähteenmäki
  • , Sirkku Peltonen
  • , Sirpa Soini
  • , Slavé Petrovski
  • , Soumitra Ghosh
  • , Stefan McDonough
  • , Stephanie Loomis
  • , Steven Greenberg
  • , Susan Eaton
  • , Susanna Lemmelä
  • , Tai-He Xia
  • , Tarja Laitinen
  • , Taru Tukiainen
  • , Teea Salmi
  • , Teemu Niiranen
  • , Teemu Paajanen
  • , Teijo Kuopio
  • , Terhi Kilpi
  • , Terhi Ollila
  • , Tero Hiekkalinna
  • , Tero Jyrhämä
  • , Terttu Harju
  • , Tiina Luukkaala
  • , Tiinamaija Tuomi
  • , Tim Behrens
  • , Tim Lu
  • , Timo Blomster
  • , Timo P. Sipilä
  • , Tom Southerington
  • , Tomi Mäkelä
  • , Tuomo Kiiskinen
  • , Tuomo Mantere
  • , Tuomo Meretoja
  • , Tushar Bhangale
  • , Tuula Salo
  • , Tuuli Sistonen
  • , Ulla Palotie
  • , Ulvi Gursoy
  • , Urho Kujala
  • , Valtteri Julkunen
  • , Veikko Salomaa
  • , Veli-Matti Kosma
  • , Venkat Subramaniam Rathinakannan
  • , Venla Kurra
  • , Vesa Aaltonen
  • , Victor Neduva
  • , Vincent Llorens
  • , Vishal Sinha
  • , Vuokko Anttonen
  • , Wei Zhou
  • , Wilco Fleuren
  • , Xing Chen
  • , Xinli Hu
  • , Ying Wu
  •  & Yunfeng Huang

Contributions

S.M.Z., S.-H.L., C.W., M.J.M. and P.N. performed statistical modeling of the UKB, FinnGen and MGB data. C.W. collected and analyzed the CUB data. S.M.Z. carried out the analyses of the GWAS and TWAS. P.-R.L. and G.G. carried out the mCA calls. M.J.M. and P.N. supervised the study. S.M.Z. and S.-H.L. drafted the manuscript. All authors critically reviewed the manuscript.

Corresponding author

Correspondence to Pradeep Natarajan.

Ethics declarations

Competing interests

P.N. reports grants from Amgen during the conduct of the study and grants from Boston Scientific; grants and personal fees from Apple; personal fees from Novartis and Blackstone Life Sciences; and other support from Vertex outside the submitted work. P.T.E. has received grant support from Bayer AG and has served on advisory boards or consulted for Bayer AG, Quest Diagnostics, MyoKardia and Novartis, outside of the present work. S.M.Z., S.-H.L., M.J.M., G.G., and P.N. have filed a patent application (serial no. 63/079,74) on the prediction of infection from mCAs. G.G. and S.A.M. have filed a patent application (PCT/WO2019/079493) for the MoChA mCA detection method used in the present study. All other authors have no competing interests.

Additional information

Peer review information Nature Medicine thanks Alexander Mentzer and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Michael Basson was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 mCA calls by chromosome.

mCA calls by chromosome in the a) MGBB b) FinnGen, and c) CUB. CN-LOH = copy neutral loss of heterozygosity, CUB = Columbia University Biobank, MGBB = Mass-General Brigham Biobank.

Extended Data Fig. 2 Visualization of the diverse range of expanded autosomal mCAs detected across the genome among individuals with a. incident sepsis and b. incident pneumonia in the UKB.

Each point represents one mCA carried by a case, with the x-axis as the chromosome, y-axis as the mCA size in mega-bases of DNA (Mb), color as the copy change, and size of the point as the cell fraction of that mCA. CNN-LOH = copy number neutral loss of heterozygosity, Mb = megabases of DNA, mCA = mosaic chromosomal alterations.

Extended Data Fig. 3 Suggestive associations (P < 0.05) of expanded autosomal mCAs with specific incident infections by Cox proportional-hazards models.

Analyses are adjusted for age, age2, sex, smoking status, and principal components 1-10 of ancestry. Bonferroni correction was used to determine the level of statistical significance (0.05/20 or P < 0.0025). Overall estimates across studies are generated via fixed effect meta-analysis. Error bars show 95% confidence intervals. mCA = mosaic chromosomal alterations.

Extended Data Fig. 4 Associations of a) expanded ChrY and b) expanded ChrX mCAs with incident infections.

Both panels employ Cox proportional-hazards model adjusting for age, age2, sex, smoking status, and principal components 1–10 of ancestry. Error bars show 95% confidence intervals. Bonferroni correction was used to determine the level of statistical significance for each mCA category (P < 0.005). mCA = mosaic chromosomal alterations.

Extended Data Fig. 5

Suggestive associations (P < 0.05) of mCAs with incident infection-related mortality in Biobank Japan Associations of autosomal mCAs with a) organ-system level infections and b) specific infection categories. c) Association of expanded autosomal mCAs with Sepsis. All panels employ Cox proportional-hazards model adjusting for age, age2, sex, smoking status, and principal components 1–10 of ancestry. Error bars show 95% confidence intervals. Bonferroni correction was used to determine the level of statistical significance. Full results are in Supplementary Table 6. Associations are presented among individuals without any cancer history. mCA = mosaic chromosomal alterations.

Extended Data Fig. 6 Incidence rate of at risk population developing each disease (N = 445,101 UKB participants).

95% confidence intervals were calculated based on normal approximation. mCA = mosaic chromosomal alterations.

Extended Data Fig. 7 Associations of expanded mCAs in the UK Biobank with COVID-19 and incident pneumonia.

Associations of expanded mCAs with a. COVID-19 hospitalization across different adjustment models, and b. different COVID-19 phenotypes in fully adjusted logistic regression models. Adjustment models include (1) an unadjusted model, (2) a sparsely adjusted model which adjusts for age, age2, sex, smoking status, and principal components of ancestry, and (3) a fully adjusted model which additionally adjusts for Townsend deprivation index, BMI, and the following comorbidities: Asthma, COPD, CAD, T2D, any cancer, and HTN. Bonferroni correction was used to determine the level of statistical significance. mCA = mosaic chromosomal alterations, COPD = chronic obstructive pulmonary disease, CAD = coronary artery disease, T2D = type 2 diabetes mellitus. c. Association of expanded mCAs with incident pneumonia stratified by sex, adjusted for age, age2, sex (in the All model only), smoking status, and principal components of ancestry. Error bars show 95% confidence intervals. mCA = mosaic chromosomal alterations.

Extended Data Fig. 8 Correlated associations of 63 independent genome-wide significant variants associated with expanded mCAs between different mCA categories in the UKB.

Bonferroni correction was used to determine the level of statistical significance for the correlation analyses (P < 0.05/6 = 0.0083). Across all panels except for panel (a), the labeled genes represent genes attributed to variants that have P < 0.05 across the mCA categories in both axes. mCA = mosaic chromosomal alterations, rp = Pearson correlation.

Extended Data Fig. 9 Association of a mLOY PRS consisting of 156 previously identified20 independent genome-wide significant variants associated with mLOY, with different expanded mCA categories in UKB Females.

Error bands were derived from binomial proportion standard errors. mCA = mosaic chromosomal alterations, mLOY = mosaic Loss-of-chromosome Y, PRS = polygenic risk score.

Extended Data Fig. 10 Pathway enrichment of TWAS results using the Elsevier Pathways.

a. Top results from pathway enrichment analysis of the TWAS results using the Elsevier Pathways. b. Highlighting the GWAS locus-zoom plots for some of the TWAS genes implicated in the top pathways from panel a. Red boxes highlight the gene(s) with strongest association in the TWAS analyses. GWAS = genome-wide association study, TWAS = transcriptome-wide association study.

Supplementary information

Supplementary Information

Supplementary Figs. 1–19 and Supplementary Tables 1–3, 6–12 and 14.

Reporting Summary

41591_2021_1371_MOESM3_ESM.xlsx

Supplementary Table 4: Infection phenotypic grouping categories across organ systems used in the UK Biobank, MGB Biobank, and Biobank Japan. Supplementary Table 5: Infection phenotypic grouping categories across organ systems used in FinnGen (includes ICD-10, -9, and -8 codes as specified). Supplementary Table 13: 63 independent loci identified in the expanded mCA GWAS. Supplementary Table 15: Transcriptome-wide association using GTExv8-whole blood (n = 670). Supplementary Table 16: Gene set pathway enrichment analysis of the transcriptome-wide analyses (from Supplementary Data 4) using the Elsevier Pathway Collection through EnrichR. Supplementary Table 17: Other phenotype definitions used in the UK Biobank COVID-19 sensitivity analyses. Supplementary Table 18: Other phenotype definitions used in MGB Biobank. Supplementary Table 19: Other phenotype definitions used in FinnGen.

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Zekavat, S.M., Lin, SH., Bick, A.G. et al. Hematopoietic mosaic chromosomal alterations increase the risk for diverse types of infection. Nat Med 27, 1012–1024 (2021). https://doi.org/10.1038/s41591-021-01371-0

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