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1
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Schuermans A, Vlasschaert C, Nauffal V, Cho SMJ, Uddin MM, Nakao T, Niroula A, Klarqvist MDR, Weeks LD, Lin AE, Saadatagah S, Lannery K, Wong M, Hornsby W, Lubitz SA, Ballantyne C, Jaiswal S, Libby P, Ebert BL, Bick AG, Ellinor PT, Natarajan P, Honigberg MC. Clonal haematopoiesis of indeterminate potential predicts incident cardiac arrhythmias. Eur Heart J 2024; 45:791-805. [PMID: 37952204 PMCID: PMC10919923 DOI: 10.1093/eurheartj/ehad670] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 09/07/2023] [Accepted: 09/26/2023] [Indexed: 11/14/2023] Open
Abstract
BACKGROUND AND AIMS Clonal haematopoiesis of indeterminate potential (CHIP), the age-related expansion of blood cells with preleukemic mutations, is associated with atherosclerotic cardiovascular disease and heart failure. This study aimed to test the association of CHIP with new-onset arrhythmias. METHODS UK Biobank participants without prevalent arrhythmias were included. Co-primary study outcomes were supraventricular arrhythmias, bradyarrhythmias, and ventricular arrhythmias. Secondary outcomes were cardiac arrest, atrial fibrillation, and any arrhythmia. Associations of any CHIP [variant allele fraction (VAF) ≥ 2%], large CHIP (VAF ≥10%), and gene-specific CHIP subtypes with incident arrhythmias were evaluated using multivariable-adjusted Cox regression. Associations of CHIP with myocardial interstitial fibrosis [T1 measured using cardiac magnetic resonance (CMR)] were also tested. RESULTS This study included 410 702 participants [CHIP: n = 13 892 (3.4%); large CHIP: n = 9191 (2.2%)]. Any and large CHIP were associated with multi-variable-adjusted hazard ratios of 1.11 [95% confidence interval (CI) 1.04-1.18; P = .001] and 1.13 (95% CI 1.05-1.22; P = .001) for supraventricular arrhythmias, 1.09 (95% CI 1.01-1.19; P = .031) and 1.13 (95% CI 1.03-1.25; P = .011) for bradyarrhythmias, and 1.16 (95% CI, 1.00-1.34; P = .049) and 1.22 (95% CI 1.03-1.45; P = .021) for ventricular arrhythmias, respectively. Associations were independent of coronary artery disease and heart failure. Associations were also heterogeneous across arrhythmia subtypes and strongest for cardiac arrest. Gene-specific analyses revealed an increased risk of arrhythmias across driver genes other than DNMT3A. Large CHIP was associated with 1.31-fold odds (95% CI 1.07-1.59; P = .009) of being in the top quintile of myocardial fibrosis by CMR. CONCLUSIONS CHIP may represent a novel risk factor for incident arrhythmias, indicating a potential target for modulation towards arrhythmia prevention and treatment.
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Affiliation(s)
- Art Schuermans
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, 75 Ames St., Cambridge, MA 02142, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, 185 Cambridge St., Boston, MA 02114, USA
- Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
| | | | - Victor Nauffal
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, 75 Ames St., Cambridge, MA 02142, USA
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - So Mi Jemma Cho
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, 75 Ames St., Cambridge, MA 02142, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, 185 Cambridge St., Boston, MA 02114, USA
- Integrative Research Center for Cerebrovascular and Cardiovascular Diseases, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Md Mesbah Uddin
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, 75 Ames St., Cambridge, MA 02142, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, 185 Cambridge St., Boston, MA 02114, USA
| | - Tetsushi Nakao
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, 75 Ames St., Cambridge, MA 02142, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, 185 Cambridge St., Boston, MA 02114, USA
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Abhishek Niroula
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, 75 Ames St., Cambridge, MA 02142, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Laboratory Medicine, Lund University, Lund, Sweden
| | | | - Lachelle D Weeks
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Amy E Lin
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Kim Lannery
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, 75 Ames St., Cambridge, MA 02142, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, 185 Cambridge St., Boston, MA 02114, USA
| | - Megan Wong
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, 75 Ames St., Cambridge, MA 02142, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, 185 Cambridge St., Boston, MA 02114, USA
| | - Whitney Hornsby
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, 75 Ames St., Cambridge, MA 02142, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, 185 Cambridge St., Boston, MA 02114, USA
| | - Steven A Lubitz
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, 75 Ames St., Cambridge, MA 02142, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, 185 Cambridge St., Boston, MA 02114, USA
- Department of Medicine, Harvard Medical School, 25 Shattuck St., Boston, MA 02115, USA
| | | | - Siddhartha Jaiswal
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Peter Libby
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Benjamin L Ebert
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medicine, Harvard Medical School, 25 Shattuck St., Boston, MA 02115, USA
- Howard Hughes Medical Institute, Boston, MA, USA
| | - Alexander G Bick
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Patrick T Ellinor
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, 75 Ames St., Cambridge, MA 02142, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, 185 Cambridge St., Boston, MA 02114, USA
- Department of Medicine, Harvard Medical School, 25 Shattuck St., Boston, MA 02115, USA
| | - Pradeep Natarajan
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, 75 Ames St., Cambridge, MA 02142, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, 185 Cambridge St., Boston, MA 02114, USA
- Department of Medicine, Harvard Medical School, 25 Shattuck St., Boston, MA 02115, USA
| | - Michael C Honigberg
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, 75 Ames St., Cambridge, MA 02142, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, 185 Cambridge St., Boston, MA 02114, USA
- Department of Medicine, Harvard Medical School, 25 Shattuck St., Boston, MA 02115, USA
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2
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Nauffal V, Di Achille P, Klarqvist MDR, Cunningham JW, Hill MC, Pirruccello JP, Weng LC, Morrill VN, Choi SH, Khurshid S, Friedman SF, Nekoui M, Roselli C, Ng K, Philippakis AA, Batra P, Ellinor PT, Lubitz SA. Genetics of myocardial interstitial fibrosis in the human heart and association with disease. Nat Genet 2023; 55:777-786. [PMID: 37081215 DOI: 10.1038/s41588-023-01371-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 03/13/2023] [Indexed: 04/22/2023]
Abstract
Myocardial interstitial fibrosis is associated with cardiovascular disease and adverse prognosis. Here, to investigate the biological pathways that underlie fibrosis in the human heart, we developed a machine learning model to measure native myocardial T1 time, a marker of myocardial fibrosis, in 41,505 UK Biobank participants who underwent cardiac magnetic resonance imaging. Greater T1 time was associated with diabetes mellitus, renal disease, aortic stenosis, cardiomyopathy, heart failure, atrial fibrillation, conduction disease and rheumatoid arthritis. Genome-wide association analysis identified 11 independent loci associated with T1 time. The identified loci implicated genes involved in glucose transport (SLC2A12), iron homeostasis (HFE, TMPRSS6), tissue repair (ADAMTSL1, VEGFC), oxidative stress (SOD2), cardiac hypertrophy (MYH7B) and calcium signaling (CAMK2D). Using a transforming growth factor β1-mediated cardiac fibroblast activation assay, we found that 9 of the 11 loci consisted of genes that exhibited temporal changes in expression or open chromatin conformation supporting their biological relevance to myofibroblast cell state acquisition. By harnessing machine learning to perform large-scale quantification of myocardial interstitial fibrosis using cardiac imaging, we validate associations between cardiac fibrosis and disease, and identify new biologically relevant pathways underlying fibrosis.
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Grants
- 1R01HL139731 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- T32HL007604 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- K08HL159346 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- 1R01HL139731 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- K24HL105780 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- 1R01HL092577 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- 5T32HL007208-42 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
- 18SFRN34250007 American Heart Association (American Heart Association, Inc.)
- 18SFRN34110082 American Heart Association (American Heart Association, Inc.)
- 18SFRN34110082 American Heart Association (American Heart Association, Inc.)
- 14CVD01 Fondation Leducq
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Affiliation(s)
- Victor Nauffal
- Cardiovascular Division, Brigham and Women's Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Paolo Di Achille
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Jonathan W Cunningham
- Cardiovascular Division, Brigham and Women's Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Matthew C Hill
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - James P Pirruccello
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiology Division, Massachusetts General Hospital, Boston, MA, USA
- Division of Cardiology, University of California San Francisco, San Francisco, CA, USA
| | - Lu-Chen Weng
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Valerie N Morrill
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Seung Hoan Choi
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Shaan Khurshid
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA
| | - Samuel F Friedman
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Mahan Nekoui
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Carolina Roselli
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Kenney Ng
- Center for Computational Health, IBM Research, Cambridge, MA, USA
| | - Anthony A Philippakis
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Puneet Batra
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Patrick T Ellinor
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA.
| | - Steven A Lubitz
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA.
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Agrawal S, Klarqvist MDR, Diamant N, Stanley TL, Ellinor PT, Mehta NN, Philippakis A, Ng K, Claussnitzer M, Grinspoon SK, Batra P, Khera AV. BMI-adjusted adipose tissue volumes exhibit depot-specific and divergent associations with cardiometabolic diseases. Nat Commun 2023; 14:266. [PMID: 36650173 PMCID: PMC9844175 DOI: 10.1038/s41467-022-35704-5] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Accepted: 12/20/2022] [Indexed: 01/18/2023] Open
Abstract
For any given body mass index (BMI), individuals vary substantially in fat distribution, and this variation may have important implications for cardiometabolic risk. Here, we study disease associations with BMI-independent variation in visceral (VAT), abdominal subcutaneous (ASAT), and gluteofemoral (GFAT) fat depots in 40,032 individuals of the UK Biobank with body MRI. We apply deep learning models based on two-dimensional body MRI projections to enable near-perfect estimation of fat depot volumes (R2 in heldout dataset = 0.978-0.991 for VAT, ASAT, and GFAT). Next, we derive BMI-adjusted metrics for each fat depot (e.g. VAT adjusted for BMI, VATadjBMI) to quantify local adiposity burden. VATadjBMI is associated with increased risk of type 2 diabetes and coronary artery disease, ASATadjBMI is largely neutral, and GFATadjBMI is associated with reduced risk. These results - describing three metabolically distinct fat depots at scale - clarify the cardiometabolic impact of BMI-independent differences in body fat distribution.
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Affiliation(s)
- Saaket Agrawal
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | | | - Nathaniel Diamant
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Takara L Stanley
- Metabolism Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Patrick T Ellinor
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Nehal N Mehta
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Anthony Philippakis
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kenney Ng
- Center for Computational Health, IBM Research, Cambridge, MA, USA
| | - Melina Claussnitzer
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Steven K Grinspoon
- Metabolism Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Puneet Batra
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Amit V Khera
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
- Verve Therapeutics, Cambridge, MA, USA.
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4
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Agrawal S, Wang M, Klarqvist MDR, Smith K, Shin J, Dashti H, Diamant N, Choi SH, Jurgens SJ, Ellinor PT, Philippakis A, Claussnitzer M, Ng K, Udler MS, Batra P, Khera AV. Inherited basis of visceral, abdominal subcutaneous and gluteofemoral fat depots. Nat Commun 2022; 13:3771. [PMID: 35773277 PMCID: PMC9247093 DOI: 10.1038/s41467-022-30931-2] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 05/25/2022] [Indexed: 12/11/2022] Open
Abstract
For any given level of overall adiposity, individuals vary considerably in fat distribution. The inherited basis of fat distribution in the general population is not fully understood. Here, we study up to 38,965 UK Biobank participants with MRI-derived visceral (VAT), abdominal subcutaneous (ASAT), and gluteofemoral (GFAT) adipose tissue volumes. Because these fat depot volumes are highly correlated with BMI, we additionally study six local adiposity traits: VAT adjusted for BMI and height (VATadj), ASATadj, GFATadj, VAT/ASAT, VAT/GFAT, and ASAT/GFAT. We identify 250 independent common variants (39 newly-identified) associated with at least one trait, with many associations more pronounced in female participants. Rare variant association studies extend prior evidence for PDE3B as an important modulator of fat distribution. Local adiposity traits (1) highlight depot-specific genetic architecture and (2) enable construction of depot-specific polygenic scores that have divergent associations with type 2 diabetes and coronary artery disease. These results - using MRI-derived, BMI-independent measures of local adiposity - confirm fat distribution as a highly heritable trait with important implications for cardiometabolic health outcomes.
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Affiliation(s)
- Saaket Agrawal
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Minxian Wang
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | | | - Kirk Smith
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Joseph Shin
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Hesam Dashti
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nathaniel Diamant
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Seung Hoan Choi
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Sean J Jurgens
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Experimental Cardiology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Patrick T Ellinor
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Anthony Philippakis
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Melina Claussnitzer
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Kenney Ng
- Center for Computational Health, IBM Research, Cambridge, MA, USA
| | - Miriam S Udler
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Puneet Batra
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Amit V Khera
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
- Verve Therapeutics, Cambridge, MA, USA.
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5
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Pirruccello JP, Di Achille P, Nauffal V, Nekoui M, Friedman SF, Klarqvist MDR, Chaffin MD, Weng LC, Cunningham JW, Khurshid S, Roselli C, Lin H, Koyama S, Ito K, Kamatani Y, Komuro I, Jurgens SJ, Benjamin EJ, Batra P, Natarajan P, Ng K, Hoffmann U, Lubitz SA, Ho JE, Lindsay ME, Philippakis AA, Ellinor PT. Genetic analysis of right heart structure and function in 40,000 people. Nat Genet 2022; 54:792-803. [PMID: 35697867 PMCID: PMC10313645 DOI: 10.1038/s41588-022-01090-3] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 04/26/2022] [Indexed: 01/29/2023]
Abstract
Congenital heart diseases often involve maldevelopment of the evolutionarily recent right heart chamber. To gain insight into right heart structure and function, we fine-tuned deep learning models to recognize the right atrium, right ventricle and pulmonary artery, measuring right heart structures in 40,000 individuals from the UK Biobank with magnetic resonance imaging. Genome-wide association studies identified 130 distinct loci associated with at least one right heart measurement, of which 72 were not associated with left heart structures. Loci were found near genes previously linked with congenital heart disease, including NKX2-5, TBX5/TBX3, WNT9B and GATA4. A genome-wide polygenic predictor of right ventricular ejection fraction was associated with incident dilated cardiomyopathy (hazard ratio, 1.33 per standard deviation; P = 7.1 × 10-13) and remained significant after accounting for a left ventricular polygenic score. Harnessing deep learning to perform large-scale cardiac phenotyping, our results yield insights into the genetic determinants of right heart structure and function.
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Affiliation(s)
- James P Pirruccello
- Cardiology Division, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Paolo Di Achille
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Victor Nauffal
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Mahan Nekoui
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Samuel F Friedman
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Marcus D R Klarqvist
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Mark D Chaffin
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Lu-Chen Weng
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jonathan W Cunningham
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Shaan Khurshid
- Cardiology Division, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Carolina Roselli
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Honghuang Lin
- Framingham Heart Study, Boston University and National Heart, Lung, and Blood Institute, Framingham, MA, USA
- Division of Clinical Informatics, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Satoshi Koyama
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Laboratory for Cardiovascular Genomics and Informatics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Kaoru Ito
- Laboratory for Cardiovascular Genomics and Informatics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Yoichiro Kamatani
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Issei Komuro
- Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Sean J Jurgens
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Experimental Cardiology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Emelia J Benjamin
- Framingham Heart Study, Boston University and National Heart, Lung, and Blood Institute, Framingham, MA, USA
- Department of Medicine, Cardiology and Preventive Medicine Sections, Boston University School of Medicine, Boston, MA, USA
- Epidemiology Department, Boston University School of Public Health, Boston, MA, USA
| | - Puneet Batra
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Pradeep Natarajan
- Cardiology Division, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
| | | | - Udo Hoffmann
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Steven A Lubitz
- Cardiology Division, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA
| | - Jennifer E Ho
- Harvard Medical School, Boston, MA, USA
- CardioVascular Institute and Division of Cardiology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Mark E Lindsay
- Cardiology Division, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
- Thoracic Aortic Center, Massachusetts General Hospital, Boston, MA, USA
| | | | - Patrick T Ellinor
- Cardiology Division, Massachusetts General Hospital, Boston, MA, USA.
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Harvard Medical School, Boston, MA, USA.
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA.
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Khurshid S, Reeder C, Harrington LX, Singh P, Sarma G, Friedman SF, Di Achille P, Diamant N, Cunningham JW, Turner AC, Lau ES, Haimovich JS, Al-Alusi MA, Wang X, Klarqvist MDR, Ashburner JM, Diedrich C, Ghadessi M, Mielke J, Eilken HM, McElhinney A, Derix A, Atlas SJ, Ellinor PT, Philippakis AA, Anderson CD, Ho JE, Batra P, Lubitz SA. Cohort design and natural language processing to reduce bias in electronic health records research. NPJ Digit Med 2022; 5:47. [PMID: 35396454 PMCID: PMC8993873 DOI: 10.1038/s41746-022-00590-0] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 03/09/2022] [Indexed: 01/04/2023] Open
Abstract
Electronic health record (EHR) datasets are statistically powerful but are subject to ascertainment bias and missingness. Using the Mass General Brigham multi-institutional EHR, we approximated a community-based cohort by sampling patients receiving longitudinal primary care between 2001-2018 (Community Care Cohort Project [C3PO], n = 520,868). We utilized natural language processing (NLP) to recover vital signs from unstructured notes. We assessed the validity of C3PO by deploying established risk models for myocardial infarction/stroke and atrial fibrillation. We then compared C3PO to Convenience Samples including all individuals from the same EHR with complete data, but without a longitudinal primary care requirement. NLP reduced the missingness of vital signs by 31%. NLP-recovered vital signs were highly correlated with values derived from structured fields (Pearson r range 0.95-0.99). Atrial fibrillation and myocardial infarction/stroke incidence were lower and risk models were better calibrated in C3PO as opposed to the Convenience Samples (calibration error range for myocardial infarction/stroke: 0.012-0.030 in C3PO vs. 0.028-0.046 in Convenience Samples; calibration error for atrial fibrillation 0.028 in C3PO vs. 0.036 in Convenience Samples). Sampling patients receiving regular primary care and using NLP to recover missing data may reduce bias and maximize generalizability of EHR research.
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Affiliation(s)
- Shaan Khurshid
- Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Christopher Reeder
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Lia X Harrington
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Pulkit Singh
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Gopal Sarma
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Samuel F Friedman
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Paolo Di Achille
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Nathaniel Diamant
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jonathan W Cunningham
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
- Division of Cardiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Ashby C Turner
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, USA
| | - Emily S Lau
- Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Julian S Haimovich
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Mostafa A Al-Alusi
- Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Xin Wang
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Marcus D R Klarqvist
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jeffrey M Ashburner
- Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Christian Diedrich
- Bayer AG, Research and Development, Pharmaceuticals, Leverkusen, Germany
| | - Mercedeh Ghadessi
- Bayer AG, Research and Development, Pharmaceuticals, Leverkusen, Germany
| | - Johanna Mielke
- Bayer AG, Research and Development, Pharmaceuticals, Leverkusen, Germany
| | - Hanna M Eilken
- Bayer AG, Research and Development, Pharmaceuticals, Leverkusen, Germany
| | - Alice McElhinney
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Andrea Derix
- Bayer AG, Research and Development, Pharmaceuticals, Leverkusen, Germany
| | - Steven J Atlas
- Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Patrick T Ellinor
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA
| | - Anthony A Philippakis
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
- Eric and Wendy Schmidt Center, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Christopher D Anderson
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Jennifer E Ho
- Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Puneet Batra
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Steven A Lubitz
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA.
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA.
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7
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Ribeiro D, Klarqvist MDR, Westermark UK, Oliynyk G, Dzieran J, Kock A, Savatier Banares C, Hertwig F, Johnsen JI, Fischer M, Kogner P, Lovén J, Arsenian Henriksson M. Regulation of Nuclear Hormone Receptors by MYCN-Driven miRNAs Impacts Neural Differentiation and Survival in Neuroblastoma Patients. Cell Rep 2016; 16:979-993. [PMID: 27396325 DOI: 10.1016/j.celrep.2016.06.052] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2015] [Revised: 05/01/2016] [Accepted: 06/12/2016] [Indexed: 01/04/2023] Open
Abstract
MYCN amplification and MYC signaling are associated with high-risk neuroblastoma with poor prognosis. Treating these tumors remains challenging, although therapeutic approaches stimulating differentiation have generated considerable interest. We have previously shown that the MYCN-regulated miR-17∼92 cluster inhibits neuroblastoma differentiation by repressing estrogen receptor alpha. Here, we demonstrate that this microRNA (miRNA) cluster selectively targets several members of the nuclear hormone receptor (NHR) superfamily, and we present a unique NHR signature associated with the survival of neuroblastoma patients. We found that suppressing glucocorticoid receptor (GR) expression in MYCN-driven patient and mouse tumors was associated with an undifferentiated phenotype and decreased survival. Importantly, MYCN inhibition and subsequent reactivation of GR signaling promotes neural differentiation and reduces tumor burden. Our findings reveal a key role for the miR-17∼92-regulated NHRs in neuroblastoma biology, thereby providing a potential differentiation approach for treating neuroblastoma patients.
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Affiliation(s)
- Diogo Ribeiro
- Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, 171 77 Stockholm, Sweden.
| | - Marcus D R Klarqvist
- Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Ulrica K Westermark
- Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Ganna Oliynyk
- Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Johanna Dzieran
- Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Anna Kock
- Childhood Cancer Research Unit, Department of Women's and Children's Health, Karolinska Institutet, 171 76 Stockholm, Sweden
| | - Carolina Savatier Banares
- Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Falk Hertwig
- Department of Pediatric Oncology and Hematology, University Children's Hospital and Center for Molecular Medicine Cologne (CMMC), University of Cologne, 50931 Cologne, Germany
| | - John Inge Johnsen
- Childhood Cancer Research Unit, Department of Women's and Children's Health, Karolinska Institutet, 171 76 Stockholm, Sweden
| | - Matthias Fischer
- Department of Pediatric Oncology and Hematology, University Children's Hospital and Center for Molecular Medicine Cologne (CMMC), University of Cologne, 50931 Cologne, Germany
| | - Per Kogner
- Childhood Cancer Research Unit, Department of Women's and Children's Health, Karolinska Institutet, 171 76 Stockholm, Sweden
| | - Jakob Lovén
- Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, 171 77 Stockholm, Sweden
| | - Marie Arsenian Henriksson
- Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, 171 77 Stockholm, Sweden.
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8
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Rainbow DB, Yang X, Burren O, Pekalski ML, Smyth DJ, Klarqvist MDR, Penkett CJ, Brugger K, Martin H, Todd JA, Wallace C, Wicker LS. Epigenetic analysis of regulatory T cells using multiplex bisulfite sequencing. Eur J Immunol 2015; 45:3200-3. [PMID: 26420295 PMCID: PMC4832369 DOI: 10.1002/eji.201545646] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2015] [Revised: 08/14/2015] [Accepted: 09/01/2015] [Indexed: 11/09/2022]
Affiliation(s)
- Daniel B Rainbow
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge Biomedical Research Campus, Cambridge, UK
| | - Xin Yang
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge Biomedical Research Campus, Cambridge, UK
| | - Oliver Burren
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge Biomedical Research Campus, Cambridge, UK
| | - Marcin L Pekalski
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge Biomedical Research Campus, Cambridge, UK
| | - Deborah J Smyth
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge Biomedical Research Campus, Cambridge, UK
| | - Marcus D R Klarqvist
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge Biomedical Research Campus, Cambridge, UK
| | | | - Kim Brugger
- East Anglian Medical Genetics Service, Molecular Genetics Laboratories, Level 6 Addenbrooke's Treatment Centre, Addenbrooke's Hospital, Cambridge, UK
| | - Howard Martin
- East Anglian Medical Genetics Service, Molecular Genetics Laboratories, Level 6 Addenbrooke's Treatment Centre, Addenbrooke's Hospital, Cambridge, UK
| | - John A Todd
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge Biomedical Research Campus, Cambridge, UK
| | - Chris Wallace
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge Biomedical Research Campus, Cambridge, UK.,MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge, UK
| | - Linda S Wicker
- JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge Biomedical Research Campus, Cambridge, UK
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