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Rivera NV. Big data in sarcoidosis. Curr Opin Pulm Med 2024; 30:561-569. [PMID: 38967053 PMCID: PMC11309342 DOI: 10.1097/mcp.0000000000001102] [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] [Indexed: 07/06/2024]
Abstract
PURPOSE OF REVIEW This review provides an overview of recent advancements in sarcoidosis research, focusing on collaborative networks, phenotype characterization, and molecular studies. It highlights the importance of collaborative efforts, phenotype characterization, and the integration of multilevel molecular data for advancing sarcoidosis research and paving the way toward personalized medicine. RECENT FINDINGS Sarcoidosis exhibits heterogeneous clinical manifestations influenced by various factors. Efforts to define sarcoidosis endophenotypes show promise, while technological advancements enable extensive molecular data generation. Collaborative networks and biobanks facilitate large-scale studies, enhancing biomarker discovery and therapeutic protocols. SUMMARY Sarcoidosis presents a complex challenge due to its unknown cause and heterogeneous clinical manifestations. Collaborative networks, comprehensive phenotype delineation, and the utilization of cutting-edge technologies are essential for advancing our understanding of sarcoidosis biology and developing personalized medicine approaches. Leveraging large-scale epidemiological resources and biobanks and integrating multilevel molecular data offer promising avenues for unraveling the disease's heterogeneity and improving patient outcomes.
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Affiliation(s)
- Natalia V Rivera
- Division of Respiratory Medicine, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
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2
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Rendel MD, Vitali C, Creasy KT, Zhang D, Scorletti E, Huang H, Seeling KS, Park J, Hehl L, Vell MS, Conlon D, Hayat S, Phillips MC, Schneider KM, Rader DJ, Schneider CV. The common p.Ile291Val variant of ERLIN1 enhances TM6SF2 function and is associated with protection against MASLD. MED 2024; 5:963-980.e5. [PMID: 38776916 DOI: 10.1016/j.medj.2024.04.010] [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: 11/04/2023] [Revised: 02/20/2024] [Accepted: 04/25/2024] [Indexed: 05/25/2024]
Abstract
BACKGROUND The ERLIN1 p.Ile291Val single-nucleotide polymorphism (rs2862954) is associated with protection from steatotic liver disease (SLD), but effects of this variant on metabolic phenotypes remain uncertain. METHODS Metabolic phenotypes and outcomes associated with ERLIN1 p.Ile291Val were analyzed by using a genome-first approach in the UK Biobank (UKB), Penn Medicine BioBank (PMBB), and All of Us cohort. FINDINGS ERLIN1 p.Ile291Val carriers exhibited significantly lower serum levels of alanine aminotransferase and aspartate aminotransferase as well as higher levels of triglycerides, low-density lipoprotein cholesterol, Apolipoprotein B, high-density lipoprotein cholesterol, and Apolipoprotein A1 in UKB, and these values were affected by ERLIN1 p.Ile291Val in an allele-dose-dependent manner. Homozygous ERLIN1 p.Ile291Val carriers had a significantly reduced risk of developing metabolic dysfunction-associated SLD (MASLD, adjusted odds ratio [aOR] = 0.92, 95% confidence interval [CI], 0.88-0.96). The protective effect of this variant was enhanced in patients with alcoholic liver disease. Our results were replicated in PMBB and the All of Us cohort. Strikingly, the protective effects of ERLIN1 p.Ile291Val were not apparent in individuals carrying the TM6SF2 p.Glu167Lys variant associated with increased risk of SLD. We analyzed the effects of predicted loss-of-function ERLIN1 variants and found that they had opposite effects, namely reduced plasma lipids, suggesting that ERLIN1 p.Ile291Val may be a gain-of-function variant. CONCLUSION Our study contributes to a better understanding of ERLIN1 by investigating a coding variant that has emerged as a potential gain-of-function mutation with protective effects against MASLD development.
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Affiliation(s)
- Miriam Daphne Rendel
- Medical Clinic III, Gastroenterology, Metabolic Diseases and Intensive Care, University Hospital RWTH Aachen, 52074 Aachen, Germany
| | - Cecilia Vitali
- Department of Medicine, Division of Translational Medicine and Human Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kate Townsend Creasy
- Department of Medicine, Division of Translational Medicine and Human Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Biobehavioral Health Sciences, School of Nursing, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - David Zhang
- Department of Medicine, Division of Translational Medicine and Human Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Eleonora Scorletti
- The Institute for Translational Medicine and Therapeutics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Helen Huang
- Department of Medicine, Division of Translational Medicine and Human Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Katharina Sophie Seeling
- Medical Clinic III, Gastroenterology, Metabolic Diseases and Intensive Care, University Hospital RWTH Aachen, 52074 Aachen, Germany
| | - Joseph Park
- The Institute for Translational Medicine and Therapeutics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Leonida Hehl
- Medical Clinic III, Gastroenterology, Metabolic Diseases and Intensive Care, University Hospital RWTH Aachen, 52074 Aachen, Germany
| | - Mara Sophie Vell
- Medical Clinic III, Gastroenterology, Metabolic Diseases and Intensive Care, University Hospital RWTH Aachen, 52074 Aachen, Germany
| | - Donna Conlon
- The Institute for Translational Medicine and Therapeutics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sikander Hayat
- Department of Medicine 2, University Hospital RWTH Aachen, 52074 Aachen, Germany
| | - Michael C Phillips
- The Institute for Translational Medicine and Therapeutics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kai Markus Schneider
- Medical Clinic III, Gastroenterology, Metabolic Diseases and Intensive Care, University Hospital RWTH Aachen, 52074 Aachen, Germany; Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Daniel J Rader
- The Institute for Translational Medicine and Therapeutics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Carolin Victoria Schneider
- Medical Clinic III, Gastroenterology, Metabolic Diseases and Intensive Care, University Hospital RWTH Aachen, 52074 Aachen, Germany; The Institute for Translational Medicine and Therapeutics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
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Huang J, Kleman N, Basu S, Shriver MD, Zaidi AA. Interpreting SNP heritability in admixed populations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.08.04.551959. [PMID: 37577588 PMCID: PMC10418213 DOI: 10.1101/2023.08.04.551959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
SNP heritability is defined as the proportion of phenotypic variance explained by genotyped SNPs and is believed to be a lower bound of heritability ( h 2 ), being equal to it if all causal variants are known. Despite the simple intuition behind , its interpretation and equivalence to h 2 is unclear, particularly in the presence of population structure and assortative mating. It is well known that population structure can lead to inflation in estimates because of confounding due to linkage disequilibrium (LD) or shared environment. Here we use analytical theory and simulations to demonstrate that estimates can be biased in admixed populations, even in the absence of confounding and even if all causal variants are known. This is because admixture generates LD, which contributes to the genetic variance, and therefore to heritability. Genome-wide restricted maximum likelihood (GREML) does not capture this contribution leading to under-or over-estimates of relative to h 2 , depending on the genetic architecture. In contrast, Haseman-Elston (HE) regression exaggerates the LD contribution leading to biases in the opposite direction. For the same reason, GREML and HE estimates of local ancestry heritability are also biased. We describe this bias in and as a function of admixture history and the genetic architecture of the trait and show that it can be recovered under some conditions. We clarify the interpretation of in admixed populations and discuss its implication for genome-wide association studies and polygenic prediction.
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Obayemi JE, Callans L, Nair N, Gao H, Gandla D, Loza BL, Gao S, Mohebnasab M, Trofe-Clark J, Jacobson P, Keating B. Assessing the Utility of a Genotype-Guided Tacrolimus Equation in African American Kidney Transplant Recipients: A Single Institution Retrospective Study. J Clin Pharmacol 2024; 64:944-952. [PMID: 38766706 DOI: 10.1002/jcph.2461] [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: 11/21/2023] [Accepted: 02/26/2024] [Indexed: 05/22/2024]
Abstract
Tacrolimus metabolism is heavily influenced by the CYP3A5 genotype, which varies widely among African Americans (AA). We aimed to assess the performance of a published genotype-informed tacrolimus dosing model in an independent set of adult AA kidney transplant (KTx) recipients. CYP3A5 genotypes were obtained for all AA KTx recipients (n = 232) from 2010 to 2019 who met inclusion criteria at a single transplant center in Philadelphia, Pennsylvania, USA. Medical record data were used to calculate predicted tacrolimus clearance using the published AA KTx dosing equation and two modified iterations. Observed and model-predicted trough levels were compared at 3 days, 3 months, and 6 months post-transplant. The mean prediction error at day 3 post-transplant was 3.05 ng/mL, indicating that the model tended to overpredict the tacrolimus trough. This bias improved over time to 1.36 and 0.78 ng/mL at 3 and 6 months post-transplant, respectively. Mean absolute prediction error-a marker of model precision-improved with time to 2.33 ng/mL at 6 months. Limiting genotype data in the model decreased bias and improved precision. The bias and precision of the published model improved over time and were comparable to studies in previous cohorts. The overprediction observed by the published model may represent overfitting to the initial cohort, possibly limiting generalizability.
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Affiliation(s)
- Joy E Obayemi
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Surgery, University of Michigan, Ann Arbor, MI, USA
| | - Lauren Callans
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Transplant Institute, University of Pennsylvania, Philadelphia, PA, USA
| | - Nikhil Nair
- Penn Transplant Institute, University of Pennsylvania, Philadelphia, PA, USA
| | - Hui Gao
- Penn Transplant Institute, University of Pennsylvania, Philadelphia, PA, USA
| | - Divya Gandla
- Penn Transplant Institute, University of Pennsylvania, Philadelphia, PA, USA
| | - Bao-Li Loza
- Penn Transplant Institute, University of Pennsylvania, Philadelphia, PA, USA
| | - Sarah Gao
- Penn Transplant Institute, University of Pennsylvania, Philadelphia, PA, USA
| | - Maedeh Mohebnasab
- Penn Transplant Institute, University of Pennsylvania, Philadelphia, PA, USA
| | - Jennifer Trofe-Clark
- Penn Transplant Institute, University of Pennsylvania, Philadelphia, PA, USA
- Department of Medicine, Renal Electrolyte and Hypertension Division, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Pamala Jacobson
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN, USA
| | - Brendan Keating
- Penn Transplant Institute, University of Pennsylvania, Philadelphia, PA, USA
- Department of Surgery, New York University, New York, NY, USA
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5
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Jasper EA, Hellwege JN, Breeyear JH, Xiao B, Jarvik GP, Stanaway IB, Leppig KA, Chittoor G, Hayes MG, Dikilitas O, Kullo IJ, Holm IA, Verma SS, Edwards TL, Velez Edwards DR. Genetic predictors of blood pressure traits are associated with preeclampsia. Sci Rep 2024; 14:17613. [PMID: 39080328 PMCID: PMC11289248 DOI: 10.1038/s41598-024-68469-6] [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: 11/08/2023] [Accepted: 07/24/2024] [Indexed: 08/02/2024] Open
Abstract
Preeclampsia, a pregnancy complication characterized by hypertension after 20 gestational weeks, is a major cause of maternal and neonatal morbidity and mortality. Mechanisms leading to preeclampsia are unclear; however, there is evidence of high heritability. We evaluated the association of polygenic scores (PGS) for blood pressure traits and preeclampsia to assess whether there is shared genetic architecture. Non-Hispanic Black and White reproductive age females with pregnancy indications and genotypes were obtained from Vanderbilt University's BioVU, Electronic Medical Records and Genomics network, and Penn Medicine Biobank. Preeclampsia was defined by ICD codes. Summary statistics for diastolic blood pressure (DBP), systolic blood pressure (SBP), and pulse pressure (PP) PGS were acquired from Giri et al. Associations between preeclampsia and each PGS were evaluated separately by race and data source before subsequent meta-analysis. Ten-fold cross validation was used for prediction modeling. In 3504 Black and 5009 White included individuals, the rate of preeclampsia was 15.49%. In cross-ancestry meta-analysis, all PGSs were associated with preeclampsia (ORDBP = 1.10, 95% CI 1.02-1.17, p = 7.68 × 10-3; ORSBP = 1.16, 95% CI 1.09-1.23, p = 2.23 × 10-6; ORPP = 1.14, 95% CI 1.07-1.27, p = 9.86 × 10-5). Addition of PGSs to clinical prediction models did not improve predictive performance. Genetic factors contributing to blood pressure regulation in the general population also predispose to preeclampsia.
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Affiliation(s)
- Elizabeth A Jasper
- Division of Quantitative and Clinical Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, 2525 West End Avenue, Suite 600, Rm 616, Nashville, TN, 37203, USA
- Center for Precision Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Epidemiology Center, Vanderbilt University, Nashville, TN, USA
- Institute for Medicine and Public Health, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jacklyn N Hellwege
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Epidemiology Center, Vanderbilt University, Nashville, TN, USA
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Joseph H Breeyear
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA
| | - Brenda Xiao
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Gail P Jarvik
- Departments of Medicine (Medical Genetics) and Genome Sciences, University of Washington Medical Center, Seattle, WA, USA
| | - Ian B Stanaway
- Division of Nephrology and Harborview Medical Center Kidney Research Institute, Department of Medicine, University of Washington Medical Center, Seattle, WA, USA
| | | | - Geetha Chittoor
- Department of Population Health Sciences, Geisinger, Danville, PA, USA
| | - M Geoffrey Hayes
- Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Anthropology, Northwestern University, Evanston, IL, USA
| | - Ozan Dikilitas
- Departments of Internal Medicine, Cardiovascular Medicine, Mayo Clinician-Investigator Training Program, Mayo Clinic, Rochester, MN, USA
| | - Iftikhar J Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Ingrid A Holm
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Shefali Setia Verma
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Todd L Edwards
- Vanderbilt Epidemiology Center, Vanderbilt University, Nashville, TN, USA
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Digna R Velez Edwards
- Division of Quantitative and Clinical Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, 2525 West End Avenue, Suite 600, Rm 616, Nashville, TN, 37203, USA.
- Vanderbilt Epidemiology Center, Vanderbilt University, Nashville, TN, USA.
- Institute for Medicine and Public Health, Vanderbilt University Medical Center, Nashville, TN, USA.
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, USA.
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Abramowitz SA, Boulier K, Keat K, Cardone KM, Shivakumar M, DePaolo J, Judy R, Kim D, Rader DJ, Ritchie, Voight BF, Pasaniuc B, Levin MG, Damrauer SM. Population Performance and Individual Agreement of Coronary Artery Disease Polygenic Risk Scores. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.25.24310931. [PMID: 39108513 PMCID: PMC11302700 DOI: 10.1101/2024.07.25.24310931] [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: 08/12/2024]
Abstract
Importance Polygenic risk scores (PRSs) for coronary artery disease (CAD) are a growing clinical and commercial reality. Whether existing scores provide similar individual-level assessments of disease liability is a critical consideration for clinical implementation that remains uncharacterized. Objective Characterize the reliability of CAD PRSs that perform equivalently at the population level at predicting individual-level risk. Design Cross-sectional Study. Setting All of Us Research Program (AOU), Penn Medicine Biobank (PMBB), and UCLA ATLAS Precision Health Biobank. Participants Volunteers of diverse genetic backgrounds enrolled in AOU, PMBB, and UCLA with available electronic health record and genotyping data. Exposures Polygenic risk for CAD from previously published PRSs and new PRSs developed separately from the testing cohorts. Main Outcomes and Measures Sets of CAD PRSs that perform population prediction equivalently were identified by comparing calibration and discrimination (Brier score and AUROC) of generalized linear models of prevalent CAD using Bayesian analysis of variance. Among equivalently performing scores, individual-level agreement between risk estimates was tested with intraclass correlation (ICC) and Light's Kappa, measures of inter-rater reliability. Results 50 PRSs were calculated for 171,095 AOU participants. When included in a model of prevalent CAD, 48 scores had practically equivalent Brier scores and AUROCs (region of practical equivalence = 0.02). Across these scores, 84% of participants had at least one score in both the top and bottom risk quintile. Continuous agreement of individual risk predictions from the 48 scores was poor, with an ICC of 0.351 (95% CI; 0.349, 0.352). Agreement between two statistically equivalent scores was moderate, with an ICC of 0.649 (95% CI; 0.646, 0.652). Light's Kappa, used to evaluate consistency of assignment to high-risk thresholds, did not exceed 0.56 (interpreted as 'fair') across statistically and practically equivalent scores. Repeating the analysis among 41,193 PMBB and 50,748 UCLA participants yielded different sets of statistically and practically equivalent scores which also lacked strong individual agreement. Conclusions and Relevance Across three diverse biobanks, CAD PRSs that performed equivalently at the population level produced unreliable individual risk estimates. Approaches to clinical implementation of CAD PRSs must consider the potential for discordant individual risk estimates from otherwise indistinguishable scores.
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Affiliation(s)
- Sarah A. Abramowitz
- Department of Surgery, University of Pennsylvania Perelman School of Medicine
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell
| | - Kristin Boulier
- Department of Computational Medicine, University of California, Los Angeles
| | - Karl Keat
- Department of Genetics, University of Pennsylvania Perelman School of Medicine
| | - Katie M. Cardone
- Department of Genetics, University of Pennsylvania Perelman School of Medicine
| | - Manu Shivakumar
- Department of Genetics, University of Pennsylvania Perelman School of Medicine
| | - John DePaolo
- Department of Surgery, University of Pennsylvania Perelman School of Medicine
| | - Renae Judy
- Department of Surgery, University of Pennsylvania Perelman School of Medicine
| | - Dokyoon Kim
- Institute of Biomedical Informatics, University of Pennsylvania
| | - Daniel J. Rader
- Department of Genetics, University of Pennsylvania Perelman School of Medicine
| | - Ritchie
- Department of Genetics, University of Pennsylvania Perelman School of Medicine
| | - Benjamin F. Voight
- Department of Genetics, University of Pennsylvania Perelman School of Medicine
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine
- Institute of Translational Medicine and Therapeutics, University of Pennsylvania Perelman School of Medicine
| | - Bogdan Pasaniuc
- Department of Computational Medicine, University of California, Los Angeles
| | - Michael G. Levin
- Cardiovascular Institute, University of Pennsylvania Perelman School of Medicine
- Corporal Michael J. Crescenz VA Medical Center
- Division of Cardiovascular Medicine, University of Pennsylvania Perelman School of Medicine
| | - Scott M. Damrauer
- Department of Surgery, University of Pennsylvania Perelman School of Medicine
- Department of Genetics, University of Pennsylvania Perelman School of Medicine
- Cardiovascular Institute, University of Pennsylvania Perelman School of Medicine
- Corporal Michael J. Crescenz VA Medical Center
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7
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Vu PT, Chahine C, Chatterjee N, MacLean MT, Swago S, Bhattaru A, Thompson EW, Ikhlas A, Oteng E, Davidson L, Tran R, Hazim M, Raghupathy P, Verma A, Duda J, Gee J, Luks V, Gershuni V, Wu G, Rader D, Sagreiya H, Witschey WR. CT imaging-derived phenotypes for abdominal muscle and their association with age and sex in a medical biobank. Sci Rep 2024; 14:14807. [PMID: 38926479 PMCID: PMC11208425 DOI: 10.1038/s41598-024-64603-6] [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: 05/10/2023] [Accepted: 06/11/2024] [Indexed: 06/28/2024] Open
Abstract
The study of muscle mass as an imaging-derived phenotype (IDP) may yield new insights into determining the normal and pathologic variations in muscle mass in the population. This can be done by determining 3D abdominal muscle mass from 12 distinct abdominal muscle regions and groups using computed tomography (CT) in a racially diverse medical biobank. To develop a fully automatic technique for assessment of CT abdominal muscle IDPs and preliminarily determine abdominal muscle IDP variations with age and sex in a clinically and racially diverse medical biobank. This retrospective study was conducted using the Penn Medicine BioBank (PMBB), a research protocol that recruits adult participants during outpatient visits at hospitals in the Penn Medicine network. We developed a deep residual U-Net (ResUNet) to segment 12 abdominal muscle groups including the left and right psoas, quadratus lumborum, erector spinae, gluteus medius, rectus abdominis, and lateral abdominals. 110 CT studies were randomly selected for training, validation, and testing. 44 of the 110 CT studies were selected to enrich the dataset with representative cases of intra-abdominal and abdominal wall pathology. The studies were divided into non-overlapping training, validation and testing sets. Model performance was evaluated using the Sørensen-Dice coefficient. Volumes of individual muscle groups were plotted to distribution curves. To investigate associations between muscle IDPs, age, and sex, deep learning model segmentations were performed on a larger abdominal CT dataset from PMBB consisting of 295 studies. Multivariable models were used to determine relationships between muscle mass, age and sex. The model's performance (Dice scores) on the test data was the following: psoas: 0.85 ± 0.12, quadratus lumborum: 0.72 ± 0.14, erector spinae: 0.92 ± 0.07, gluteus medius: 0.90 ± 0.08, rectus abdominis: 0.85 ± 0.08, lateral abdominals: 0.85 ± 0.09. The average Dice score across all muscle groups was 0.86 ± 0.11. Average total muscle mass for females was 2041 ± 560.7 g with a high of 2256 ± 560.1 g (41-50 year old cohort) and a change of - 0.96 g/year, declining to an average mass of 1579 ± 408.8 g (81-100 year old cohort). Average total muscle mass for males was 3086 ± 769.1 g with a high of 3385 ± 819.3 g (51-60 year old cohort) and a change of - 1.73 g/year, declining to an average mass of 2629 ± 536.7 g (81-100 year old cohort). Quadratus lumborum was most highly correlated with age for both sexes (correlation coefficient of - 0.5). Gluteus medius mass in females was positively correlated with age with a coefficient of 0.22. These preliminary findings show that our CNN can automate detailed abdominal muscle volume measurement. Unlike prior efforts, this technique provides 3D muscle segmentations of individual muscles. This technique will dramatically impact sarcopenia diagnosis and research, elucidating its clinical and public health implications. Our results suggest a peak age range for muscle mass and an expected rate of decline, both of which vary between genders. Future goals are to investigate genetic variants for sarcopenia and malnutrition, while describing genotype-phenotype associations of muscle mass in healthy humans using imaging-derived phenotypes. It is feasible to obtain 3D abdominal muscle IDPs with high accuracy from patients in a medical biobank using fully automated machine learning methods. Abdominal muscle IDPs showed significant variations in lean mass by age and sex. In the future, this tool can be leveraged to perform a genome-wide association study across the medical biobank and determine genetic variants associated with early or accelerated muscle wasting.
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Affiliation(s)
- Phuong T Vu
- Department of Radiology, Perelman School of Medicine, Perelman Center for Advanced Medicine, University of Pennsylvania, 3400 Civic Center Boulevard, Philadelphia, PA, 19104, USA
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Chantal Chahine
- Department of Radiology, Perelman School of Medicine, Perelman Center for Advanced Medicine, University of Pennsylvania, 3400 Civic Center Boulevard, Philadelphia, PA, 19104, USA
| | - Neil Chatterjee
- Department of Radiology, Perelman School of Medicine, Perelman Center for Advanced Medicine, University of Pennsylvania, 3400 Civic Center Boulevard, Philadelphia, PA, 19104, USA
| | - Matthew T MacLean
- Department of Radiology, Perelman School of Medicine, Perelman Center for Advanced Medicine, University of Pennsylvania, 3400 Civic Center Boulevard, Philadelphia, PA, 19104, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sophia Swago
- Department of Radiology, Perelman School of Medicine, Perelman Center for Advanced Medicine, University of Pennsylvania, 3400 Civic Center Boulevard, Philadelphia, PA, 19104, USA
| | - Abhi Bhattaru
- Department of Radiology, Perelman School of Medicine, Perelman Center for Advanced Medicine, University of Pennsylvania, 3400 Civic Center Boulevard, Philadelphia, PA, 19104, USA
| | - Elizabeth W Thompson
- Department of Radiology, Perelman School of Medicine, Perelman Center for Advanced Medicine, University of Pennsylvania, 3400 Civic Center Boulevard, Philadelphia, PA, 19104, USA
| | - Anooshey Ikhlas
- Department of Radiology, Perelman School of Medicine, Perelman Center for Advanced Medicine, University of Pennsylvania, 3400 Civic Center Boulevard, Philadelphia, PA, 19104, USA
| | - Edith Oteng
- Department of Radiology, Perelman School of Medicine, Perelman Center for Advanced Medicine, University of Pennsylvania, 3400 Civic Center Boulevard, Philadelphia, PA, 19104, USA
| | - Lauren Davidson
- Department of Radiology, Perelman School of Medicine, Perelman Center for Advanced Medicine, University of Pennsylvania, 3400 Civic Center Boulevard, Philadelphia, PA, 19104, USA
| | - Richard Tran
- Department of Radiology, Perelman School of Medicine, Perelman Center for Advanced Medicine, University of Pennsylvania, 3400 Civic Center Boulevard, Philadelphia, PA, 19104, USA
| | - Mohamad Hazim
- Department of Radiology, Perelman School of Medicine, Perelman Center for Advanced Medicine, University of Pennsylvania, 3400 Civic Center Boulevard, Philadelphia, PA, 19104, USA
| | - Pavan Raghupathy
- Department of Radiology, Perelman School of Medicine, Perelman Center for Advanced Medicine, University of Pennsylvania, 3400 Civic Center Boulevard, Philadelphia, PA, 19104, USA
| | - Anurag Verma
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jeffrey Duda
- Department of Radiology, Perelman School of Medicine, Perelman Center for Advanced Medicine, University of Pennsylvania, 3400 Civic Center Boulevard, Philadelphia, PA, 19104, USA
| | - James Gee
- Department of Radiology, Perelman School of Medicine, Perelman Center for Advanced Medicine, University of Pennsylvania, 3400 Civic Center Boulevard, Philadelphia, PA, 19104, USA
| | - Valerie Luks
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Victoria Gershuni
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Gary Wu
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Daniel Rader
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Hersh Sagreiya
- Department of Radiology, Perelman School of Medicine, Perelman Center for Advanced Medicine, University of Pennsylvania, 3400 Civic Center Boulevard, Philadelphia, PA, 19104, USA
| | - Walter R Witschey
- Department of Radiology, Perelman School of Medicine, Perelman Center for Advanced Medicine, University of Pennsylvania, 3400 Civic Center Boulevard, Philadelphia, PA, 19104, USA.
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Newby D, Taylor N, Joyce DW, Winchester LM. Optimising the use of electronic medical records for large scale research in psychiatry. Transl Psychiatry 2024; 14:232. [PMID: 38824136 PMCID: PMC11144247 DOI: 10.1038/s41398-024-02911-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 04/13/2024] [Accepted: 04/15/2024] [Indexed: 06/03/2024] Open
Abstract
The explosion and abundance of digital data could facilitate large-scale research for psychiatry and mental health. Research using so-called "real world data"-such as electronic medical/health records-can be resource-efficient, facilitate rapid hypothesis generation and testing, complement existing evidence (e.g. from trials and evidence-synthesis) and may enable a route to translate evidence into clinically effective, outcomes-driven care for patient populations that may be under-represented. However, the interpretation and processing of real-world data sources is complex because the clinically important 'signal' is often contained in both structured and unstructured (narrative or "free-text") data. Techniques for extracting meaningful information (signal) from unstructured text exist and have advanced the re-use of routinely collected clinical data, but these techniques require cautious evaluation. In this paper, we survey the opportunities, risks and progress made in the use of electronic medical record (real-world) data for psychiatric research.
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Affiliation(s)
- Danielle Newby
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Centre for Statistics in Medicine, University of Oxford, Oxford, UK
| | - Niall Taylor
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Dan W Joyce
- Department of Primary Care and Mental Health and Civic Health, Innovation Labs, Institute of Population Health, University of Liverpool, Liverpool, UK
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9
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Toikumo S, Jennings MV, Pham BK, Lee H, Mallard TT, Bianchi SB, Meredith JJ, Vilar-Ribó L, Xu H, Hatoum AS, Johnson EC, Pazdernik VK, Jinwala Z, Pakala SR, Leger BS, Niarchou M, Ehinmowo M, Jenkins GD, Batzler A, Pendegraft R, Palmer AA, Zhou H, Biernacka JM, Coombes BJ, Gelernter J, Xu K, Hancock DB, Cox NJ, Smoller JW, Davis LK, Justice AC, Kranzler HR, Kember RL, Sanchez-Roige S. Multi-ancestry meta-analysis of tobacco use disorder identifies 461 potential risk genes and reveals associations with multiple health outcomes. Nat Hum Behav 2024; 8:1177-1193. [PMID: 38632388 PMCID: PMC11199106 DOI: 10.1038/s41562-024-01851-6] [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] [Received: 03/24/2023] [Accepted: 02/21/2024] [Indexed: 04/19/2024]
Abstract
Tobacco use disorder (TUD) is the most prevalent substance use disorder in the world. Genetic factors influence smoking behaviours and although strides have been made using genome-wide association studies to identify risk variants, most variants identified have been for nicotine consumption, rather than TUD. Here we leveraged four US biobanks to perform a multi-ancestral meta-analysis of TUD (derived via electronic health records) in 653,790 individuals (495,005 European, 114,420 African American and 44,365 Latin American) and data from UK Biobank (ncombined = 898,680). We identified 88 independent risk loci; integration with functional genomic tools uncovered 461 potential risk genes, primarily expressed in the brain. TUD was genetically correlated with smoking and psychiatric traits from traditionally ascertained cohorts, externalizing behaviours in children and hundreds of medical outcomes, including HIV infection, heart disease and pain. This work furthers our biological understanding of TUD and establishes electronic health records as a source of phenotypic information for studying the genetics of TUD.
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Affiliation(s)
- Sylvanus Toikumo
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Mariela V Jennings
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Benjamin K Pham
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Hyunjoon Lee
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Travis T Mallard
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA, USA
| | - Sevim B Bianchi
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - John J Meredith
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Laura Vilar-Ribó
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Heng Xu
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Alexander S Hatoum
- Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Emma C Johnson
- Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | | | - Zeal Jinwala
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Shreya R Pakala
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Brittany S Leger
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Program in Biomedical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Maria Niarchou
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, USA
| | | | - Greg D Jenkins
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Anthony Batzler
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Richard Pendegraft
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Abraham A Palmer
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Hang Zhou
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Joanna M Biernacka
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Brandon J Coombes
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Joel Gelernter
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Ke Xu
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | | | - Nancy J Cox
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA, USA
| | - Lea K Davis
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Amy C Justice
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Yale University School of Public Health, New Haven, CT, USA
- Yale University School of Medicine, New Haven, CT, USA
| | - Henry R Kranzler
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Rachel L Kember
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Sandra Sanchez-Roige
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, USA.
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA.
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10
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Rodriguez A, Kim Y, Nandi TN, Keat K, Kumar R, Bhukar R, Conery M, Liu M, Hessington J, Maheshwari K, Schmidt D, Begoli E, Tourassi G, Muralidhar S, Natarajan P, Voight BF, Cho K, Gaziano JM, Damrauer SM, Liao KP, Zhou W, Huffman JE, Verma A, Madduri RK. Accelerating Genome- and Phenome-Wide Association Studies using GPUs - A case study using data from the Million Veteran Program. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.17.594583. [PMID: 38826407 PMCID: PMC11142062 DOI: 10.1101/2024.05.17.594583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
The expansion of biobanks has significantly propelled genomic discoveries yet the sheer scale of data within these repositories poses formidable computational hurdles, particularly in handling extensive matrix operations required by prevailing statistical frameworks. In this work, we introduce computational optimizations to the SAIGE (Scalable and Accurate Implementation of Generalized Mixed Model) algorithm, notably employing a GPU-based distributed computing approach to tackle these challenges. We applied these optimizations to conduct a large-scale genome-wide association study (GWAS) across 2,068 phenotypes derived from electronic health records of 635,969 diverse participants from the Veterans Affairs (VA) Million Veteran Program (MVP). Our strategies enabled scaling up the analysis to over 6,000 nodes on the Department of Energy (DOE) Oak Ridge Leadership Computing Facility (OLCF) Summit High-Performance Computer (HPC), resulting in a 20-fold acceleration compared to the baseline model. We also provide a Docker container with our optimizations that was successfully used on multiple cloud infrastructures on UK Biobank and All of Us datasets where we showed significant time and cost benefits over the baseline SAIGE model.
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Affiliation(s)
- Alex Rodriguez
- Data Science and Learning, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Youngdae Kim
- Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Tarak Nath Nandi
- Data Science and Learning, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Karl Keat
- Institute for Biomedical Informatics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Rachit Kumar
- Institute for Biomedical Informatics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Rohan Bhukar
- Program in Medical and Population Genetics, Cambridge, MA, 02142, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Mitchell Conery
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Molei Liu
- Department of Biostatistics, Columbia University's Mailman School of Public Health, New York, NY, 10032, USA
| | - John Hessington
- Information systems, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | | | - Drew Schmidt
- Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Edmon Begoli
- Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Georgia Tourassi
- Computing and Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA
| | - Sumitra Muralidhar
- Office of Research and Development, Department of Veterans Affairs, Washington, DC, 20420, USA
| | - Pradeep Natarajan
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, 02114, USA
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cardiology Division, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Benjamin F Voight
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, 19104, USA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Department of Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Institute of Translational Medicine and Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Kelly Cho
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA, 02111, USA
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
- Department of Medicine, Division of Aging, Brigham and Women's Hospital, Boston, MA, 02115, USA
| | - J Michael Gaziano
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA, 02111, USA
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
- Department of Medicine, Division of Aging, Brigham and Women's Hospital, Boston, MA, 02115, USA
| | - Scott M Damrauer
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, 19104, USA
- Department of Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Department of Surgery, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Cardiovascular Institute, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Katherine P Liao
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, 02130, USA
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA
- Medicine, Rheumatology, VA Boston Healthcare System, Boston, MA, 02130, USA
- Department of Medicine, Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Boston, MA, 02115, USA
| | - Wei Zhou
- Program in Medical and Population Genetics, Cambridge, MA, 02142, USA
- Department of Medicine, Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, 02114, USA
- Stanley Center for Psychiatric Research, Cambridge, MA, 02142, USA
| | - Jennifer E Huffman
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, 02130, USA
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
- Palo Alto Veterans Institute for Research (PAVIR), Palo Alto Health Care System, Palo Alto, CA, 94304, USA
| | - Anurag Verma
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, 19104, USA
- Institute for Biomedical Informatics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Department of Medicine, Division of Translational Medicine and Human Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Ravi K Madduri
- Data Science and Learning, Argonne National Laboratory, Lemont, IL, 60439, USA
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11
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Beeche C, Dib MJ, Zhao B, Azzo JD, Maynard H, Duda J, Gee J, Salman O, Witschey WR, Chirinos JA. Three-dimensional aortic geometry: clinical correlates, prognostic value and genetic architecture. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.09.593413. [PMID: 38798566 PMCID: PMC11118285 DOI: 10.1101/2024.05.09.593413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Aortic structure and function impact cardiovascular health through multiple mechanisms. Aortic structural degeneration increases left ventricular afterload, pulse pressure and promotes target organ damage. Despite the impact of aortic structure on cardiovascular health, aortic 3D-geometry has yet to be comprehensively assessed. Using a convolutional neural network (U-Net) combined with morphological operations, we quantified aortic 3D-geometric phenotypes (AGPs) from 53,612 participants in the UK Biobank and 8,066 participants in the Penn Medicine Biobank. AGPs reflective of structural aortic degeneration, characterized by arch unfolding, descending aortic lengthening and luminal dilation exhibited cross-sectional associations with hypertension and cardiac diseases, and were predictive for new-onset hypertension, heart failure, cardiomyopathy, and atrial fibrillation. We identified 237 novel genetic loci associated with 3D-AGPs. Fibrillin-2 gene polymorphisms were identified as key determinants of aortic arch-3D structure. Mendelian randomization identified putative causal effects of aortic geometry on the risk of chronic kidney disease and stroke.
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12
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Khan Y, Davis CN, Jinwala Z, Feuer KL, Toikumo S, Hartwell EE, Sanchez-Roige S, Peterson RE, Hatoum AS, Kranzler HR, Kember RL. Combining Transdiagnostic and Disorder-Level GWAS Enhances Precision of Psychiatric Genetic Risk Profiles in a Multi-Ancestry Sample. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.09.24307111. [PMID: 38766259 PMCID: PMC11100926 DOI: 10.1101/2024.05.09.24307111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
The etiology of substance use disorders (SUDs) and psychiatric disorders reflects a combination of both transdiagnostic (i.e., common) and disorder-level (i.e., independent) genetic risk factors. We applied genomic structural equation modeling to examine these genetic factors across SUDs, psychotic, mood, and anxiety disorders using genome-wide association studies (GWAS) of European- (EUR) and African-ancestry (AFR) individuals. In EUR individuals, transdiagnostic genetic factors represented SUDs (143 lead single nucleotide polymorphisms [SNPs]), psychotic (162 lead SNPs), and mood/anxiety disorders (112 lead SNPs). We identified two novel SNPs for mood/anxiety disorders that have probable regulatory roles on FOXP1, NECTIN3, and BTLA genes. In AFR individuals, genetic factors represented SUDs (1 lead SNP) and psychiatric disorders (no significant SNPs). The SUD factor lead SNP, although previously significant in EUR- and cross-ancestry GWAS, is a novel finding in AFR individuals. Shared genetic variance accounted for overlap between SUDs and their psychiatric comorbidities, with second-order GWAS identifying up to 12 SNPs not significantly associated with either first-order factor in EUR individuals. Finally, common and independent genetic effects showed different associations with psychiatric, sociodemographic, and medical phenotypes. For example, the independent components of schizophrenia and bipolar disorder had distinct associations with affective and risk-taking behaviors, and phenome-wide association studies identified medical conditions associated with tobacco use disorder independent of the broader SUDs factor. Thus, combining transdiagnostic and disorder-level genetic approaches can improve our understanding of co-occurring conditions and increase the specificity of genetic discovery, which is critical for psychiatric disorders that demonstrate considerable symptom and etiological overlap.
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Affiliation(s)
- Yousef Khan
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
| | - Christal N. Davis
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA 19104
| | - Zeal Jinwala
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
| | - Kyra L. Feuer
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
| | - Sylvanus Toikumo
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA 19104
| | - Emily E. Hartwell
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA 19104
| | - Sandra Sanchez-Roige
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, United States
- Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN 37235, United States
- Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Roseann E. Peterson
- Institute for Department of Psychiatry and Behavioral Sciences, Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, United States
| | - Alexander S. Hatoum
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO 63130, United States
| | - Henry R. Kranzler
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA 19104
| | - Rachel L. Kember
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA 19104
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13
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Koehler S, Hengel FE, Dumoulin B, Damashek L, Holzman LB, Susztak K, Huber TB. The 14th International Podocyte Conference 2023: from podocyte biology to glomerular medicine. Kidney Int 2024; 105:935-952. [PMID: 38447880 DOI: 10.1016/j.kint.2024.01.042] [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: 10/30/2023] [Revised: 12/11/2023] [Accepted: 01/02/2024] [Indexed: 03/08/2024]
Abstract
The 14th International Podocyte Conference took place in Philadelphia, Pennsylvania, USA from May 23 to 26, 2023. It commenced with an early-career researchers' meeting on May 23, providing young scientists with a platform to present and discuss their research findings. Throughout the main conference, 29 speakers across 9 sessions shared their insights on podocyte biology, glomerular medicine, novel technologic advancements, and translational approaches. Additionally, the event featured 3 keynote lectures addressing engineered chimeric antigen receptor T cell- and mRNA-based therapies and the use of biobanks for enhanced disease comprehension. Furthermore, 4 brief oral abstract sessions allowed scientists to present their findings to a broad audience. The program also included a panel discussion addressing the challenges of conducting human research within the American Black community. Remarkably, after a 5-year hiatus from in-person conferences, the 14th International Podocyte Conference successfully convened scientists from around the globe, fostering the presentation and discussion of crucial research findings, as summarized in this review. Furthermore, to ensure continuous and sustainable education, research, translation, and trial medicine related to podocyte and glomerular diseases for the benefit of patients, the International Society of Glomerular Disease was officially launched during the conference.
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Affiliation(s)
- Sybille Koehler
- III. Department of Medicine and Hamburg Center for Kidney Health (HCKH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Felicitas E Hengel
- III. Department of Medicine and Hamburg Center for Kidney Health (HCKH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Bernhard Dumoulin
- III. Department of Medicine and Hamburg Center for Kidney Health (HCKH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany; Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Laurel Damashek
- International Society of Glomerular Disease, Florence, Massachusetts, USA
| | - Lawrence B Holzman
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Katalin Susztak
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA; Institute of Diabetes, Obesity and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Tobias B Huber
- III. Department of Medicine and Hamburg Center for Kidney Health (HCKH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany; International Society of Glomerular Disease, Florence, Massachusetts, USA.
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14
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Guare L, Humphrey LA, Rush M, Pollie M, Luo Y, Weng C, Wei WQ, Kottyan L, Jarvik G, Elhadad N, Zondervan K, Missmer S, Vujkovic M, Velez-Edwards D, Senapati S, Setia-Verma S. Enhancing Genetic Association Power in Endometriosis through Unsupervised Clustering of Clinical Subtypes Identified from Electronic Health Records. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.22.24306092. [PMID: 38712122 PMCID: PMC11071578 DOI: 10.1101/2024.04.22.24306092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Background Endometriosis affects 10% of reproductive-age women, and yet, it goes undiagnosed for 3.6 years on average after symptoms onset. Despite large GWAS meta-analyses (N > 750,000), only a few dozen causal loci have been identified. We hypothesized that the challenges in identifying causal genes for endometriosis stem from heterogeneity across clinical and biological factors underlying endometriosis diagnosis. Methods We extracted known endometriosis risk factors, symptoms, and concomitant conditions from the Penn Medicine Biobank (PMBB) and performed unsupervised spectral clustering on 4,078 women with endometriosis. The 5 clusters were characterized by utilizing additional electronic health record (EHR) variables, such as endometriosis-related comorbidities and confirmed surgical phenotypes. From four EHR-linked genetic datasets, PMBB, eMERGE, AOU, and UKBB, we extracted lead variants and tag variants 39 known endometriosis loci for association testing. We meta-analyzed ancestry-stratified case/control tests for each locus and cluster in addition to a positive control (Total N endometriosis cases = 10,108). Results We have designated the five subtype clusters as pain comorbidities, uterine disorders, pregnancy complications, cardiometabolic comorbidities, and EHR-asymptomatic based on enriched features from each group. One locus, RNLS , surpassed the genome-wide significant threshold in the positive control. Thirteen more loci reached a Bonferroni threshold of 1.3 x 10 -3 (0.05 / 39) in the positive control. The cluster-stratified tests yielded more significant associations than the positive control for anywhere from 5 to 15 loci depending on the cluster. Bonferroni significant loci were identified for four out of five clusters, including WNT4 and GREB1 for the uterine disorders cluster, RNLS for the cardiometabolic cluster, FSHB for the pregnancy complications cluster, and SYNE1 and CDKN2B-AS1 for the EHR-asymptomatic cluster. This study enhances our understanding of the clinical presentation patterns of endometriosis subtypes, showcasing the innovative approach employed to investigate this complex disease.
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15
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Boruah N, Hoyos D, Moses R, Hausler R, Desai H, Le AN, Good M, Kelly G, Raghavakaimal A, Tayeb M, Narasimhamurthy M, Doucette A, Gabriel P, Feldman MJ, Park J, de Rodas ML, Schalper KA, Goldfarb SB, Nayak A, Levine AJ, Greenbaum BD, Maxwell KN. Distinct genomic and immunologic tumor evolution in germline TP53-driven breast cancers. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.03.588009. [PMID: 38617260 PMCID: PMC11014613 DOI: 10.1101/2024.04.03.588009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Pathogenic germline TP53 alterations cause Li-Fraumeni Syndrome (LFS), and breast cancer is the most common cancer in LFS females. We performed first of its kind multimodal analysis of LFS breast cancer (LFS-BC) compared to sporadic premenopausal BC. Nearly all LFS-BC underwent biallelic loss of TP53 with no recurrent oncogenic variants except ERBB2 (HER2) amplification. Compared to sporadic BC, in situ and invasive LFS-BC exhibited a high burden of short amplified aneuploid segments (SAAS). Pro-apoptotic p53 target genes BAX and TP53I3 failed to be up-regulated in LFS-BC as was seen in sporadic BC compared to normal breast tissue. LFS-BC had lower CD8+ T-cell infiltration compared to sporadic BC yet higher levels of proliferating cytotoxic T-cells. Within LFS-BC, progression from in situ to invasive BC was marked by an increase in chromosomal instability with a decrease in proliferating cytotoxic T-cells. Our study uncovers critical events in mutant p53-driven tumorigenesis in breast tissue.
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Affiliation(s)
- Nabamita Boruah
- Department of Medicine, Division of Hematology-Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - David Hoyos
- Computational Oncology, Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Renyta Moses
- Department of Medicine, Division of Hematology-Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Ryan Hausler
- Department of Medicine, Division of Hematology-Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Heena Desai
- Department of Medicine, Division of Hematology-Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Anh N Le
- Department of Medicine, Division of Hematology-Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Madeline Good
- Department of Medicine, Division of Hematology-Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Gregory Kelly
- Department of Medicine, Division of Hematology-Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Ashvathi Raghavakaimal
- Department of Medicine, Division of Hematology-Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Maliha Tayeb
- Department of Medicine, Division of Hematology-Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Mohana Narasimhamurthy
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA
| | - Abigail Doucette
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Peter Gabriel
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Michael J. Feldman
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA
| | - Jinae Park
- Departments of Medicine and Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | - Shari B. Goldfarb
- Departments of Medicine and Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY
- Department of Medicine, Weill Cornell Medical Center, New York, NY
| | - Anupma Nayak
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA
| | | | - Benjamin D. Greenbaum
- Computational Oncology, Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY
- Department of Physiology, Biophysics & Systems Biology, Weill Cornell Medical Center, New York, NY:
| | - Kara N. Maxwell
- Department of Medicine, Division of Hematology-Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
- Corporal Michael Crescenz Veterans Affairs Medical Center, Philadelphia, PA
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16
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Toikumo S, Vickers-Smith R, Jinwala Z, Xu H, Saini D, Hartwell EE, Pavicic M, Sullivan KA, Xu K, Jacobson DA, Gelernter J, Rentsch CT, Stahl E, Cheatle M, Zhou H, Waxman SG, Justice AC, Kember RL, Kranzler HR. A multi-ancestry genetic study of pain intensity in 598,339 veterans. Nat Med 2024; 30:1075-1084. [PMID: 38429522 DOI: 10.1038/s41591-024-02839-5] [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] [Received: 03/08/2023] [Accepted: 01/27/2024] [Indexed: 03/03/2024]
Abstract
Chronic pain is a common problem, with more than one-fifth of adult Americans reporting pain daily or on most days. It adversely affects the quality of life and imposes substantial personal and economic costs. Efforts to treat chronic pain using opioids had a central role in precipitating the opioid crisis. Despite an estimated heritability of 25-50%, the genetic architecture of chronic pain is not well-characterized, in part because studies have largely been limited to samples of European ancestry. To help address this knowledge gap, we conducted a cross-ancestry meta-analysis of pain intensity in 598,339 participants in the Million Veteran Program, which identified 126 independent genetic loci, 69 of which are new. Pain intensity was genetically correlated with other pain phenotypes, level of substance use and substance use disorders, other psychiatric traits, education level and cognitive traits. Integration of the genome-wide association studies findings with functional genomics data shows enrichment for putatively causal genes (n = 142) and proteins (n = 14) expressed in brain tissues, specifically in GABAergic neurons. Drug repurposing analysis identified anticonvulsants, β-blockers and calcium-channel blockers, among other drug groups, as having potential analgesic effects. Our results provide insights into key molecular contributors to the experience of pain and highlight attractive drug targets.
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Affiliation(s)
- Sylvanus Toikumo
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Rachel Vickers-Smith
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Epidemiology and Environmental Health, University of Kentucky College of Public Health, Lexington, KY, USA
| | - Zeal Jinwala
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Heng Xu
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Divya Saini
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Emily E Hartwell
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Mirko Pavicic
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Kyle A Sullivan
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Ke Xu
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Daniel A Jacobson
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Joel Gelernter
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Christopher T Rentsch
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Medicine, Yale University School of Medicine, New Haven, CT, USA
- London School of Hygiene & Tropical Medicine, London, UK
| | - Eli Stahl
- Regeneron Genetics Center, Tarrytown, NY, USA
| | - Martin Cheatle
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Hang Zhou
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Section of Biomedical Informatics and Data Science, Yale University School of Medicine, New Haven, CT, USA
| | - Stephen G Waxman
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Neurology, Yale University School of Medicine, New Haven, CT, USA
| | - Amy C Justice
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Medicine, Yale University School of Medicine, New Haven, CT, USA
- Yale University School of Public Health, New Haven, CT, USA
| | - Rachel L Kember
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Henry R Kranzler
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA.
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
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Gold J, Kripke CM, Drivas TG. Universal Exome Sequencing in Critically Ill Adults: A Diagnostic Yield of 25% and Race-Based Disparities in Access to Genetic Testing. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.11.24304088. [PMID: 38559092 PMCID: PMC10980115 DOI: 10.1101/2024.03.11.24304088] [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: 04/04/2024]
Abstract
Numerous studies have underscored the diagnostic and therapeutic potential of exome or genome sequencing in critically ill pediatric populations. However, an equivalent investigation in critically ill adults remains conspicuously absent. We retrospectively analyzed whole exome sequencing (WES) data available through the PennMedicine Biobank (PMBB) from all 365 young adult patients, aged 18-40 years, with intensive care unit (ICU) admissions at the University of Pennsylvania Health System who met inclusion criteria for our study. For each participant, two Medical Genetics and Internal Medicine-trained clinicians reviewed WES reports and patient charts for variant classification, result interpretation, and identification of genetic diagnoses related to their critical illness. Of the 365 individuals in our study, 90 (24.7%) were found to have clearly diagnostic results on WES; an additional 40 (11.0%) had a suspicious variant of uncertain significance (VUS) identified; and an additional 16 (4.4%) had a medically actionable incidental finding. The diagnostic rate of exome sequencing did not decrease with increasing patient age. Affected genes were primarily involved in cardiac function (18.8%), vascular health (16.7%), cancer (16.7%), and pulmonary disease (11.5%). Only half of all diagnostic findings were known and documented in the patient chart at the time of ICU admission. Significant disparities emerged in subgroup analysis by EHR-reported race, with genetic diagnoses known/documented for 63.5% of White patients at the time of ICU admission but only for 28.6% of Black or Hispanic patients. There was a trend towards patients with undocumented genetic diagnoses having a 66% increased mortality rate, making these race-based disparities in genetic diagnosis even more concerning. Altogether, universal exome sequencing in ICU-admitted adult patients was found to yield a new definitive diagnosis in 11.2% of patients. Of these diagnoses, 76.6% conferred specific care-altering medical management recommendations. Our study suggests that the diagnostic utility of exome sequencing in critically ill young adults is similar to that observed in neonatal and pediatric populations and is age-independent. The high diagnostic rate and striking race-based disparities we find in genetic diagnoses argue for broad and universal approaches to genetic testing for critically ill adults. The widespread implementation of comprehensive genetic sequencing in the adult population promises to enhance medical care for all individuals and holds the potential to rectify disparities in genetic testing referrals, ultimately promoting more equitable healthcare delivery.
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Affiliation(s)
- Jessica Gold
- Division of Clinical Genetics, Department of Pediatrics, Northwell Health, Great Neck, NY 11021, USA
| | - Colleen M. Kripke
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19194, USA
| | | | | | - Theodore G. Drivas
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19194, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19194, USA
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18
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Jung SH, Lee YC, Shivakumar M, Kim J, Yun JS, Park WY, Won HH, Kim D. Association between genetic risk and adherence to healthy lifestyle for developing age-related hearing loss. BMC Med 2024; 22:141. [PMID: 38532472 DOI: 10.1186/s12916-024-03364-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 03/18/2024] [Indexed: 03/28/2024] Open
Abstract
BACKGROUND Previous studies have shown that lifestyle/environmental factors could accelerate the development of age-related hearing loss (ARHL). However, there has not yet been a study investigating the joint association among genetics, lifestyle/environmental factors, and adherence to healthy lifestyle for risk of ARHL. We aimed to assess the association between ARHL genetic variants, lifestyle/environmental factors, and adherence to healthy lifestyle as pertains to risk of ARHL. METHODS This case-control study included 376,464 European individuals aged 40 to 69 years, enrolled between 2006 and 2010 in the UK Biobank (UKBB). As a replication set, we also included a total of 26,523 individuals considered of European ancestry and 9834 individuals considered of African-American ancestry through the Penn Medicine Biobank (PMBB). The polygenic risk score (PRS) for ARHL was derived from a sensorineural hearing loss genome-wide association study from the FinnGen Consortium and categorized as low, intermediate, high, and very high. We selected lifestyle/environmental factors that have been previously studied in association with hearing loss. A composite healthy lifestyle score was determined using seven selected lifestyle behaviors and one environmental factor. RESULTS Of the 376,464 participants, 87,066 (23.1%) cases belonged to the ARHL group, and 289,398 (76.9%) individuals comprised the control group in the UKBB. A very high PRS for ARHL had a 49% higher risk of ARHL than those with low PRS (adjusted OR, 1.49; 95% CI, 1.36-1.62; P < .001), which was replicated in the PMBB cohort. A very poor lifestyle was also associated with risk of ARHL (adjusted OR, 3.03; 95% CI, 2.75-3.35; P < .001). These risk factors showed joint effects with the risk of ARHL. Conversely, adherence to healthy lifestyle in relation to hearing mostly attenuated the risk of ARHL even in individuals with very high PRS (adjusted OR, 0.21; 95% CI, 0.09-0.52; P < .001). CONCLUSIONS Our findings of this study demonstrated a significant joint association between genetic and lifestyle factors regarding ARHL. In addition, our analysis suggested that lifestyle adherence in individuals with high genetic risk could reduce the risk of ARHL.
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Affiliation(s)
- Sang-Hyuk Jung
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Young Chan Lee
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Otolaryngology-Head and Neck Surgery, School of Medicine, Kyung Hee University, Kyung Hee University Hospital at Gangdong, Seoul, Republic of Korea
| | - Manu Shivakumar
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jaeyoung Kim
- Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Jae-Seung Yun
- Division of Endocrinology and Metabolism, Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Woong-Yang Park
- Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hong-Hee Won
- Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
- Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, USA.
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19
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Lee YC, Jung SH, Shivakumar M, Cha S, Park WY, Won HH, Eun YG, Biobank PM, Kim D. Polygenic risk score-based phenome-wide association study of head and neck cancer across two large biobanks. BMC Med 2024; 22:120. [PMID: 38486201 PMCID: PMC10941505 DOI: 10.1186/s12916-024-03305-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Accepted: 02/15/2024] [Indexed: 03/17/2024] Open
Abstract
BACKGROUND Numerous observational studies have highlighted associations of genetic predisposition of head and neck squamous cell carcinoma (HNSCC) with diverse risk factors, but these findings are constrained by design limitations of observational studies. In this study, we utilized a phenome-wide association study (PheWAS) approach, incorporating a polygenic risk score (PRS) derived from a wide array of genomic variants, to systematically investigate phenotypes associated with genetic predisposition to HNSCC. Furthermore, we validated our findings across heterogeneous cohorts, enhancing the robustness and generalizability of our results. METHODS We derived PRSs for HNSCC and its subgroups, oropharyngeal cancer and oral cancer, using large-scale genome-wide association study summary statistics from the Genetic Associations and Mechanisms in Oncology Network. We conducted a comprehensive investigation, leveraging genotyping data and electronic health records from 308,492 individuals in the UK Biobank and 38,401 individuals in the Penn Medicine Biobank (PMBB), and subsequently performed PheWAS to elucidate the associations between PRS and a wide spectrum of phenotypes. RESULTS We revealed the HNSCC PRS showed significant association with phenotypes related to tobacco use disorder (OR, 1.06; 95% CI, 1.05-1.08; P = 3.50 × 10-15), alcoholism (OR, 1.06; 95% CI, 1.04-1.09; P = 6.14 × 10-9), alcohol-related disorders (OR, 1.08; 95% CI, 1.05-1.11; P = 1.09 × 10-8), emphysema (OR, 1.11; 95% CI, 1.06-1.16; P = 5.48 × 10-6), chronic airway obstruction (OR, 1.05; 95% CI, 1.03-1.07; P = 2.64 × 10-5), and cancer of bronchus (OR, 1.08; 95% CI, 1.04-1.13; P = 4.68 × 10-5). These findings were replicated in the PMBB cohort, and sensitivity analyses, including the exclusion of HNSCC cases and the major histocompatibility complex locus, confirmed the robustness of these associations. Additionally, we identified significant associations between HNSCC PRS and lifestyle factors related to smoking and alcohol consumption. CONCLUSIONS The study demonstrated the potential of PRS-based PheWAS in revealing associations between genetic risk factors for HNSCC and various phenotypic traits. The findings emphasized the importance of considering genetic susceptibility in understanding HNSCC and highlighted shared genetic bases between HNSCC and other health conditions and lifestyles.
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Affiliation(s)
- Young Chan Lee
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Otolaryngology-Head and Neck Surgery, School of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Sang-Hyuk Jung
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Manu Shivakumar
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Soojin Cha
- Hanyang University Institute for Rheumatology Research, Seoul, Republic of Korea
| | - Woong-Yang Park
- Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hong-Hee Won
- Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Samsung Medical Center, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, Republic of Korea
| | - Young-Gyu Eun
- Department of Otolaryngology-Head and Neck Surgery, School of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Penn Medicine Biobank
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA.
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20
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Schuermans A, Truong B, Ardissino M, Bhukar R, Slob EAW, Nakao T, Dron JS, Small AM, Cho SMJ, Yu Z, Hornsby W, Antoine T, Lannery K, Postupaka D, Gray KJ, Yan Q, Butterworth AS, Burgess S, Wood MJ, Scott NS, Harrington CM, Sarma AA, Lau ES, Roh JD, Januzzi JL, Natarajan P, Honigberg MC. Genetic Associations of Circulating Cardiovascular Proteins With Gestational Hypertension and Preeclampsia. JAMA Cardiol 2024; 9:209-220. [PMID: 38170504 PMCID: PMC10765315 DOI: 10.1001/jamacardio.2023.4994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Accepted: 11/01/2023] [Indexed: 01/05/2024]
Abstract
Importance Hypertensive disorders of pregnancy (HDPs), including gestational hypertension and preeclampsia, are important contributors to maternal morbidity and mortality worldwide. In addition, women with HDPs face an elevated long-term risk of cardiovascular disease. Objective To identify proteins in the circulation associated with HDPs. Design, Setting, and Participants Two-sample mendelian randomization (MR) tested the associations of genetic instruments for cardiovascular disease-related proteins with gestational hypertension and preeclampsia. In downstream analyses, a systematic review of observational data was conducted to evaluate the identified proteins' dynamics across gestation in hypertensive vs normotensive pregnancies, and phenome-wide MR analyses were performed to identify potential non-HDP-related effects associated with the prioritized proteins. Genetic association data for cardiovascular disease-related proteins were obtained from the Systematic and Combined Analysis of Olink Proteins (SCALLOP) consortium. Genetic association data for the HDPs were obtained from recent European-ancestry genome-wide association study meta-analyses for gestational hypertension and preeclampsia. Study data were analyzed October 2022 to October 2023. Exposures Genetic instruments for 90 candidate proteins implicated in cardiovascular diseases, constructed using cis-protein quantitative trait loci (cis-pQTLs). Main Outcomes and Measures Gestational hypertension and preeclampsia. Results Genetic association data for cardiovascular disease-related proteins were obtained from 21 758 participants from the SCALLOP consortium. Genetic association data for the HDPs were obtained from 393 238 female individuals (8636 cases and 384 602 controls) for gestational hypertension and 606 903 female individuals (16 032 cases and 590 871 controls) for preeclampsia. Seventy-five of 90 proteins (83.3%) had at least 1 valid cis-pQTL. Of those, 10 proteins (13.3%) were significantly associated with HDPs. Four were robust to sensitivity analyses for gestational hypertension (cluster of differentiation 40, eosinophil cationic protein [ECP], galectin 3, N-terminal pro-brain natriuretic peptide [NT-proBNP]), and 2 were robust for preeclampsia (cystatin B, heat shock protein 27 [HSP27]). Consistent with the MR findings, observational data revealed that lower NT-proBNP (0.76- to 0.88-fold difference vs no HDPs) and higher HSP27 (2.40-fold difference vs no HDPs) levels during the first trimester of pregnancy were associated with increased risk of HDPs, as were higher levels of ECP (1.60-fold difference vs no HDPs). Phenome-wide MR analyses identified 37 unique non-HDP-related protein-disease associations, suggesting potential on-target effects associated with interventions lowering HDP risk through the identified proteins. Conclusions and Relevance Study findings suggest genetic associations of 4 cardiovascular disease-related proteins with gestational hypertension and 2 associated with preeclampsia. Future studies are required to test the efficacy of targeting the corresponding pathways to reduce HDP risk.
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Affiliation(s)
- Art Schuermans
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, Massachusetts
- Cardiovascular Research Center, Massachusetts General Hospital, Boston
- Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
| | - Buu Truong
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, Massachusetts
- Cardiovascular Research Center, Massachusetts General Hospital, Boston
| | - Maddalena Ardissino
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Rohan Bhukar
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, Massachusetts
- Cardiovascular Research Center, Massachusetts General Hospital, Boston
| | - Eric A. W. Slob
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
- Department of Applied Economics, Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, the Netherlands
- Erasmus University Rotterdam Institute for Behavior and Biology, Erasmus University Rotterdam, Rotterdam, the Netherlands
| | - Tetsushi Nakao
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, Massachusetts
- Cardiovascular Research Center, Massachusetts General Hospital, Boston
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Jacqueline S. Dron
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, Massachusetts
- Cardiovascular Research Center, Massachusetts General Hospital, Boston
| | - Aeron M. Small
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, Massachusetts
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
| | - So Mi Jemma Cho
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, Massachusetts
- Cardiovascular Research Center, Massachusetts General Hospital, Boston
- Integrative Research Center for Cerebrovascular and Cardiovascular Diseases, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Zhi Yu
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, Massachusetts
- Cardiovascular Research Center, Massachusetts General Hospital, Boston
| | - Whitney Hornsby
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, Massachusetts
- Cardiovascular Research Center, Massachusetts General Hospital, Boston
| | - Tajmara Antoine
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, Massachusetts
- Cardiovascular Research Center, Massachusetts General Hospital, Boston
| | - Kim Lannery
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, Massachusetts
- Cardiovascular Research Center, Massachusetts General Hospital, Boston
| | - Darina Postupaka
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, Massachusetts
- Cardiovascular Research Center, Massachusetts General Hospital, Boston
| | - Kathryn J. Gray
- Division of Maternal-Fetal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Qi Yan
- Department of Obstetrics and Gynecology, Columbia University, New York, New York
| | - Adam S. Butterworth
- BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- BHF Centre of Research Excellence, University of Cambridge, Cambridge, United Kingdom
- National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, United Kingdom
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, United Kingdom
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, United Kingdom
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | - Malissa J. Wood
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Cardiology Division, Massachusetts General Hospital, Boston
- Lee Health, Fort Myers, Florida
| | - Nandita S. Scott
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Cardiology Division, Massachusetts General Hospital, Boston
| | - Colleen M. Harrington
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Cardiology Division, Massachusetts General Hospital, Boston
| | - Amy A. Sarma
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Cardiology Division, Massachusetts General Hospital, Boston
| | - Emily S. Lau
- Cardiovascular Research Center, Massachusetts General Hospital, Boston
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Cardiology Division, Massachusetts General Hospital, Boston
| | - Jason D. Roh
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Cardiology Division, Massachusetts General Hospital, Boston
| | - James L. Januzzi
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Cardiology Division, Massachusetts General Hospital, Boston
- Baim Institute for Clinical Research, Boston, Massachusetts
| | - Pradeep Natarajan
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, Massachusetts
- Cardiovascular Research Center, Massachusetts General Hospital, Boston
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Cardiology Division, Massachusetts General Hospital, Boston
| | - Michael C. Honigberg
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, Massachusetts
- Cardiovascular Research Center, Massachusetts General Hospital, Boston
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Cardiology Division, Massachusetts General Hospital, Boston
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21
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Lo Faro V, Bhattacharya A, Zhou W, Zhou D, Wang Y, Läll K, Kanai M, Lopera-Maya E, Straub P, Pawar P, Tao R, Zhong X, Namba S, Sanna S, Nolte IM, Okada Y, Ingold N, MacGregor S, Snieder H, Surakka I, Shortt J, Gignoux C, Rafaels N, Crooks K, Verma A, Verma SS, Guare L, Rader DJ, Willer C, Martin AR, Brantley MA, Gamazon ER, Jansonius NM, Joos K, Cox NJ, Hirbo J. Novel ancestry-specific primary open-angle glaucoma loci and shared biology with vascular mechanisms and cell proliferation. Cell Rep Med 2024; 5:101430. [PMID: 38382466 PMCID: PMC10897632 DOI: 10.1016/j.xcrm.2024.101430] [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: 01/05/2022] [Revised: 03/28/2023] [Accepted: 01/25/2024] [Indexed: 02/23/2024]
Abstract
Primary open-angle glaucoma (POAG), a leading cause of irreversible blindness globally, shows disparity in prevalence and manifestations across ancestries. We perform meta-analysis across 15 biobanks (of the Global Biobank Meta-analysis Initiative) (n = 1,487,441: cases = 26,848) and merge with previous multi-ancestry studies, with the combined dataset representing the largest and most diverse POAG study to date (n = 1,478,037: cases = 46,325) and identify 17 novel significant loci, 5 of which were ancestry specific. Gene-enrichment and transcriptome-wide association analyses implicate vascular and cancer genes, a fifth of which are primary ciliary related. We perform an extensive statistical analysis of SIX6 and CDKN2B-AS1 loci in human GTEx data and across large electronic health records showing interaction between SIX6 gene and causal variants in the chr9p21.3 locus, with expression effect on CDKN2A/B. Our results suggest that some POAG risk variants may be ancestry specific, sex specific, or both, and support the contribution of genes involved in programmed cell death in POAG pathogenesis.
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Affiliation(s)
- Valeria Lo Faro
- Department of Ophthalmology, Amsterdam University Medical Center (AMC), Amsterdam, the Netherlands; Department of Clinical Genetics, Amsterdam University Medical Center (AMC), Amsterdam, the Netherlands; Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Institute for Quantitative and Computational Biosciences, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Wei Zhou
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Dan Zhou
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ying Wang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Kristi Läll
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Masahiro Kanai
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Esteban Lopera-Maya
- University of Groningen, UMCG, Department of Genetics, Groningen, the Netherlands
| | - Peter Straub
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Priyanka Pawar
- Vanderbilt Eye Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ran Tao
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xue Zhong
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Shinichi Namba
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Serena Sanna
- University of Groningen, UMCG, Department of Genetics, Groningen, the Netherlands; Institute for Genetics and Biomedical Research (IRGB), National Research Council (CNR), Cagliari, Italy
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan; Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan; Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka, Japan; Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka, Japan; Center for Infectious Disease Education and Research (CiDER), Osaka University, Osaka, Japan
| | - Nathan Ingold
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Queensland University of Technology, Brisbane, QLD, Australia; School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia
| | - Stuart MacGregor
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Queensland University of Technology, Brisbane, QLD, Australia
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Ida Surakka
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Jonathan Shortt
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Chris Gignoux
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Nicholas Rafaels
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Kristy Crooks
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Anurag Verma
- Department of Medicine, Division of Translational Medicine and Human Genetics, University of Pennsylvania, Philadelphia, PA, USA; Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - Shefali S Verma
- Department of Pathology, University of Pennsylvania, Philadelphia, PA, USA
| | - Lindsay Guare
- Department of Pathology, University of Pennsylvania, Philadelphia, PA, USA; Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Daniel J Rader
- Department of Medicine, Division of Translational Medicine and Human Genetics, University of Pennsylvania, Philadelphia, PA, USA; Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA; Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Cristen Willer
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway; Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA; Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Alicia R Martin
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Milam A Brantley
- Vanderbilt Eye Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Eric R Gamazon
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nomdo M Jansonius
- Department of Ophthalmology, Amsterdam University Medical Center (AMC), Amsterdam, the Netherlands
| | - Karen Joos
- Vanderbilt Eye Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nancy J Cox
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jibril Hirbo
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
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22
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Verma SS, Gudiseva HV, Chavali VRM, Salowe RJ, Bradford Y, Guare L, Lucas A, Collins DW, Vrathasha V, Nair RM, Rathi S, Zhao B, He J, Lee R, Zenebe-Gete S, Bowman AS, McHugh CP, Zody MC, Pistilli M, Khachatryan N, Daniel E, Murphy W, Henderer J, Kinzy TG, Iyengar SK, Peachey NS, Taylor KD, Guo X, Chen YDI, Zangwill L, Girkin C, Ayyagari R, Liebmann J, Chuka-Okosa CM, Williams SE, Akafo S, Budenz DL, Olawoye OO, Ramsay M, Ashaye A, Akpa OM, Aung T, Wiggs JL, Ross AG, Cui QN, Addis V, Lehman A, Miller-Ellis E, Sankar PS, Williams SM, Ying GS, Cooke Bailey J, Rotter JI, Weinreb R, Khor CC, Hauser MA, Ritchie MD, O'Brien JM. A multi-cohort genome-wide association study in African ancestry individuals reveals risk loci for primary open-angle glaucoma. Cell 2024; 187:464-480.e10. [PMID: 38242088 DOI: 10.1016/j.cell.2023.12.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 07/24/2023] [Accepted: 12/04/2023] [Indexed: 01/21/2024]
Abstract
Primary open-angle glaucoma (POAG), the leading cause of irreversible blindness worldwide, disproportionately affects individuals of African ancestry. We conducted a genome-wide association study (GWAS) for POAG in 11,275 individuals of African ancestry (6,003 cases; 5,272 controls). We detected 46 risk loci associated with POAG at genome-wide significance. Replication and post-GWAS analyses, including functionally informed fine-mapping, multiple trait co-localization, and in silico validation, implicated two previously undescribed variants (rs1666698 mapping to DBF4P2; rs34957764 mapping to ROCK1P1) and one previously associated variant (rs11824032 mapping to ARHGEF12) as likely causal. For individuals of African ancestry, a polygenic risk score (PRS) for POAG from our mega-analysis (African ancestry individuals) outperformed a PRS from summary statistics of a much larger GWAS derived from European ancestry individuals. This study quantifies the genetic architecture similarities and differences between African and non-African ancestry populations for this blinding disease.
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Affiliation(s)
- Shefali S Verma
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Harini V Gudiseva
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Venkata R M Chavali
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rebecca J Salowe
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yuki Bradford
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Lindsay Guare
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anastasia Lucas
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - David W Collins
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Vrathasha Vrathasha
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rohini M Nair
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sonika Rathi
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Bingxin Zhao
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA
| | - Jie He
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Roy Lee
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Selam Zenebe-Gete
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anita S Bowman
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | - Maxwell Pistilli
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Naira Khachatryan
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ebenezer Daniel
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Jeffrey Henderer
- Department of Ophthalmology, Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA
| | - Tyler G Kinzy
- Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA; Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA
| | - Sudha K Iyengar
- Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA; Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA
| | - Neal S Peachey
- Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA; Cole Eye Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Kent D Taylor
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Xiuqing Guo
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Yii-Der Ida Chen
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Linda Zangwill
- Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA
| | - Christopher Girkin
- Department of Ophthalmology and Visual Sciences, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Radha Ayyagari
- Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA
| | - Jeffrey Liebmann
- Department of Ophthalmology, Columbia University Medical Center, Columbia University, New York, NY, USA
| | | | - Susan E Williams
- Division of Ophthalmology, Department of Neurosciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Stephen Akafo
- Unit of Ophthalmology, Department of Surgery, University of Ghana Medical School, Accra, Ghana
| | - Donald L Budenz
- Department of Ophthalmology, University of North Carolina, Chapel Hill, NC, USA
| | | | - Michele Ramsay
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Adeyinka Ashaye
- Department of Ophthalmology, University of Ibadan, Ibadan, Nigeria
| | - Onoja M Akpa
- Department of Epidemiology and Medical Statistics, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Tin Aung
- Singapore Eye Research Institute, Singapore, Singapore
| | - Janey L Wiggs
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Ahmara G Ross
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Qi N Cui
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Victoria Addis
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Amanda Lehman
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Eydie Miller-Ellis
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Prithvi S Sankar
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Scott M Williams
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Gui-Shuang Ying
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jessica Cooke Bailey
- Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA; Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA; Department of Pharmacology and Toxicology, Center for Health Disparities, Brody School of Medicine. East Carolina University, Greenville, NC, 27834, USA
| | - Jerome I Rotter
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Robert Weinreb
- Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA
| | | | | | - Marylyn D Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Joan M O'Brien
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. joan.o'
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23
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DePaolo J, Bornstein M, Judy R, Abramowitz S, Verma SS, Levin MG, Arany Z, Damrauer SM. Titin-Truncating variants Predispose to Dilated Cardiomyopathy in Diverse Populations. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.17.24301405. [PMID: 38293092 PMCID: PMC10827233 DOI: 10.1101/2024.01.17.24301405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Importance The effect of high percentage spliced in (hiPSI) TTN truncating variants (TTNtvs) on risk of dilated cardiomyopathy (DCM) has historically been studied among population subgroups defined by genetic similarity to European reference populations. This has raised questions about the effect of TTNtvs in diverse populations, especially among individuals genetically similar to African reference populations. Objective To determine the effect of TTNtvs on risk of DCM in diverse population as measured by genetic distance (GD) in principal component (PC) space. Design Cohort study. Setting Penn Medicine Biobank (PMBB) is a large, diverse biobank. Participants Participants were recruited from across the Penn Medicine healthcare system and volunteered to have their electronic health records linked to biospecimen data including DNA which has undergone whole exome sequencing. Main Outcomes and Measures Risk of DCM among individuals carrying a hiPSI TTNtv. Results Carrying a hiPSI TTNtv was associated with DCM among PMBB participants across a range of GD deciles from the 1000G European centroid; the effect estimates ranged from odds ratio (OR) = 3.29 (95% confidence interval [CI] 1.26 to 8.56) to OR = 9.39 (95% CI 3.82 to 23.13). When individuals were assigned to population subgroups based on genetic similarity to the 1000G reference populations, hiPSI TTNtvs conferred significant risk of DCM among those genetically similar to the 1000G European reference population (OR = 7.55, 95% CI 4.99 to 11.42, P<0.001) and individuals genetically similar to the 1000G African reference population (OR 3.50, 95% CI 1.48 to 8.24, P=0.004). Conclusions and Relevance TTNtvs are associated with increased risk of DCM among a diverse cohort. There is no significant difference in effect of TTNtvs on DCM risk across deciles of GD from the 1000G European centroid, suggesting genetic background should not be considered when screening individuals for titin-related DCM.
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Affiliation(s)
- John DePaolo
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Marc Bornstein
- Cardiovascular Institute, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
| | - Renae Judy
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sarah Abramowitz
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Shefali S Verma
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
| | - Michael G Levin
- Cardiovascular Institute, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA 19104, USA
| | - Zoltan Arany
- Cardiovascular Institute, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
| | - Scott M Damrauer
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Cardiovascular Institute, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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24
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Compher CW, Quinn R, Haslam R, Bader E, Weaver J, Dudek S, Ritchie MD, Lewis JD, Wu GD. Penn Healthy Diet survey: pilot validation and scoring. Br J Nutr 2024; 131:156-162. [PMID: 37519237 DOI: 10.1017/s0007114523001642] [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] [Indexed: 08/01/2023]
Abstract
Though diet quality is widely recognised as linked to risk of chronic disease, health systems have been challenged to find a user-friendly, efficient way to obtain information about diet. The Penn Healthy Diet (PHD) survey was designed to fill this void. The purposes of this pilot project were to assess the patient experience with the PHD, to validate the accuracy of the PHD against related items in a diet recall and to explore scoring algorithms with relationship to the Healthy Eating Index (HEI)-2015 computed from the recall data. A convenience sample of participants in the Penn Health BioBank was surveyed with the PHD, the Automated Self-Administered 24-hour recall (ASA24) and experience questions. Kappa scores and Spearman correlations were used to compare related questions in the PHD to the ASA24. Numerical scoring, regression tree and weighted regressions were computed for scoring. Participants assessed the PHD as easy to use and were willing to repeat the survey at least annually. The three scoring algorithms were strongly associated with HEI-2015 scores using National Health and Nutrition Examination Survey 2017-2018 data from which the PHD was developed and moderately associated with the pilot replication data. The PHD is acceptable to participants and at least moderately correlated with the HEI-2015. Further validation in a larger sample will enable the selection of the strongest scoring approach.
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Affiliation(s)
- Charlene W Compher
- University of Pennsylvania School of Nursing, Department of Biobehavioral Health Science, Philadelphia, PA, USA
| | - Ryan Quinn
- University of Pennsylvania School of Nursing, Department of Biostatistics, Philadelphia, PA, USA
| | - Richard Haslam
- University of Dublin, School of Medicine, Dublin, Republic of Ireland
| | | | - Joellen Weaver
- University of Pennsylvania Health System, Penn Medicine Biobank, Philadelphia, PA, USA
| | - Scott Dudek
- Department of Genetics and Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Marylyn D Ritchie
- Department of Genetics and Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - James D Lewis
- University of Pennsylvania Perelman School of Medicine, Division of Gastroenterology and Hepatology, Philadelphia, PA, USA
| | - Gary D Wu
- University of Pennsylvania Perelman School of Medicine, Division of Gastroenterology and Hepatology, Philadelphia, PA, USA
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25
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de Andrade KC, Strande NT, Kim J, Haley JS, Hatton JN, Frone MN, Khincha PP, Thone GM, Mirshahi UL, Schneider C, Desai H, Dove JT, Smelser DT, Levine AJ, Maxwell KN, Stewart DR, Carey DJ, Savage SA. Genome-first approach of the prevalence and cancer phenotypes of pathogenic or likely pathogenic germline TP53 variants. HGG ADVANCES 2024; 5:100242. [PMID: 37777824 PMCID: PMC10589747 DOI: 10.1016/j.xhgg.2023.100242] [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: 07/26/2023] [Revised: 09/20/2023] [Accepted: 09/20/2023] [Indexed: 10/02/2023] Open
Abstract
Pathogenic or likely pathogenic (P/LP) germline TP53 variants are the primary cause of Li-Fraumeni syndrome (LFS), a hereditary cancer predisposition disorder characterized by early-onset cancers. The population prevalence of P/LP germline TP53 variants is estimated to be approximately one in every 3,500 to 20,000 individuals. However, these estimates are likely impacted by ascertainment biases and lack of clinical and genetic data to account for potential confounding factors, such as clonal hematopoiesis. Genome-first approaches of cohorts linked to phenotype data can further refine these estimates by identifying individuals with variants of interest and then assessing their phenotypes. This study evaluated P/LP germline (variant allele fraction ≥30%) TP53 variants in three cohorts: UK Biobank (UKB, n = 200,590), Geisinger (n = 170,503), and Penn Medicine Biobank (PMBB, n = 43,731). A total of 109 individuals were identified with P/LP germline TP53 variants across the three databases. The TP53 p.R181H variant was the most frequently identified (9 of 109 individuals, 8%). A total of 110 cancers, including 47 hematologic cancers (47 of 110, 43%), were reported in 71 individuals. The prevalence of P/LP germline TP53 variants was conservatively estimated as 1:10,439 in UKB, 1:3,790 in Geisinger, and 1:2,983 in PMBB. These estimates were calculated after excluding related individuals and accounting for the potential impact of clonal hematopoiesis by excluding heterozygotes who ever developed a hematologic cancer. These varying estimates likely reflect intrinsic selection biases of each database, such as healthcare or population-based contexts. Prospective studies of diverse, young cohorts are required to better understand the population prevalence of germline TP53 variants and their associated cancer penetrance.
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Affiliation(s)
- Kelvin C de Andrade
- Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Natasha T Strande
- Department of Genomic Health, Geisinger Clinic, Geisinger, Danville, PA, USA
| | - Jung Kim
- Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jeremy S Haley
- Department of Genomic Health, Geisinger Clinic, Geisinger, Danville, PA, USA
| | - Jessica N Hatton
- Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Megan N Frone
- Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Payal P Khincha
- Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Gretchen M Thone
- Department of Genomic Health, Geisinger Clinic, Geisinger, Danville, PA, USA
| | - Uyenlinh L Mirshahi
- Department of Genomic Health, Geisinger Clinic, Geisinger, Danville, PA, USA
| | - Cynthia Schneider
- Division of Hematology/Oncology, Department of Medicine and Department of Genetics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Heena Desai
- Division of Hematology/Oncology, Department of Medicine and Department of Genetics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - James T Dove
- Department of Genomic Health, Geisinger Clinic, Geisinger, Danville, PA, USA
| | - Diane T Smelser
- Department of Genomic Health, Geisinger Clinic, Geisinger, Danville, PA, USA
| | - Arnold J Levine
- Simons Center for Systems Biology, Institute for Advanced Study, Princeton, NJ, USA
| | - Kara N Maxwell
- Division of Hematology/Oncology, Department of Medicine and Department of Genetics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Douglas R Stewart
- Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - David J Carey
- Department of Genomic Health, Geisinger Clinic, Geisinger, Danville, PA, USA
| | - Sharon A Savage
- Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
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26
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Kember RL, Verma SS, Verma A, Xiao B, Lucas A, Kripke CM, Judy R, Chen J, Damrauer SM, Rader DJ, Ritchie MD. Polygenic risk scores for cardiometabolic traits demonstrate importance of ancestry for predictive precision medicine. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2024; 29:611-626. [PMID: 38160310 PMCID: PMC10947742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
Polygenic risk scores (PRS) have predominantly been derived from genome-wide association studies (GWAS) conducted in European ancestry (EUR) individuals. In this study, we present an in-depth evaluation of PRS based on multi-ancestry GWAS for five cardiometabolic phenotypes in the Penn Medicine BioBank (PMBB) followed by a phenome-wide association study (PheWAS). We examine the PRS performance across all individuals and separately in African ancestry (AFR) and EUR ancestry groups. For AFR individuals, PRS derived using the multi-ancestry LD panel showed a higher effect size for four out of five PRSs (DBP, SBP, T2D, and BMI) than those derived from the AFR LD panel. In contrast, for EUR individuals, the multi-ancestry LD panel PRS demonstrated a higher effect size for two out of five PRSs (SBP and T2D) compared to the EUR LD panel. These findings underscore the potential benefits of utilizing a multi-ancestry LD panel for PRS derivation in diverse genetic backgrounds and demonstrate overall robustness in all individuals. Our results also revealed significant associations between PRS and various phenotypic categories. For instance, CAD PRS was linked with 18 phenotypes in AFR and 82 in EUR, while T2D PRS correlated with 84 phenotypes in AFR and 78 in EUR. Notably, associations like hyperlipidemia, renal failure, atrial fibrillation, coronary atherosclerosis, obesity, and hypertension were observed across different PRSs in both AFR and EUR groups, with varying effect sizes and significance levels. However, in AFR individuals, the strength and number of PRS associations with other phenotypes were generally reduced compared to EUR individuals. Our study underscores the need for future research to prioritize 1) conducting GWAS in diverse ancestry groups and 2) creating a cosmopolitan PRS methodology that is universally applicable across all genetic backgrounds. Such advances will foster a more equitable and personalized approach to precision medicine.
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Affiliation(s)
- Rachel L Kember
- Department of Psychiatry, University of Pennsylvania, 3535 Market Street, Philadelphia, PA 19104, USA,
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27
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Cardone KM, Dudek S, Keat K, Bradford Y, Cindi Z, Daar ES, Gulick R, Riddler SA, Lennox JL, Sinxadi P, Haas DW, Ritchie MD. Lymphocyte Count Derived Polygenic Score and Interindividual Variability in CD4 T-cell Recovery in Response to Antiretroviral Therapy. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2024; 29:594-610. [PMID: 38160309 PMCID: PMC10764076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
Access to safe and effective antiretroviral therapy (ART) is a cornerstone in the global response to the HIV pandemic. Among people living with HIV, there is considerable interindividual variability in absolute CD4 T-cell recovery following initiation of virally suppressive ART. The contribution of host genetics to this variability is not well understood. We explored the contribution of a polygenic score which was derived from large, publicly available summary statistics for absolute lymphocyte count from individuals in the general population (PGSlymph) due to a lack of publicly available summary statistics for CD4 T-cell count. We explored associations with baseline CD4 T-cell count prior to ART initiation (n=4959) and change from baseline to week 48 on ART (n=3274) among treatment-naïve participants in prospective, randomized ART studies of the AIDS Clinical Trials Group. We separately examined an African-ancestry-derived and a European-ancestry-derived PGSlymph, and evaluated their performance across all participants, and also in the African and European ancestral groups separately. Multivariate models that included PGSlymph, baseline plasma HIV-1 RNA, age, sex, and 15 principal components (PCs) of genetic similarity explained ∼26-27% of variability in baseline CD4 T-cell count, but PGSlymph accounted for <1% of this variability. Models that also included baseline CD4 T-cell count explained ∼7-9% of variability in CD4 T-cell count increase on ART, but PGSlymph accounted for <1% of this variability. In univariate analyses, PGSlymph was not significantly associated with baseline or change in CD4 T-cell count. Among individuals of African ancestry, the African PGSlymph term in the multivariate model was significantly associated with change in CD4 T-cell count while not significant in the univariate model. When applied to lymphocyte count in a general medical biobank population (Penn Medicine BioBank), PGSlymph explained ∼6-10% of variability in multivariate models (including age, sex, and PCs) but only ∼1% in univariate models. In summary, a lymphocyte count PGS derived from the general population was not consistently associated with CD4 T-cell recovery on ART. Nonetheless, adjusting for clinical covariates is quite important when estimating such polygenic effects.
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Affiliation(s)
- Kathleen M Cardone
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
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28
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Stewart DR. Genomic ascertainment of primary central nervous system cancers in adolescents and young adults. Neurooncol Adv 2024; 6:vdae048. [PMID: 38800695 PMCID: PMC11125399 DOI: 10.1093/noajnl/vdae048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/29/2024] Open
Abstract
Genomic ascertainment is the inversion of the traditional phenotype-first approach; with a "genome-first" approach, a cohort linked to electronic health records (EHR) undergoes germline sequencing (array, panel, exome, and genome) and deleterious variation of interest in a gene (or set of genes) are identified. Phenotype is then queried from the linked EHR and from call-back investigation and estimates of variant prevalence, disease penetrance, and phenotype can be determined. This should permit a better estimate of the full phenotypic spectrum, severity, and penetrance linked to a deleterious variant. For now, given the modest size, limited EHR, and age of participants in sequenced cohorts, genomic ascertainment approaches to investigate cancer in children and young adults will likely be restricted to descriptive studies and complement traditional phenotype-first work. Another issue is the ascertainment of the cohort itself: Participants need to survive long enough to enroll. Not accounting for this may lead to bias and incorrect estimates of variant prevalence. Adult-focused cohorts with EHR extending back into childhood, linked to cancer registries, and/or studies that permit recontact with participants may facilitate genomic ascertainment in pediatric cancer research. In summary, genomic ascertainment in pediatric primary brain cancer research remains largely untapped and merits further investigation.
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Affiliation(s)
- Douglas R Stewart
- Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Rockville, Maryland, USA
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29
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Zhou H, Kember RL, Deak JD, Xu H, Toikumo S, Yuan K, Lind PA, Farajzadeh L, Wang L, Hatoum AS, Johnson J, Lee H, Mallard TT, Xu J, Johnston KJA, Johnson EC, Nielsen TT, Galimberti M, Dao C, Levey DF, Overstreet C, Byrne EM, Gillespie NA, Gordon S, Hickie IB, Whitfield JB, Xu K, Zhao H, Huckins LM, Davis LK, Sanchez-Roige S, Madden PAF, Heath AC, Medland SE, Martin NG, Ge T, Smoller JW, Hougaard DM, Børglum AD, Demontis D, Krystal JH, Gaziano JM, Edenberg HJ, Agrawal A, Justice AC, Stein MB, Kranzler HR, Gelernter J. Multi-ancestry study of the genetics of problematic alcohol use in over 1 million individuals. Nat Med 2023; 29:3184-3192. [PMID: 38062264 PMCID: PMC10719093 DOI: 10.1038/s41591-023-02653-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 10/18/2023] [Indexed: 12/17/2023]
Abstract
Problematic alcohol use (PAU), a trait that combines alcohol use disorder and alcohol-related problems assessed with a questionnaire, is a leading cause of death and morbidity worldwide. Here we conducted a large cross-ancestry meta-analysis of PAU in 1,079,947 individuals (European, N = 903,147; African, N = 122,571; Latin American, N = 38,962; East Asian, N = 13,551; and South Asian, N = 1,716 ancestries). We observed a high degree of cross-ancestral similarity in the genetic architecture of PAU and identified 110 independent risk variants in within- and cross-ancestry analyses. Cross-ancestry fine mapping improved the identification of likely causal variants. Prioritizing genes through gene expression and chromatin interaction in brain tissues identified multiple genes associated with PAU. We identified existing medications for potential pharmacological studies by a computational drug repurposing analysis. Cross-ancestry polygenic risk scores showed better performance of association in independent samples than single-ancestry polygenic risk scores. Genetic correlations between PAU and other traits were observed in multiple ancestries, with other substance use traits having the highest correlations. This study advances our knowledge of the genetic etiology of PAU, and these findings may bring possible clinical applicability of genetics insights-together with neuroscience, biology and data science-closer.
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Affiliation(s)
- Hang Zhou
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA.
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA.
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA.
| | - Rachel L Kember
- Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Joseph D Deak
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Heng Xu
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Sylvanus Toikumo
- Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Kai Yuan
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Penelope A Lind
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
- School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
- Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia
| | - Leila Farajzadeh
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Lu Wang
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Alexander S Hatoum
- Department of Psychological and Brain Sciences, Washington University in St. Louis, Saint Louis, MO, USA
| | - Jessica Johnson
- Pamela Sklar Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hyunjoon Lee
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Travis T Mallard
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jiayi Xu
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | | | - Emma C Johnson
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Trine Tollerup Nielsen
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Marco Galimberti
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Cecilia Dao
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Daniel F Levey
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Cassie Overstreet
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Enda M Byrne
- Child Health Research Centre, The University of Queensland, Brisbane, Queensland, Australia
| | - Nathan A Gillespie
- Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Scott Gordon
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Ian B Hickie
- Brain and Mind Centre, University of Sydney, Camperdown, New South Wales, Australia
| | - John B Whitfield
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Ke Xu
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
| | - Laura M Huckins
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Lea K Davis
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Division of Medical Genetics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Sandra Sanchez-Roige
- Department of Medicine, Division of Medical Genetics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Pamela A F Madden
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Andrew C Heath
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Sarah E Medland
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
- Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia
- School of Psychology, University of Queensland, Brisbane, Queensland, Australia
| | - Nicholas G Martin
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Tian Ge
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Jordan W Smoller
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - David M Hougaard
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Anders D Børglum
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Ditte Demontis
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - John H Krystal
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA
- National Center for PTSD, US Department of Veterans Affairs, West Haven, CT, USA
- Department of Psychology, Yale University, New Haven, CT, USA
- Psychiatry and Behavioral Health Services, Yale-New Haven Hospital, New Haven, CT, USA
| | - J Michael Gaziano
- Massachusetts Veterans Epidemiology and Research Information Center (MAVERIC), Boston Veterans Affairs Healthcare System, Boston, MA, USA
- Department of Medicine, Divisions of Aging and Preventative Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Howard J Edenberg
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Amy C Justice
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Interdisciplinary Research on AIDS, Yale School of Public Health, New Haven, CT, USA
| | - Murray B Stein
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Psychiatry Service, VA San Diego Healthcare System, San Diego, CA, USA
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Henry R Kranzler
- Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Joel Gelernter
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA.
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA.
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA.
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA.
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30
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Symecko H, Schnoll R, Beidas RS, Bekelman JE, Blumenthal D, Bauer AM, Gabriel P, Boisseau L, Doucette A, Powers J, Cappadocia J, McKenna DB, Richardville R, Cuff L, Offer R, Clement EG, Buttenheim AM, Asch DA, Rendle KA, Shelton RC, Fayanju OM, Wileyto EP, Plag M, Ware S, Shulman LN, Nathanson KL, Domchek SM. Protocol to evaluate sequential electronic health record-based strategies to increase genetic testing for breast and ovarian cancer risk across diverse patient populations in gynecology practices. Implement Sci 2023; 18:57. [PMID: 37932730 PMCID: PMC10629034 DOI: 10.1186/s13012-023-01308-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: 08/29/2023] [Accepted: 09/29/2023] [Indexed: 11/08/2023] Open
Abstract
BACKGROUND Germline genetic testing is recommended by the National Comprehensive Cancer Network (NCCN) for individuals including, but not limited to, those with a personal history of ovarian cancer, young-onset (< 50 years) breast cancer, and a family history of ovarian cancer or male breast cancer. Genetic testing is underused overall, and rates are consistently lower among Black and Hispanic populations. Behavioral economics-informed implementation strategies, or nudges, directed towards patients and clinicians may increase the use of this evidence-based clinical practice. METHODS Patients meeting eligibility for germline genetic testing for breast and ovarian cancer will be identified using electronic phenotyping algorithms. A pragmatic cohort study will test three sequential strategies to promote genetic testing, two directed at patients and one directed at clinicians, deployed in the electronic health record (EHR) for patients in OB-GYN clinics across a diverse academic medical center. We will use rapid cycle approaches informed by relevant clinician and patient experiences, health equity, and behavioral economics to optimize and de-risk our strategies and methods before trial initiation. Step 1 will send patients messages through the health system patient portal. For non-responders, step 2 will reach out to patients via text message. For non-responders, Step 3 will contact patients' clinicians using a novel "pend and send" tool in the EHR. The primary implementation outcome is engagement with germline genetic testing for breast and ovarian cancer predisposition, defined as a scheduled genetic counseling appointment. Patient data collected through the EHR (e.g., race/ethnicity, geocoded address) will be examined as moderators of the impact of the strategies. DISCUSSION This study will be one of the first to sequentially examine the effects of patient- and clinician-directed strategies informed by behavioral economics on engagement with breast and ovarian cancer genetic testing. The pragmatic and sequential design will facilitate a large and diverse patient sample, allow for the assessment of incremental gains from different implementation strategies, and permit the assessment of moderators of strategy effectiveness. The findings may help determine the impact of low-cost, highly transportable implementation strategies that can be integrated into healthcare systems to improve the use of genomic medicine. TRIAL REGISTRATION ClinicalTrials.gov. NCT05721326. Registered February 10, 2023. https://www. CLINICALTRIALS gov/study/NCT05721326.
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Affiliation(s)
- Heather Symecko
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Basser Center for BRCA, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Robert Schnoll
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, USA
- Center for Interdisciplinary Research On Nicotine Addiction, University of Pennsylvania, Philadelphia, PA, USA
| | - Rinad S Beidas
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Justin E Bekelman
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, USA
| | - Daniel Blumenthal
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Interdisciplinary Research On Nicotine Addiction, University of Pennsylvania, Philadelphia, PA, USA
| | - Anna-Marika Bauer
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Interdisciplinary Research On Nicotine Addiction, University of Pennsylvania, Philadelphia, PA, USA
| | - Peter Gabriel
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, USA
| | - Leland Boisseau
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Abigail Doucette
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, USA
| | - Jacquelyn Powers
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Basser Center for BRCA, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jacqueline Cappadocia
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Basser Center for BRCA, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Danielle B McKenna
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Basser Center for BRCA, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Robert Richardville
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Basser Center for BRCA, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Lauren Cuff
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Basser Center for BRCA, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ryan Offer
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth G Clement
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Alison M Buttenheim
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, USA
- School of Nursing, University of Pennsylvania, Philadelphia, PA, USA
| | - David A Asch
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, USA
| | - Katharine A Rendle
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, USA
| | - Rachel C Shelton
- Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Oluwadamilola M Fayanju
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, USA
| | - E Paul Wileyto
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Interdisciplinary Research On Nicotine Addiction, University of Pennsylvania, Philadelphia, PA, USA
| | - Martina Plag
- Center for Healthcare Transformation and Innovation, Penn Medicine, Philadelphia, PA, USA
| | - Sue Ware
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Interdisciplinary Research On Nicotine Addiction, University of Pennsylvania, Philadelphia, PA, USA
| | - Lawrence N Shulman
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, USA
| | - Katherine L Nathanson
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Basser Center for BRCA, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, USA
| | - Susan M Domchek
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Basser Center for BRCA, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, USA.
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31
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Koyama S, Wang Y, Paruchuri K, Uddin MM, Cho SMJ, Urbut SM, Haidermota S, Hornsby WE, Green RC, Daly MJ, Neale BM, Ellinor PT, Smoller JW, Lebo MS, Karlson EW, Martin AR, Natarajan P. Decoding Genetics, Ancestry, and Geospatial Context for Precision Health. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.10.24.23297096. [PMID: 37961173 PMCID: PMC10635180 DOI: 10.1101/2023.10.24.23297096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Mass General Brigham, an integrated healthcare system based in the Greater Boston area of Massachusetts, annually serves 1.5 million patients. We established the Mass General Brigham Biobank (MGBB), encompassing 142,238 participants, to unravel the intricate relationships among genomic profiles, environmental context, and disease manifestations within clinical practice. In this study, we highlight the impact of ancestral diversity in the MGBB by employing population genetics, geospatial assessment, and association analyses of rare and common genetic variants. The population structures captured by the genetics mirror the sequential immigration to the Greater Boston area throughout American history, highlighting communities tied to shared genetic and environmental factors. Our investigation underscores the potency of unbiased, large-scale analyses in a healthcare-affiliated biobank, elucidating the dynamic interplay across genetics, immigration, structural geospatial factors, and health outcomes in one of the earliest American sites of European colonization.
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Affiliation(s)
- Satoshi Koyama
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Ying Wang
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kaavya Paruchuri
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Md Mesbah Uddin
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - So Mi J. Cho
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Integrative Research Center for Cerebrovascular and Cardiovascular Diseases, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Sarah M. Urbut
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Sara Haidermota
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Whitney E. Hornsby
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Robert C. Green
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Medicine (Genetics), MassGeneralBrigham, Boston, MA, USA
- Broad Institute and Ariadne Labs, Boston, MA, USA
| | - Mark J. Daly
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Molecular Medicine Finland (FIMM), Finland
- University of Helsinki, Helsinki, Finland
| | - Benjamin M. Neale
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Patrick T. Ellinor
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Jordan W. Smoller
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Matthew S. Lebo
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Mass General Brigham Personalized Medicine, Cambridge, MA, USA
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Elizabeth W. Karlson
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Mass General Brigham Personalized Medicine, Cambridge, MA, USA
- Division of Rheumatology, Inflammation and Immunity, Department of Medicine, Brigham and Women’s Hospital., Boston, MA, USA
| | - Alicia R. Martin
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Pradeep Natarajan
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
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Gold JI, Madhavan S, Park J, Zouk H, Perez E, Strong A, Drivas TG, Karaa A, Yudkoff M, Rader D, Green RC, Gold NB. Phenotypes of undiagnosed adults with actionable OTC and GLA variants. HGG ADVANCES 2023; 4:100226. [PMID: 37593415 PMCID: PMC10428110 DOI: 10.1016/j.xhgg.2023.100226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 07/21/2023] [Indexed: 08/19/2023] Open
Abstract
Inherited metabolic disorders (IMDs) are variably expressive, complicating identification of affected individuals. A genotype-first approach can identify individuals at risk for morbidity and mortality from undiagnosed IMDs and can lead to protocols that improve clinical detection, counseling, and management. Using data from 57,340 participants in two hospital biobanks, we assessed the frequency and phenotypes of individuals with pathogenic/likely pathogenic variants (PLPVs) in two IMD genes: GLA, associated with Fabry disease, and OTC, associated with ornithine transcarbamylase deficiency. Approximately 1 in 19,100 participants harbored an undiagnosed PLPV in GLA or OTC. We identified three individuals (2 male, 1 female) with PLPVs in GLA, all of whom were undiagnosed, and three individuals (3 female) with PLPVs in OTC, two of whom were undiagnosed. All three individuals with PLPVs in GLA (100%) had symptoms suggestive of mild Fabry disease, and one individual (14.2%) had an ischemic stroke at age 33, likely indicating the presence of classic disease. No individuals with PLPVs in OTC had documented hyperammonemia despite exposure to catabolic states, but all (100%) had chronic symptoms suggestive of attenuated disease, including mood disorders and migraines. Our findings suggest that GLA and OTC variants identified via a genotype-first approach are of high penetrance and that population screening of these genes can be used to facilitate stepwise phenotyping and appropriate care.
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Affiliation(s)
- Jessica I. Gold
- Division of Human Genetics, Department of Pediatrics, Children’s Hospital of Philadelphia; Philadelphia, PA, USA
| | - Sarina Madhavan
- Harvard Medical School, Boston, MA, USA
- Harvard Business School, Cambridge, MA, USA
| | - Joseph Park
- Department of Genetics, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Hana Zouk
- Harvard Medical School, Boston, MA, USA
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge MA, USA
- Department of Pathology, Massachusetts General Hospital, Boston, MA, USA
| | - Emma Perez
- Mass General Brigham Personalized Medicine, Cambridge MA, USA
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Alanna Strong
- Division of Human Genetics, Department of Pediatrics, Children’s Hospital of Philadelphia; Philadelphia, PA, USA
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Theodore G. Drivas
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Amel Karaa
- Massachusetts General Hospital for Children, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Marc Yudkoff
- Division of Human Genetics, Department of Pediatrics, Children’s Hospital of Philadelphia; Philadelphia, PA, USA
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Daniel Rader
- Department of Genetics, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Regeneron Genetics Center
- Division of Human Genetics, Department of Pediatrics, Children’s Hospital of Philadelphia; Philadelphia, PA, USA
- Harvard Medical School, Boston, MA, USA
- Harvard Business School, Cambridge, MA, USA
- Department of Genetics, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge MA, USA
- Department of Pathology, Massachusetts General Hospital, Boston, MA, USA
- Mass General Brigham Personalized Medicine, Cambridge MA, USA
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Massachusetts General Hospital for Children, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Broad Institute, Boston, MA, USA
- Ariadne Labs, Boston, MA, USA
| | - Penn Medicine BioBank
- Division of Human Genetics, Department of Pediatrics, Children’s Hospital of Philadelphia; Philadelphia, PA, USA
- Harvard Medical School, Boston, MA, USA
- Harvard Business School, Cambridge, MA, USA
- Department of Genetics, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge MA, USA
- Department of Pathology, Massachusetts General Hospital, Boston, MA, USA
- Mass General Brigham Personalized Medicine, Cambridge MA, USA
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Massachusetts General Hospital for Children, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Broad Institute, Boston, MA, USA
- Ariadne Labs, Boston, MA, USA
| | - Robert C. Green
- Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Broad Institute, Boston, MA, USA
- Ariadne Labs, Boston, MA, USA
| | - Nina B. Gold
- Massachusetts General Hospital for Children, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
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Lee DSM, Cardone KM, Zhang DY, Abramowitz S, DePaolo JS, Aragam KG, Biddinger K, Conery M, Dilitikas O, Hoffman-Andrews L, Judy RL, Khan A, Kulo I, Puckelwartz MJ, Reza N, Satterfield BA, Singhal P, Arany ZP, Cappola TP, Carruth E, Day SM, Do R, Haggarty CM, Joseph J, McNally E, Nadkarni G, Owens AT, Rader DJ, Ritchie MD, Sun Y, Voight BF, Levin MG, Damrauer SM. Common- and rare-variant genetic architecture of heart failure across the allele frequency spectrum. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.07.16.23292724. [PMID: 37503172 PMCID: PMC10371173 DOI: 10.1101/2023.07.16.23292724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Heart failure (HF) is a complex trait, influenced by environmental and genetic factors, that affects over 30 million individuals worldwide. Historically, the genetics of HF have been studied in Mendelian forms of disease, where rare genetic variants have been linked to familial cardiomyopathies. More recently, genome-wide association studies (GWAS) have successfully identified common genetic variants associated with risk of HF. However, the relative importance of genetic variants across the allele-frequency spectrum remains incompletely characterized. Here, we report the results of common- and rare-variant association studies of all-cause heart failure, applying recently developed methods to quantify the heritability of HF attributable to different classes of genetic variation. We combine GWAS data across multiple populations including 207,346 individuals with HF and 2,151,210 without, identifying 176 risk loci at genome-wide significance (p < 5×10-8). Signals at newly identified common-variant loci include coding variants in Mendelian cardiomyopathy genes (MYBPC3, BAG3), as well as regulators of lipoprotein (LPL) and glucose metabolism (GIPR, GLP1R), and are enriched in cardiac, muscle, nerve, and vascular tissues, as well as myocyte and adipocyte cell types. Gene burden studies across three biobanks (PMBB, UKB, AOU) including 27,208 individuals with HF and 349,126 without uncover exome-wide significant (p < 3.15×10-6) associations for HF and rare predicted loss-of-function (pLoF) variants in TTN, MYBPC3, FLNC, and BAG3. Total burden heritability of rare coding variants (2.2%, 95% CI 0.99-3.5%) is highly concentrated in a small set of Mendelian cardiomyopathy genes, and is lower than heritability attributable to common variants (4.3%, 95% CI 3.9-4.7%) which is more diffusely spread throughout the genome. Finally, we demonstrate that common-variant background, in the form of a polygenic risk score (PRS), significantly modifies the risk of HF among carriers of pathogenic truncating variants in the Mendelian cardiomyopathy gene TTN. These findings suggest a significant polygenic component to HF exists that is not captured by current clinical genetic testing.
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Affiliation(s)
- David S M Lee
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Katie M Cardone
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - David Y Zhang
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Sarah Abramowitz
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - John S DePaolo
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Krishna G Aragam
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA
| | - Kiran Biddinger
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA
| | - Mitchell Conery
- Genomics and Computational Biology Graduate Group, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Ozan Dilitikas
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Lily Hoffman-Andrews
- Division of Cardiovascular Medicine, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Renae L Judy
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Atlas Khan
- Division of Nephrology, Department of Medicine, Columbia University Vagelos College of Physicians and Surgeons, New York, NY
| | - Iftikhar Kulo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Megan J Puckelwartz
- Department of Pharmacology, Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Nosheen Reza
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | | | - Pankhuri Singhal
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Zoltan P Arany
- Division of Cardiovascular Medicine, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
- Cardiovascular Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Thomas P Cappola
- Division of Cardiovascular Medicine, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Eric Carruth
- Department of Translational Data Science and Informatics, Geisinger, Danville, PA
| | - Sharlene M Day
- Division of Cardiovascular Medicine, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
- Cardiovascular Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Ron Do
- The Charles Bronfman Institute for Personalized Medicine, Mount Sinai Icahn School of Medicine, New York, NY
- Biome Phenomics Center, Mount Sinai Icahn School of Medicine, New York, NY
- Department of Genetics and Genomic Sciences, Mount Sinai Icahn School of Medicine, New York, NY
| | | | - Jacob Joseph
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Elizabeth McNally
- Center for Genetic Medicine, Bluhm Cardiovascular Institute, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Girish Nadkarni
- Division of Nephrology, Department of Medicine, Mount Sinai Icahn School of Medicine, New York, NY
| | - Anjali T Owens
- Cardiovascular Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Daniel J Rader
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
- Division of Translational Medicine and Human Genetics, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Yan Sun
- Deparment of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA
| | - Benjamin F Voight
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA
| | - Michael G Levin
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA
| | - Scott M Damrauer
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
- Cardiovascular Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA
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34
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Xu H, Toikumo S, Crist RC, Glogowska K, Jinwala Z, Deak JD, Justice AC, Gelernter J, Johnson EC, Kranzler HR, Kember RL. Identifying genetic loci and phenomic associations of substance use traits: A multi-trait analysis of GWAS (MTAG) study. Addiction 2023; 118:1942-1952. [PMID: 37156939 PMCID: PMC10754226 DOI: 10.1111/add.16229] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 04/19/2023] [Indexed: 05/10/2023]
Abstract
BACKGROUND AND AIMS Genome-wide association studies (GWAS) of opioid use disorder (OUD) and cannabis use disorder (CUD) have lagged behind those of alcohol use disorder (AUD) and smoking, where many more loci have been identified. We sought to identify novel loci for substance use traits (SUTs) in both African- (AFR) and European- (EUR) ancestry individuals to enhance our understanding of the traits' genetic architecture. DESIGN We used multi-trait analysis of GWAS (MTAG) to analyze four SUTs in EUR subjects (OUD, CUD, AUD and smoking initiation [SMKinitiation]), and three SUTs in AFR subjects (OUD, AUD and smoking trajectory [SMKtrajectory]). We conducted gene-set and protein-protein interaction analyses and calculated polygenic risk scores (PRS) in two independent samples. SETTING This study was conducted in the United States. PARTICIPANTS A total of 5692 EUR and 4918 AFR individuals in the Yale-Penn sample and 29 054 EUR and 10 265 AFR individuals in the Penn Medicine BioBank sample. FINDINGS MTAG identified genome-wide significant (GWS) single nucleotide polymorphisms (SNPs) for all four traits in EUR: 41 SNPs in 36 loci for OUD; 74 SNPs in 60 loci for CUD; 63 SNPs in 52 loci for AUD; and 183 SNPs in 144 loci for SMKinitiation. MTAG also identified GWS SNPs in AFR: 2 SNPs in 2 loci for OUD; 3 SNPs in 3 loci for AUD; and 1 SNP in 1 locus for SMKtrajectory. In the Yale-Penn sample, the MTAG-derived PRS consistently yielded more significant associations with both the corresponding substance use disorder diagnosis and multiple related phenotypes than the GWAS-derived PRS. CONCLUSIONS Multi-trait analysis of genome-wide association studies boosted the number of loci found for substance use traits, identifying genes not previously linked to any substance, and increased the power of polygenic risk scores. Multi-trait analysis of genome-wide association studies can be used to identify novel associations for substance use, especially those for which the samples are smaller than those for historically legal substances.
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Affiliation(s)
- Heng Xu
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Sylvanus Toikumo
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- Mental Illness Research, Education and Clinical Center, Veterans Integrated Service Network 4, Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania, USA
| | - Richard C. Crist
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- Mental Illness Research, Education and Clinical Center, Veterans Integrated Service Network 4, Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania, USA
| | - Klaudia Glogowska
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Zeal Jinwala
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Joseph D. Deak
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
- Veterans Affairs Connecticut Healthcare Center, West Haven, Connecticut, USA
| | - Amy C. Justice
- Veterans Affairs Connecticut Healthcare Center, West Haven, Connecticut, USA
- Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut, USA
- Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut, USA
| | - Joel Gelernter
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
- Veterans Affairs Connecticut Healthcare Center, West Haven, Connecticut, USA
| | - Emma C. Johnson
- Department of Psychiatry, Washington University School of Medicine, St Louis, Missouri, USA
| | - Henry R. Kranzler
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- Mental Illness Research, Education and Clinical Center, Veterans Integrated Service Network 4, Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania, USA
| | - Rachel L. Kember
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- Mental Illness Research, Education and Clinical Center, Veterans Integrated Service Network 4, Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania, USA
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Toikumo S, Jennings MV, Pham BK, Lee H, Mallard TT, Bianchi SB, Meredith JJ, Vilar-Ribó L, Xu H, Hatoum AS, Johnson EC, Pazdernik V, Jinwala Z, Pakala SR, Leger BS, Niarchou M, Ehinmowo M, Jenkins GD, Batzler A, Pendegraft R, Palmer AA, Zhou H, Biernacka JM, Coombes BJ, Gelernter J, Xu K, Hancock DB, Cox NJ, Smoller JW, Davis LK, Justice AC, Kranzler HR, Kember RL, Sanchez-Roige S. Multi-ancestry meta-analysis of tobacco use disorder prioritizes novel candidate risk genes and reveals associations with numerous health outcomes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.27.23287713. [PMID: 37034728 PMCID: PMC10081388 DOI: 10.1101/2023.03.27.23287713] [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: 04/29/2023]
Abstract
Tobacco use disorder (TUD) is the most prevalent substance use disorder in the world. Genetic factors influence smoking behaviors, and although strides have been made using genome-wide association studies (GWAS) to identify risk variants, the majority of variants identified have been for nicotine consumption, rather than TUD. We leveraged five biobanks to perform a multi-ancestral meta-analysis of TUD (derived via electronic health records, EHR) in 898,680 individuals (739,895 European, 114,420 African American, 44,365 Latin American). We identified 88 independent risk loci; integration with functional genomic tools uncovered 461 potential risk genes, primarily expressed in the brain. TUD was genetically correlated with smoking and psychiatric traits from traditionally ascertained cohorts, externalizing behaviors in children, and hundreds of medical outcomes, including HIV infection, heart disease, and pain. This work furthers our biological understanding of TUD and establishes EHR as a source of phenotypic information for studying the genetics of TUD.
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Affiliation(s)
- Sylvanus Toikumo
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Mariela V Jennings
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Benjamin K Pham
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Hyunjoon Lee
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Travis T Mallard
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Sevim B Bianchi
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - John J Meredith
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Laura Vilar-Ribó
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d’Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Heng Xu
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Alexander S Hatoum
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, Missouri, USA
| | - Emma C Johnson
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, Missouri, USA
| | - Vanessa Pazdernik
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Zeal Jinwala
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Shreya R Pakala
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Brittany S Leger
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
- Program in Biomedical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Maria Niarchou
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | | | - Greg D Jenkins
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Anthony Batzler
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Richard Pendegraft
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Abraham A Palmer
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Hang Zhou
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Joanna M Biernacka
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry & Psychology, Mayo Clinic, Rochester, MN, USA
| | - Brandon J Coombes
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Joel Gelernter
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Ke Xu
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Dana B Hancock
- Behavioral and Urban Health Program, Behavioral Health and Criminal Justice Division, RTI International, Research Triangle Park, NC, USA
| | - Nancy J Cox
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, USA
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Lea K Davis
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Amy C Justice
- Yale University School of Public Health, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Yale University School of Medicine, New Haven, CT, USA
| | - Henry R Kranzler
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Rachel L Kember
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Sandra Sanchez-Roige
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, USA
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DePaolo J, Biagetti G, Judy R, Wang GJ, Kelly J, Iyengar A, Goel NJ, Desai N, Szeto WY, Bavaria JE, Levin MG, Damrauer SM. Using a polygenic score to account for genomic risk factors in a model to detect individuals with dilated ascending thoracic aortas. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.06.23295145. [PMID: 37732226 PMCID: PMC10508815 DOI: 10.1101/2023.09.06.23295145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
Background Ascending thoracic aortic dilation is a complex trait that involves modifiable and non-modifiable risk factors and can lead to thoracic aortic aneurysm and dissection. Clinical risk factors have been shown to predict ascending thoracic aortic diameter. Polygenic scores (PGS) are increasingly used to assess clinical risk for multifactorial diseases. The degree to which a PGS can improve aortic diameter prediction is not known. In this study we tested the extent to which the addition of a PGS to clinical prediction algorithms improves the prediction of aortic diameter. Methods The patient cohort comprised 6,790 Penn Medicine Biobank (PMBB) participants with available echocardiography and clinical data linked to genome-wide genotype data. Linear regression models were used to integrate PGS weights derived from a large genome wide association study of thoracic aortic diameter in the UK biobank and were compared to the performance of the standard and a reweighted variation of the recently published AORTA Score. Results Cohort participants were 56% male, had a median age of 61 years (IQR 52-70) with a mean ascending aortic diameter of 3.4 cm (SD 0.5). Compared to the AORTA Score which explained 28.4% (95% CI 28.1% to 29.2%) of the variance in aortic diameter, AORTA Score + PGS explained 28.8%, (95% CI 28.1% to 29.6%), the reweighted AORTA score explained 30.4% (95% CI 29.6% to 31.2%), and the reweighted AORTA Score + PGS explained 31.0% (95% CI 30.2% to 31.8%). The addition of a PGS to either the AORTA Score or the reweighted AORTA Score improved model sensitivity for the identifying individuals with a thoracic aortic diameter ≥ 4 cm. The respective areas under the receiver operator characteristic curve for the AORTA Score + PGS (0.771, 95% CI 0.756 to 0.787) and reweighted AORTA Score + PGS (0.785, 95% CI 0.770 to 0.800) were greater than the standard AORTA Score (0.767, 95% CI 0.751 to 0.783) and reweighted AORTA Score (0.780 95% CI 0.765 to 0.795). Conclusions We demonstrated that inclusion of a PGS to the AORTA Score results in a small but clinically meaningful performance enhancement. Further investigation is necessary to determine if combining genetic and clinical risk prediction improves outcomes for thoracic aortic disease.
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Affiliation(s)
- John DePaolo
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Gina Biagetti
- Division of Vascular Surgery, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Renae Judy
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Grace J Wang
- Division of Vascular Surgery, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - John Kelly
- Division of Cardiovascular Surgery, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Amit Iyengar
- Division of Cardiovascular Surgery, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Nicholas J Goel
- Division of Cardiovascular Surgery, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Nimesh Desai
- Division of Cardiovascular Surgery, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Wilson Y Szeto
- Division of Cardiovascular Surgery, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Joseph E Bavaria
- Division of Cardiovascular Surgery, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael G Levin
- Cardiovascular Institute, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
| | - Scott M Damrauer
- Division of Vascular Surgery, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Cardiovascular Institute, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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Safonov A, Nomakuchi TT, Chao E, Horton C, Dolinsky JS, Yussuf A, Richardson M, Speare V, Li S, Bogus ZC, Bonanni M, Raper A, Kallish S, Ritchie MD, Nathanson KL, Drivas TG. A genotype-first approach identifies high incidence of NF1 pathogenic variants with distinct disease associations. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.08.23293676. [PMID: 37609227 PMCID: PMC10441497 DOI: 10.1101/2023.08.08.23293676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Loss of function variants in the NF1 gene cause neurofibromatosis type 1 (NF1), a genetic disorder characterized by complete penetrance, prevalence of 1 in 3,000, characteristic physical exam findings, and a substantially increased risk for malignancy. However, our understanding of the disorder is entirely based on patients ascertained through phenotype-first approaches. Leveraging a genotype-first approach in two large patient cohorts, we demonstrate unexpectedly high prevalence (1 in 450-750) of NF1 pathogenic variants. Half were identified in individuals lacking clinical features of NF1, with many appearing to have post-zygotic mosaicism for the identified variant. Incidentally discovered variants were not associated with classic NF1 features but were associated with an increased incidence of malignancy compared to a control population. Our findings suggest that NF1 pathogenic variants are substantially more common than previously thought, often characterized by somatic mosaicism and reduced penetrance, and are important contributors to cancer risk in the general population.
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Cruz LA, Cooke Bailey JN, Crawford DC. Importance of Diversity in Precision Medicine: Generalizability of Genetic Associations Across Ancestry Groups Toward Better Identification of Disease Susceptibility Variants. Annu Rev Biomed Data Sci 2023; 6:339-356. [PMID: 37196357 PMCID: PMC10720270 DOI: 10.1146/annurev-biodatasci-122220-113250] [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] [Indexed: 05/19/2023]
Abstract
Genome-wide association studies (GWAS) revolutionized our understanding of common genetic variation and its impact on common human disease and traits. Developed and adopted in the mid-2000s, GWAS led to searchable genotype-phenotype catalogs and genome-wide datasets available for further data mining and analysis for the eventual development of translational applications. The GWAS revolution was swift and specific, including almost exclusively populations of European descent, to the neglect of the majority of the world's genetic diversity. In this narrative review, we recount the GWAS landscape of the early years that established a genotype-phenotype catalog that is now universally understood to be inadequate for a complete understanding of complex human genetics. We then describe approaches taken to augment the genotype-phenotype catalog, including the study populations, collaborative consortia, and study design approaches aimed to generalize and then ultimately discover genome-wide associations in non-European descent populations. The collaborations and data resources established in the efforts to diversify genomic findings undoubtedly provide the foundations of the next chapters of genetic association studies with the advent of budget-friendly whole-genome sequencing.
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Affiliation(s)
- Lauren A Cruz
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA;
- Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Jessica N Cooke Bailey
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA;
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio, USA
- Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Dana C Crawford
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA;
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio, USA
- Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, USA
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Wineland D, Le AN, Hausler R, Kelly G, Barrett E, Desai H, Wubbenhorst B, Pluta J, Bastian P, Symecko H, D'Andrea K, Doucette A, Gabriel P, Reiss KA, Nayak A, Feldman M, Domchek SM, Nathanson KL, Maxwell KN. Biallelic BRCA Loss and Homologous Recombination Deficiency in Nonbreast/Ovarian Tumors in Germline BRCA1/2 Carriers. JCO Precis Oncol 2023; 7:e2300036. [PMID: 37535879 PMCID: PMC10581613 DOI: 10.1200/po.23.00036] [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] [Received: 01/24/2023] [Revised: 05/02/2023] [Accepted: 06/02/2023] [Indexed: 08/05/2023] Open
Abstract
PURPOSE Breast and ovarian tumors in germline BRCA1/2 carriers undergo allele-specific loss of heterozygosity, resulting in homologous recombination deficiency (HRD) and sensitivity to poly-ADP-ribose polymerase (PARP) inhibitors. This study investigated whether biallelic loss and HRD also occur in primary nonbreast/ovarian tumors that arise in germline BRCA1/2 carriers. METHODS A clinically ascertained cohort of BRCA1/2 carriers with a primary nonbreast/ovarian cancer was identified, including canonical (prostate and pancreatic cancers) and noncanonical (all other) tumor types. Whole-exome sequencing or clinical sequencing results (n = 45) were analyzed. A pan-cancer analysis of nonbreast/ovarian primary tumors from germline BRCA1/2 carriers from The Cancer Genome Atlas (TCGA, n = 73) was used as a validation cohort. RESULTS Ages of nonbreast/ovarian cancer diagnosis in germline BRCA1/2 carriers were similar to controls for the majority of cancer types. Nine of 45 (20%) primary nonbreast/ovarian tumors from germline BRCA1/2 carriers had biallelic loss of BRCA1/2 in the clinical cohort, and 23 of 73 (32%) in the TCGA cohort. In the combined cohort, 35% and 27% of primary canonical and noncanonical BRCA tumor types, respectively, had biallelic loss. High HRD scores (HRDex > 42) were detected in 81% of tumors with biallelic BRCA loss compared with 22% (P < .001) of tumors without biallelic BRCA loss. No differences in genomic profile, including mutational signatures, mutation spectrum, tumor mutational burden, or microsatellite instability, were found in primary nonbreast/ovarian tumors with or without biallelic BRCA1/2 loss. CONCLUSION A proportion of noncanonical primary tumors have biallelic loss and evidence of HRD. Our data suggest that assessment of biallelic loss and HRD could supplement identification of germline BRCA1/2 mutations in selection of patients for platinum or PARP inhibitor therapy.
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Affiliation(s)
- Dylane Wineland
- Arcadia University and Chester County Hospital, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Anh N. Le
- Division of Hematology/Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Ryan Hausler
- Division of Hematology/Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Gregory Kelly
- Division of Hematology/Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Emanuel Barrett
- Division of Hematology/Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Heena Desai
- Division of Hematology/Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Bradley Wubbenhorst
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - John Pluta
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Paul Bastian
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Heather Symecko
- Division of Hematology/Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Kurt D'Andrea
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Abigail Doucette
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Peter Gabriel
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Kim A. Reiss
- Division of Hematology/Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Anupma Nayak
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Michael Feldman
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Susan M. Domchek
- Division of Hematology/Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Katherine L. Nathanson
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Kara N. Maxwell
- Division of Hematology/Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
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Verma A, Huffman JE, Rodriguez A, Conery M, Liu M, Ho YL, Kim Y, Heise DA, Guare L, Panickan VA, Garcon H, Linares F, Costa L, Goethert I, Tipton R, Honerlaw J, Davies L, Whitbourne S, Cohen J, Posner DC, Sangar R, Murray M, Wang X, Dochtermann DR, Devineni P, Shi Y, Nandi TN, Assimes TL, Brunette CA, Carroll RJ, Clifford R, Duvall S, Gelernter J, Hung A, Iyengar SK, Joseph J, Kember R, Kranzler H, Levey D, Luoh SW, Merritt VC, Overstreet C, Deak JD, Grant SFA, Polimanti R, Roussos P, Sun YV, Venkatesh S, Voloudakis G, Justice A, Begoli E, Ramoni R, Tourassi G, Pyarajan S, Tsao PS, O’Donnell CJ, Muralidhar S, Moser J, Casas JP, Bick AG, Zhou W, Cai T, Voight BF, Cho K, Gaziano MJ, Madduri RK, Damrauer SM, Liao KP. Diversity and Scale: Genetic Architecture of 2,068 Traits in the VA Million Veteran Program. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.28.23291975. [PMID: 37425708 PMCID: PMC10327290 DOI: 10.1101/2023.06.28.23291975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Genome-wide association studies (GWAS) have underrepresented individuals from non-European populations, impeding progress in characterizing the genetic architecture and consequences of health and disease traits. To address this, we present a population-stratified phenome-wide GWAS followed by a multi-population meta-analysis for 2,068 traits derived from electronic health records of 635,969 participants in the Million Veteran Program (MVP), a longitudinal cohort study of diverse U.S. Veterans genetically similar to the respective African (121,177), Admixed American (59,048), East Asian (6,702), and European (449,042) superpopulations defined by the 1000 Genomes Project. We identified 38,270 independent variants associating with one or more traits at experiment-wide P < 4.6 × 10 - 11 significance; fine-mapping 6,318 signals identified from 613 traits to single-variant resolution. Among these, a third (2,069) of the associations were found only among participants genetically similar to non-European reference populations, demonstrating the importance of expanding diversity in genetic studies. Our work provides a comprehensive atlas of phenome-wide genetic associations for future studies dissecting the architecture of complex traits in diverse populations.
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Affiliation(s)
- Anurag Verma
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, 19104, USA
- Department of Medicine, Division of Translational Medicine and Human Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Institute for Biomedical Informatics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Jennifer E Huffman
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, 02130, USA
- Palo Alto Veterans Institute for Research (PAVIR), Palo Alto Health Care System, Palo Alto, CA, 94304, USA
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
| | - Alex Rodriguez
- Data Science and Learning, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Mitchell Conery
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Molei Liu
- Department of Biostatistics, Columbia University’s Mailman School of Public Health, New York, NY, 10032, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, 02130, USA
| | - Youngdae Kim
- Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - David A Heise
- National Security Sciences Directorate, Cyber Resilience and Intelligence Division, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN, 37831, USA
| | - Lindsay Guare
- Department of Medicine, Division of Translational Medicine and Human Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | | | - Helene Garcon
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, 02130, USA
| | - Franciel Linares
- R&D Systems Engineering, Information Technology Services Directorate, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN, 37831, USA
| | - Lauren Costa
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA, 02111, USA
| | - Ian Goethert
- Data Management and Engineering, Information Technology Services Division, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN, 37831, USA
| | - Ryan Tipton
- Knowledge Discovery Infrastructure, Information Technology Services Division, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN, 37831, USA
| | - Jacqueline Honerlaw
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, 02130, USA
| | - Laura Davies
- Computing and Computational Sciences Dir PMO, PMO, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN, 37831, USA
| | - Stacey Whitbourne
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA, 02111, USA
- Department of Medicine, Division of Aging, Brigham and Women’s Hospital, Boston, MA, 02115, USA
| | - Jeremy Cohen
- National Security Sciences Directorate, Cyber Resilience and Intelligence Division, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN, 37831, USA
| | - Daniel C Posner
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, 02130, USA
| | - Rahul Sangar
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA, 02111, USA
| | - Michael Murray
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA, 02111, USA
| | - Xuan Wang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA
| | - Daniel R Dochtermann
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA, 02130, USA
| | - Poornima Devineni
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA, 02130, USA
| | - Yunling Shi
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA, 02130, USA
| | - Tarak Nath Nandi
- Data Science and Learning, Argonne National Laboratory, Lemont, IL, 60439, USA
| | | | - Charles A Brunette
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
- Research Service, VA Boston Healthcare System, Boston, MA, 02130, USA
| | - Robert J Carroll
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37211, USA
| | - Royce Clifford
- Research Department, VA San Diego Healthcare System, San Diego, CA, 92161, USA
- Surgery, Otolaryngology, UCSD San Diego, La Jolla, California, 92093, USA
| | - Scott Duvall
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, 84148, USA
- Internal Medicine, Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, 84132, USA
| | - Joel Gelernter
- Psychiatry, Human Genetics, Yale University, New Haven, CT, 06520, USA
- VA Connecticut Healthcare System West Haven, West Haven, CT, 06516, USA
| | - Adriana Hung
- Medicine, Nephrology & Hypertension, VA Tennessee Valley Healthcare System & Vanderbilt University, Nashville, TN, 37232, USA
| | - Sudha K Iyengar
- Population and Quantitative Health Sciences, Case Western Reserve University, School of Medicine, Cleveland, OH, 44106, USA
| | - Jacob Joseph
- Medicine, Cardiology Section, VA Providence Healthcare System, Providence, RI, 02908, USA
- Department of Medicine, Brown University, Providence, RI, 02908, USA
| | - Rachel Kember
- Mental Illness Research, Education and Clinical Center, Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, 19104, USA
- Department of Psychiatry, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Henry Kranzler
- Mental Illness Research, Education and Clinical Center, Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, 19104, USA
- Department of Psychiatry, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Daniel Levey
- Psychiatry, Human Genetics, Yale University, New Haven, CT, 06520, USA
- Medicine, VA Connecticut Healthcare System West Haven, West Haven, CT, 06516, USA
| | - Shiuh-Wen Luoh
- VA Portland Health Care System, Portland, OR, 97239, USA
- Division of Hematology and Medical Oncology, Knight Cancer Institute, Oregon Health and Science University, Portland, OR, 97239, USA
| | - Victoria C Merritt
- Research Department, VA San Diego Healthcare System, San Diego, CA, 92161, USA
| | - Cassie Overstreet
- Psychiatry, Human Genetics, Yale University, New Haven, CT, 06520, USA
| | - Joseph D Deak
- Psychiatry, Yale University, New Haven, CT, 06520, USA
- Psychiatry, VA Connecticut Healthcare System West Haven, West Haven, CT, 06516, USA
| | - Struan F A Grant
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Department of Pediatrics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Divisions of Human Genetics and Endocrinology and Diabetes, Children’s Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Department of Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | | | - Panos Roussos
- Psychiatry, Mental Illness Research, Education and Clinical Center, James J. Peters VA Medical Center; Icahn School of Medicine at Mount Sinai, Bronx, NY, 10468, USA
| | - Yan V Sun
- Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, 30322, USA
| | - Sanan Venkatesh
- Psychiatry, Mental Illness Research, Education and Clinical Center, James J. Peters VA Medical Center; Icahn School of Medicine at Mount Sinai, Bronx, NY, 10468, USA
| | - Georgios Voloudakis
- Psychiatry, Mental Illness Research, Education and Clinical Center, James J. Peters VA Medical Center; Icahn School of Medicine at Mount Sinai, Bronx, NY, 10468, USA
| | - Amy Justice
- Medicine, VA Connecticut Healthcare System West Haven, West Haven, CT, 06516, USA
- Internal Medicine, General Medicine, Yale University, New Haven, CT, 06520, USA
- Health Policy, Yale School of Public Health, New Haven, CT, 06520, USA
| | - Edmon Begoli
- Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN, 37831, USA
| | - Rachel Ramoni
- Office of Research and Development, Department of Veterans Affairs, Washington, DC, 20420, USA
| | - Georgia Tourassi
- National Center for Computational Sciences, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN, 37831, USA
| | - Saiju Pyarajan
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA, 02130, USA
| | - Philip S Tsao
- Medicine, Cardiology, VA Palo Alto Healthcare System, Palo Alto, CA, 94304, USA
- Department of Medicine, Stanford University, Palo Alto, CA, 94304, USA
| | | | - Sumitra Muralidhar
- Office of Research and Development, Department of Veterans Affairs, Washington, DC, 20420, USA
| | - Jennifer Moser
- Office of Research and Development, Department of Veterans Affairs, Washington, DC, 20420, USA
| | - Juan P Casas
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, 02130, USA
| | - Alexander G Bick
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, 37325, USA
| | - Wei Zhou
- Department of Medicine, Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, 02114, USA
- Stanley Center for Psychiatric Research, Cambridge, MA, 02142, USA
- Program in Medical and Population Genetics, Cambridge, MA, 02142, USA
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA
| | - Benjamin F Voight
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, 19104, USA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Department of Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Institute of Translational Medicine and Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Kelly Cho
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA, 02111, USA
- Department of Medicine, Division of Aging, Brigham and Women’s Hospital, Boston, MA, 02115, USA
| | - Michael J Gaziano
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA, 02111, USA
- Department of Medicine, Division of Aging, Brigham and Women’s Hospital, Boston, MA, 02115, USA
| | - Ravi K Madduri
- Data Science and Learning, Argonne National Laboratory, Lemont, IL, 60439, USA
| | - Scott M Damrauer
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, 19104, USA
- Department of Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Department of Surgery, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Cardiovascular Institute, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Katherine P Liao
- Medicine, Rheumatology, VA Boston Healthcare System, Boston, MA, 02130, USA
- Department of Medicine, Division of Rheumatology, Inflammation, and Immunity, Brigham and Women’s Hospital, Boston, MA, 02115, USA
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41
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Toikumo S, Vickers-Smith R, Jinwala Z, Xu H, Saini D, Hartwell E, Venegas MP, Sullivan KA, Xu K, Jacobson DA, Gelernter J, Rentsch CT, Stahl E, Cheatle M, Zhou H, Waxman SG, Justice AC, Kember RL, Kranzler HR. The genetic architecture of pain intensity in a sample of 598,339 U.S. veterans. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.09.23286958. [PMID: 36993749 PMCID: PMC10055465 DOI: 10.1101/2023.03.09.23286958] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Chronic pain is a common problem, with more than one-fifth of adult Americans reporting pain daily or on most days. It adversely affects quality of life and imposes substantial personal and economic costs. Efforts to treat chronic pain using opioids played a central role in precipitating the opioid crisis. Despite an estimated heritability of 25-50%, the genetic architecture of chronic pain is not well characterized, in part because studies have largely been limited to samples of European ancestry. To help address this knowledge gap, we conducted a cross-ancestry meta-analysis of pain intensity in 598,339 participants in the Million Veteran Program, which identified 125 independent genetic loci, 82 of which are novel. Pain intensity was genetically correlated with other pain phenotypes, level of substance use and substance use disorders, other psychiatric traits, education level, and cognitive traits. Integration of the GWAS findings with functional genomics data shows enrichment for putatively causal genes (n = 142) and proteins (n = 14) expressed in brain tissues, specifically in GABAergic neurons. Drug repurposing analysis identified anticonvulsants, beta-blockers, and calcium-channel blockers, among other drug groups, as having potential analgesic effects. Our results provide insights into key molecular contributors to the experience of pain and highlight attractive drug targets.
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Affiliation(s)
- Sylvanus Toikumo
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Rachel Vickers-Smith
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Epidemiology, University of Kentucky College of Public Health; Center on Drug and Alcohol Research, University of Kentucky College of Medicine, Lexington, KY, USA
| | - Zeal Jinwala
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Heng Xu
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Divya Saini
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Emily Hartwell
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Mirko P. Venegas
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Kyle A. Sullivan
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Ke Xu
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Yale University School of Medicine, New Haven, CT, USA
| | | | - Joel Gelernter
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Yale University School of Medicine, New Haven, CT, USA
| | - Christopher T. Rentsch
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Yale University School of Medicine, New Haven, CT, USA
- London School of Hygiene & Tropical Medicine, London, UK
| | | | - Eli Stahl
- Regeneron Genetics Center, Tarrytown, NY, USA
| | - Martin Cheatle
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Hang Zhou
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Yale University School of Medicine, New Haven, CT, USA
| | - Stephen G. Waxman
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Yale University School of Medicine, New Haven, CT, USA
| | - Amy C. Justice
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Yale University School of Medicine, New Haven, CT, USA
- Yale University School of Public Health, New Haven, CT, USA
| | - Rachel L. Kember
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Henry R. Kranzler
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
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Jasper EA, Hellwege JN, Breeyear JH, Xiao B, Jarvik GP, Stanaway IB, Leppig KA, Chittoor G, Hayes MG, Dikilitas O, Kullo IJ, Holm IA, Verma SS, Edwards TL, Velez Edwards DR. Genetic Predictors of Blood Pressure Traits are Associated with Preeclampsia. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.09.23285734. [PMID: 36824881 PMCID: PMC9949198 DOI: 10.1101/2023.02.09.23285734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
Background Preeclampsia, a pregnancy complication characterized by hypertension after 20 gestational weeks, is a major cause of maternal and neonatal morbidity and mortality. The mechanisms leading to preeclampsia are unclear; however, there is evidence that preeclampsia is highly heritable. We evaluated the association of polygenic risk scores (PRS) for blood pressure traits and preeclampsia to assess whether there is shared genetic architecture. Methods Participants were obtained from Vanderbilt University's BioVU, the Electronic Medical Records and Genomics network, and the Penn Medicine Biobank. Non-Hispanic Black and White females of reproductive age with indications of pregnancy and genotype information were included. Preeclampsia was defined by ICD codes. Summary statistics for diastolic blood pressure (DBP), systolic blood pressure (SBP), and pulse pressure (PP) PRS were obtained from Giri et al 2019. Associations between preeclampsia and each PRS were evaluated separately by race and study population before evidence was meta-analyzed. Prediction models were developed and evaluated using 10-fold cross validation. Results In the 3,504 Black and 5,009 White individuals included, the rate of preeclampsia was 15.49%. The DBP and SBP PRSs were associated with preeclampsia in Whites but not Blacks. The PP PRS was significantly associated with preeclampsia in Blacks and Whites. In trans-ancestry meta-analysis, all PRSs were associated with preeclampsia (OR DBP =1.10, 95% CI=1.02-1.17, p =7.68×10 -3 ; OR SBP =1.16, 95% CI=1.09-1.23, p =2.23×10 -6 ; OR PP =1.14, 95% CI=1.07-1.27, p =9.86×10 -5 ). However, addition of PRSs to clinical prediction models did not improve predictive performance. Conclusions Genetic factors contributing to blood pressure regulation in the general population also predispose to preeclampsia.
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Zhou H, Kember RL, Deak JD, Xu H, Toikumo S, Yuan K, Lind PA, Farajzadeh L, Wang L, Hatoum AS, Johnson J, Lee H, Mallard TT, Xu J, Johnston KJ, Johnson EC, Galimberti M, Dao C, Levey DF, Overstreet C, Byrne EM, Gillespie NA, Gordon S, Hickie IB, Whitfield JB, Xu K, Zhao H, Huckins LM, Davis LK, Sanchez-Roige S, Madden PAF, Heath AC, Medland SE, Martin NG, Ge T, Smoller JW, Hougaard DM, Børglum AD, Demontis D, Krystal JH, Gaziano JM, Edenberg HJ, Agrawal A, Justice AC, Stein MB, Kranzler HR, Gelernter J. Multi-ancestry study of the genetics of problematic alcohol use in >1 million individuals. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.24.23284960. [PMID: 36747741 PMCID: PMC9901058 DOI: 10.1101/2023.01.24.23284960] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Problematic alcohol use (PAU) is a leading cause of death and disability worldwide. To improve our understanding of the genetics of PAU, we conducted a large cross-ancestry meta-analysis of PAU in 1,079,947 individuals. We observed a high degree of cross-ancestral similarity in the genetic architecture of PAU and identified 110 independent risk variants in within- and cross-ancestry analyses. Cross-ancestry fine-mapping improved the identification of likely causal variants. Prioritizing genes through gene expression and/or chromatin interaction in brain tissues identified multiple genes associated with PAU. We identified existing medications for potential pharmacological studies by drug repurposing analysis. Cross-ancestry polygenic risk scores (PRS) showed better performance in independent sample than single-ancestry PRS. Genetic correlations between PAU and other traits were observed in multiple ancestries, with other substance use traits having the highest correlations. The analysis of diverse ancestries contributed significantly to the findings, and fills an important gap in the literature.
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Affiliation(s)
- Hang Zhou
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- These authors contributed equally
| | - Rachel L. Kember
- Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- These authors contributed equally
| | - Joseph D. Deak
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Heng Xu
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Sylvanus Toikumo
- Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Kai Yuan
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Penelope A. Lind
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Leila Farajzadeh
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Lu Wang
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Alexander S. Hatoum
- Department of Psychological and Brain Sciences, Washington University in St. Louis, Saint Louis, MO, USA
| | - Jessica Johnson
- Pamela Sklar Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hyunjoon Lee
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Travis T. Mallard
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jiayi Xu
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | | | - Emma C. Johnson
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Marco Galimberti
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Cecilia Dao
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Chronic Disease Epidemiology, Yale University School of Public Health, New Haven, CT, USA
| | - Daniel F. Levey
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Cassie Overstreet
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Enda M. Byrne
- Child Health Research Centre, The University of Queensland, Brisbane, QLD, Australia
| | - Nathan A. Gillespie
- Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Scott Gordon
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Ian B. Hickie
- Brain and Mind Centre, University of Sydney, Camperdown, NSW, Australia
| | - John B. Whitfield
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Ke Xu
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
| | - Laura M. Huckins
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Lea K. Davis
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Division of Medical Genetics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Sandra Sanchez-Roige
- Department of Medicine, Division of Medical Genetics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Pamela A. F. Madden
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Andrew C. Heath
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Sarah E. Medland
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
- School of Psychology, University of Queensland, Brisbane, QLD, Australia
| | - Nicholas G. Martin
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Tian Ge
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Jordan W. Smoller
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - David M. Hougaard
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Anders D. Børglum
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Ditte Demontis
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - John H. Krystal
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA
- National Center for PTSD, US Department of Veterans Affairs, West Haven, CT, USA
| | - J. Michael Gaziano
- Massachusetts Veterans Epidemiology and Research Information Center (MAVERIC), Boston Veterans Affairs Healthcare System, Boston, MA, USA
- Department of Medicine, Divisions of Aging and Preventative Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Howard J. Edenberg
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | | | - Amy C. Justice
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Interdisciplinary Research on AIDS, Yale School of Public Health, New Haven, CT, USA
| | - Murray B. Stein
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- VA San Diego Healthcare System, Psychiatry Service, San Diego, CA, USA
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Henry R. Kranzler
- Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- These authors jointly supervised this work
| | - Joel Gelernter
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA
- These authors jointly supervised this work
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