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Lee S, Hecker J, Hahn G, Mullin K, Lutz SM, Tanzi RE, Lange C, Prokopenko D. On the effect heterogeneity of established disease susceptibility loci for Alzheimer's disease across different genetic ancestries. Alzheimers Dement 2024. [PMID: 38563508 DOI: 10.1002/alz.13796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 02/14/2024] [Accepted: 02/23/2024] [Indexed: 04/04/2024]
Abstract
INTRODUCTION Genome-wide association studies have identified numerous disease susceptibility loci (DSLs) for Alzheimer's disease (AD). However, only a limited number of studies have investigated the dependence of the genetic effect size of established DSLs on genetic ancestry. METHODS We utilized the whole genome sequencing data from the Alzheimer's Disease Sequencing Project (ADSP) including 35,569 participants. A total of 25,459 subjects in four distinct populations (African ancestry, non-Hispanic White, admixed Hispanic, and Asian) were analyzed. RESULTS We found that nine DSLs showed significant heterogeneity across populations. Single nucleotide polymorphism (SNP) rs2075650 in translocase of outer mitochondrial membrane 40 (TOMM40) showed the largest heterogeneity (Cochran's Q = 0.00, I2 = 90.08), followed by other SNPs in apolipoprotein C1 (APOC1) and apolipoprotein E (APOE). Two additional loci, signal-induced proliferation-associated 1 like 2 (SIPA1L2) and solute carrier 24 member 4 (SLC24A4), showed significant heterogeneity across populations. DISCUSSION We observed substantial heterogeneity for the APOE-harboring 19q13.32 region with TOMM40/APOE/APOC1 genes. The largest risk effect was seen among African Americans, while Asians showed a surprisingly small risk effect.
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Affiliation(s)
- Sanghun Lee
- Department of Medical Consilience, Division of Medicine, Graduate school, Dankook University, Yongin-si, Gyeonggi-do, South Korea
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Julian Hecker
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Georg Hahn
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Kristina Mullin
- Genetics and Aging Unit and McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Sharon M Lutz
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Healthcare Institute, Boston, Massachusetts, USA
| | - Rudolph E Tanzi
- Genetics and Aging Unit and McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Christoph Lange
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Dmitry Prokopenko
- Genetics and Aging Unit and McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Charlestown, Massachusetts, USA
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Liu T, Liu S, Rui X, Cao Y, Hecker J, Guo F, Zhang Y, Gong L, Zhou Y, Yu Y, Krishnamoorthyni N, Bates S, Chun S, Boyer N, Xu S, Park JA, Perrella MA, Levy BD, Weiss ST, Mou H, Raby BA, Zhou X. Gasdermin B, an asthma-susceptibility gene, promotes MAVS-TBK1 signaling and airway inflammation. Eur Respir J 2024:2301232. [PMID: 38514093 DOI: 10.1183/13993003.01232-2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 12/30/2023] [Indexed: 03/23/2024]
Abstract
RATIONALE Respiratory virus-induced inflammation is the leading cause of asthma exacerbation, frequently accompanied by induction of IFN-stimulated genes (ISGs). How asthma genetic susceptible genes modulate cellular response upon viral infection through fine-tuning ISGs induction and subsequent airway inflammation in genetically susceptible asthmatics remains largely unknown. OBJECTIVES To decipher the functions of GSDMB in respiratory virus-induced lung inflammation. METHODS In two independent cohorts, we analyzed expression correlation between GSDMB and ISGs. In human bronchial epithelial cell line or primary cells, we generated GSDMB-overexpressing and -deficient cells. A series of qPCR, ELISA and co-immunoprecipitation assays were performed to determine the function and mechanism of GSDMB for ISGs induction. We also generated a novel transgenic mouse line with inducible expression of human unique GSDMB gene in airway epithelial cells and applied respiratory syncytial virus (RSV) infection to determine the role of GSDMB on RSV-induced lung inflammation in vivo. MEASUREMENTS AND MAIN RESULTS Gasdermin B encoded by GSDMB, one of the most significant asthma-susceptible genes at 17q21, acts as a novel RNA sensor, promoting MAVS-TBK1 signaling and subsequent inflammation. In airway epithelium, GSDMB is induced by respiratory viral infections. Expression of GSDMB and ISGs significantly correlated in respiratory epithelium from two independent asthma cohorts. Notably, inducible expression of human GSDMB gene in mouse airway epithelium leads to enhanced ISGs induction, increased airway inflammation with mucus hyper-secretion upon RSV infection. CONCLUSIONS GSDMB promotes ISGs expression and airway inflammation upon respiratory virus infection, thereby conferring asthma risk in risk allele carriers.
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Affiliation(s)
- Tao Liu
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Siqi Liu
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
- These authors contributed equally
| | - Xianliang Rui
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
- These authors contributed equally
| | - Ye Cao
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Julian Hecker
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Feng Guo
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Yihan Zhang
- The Mucosal Immunology and Biology Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Lu Gong
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Yihan Zhou
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Yuzhen Yu
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Nandini Krishnamoorthyni
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Samuel Bates
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Sung Chun
- Division of Pulmonary Medicine, Department of Pediatrics, Boston Children's Hospital and Harvard Medical School, Boston, MA
| | - Nathan Boyer
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Shuang Xu
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Jin-Ah Park
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Mark A Perrella
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Bruce D Levy
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Scott T Weiss
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Hongmei Mou
- The Mucosal Immunology and Biology Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Benjamin A Raby
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
- Division of Pulmonary Medicine, Department of Pediatrics, Boston Children's Hospital and Harvard Medical School, Boston, MA
- These authors jointly conceptualized and supervised this work
| | - Xiaobo Zhou
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
- These authors jointly conceptualized and supervised this work
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Hahn G, Lutz SM, Hecker J, Prokopenko D, Cho MH, Silverman EK, Weiss ST, Lange C. Fast computation of the eigensystem of genomic similarity matrices. BMC Bioinformatics 2024; 25:43. [PMID: 38273228 PMCID: PMC10811951 DOI: 10.1186/s12859-024-05650-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 01/11/2024] [Indexed: 01/27/2024] Open
Abstract
The computation of a similarity measure for genomic data is a standard tool in computational genetics. The principal components of such matrices are routinely used to correct for biases due to confounding by population stratification, for instance in linear regressions. However, the calculation of both a similarity matrix and its singular value decomposition (SVD) are computationally intensive. The contribution of this article is threefold. First, we demonstrate that the calculation of three matrices (called the covariance matrix, the weighted Jaccard matrix, and the genomic relationship matrix) can be reformulated in a unified way which allows for the application of a randomized SVD algorithm, which is faster than the traditional computation. The fast SVD algorithm we present is adapted from an existing randomized SVD algorithm and ensures that all computations are carried out in sparse matrix algebra. The algorithm only assumes that row-wise and column-wise subtraction and multiplication of a vector with a sparse matrix is available, an operation that is efficiently implemented in common sparse matrix packages. An exception is the so-called Jaccard matrix, which does not have a structure applicable for the fast SVD algorithm. Second, an approximate Jaccard matrix is introduced to which the fast SVD computation is applicable. Third, we establish guaranteed theoretical bounds on the accuracy (in [Formula: see text] norm and angle) between the principal components of the Jaccard matrix and the ones of our proposed approximation, thus putting the proposed Jaccard approximation on a solid mathematical foundation, and derive the theoretical runtime of our algorithm. We illustrate that the approximation error is low in practice and empirically verify the theoretical runtime scalings on both simulated data and data of the 1000 Genome Project.
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Affiliation(s)
- Georg Hahn
- T.H. Chan School of Public Health, Harvard University, Boston, MA, 02115, USA.
| | - Sharon M Lutz
- T.H. Chan School of Public Health, Harvard University, Boston, MA, 02115, USA
| | - Julian Hecker
- Channing Divsion of Network Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA
| | - Dmitry Prokopenko
- Massachusetts General Hospital, Harvard University, Boston, MA, 02114, USA
| | - Michael H Cho
- Channing Divsion of Network Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA
| | - Edwin K Silverman
- Channing Divsion of Network Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA
| | - Scott T Weiss
- Channing Divsion of Network Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA
| | - Christoph Lange
- T.H. Chan School of Public Health, Harvard University, Boston, MA, 02115, USA
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Hecker J, Lee S, Kachroo P, Prokopenko D, Maaser-Hecker A, Lutz SM, Hahn G, Irizarry R, Weiss ST, DeMeo DL, Lange C. A consistent pattern of slide effects in Illumina DNA methylation BeadChip array data. Epigenetics 2023; 18:2257437. [PMID: 37731367 DOI: 10.1080/15592294.2023.2257437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 09/01/2023] [Indexed: 09/22/2023] Open
Abstract
Background: Recent studies have identified thousands of associations between DNA methylation CpGs and complex diseases/traits, emphasizing the critical role of epigenetics in understanding disease aetiology and identifying biomarkers. However, association analyses based on methylation array data are susceptible to batch/slide effects, which can lead to inflated false positive rates or reduced statistical powerResults: We use multiple DNA methylation datasets based on the popular Illumina Infinium MethylationEPIC BeadChip array to describe consistent patterns and the joint distribution of slide effects across CpGs, confirming and extending previous results. The susceptible CpGs overlap with the Illumina Infinium HumanMethylation450 BeadChip array content.Conclusions: Our findings reveal systematic patterns in slide effects. The observations provide further insights into the characteristics of these effects and can improve existing adjustment approaches.
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Affiliation(s)
- Julian Hecker
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Sanghun Lee
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Medical Consilience, Division of Medicine, Graduate School, Dankook University, Yongin-si, South Korea
| | - Priyadarshini Kachroo
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Dmitry Prokopenko
- Genetics and Aging Unit and McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Anna Maaser-Hecker
- Genetics and Aging Unit and McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Sharon M Lutz
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Population Medicine, PRecisiOn Medicine Translational Research (PROMoTeR) Center, Harvard Pilgrim Health Care and Harvard Medical School, Boston, MA, USA
| | - Georg Hahn
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Rafael Irizarry
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Scott T Weiss
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Dawn L DeMeo
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Christoph Lange
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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Moll M, Peljto AL, Kim JS, Xu H, Debban CL, Chen X, Menon A, Putman RK, Ghosh AJ, Saferali A, Nishino M, Hatabu H, Hobbs BD, Hecker J, McDermott G, Sparks JA, Wain LV, Allen RJ, Tobin MD, Raby BA, Chun S, Silverman EK, Zamora AC, Ortega VE, Garcia CK, Barr RG, Bleecker ER, Meyers DA, Kaner RJ, Rich SS, Manichaikul A, Rotter JI, Dupuis J, O’Connor GT, Fingerlin TE, Hunninghake GM, Schwartz DA, Cho MH. A Polygenic Risk Score for Idiopathic Pulmonary Fibrosis and Interstitial Lung Abnormalities. Am J Respir Crit Care Med 2023; 208:791-801. [PMID: 37523715 PMCID: PMC10563194 DOI: 10.1164/rccm.202212-2257oc] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 07/31/2023] [Indexed: 08/02/2023] Open
Abstract
Rationale: In addition to rare genetic variants and the MUC5B locus, common genetic variants contribute to idiopathic pulmonary fibrosis (IPF) risk. The predictive power of common variants outside the MUC5B locus for IPF and interstitial lung abnormalities (ILAs) is unknown. Objectives: We tested the predictive value of IPF polygenic risk scores (PRSs) with and without the MUC5B region on IPF, ILA, and ILA progression. Methods: We developed PRSs that included (PRS-M5B) and excluded (PRS-NO-M5B) the MUC5B region (500-kb window around rs35705950-T) using an IPF genome-wide association study. We assessed PRS associations with area under the receiver operating characteristic curve (AUC) metrics for IPF, ILA, and ILA progression. Measurements and Main Results: We included 14,650 participants (1,970 IPF; 1,068 ILA) from six multi-ancestry population-based and case-control cohorts. In cases excluded from genome-wide association study, the PRS-M5B (odds ratio [OR] per SD of the score, 3.1; P = 7.1 × 10-95) and PRS-NO-M5B (OR per SD, 2.8; P = 2.5 × 10-87) were associated with IPF. Participants in the top PRS-NO-M5B quintile had ∼sevenfold odds for IPF compared with those in the first quintile. A clinical model predicted IPF (AUC, 0.61); rs35705950-T and PRS-NO-M5B demonstrated higher AUCs (0.73 and 0.7, respectively), and adding both genetic predictors to a clinical model yielded the highest performance (AUC, 0.81). The PRS-NO-M5B was associated with ILA (OR, 1.25) and ILA progression (OR, 1.16) in European ancestry participants. Conclusions: A common genetic variant risk score complements the MUC5B variant to identify individuals at high risk of interstitial lung abnormalities and pulmonary fibrosis.
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Affiliation(s)
- Matthew Moll
- Division of Pulmonary and Critical Care Medicine, and
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Anna L. Peljto
- Department of Medicine and
- Department of Immunology, Division of Pulmonary Medicine, University of Colorado, Aurora, Colorado
| | - John S. Kim
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Virginia, Charlottesville, Virginia
| | - Hanfei Xu
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - Catherine L. Debban
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, Virginia
| | - Xianfeng Chen
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Phoenix, Arizona
| | - Aravind Menon
- Division of Pulmonary and Critical Care Medicine, and
| | | | - Auyon J. Ghosh
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, State University of New York Upstate Medical Center, Syracuse, New York
| | - Aabida Saferali
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Mizuki Nishino
- Center for Pulmonary Functional Imaging, Department of Radiology
| | - Hiroto Hatabu
- Center for Pulmonary Functional Imaging, Department of Radiology
| | - Brian D. Hobbs
- Division of Pulmonary and Critical Care Medicine, and
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Julian Hecker
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Gregory McDermott
- Division of Rheumatology, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Jeffrey A. Sparks
- Division of Rheumatology, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Louise V. Wain
- Department of Health Sciences, University of Leicester, Leicester, United Kingdom
- National Institute for Health Research, Leicester Respiratory Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Richard J. Allen
- Department of Health Sciences, University of Leicester, Leicester, United Kingdom
- National Institute for Health Research, Leicester Respiratory Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Martin D. Tobin
- Department of Health Sciences, University of Leicester, Leicester, United Kingdom
- National Institute for Health Research, Leicester Respiratory Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Benjamin A. Raby
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
- Department of Pediatrics
- Division of Pulmonary Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Sung Chun
- Division of Pulmonary Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Edwin K. Silverman
- Division of Pulmonary and Critical Care Medicine, and
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Ana C. Zamora
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Phoenix, Arizona
| | - Victor E. Ortega
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Phoenix, Arizona
| | - Christine K. Garcia
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Columbia University Irving Medical Center, New York, New York
| | - R. Graham Barr
- Department of Medicine and
- Division of General Medicine, Department of Epidemiology, Columbia University Medical Center, New York, New York
| | - Eugene R. Bleecker
- Division of Genetics, Genomics, and Precision Medicine, Department of Medicine, University of Arizona, Tucson, Arizona
| | - Deborah A. Meyers
- Division of Genetics, Genomics, and Precision Medicine, Department of Medicine, University of Arizona, Tucson, Arizona
| | - Robert J. Kaner
- Division of Pulmonary Medicine, Weill Cornell School of Medicine, New York, New York
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, Virginia
| | - Ani Manichaikul
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, Virginia
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-University of California, Los Angeles Medical Center, Torrance, California
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Biostatistics and Occupational Health, School of Population and Global Health, McGill University Faculty of Medicine and Health Sciences, Montreal, Quebec, Canada
| | - George T. O’Connor
- Department of Medicine, Pulmonary Center, Boston University School of Medicine, Boston, Massachusetts; and
| | - Tasha E. Fingerlin
- The National Jewish Health Cohen Family Asthma Institute, Division of Allergy and Immunology, National Jewish Health, Denver, Colorado
| | | | - David A. Schwartz
- Department of Medicine and
- Department of Immunology, Division of Pulmonary Medicine, University of Colorado, Aurora, Colorado
| | - Michael H. Cho
- Division of Pulmonary and Critical Care Medicine, and
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
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Schreck C, Schönhals E, Loersch A, Bassermann F, Oostendorp R, Hecker J. 107P The actin cytoskeleton as a new target structure for metastasizing osteosarcoma. ESMO Open 2023. [DOI: 10.1016/j.esmoop.2023.101144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023] Open
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Wang XW, Wang T, Schaub DP, Chen C, Sun Z, Ke S, Hecker J, Maaser-Hecker A, Zeleznik OA, Zeleznik R, Litonjua AA, DeMeo DL, Lasky-Su J, Silverman EK, Liu YY, Weiss ST. Benchmarking omics-based prediction of asthma development in children. Respir Res 2023; 24:63. [PMID: 36842969 PMCID: PMC9969629 DOI: 10.1186/s12931-023-02368-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 02/16/2023] [Indexed: 02/27/2023] Open
Abstract
BACKGROUND Asthma is a heterogeneous disease with high morbidity. Advancement in high-throughput multi-omics approaches has enabled the collection of molecular assessments at different layers, providing a complementary perspective of complex diseases. Numerous computational methods have been developed for the omics-based patient classification or disease outcome prediction. Yet, a systematic benchmarking of those methods using various combinations of omics data for the prediction of asthma development is still lacking. OBJECTIVE We aimed to investigate the computational methods in disease status prediction using multi-omics data. METHOD We systematically benchmarked 18 computational methods using all the 63 combinations of six omics data (GWAS, miRNA, mRNA, microbiome, metabolome, DNA methylation) collected in The Vitamin D Antenatal Asthma Reduction Trial (VDAART) cohort. We evaluated each method using standard performance metrics for each of the 63 omics combinations. RESULTS Our results indicate that overall Logistic Regression, Multi-Layer Perceptron, and MOGONET display superior performance, and the combination of transcriptional, genomic and microbiome data achieves the best prediction. Moreover, we find that including the clinical data can further improve the prediction performance for some but not all the omics combinations. CONCLUSIONS Specific omics combinations can reach the optimal prediction of asthma development in children. And certain computational methods showed superior performance than other methods.
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Affiliation(s)
- Xu-Wen Wang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Tong Wang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Darius P Schaub
- Department of Mathematics, University of Hamburg, 21109, Hamburg, Germany
| | - Can Chen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Zheng Sun
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Shanlin Ke
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Julian Hecker
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Anna Maaser-Hecker
- Genetics and Aging Research Unit, Department of Neurology, McCance Center for Brain Health, Mass General Institute for Neurodegenerative Disease, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Oana A Zeleznik
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Roman Zeleznik
- Department of Radiation Oncology, Brigham and Women's Hospital, Boston, MA, USA
| | - Augusto A Litonjua
- Division of Pediatric Pulmonology, Golisano Children's Hospital, Rochester, NY, USA
| | - Dawn L DeMeo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Jessica Lasky-Su
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Yang-Yu Liu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
- Center for Artificial Intelligence and Modeling, The Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
| | - Scott T Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
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8
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Hecker J, Chun S, Samiei A, Liu C, Laurie C, Kachroo P, Lutz SM, Lee S, Smith AV, Lasky-Su J, Cho MH, Sharma S, Soto Quirós ME, Avila L, Celedón JC, Raby B, Zhou X, Silverman EK, DeMeo DL, Lange C, Weiss ST. FGF20 and PGM2 variants are associated with childhood asthma in family-based whole-genome sequencing studies. Hum Mol Genet 2023; 32:696-707. [PMID: 36255742 PMCID: PMC9896483 DOI: 10.1093/hmg/ddac258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 10/12/2022] [Accepted: 10/13/2022] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Asthma is a heterogeneous common respiratory disease that remains poorly understood. The established genetic associations fail to explain the high estimated heritability, and the prevalence of asthma differs between populations and geographic regions. Robust association analyses incorporating different genetic ancestries and whole-genome sequencing data may identify novel genetic associations. METHODS We performed family-based genome-wide association analyses of childhood-onset asthma based on whole-genome sequencing (WGS) data for the 'The Genetic Epidemiology of Asthma in Costa Rica' study (GACRS) and the Childhood Asthma Management Program (CAMP). Based on parent-child trios with children diagnosed with asthma, we performed a single variant analysis using an additive and a recessive genetic model and a region-based association analysis of low-frequency and rare variants. RESULTS Based on 1180 asthmatic trios (894 GACRS trios and 286 CAMP trios, a total of 3540 samples with WGS data), we identified three novel genetic loci associated with childhood-onset asthma: rs4832738 on 4p14 ($P=1.72\ast{10}^{-9}$, recessive model), rs1581479 on 8p22 ($P=1.47\ast{10}^{-8}$, additive model) and rs73367537 on 10q26 ($P=1.21\ast{10}^{-8}$, additive model in GACRS only). Integrative analyses suggested potential novel candidate genes underlying these associations: PGM2 on 4p14 and FGF20 on 8p22. CONCLUSION Our family-based whole-genome sequencing analysis identified three novel genetic loci for childhood-onset asthma. Gene expression data and integrative analyses point to PGM2 on 4p14 and FGF20 on 8p22 as linked genes. Furthermore, region-based analyses suggest independent potential low-frequency/rare variant associations on 8p22. Follow-up analyses are needed to understand the functional mechanisms and generalizability of these associations.
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Affiliation(s)
- Julian Hecker
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Sung Chun
- Division of Pulmonary Medicine, Boston Children’s Hospital, Boston, MA 02115, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
| | - Ahmad Samiei
- Division of Pulmonary Medicine, Boston Children’s Hospital, Boston, MA 02115, USA
| | - Cuining Liu
- Division of Pulmonary Sciences and Critical Care Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Cecelia Laurie
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Priyadarshini Kachroo
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Sharon M Lutz
- Harvard Medical School, Boston, MA 02115, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Population Medicine, PRecisiOn Medicine Translational Research (PROMoTeR) Center, Harvard Pilgrim Health Care, Boston, MA 02215, USA
| | - Sanghun Lee
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Medical Consilience, Division of Medicine, Graduate School, Dankook University, Yongin-si, 16890, South Korea
| | - Albert V Smith
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jessica Lasky-Su
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Michael H Cho
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Boston, MA 02115, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Sunita Sharma
- Division of Pulmonary Sciences and Critical Care Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | | | - Lydiana Avila
- Department of Pediatrics, Hospital Nacional de Niños, 10101 San José, Costa Rica
| | - Juan C Celedón
- Division of Pediatric Pulmonary Medicine, UPMC Children's Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, PA 15224, USA
| | - Benjamin Raby
- Harvard Medical School, Boston, MA 02115, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
| | - Xiaobo Zhou
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Boston, MA 02115, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Dawn L DeMeo
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Boston, MA 02115, USA
| | | | - Christoph Lange
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Boston, MA 02115, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Scott T Weiss
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Boston, MA 02115, USA
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9
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Lee S, Hahn G, Hecker J, Lutz SM, Mullin K, Hide W, Bertram L, DeMeo DL, Tanzi RE, Lange C, Prokopenko D. A comparison between similarity matrices for principal component analysis to assess population stratification in sequenced genetic data sets. Brief Bioinform 2023; 24:bbac611. [PMID: 36585781 PMCID: PMC9851291 DOI: 10.1093/bib/bbac611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 12/07/2022] [Accepted: 12/11/2022] [Indexed: 01/01/2023] Open
Abstract
Genetic similarity matrices are commonly used to assess population substructure (PS) in genetic studies. Through simulation studies and by the application to whole-genome sequencing (WGS) data, we evaluate the performance of three genetic similarity matrices: the unweighted and weighted Jaccard similarity matrices and the genetic relationship matrix. We describe different scenarios that can create numerical pitfalls and lead to incorrect conclusions in some instances. We consider scenarios in which PS is assessed based on loci that are located across the genome ('globally') and based on loci from a specific genomic region ('locally'). We also compare scenarios in which PS is evaluated based on loci from different minor allele frequency bins: common (>5%), low-frequency (5-0.5%) and rare (<0.5%) single-nucleotide variations (SNVs). Overall, we observe that all approaches provide the best clustering performance when computed based on rare SNVs. The performance of the similarity matrices is very similar for common and low-frequency variants, but for rare variants, the unweighted Jaccard matrix provides preferable clustering features. Based on visual inspection and in terms of standard clustering metrics, its clusters are the densest and the best separated in the principal component analysis of variants with rare SNVs compared with the other methods and different allele frequency cutoffs. In an application, we assessed the role of rare variants on local and global PS, using WGS data from multiethnic Alzheimer's disease data sets and European or East Asian populations from the 1000 Genome Project.
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Affiliation(s)
- Sanghun Lee
- Department of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Medical Consilience, Division of Medicine, Graduate school, Dankook University, South Korea
- NH Institute for Natural Product Research, Myungji Hospital, South Korea
| | - Georg Hahn
- Department of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Julian Hecker
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Sharon M Lutz
- Department of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Kristina Mullin
- Genetics and Aging Unit and McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Winston Hide
- Harvard Medical School, Boston, MA, USA
- Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Lars Bertram
- Lübeck Interdisciplinary Platform for Genome Analytics, University of Lübeck, Lübeck, Germany
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Dawn L DeMeo
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Rudolph E Tanzi
- Harvard Medical School, Boston, MA, USA
- Genetics and Aging Unit and McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Christoph Lange
- Department of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Dmitry Prokopenko
- Harvard Medical School, Boston, MA, USA
- Genetics and Aging Unit and McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
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10
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Lee S, Prokopenko D, Kelly RS, Lutz S, Ann Lasky-Su J, Cho MH, Laurie C, Celedón JC, Lange C, Weiss ST, Hecker J, DeMeo DL. Zinc finger protein 33B demonstrates sex interaction with atopy-related markers in childhood asthma. Eur Respir J 2023; 61:2200479. [PMID: 35953101 PMCID: PMC10124713 DOI: 10.1183/13993003.00479-2022] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Accepted: 07/14/2022] [Indexed: 01/11/2023]
Abstract
BACKGROUND Sex differences related to immune responses can influence atopic manifestations in childhood asthma. While genome-wide association studies have investigated a sex-specific genetic architecture of the immune response, gene-by-sex interactions have not been extensively analysed for atopy-related markers including allergy skin tests, IgE and eosinophils in asthmatic children. METHODS We performed a genome-wide gene-by-sex interaction analysis for atopy-related markers using whole-genome sequencing data based on 889 trios from the Genetic Epidemiology of Asthma in Costa Rica Study (GACRS) and 284 trios from the Childhood Asthma Management Program (CAMP). We also tested the findings in UK Biobank participants with self-reported childhood asthma. Furthermore, downstream analyses in GACRS integrated gene expression to disentangle observed associations. RESULTS Single nucleotide polymorphism (SNP) rs1255383 at 10q11.21 demonstrated a genome-wide significant gene-by-sex interaction (pinteraction=9.08×10-10) for atopy (positive skin test) with opposite direction of effects between females and males. In the UK Biobank participants with a history of childhood asthma, the signal was consistently observed with the same sex-specific effect directions for high eosinophil count (pinteraction=0.0058). Gene expression of ZNF33B (zinc finger protein 33B), located at 10q11.21, was moderately associated with atopy in girls, but not in boys. CONCLUSIONS We report SNPs in/near a zinc finger gene as novel sex-differential loci for atopy-related markers with opposite effect directions in females and males. A potential role for ZNF33B should be studied further as an important driver of sex-divergent features of atopy in childhood asthma.
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Affiliation(s)
- Sanghun Lee
- Department of Medical Consilience, Division of Medicine, Graduate School, Dankook University, Yongin, South Korea
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- NH Institute for Natural Product Research, Myungji Hospital, Goyang-si, South Korea
| | - Dmitry Prokopenko
- Genetics and Aging Unit and McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Rachel S Kelly
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Sharon Lutz
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jessica Ann Lasky-Su
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Michael H Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Cecelia Laurie
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Juan C Celedón
- Division of Pediatric Pulmonary Medicine, UPMC Children's Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, PA, USA
| | - Christoph Lange
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Scott T Weiss
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Julian Hecker
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Dawn L DeMeo
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
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11
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Hahn G, Lee S, Prokopenko D, Abraham J, Novak T, Hecker J, Cho M, Khurana S, Baden LR, Randolph AG, Weiss ST, Lange C. Unsupervised outlier detection applied to SARS-CoV-2 nucleotide sequences can identify sequences of common variants and other variants of interest. BMC Bioinformatics 2022; 23:547. [PMID: 36536276 PMCID: PMC9761049 DOI: 10.1186/s12859-022-05105-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 12/07/2022] [Indexed: 12/23/2022] Open
Abstract
As of June 2022, the GISAID database contains more than 11 million SARS-CoV-2 genomes, including several thousand nucleotide sequences for the most common variants such as delta or omicron. These SARS-CoV-2 strains have been collected from patients around the world since the beginning of the pandemic. We start by assessing the similarity of all pairs of nucleotide sequences using the Jaccard index and principal component analysis. As shown previously in the literature, an unsupervised cluster analysis applied to the SARS-CoV-2 genomes results in clusters of sequences according to certain characteristics such as their strain or their clade. Importantly, we observe that nucleotide sequences of common variants are often outliers in clusters of sequences stemming from variants identified earlier on during the pandemic. Motivated by this finding, we are interested in applying outlier detection to nucleotide sequences. We demonstrate that nucleotide sequences of common variants (such as alpha, delta, or omicron) can be identified solely based on a statistical outlier criterion. We argue that outlier detection might be a useful surveillance tool to identify emerging variants in real time as the pandemic progresses.
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Affiliation(s)
- Georg Hahn
- Department of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, MA, 02115, USA.
| | - Sanghun Lee
- Department of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, MA, 02115, USA
- Department of Medical Consilience, Graduate School, Dankook University, Yongin, South Korea
| | - Dmitry Prokopenko
- Genetics and Aging Research Unit, Department of Neurology, McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Jonathan Abraham
- Department of Microbiology, Harvard Medical School, Blavatnik Institute, 77 Avenue Louis Pasteur, Boston, MA, 02115, USA
| | - Tanya Novak
- Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Boston, MA, 02115, USA
| | - Julian Hecker
- Harvard Medical School, Harvard University, Boston, MA, 02115, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA
| | - Michael Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA
| | | | - Lindsey R Baden
- Division of Infectious Diseases, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, 02115, USA
| | - Adrienne G Randolph
- Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Boston, MA, 02115, USA
- Harvard Medical School, Harvard University, Boston, MA, 02115, USA
| | - Scott T Weiss
- Harvard Medical School, Harvard University, Boston, MA, 02115, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA
| | - Christoph Lange
- Department of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, MA, 02115, USA
- Harvard Medical School, Harvard University, Boston, MA, 02115, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA
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12
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Kim W, Hecker J, Barr RG, Boerwinkle E, Cade B, Correa A, Dupuis J, Gharib SA, Lange L, London SJ, Morrison AC, O'Connor GT, Oelsner EC, Psaty BM, Vasan RS, Redline S, Rich SS, Rotter JI, Yu B, Lange C, Manichaikul A, Zhou JJ, Sofer T, Silverman EK, Qiao D, Cho MH. Assessing the contribution of rare genetic variants to phenotypes of chronic obstructive pulmonary disease using whole-genome sequence data. Hum Mol Genet 2022; 31:3873-3885. [PMID: 35766891 PMCID: PMC9652112 DOI: 10.1093/hmg/ddac117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 04/13/2022] [Accepted: 05/16/2021] [Indexed: 01/04/2023] Open
Abstract
RATIONALE Genetic variation has a substantial contribution to chronic obstructive pulmonary disease (COPD) and lung function measurements. Heritability estimates using genome-wide genotyping data can be biased if analyses do not appropriately account for the nonuniform distribution of genetic effects across the allele frequency and linkage disequilibrium (LD) spectrum. In addition, the contribution of rare variants has been unclear. OBJECTIVES We sought to assess the heritability of COPD and lung function using whole-genome sequence data from the Trans-Omics for Precision Medicine program. METHODS Using the genome-based restricted maximum likelihood method, we partitioned the genome into bins based on minor allele frequency and LD scores and estimated heritability of COPD, FEV1% predicted and FEV1/FVC ratio in 11 051 European ancestry and 5853 African-American participants. MEASUREMENTS AND MAIN RESULTS In European ancestry participants, the estimated heritability of COPD, FEV1% predicted and FEV1/FVC ratio were 35.5%, 55.6% and 32.5%, of which 18.8%, 19.7%, 17.8% were from common variants, and 16.6%, 35.8%, and 14.6% were from rare variants. These estimates had wide confidence intervals, with common variants and some sets of rare variants showing a statistically significant contribution (P-value < 0.05). In African-Americans, common variant heritability was similar to European ancestry participants, but lower sample size precluded calculation of rare variant heritability. CONCLUSIONS Our study provides updated and unbiased estimates of heritability for COPD and lung function, and suggests an important contribution of rare variants. Larger studies of more diverse ancestry will improve accuracy of these estimates.
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Affiliation(s)
- Wonji Kim
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Julian Hecker
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - R Graham Barr
- Departments of Medicine and Epidemiology, Columbia University Medical Center, New York, NY 10032, USA
| | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Brian Cade
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Adolfo Correa
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Josée Dupuis
- Department of Biostatistics, Boston University of Public Health, Boston, MA 02118, USA
| | - Sina A Gharib
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA 98101, USA
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, WA 98109, USA
| | - Leslie Lange
- Department of Medicine, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Stephanie J London
- Epidemiology Branch, National Institute of Environmental Health Sciences, Department of Health and Human Services, National Institutes of Health, Research Triangle Park, NC 27709, USA
| | - Alanna C Morrison
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - George T O'Connor
- Pulmonary Center, Boston University School of Medicine, Boston, MA 02118, USA
| | - Elizabeth C Oelsner
- Departments of Medicine and Epidemiology, Columbia University Medical Center, New York, NY 10032, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA 98101, USA
- Departments of Epidemiology and Health Services, University of Washington, Seattle, WA 98101, USA
| | - Ramachandran S Vasan
- Lung and Blood Institute Framingham Heart Study, Boston University and National Heart, Framingham, MA 01702, USA
- Department of Preventive Medicine and Epidemiology, School of Medicine and Public Health, Boston University, Boston, MA 02118, USA
| | - Susan Redline
- Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Bing Yu
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Christoph Lange
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Ani Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Jin J Zhou
- Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ 85721, USA
| | - Tamar Sofer
- Division of Sleep and Circadian Disorder, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Dandi Qiao
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Michael H Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
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13
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Hecker J, Prokopenko D, Moll M, Lee S, Kim W, Qiao D, Voorhies K, Kim W, Vansteelandt S, Hobbs BD, Cho MH, Silverman EK, Lutz SM, DeMeo DL, Weiss ST, Lange C. A robust and adaptive framework for interaction testing in quantitative traits between multiple genetic loci and exposure variables. PLoS Genet 2022; 18:e1010464. [PMID: 36383614 PMCID: PMC9668174 DOI: 10.1371/journal.pgen.1010464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 10/04/2022] [Indexed: 11/17/2022] Open
Abstract
The identification and understanding of gene-environment interactions can provide insights into the pathways and mechanisms underlying complex diseases. However, testing for gene-environment interaction remains a challenge since a.) statistical power is often limited and b.) modeling of environmental effects is nontrivial and such model misspecifications can lead to false positive interaction findings. To address the lack of statistical power, recent methods aim to identify interactions on an aggregated level using, for example, polygenic risk scores. While this strategy can increase the power to detect interactions, identifying contributing genes and pathways is difficult based on these relatively global results. Here, we propose RITSS (Robust Interaction Testing using Sample Splitting), a gene-environment interaction testing framework for quantitative traits that is based on sample splitting and robust test statistics. RITSS can incorporate sets of genetic variants and/or multiple environmental factors. Based on the user's choice of statistical/machine learning approaches, a screening step selects and combines potential interactions into scores with improved interpretability. In the testing step, the application of robust statistics minimizes the susceptibility to main effect misspecifications. Using extensive simulation studies, we demonstrate that RITSS controls the type 1 error rate in a wide range of scenarios, and we show how the screening strategy influences statistical power. In an application to lung function phenotypes and human height in the UK Biobank, RITSS identified highly significant interactions based on subcomponents of genetic risk scores. While the contributing single variant interaction signals are weak, our results indicate interaction patterns that result in strong aggregated effects, providing potential insights into underlying gene-environment interaction mechanisms.
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Affiliation(s)
- Julian Hecker
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Dmitry Prokopenko
- Harvard Medical School, Boston, Massachusetts, United States of America
- Genetics and Aging Unit and McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Matthew Moll
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Sanghun Lee
- Department of Medical Consilience, Division of Medicine, Graduate School, Dankook University, Yongin, South Korea
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Wonji Kim
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Dandi Qiao
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Kirsten Voorhies
- Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Population Medicine, PRecisiOn Medicine Translational Research (PROMoTeR) Center, Harvard Pilgrim Health Care, Boston, Massachusetts, United States of America
| | - Woori Kim
- Harvard Medical School, Boston, Massachusetts, United States of America
- Systems Biology and Computer Science Program, Ann Romney Center for Neurological Diseases, Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Stijn Vansteelandt
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Gent, Belgium
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Brian D. Hobbs
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Michael H. Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Edwin K. Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
| | - Sharon M. Lutz
- Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Department of Population Medicine, PRecisiOn Medicine Translational Research (PROMoTeR) Center, Harvard Pilgrim Health Care, Boston, Massachusetts, United States of America
| | - Dawn L. DeMeo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Scott T. Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Christoph Lange
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
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14
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Prokopenko D, Lee S, Hecker J, Mullin K, Morgan S, Katsumata Y, Weiner MW, Fardo DW, Laird N, Bertram L, Hide W, Lange C, Tanzi RE. Region-based analysis of rare genomic variants in whole-genome sequencing datasets reveal two novel Alzheimer's disease-associated genes: DTNB and DLG2. Mol Psychiatry 2022; 27:1963-1969. [PMID: 35246634 PMCID: PMC9126808 DOI: 10.1038/s41380-022-01475-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 01/25/2022] [Accepted: 02/04/2022] [Indexed: 01/01/2023]
Abstract
Alzheimer's disease (AD) is a genetically complex disease for which nearly 40 loci have now been identified via genome-wide association studies (GWAS). We attempted to identify groups of rare variants (alternate allele frequency <0.01) associated with AD in a region-based, whole-genome sequencing (WGS) association study (rvGWAS) of two independent AD family datasets (NIMH/NIA; 2247 individuals; 605 families). Employing a sliding window approach across the genome, we identified several regions that achieved association p values <10-6, using the burden test or the SKAT statistic. The genomic region around the dystobrevin beta (DTNB) gene was identified with the burden and SKAT test and replicated in case/control samples from the ADSP study reaching genome-wide significance after meta-analysis (pmeta = 4.74 × 10-8). SKAT analysis also revealed region-based association around the Discs large homolog 2 (DLG2) gene and replicated in case/control samples from the ADSP study (pmeta = 1 × 10-6). In conclusion, in a region-based rvGWAS of AD we identified two novel AD genes, DLG2 and DTNB, based on association with rare variants.
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Affiliation(s)
- Dmitry Prokopenko
- grid.32224.350000 0004 0386 9924Genetics and Aging Research Unit and The Henry and Allison McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, MA USA ,grid.38142.3c000000041936754XHarvard Medical School, Boston, MA USA
| | - Sanghun Lee
- grid.411982.70000 0001 0705 4288Department of Medical Consilience, Graduate School, Dankook University, Yongin, South Korea ,grid.38142.3c000000041936754XDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA USA
| | - Julian Hecker
- grid.38142.3c000000041936754XHarvard Medical School, Boston, MA USA ,grid.62560.370000 0004 0378 8294Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA USA
| | - Kristina Mullin
- grid.32224.350000 0004 0386 9924Genetics and Aging Research Unit and The Henry and Allison McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, MA USA
| | - Sarah Morgan
- grid.38142.3c000000041936754XHarvard Medical School, Boston, MA USA ,grid.239395.70000 0000 9011 8547Department of Pathology, Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA USA
| | - Yuriko Katsumata
- grid.266539.d0000 0004 1936 8438Department of Biostatistics, University of Kentucky, Lexington, KY USA ,grid.266539.d0000 0004 1936 8438Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY USA
| | | | - Michael W. Weiner
- grid.266102.10000 0001 2297 6811Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA USA
| | - David W. Fardo
- grid.266539.d0000 0004 1936 8438Department of Biostatistics, University of Kentucky, Lexington, KY USA ,grid.266539.d0000 0004 1936 8438Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY USA
| | - Nan Laird
- grid.38142.3c000000041936754XDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA USA
| | - Lars Bertram
- grid.4562.50000 0001 0057 2672Lübeck Interdisciplinary Platform for Genome Analytics, University of Lübeck, Lübeck, Germany ,grid.5510.10000 0004 1936 8921Department of Psychology, University of Oslo, Oslo, Norway
| | - Winston Hide
- grid.38142.3c000000041936754XHarvard Medical School, Boston, MA USA ,grid.239395.70000 0000 9011 8547Department of Pathology, Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA USA
| | - Christoph Lange
- grid.38142.3c000000041936754XDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA USA
| | - Rudolph E. Tanzi
- grid.32224.350000 0004 0386 9924Genetics and Aging Research Unit and The Henry and Allison McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, MA USA ,grid.38142.3c000000041936754XHarvard Medical School, Boston, MA USA
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15
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Lee S, Lasky-Su J, Won S, Laurie C, Celedón JC, Lange C, Weiss S, Hecker J. Novel recessive locus for body mass index in childhood asthma. Thorax 2021; 76:1227-1230. [PMID: 33888571 PMCID: PMC8531156 DOI: 10.1136/thoraxjnl-2020-215742] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 11/16/2020] [Accepted: 03/16/2021] [Indexed: 11/03/2022]
Abstract
Most genome-wide association studies of obesity and body mass index (BMI) have so far assumed an additive mode of inheritance in their analysis, although association testing supports a recessive effect for some of the established loci, for example, rs1421085 in FTO In two whole-genome sequencing (WGS) studies of children with asthma and their parents (892 Costa Rican trios and 286 North American trios), we discovered an association between a locus (rs9292139) in LOC102724122 and BMI that reaches genome-wide significance under a recessive model in the combined analysis. As the association does not achieve significance under an additive model, our finding illustrates the benefits of the recessive model in WGS analyses.
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Affiliation(s)
- Sanghun Lee
- Department of Medical Consilience, Division of Medicine, Graduate School, Dankook University-Jukjeon Campus, Yongin, South Korea
| | - Jessica Lasky-Su
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Sungho Won
- Department of Public Health Science, Seoul National University, Gwanak-gu, South Korea
| | - Cecelia Laurie
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
| | - Juan Carlos Celedón
- Division of Pediatric Pulmonary Medicine, Children's Hospital of Pittsburgh of UPMC, Pittsburgh, Pennsylvania, USA
| | - Christoph Lange
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Scott Weiss
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Julian Hecker
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
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16
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Hartmann L, Hecker J, Rothenberg-Thurley M, Rivière J, Ksienzyk B, Buck M, Van Der Garde M, Fischer L, Winter S, Rauner M, Tsourdi E, Sockel K, Schneider M, Kubasch A, Nolde M, Hausmann D, Lützner J, Roth A, Bassermann F, Spiekermann K, Hofbauer L, Platzbecker U, Götze K, Metzeler K. Topic: AS04-MDS Biology and Pathogenesis/AS04b-Clonal diversity & evolution. Leuk Res 2021. [DOI: 10.1016/j.leukres.2021.106681.17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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17
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Hahn G, Wu CM, Lee S, Lutz SM, Khurana S, Baden LR, Haneuse S, Qiao D, Hecker J, DeMeo DL, Tanzi RE, Choudhary MC, Etemad B, Mohammadi A, Esmaeilzadeh E, Cho MH, Li JZ, Randolph AG, Laird NM, Weiss ST, Silverman EK, Ribbeck K, Lange C. Genome-wide association analysis of COVID-19 mortality risk in SARS-CoV-2 genomes identifies mutation in the SARS-CoV-2 spike protein that colocalizes with P.1 of the Brazilian strain. Genet Epidemiol 2021; 45:685-693. [PMID: 34159627 PMCID: PMC8426743 DOI: 10.1002/gepi.22421] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 05/10/2021] [Accepted: 05/17/2021] [Indexed: 12/05/2022]
Abstract
SARS‐CoV‐2 mortality has been extensively studied in relation to host susceptibility. How sequence variations in the SARS‐CoV‐2 genome affect pathogenicity is poorly understood. Starting in October 2020, using the methodology of genome‐wide association studies (GWAS), we looked at the association between whole‐genome sequencing (WGS) data of the virus and COVID‐19 mortality as a potential method of early identification of highly pathogenic strains to target for containment. Although continuously updating our analysis, in December 2020, we analyzed 7548 single‐stranded SARS‐CoV‐2 genomes of COVID‐19 patients in the GISAID database and associated variants with mortality using a logistic regression. In total, evaluating 29,891 sequenced loci of the viral genome for association with patient/host mortality, two loci, at 12,053 and 25,088 bp, achieved genome‐wide significance (p values of 4.09e−09 and 4.41e−23, respectively), though only 25,088 bp remained significant in follow‐up analyses. Our association findings were exclusively driven by the samples that were submitted from Brazil (p value of 4.90e−13 for 25,088 bp). The mutation frequency of 25,088 bp in the Brazilian samples on GISAID has rapidly increased from about 0.4 in October/December 2020 to 0.77 in March 2021. Although GWAS methodology is suitable for samples in which mutation frequencies varies between geographical regions, it cannot account for mutation frequencies that change rapidly overtime, rendering a GWAS follow‐up analysis of the GISAID samples that have been submitted after December 2020 as invalid. The locus at 25,088 bp is located in the P.1 strain, which later (April 2021) became one of the distinguishing loci (precisely, substitution V1176F) of the Brazilian strain as defined by the Centers for Disease Control. Specifically, the mutations at 25,088 bp occur in the S2 subunit of the SARS‐CoV‐2 spike protein, which plays a key role in viral entry of target host cells. Since the mutations alter amino acid coding sequences, they potentially imposing structural changes that could enhance viral infectivity and symptom severity. Our analysis suggests that GWAS methodology can provide suitable analysis tools for the real‐time detection of new more transmissible and pathogenic viral strains in databases such as GISAID, though new approaches are needed to accommodate rapidly changing mutation frequencies over time, in the presence of simultaneously changing case/control ratios. Improvements of the associated metadata/patient information in terms of quality and availability will also be important to fully utilize the potential of GWAS methodology in this field.
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Affiliation(s)
- Georg Hahn
- Department of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA
| | - Chloe M Wu
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Sanghun Lee
- Department of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA.,Department of Medical Consilience, Graduate School, Dankook University, Yongin, South Korea
| | - Sharon M Lutz
- Department of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA.,PRecisiOn Medicine Translational Research (PROMoTeR) Center, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | | | - Lindsey R Baden
- Division of Infectious Diseases, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Sebastien Haneuse
- Department of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA
| | - Dandi Qiao
- Harvard Medical School, Harvard University, Boston, Massachusetts, USA.,Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Julian Hecker
- PRecisiOn Medicine Translational Research (PROMoTeR) Center, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA.,Harvard Medical School, Harvard University, Boston, Massachusetts, USA
| | - Dawn L DeMeo
- Harvard Medical School, Harvard University, Boston, Massachusetts, USA.,Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Rudolph E Tanzi
- Genetics and Aging Research Unit, McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Behzad Etemad
- Harvard Medical School, Harvard University, Boston, Massachusetts, USA
| | - Abbas Mohammadi
- Harvard Medical School, Harvard University, Boston, Massachusetts, USA
| | | | - Michael H Cho
- Harvard Medical School, Harvard University, Boston, Massachusetts, USA.,Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Jonathan Z Li
- Harvard Medical School, Harvard University, Boston, Massachusetts, USA
| | - Adrienne G Randolph
- Harvard Medical School, Harvard University, Boston, Massachusetts, USA.,Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Nan M Laird
- Department of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA
| | - Scott T Weiss
- Harvard Medical School, Harvard University, Boston, Massachusetts, USA.,Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Edwin K Silverman
- Harvard Medical School, Harvard University, Boston, Massachusetts, USA.,Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Katharina Ribbeck
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Christoph Lange
- Department of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA.,Harvard Medical School, Harvard University, Boston, Massachusetts, USA.,Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
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18
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Hecker J, Townes FW, Kachroo P, Laurie C, Lasky-Su J, Ziniti J, Cho MH, Weiss ST, Laird NM, Lange C. A unifying framework for rare variant association testing in family-based designs, including higher criticism approaches, SKATs, and burden tests. Bioinformatics 2021; 36:5432-5438. [PMID: 33367522 PMCID: PMC8016468 DOI: 10.1093/bioinformatics/btaa1055] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 11/20/2020] [Accepted: 12/10/2020] [Indexed: 12/13/2022] Open
Abstract
MOTIVATION Analysis of rare variants in family-based studies remains a challenge. Transmission-based approaches provide robustness against population stratification, but the evaluation of the significance of test statistics based on asymptotic theory can be imprecise. Also, power will depend heavily on the choice of the test statistic and on the underlying genetic architecture of the locus, which will be generally unknown. RESULTS In our proposed framework, we utilize the FBAT haplotype algorithm to obtain the conditional offspring genotype distribution under the null hypothesis given the sufficient statistic. Based on this conditional offspring genotype distribution, the significance of virtually any association test statistic can be evaluated based on simulations or exact computations, without the need for asymptotic approximations. Besides standard linear burden-type statistics, this enables our approach to also evaluate other test statistics such as variance components statistics, higher criticism approaches, and maximum-single-variant-statistics, where asymptotic theory might be involved or does not provide accurate approximations for rare variant data. Based on these P-values, combined test statistics such as the aggregated Cauchy association test (ACAT) can also be utilized. In simulation studies, we show that our framework outperforms existing approaches for family-based studies in several scenarios. We also applied our methodology to a TOPMed whole-genome sequencing dataset with 897 asthmatic trios from Costa Rica. AVAILABILITY AND IMPLEMENTATION FBAT software is available at https://sites.google.com/view/fbatwebpage. Simulation code is available at https://github.com/julianhecker/FBAT_rare_variant_test_simulations. Whole-genome sequencing data for 'NHLBI TOPMed: The Genetic Epidemiology of Asthma in Costa Rica' is available at https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000988.v4.p1. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Julian Hecker
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - F William Townes
- Department of Computer Science, Princeton University, Princeton, NJ 08540-5233, USA
| | - Priyadarshini Kachroo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Cecelia Laurie
- Department of Biostatistics, University of Washington, Seattle, WA 98195-1617, USA
| | - Jessica Lasky-Su
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - John Ziniti
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Michael H Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Scott T Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Nan M Laird
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Christoph Lange
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
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19
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Lee S, Lasky-Su JA, Lange C, Kim W, Kumar PL, McDonald MLN, Vaz Fragoso CA, Laurie C, Raby BA, Celedón JC, Cho MH, Won S, Weiss ST, Hecker J. A novel locus for exertional dyspnoea in childhood asthma. Eur Respir J 2021; 57:13993003.01224-2020. [PMID: 32855217 DOI: 10.1183/13993003.01224-2020] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 07/31/2020] [Indexed: 12/18/2022]
Abstract
Most children diagnosed with asthma have respiratory symptoms such as cough, dyspnoea and wheezing, which are also important markers of overall respiratory function. A decade of genome-wide association studies (GWAS) have investigated genetic susceptibility to asthma itself, but few have focused on important respiratory symptoms that characterise childhood asthma.Using whole-genome sequencing (WGS) data for 894 asthmatic trios from a Costa Rican cohort, we performed family-based association tests (FBATs) to assess the association between genetic variants and multiple asthma-relevant respiratory phenotypes: cough, phlegm, wheezing, exertional dyspnoea and exertional chest tightness. We tested whether genome-wide significant associations were replicated in two additional studies: 1) 286 asthmatic trios from the Childhood Asthma Management Program (CAMP), and 2) 2691 African American current or former smokers from the COPDGene study.In the 894 Costa Rican trios, we identified a genome-wide significant association (p=2.16×10-9) between exertional dyspnoea and the single nucleotide polymorphism (SNP) rs10165869, located on chromosome 2q37.3, that was replicated in the CAMP cohort (p=0.023) with the same direction of association (combined p=3.28×10-10). This association was not found in the African American participants from COPDGene. We also found suggestive evidence for an association between SNP rs10165869 and the atypical chemokine receptor 3 (ACKR3).Our finding encourages the secondary association analysis of a wider range of phenotypes that characterise respiratory symptoms in other airway diseases/studies.
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Affiliation(s)
- Sanghun Lee
- Dept of Medical Consilience, Division of Medicine, Graduate School, Dankook University, Yongin, South Korea.,Dept of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jessica Ann Lasky-Su
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Christoph Lange
- Dept of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Wonji Kim
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Preeti Lakshman Kumar
- Division of Pulmonary, Allergy and Critical Care Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Merry-Lynn N McDonald
- Division of Pulmonary, Allergy and Critical Care Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | | | - Cecelia Laurie
- Dept of Biostatistics, University of Washington, Seattle, WA, USA
| | - Benjamin A Raby
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Juan C Celedón
- Division of Pediatric Pulmonary Medicine, UPMC Children's Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, PA, USA
| | - Michael H Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Sungho Won
- Dept of Public Health Science, Seoul National University, Seoul, South Korea
| | - Scott T Weiss
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Julian Hecker
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
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20
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Lee S, Lasky-Su J, Kim W, Won S, Laurie C, Celedón JC, Lange C, Weiss ST, Hecker J. An interaction of the 17q12-21 locus with mold exposure in childhood asthma. Pediatr Allergy Immunol 2021; 32:373-376. [PMID: 32946604 PMCID: PMC8277824 DOI: 10.1111/pai.13376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 09/08/2020] [Accepted: 09/09/2020] [Indexed: 11/29/2022]
Affiliation(s)
- Sanghun Lee
- Department of Medical Consilience, Division of Medicine, Graduate school, Dankook University, Yongin, South Korea.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jessica Lasky-Su
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Wonji Kim
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Sungho Won
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Department of Public Health Science, Seoul National University, Seoul, South Korea
| | - Cecelia Laurie
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Juan C Celedón
- Division of Pediatric Pulmonary Medicine, UPMC Children's Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, PA, USA
| | - Christoph Lange
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Scott T Weiss
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Julian Hecker
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
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21
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Hahn G, Lutz SM, Hecker J, Prokopenko D, Cho MH, Silverman EK, Weiss ST, Lange C. locStra: Fast analysis of regional/global stratification in whole-genome sequencing studies. Genet Epidemiol 2020; 45:82-98. [PMID: 32929743 DOI: 10.1002/gepi.22356] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Revised: 08/05/2020] [Accepted: 08/24/2020] [Indexed: 01/08/2023]
Abstract
locStra is an R -package for the analysis of regional and global population stratification in whole-genome sequencing (WGS) studies, where regional stratification refers to the substructure defined by the loci in a particular region on the genome. Population substructure can be assessed based on the genetic covariance matrix, the genomic relationship matrix, and the unweighted/weighted genetic Jaccard similarity matrix. Using a sliding window approach, the regional similarity matrices are compared with the global ones, based on user-defined window sizes and metrics, for example, the correlation between regional and global eigenvectors. An algorithm for the specification of the window size is provided. As the implementation fully exploits sparse matrix algebra and is written in C++, the analysis is highly efficient. Even on single cores, for realistic study sizes (several thousand subjects, several million rare variants per subject), the runtime for the genome-wide computation of all regional similarity matrices does typically not exceed one hour, enabling an unprecedented investigation of regional stratification across the entire genome. The package is applied to three WGS studies, illustrating the varying patterns of regional substructure across the genome and its beneficial effects on association testing.
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Affiliation(s)
- Georg Hahn
- Department of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA
| | - Sharon M Lutz
- Department of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA
| | - Julian Hecker
- Department of Medicine, Brigham and Women's Hospital, Harvard University, Boston, Massachusetts, USA
| | - Dmitry Prokopenko
- Massachusetts General Hospital, Harvard University, Boston, Massachusetts, USA
| | - Michael H Cho
- Department of Medicine, Brigham and Women's Hospital, Harvard University, Boston, Massachusetts, USA
| | - Edwin K Silverman
- Department of Medicine, Brigham and Women's Hospital, Harvard University, Boston, Massachusetts, USA
| | - Scott T Weiss
- Department of Medicine, Brigham and Women's Hospital, Harvard University, Boston, Massachusetts, USA
| | - Christoph Lange
- Department of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA
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22
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Tomasko D, Alderson M, Burnes R, Hecker J, Iadevaia N, Leverone J, Raulerson G, Sherwood E. The effects of Hurricane Irma on seagrass meadows in previously eutrophic estuaries in Southwest Florida (USA). Mar Pollut Bull 2020; 156:111247. [PMID: 32510389 DOI: 10.1016/j.marpolbul.2020.111247] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 05/04/2020] [Accepted: 05/05/2020] [Indexed: 06/11/2023]
Abstract
In six contiguous estuaries in Southwest Florida (USA) focused management actions over the past several decades have reduced watershed nutrient loads, resulting in an additional 11,672 ha of seagrass meadows between 1999 and 2016, an improvement of 32%. However, in September of 2017, Hurricane Irma made landfall in the state of Florida, affecting the open water and watersheds of each of these six estuaries. In response, seagrass coverage declined by 1203 ha between 2016 and 2018, a system-wide decrease of 3%. The range of decreases associated with Hurricane Irma varied from less than a 1% loss of seagrass coverage in St. Joseph Sound to declines of 7 and 11% in Clearwater Harbor and Lemon Bay, respectively. Areas with the largest losses between 2016 and 2018 were those systems where seagrass coverage had declined in prior years, indicating the effects of Hurricane Irma might have been intensified by prior impacts.
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Affiliation(s)
- D Tomasko
- Environmental Science Associates, Tampa, FL, USA.
| | - M Alderson
- Sarasota Bay Estuary Program, Sarasota, FL, USA
| | - R Burnes
- Pinellas County Environmental Management, Clearwater, FL, USA
| | - J Hecker
- Coastal & Heartland National Estuary Partnership, Punta Gorda, FL, USA
| | - N Iadevaia
- Coastal & Heartland National Estuary Partnership, Punta Gorda, FL, USA
| | - J Leverone
- Sarasota Bay Estuary Program, Sarasota, FL, USA
| | - G Raulerson
- Tampa Bay Estuary Program, St. Petersburg, FL, USA
| | - E Sherwood
- Tampa Bay Estuary Program, St. Petersburg, FL, USA
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23
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Prokopenko D, Hecker J, Kirchner R, Chapman BA, Hoffman O, Mullin K, Hide W, Bertram L, Laird N, DeMeo DL, Lange C, Tanzi RE. Identification of Novel Alzheimer's Disease Loci Using Sex-Specific Family-Based Association Analysis of Whole-Genome Sequence Data. Sci Rep 2020; 10:5029. [PMID: 32193444 PMCID: PMC7081222 DOI: 10.1038/s41598-020-61883-6] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Accepted: 02/17/2020] [Indexed: 11/21/2022] Open
Abstract
With the advent of whole genome-sequencing (WGS) studies, family-based designs enable sex-specific analysis approaches that can be applied to only affected individuals; tests using family-based designs are attractive because they are completely robust against the effects of population substructure. These advantages make family-based association tests (FBATs) that use siblings as well as parents especially suited for the analysis of late-onset diseases such as Alzheimer's Disease (AD). However, the application of FBATs to assess sex-specific effects can require additional filtering steps, as sensitivity to sequencing errors is amplified in this type of analysis. Here, we illustrate the implementation of robust analysis approaches and additional filtering steps that can minimize the chances of false positive-findings due to sex-specific sequencing errors. We apply this approach to two family-based AD datasets and identify four novel loci (GRID1, RIOK3, MCPH1, ZBTB7C) showing sex-specific association with AD risk. Following stringent quality control filtering, the strongest candidate is ZBTB7C (Pinter = 1.83 × 10-7), in which the minor allele of rs1944572 confers increased risk for AD in females and protection in males. ZBTB7C encodes the Zinc Finger and BTB Domain Containing 7C, a transcriptional repressor of membrane metalloproteases (MMP). Members of this MMP family were implicated in AD neuropathology.
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Affiliation(s)
- Dmitry Prokopenko
- Genetics and Aging Unit and McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Julian Hecker
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Rory Kirchner
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Brad A Chapman
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Oliver Hoffman
- Department of Clinical Pathology, University of Melbourne, Victoria, 3000, Melbourne, Australia
| | - Kristina Mullin
- Genetics and Aging Unit and McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Winston Hide
- Harvard Medical School, Boston, MA, USA
- Department of Neuroscience, Sheffield Institute for Translational Neurosciences, University of Sheffield, Sheffield, UK
- Department of Pathology, Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA, US
| | - Lars Bertram
- Lübeck Interdisciplinary Platform for Genome Analytics, Institutes of Neurogenetics and Cardiogenetics, University of Lübeck, Lübeck, Germany
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Nan Laird
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Dawn L DeMeo
- Harvard Medical School, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Christoph Lange
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA.
| | - Rudolph E Tanzi
- Genetics and Aging Unit and McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
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24
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Ragland MF, Benway CJ, Lutz SM, Bowler RP, Hecker J, Hokanson JE, Crapo JD, Castaldi PJ, DeMeo DL, Hersh CP, Hobbs BD, Lange C, Beaty TH, Cho MH, Silverman EK. Genetic Advances in Chronic Obstructive Pulmonary Disease. Insights from COPDGene. Am J Respir Crit Care Med 2020; 200:677-690. [PMID: 30908940 DOI: 10.1164/rccm.201808-1455so] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Chronic obstructive pulmonary disease (COPD) is a common and progressive disease that is influenced by both genetic and environmental factors. For many years, knowledge of the genetic basis of COPD was limited to Mendelian syndromes, such as alpha-1 antitrypsin deficiency and cutis laxa, caused by rare genetic variants. Over the past decade, the proliferation of genome-wide association studies, the accessibility of whole-genome sequencing, and the development of novel methods for analyzing genetic variation data have led to a substantial increase in the understanding of genetic variants that play a role in COPD susceptibility and COPD-related phenotypes. COPDGene (Genetic Epidemiology of COPD), a multicenter, longitudinal study of over 10,000 current and former cigarette smokers, has been pivotal to these breakthroughs in understanding the genetic basis of COPD. To date, over 20 genetic loci have been convincingly associated with COPD affection status, with additional loci demonstrating association with COPD-related phenotypes such as emphysema, chronic bronchitis, and hypoxemia. In this review, we discuss the contributions of the COPDGene study to the discovery of these genetic associations as well as the ongoing genetic investigations of COPD subtypes, protein biomarkers, and post-genome-wide association study analysis.
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Affiliation(s)
- Margaret F Ragland
- Division of Pulmonary Sciences and Critical Care Medicine, School of Medicine, and
| | | | | | | | - Julian Hecker
- Harvard T. H. Chan School of Public Health, Boston, Massachusetts; and
| | - John E Hokanson
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Aurora, Colorado
| | | | | | - Dawn L DeMeo
- Channing Division of Network Medicine and.,Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Craig P Hersh
- Channing Division of Network Medicine and.,Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Brian D Hobbs
- Channing Division of Network Medicine and.,Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Christoph Lange
- Harvard T. H. Chan School of Public Health, Boston, Massachusetts; and
| | - Terri H Beaty
- Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
| | - Michael H Cho
- Channing Division of Network Medicine and.,Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Edwin K Silverman
- Channing Division of Network Medicine and.,Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Massachusetts
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25
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Forstner AJ, Fischer SB, Schenk LM, Strohmaier J, Maaser-Hecker A, Reinbold CS, Sivalingam S, Hecker J, Streit F, Degenhardt F, Witt SH, Schumacher J, Thiele H, Nürnberg P, Guzman-Parra J, Orozco Diaz G, Auburger G, Albus M, Borrmann-Hassenbach M, González MJ, Gil Flores S, Cabaleiro Fabeiro FJ, del Río Noriega F, Perez Perez F, Haro González J, Rivas F, Mayoral F, Bauer M, Pfennig A, Reif A, Herms S, Hoffmann P, Pirooznia M, Goes FS, Rietschel M, Nöthen MM, Cichon S. Whole-exome sequencing of 81 individuals from 27 multiply affected bipolar disorder families. Transl Psychiatry 2020; 10:57. [PMID: 32066727 PMCID: PMC7026119 DOI: 10.1038/s41398-020-0732-y] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 12/18/2019] [Accepted: 01/08/2020] [Indexed: 01/01/2023] Open
Abstract
Bipolar disorder (BD) is a highly heritable neuropsychiatric disease characterized by recurrent episodes of depression and mania. Research suggests that the cumulative impact of common alleles explains 25-38% of phenotypic variance, and that rare variants may contribute to BD susceptibility. To identify rare, high-penetrance susceptibility variants for BD, whole-exome sequencing (WES) was performed in three affected individuals from each of 27 multiply affected families from Spain and Germany. WES identified 378 rare, non-synonymous, and potentially functional variants. These spanned 368 genes, and were carried by all three affected members in at least one family. Eight of the 368 genes harbored rare variants that were implicated in at least two independent families. In an extended segregation analysis involving additional family members, five of these eight genes harbored variants showing full or nearly full cosegregation with BD. These included the brain-expressed genes RGS12 and NCKAP5, which were considered the most promising BD candidates on the basis of independent evidence. Gene enrichment analysis for all 368 genes revealed significant enrichment for four pathways, including genes reported in de novo studies of autism (padj < 0.006) and schizophrenia (padj = 0.015). These results suggest a possible genetic overlap with BD for autism and schizophrenia at the rare-sequence-variant level. The present study implicates novel candidate genes for BD development, and may contribute to an improved understanding of the biological basis of this common and often devastating disease.
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Affiliation(s)
- Andreas J. Forstner
- 0000 0004 1936 9756grid.10253.35Centre for Human Genetics, University of Marburg, Marburg, Germany ,0000 0001 2240 3300grid.10388.32Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany ,0000 0004 1937 0642grid.6612.3Department of Biomedicine, University of Basel, Basel, Switzerland ,0000 0004 1937 0642grid.6612.3Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
| | - Sascha B. Fischer
- 0000 0004 1937 0642grid.6612.3Department of Biomedicine, University of Basel, Basel, Switzerland ,grid.410567.1Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
| | - Lorena M. Schenk
- 0000 0001 2240 3300grid.10388.32Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Jana Strohmaier
- 0000 0001 2190 4373grid.7700.0Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany ,SRH University Heidelberg, Academy for Psychotherapy, Heidelberg, Germany
| | - Anna Maaser-Hecker
- 0000 0001 2240 3300grid.10388.32Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Céline S. Reinbold
- 0000 0004 1937 0642grid.6612.3Department of Biomedicine, University of Basel, Basel, Switzerland ,grid.410567.1Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland ,0000 0004 1936 8921grid.5510.1Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, Oslo, Norway
| | - Sugirthan Sivalingam
- 0000 0001 2240 3300grid.10388.32Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Julian Hecker
- 000000041936754Xgrid.38142.3cDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA USA
| | - Fabian Streit
- 0000 0001 2190 4373grid.7700.0Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Franziska Degenhardt
- 0000 0001 2240 3300grid.10388.32Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Stephanie H. Witt
- 0000 0001 2190 4373grid.7700.0Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Johannes Schumacher
- 0000 0004 1936 9756grid.10253.35Centre for Human Genetics, University of Marburg, Marburg, Germany ,0000 0001 2240 3300grid.10388.32Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Holger Thiele
- 0000 0000 8580 3777grid.6190.eCologne Center for Genomics, University of Cologne, Cologne, Germany
| | - Peter Nürnberg
- 0000 0000 8580 3777grid.6190.eCologne Center for Genomics, University of Cologne, Cologne, Germany
| | - José Guzman-Parra
- grid.452525.1Department of Mental Health, University Regional Hospital of Málaga, Institute of Biomedicine of Málaga (IBIMA), Málaga, Spain
| | - Guillermo Orozco Diaz
- Unidad de Gestión Clínica del Dispositivo de Cuidados Críticos y Urgencias del Distrito Sanitario Málaga - Coin-Gudalhorce, Málaga, Spain
| | - Georg Auburger
- 0000 0004 0578 8220grid.411088.4Experimental Neurology, Department of Neurology, Goethe University Hospital, Frankfurt am Main, Germany
| | - Margot Albus
- 0000 0001 0690 3065grid.419834.3Isar Amper Klinikum München Ost, kbo, Haar, Germany
| | | | - Maria José González
- grid.452525.1Department of Mental Health, University Regional Hospital of Málaga, Institute of Biomedicine of Málaga (IBIMA), Málaga, Spain
| | - Susana Gil Flores
- 0000 0004 1771 4667grid.411349.aDepartment of Mental Health, University Hospital of Reina Sofia, Cordoba, Spain
| | | | - Francisco del Río Noriega
- grid.477360.1Department of Mental Health, Hospital of Jerez de la Frontera, Jerez de la Frontera, Spain
| | | | | | - Fabio Rivas
- Department of Psychiatry, Carlos Haya Regional University Hospital, Malaga, Spain
| | - Fermin Mayoral
- Department of Psychiatry, Carlos Haya Regional University Hospital, Malaga, Spain
| | - Michael Bauer
- Department of Psychiatry and Psychotherapy, Medical Faculty, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Andrea Pfennig
- Department of Psychiatry and Psychotherapy, Medical Faculty, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Andreas Reif
- 0000 0004 0578 8220grid.411088.4Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt am Main, Frankfurt am Main, Germany
| | - Stefan Herms
- 0000 0001 2240 3300grid.10388.32Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany ,0000 0004 1937 0642grid.6612.3Department of Biomedicine, University of Basel, Basel, Switzerland ,grid.410567.1Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
| | - Per Hoffmann
- 0000 0001 2240 3300grid.10388.32Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany ,0000 0004 1937 0642grid.6612.3Department of Biomedicine, University of Basel, Basel, Switzerland ,grid.410567.1Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland ,0000 0001 2297 375Xgrid.8385.6Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany
| | - Mehdi Pirooznia
- 0000 0001 2171 9311grid.21107.35Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD USA
| | - Fernando S. Goes
- 0000 0001 2171 9311grid.21107.35Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD USA
| | - Marcella Rietschel
- 0000 0001 2190 4373grid.7700.0Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Markus M. Nöthen
- 0000 0001 2240 3300grid.10388.32Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Sven Cichon
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany. .,Department of Biomedicine, University of Basel, Basel, Switzerland. .,Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland. .,Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany.
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26
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Kachroo P, Hecker J, Chawes BL, Ahluwalia TS, Cho MH, Qiao D, Kelly RS, Chu SH, Virkud YV, Huang M, Barnes KC, Burchard EG, Eng C, Hu D, Celedón JC, Daya M, Levin AM, Gui H, Williams LK, Forno E, Mak ACY, Avila L, Soto-Quiros ME, Cloutier MM, Acosta-Pérez E, Canino G, Bønnelykke K, Bisgaard H, Raby BA, Lange C, Weiss ST, Lasky-Su JA. Whole Genome Sequencing Identifies CRISPLD2 as a Lung Function Gene in Children With Asthma. Chest 2019; 156:1068-1079. [PMID: 31557467 PMCID: PMC6904857 DOI: 10.1016/j.chest.2019.08.2202] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 08/02/2019] [Accepted: 08/22/2019] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Asthma is a common respiratory disorder with a highly heterogeneous nature that remains poorly understood. The objective was to use whole genome sequencing (WGS) data to identify regions of common genetic variation contributing to lung function in individuals with a diagnosis of asthma. METHODS WGS data were generated for 1,053 individuals from trios and extended pedigrees participating in the family-based Genetic Epidemiology of Asthma in Costa Rica study. Asthma affection status was defined through a physician's diagnosis of asthma, and most participants with asthma also had airway hyperresponsiveness (AHR) to methacholine. Family-based association tests for single variants were performed to assess the associations with lung function phenotypes. RESULTS A genome-wide significant association was identified between baseline FEV1/FVC ratio and a single-nucleotide polymorphism in the top hit cysteine-rich secretory protein LCCL domain-containing 2 (CRISPLD2) (rs12051168; P = 3.6 × 10-8 in the unadjusted model) that retained suggestive significance in the covariate-adjusted model (P = 5.6 × 10-6). Rs12051168 was also nominally associated with other related phenotypes: baseline FEV1 (P = 3.3 × 10-3), postbronchodilator (PB) FEV1 (7.3 × 10-3), and PB FEV1/FVC ratio (P = 2.7 × 10-3). The identified baseline FEV1/FVC ratio and rs12051168 association was meta-analyzed and replicated in three independent cohorts in which most participants with asthma also had confirmed AHR (combined weighted z-score P = .015) but not in cohorts without information about AHR. CONCLUSIONS These findings suggest that using specific asthma characteristics, such as AHR, can help identify more genetically homogeneous asthma subgroups with genotype-phenotype associations that may not be observed in all children with asthma. CRISPLD2 also may be important for baseline lung function in individuals with asthma who also may have AHR.
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Affiliation(s)
- Priyadarshini Kachroo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Julian Hecker
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Bo L Chawes
- Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Tarunveer S Ahluwalia
- Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Michael H Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Dandi Qiao
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Rachel S Kelly
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Su H Chu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Yamini V Virkud
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Department of Pediatrics, Massachusetts General Hospital for Children and Harvard Medical School, Boston, MA
| | - Mengna Huang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Kathleen C Barnes
- Division of Biomedical Informatics and Personalized Medicine, University of Colorado Anschutz Medical Campus, Colorado, CO
| | - Esteban G Burchard
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA
| | - Celeste Eng
- Department of Medicine, University of California San Francisco, San Francisco, CA
| | - Donglei Hu
- Department of Medicine, University of California San Francisco, San Francisco, CA
| | - Juan C Celedón
- Division of Pediatric Pulmonary Medicine, Allergy and Immunology, UPMC Children's Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, PA
| | - Michelle Daya
- Division of Biomedical Informatics and Personalized Medicine, University of Colorado Anschutz Medical Campus, Colorado, CO
| | - Albert M Levin
- Department of Public Health Sciences, Henry Ford Health System, Detroit, MI; Center for Bioinformatics, Henry Ford Health System, Detroit, MI
| | - Hongsheng Gui
- Center for Individualized and Genomic Medicine Research, Henry Ford Health System, Detroit, MI; Department of Internal Medicine, Henry Ford Health System, Detroit, MI
| | - L Keoki Williams
- Center for Individualized and Genomic Medicine Research, Henry Ford Health System, Detroit, MI; Department of Internal Medicine, Henry Ford Health System, Detroit, MI
| | - Erick Forno
- Division of Pediatric Pulmonary Medicine, Allergy and Immunology, UPMC Children's Hospital of Pittsburgh, University of Pittsburgh, Pittsburgh, PA
| | - Angel C Y Mak
- Department of Medicine, University of California San Francisco, San Francisco, CA
| | - Lydiana Avila
- Department of Pediatrics, Hospital Nacional de Niños, San José, Costa Rica
| | | | | | - Edna Acosta-Pérez
- Behavioral Sciences Research Institute, University of Puerto Rico, San Juan, Puerto Rico
| | - Glorisa Canino
- Behavioral Sciences Research Institute, University of Puerto Rico, San Juan, Puerto Rico
| | - Klaus Bønnelykke
- Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Hans Bisgaard
- Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Benjamin A Raby
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA; Boston Children's Hospital and Harvard Medical School, Boston, MA
| | - Christoph Lange
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Scott T Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Jessica A Lasky-Su
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.
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27
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Böhmer AC, Hecker J, Schröder J, Gharahkhani P, May A, Gerges C, Anders M, Becker J, Hess T, Kreuser N, Thieme R, Noder T, Venerito M, Veits L, Schmidt T, Fuchs C, Izbicki JR, Hölscher AH, Dietrich A, Moulla Y, Lyros O, Lang H, Lorenz D, Schumacher B, Mayershofer R, Vashist Y, Ott K, Vieth M, Weismüller J, Moebus S, Knapp M, Neuhaus H, Rösch T, Ell C, Nöthen MM, Whiteman DC, Tomlinson I, Jankowski J, Fitzgerald RC, Palles C, Vaughan TL, Gockel I, Thrift AP, Fier H, Schumacher J. Shared Genetic Etiology of Obesity-Related Traits and Barrett's Esophagus/Adenocarcinoma: Insights from Genome-Wide Association Studies. Cancer Epidemiol Biomarkers Prev 2019; 29:427-433. [PMID: 31748258 DOI: 10.1158/1055-9965.epi-19-0374] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 07/09/2019] [Accepted: 11/15/2019] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Obesity is a major risk factor for esophageal adenocarcinoma (EA) and its precursor Barrett's esophagus (BE). Research suggests that individuals with high genetic risk to obesity have a higher BE/EA risk. To facilitate understanding of biological factors that lead to progression from BE to EA, the present study investigated the shared genetic background of BE/EA and obesity-related traits. METHODS Cross-trait linkage disequilibrium score regression was applied to summary statistics from genome-wide association meta-analyses on BE/EA and on obesity traits. Body mass index (BMI) was used as a proxy for general obesity, and waist-to-hip ratio (WHR) for abdominal obesity. For single marker analyses, all genome-wide significant risk alleles for BMI and WHR were compared with summary statistics of the BE/EA meta-analyses. RESULTS Sex-combined analyses revealed a significant genetic correlation between BMI and BE/EA (rg = 0.13, P = 2 × 10-04) and a rg of 0.12 between WHR and BE/EA (P = 1 × 10-02). Sex-specific analyses revealed a pronounced genetic correlation between BMI and EA in females (rg = 0.17, P = 1.2 × 10-03), and WHR and EA in males (rg = 0.18, P = 1.51 × 10-02). On the single marker level, significant enrichment of concordant effects was observed for BMI and BE/EA risk variants (P = 8.45 × 10-03) and WHR and BE/EA risk variants (P = 2 × 10-02). CONCLUSIONS Our study provides evidence for sex-specific genetic correlations that might reflect specific biological mecha-nisms. The data demonstrate that shared genetic factors are particularly relevant in progression from BE to EA. IMPACT Our study quantifies the genetic correlation between BE/EA and obesity. Further research is now warranted to elucidate these effects and to understand the shared pathophysiology.
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Affiliation(s)
- Anne C Böhmer
- Institute of Human Genetics, University of Bonn, Bonn, Germany. .,Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany
| | - Julian Hecker
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Julia Schröder
- Institute of Human Genetics, University of Bonn, Bonn, Germany.,Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany
| | - Puya Gharahkhani
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Andrea May
- Department of Medicine II, Sana Klinikum, Offenbach, Germany
| | - Christian Gerges
- Department of Internal Medicine II, Evangelisches Krankenhaus, Düsseldorf, Germany
| | - Mario Anders
- Department of Interdisciplinary Endoscopy, University Hospital Hamburg-Eppendorf, Hamburg, Germany.,Department of Gastroenterology and Interdisciplinary Endoscopy, Vivantes Wenckebach-Klinikum, Berlin, Germany
| | - Jessica Becker
- Institute of Human Genetics, University of Bonn, Bonn, Germany.,Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany
| | - Timo Hess
- Center for Human Genetics, University Hospital Marburg, Marburg, Germany
| | - Nicole Kreuser
- Department of Visceral, Transplant, Thoracic and Vascular Surgery, University Hospital of Leipzig, Leipzig, Germany
| | - René Thieme
- Department of Visceral, Transplant, Thoracic and Vascular Surgery, University Hospital of Leipzig, Leipzig, Germany
| | - Tania Noder
- Department of Interdisciplinary Endoscopy, University Hospital Hamburg-Eppendorf, Hamburg, Germany
| | - Marino Venerito
- Department of Gastroenterology, Hepatology and Infectious Diseases, Otto-von-Guericke University Hospital, Magdeburg, Germany
| | - Lothar Veits
- Institute of Pathology, Klinikum Bayreuth, Bayreuth, Germany
| | - Thomas Schmidt
- Department of General, Visceral and Transplantation Surgery, University of Heidelberg, Heidelberg, Germany
| | - Claudia Fuchs
- Department of General, Visceral and Cancer Surgery, University of Cologne, Cologne, Germany
| | - Jakob R Izbicki
- Department of General, Visceral and Thoracic Surgery, University Medical Center Hamburg-Eppendorf, University of Hamburg, Hamburg, Germany
| | - Arnulf H Hölscher
- Department of General, Visceral and Cancer Surgery, University of Cologne, Cologne, Germany
| | - Arne Dietrich
- Department of Visceral, Transplant, Thoracic and Vascular Surgery, University Hospital of Leipzig, Leipzig, Germany
| | - Yusef Moulla
- Department of Visceral, Transplant, Thoracic and Vascular Surgery, University Hospital of Leipzig, Leipzig, Germany
| | - Orestis Lyros
- Department of Visceral, Transplant, Thoracic and Vascular Surgery, University Hospital of Leipzig, Leipzig, Germany
| | - Hauke Lang
- Department of General, Visceral and Transplant Surgery, University Medical Center, University of Mainz, Mainz, Germany
| | - Dietmar Lorenz
- Department of General, Visceral and Thoracic Surgery, Klinikum Darmstadt, Darmstadt, Germany
| | - Brigitte Schumacher
- Department of Internal Medicine and Gastroenterology, Elisabeth Hospital, Essen, Germany
| | | | - Yogesh Vashist
- Department of General, Visceral and Thoracic Surgery, University Medical Center Hamburg-Eppendorf, University of Hamburg, Hamburg, Germany.,Kantonsspital Aarau, Aarau, Switzerland
| | - Katja Ott
- Department of General, Visceral and Transplantation Surgery, University of Heidelberg, Heidelberg, Germany.,Department of General, Visceral and Thorax Surgery, RoMed Klinikum Rosenheim, Rosenheim, Germany
| | - Michael Vieth
- Institute of Pathology, Klinikum Bayreuth, Bayreuth, Germany
| | | | - Susanne Moebus
- Centre of Urban Epidemiology, Institute of Medical Informatics, Biometry and Epidemiology, University of Essen, Essen, Germany
| | - Michael Knapp
- Institute for Medical Biometry, Informatics, and Epidemiology, University of Bonn, Bonn, Germany
| | - Horst Neuhaus
- Department of Internal Medicine II, Evangelisches Krankenhaus, Düsseldorf, Germany
| | - Thomas Rösch
- Department of Interdisciplinary Endoscopy, University Hospital Hamburg-Eppendorf, Hamburg, Germany
| | - Christian Ell
- Department of Medicine II, Sana Klinikum, Offenbach, Germany
| | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn, Bonn, Germany.,Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany
| | - David C Whiteman
- Cancer Control, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Ian Tomlinson
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Janusz Jankowski
- University of Central Lancashire, Westlakes Science and Technology Park, Moor Row, United Kingdom.,Warwick Medical School, University of Warwick, Warwick, United Kingdom
| | - Rebecca C Fitzgerald
- Medical Research Council (MRC) Cancer Unit, Hutchison-MRC Research Centre and University of Cambridge, Cambridge, United Kingdom
| | - Claire Palles
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Thomas L Vaughan
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Ines Gockel
- Department of Visceral, Transplant, Thoracic and Vascular Surgery, University Hospital of Leipzig, Leipzig, Germany
| | - Aaron P Thrift
- Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas.,Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas
| | - Heide Fier
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Johannes Schumacher
- Institute of Human Genetics, University of Bonn, Bonn, Germany.,Center for Human Genetics, University Hospital Marburg, Marburg, Germany
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Hecker J, Ruczinski I, Cho MH, Silverman EK, Coull B, Lange C. A flexible and nearly optimal sequential testing approach to randomized testing: QUICK-STOP. Genet Epidemiol 2019; 44:139-147. [PMID: 31713269 DOI: 10.1002/gepi.22268] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Revised: 08/24/2019] [Accepted: 09/27/2019] [Indexed: 01/19/2023]
Abstract
In the analysis of current life science datasets, we often encounter scenarios in which the application of asymptotic theory to hypothesis testing can be problematic. Besides improved asymptotic results, permutation/simulation-based tests are a general approach to address this issue. However, these randomized tests can impose a massive computational burden, for example, in scenarios in which large numbers of statistical tests are computed, and the specified significance level is very small. Stopping rules aim to assess significance with the smallest possible number of draws while controlling the probabilities of errors due to statistical uncertainty. In this communication, we derive a general stopping rule, QUICK-STOP, based on the sequential testing theory that is easy to implement, controls the error probabilities rigorously, and is nearly optimal in terms of expected draws. In a simulation study, we show that our approach outperforms current stopping approaches for general randomized tests by factor 10 and does not impose an additional computational burden. We illustrate our approach by applying our stopping rule to a single-variant analysis of a whole-genome sequencing study for lung function.
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Affiliation(s)
- Julian Hecker
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.,Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Ingo Ruczinski
- Department of Biostatistics, Bloomberg School of Public Health, Baltimore, Maryland
| | - Michael H Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Edwin K Silverman
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Brent Coull
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Christoph Lange
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.,Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts
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29
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Lobbes MBI, Hecker J, Houben IPL, Pluymakers R, Jeukens C, Laji UC, Gommers S, Wildberger JE, Nelemans PJ. Evaluation of single-view contrast-enhanced mammography as novel reading strategy: a non-inferiority feasibility study. Eur Radiol 2019; 29:6211-6219. [PMID: 31073859 PMCID: PMC6795610 DOI: 10.1007/s00330-019-06215-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Revised: 03/13/2019] [Accepted: 04/02/2019] [Indexed: 11/27/2022]
Abstract
BACKGROUND Guidelines recommend screening of high-risk women using breast magnetic resonance imaging (MRI). Contrast-enhanced mammography (CEM) has matured, providing excellent diagnostic accuracy. To lower total radiation dose, evaluation of single-view (1 V) CEM exams might be considered instead of double-view (2 V) readings as an alternative reading strategy in women who cannot undergo MRI. METHODS This retrospective non-inferiority feasibility study evaluates whether the use of 1 V results in an acceptable sensitivity for detecting breast cancer (non-inferiority margin, - 10%). CEM images from May 2013 to December 2017 were included. 1 V readings were performed by consensus opinion of three radiologists, followed by 2 V readings being performed after 6 weeks. Cases were considered "malignant" if the final BI-RADS score was ≥ 4, enabling calculation of sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Histopathological results or follow-up served as a gold standard. RESULTS A total of 368 cases were evaluated. Mean follow-up for benign or negative cases was 20.9 months. Sensitivity decreased by 9.6% from 92.9 to 83.3% when only 1 V was used for evaluation (p < 0.001). The lower limit of the 90% confidence interval around the difference in sensitivity between 1 V and 2 V readings was - 15% and lies below the predefined non-inferiority margin of - 10%. Hence, non-inferiority of 1 V to 2 V reading cannot be concluded. AUC for 1 V was significantly lower, 0.861 versus 0.899 for 2 V (p = 0.0174). CONCLUSION Non-inferiority of 1 V evaluations as an alternative reading strategy to standard 2 V evaluations could not be concluded. 1 V evaluations had lower diagnostic performance compared with 2 V evaluations. KEY POINTS • To lower radiation exposure used in contrast-enhanced mammography, we studied a hypothetical alternative strategy: single-view readings (1 V) versus (standard) double-view readings (2 V). • Based on our predefined margin of - 10%, non-inferiority of 1 V could not be concluded. • 1 V evaluation is not recommended as an alternative reading strategy to lower CEM-related radiation exposure.
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Affiliation(s)
- M B I Lobbes
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, P.O. Box 5800, 6202 AZ, Maastricht, The Netherlands.
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.
| | - J Hecker
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, P.O. Box 5800, 6202 AZ, Maastricht, The Netherlands
| | - I P L Houben
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, P.O. Box 5800, 6202 AZ, Maastricht, The Netherlands
| | - R Pluymakers
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, P.O. Box 5800, 6202 AZ, Maastricht, The Netherlands
| | - C Jeukens
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, P.O. Box 5800, 6202 AZ, Maastricht, The Netherlands
| | - U C Laji
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, P.O. Box 5800, 6202 AZ, Maastricht, The Netherlands
| | - S Gommers
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, P.O. Box 5800, 6202 AZ, Maastricht, The Netherlands
| | - J E Wildberger
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, P.O. Box 5800, 6202 AZ, Maastricht, The Netherlands
| | - P J Nelemans
- Department of Epidemiology, Maastricht University, Maastricht, The Netherlands
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30
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An J, Won S, Lutz SM, Hecker J, Lange C. Effect of population stratification on SNP-by-environment interaction. Genet Epidemiol 2019; 43:1046-1055. [PMID: 31429121 DOI: 10.1002/gepi.22250] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Revised: 06/04/2019] [Accepted: 07/11/2019] [Indexed: 11/10/2022]
Abstract
Proportions of false-positive rates in genome-wide association analysis are affected by population stratification, and if it is not correctly adjusted, the statistical analysis can produce the large false-negative finding. Therefore various approaches have been proposed to adjust such problems in genome-wide association studies. However, in spite of its importance, a few studies have been conducted in genome-wide single nucleotide polymorphism (SNP)-by-environment interaction studies. In this report, we illustrate in which scenarios can lead to the false-positive rates in association mapping and approach to maintaining the overall type-1 error rate.
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Affiliation(s)
- Jaehoon An
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, South Korea
| | - Sungho Won
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, South Korea.,Interdisciplinary Program for Bioinformatics, College of Natural Science, Seoul National University, Seoul, South Korea.,Institute of Health and Environment, Seoul National University, Seoul, South Korea.,Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
| | - Sharon M Lutz
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
| | - Julian Hecker
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts.,Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Christoph Lange
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts.,Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts
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Hecker J, Prokopenko D, Lange C, Fier HL. PolyGEE: a generalized estimating equation approach to the efficient and robust estimation of polygenic effects in large-scale association studies. Biostatistics 2019; 19:295-306. [PMID: 28968646 PMCID: PMC5991211 DOI: 10.1093/biostatistics/kxx040] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Accepted: 07/21/2017] [Indexed: 11/17/2022] Open
Abstract
To quantify polygenic effects, i.e. undetected genetic effects, in large-scale association studies, we propose a generalized estimating equation (GEE) based estimation framework. We develop a marginal model for single-variant association test statistics of complex diseases that generalizes existing approaches such as LD Score regression and that is applicable to population-based designs, to family-based designs or to arbitrary combinations of both. We extend the standard GEE approach so that the parameters of the proposed marginal model can be estimated based on working-correlation/linkage-disequilibrium (LD) matrices from external reference panels. Our method achieves substantial efficiency gains over standard approaches, while it is robust against misspecification of the LD structure, i.e. the LD structure of the reference panel can differ substantially from the true LD structure in the study population. In simulation studies and in applications to population-based and family-based studies, we illustrate the features of the proposed GEE framework. Our results suggest that our approach can be up to 100% more efficient than existing methodology.
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Affiliation(s)
- Julian Hecker
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA 02115, USA and Department of Genomic Mathematics, University of Bonn, Sigmund-Freud-Strasse 25, 53127 Bonn, Germany
| | - Dmitry Prokopenko
- Channing Division of Network Medicine, Brigham and Women's Hospital, 181 Longwood Avenue, Boston, MA 02115, USA
| | - Christoph Lange
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA 02115, USA and Channing Division of Network Medicine, Brigham and Women's Hospital, 181 Longwood Avenue, Boston, MA 02115, USA
| | - Heide Loehlein Fier
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA 02115, USA and Department of Genomic Mathematics, University of Bonn, Sigmund-Freud-Strasse 25, 53127 Bonn, Germany
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32
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Hecker J, Laird N, Lange C. A comparison of popular TDT-generalizations for family-based association analysis. Genet Epidemiol 2019; 43:300-317. [PMID: 30609057 DOI: 10.1002/gepi.22181] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Revised: 09/26/2018] [Accepted: 11/26/2018] [Indexed: 12/31/2022]
Abstract
The transmission disequilibrium test (TDT) is the gold standard for testing the association between a genetic variant and disease in samples consisting of affected individuals and their parents. In practice, more complex pedigree structures, that is siblings with no parents, or three-generational pedigrees with possibly missing genotypes, are common. There are several generalizations of the TDT that are suitable for use with arbitrary pedigree structures. We consider three such frequently used generalizations, family-based association test, pedigree disequilibrium test, and generalized disequilibrium test, that have accompanying software and compare them regarding validity and power in the single variant setting. We use simulations to study the effects of population admixture, populations whose genotypes are not in Hardy-Weinberg equilibrium (HWE), different pedigree structures, and the presence of linkage. Whereas our results show that some TDT generalizations can have a substantially increased Type 1 error, these tests are often used in substantive research without caveats about the validity of their Type 1 error. For the association analysis of rare variants in sequencing studies, region-based extensions of the TDT generalizations, that rely on the postulated robustness of the single variant tests, have been proposed. We discuss the implications of our results for these region-based extensions.
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Affiliation(s)
- Julian Hecker
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Nan Laird
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Christoph Lange
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
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Maaser A, Forstner AJ, Strohmaier J, Hecker J, Ludwig KU, Sivalingam S, Streit F, Degenhardt F, Witt SH, Reinbold CS, Koller AC, Raff R, Heilmann-Heimbach S, Fischer SB, Herms S, Hoffmann P, Thiele H, Nürnberg P, Löhlein Fier H, Orozco-Díaz G, Carmenate-Naranjo D, Proenza-Barzaga N, Auburger GWJ, Andlauer TFM, Cichon S, Marcheco-Teruel B, Mors O, Rietschel M, Nöthen MM. Exome sequencing in large, multiplex bipolar disorder families from Cuba. PLoS One 2018; 13:e0205895. [PMID: 30379966 PMCID: PMC6209204 DOI: 10.1371/journal.pone.0205895] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Accepted: 10/03/2018] [Indexed: 12/17/2022] Open
Abstract
Bipolar disorder (BD) is a major psychiatric illness affecting around 1% of the global population. BD is characterized by recurrent manic and depressive episodes, and has an estimated heritability of around 70%. Research has identified the first BD susceptibility genes. However, the underlying pathways and regulatory networks remain largely unknown. Research suggests that the cumulative impact of common alleles with small effects explains only around 25-38% of the phenotypic variance for BD. A plausible hypothesis therefore is that rare, high penetrance variants may contribute to BD risk. The present study investigated the role of rare, nonsynonymous, and potentially functional variants via whole exome sequencing in 15 BD cases from two large, multiply affected families from Cuba. The high prevalence of BD in these pedigrees renders them promising in terms of the identification of genetic risk variants with large effect sizes. In addition, SNP array data were used to calculate polygenic risk scores for affected and unaffected family members. After correction for multiple testing, no significant increase in polygenic risk scores for common, BD-associated genetic variants was found in BD cases compared to healthy relatives. Exome sequencing identified a total of 17 rare and potentially damaging variants in 17 genes. The identified variants were shared by all investigated BD cases in the respective pedigree. The most promising variant was located in the gene SERPING1 (p.L349F), which has been reported previously as a genome-wide significant risk gene for schizophrenia. The present data suggest novel candidate genes for BD susceptibility, and may facilitate the discovery of disease-relevant pathways and regulatory networks.
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Affiliation(s)
- Anna Maaser
- Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany
- Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
| | - Andreas J. Forstner
- Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany
- Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
- Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
- * E-mail:
| | - Jana Strohmaier
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Julian Hecker
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Kerstin U. Ludwig
- Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany
- Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
| | - Sugirthan Sivalingam
- Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany
- Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
| | - Fabian Streit
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Franziska Degenhardt
- Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany
- Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
| | - Stephanie H. Witt
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Céline S. Reinbold
- Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
| | - Anna C. Koller
- Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany
- Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
| | - Ruth Raff
- Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Stefanie Heilmann-Heimbach
- Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany
- Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
| | - Sascha B. Fischer
- Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
| | | | - Stefan Herms
- Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany
- Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
- Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
| | - Per Hoffmann
- Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany
- Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
- Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
- Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany
| | - Holger Thiele
- Cologne Center for Genomics, University of Cologne, Cologne, Germany
| | - Peter Nürnberg
- Cologne Center for Genomics, University of Cologne, Cologne, Germany
| | - Heide Löhlein Fier
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
- Institute of Genomic Mathematics, University of Bonn, Bonn, Germany
| | | | | | | | | | - Till F. M. Andlauer
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
- Department of Neurology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Sven Cichon
- Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany
- Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
- Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
- Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany
| | | | - Ole Mors
- Psychosis Research Unit, Aarhus University Hospital, Risskov, Denmark
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Markus M. Nöthen
- Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany
- Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
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Tomasko D, Alderson M, Burnes R, Hecker J, Leverone J, Raulerson G, Sherwood E. Widespread recovery of seagrass coverage in Southwest Florida (USA): Temporal and spatial trends and management actions responsible for success. Mar Pollut Bull 2018; 135:1128-1137. [PMID: 30301011 DOI: 10.1016/j.marpolbul.2018.08.049] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Revised: 08/17/2018] [Accepted: 08/22/2018] [Indexed: 06/08/2023]
Abstract
In Southwest Florida, a variety of human impacts had caused widespread losses of seagrass coverage from historical conditions. St. Joseph Sound and Clearwater Harbor lost approximately 24 and 51%, respectively, of their seagrass coverage between 1950 and 1999, while Tampa Bay and Sarasota Bay had lost 46% and 15%, respectively, of their seagrass coverage between 1950 and the 1980s. However, over the period of 1999 to 2016, the largest of the six estuaries, Tampa Bay, added 408 ha of seagrass per year, while the remaining five estuaries examined in this paper added approximately 269 ha per year. In total, seagrass coverage in these six estuaries increased 12,171 ha between the 1980s and 2016. Focused resource management plans have held the line on nitrogen loads from non-point sources, allowing seagrass resources to expand in response to reductions in point source loads that have been implemented over the past few decades.
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Affiliation(s)
- D Tomasko
- Environmental Science Associates, 4200 West Cypress Street, Suite 450, Tampa, FL 33607, USA.
| | - M Alderson
- Sarasota Bay Estuary Program, 111 South Orange Avenue, Sarasota, FL 34236, USA
| | - R Burnes
- Pinellas County Environmental Management, 22211 U.S. Highway 19, North, Clearwater, FL 33765, USA
| | - J Hecker
- Charlotte Harbor Estuary Program, 326 West Marion Avenue, Punta Gorda, FL 33950, USA
| | - J Leverone
- Sarasota Bay Estuary Program, 111 South Orange Avenue, Sarasota, FL 34236, USA
| | - G Raulerson
- Tampa Bay Estuary Program, 263 13th Avenue, South, St. Petersburg, FL 33701, USA
| | - E Sherwood
- Tampa Bay Estuary Program, 263 13th Avenue, South, St. Petersburg, FL 33701, USA
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Moehler M, Maderer A, Thuss-Patience P, Brenner B, Hecker J, Muñoz FL, Meiler J, Ettrich T, Hofheinz R, Al-Batran S, Vogel A, Mueller L, Lutz M, Borchert K, Greil R, Alsina M, Karatas A, Van Cutsem E, Keller R, Larcher-Senn J, Lorenzen S. Cisplatin/5-fluorouracil +/- panitumumab for patients with non-resectable, advanced or metastatic esophageal squamous cell cancer: A randomized phase III AIO/EORTC trial with an extensive biomarker program. Ann Oncol 2018. [DOI: 10.1093/annonc/mdy149.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
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Reinbold CS, Forstner AJ, Hecker J, Fullerton JM, Hoffmann P, Hou L, Heilbronner U, Degenhardt F, Adli M, Akiyama K, Akula N, Ardau R, Arias B, Backlund L, Benabarre A, Bengesser S, Bhattacharjee AK, Biernacka JM, Birner A, Marie-Claire C, Cervantes P, Chen GB, Chen HC, Chillotti C, Clark SR, Colom F, Cousins DA, Cruceanu C, Czerski PM, Dayer A, Étain B, Falkai P, Frisén L, Gard S, Garnham JS, Goes FS, Grof P, Gruber O, Hashimoto R, Hauser J, Herms S, Jamain S, Jiménez E, Kahn JP, Kassem L, Kittel-Schneider S, Kliwicki S, König B, Kusumi I, Lackner N, Laje G, Landén M, Lavebratt C, Leboyer M, Leckband SG, López Jaramillo CA, MacQueen G, Manchia M, Martinsson L, Mattheisen M, McCarthy MJ, McElroy SL, Mitjans M, Mondimore FM, Monteleone P, Nievergelt CM, Ösby U, Ozaki N, Perlis RH, Pfennig A, Reich-Erkelenz D, Rouleau GA, Schofield PR, Schubert KO, Schweizer BW, Seemüller F, Severino G, Shekhtman T, Shilling PD, Shimoda K, Simhandl C, Slaney CM, Smoller JW, Squassina A, Stamm TJ, Stopkova P, Tighe SK, Tortorella A, Turecki G, Volkert J, Witt SH, Wright AJ, Young LT, Zandi PP, Potash JB, DePaulo JR, Bauer M, Reininghaus E, Novák T, Aubry JM, Maj M, Baune BT, Mitchell PB, Vieta E, Frye MA, Rybakowski JK, Kuo PH, Kato T, Grigoroiu-Serbanescu M, Reif A, Del Zompo M, Bellivier F, Schalling M, Wray NR, Kelsoe JR, Alda M, McMahon FJ, Schulze TG, Rietschel M, Nöthen MM, Cichon S. Analysis of the Influence of microRNAs in Lithium Response in Bipolar Disorder. Front Psychiatry 2018; 9:207. [PMID: 29904359 PMCID: PMC5991073 DOI: 10.3389/fpsyt.2018.00207] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2018] [Accepted: 05/03/2018] [Indexed: 12/30/2022] Open
Abstract
Bipolar disorder (BD) is a common, highly heritable neuropsychiatric disease characterized by recurrent episodes of mania and depression. Lithium is the best-established long-term treatment for BD, even though individual response is highly variable. Evidence suggests that some of this variability has a genetic basis. This is supported by the largest genome-wide association study (GWAS) of lithium response to date conducted by the International Consortium on Lithium Genetics (ConLiGen). Recently, we performed the first genome-wide analysis of the involvement of miRNAs in BD and identified nine BD-associated miRNAs. However, it is unknown whether these miRNAs are also associated with lithium response in BD. In the present study, we therefore tested whether common variants at these nine candidate miRNAs contribute to the variance in lithium response in BD. Furthermore, we systematically analyzed whether any other miRNA in the genome is implicated in the response to lithium. For this purpose, we performed gene-based tests for all known miRNA coding genes in the ConLiGen GWAS dataset (n = 2,563 patients) using a set-based testing approach adapted from the versatile gene-based test for GWAS (VEGAS2). In the candidate approach, miR-499a showed a nominally significant association with lithium response, providing some evidence for involvement in both development and treatment of BD. In the genome-wide miRNA analysis, 71 miRNAs showed nominally significant associations with the dichotomous phenotype and 106 with the continuous trait for treatment response. A total of 15 miRNAs revealed nominal significance in both phenotypes with miR-633 showing the strongest association with the continuous trait (p = 9.80E-04) and miR-607 with the dichotomous phenotype (p = 5.79E-04). No association between miRNAs and treatment response to lithium in BD in either of the tested conditions withstood multiple testing correction. Given the limited power of our study, the investigation of miRNAs in larger GWAS samples of BD and lithium response is warranted.
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Affiliation(s)
- Céline S Reinbold
- Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland.,Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
| | - Andreas J Forstner
- Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland.,Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland.,Institute of Human Genetics, University of Bonn, Bonn, Germany.,Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany.,Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
| | - Julian Hecker
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Janice M Fullerton
- Neuroscience Research Australia, Sydney, NSW, Australia.,School of Medical Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Per Hoffmann
- Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland.,Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland.,Institute of Human Genetics, University of Bonn, Bonn, Germany.,Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
| | - Liping Hou
- Intramural Research Program, US Department of Health & Human Services, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Urs Heilbronner
- Department Psychiatry and Psychotherapy, Institute of Psychiatric Phenomics and Genomics, University Hospital, LMU Munich, Munich, Germany
| | - Franziska Degenhardt
- Institute of Human Genetics, University of Bonn, Bonn, Germany.,Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
| | - Mazda Adli
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin, Berlin, Germany
| | - Kazufumi Akiyama
- Department of Biological Psychiatry and Neuroscience, Dokkyo Medical University School of Medicine, Mibu, Japan
| | - Nirmala Akula
- Intramural Research Program, US Department of Health & Human Services, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Raffaella Ardau
- Unit of Clinical Pharmacology, University Hospital of Cagliari, Cagliari, Italy
| | - Bárbara Arias
- Zoology and Biological Anthropology Section (Department of Evolutive Biology, Ecology and Environmental Sciences), Facultat de Biologia and Institut de Biomedicina, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - Lena Backlund
- Department of Molecular Medicine and Surgery, Karolinska Institutet and Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Antonio Benabarre
- Bipolar Disorder Program, Institute of Neuroscience, Hospital Clinic, CIBERSAM, IDIBAPS, University of Barcelona, Barcelona, Spain
| | - Susanne Bengesser
- Special Outpatient Center for Bipolar Affective Disorder, Medical University of Graz, Graz, Austria
| | | | - Joanna M Biernacka
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States.,Institut National de la Santé et de la Recherche Médicale, U1144, Paris, France
| | - Armin Birner
- Special Outpatient Center for Bipolar Affective Disorder, Medical University of Graz, Graz, Austria
| | - Cynthia Marie-Claire
- Université Paris Diderot, Sorbonne Paris Cité, UMR-S 1144, Paris, France.,Department of Psychiatry, University of California, San Diego, San Diego, CA, United States
| | - Pablo Cervantes
- The Neuromodulation Unit, McGill University Health Centre, Montreal, QC, Canada
| | - Guo-Bo Chen
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Hsi-Chung Chen
- Department of Psychiatry & Center of Sleep Disorders, National Taiwan University Hospital, Taipei, Taiwan
| | - Caterina Chillotti
- Unit of Clinical Pharmacology, University Hospital of Cagliari, Cagliari, Italy
| | - Scott R Clark
- Discipline of Psychiatry, University of Adelaide, Adelaide, SA, Australia
| | - Francesc Colom
- Mental Health Research Group, IMIM-Hospital del Mar, Barcelona, Spain
| | - David A Cousins
- Campus for Ageing and Vitality, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Cristiana Cruceanu
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
| | - Piotr M Czerski
- Psychiatric Genetic Unit, Poznan University of Medical Sciences, Poznan, Poland
| | - Alexandre Dayer
- Mood Disorders Unit, HUG - Geneva University Hospitals, Geneva, Switzerland
| | - Bruno Étain
- Université Paris Diderot, Sorbonne Paris Cité, UMR-S 1144, Paris, France.,Department of Psychiatry, University of California, San Diego, San Diego, CA, United States.,AP-HP, GH Saint-Louis - Lariboisière - F. Widal, Département de Psychiatrie et de Médecine Addictologique, Paris, France
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Louise Frisén
- Department of Molecular Medicine and Surgery, Karolinska Institutet and Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Sébastien Gard
- Service de Psychiatrie, Hôpital Charles Perrens, Bordeaux, France
| | - Julie S Garnham
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Fernando S Goes
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, United States
| | - Paul Grof
- Mood Disorders Center of Ottawa, Ottawa, ON, Canada
| | - Oliver Gruber
- Department of Psychiatry and Psychotherapy, University Medical Center, Georg-August University Göttingen, Göttingen, Germany
| | - Ryota Hashimoto
- Molecular Research Center for Children's Mental Development, United Graduate School of Child Development, Osaka University, Osaka, Japan
| | - Joanna Hauser
- Psychiatric Genetic Unit, Poznan University of Medical Sciences, Poznan, Poland
| | - Stefan Herms
- Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland.,Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland.,Institute of Human Genetics, University of Bonn, Bonn, Germany.,Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
| | - Stéphane Jamain
- Institut National de la Santé et de la Recherche Médicale U955, Psychiatrie Translationnelle, Créteil, France
| | - Esther Jiménez
- Bipolar Disorder Program, Institute of Neuroscience, Hospital Clinic, CIBERSAM, IDIBAPS, University of Barcelona, Barcelona, Spain
| | - Jean-Pierre Kahn
- Service de Psychiatrie et Psychologie Clinique, Centre Psychothérapique de Nancy - Université de Lorraine, Nancy, France
| | - Layla Kassem
- Intramural Research Program, US Department of Health & Human Services, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Sarah Kittel-Schneider
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Frankfurt, Germany
| | - Sebastian Kliwicki
- Department of Adult Psychiatry, Poznan University of Medical Sciences, Poznan, Poland
| | - Barbara König
- Department of Psychiatry and Psychotherapeuthic Medicine, Landesklinikum Neunkirchen, Neunkirchen, Austria
| | - Ichiro Kusumi
- Department of Psychiatry, Hokkaido University Graduate School of Medicine, Sapporo, Japan
| | - Nina Lackner
- Special Outpatient Center for Bipolar Affective Disorder, Medical University of Graz, Graz, Austria
| | - Gonzalo Laje
- Intramural Research Program, US Department of Health & Human Services, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Mikael Landén
- Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the Gothenburg University, Gothenburg, Sweden.,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Catharina Lavebratt
- Department of Molecular Medicine and Surgery, Karolinska Institutet and Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Marion Leboyer
- Assistance Publique-Hôpitaux de Paris, Hôpital Albert Chenevier - Henri Mondor, Pôle de Psychiatrie, Créteil, France
| | - Susan G Leckband
- Department of Pharmacy, VA San Diego Healthcare System, San Diego, CA, United States
| | | | - Glenda MacQueen
- Department of Psychiatry, University of Calgary, Calgary, AB, Canada
| | - Mirko Manchia
- Section of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy.,Department of Pharmacology, Dalhousie University, Halifax, NS, Canada
| | - Lina Martinsson
- Department of Clinical Neurosciences, Karolinska Institutet, Stockholm, Sweden
| | | | - Michael J McCarthy
- Department of Psychiatry, VA San Diego Healthcare System, San Diego, CA, United States
| | - Susan L McElroy
- Department of Psychiatry, Lindner Center of Hope/University of Cincinnati, Mason, OH, United States
| | - Marina Mitjans
- Zoology and Biological Anthropology Section (Department of Evolutive Biology, Ecology and Environmental Sciences), Facultat de Biologia and Institut de Biomedicina, CIBERSAM, Universitat de Barcelona, Barcelona, Spain
| | - Francis M Mondimore
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, United States
| | - Palmiero Monteleone
- Neurosciences Section, Department of Medicine and Surgery, University of Salerno, Salerno, Italy.,Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | | | - Urban Ösby
- Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet and Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Norio Ozaki
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Roy H Perlis
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Andrea Pfennig
- Department of Psychiatry and Psychotherapy, Medical Faculty, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Daniela Reich-Erkelenz
- Department Psychiatry and Psychotherapy, Institute of Psychiatric Phenomics and Genomics, University Hospital, LMU Munich, Munich, Germany
| | - Guy A Rouleau
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Peter R Schofield
- School of Medical Sciences, University of New South Wales, Sydney, NSW, Australia.,Mental Illness, Neuroscience Research Australia, Sydney, NSW, Australia
| | - K Oliver Schubert
- Discipline of Psychiatry, University of Adelaide, Adelaide, SA, Australia
| | - Barbara W Schweizer
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, United States
| | - Florian Seemüller
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Giovanni Severino
- Department of Biomedical Sciences, University of Cagliari, Cagliari, Italy
| | | | - Paul D Shilling
- Health Sciences Research, Mayo Clinic, Rochester, MN, United States
| | - Kazutaka Shimoda
- Department of Psychiatry, Dokkyo Medical University School of Medicine, Mibu, Japan
| | - Christian Simhandl
- Medical school, Sigmund Freud University, Vienna, Austria.,Bipolar Center Wiener Neustadt, Vienna, Austria
| | - Claire M Slaney
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Jordan W Smoller
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Alessio Squassina
- Department of Biomedical Sciences, University of Cagliari, Cagliari, Italy
| | - Thomas J Stamm
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin, Berlin, Germany.,Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School Brandenburg, Neuruppin, Germany
| | - Pavla Stopkova
- Department of Psychiatry, National Institute of Mental Health, Klecany, Czechia
| | - Sarah K Tighe
- Department of Psychiatry, University of Iowa, Iowa, IA, United States
| | - Alfonso Tortorella
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Gustavo Turecki
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
| | - Julia Volkert
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Frankfurt, Germany
| | - Stephanie H Witt
- Department of Genetic Epidemiology in Psychiatry, Medical Faculty Mannheim, Central Institute of Mental Health, University of Heidelberg, Mannheim, Germany
| | - Adam J Wright
- School of Psychiatry, University of New South Wales, and Black Dog Institute, Sydney, NSW, Australia
| | - L Trevor Young
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Peter P Zandi
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - James B Potash
- Department of Psychiatry, University of Iowa, Iowa, IA, United States
| | - J Raymond DePaulo
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, United States
| | - Michael Bauer
- Department of Psychiatry and Psychotherapy, Medical Faculty, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Eva Reininghaus
- Special Outpatient Center for Bipolar Affective Disorder, Medical University of Graz, Graz, Austria
| | - Tomáš Novák
- Department of Psychiatry, National Institute of Mental Health, Klecany, Czechia
| | - Jean-Michel Aubry
- Mood Disorders Unit, HUG - Geneva University Hospitals, Geneva, Switzerland
| | - Mario Maj
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Bernhard T Baune
- Discipline of Psychiatry, University of Adelaide, Adelaide, SA, Australia
| | - Philip B Mitchell
- School of Psychiatry, University of New South Wales, and Black Dog Institute, Sydney, NSW, Australia
| | - Eduard Vieta
- Bipolar Disorder Program, Institute of Neuroscience, Hospital Clinic, CIBERSAM, IDIBAPS, University of Barcelona, Barcelona, Spain
| | - Mark A Frye
- Institut National de la Santé et de la Recherche Médicale, U1144, Paris, France
| | - Janusz K Rybakowski
- Department of Adult Psychiatry, Poznan University of Medical Sciences, Poznan, Poland
| | - Po-Hsiu Kuo
- Department of Public Health and Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan
| | - Tadafumi Kato
- Laboratory for Molecular Dynamics of Mental Disorders, RIKEN Brain Science Institute, Saitama, Japan
| | - Maria Grigoroiu-Serbanescu
- Biometric Psychiatric Genetics Research Unit, Alexandru Obregia Clinical Psychiatric Hospital, Bucharest, Romania
| | - Andreas Reif
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Frankfurt, Germany
| | - Maria Del Zompo
- Department of Biomedical Sciences, University of Cagliari, Cagliari, Italy
| | - Frank Bellivier
- Université Paris Diderot, Sorbonne Paris Cité, UMR-S 1144, Paris, France.,Department of Psychiatry, University of California, San Diego, San Diego, CA, United States.,AP-HP, GH Saint-Louis - Lariboisière - F. Widal, Département de Psychiatrie et de Médecine Addictologique, Paris, France
| | - Martin Schalling
- Department of Molecular Medicine and Surgery, Karolinska Institutet and Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Naomi R Wray
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - John R Kelsoe
- Health Sciences Research, Mayo Clinic, Rochester, MN, United States
| | - Martin Alda
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada.,Department of Psychiatry, National Institute of Mental Health, Klecany, Czechia
| | - Francis J McMahon
- Intramural Research Program, US Department of Health & Human Services, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Thomas G Schulze
- Intramural Research Program, US Department of Health & Human Services, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States.,Department Psychiatry and Psychotherapy, Institute of Psychiatric Phenomics and Genomics, University Hospital, LMU Munich, Munich, Germany.,Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, United States.,Department of Psychiatry and Psychotherapy, University Medical Center, Georg-August University Göttingen, Göttingen, Germany.,Department of Genetic Epidemiology in Psychiatry, Medical Faculty Mannheim, Central Institute of Mental Health, University of Heidelberg, Mannheim, Germany
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Medical Faculty Mannheim, Central Institute of Mental Health, University of Heidelberg, Mannheim, Germany
| | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn, Bonn, Germany.,Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
| | - Sven Cichon
- Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland.,Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland.,Institute of Human Genetics, University of Bonn, Bonn, Germany.,Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany.,Research Centre Jülich, Institute of Neuroscience and Medicine (INM-1), Jülich, Germany
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Hecker J, Xu X, Townes FW, Loehlein Fier H, Corcoran C, Laird N, Lange C. Family-based tests for associating haplotypes with general phenotype data: Improving the FBAT-haplotype algorithm. Genet Epidemiol 2017; 42:123-126. [PMID: 29159827 DOI: 10.1002/gepi.22094] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2017] [Revised: 09/29/2017] [Accepted: 10/10/2017] [Indexed: 11/08/2022]
Abstract
For family-based association studies, Horvath et al. proposed an algorithm for the association analysis between haplotypes and arbitrary phenotypes when the phase of the haplotypes is unknown, that is, genotype data is given. Their approach to haplotype analysis maintains the original features of the TDT/FBAT-approach, that is, complete robustness against genetic confounding and misspecification of the phenotype. The algorithm has been implemented in the FBAT and PBAT software package and has been used in numerous substantive manuscripts. Here, we propose a simplification of the original algorithm that maintains the original approach but reduces the computational burden of the approach substantially and gives valuable insights regarding the conditional distribution. With the modified algorithm, the application to whole-genome sequencing (WGS) studies becomes feasible; for example, in sliding window approaches or spatial-clustering approaches. The reduction of the computational burden that our modification provides is especially dramatic when both parental genotypes are missing. For example, for eight variants and 441 nuclear families with mostly offspring-only families, in a WGS study at the APOE locus, the running time decreased from approximately 21 hr for the original algorithm to 0.11 sec after our modification.
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Affiliation(s)
- Julian Hecker
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.,Department of Genomic Mathematics, University of Bonn, Bonn, Germany
| | - Xin Xu
- Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - F William Townes
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Heide Loehlein Fier
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.,Department of Genomic Mathematics, University of Bonn, Bonn, Germany
| | - Chris Corcoran
- Department of Mathematics and Statistics, Utah State University, Logan, UT, USA
| | - Nan Laird
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Christoph Lange
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.,Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
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Müller-Thomas C, Tüchler H, Hecker J, Wenk C, Peschel C, Götze K. Vitamin D is Associated with Severity of Disease as Expressed by Subdiagnosis and IPSS-R in Patients with Myelodysplastic Syndromes. Leuk Res 2017. [DOI: 10.1016/s0145-2126(17)30326-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Loehlein Fier H, Prokopenko D, Hecker J, Cho MH, Silverman EK, Weiss ST, Tanzi RE, Lange C. On the association analysis of genome-sequencing data: A spatial clustering approach for partitioning the entire genome into nonoverlapping windows. Genet Epidemiol 2017; 41:332-340. [PMID: 28318110 DOI: 10.1002/gepi.22040] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2016] [Revised: 12/20/2016] [Accepted: 02/04/2017] [Indexed: 12/16/2022]
Abstract
For the association analysis of whole-genome sequencing (WGS) studies, we propose an efficient and fast spatial-clustering algorithm. Compared to existing analysis approaches for WGS data, that define the tested regions either by sliding or consecutive windows of fixed sizes along variants, a meaningful grouping of nearby variants into consecutive regions has the advantage that, compared to sliding window approaches, the number of tested regions is likely to be smaller. In comparison to consecutive, fixed-window approaches, our approach is likely to group nearby variants together. Given existing biological evidence that disease-associated mutations tend to physically cluster in specific regions along the chromosome, the identification of meaningful groups of nearby located variants could thus lead to a potential power gain for association analysis. Our algorithm defines consecutive genomic regions based on the physical positions of the variants, assuming an inhomogeneous Poisson process and groups together nearby variants. As parameters are estimated locally, the algorithm takes the differing variant density along the chromosome into account and provides locally optimal partitioning of variants into consecutive regions. An R-implementation of the algorithm is provided. We discuss the theoretical advances of our algorithm compared to existing, window-based approaches and show the performance and advantage of our introduced algorithm in a simulation study and by an application to Alzheimer's disease WGS data. Our analysis identifies a region in the ITGB3 gene that potentially harbors disease susceptibility loci for Alzheimer's disease. The region-based association signal of ITGB3 replicates in an independent data set and achieves formally genome-wide significance. Software Implementation: An implementation of the algorithm in R is available at: https://github.com/heidefier/cluster_wgs_data.
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Affiliation(s)
- Heide Loehlein Fier
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.,Working Group of Genomic Mathematics, University of Bonn, Bonn, Germany
| | - Dmitry Prokopenko
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Julian Hecker
- Working Group of Genomic Mathematics, University of Bonn, Bonn, Germany
| | - Michael H Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Scott T Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Rudolph E Tanzi
- Genetics and Aging Research Unit, MassGeneral Institute for Neurodegenerative Disease, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, United States of America
| | - Christoph Lange
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
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Forstner AJ, Hecker J, Hofmann A, Maaser A, Reinbold CS, Mühleisen TW, Leber M, Strohmaier J, Degenhardt F, Treutlein J, Mattheisen M, Schumacher J, Streit F, Meier S, Herms S, Hoffmann P, Lacour A, Witt SH, Reif A, Müller-Myhsok B, Lucae S, Maier W, Schwarz M, Vedder H, Kammerer-Ciernioch J, Pfennig A, Bauer M, Hautzinger M, Moebus S, Schenk LM, Fischer SB, Sivalingam S, Czerski PM, Hauser J, Lissowska J, Szeszenia-Dabrowska N, Brennan P, McKay JD, Wright A, Mitchell PB, Fullerton JM, Schofield PR, Montgomery GW, Medland SE, Gordon SD, Martin NG, Krasnov V, Chuchalin A, Babadjanova G, Pantelejeva G, Abramova LI, Tiganov AS, Polonikov A, Khusnutdinova E, Alda M, Cruceanu C, Rouleau GA, Turecki G, Laprise C, Rivas F, Mayoral F, Kogevinas M, Grigoroiu-Serbanescu M, Becker T, Schulze TG, Rietschel M, Cichon S, Fier H, Nöthen MM. Identification of shared risk loci and pathways for bipolar disorder and schizophrenia. PLoS One 2017; 12:e0171595. [PMID: 28166306 PMCID: PMC5293228 DOI: 10.1371/journal.pone.0171595] [Citation(s) in RCA: 71] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2016] [Accepted: 01/23/2017] [Indexed: 12/21/2022] Open
Abstract
Bipolar disorder (BD) is a highly heritable neuropsychiatric disease characterized by recurrent episodes of mania and depression. BD shows substantial clinical and genetic overlap with other psychiatric disorders, in particular schizophrenia (SCZ). The genes underlying this etiological overlap remain largely unknown. A recent SCZ genome wide association study (GWAS) by the Psychiatric Genomics Consortium identified 128 independent genome-wide significant single nucleotide polymorphisms (SNPs). The present study investigated whether these SCZ-associated SNPs also contribute to BD development through the performance of association testing in a large BD GWAS dataset (9747 patients, 14278 controls). After re-imputation and correction for sample overlap, 22 of 107 investigated SCZ SNPs showed nominal association with BD. The number of shared SCZ-BD SNPs was significantly higher than expected (p = 1.46x10-8). This provides further evidence that SCZ-associated loci contribute to the development of BD. Two SNPs remained significant after Bonferroni correction. The most strongly associated SNP was located near TRANK1, which is a reported genome-wide significant risk gene for BD. Pathway analyses for all shared SCZ-BD SNPs revealed 25 nominally enriched gene-sets, which showed partial overlap in terms of the underlying genes. The enriched gene-sets included calcium- and glutamate signaling, neuropathic pain signaling in dorsal horn neurons, and calmodulin binding. The present data provide further insights into shared risk loci and disease-associated pathways for BD and SCZ. This may suggest new research directions for the treatment and prevention of these two major psychiatric disorders.
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Affiliation(s)
- Andreas J. Forstner
- Institute of Human Genetics, University of Bonn, Bonn, Germany
- Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
- Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
| | - Julian Hecker
- Institute for Genomics Mathematics, University of Bonn, Bonn, Germany
| | - Andrea Hofmann
- Institute of Human Genetics, University of Bonn, Bonn, Germany
- Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
- Institute of Medical Microbiology, Immunology and Parasitology, University of Bonn, Bonn, Germany
| | - Anna Maaser
- Institute of Human Genetics, University of Bonn, Bonn, Germany
- Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
| | - Céline S. Reinbold
- Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
| | - Thomas W. Mühleisen
- Institute of Human Genetics, University of Bonn, Bonn, Germany
- Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
- Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
- Institute of Neuroscience and Medicine (INM-1), Research Center Juelich, Juelich, Germany
| | - Markus Leber
- Department of Psychiatry & Psychotherapy, University of Cologne, Cologne, Germany
| | - Jana Strohmaier
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim/University of Heidelberg, Mannheim, Germany
| | - Franziska Degenhardt
- Institute of Human Genetics, University of Bonn, Bonn, Germany
- Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
| | - Jens Treutlein
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim/University of Heidelberg, Mannheim, Germany
| | - Manuel Mattheisen
- Institute of Human Genetics, University of Bonn, Bonn, Germany
- Department of Biomedicine and Centre for integrative Sequencing, iSEQ, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for integrative Psychiatric Research, iPSYCH, Aarhus and Copenhagen, Denmark
| | - Johannes Schumacher
- Institute of Human Genetics, University of Bonn, Bonn, Germany
- Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
| | - Fabian Streit
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim/University of Heidelberg, Mannheim, Germany
| | - Sandra Meier
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim/University of Heidelberg, Mannheim, Germany
- The Lundbeck Foundation Initiative for integrative Psychiatric Research, iPSYCH, Aarhus and Copenhagen, Denmark
- National Centre Register-Based Research, Aarhus University, Aarhus, Denmark
| | - Stefan Herms
- Institute of Human Genetics, University of Bonn, Bonn, Germany
- Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
- Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
| | - Per Hoffmann
- Institute of Human Genetics, University of Bonn, Bonn, Germany
- Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
- Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
| | - André Lacour
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Stephanie H. Witt
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim/University of Heidelberg, Mannheim, Germany
| | - Andreas Reif
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt am Main, Frankfurt am Main, Germany
| | - Bertram Müller-Myhsok
- Max Planck Institute of Psychiatry, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- University of Liverpool, Institute of Translational Medicine, Liverpool, United Kingdom
| | | | - Wolfgang Maier
- Department of Psychiatry, University of Bonn, Bonn, Germany
| | | | | | | | - Andrea Pfennig
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Michael Bauer
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Martin Hautzinger
- Department of Psychology, Clinical Psychology and Psychotherapy, Eberhard Karls University Tübingen, Tübingen, Germany
| | - Susanne Moebus
- Institute of Medical Informatics, Biometry and Epidemiology, University Duisburg-Essen, Essen, Germany
| | - Lorena M. Schenk
- Institute of Human Genetics, University of Bonn, Bonn, Germany
- Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
| | - Sascha B. Fischer
- Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
| | - Sugirthan Sivalingam
- Institute of Human Genetics, University of Bonn, Bonn, Germany
- Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
| | - Piotr M. Czerski
- Laboratory of Psychiatric Genetics, Department of Psychiatry, Poznan University of Medical Sciences, Poznan, Poland
| | - Joanna Hauser
- Laboratory of Psychiatric Genetics, Department of Psychiatry, Poznan University of Medical Sciences, Poznan, Poland
| | - Jolanta Lissowska
- Department of Cancer Epidemiology and Prevention, Maria Sklodowska-Curie Memorial Cancer Centre and Institute of Oncology Warsaw, Warsaw, Poland
| | | | - Paul Brennan
- Genetic Epidemiology Group, International Agency for Research on Cancer (IARC), Lyon, France
| | - James D. McKay
- Genetic Cancer Susceptibility Group, International Agency for Research on Cancer (IARC), Lyon, France
| | - Adam Wright
- School of Psychiatry, University of New South Wales, Randwick, Australia
- Black Dog Institute, Prince of Wales Hospital, Randwick, Australia
| | - Philip B. Mitchell
- School of Psychiatry, University of New South Wales, Randwick, Australia
- Black Dog Institute, Prince of Wales Hospital, Randwick, Australia
| | - Janice M. Fullerton
- Neuroscience Research Australia, Sydney, Australia
- School of Medical Sciences Faculty of Medicine, University of New South Wales, Sydney, Australia
| | - Peter R. Schofield
- Neuroscience Research Australia, Sydney, Australia
- School of Medical Sciences Faculty of Medicine, University of New South Wales, Sydney, Australia
| | | | - Sarah E. Medland
- Queensland Institute of Medical Research (QIMR), Brisbane, Australia
| | - Scott D. Gordon
- Queensland Institute of Medical Research (QIMR), Brisbane, Australia
| | | | - Valery Krasnov
- Moscow Research Institute of Psychiatry, Moscow, Russian Federation
| | - Alexander Chuchalin
- Institute of Pulmonology, Russian State Medical University, Moscow, Russian Federation
| | - Gulja Babadjanova
- Institute of Pulmonology, Russian State Medical University, Moscow, Russian Federation
| | - Galina Pantelejeva
- Russian Academy of Medical Sciences, Mental Health Research Center, Moscow, Russian Federation
| | - Lilia I. Abramova
- Russian Academy of Medical Sciences, Mental Health Research Center, Moscow, Russian Federation
| | - Alexander S. Tiganov
- Russian Academy of Medical Sciences, Mental Health Research Center, Moscow, Russian Federation
| | - Alexey Polonikov
- Department of Biology, Medical Genetics and Ecology, Kursk State Medical University, Kursk, Russian Federation
- Research Institute for Genetic and Molecular Epidemiology, Kursk State Medical University, Kursk, Russian Federation
| | - Elza Khusnutdinova
- Institute of Biochemistry and Genetics, Ufa Scientific Center of Russian Academy of Sciences, Ufa, Russian Federation
- Department of Genetics and Fundamental Medicine of Bashkir State University, Ufa, Russian Federation
| | - Martin Alda
- Department of Psychiatry, Dalhousie University, Halifax, Canada
- National Institute of Mental Health, Klecany, Czech Republic
| | - Cristiana Cruceanu
- Montreal Neurological Institute, McGill University, Montreal, Canada
- Department of Human Genetics, McGill University, Montreal, Canada
- McGill Group for Suicide Studies & Douglas Research Institute, Montreal, Canada
| | - Guy A. Rouleau
- Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Gustavo Turecki
- Department of Human Genetics, McGill University, Montreal, Canada
- McGill Group for Suicide Studies & Douglas Research Institute, Montreal, Canada
- Department of Psychiatry, McGill University, Montreal, Canada
| | - Catherine Laprise
- Département des sciences fondamentales, Université du Québec à Chicoutimi (UQAC), Saguenay, Canada
| | - Fabio Rivas
- Department of Psychiatry, Hospital Regional Universitario, Biomedical Institute of Malaga, Malaga, Spain
| | - Fermin Mayoral
- Department of Psychiatry, Hospital Regional Universitario, Biomedical Institute of Malaga, Malaga, Spain
| | - Manolis Kogevinas
- Center for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
| | - Maria Grigoroiu-Serbanescu
- Biometric Psychiatric Genetics Research Unit, Alexandru Obregia Clinical Psychiatric Hospital, Bucharest, Romania
| | - Tim Becker
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Institute for Medical Biometry, Informatics and Epidemiology, University of Bonn, Bonn, Germany
| | - Thomas G. Schulze
- Institute of Psychiatric Phenomics and Genomics, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim/University of Heidelberg, Mannheim, Germany
| | - Sven Cichon
- Institute of Human Genetics, University of Bonn, Bonn, Germany
- Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
- Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, Switzerland
- Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
- Institute of Neuroscience and Medicine (INM-1), Research Center Juelich, Juelich, Germany
| | - Heide Fier
- Institute for Genomics Mathematics, University of Bonn, Bonn, Germany
| | - Markus M. Nöthen
- Institute of Human Genetics, University of Bonn, Bonn, Germany
- Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
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Chang H, Li L, Peng T, Grigoroiu-Serbanescu M, Bergen SE, Landén M, Hultman CM, Forstner AJ, Strohmaier J, Hecker J, Schulze TG, Müller-Myhsok B, Reif A, Mitchell PB, Martin NG, Cichon S, Nöthen MM, Jamain S, Leboyer M, Bellivier F, Etain B, Kahn JP, Henry C, Rietschel M, Xiao X, Li M. Identification of a Bipolar Disorder Vulnerable Gene CHDH at 3p21.1. Mol Neurobiol 2016; 54:5166-5176. [DOI: 10.1007/s12035-016-0041-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2016] [Accepted: 08/05/2016] [Indexed: 10/21/2022]
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42
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Degenhardt F, Krämer L, Frank J, Treutlein J, Heilmann-Heimbach S, Hecker J, Fier HL, Lang M, Witt SH, Koller AC, Mann K, Hoffmann S, Kiefer F, Spanagel R, Rietschel M, Nöthen MM. Analysis of Rare Variants in the Alcohol Dependence Candidate Gene GATA4. Alcohol Clin Exp Res 2016; 40:1627-32. [PMID: 27374936 PMCID: PMC5108491 DOI: 10.1111/acer.13125] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2015] [Accepted: 05/08/2016] [Indexed: 11/30/2022]
Abstract
Background Common variants in the gene GATA binding protein 4 (GATA4) show association with alcohol dependence (AD). The aim of this study was to identify rare variants in GATA4 in order to elucidate the role of this gene in AD susceptibility. Identification of rare variants may provide a more complete picture of the allelic architecture at this risk locus. Methods Sanger sequencing of all 6 coding exons of GATA4 was performed in 528 patients and 517 controls. Four in silico prediction tools were used to determine the effect of a DNA variant on the amino acid sequence and protein function. Five variants were included in the replication step. Of these, 4 were successfully genotyped in our replication cohort of 655 patients and 1,501 controls. All patients fulfilled DSM‐IV criteria for AD, and all individuals were of German descent. Results In the discovery step, 19 different heterozygous variants were identified. Four patient‐specific and potentially functionally relevant variants were followed up. Only the variant S379S (c.1137C>T) remained patient specific (1/1,166 patients vs. 0/1,997 controls). None of the variants showed a statistically significant association with AD. Conclusions The present study elucidated the role of GATA4 in AD susceptibility by identifying rare variants via Sanger sequencing and subsequent replication. Although novel patient‐specific rare variants of GATA4 were identified, none received support in the independent replication step. However, given previous robust findings of association with common variants, GATA4 remains a promising candidate gene for AD.
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Affiliation(s)
- Franziska Degenhardt
- Institute of Human Genetics, University of Bonn, Bonn, Germany.,Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany
| | - Laurenz Krämer
- Institute of Human Genetics, University of Bonn, Bonn, Germany.,Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany
| | - Josef Frank
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Jens Treutlein
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Stefanie Heilmann-Heimbach
- Institute of Human Genetics, University of Bonn, Bonn, Germany.,Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany
| | - Julian Hecker
- Institute of Human Genetics, University of Bonn, Bonn, Germany.,Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany.,Institute of Genomic Mathematics, University of Bonn, Bonn, Germany
| | - Heide Löhlein Fier
- Institute of Human Genetics, University of Bonn, Bonn, Germany.,Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany.,Institute of Genomic Mathematics, University of Bonn, Bonn, Germany.,Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts
| | - Maren Lang
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Stephanie H Witt
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Anna C Koller
- Institute of Human Genetics, University of Bonn, Bonn, Germany.,Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany
| | - Karl Mann
- Department of Addictive Behavior and Addiction Medicine, Central Institute of Mental Health, Heidelberg University, Mannheim, Germany
| | - Sabine Hoffmann
- Department of Addictive Behavior and Addiction Medicine, Central Institute of Mental Health, Heidelberg University, Mannheim, Germany
| | - Falk Kiefer
- Department of Addictive Behavior and Addiction Medicine, Central Institute of Mental Health, Heidelberg University, Mannheim, Germany
| | - Rainer Spanagel
- Institute of Psychopharmacology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn, Bonn, Germany.,Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany
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Avsar M, Jansson K, Sommer W, Kruse B, Thissen S, Dreckmann K, Knoefel AK, Salman J, Hafer C, Hecker J, Buechler G, Karstens JH, Jonigk D, Länger F, Kaever V, Falk CS, Hewicker-Trautwein M, Ungefroren H, Haverich A, Strüber M, Warnecke G. Augmentation of Transient Donor Cell Chimerism and Alloantigen-Specific Regulation of Lung Transplants in Miniature Swine. Am J Transplant 2016; 16:1371-82. [PMID: 26602894 DOI: 10.1111/ajt.13629] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2015] [Revised: 10/29/2015] [Accepted: 11/13/2015] [Indexed: 01/25/2023]
Abstract
Donor alloantigen infusion induces T cell regulation and transplant tolerance in small animals. Here, we study donor splenocyte infusion in a large animal model of pulmonary transplantation. Major histocompatibility complex-mismatched single lung transplantation was performed in 28 minipigs followed by a 28-day course of methylprednisolone and tacrolimus. Some animals received a perioperative donor or third party splenocyte infusion, with or without low-dose irradiation (IRR) before surgery. Graft survival was significantly prolonged in animals receiving both donor splenocytes and IRR compared with controls with either donor splenocytes or IRR only. In animals with donor splenocytes and IRR, increased donor cell chimerism and CD4(+) CD25(high+) T cell frequencies were detected in peripheral blood associated with decreased interferon-γ production of leukocytes. Secondary third-party kidney transplants more than 2 years after pulmonary transplantation were acutely rejected despite maintained tolerance of the lung allografts. As a cellular control, additional animals received third-party splenocytes or donor splenocyte protein extracts. While animals treated with third-party splenocytes showed significant graft survival prolongation, the subcellular antigen infusion showed no such effect. In conclusion, minipigs conditioned with preoperative IRR and donor, or third-party, splenocyte infusions may develop long-term donor-specific pulmonary allograft survival in the presence of high levels of circulating regulatory T cells.
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Affiliation(s)
- M Avsar
- Division of Cardiac, Thoracic, Transplantation and Vascular Surgery, Hannover Medical School, Hannover, Germany
| | - K Jansson
- Division of Cardiac, Thoracic, Transplantation and Vascular Surgery, Hannover Medical School, Hannover, Germany.,German Centre for Lung Research, Hannover Medical School, Hannover, Germany
| | - W Sommer
- Division of Cardiac, Thoracic, Transplantation and Vascular Surgery, Hannover Medical School, Hannover, Germany.,German Centre for Lung Research, Hannover Medical School, Hannover, Germany
| | - B Kruse
- Division of Cardiac, Thoracic, Transplantation and Vascular Surgery, Hannover Medical School, Hannover, Germany
| | - S Thissen
- Division of Cardiac, Thoracic, Transplantation and Vascular Surgery, Hannover Medical School, Hannover, Germany
| | - K Dreckmann
- Division of Cardiac, Thoracic, Transplantation and Vascular Surgery, Hannover Medical School, Hannover, Germany
| | - A-K Knoefel
- Division of Cardiac, Thoracic, Transplantation and Vascular Surgery, Hannover Medical School, Hannover, Germany.,German Centre for Lung Research, Hannover Medical School, Hannover, Germany
| | - J Salman
- Division of Cardiac, Thoracic, Transplantation and Vascular Surgery, Hannover Medical School, Hannover, Germany
| | - C Hafer
- Department of Nephrology, Hannover Medical School, Hannover, Germany
| | - J Hecker
- Division of Visceral and Transplantation Surgery, Hannover Medical School, Hannover, Germany
| | - G Buechler
- Division of Cardiac, Thoracic, Transplantation and Vascular Surgery, Hannover Medical School, Hannover, Germany
| | - J H Karstens
- Department of Nuclear Medicine and Radiation Oncology, Hannover Medical School, Hannover, Germany
| | - D Jonigk
- Institute for Pathology, Hannover Medical School, Hannover, Germany
| | - F Länger
- Institute for Pathology, Hannover Medical School, Hannover, Germany
| | - V Kaever
- Institute for Pharmacology, Hannover Medical School, Hannover, Germany
| | - C S Falk
- Institute for Transplant Immunology, IFB-Tx, Hannover Medical School, Hannover, Germany
| | | | - H Ungefroren
- Department of Applied Cellular Therapy, University of Kiel, Kiel, Germany
| | - A Haverich
- Division of Cardiac, Thoracic, Transplantation and Vascular Surgery, Hannover Medical School, Hannover, Germany.,German Centre for Lung Research, Hannover Medical School, Hannover, Germany
| | - M Strüber
- Division of Cardiac, Thoracic, Transplantation and Vascular Surgery, Hannover Medical School, Hannover, Germany
| | - G Warnecke
- Division of Cardiac, Thoracic, Transplantation and Vascular Surgery, Hannover Medical School, Hannover, Germany.,German Centre for Lung Research, Hannover Medical School, Hannover, Germany
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Prokopenko D, Hecker J, Silverman EK, Pagano M, Nöthen MM, Dina C, Lange C, Fier HL. Utilizing the Jaccard index to reveal population stratification in sequencing data: a simulation study and an application to the 1000 Genomes Project. Bioinformatics 2015; 32:1366-72. [PMID: 26722118 DOI: 10.1093/bioinformatics/btv752] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2015] [Accepted: 12/19/2015] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Population stratification is one of the major sources of confounding in genetic association studies, potentially causing false-positive and false-negative results. Here, we present a novel approach for the identification of population substructure in high-density genotyping data/next generation sequencing data. The approach exploits the co-appearances of rare genetic variants in individuals. The method can be applied to all available genetic loci and is computationally fast. Using sequencing data from the 1000 Genomes Project, the features of the approach are illustrated and compared to existing methodology (i.e. EIGENSTRAT). We examine the effects of different cutoffs for the minor allele frequency on the performance of the approach. We find that our approach works particularly well for genetic loci with very small minor allele frequencies. The results suggest that the inclusion of rare-variant data/sequencing data in our approach provides a much higher resolution picture of population substructure than it can be obtained with existing methodology. Furthermore, in simulation studies, we find scenarios where our method was able to control the type 1 error more precisely and showed higher power. AVAILABILITY AND IMPLEMENTATION CONTACT dmitry.prokopenko@uni-bonn.de SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Julian Hecker
- Institute of Genomic Mathematics, University of Bonn, Bonn, Germany
| | | | - Marcello Pagano
- Department of Biostatistics, Harvard School of Public Health, Boston, USA
| | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn, Bonn, Germany
| | - Christian Dina
- Institut National de la Santé et de la Recherche Médicale (INSERM) Unité Mixte de Recherche (UMR) 1087, l'institut du thorax, Nantes, France, Centre National de la Recherche Scientifique (CNRS) UMR 6291, l'institut du thorax, Nantes, France, Université de Nantes, l'institut du thorax, Nantes, France and Centre Hospitalier Universitaire (CHU) de Nantes, l'institut du thorax, Service de Cardiologie, Nantes, France
| | - Christoph Lange
- Channing Division of Network Medicine, Brigham and Women's Hospital, Department of Biostatistics, Harvard School of Public Health, Boston, USA
| | - Heide Loehlein Fier
- Institute of Genomic Mathematics, University of Bonn, Bonn, Germany, Department of Biostatistics, Harvard School of Public Health, Boston, USA
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Hecker J, Prokopenko D, Lange C, Fier HL. On the Recombination Rate Estimation in the Presence of Population Substructure. PLoS One 2015; 10:e0145152. [PMID: 26716445 PMCID: PMC4696844 DOI: 10.1371/journal.pone.0145152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2015] [Accepted: 11/25/2015] [Indexed: 11/19/2022] Open
Abstract
As recombination events are not uniformly distributed along the human genome, the estimation of fine-scale recombination maps, e.g. HapMap Project, has been one of the major research endeavors over the last couple of years. For simulation studies, these estimates provide realistic reference scenarios to design future study and to develop novel methodology. To achieve a feasible framework for the estimation of such recombination maps, existing methodology uses sample probabilities for a two-locus model with recombination, with recent advances allowing for computationally fast implementations. In this work, we extend the existing theoretical framework for the recombination rate estimation to the presence of population substructure. We show under which assumptions the existing methodology can still be applied. We illustrate our extension of the methodology by an extensive simulation study.
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Affiliation(s)
- Julian Hecker
- Institute of Genomic Mathematics, University of Bonn, Bonn, Germany
- * E-mail:
| | | | - Christoph Lange
- Institute of Genomic Mathematics, University of Bonn, Bonn, Germany
- Department of Biostatistics, Harvard School of Public Health, Boston, United States of America
- Channing Laboratory, Brigham and Women’s Hospital, Boston, United States of America
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Heide Löhlein Fier
- Institute of Genomic Mathematics, University of Bonn, Bonn, Germany
- Department of Biostatistics, Harvard School of Public Health, Boston, United States of America
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Prokopenko D, Hecker J, Silverman E, Nöthen MM, Schmid M, Lange C, Loehlein Fier H. Using Network Methodology to Infer Population Substructure. PLoS One 2015; 10:e0130708. [PMID: 26098940 PMCID: PMC4476755 DOI: 10.1371/journal.pone.0130708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2015] [Accepted: 05/22/2015] [Indexed: 11/24/2022] Open
Abstract
One of the main caveats of association studies is the possible affection by bias due to population stratification. Existing methods rely on model-based approaches like structure and ADMIXTURE or on principal component analysis like EIGENSTRAT. Here we provide a novel visualization technique and describe the problem of population substructure from a graph-theoretical point of view. We group the sequenced individuals into triads, which depict the relational structure, on the basis of a predefined pairwise similarity measure. We then merge the triads into a network and apply community detection algorithms in order to identify homogeneous subgroups or communities, which can further be incorporated as covariates into logistic regression. We apply our method to populations from different continents in the 1000 Genomes Project and evaluate the type 1 error based on the empirical p-values. The application to 1000 Genomes data suggests that the network approach provides a very fine resolution of the underlying ancestral population structure. Besides we show in simulations, that in the presence of discrete population structures, our developed approach maintains the type 1 error more precisely than existing approaches.
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Affiliation(s)
- Dmitry Prokopenko
- Institute of Genomic Mathematics, University of Bonn, Bonn, Germany
- Institute of Human Genetics, University of Bonn, Bonn, Germany
- * E-mail:
| | - Julian Hecker
- Institute of Genomic Mathematics, University of Bonn, Bonn, Germany
- Institute of Human Genetics, University of Bonn, Bonn, Germany
| | - Edwin Silverman
- Channing Laboratory, Brigham and Women's Hospital, Boston, United States of America
| | | | - Matthias Schmid
- Institute of Medical Biometrics, Informatics and Epidemiology, University of Bonn, Bonn, Germany
| | - Christoph Lange
- Institute of Genomic Mathematics, University of Bonn, Bonn, Germany
- Department of Biostatistics, Harvard School of Public Health, Boston, United States of America
- Channing Laboratory, Brigham and Women's Hospital, Boston, United States of America
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Heide Loehlein Fier
- Institute of Genomic Mathematics, University of Bonn, Bonn, Germany
- Institute of Human Genetics, University of Bonn, Bonn, Germany
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Hecker J, Diethers A, Schulz D, Sabri A, Plum M, Michel Y, Mempel M, Ollert M, Jakob T, Blank S, Braren I, Spillner E. An IgE epitope of Bet v 1 and fagales PR10 proteins as defined by a human monoclonal IgE. Allergy 2012; 67:1530-7. [PMID: 23066955 DOI: 10.1111/all.12045] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/31/2012] [Indexed: 01/03/2023]
Abstract
BACKGROUND Analyses of the molecular basis underlying allergenicity and allergen cross-reactivity, as well as improvement of allergy diagnostics and therapeutics, are hampered by the lack of human monoclonal IgE antibodies and knowledge about their epitopes. Here, we addressed the consecutive generation and epitope delineation of a human monoclonal IgE against the prototypic allergen Bet v 1. METHODS Phage-display scFv hybrid libraries of allergic donor-derived VH epsilon and synthetic VL were established from 107 mononuclear cells. An obtained scFv was converted into human immunoglobulin formats including IgE. Using variants of Bet v 1, the epitope of the antibody was mapped and extrapolated to other PR10 proteins. RESULTS The obtained antibodies exhibited pronounced reactivity with Bet v 1, but were not reactive with the homologous PR10 protein Mal d 1. The epitope as defined by the IgE paratope and a set of chimeric Bet v 1 fusion proteins and fragments could be assigned to a C-terminal helix-structured motif comprised by amino acid residues 132-154, including the critical residue E149. Grafting this motif re-established the reactivity of the per se nonreactive Mal d 1 framework. Cross-reactivities predicted by primary structure analyses of different isoforms and PR10 proteins were verified by allergen chip-based analyses. CONCLUSIONS The obtained results demonstrate that hybrid IgE repertoires represent a source for human antibodies with genuine paratopes. The IgE-derived information about the IgE epitope nature of Bet v 1 and homologues allows for detailed insights into molecular aspects of allergenicity and cross-reactivity within the PR10 protein family.
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Affiliation(s)
- J. Hecker
- Department of Chemistry, Institute of Biochemistry and Molecular Biology; University of Hamburg; Hamburg; Germany
| | - A. Diethers
- Department of Chemistry, Institute of Biochemistry and Molecular Biology; University of Hamburg; Hamburg; Germany
| | - D. Schulz
- Department of Chemistry, Institute of Biochemistry and Molecular Biology; University of Hamburg; Hamburg; Germany
| | - A. Sabri
- Department of Chemistry, Institute of Biochemistry and Molecular Biology; University of Hamburg; Hamburg; Germany
| | - M. Plum
- Department of Chemistry, Institute of Biochemistry and Molecular Biology; University of Hamburg; Hamburg; Germany
| | - Y. Michel
- Department of Chemistry, Institute of Biochemistry and Molecular Biology; University of Hamburg; Hamburg; Germany
| | - M. Mempel
- Department of Dermatology, Venerology, and Allergology; Georg-August-University; Göttingen; Germany
| | - M. Ollert
- Department of Dermatology and Allergy, Clinical Research Division of Molecular and Clinical Allergotoxicology; Technische Universität München; Munich; Germany
| | - T. Jakob
- Department of Dermatology; University Medical Center Freiburg; Freiburg; Germany
| | - S. Blank
- Department of Chemistry, Institute of Biochemistry and Molecular Biology; University of Hamburg; Hamburg; Germany
| | - I. Braren
- Hamburg Center for Experimental Therapy Research; University Medical Center Hamburg; Hamburg; Germany
| | - E. Spillner
- Department of Chemistry, Institute of Biochemistry and Molecular Biology; University of Hamburg; Hamburg; Germany
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Hecker J, Lewis GBH. Ageing of intravenous glucose solutions. International Journal of Pharmacy Practice 2011. [DOI: 10.1111/j.2042-7174.1992.tb00572.x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Abstract
Abstract
The pH and acid content of 63 glucose-based solutions was measured up to eight years after their expiry. The pH ranged from 2.27 to 4.33. Newly manufactured solutions with 5 per cent or less of glucose which were packaged in plastic bags contained in the order of 0.01 mEq/L of acid per gram of glucose. Solutions with higher glucose concentrations had lower pH and greater acid. As solutions in bags became older, pH decreased and acid increased. In contrast, 25 per cent and 50 per cent glucose solutions in glass containers were much less acid and showed only slight changes with age in pH or acid. The greater rate of acid increase in glucose solutions in plastic bags may be due to the bags being permeable to oxygen and thus allowing slow oxidation of the glucose.
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Affiliation(s)
- J Hecker
- Department of Physiology, University of New England, Armidale, NSW2351, Australia
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Feldman H, Gauthier S, Hecker J, Vellas B, Hux M, Xu Y, Schwam EM, Shah S, Mastey V. Economic evaluation of donepezil in moderate to severe Alzheimer disease. Neurology 2005; 63:644-50. [PMID: 15326236 DOI: 10.1212/01.wnl.0000134663.79663.6e] [Citation(s) in RCA: 60] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE To investigate the costs to society of Alzheimer disease (AD) care in a multinational, randomized, placebo-controlled trial of donepezil in patients with moderate to severe AD. METHODS A total of 290 patients with AD (screening standardized Mini-Mental State Examination score 5 to 17) were randomized to receive either donepezil (n = 144; 5 mg/day for 28 days, followed by 10 mg/day as per clinician's judgment) or placebo (n = 146) for 24 weeks. The authors collected data on patient and caregiver health resource utilization prospectively using the Canadian Utilization of Services Tracking questionnaire. Costs were calculated for patients and caregivers in each group based on resource utilization multiplied by the unit prices for each resource. A cost (the average Ontario minimum wage for 1998 [Can 6.85 dollars per hour]) was assigned to unpaid time that caregivers spent assisting the patient with activities of daily living (ADL). RESULTS Patient and caregiver demographics at baseline were similar across the two groups. After adjusting for baseline total cost per patient, the mean total societal cost per patient for the 24-week period was donepezil, Can 9,904 dollars (US 6,686 dollars) and placebo, Can 10,236 dollars (US 6,910 dollars). This net cost saving of Can 332 dollars (US 224 dollars) included the average 24-week cost of donepezil treatment. Most of the cost-saving with donepezil treatment was due to less use of residential care by patients, and caregivers spending less time assisting patients with ADL. CONCLUSION This cost-consequence analysis reveals economic benefits of treatment of moderate to severe AD with donepezil.
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Affiliation(s)
- H Feldman
- Division of Neurology, UBC Hospital, Clinic for Alzheimer's Disease and Related Disorders, Vancouver, BC, Canada.
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