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Agogo-Mawuli PS, Mendez J, Oestreich EA, Bosch DE, Siderovski DP. Molecular Modeling and In Vitro Functional Analysis of the RGS12 PDZ Domain Variant Associated with High-Penetrance Familial Bipolar Disorder. Int J Mol Sci 2024; 25:11431. [PMID: 39518985 PMCID: PMC11546610 DOI: 10.3390/ijms252111431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2024] [Revised: 10/17/2024] [Accepted: 10/21/2024] [Indexed: 11/16/2024] Open
Abstract
Bipolar disorder's etiology involves genetics, environmental factors, and gene-environment interactions, underlying its heterogeneous nature and treatment complexity. In 2020, Forstner and colleagues catalogued 378 sequence variants co-segregating with familial bipolar disorder. A notable candidate was an R59Q missense mutation in the PDZ (PSD-95/Dlg1/ZO-1) domain of RGS12. We previously demonstrated that RGS12 loss removes negative regulation on the kappa opioid receptor, disrupting basal ganglia dopamine homeostasis and dampening responses to dopamine-eliciting psychostimulants. Here, we investigated the R59Q variation in the context of potential PDZ domain functional alterations. We first validated a new target for the wildtype RGS12 PDZ domain-the SAPAP3 C-terminus-by molecular docking, surface plasmon resonance (SPR), and co-immunoprecipitation. While initial molecular dynamics (MD) studies predicted negligible effects of the R59Q variation on ligand binding, SPR showed a significant reduction in binding affinity for the three peptide targets tested. AlphaFold2-generated models predicted a modest reduction in protein-peptide interactions, which is consistent with the reduced binding affinity observed by SPR, suggesting that the substituted glutamine side chain may weaken the affinity of RGS12 for its in vivo binding targets, likely through allosteric changes. This difference may adversely affect the CNS signaling related to dynorphin and dopamine in individuals with this R59Q variation, potentially impacting bipolar disorder pathophysiology.
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Affiliation(s)
- Percy S. Agogo-Mawuli
- Department of Pharmacology & Neuroscience, University of North Texas Health Science Center, Fort Worth, TX 76107, USA; (P.S.A.-M.)
| | - Joseph Mendez
- Department of Pharmacology & Neuroscience, University of North Texas Health Science Center, Fort Worth, TX 76107, USA; (P.S.A.-M.)
| | - Emily A. Oestreich
- Department of Biomedical Sciences, Pacific Northwest University of Health Sciences, Yakima, WA 98901, USA
| | - Dustin E. Bosch
- Department of Pathology, Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA
| | - David P. Siderovski
- Department of Pharmacology & Neuroscience, University of North Texas Health Science Center, Fort Worth, TX 76107, USA; (P.S.A.-M.)
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2
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Kawamoto M, Yoshida T, Tamura K, Dbouk M, Canto MI, Burkhart R, He J, Roberts NJ, Klein AP, Goggins M. Endoplasmic stress-inducing variants in carboxyl ester lipase and pancreatic cancer risk. Pancreatology 2022; 22:959-964. [PMID: 35995657 PMCID: PMC9669157 DOI: 10.1016/j.pan.2022.08.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 08/04/2022] [Accepted: 08/07/2022] [Indexed: 12/11/2022]
Abstract
BACKGROUND Endoplasmic reticulum (ER) stress-inducing variants in several pancreatic secretory enzymes have been associated with pancreatic disease. Multiple variants in CEL, encoding carboxyl ester lipase, are known to cause maturity-onset diabetes of the young (MODY8) but have not been implicated in pancreatic cancer risk. METHODS The prevalence of ER stress-inducing variants in the CEL gene was compared among pancreatic cancer cases vs. controls. Variants were identified by next-generation sequencing and confirmed by Sanger sequencing. Variants of uncertain significance (VUS) were assessed for their effect on the secretion of CEL protein and variants with reduced protein secretion were evaluated to determine if they induced endoplasmic reticulum stress. RESULTS ER stress-inducing CEL variants were found in 34 of 986 cases with sporadic pancreatic ductal adenocarcinoma, and 21 of 1045 controls (P = 0.055). Most of the variants were either the CEL-HYB1 variant, the I488T variant, or the combined CEL-HYB1/I488T variant; one case had a MODY8 variant. CONCLUSION This case/control analysis finds ER stress-inducing CEL variants are not associated with an increased likelihood of having pancreatic cancer.
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Affiliation(s)
- Makoto Kawamoto
- Department of Pathology, Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Takeichi Yoshida
- Department of Pathology, Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Koji Tamura
- Department of Pathology, Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Mohamad Dbouk
- Department of Pathology, Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Marcia Irene Canto
- Medicine, Johns Hopkins Medical Institutions, Baltimore, MD, USA; Oncology, Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | | | - Jin He
- Surgery, Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Nicholas J Roberts
- Department of Pathology, Johns Hopkins Medical Institutions, Baltimore, MD, USA; Oncology, Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Alison P Klein
- Department of Pathology, Johns Hopkins Medical Institutions, Baltimore, MD, USA; Oncology, Johns Hopkins Medical Institutions, Baltimore, MD, USA; The Sol Goldman Pancreatic Cancer Research Center, And the Bloomberg School of Public Health, Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Michael Goggins
- Department of Pathology, Johns Hopkins Medical Institutions, Baltimore, MD, USA; Medicine, Johns Hopkins Medical Institutions, Baltimore, MD, USA; Oncology, Johns Hopkins Medical Institutions, Baltimore, MD, USA.
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3
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Abstract
BACKGROUND To date, besides genome-wide association studies, a variety of other genetic analyses (e.g. polygenic risk scores, whole-exome sequencing and whole-genome sequencing) have been conducted, and a large amount of data has been gathered for investigating the involvement of common, rare and very rare types of DNA sequence variants in bipolar disorder. Also, non-invasive neuroimaging methods can be used to quantify changes in brain structure and function in patients with bipolar disorder. AIMS To provide a comprehensive assessment of genetic findings associated with bipolar disorder, based on the evaluation of different genomic approaches and neuroimaging studies. METHOD We conducted a PubMed search of all relevant literatures from the beginning to the present, by querying related search strings. RESULTS ANK3, CACNA1C, SYNE1, ODZ4 and TRANK1 are five genes that have been replicated as key gene candidates in bipolar disorder pathophysiology, through the investigated studies. The percentage of phenotypic variance explained by the identified variants is small (approximately 4.7%). Bipolar disorder polygenic risk scores are associated with other psychiatric phenotypes. The ENIGMA-BD studies show a replicable pattern of lower cortical thickness, altered white matter integrity and smaller subcortical volumes in bipolar disorder. CONCLUSIONS The low amount of explained phenotypic variance highlights the need for further large-scale investigations, especially among non-European populations, to achieve a more complete understanding of the genetic architecture of bipolar disorder and the missing heritability. Combining neuroimaging data with genetic data in large-scale studies might help researchers acquire a better knowledge of the engaged brain regions in bipolar disorder.
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Affiliation(s)
- Mojtaba Oraki Kohshour
- Institute of Psychiatric Phenomics and Genomics, University Hospital LMU Munich, Germany; and Department of Immunology, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Iran
| | - Sergi Papiol
- Institute of Psychiatric Phenomics and Genomics, University Hospital LMU Munich, Germany; and Department of Psychiatry and Psychotherapy, University Hospital LMU Munich, Germany
| | - Christopher R K Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, USA
| | - Thomas G Schulze
- Institute of Psychiatric Phenomics and Genomics, University Hospital LMU Munich, Germany; and Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, USA
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4
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Askland KD, Strong D, Wright MN, Moore JH. The Translational Machine: A novel machine-learning approach to illuminate complex genetic architectures. Genet Epidemiol 2021; 45:485-536. [PMID: 33942369 DOI: 10.1002/gepi.22383] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 03/05/2021] [Accepted: 03/23/2021] [Indexed: 11/08/2022]
Abstract
The Translational Machine (TM) is a machine learning (ML)-based analytic pipeline that translates genotypic/variant call data into biologically contextualized features that richly characterize complex variant architectures and permit greater interpretability and biological replication. It also reduces potentially confounding effects of population substructure on outcome prediction. The TM consists of three main components. First, replicable but flexible feature engineering procedures translate genome-scale data into biologically informative features that appropriately contextualize simple variant calls/genotypes within biological and functional contexts. Second, model-free, nonparametric ML-based feature filtering procedures empirically reduce dimensionality and noise of both original genotype calls and engineered features. Third, a powerful ML algorithm for feature selection is used to differentiate risk variant contributions across variant frequency and functional prediction spectra. The TM simultaneously evaluates potential contributions of variants operative under polygenic and heterogeneous models of genetic architecture. Our TM enables integration of biological information (e.g., genomic annotations) within conceptual frameworks akin to geneset-/pathways-based and collapsing methods, but overcomes some of these methods' limitations. The full TM pipeline is executed in R. Our approach and initial findings from its application to a whole-exome schizophrenia case-control data set are presented. These TM procedures extend the findings of the primary investigation and yield novel results.
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Affiliation(s)
- Kathleen D Askland
- Waypoint Centre for Mental Health Care Penetanguishene, University of Toronto, Toronto, Ontario, Canada
| | - David Strong
- Department of Family Medicine and Public Health, University of California San Diego, San Diego, California, USA
| | - Marvin N Wright
- Department Biometry and Data Management, Leibniz Institute for Prevention Research and Epidemiology - BIPS GmbH, Germany
| | - Jason H Moore
- Department of Biostatistics, Epidemiology, & Informatics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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5
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Raimondi D, Simm J, Arany A, Fariselli P, Cleynen I, Moreau Y. An interpretable low-complexity machine learning framework for robust exome-based in- silico diagnosis of Crohn's disease patients. NAR Genom Bioinform 2021; 2:lqaa011. [PMID: 33575557 PMCID: PMC7671306 DOI: 10.1093/nargab/lqaa011] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 01/22/2020] [Accepted: 02/05/2020] [Indexed: 02/06/2023] Open
Abstract
Whole exome sequencing (WES) data are allowing researchers to pinpoint the causes of many Mendelian disorders. In time, sequencing data will be crucial to solve the genome interpretation puzzle, which aims at uncovering the genotype-to-phenotype relationship, but for the moment many conceptual and technical problems need to be addressed. In particular, very few attempts at the in-silico diagnosis of oligo-to-polygenic disorders have been made so far, due to the complexity of the challenge, the relative scarcity of the data and issues such as batch effects and data heterogeneity, which are confounder factors for machine learning (ML) methods. Here, we propose a method for the exome-based in-silico diagnosis of Crohn’s disease (CD) patients which addresses many of the current methodological issues. First, we devise a rational ML-friendly feature representation for WES data based on the gene mutational burden concept, which is suitable for small sample sizes datasets. Second, we propose a Neural Network (NN) with parameter tying and heavy regularization, in order to limit its complexity and thus the risk of over-fitting. We trained and tested our NN on 3 CD case-controls datasets, comparing the performance with the participants of previous CAGI challenges. We show that, notwithstanding the limited NN complexity, it outperforms the previous approaches. Moreover, we interpret the NN predictions by analyzing the learned patterns at the variant and gene level and investigating the decision process leading to each prediction.
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Affiliation(s)
| | - Jaak Simm
- ESAT-STADIUS, KU Leuven, 3001 Leuven, Belgium
| | - Adam Arany
- ESAT-STADIUS, KU Leuven, 3001 Leuven, Belgium
| | - Piero Fariselli
- Department of Medical Sciences, University of Torino, Torino, 10123 Italy
| | | | - Yves Moreau
- ESAT-STADIUS, KU Leuven, 3001 Leuven, Belgium
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6
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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: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [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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7
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The impact of a fine-scale population stratification on rare variant association test results. PLoS One 2018; 13:e0207677. [PMID: 30521541 PMCID: PMC6283567 DOI: 10.1371/journal.pone.0207677] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Accepted: 11/05/2018] [Indexed: 12/28/2022] Open
Abstract
Population stratification is a well-known confounding factor in both common and rare variant association analyses. Rare variants tend to be more geographically clustered than common variants, because of their more recent origin. However, it is not yet clear if population stratification at a very fine scale (neighboring administrative regions within a country) would lead to statistical bias in rare variant analyses. As the inclusion of convenience controls from external studies is indeed a common procedure, in order to increase the power to detect genetic associations, this problem is important. We studied through simulation the impact of a fine scale population structure on different rare variant association strategies, assessing type I error and power. We showed that principal component analysis (PCA) based methods of adjustment for population stratification adequately corrected type I error inflation at the largest geographical scales, but not at finest scales. We also showed in our simulations that adding controls obviously increased power, but at a considerably lower level when controls were drawn from another population.
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8
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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, Bipolar Disorder Working Group of the Psychiatric Genomics Consortium, 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: 1.9] [Reference Citation Analysis] [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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9
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Mutations in the pancreatic secretory enzymes CPA1 and CPB1 are associated with pancreatic cancer. Proc Natl Acad Sci U S A 2018; 115:4767-4772. [PMID: 29669919 DOI: 10.1073/pnas.1720588115] [Citation(s) in RCA: 66] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
To evaluate whether germline variants in genes encoding pancreatic secretory enzymes contribute to pancreatic cancer susceptibility, we sequenced the coding regions of CPB1 and other genes encoding pancreatic secretory enzymes and known pancreatitis susceptibility genes (PRSS1, CPA1, CTRC, and SPINK1) in a hospital series of pancreatic cancer cases and controls. Variants in CPB1, CPA1 (encoding carboxypeptidase B1 and A1), and CTRC were evaluated in a second set of cases with familial pancreatic cancer and controls. More deleterious CPB1 variants, defined as having impaired protein secretion and induction of endoplasmic reticulum (ER) stress in transfected HEK 293T cells, were found in the hospital series of pancreatic cancer cases (5/986, 0.5%) than in controls (0/1,045, P = 0.027). Among familial pancreatic cancer cases, ER stress-inducing CPB1 variants were found in 4 of 593 (0.67%) vs. 0 of 967 additional controls (P = 0.020), with a combined prevalence in pancreatic cancer cases of 9/1,579 vs. 0/2,012 controls (P < 0.01). More ER stress-inducing CPA1 variants were also found in the combined set of hospital and familial cases with pancreatic cancer than in controls [7/1,546 vs. 1/2,012; P = 0.025; odds ratio, 9.36 (95% CI, 1.15-76.02)]. Overall, 16 (1%) of 1,579 pancreatic cancer cases had an ER stress-inducing CPA1 or CPB1 variant, compared with 1 of 2,068 controls (P < 0.00001). No other candidate genes had statistically significant differences in variant prevalence between cases and controls. Our study indicates ER stress-inducing variants in CPB1 and CPA1 are associated with pancreatic cancer susceptibility and implicate ER stress in pancreatic acinar cells in pancreatic cancer development.
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10
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Persyn E, Karakachoff M, Le Scouarnec S, Le Clézio C, Campion D, Consortium FE, Schott JJ, Redon R, Bellanger L, Dina C. DoEstRare: A statistical test to identify local enrichments in rare genomic variants associated with disease. PLoS One 2017; 12:e0179364. [PMID: 28742119 PMCID: PMC5524342 DOI: 10.1371/journal.pone.0179364] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2017] [Accepted: 05/29/2017] [Indexed: 01/01/2023] Open
Abstract
Next-generation sequencing technologies made it possible to assay the effect of rare variants on complex diseases. As an extension of the "common disease-common variant" paradigm, rare variant studies are necessary to get a more complete insight into the genetic architecture of human traits. Association studies of these rare variations show new challenges in terms of statistical analysis. Due to their low frequency, rare variants must be tested by groups. This approach is then hindered by the fact that an unknown proportion of the variants could be neutral. The risk level of a rare variation may be determined by its impact but also by its position in the protein sequence. More generally, the molecular mechanisms underlying the disease architecture may involve specific protein domains or inter-genic regulatory regions. While a large variety of methods are optimizing functionality weights for each single marker, few evaluate variant position differences between cases and controls. Here, we propose a test called DoEstRare, which aims to simultaneously detect clusters of disease risk variants and global allele frequency differences in genomic regions. This test estimates, for cases and controls, variant position densities in the genetic region by a kernel method, weighted by a function of allele frequencies. We compared DoEstRare with previously published strategies through simulation studies as well as re-analysis of real datasets. Based on simulation under various scenarios, DoEstRare was the sole to consistently show highest performance, in terms of type I error and power both when variants were clustered or not. DoEstRare was also applied to Brugada syndrome and early-onset Alzheimer's disease data and provided complementary results to other existing tests. DoEstRare, by integrating variant position information, gives new opportunities to explain disease susceptibility. DoEstRare is implemented in a user-friendly R package.
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Affiliation(s)
- Elodie Persyn
- INSERM, CNRS, UNIV Nantes, l’institut du thorax, Nantes, France
| | - Matilde Karakachoff
- INSERM, CNRS, UNIV Nantes, l’institut du thorax, Nantes, France
- CHU Nantes, l’institut du thorax, Nantes, France
| | | | - Camille Le Clézio
- Inserm U1079, Rouen University, Normandy Center for Genomic Medicine and Personalized Medicine, Normandy University, Rouen, France
| | - Dominique Campion
- Inserm U1079, Rouen University, Normandy Center for Genomic Medicine and Personalized Medicine, Normandy University, Rouen, France
| | | | - Jean-Jacques Schott
- INSERM, CNRS, UNIV Nantes, l’institut du thorax, Nantes, France
- CHU Nantes, l’institut du thorax, Nantes, France
| | - Richard Redon
- INSERM, CNRS, UNIV Nantes, l’institut du thorax, Nantes, France
- CHU Nantes, l’institut du thorax, Nantes, France
| | - Lise Bellanger
- Laboratoire de Mathématiques Jean Leray, UMR CNRS 6629, Nantes, France
- * E-mail: (LB); (CD)
| | - Christian Dina
- INSERM, CNRS, UNIV Nantes, l’institut du thorax, Nantes, France
- CHU Nantes, l’institut du thorax, Nantes, France
- * E-mail: (LB); (CD)
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11
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Budde M, Forstner AJ, Adorjan K, Schaupp SK, Nöthen MM, Schulze TG. Genetische Grundlagen der bipolaren Störung. DER NERVENARZT 2017; 88:755-759. [DOI: 10.1007/s00115-017-0336-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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12
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Zhang D, Cui H, Korkin D, Wu Z. Incorporation of protein binding effects into likelihood ratio test for exome sequencing data. BMC Proc 2016; 10:275-281. [PMID: 27980649 PMCID: PMC5133515 DOI: 10.1186/s12919-016-0043-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
Statistical association studies are an important tool in detecting novel disease genes. However, for sequencing data, association studies confront the challenge of low power because of relatively small data sample size and rare variants. Incorporating biological information that reflects disease mechanism is likely to strengthen the association evidence of disease genes, and thus increase the power of association studies. In this paper, we annotate non-synonymous single-nucleotide variants according to protein binding sites (BSs) by using a more accurate BS prediction method. We then incorporate this information into association study through a statistical framework of likelihood ratio test (LRT) based on weighted burden score of single-nucleotide variants (SNVs). The strategy is applied to Genetic Analysis Workshop 19 exome-sequencing data for detecting novel genes associated to hypotension. The SNV-weighting LRT idea is empirically verified by the simulated phenotypes (336 cases and 1607 controls), and the weights based on BS annotation are applied to the real phenotypes (394 cases and 1457 controls). Such strategy of weighting the prior information on protein functional sites is shown to be superior to the unweighted LRT and serves as a good complement to the existing association tests. Several putative genes are reported; some of them are functionally related to hypertension according to the previous evidence in the literature.
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Affiliation(s)
- Dongni Zhang
- Mathematics Department, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609-2280 USA
| | - Hongzhu Cui
- Computer Science Department, Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609-2280 USA
| | - Dmitry Korkin
- Computer Science Department, Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609-2280 USA
| | - Zheyang Wu
- Mathematics Department, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609-2280 USA
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13
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Friedrichs S, Malzahn D, Pugh EW, Almeida M, Liu XQ, Bailey JN. Filtering genetic variants and placing informative priors based on putative biological function. BMC Genet 2016; 17 Suppl 2:8. [PMID: 26866982 PMCID: PMC4895695 DOI: 10.1186/s12863-015-0313-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
High-density genetic marker data, especially sequence data, imply an immense multiple testing burden. This can be ameliorated by filtering genetic variants, exploiting or accounting for correlations between variants, jointly testing variants, and by incorporating informative priors. Priors can be based on biological knowledge or predicted variant function, or even be used to integrate gene expression or other omics data. Based on Genetic Analysis Workshop (GAW) 19 data, this article discusses diversity and usefulness of functional variant scores provided, for example, by PolyPhen2, SIFT, or RegulomeDB annotations. Incorporating functional scores into variant filters or weights and adjusting the significance level for correlations between variants yielded significant associations with blood pressure traits in a large family study of Mexican Americans (GAW19 data set). Marker rs218966 in gene PHF14 and rs9836027 in MAP4 significantly associated with hypertension; additionally, rare variants in SNUPN significantly associated with systolic blood pressure. Variant weights strongly influenced the power of kernel methods and burden tests. Apart from variant weights in test statistics, prior weights may also be used when combining test statistics or to informatively weight p values while controlling false discovery rate (FDR). Indeed, power improved when gene expression data for FDR-controlled informative weighting of association test p values of genes was used. Finally, approaches exploiting variant correlations included identity-by-descent mapping and the optimal strategy for joint testing rare and common variants, which was observed to depend on linkage disequilibrium structure.
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Affiliation(s)
- Stefanie Friedrichs
- Department of Genetic Epidemiology, University Medical Center, Georg-August University Göttingen, Göttingen, Germany.
| | - Dörthe Malzahn
- Department of Genetic Epidemiology, University Medical Center, Georg-August University Göttingen, Göttingen, Germany.
| | - Elizabeth W Pugh
- Center for Inherited Disease Research, Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA.
| | - Marcio Almeida
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville, TX, USA.
| | - Xiao Qing Liu
- Department of Obstetrics, Gynecology, and Reproductive Sciences, Department of Biochemistry and Medical Genetics, Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada.
- Children's Hospital Research Institute of Manitoba, Winnipeg, MB, Canada.
| | - Julia N Bailey
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA.
- Epilepsy Genetics/Genomics Laboratory, West Los Angeles Veterans Administration, Los Angeles, CA, USA.
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14
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Roberts NJ, Norris AL, Petersen GM, Bondy ML, Brand R, Gallinger S, Kurtz RC, Olson SH, Rustgi AK, Schwartz AG, Stoffel E, Syngal S, Zogopoulos G, Ali SZ, Axilbund J, Chaffee KG, Chen YC, Cote ML, Childs EJ, Douville C, Goes FS, Herman JM, Iacobuzio-Donahue C, Kramer M, Makohon-Moore A, McCombie RW, McMahon KW, Niknafs N, Parla J, Pirooznia M, Potash JB, Rhim AD, Smith AL, Wang Y, Wolfgang CL, Wood LD, Zandi PP, Goggins M, Karchin R, Eshleman JR, Papadopoulos N, Kinzler KW, Vogelstein B, Hruban RH, Klein AP. Whole Genome Sequencing Defines the Genetic Heterogeneity of Familial Pancreatic Cancer. Cancer Discov 2015; 6:166-75. [PMID: 26658419 DOI: 10.1158/2159-8290.cd-15-0402] [Citation(s) in RCA: 276] [Impact Index Per Article: 27.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2015] [Accepted: 12/02/2015] [Indexed: 12/14/2022]
Abstract
UNLABELLED Pancreatic cancer is projected to become the second leading cause of cancer-related death in the United States by 2020. A familial aggregation of pancreatic cancer has been established, but the cause of this aggregation in most families is unknown. To determine the genetic basis of susceptibility in these families, we sequenced the germline genomes of 638 patients with familial pancreatic cancer and the tumor exomes of 39 familial pancreatic adenocarcinomas. Our analyses support the role of previously identified familial pancreatic cancer susceptibility genes such as BRCA2, CDKN2A, and ATM, and identify novel candidate genes harboring rare, deleterious germline variants for further characterization. We also show how somatic point mutations that occur during hematopoiesis can affect the interpretation of genome-wide studies of hereditary traits. Our observations have important implications for the etiology of pancreatic cancer and for the identification of susceptibility genes in other common cancer types. SIGNIFICANCE The genetic basis of disease susceptibility in the majority of patients with familial pancreatic cancer is unknown. We whole genome sequenced 638 patients with familial pancreatic cancer and demonstrate that the genetic underpinning of inherited pancreatic cancer is highly heterogeneous. This has significant implications for the management of patients with familial pancreatic cancer.
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Affiliation(s)
- Nicholas J Roberts
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland. Ludwig Center and the Howard Hughes Medical Institute, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland.
| | - Alexis L Norris
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Gloria M Petersen
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Melissa L Bondy
- Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Randall Brand
- Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Steven Gallinger
- Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Robert C Kurtz
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Sara H Olson
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Anil K Rustgi
- Division of Gastroenterology, Departments of Medicine and Genetics, Pancreatic Cancer Translational Center of Excellence, Abramson Cancer Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Ann G Schwartz
- Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, Michigan
| | - Elena Stoffel
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | - Sapna Syngal
- Population Sciences Division, Dana-Farber Cancer Institute, and Gastroenterology Division, Brigham and Women's Hospital, Boston, Massachusetts
| | - George Zogopoulos
- The Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada. Goodman Cancer Research Centre, McGill University, Montreal, Quebec, Canada
| | - Syed Z Ali
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Jennifer Axilbund
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Kari G Chaffee
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Yun-Ching Chen
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Michele L Cote
- Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, Michigan
| | - Erica J Childs
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
| | - Christopher Douville
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Fernando S Goes
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Joseph M Herman
- Department of Oncology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland
| | | | - Melissa Kramer
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York
| | - Alvin Makohon-Moore
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Richard W McCombie
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York
| | - K Wyatt McMahon
- Ludwig Center and the Howard Hughes Medical Institute, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Noushin Niknafs
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Jennifer Parla
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York. inGenious Targeting Laboratory, Ronkonkoma, New York
| | - Mehdi Pirooznia
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - James B Potash
- Department of Psychiatry, University of Iowa, Iowa City, Iowa
| | - Andrew D Rhim
- Division of Gastroenterology, Departments of Medicine and Genetics, Pancreatic Cancer Translational Center of Excellence, Abramson Cancer Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania. Department of Medicine, University of Michigan, Ann Arbor, Michigan
| | - Alyssa L Smith
- The Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada. Goodman Cancer Research Centre, McGill University, Montreal, Quebec, Canada
| | - Yuxuan Wang
- Ludwig Center and the Howard Hughes Medical Institute, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Christopher L Wolfgang
- Department of Surgery, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Laura D Wood
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland. Department of Oncology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Peter P Zandi
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Michael Goggins
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland. Department of Oncology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland. Department of Medicine, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Rachel Karchin
- Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland
| | - James R Eshleman
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland. Department of Oncology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Nickolas Papadopoulos
- Ludwig Center and the Howard Hughes Medical Institute, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Kenneth W Kinzler
- Ludwig Center and the Howard Hughes Medical Institute, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland.
| | - Bert Vogelstein
- Ludwig Center and the Howard Hughes Medical Institute, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland.
| | - Ralph H Hruban
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland. Department of Oncology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland.
| | - Alison P Klein
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland. Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland. Department of Oncology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland.
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15
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Kato T. Whole genome/exome sequencing in mood and psychotic disorders. Psychiatry Clin Neurosci 2015; 69:65-76. [PMID: 25319632 DOI: 10.1111/pcn.12247] [Citation(s) in RCA: 56] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/09/2014] [Indexed: 02/06/2023]
Abstract
Recent developments in DNA sequencing technologies have allowed for genetic studies using whole genome or exome analysis, and these have been applied in the study of mood and psychotic disorders, including bipolar disorder, depression, schizophrenia, and schizoaffective disorder. In this review, the current situation, recent findings, methodological problems, and future directions of whole genome/exome analysis studies of these disorders are summarized. Whole genome/exome studies of bipolar disorder have included pedigree analysis and case-control studies, demonstrating the role of previously implicated pathways, such as calcium signaling, cyclic adenosine monophosphate response element binding protein (CREB) signaling, and potassium channels. Extensive analysis of trio families and case-control studies showed that de novo mutations play a role in the genetic architecture of schizophrenia and indicated that mutations in several molecular pathways, including chromatin regulation, activity-regulated cytoskeleton, post-synaptic density, N-methyl-D-aspartate receptor, and targets of fragile X mental retardation protein, are associated with this disorder. Depression is a heterogeneous group of diseases and studies using exome analysis have been conducted to identify rare mutations causing Mendelian diseases that accompany depression. In the near future, clarification of the genetic architecture of bipolar disorder and schizophrenia is expected. Identification of causative mutations using these new technologies will facilitate neurobiological studies of these disorders.
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Affiliation(s)
- Tadafumi Kato
- Laboratory for Molecular Dynamics of Mental Disorders, RIKEN Brain Science Institute, Wako, Japan
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16
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Abstract
The last several years have been breakthrough ones in bipolar disorder (BPD) genetics, as the field has identified robust risk variants for the first time. Leading the way have been genome-wide association studies (GWAS) that have assessed common genetic markers across very large groups of patients and controls. These have resulted in findings in genes including ANK3, CACNA1C, SYNE1, ODZ4, and TRANK1. Additional studies have begun to examine the biology of these genes and how risk variants influence aspects of brain and behavior that underlie BPD. For example, carriers of the CACNA1C risk variant have been found to exhibit hippocampal and anterior cingulate dysfunction during episodic memory recall. This work has shed additional light on the relationship of bipolar susceptibility variants to other disorders, particularly schizophrenia. Even larger BPD GWAS are expected with samples now amassed of 21,035 cases and 28,758 controls. Studies have examined the pharmacogenomics of BPD with studies of lithium response, yielding high profile results that remain to be confirmed. The next frontier in the field is the identification of rare bipolar susceptibility variants through large-scale DNA sequencing. While only a couple of papers have been published to date, many studies are underway. The Bipolar Sequencing Consortium has been formed to bring together all of the groups working in this area, and to perform meta-analyses of the data generated. The consortium, with 13 member groups, now has exome data on ~3,500 cases and ~5,000 controls, and on ~162 families. The focus will likely shift within several years from exome data to whole genome data as costs of obtaining such data continue to drop. Gene-mapping studies are now providing clear results that provide insights into the pathophysiology of the disorder. Sequencing studies should extend this process further. Findings could eventually set the stage for rational therapeutic development.
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Affiliation(s)
- Gen Shinozaki
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, IA, USA
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17
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Lin YC, Hsieh AR, Hsiao CL, Wu SJ, Wang HM, Lian IB, Fann CSJ. Identifying rare and common disease associated variants in genomic data using Parkinson's disease as a model. J Biomed Sci 2014; 21:88. [PMID: 25175702 PMCID: PMC4428531 DOI: 10.1186/s12929-014-0088-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2014] [Accepted: 08/21/2014] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Genome-wide association studies have been successful in identifying common genetic variants for human diseases. However, much of the heritable variation associated with diseases such as Parkinson's disease remains unknown suggesting that many more risk loci are yet to be identified. Rare variants have become important in disease association studies for explaining missing heritability. Methods for detecting this type of association require prior knowledge on candidate genes and combining variants within the region. These methods may suffer from power loss in situations with many neutral variants or causal variants with opposite effects. RESULTS We propose a method capable of scanning genetic variants to identify the region most likely harbouring disease gene with rare and/or common causal variants. Our method assigns a score at each individual variant based on our scoring system. It uses aggregate scores to identify the region with disease association. We evaluate performance by simulation based on 1000 Genomes sequencing data and compare with three commonly used methods. We use a Parkinson's disease case-control dataset as a model to demonstrate the application of our method. Our method has better power than CMC and WSS and similar power to SKAT-O with well-controlled type I error under simulation based on 1000 Genomes sequencing data. In real data analysis, we confirm the association of α-synuclein gene (SNCA) with Parkinson's disease (p = 0.005). We further identify association with hyaluronan synthase 2 (HAS2, p = 0.028) and kringle containing transmembrane protein 1 (KREMEN1, p = 0.006). KREMEN1 is associated with Wnt signalling pathway which has been shown to play an important role for neurodegeneration in Parkinson's disease. CONCLUSIONS Our method is time efficient and less sensitive to inclusion of neutral variants and direction effect of causal variants. It can narrow down a genomic region or a chromosome to a disease associated region. Using Parkinson's disease as a model, our method not only confirms association for a known gene but also identifies two genes previously found by other studies. In spite of many existing methods, we conclude that our method serves as an efficient alternative for exploring genomic data containing both rare and common variants.
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Affiliation(s)
- Ying-Chao Lin
- Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan. .,Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei, Taiwan. .,Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.
| | - Ai-Ru Hsieh
- Graduate Institute of Biostatistics, China Medical University, Taichung, Taiwan.
| | - Ching-Lin Hsiao
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.
| | - Shang-Jung Wu
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.
| | - Hui-Min Wang
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.
| | - Ie-Bin Lian
- Graduate Institute of Statistics and Information Science, National Changhua University of Education, Changhua, Taiwan.
| | - Cathy S J Fann
- Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei, Taiwan. .,Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan. .,Institute of Public Health, National Yang-Ming University, Taipei, Taiwan.
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19
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Valsesia A, Macé A, Jacquemont S, Beckmann JS, Kutalik Z. The Growing Importance of CNVs: New Insights for Detection and Clinical Interpretation. Front Genet 2013; 4:92. [PMID: 23750167 PMCID: PMC3667386 DOI: 10.3389/fgene.2013.00092] [Citation(s) in RCA: 47] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2013] [Accepted: 05/04/2013] [Indexed: 02/03/2023] Open
Abstract
Differences between genomes can be due to single nucleotide variants, translocations, inversions, and copy number variants (CNVs, gain or loss of DNA). The latter can range from sub-microscopic events to complete chromosomal aneuploidies. Small CNVs are often benign but those larger than 500 kb are strongly associated with morbid consequences such as developmental disorders and cancer. Detecting CNVs within and between populations is essential to better understand the plasticity of our genome and to elucidate its possible contribution to disease. Hence there is a need for better-tailored and more robust tools for the detection and genome-wide analyses of CNVs. While a link between a given CNV and a disease may have often been established, the relative CNV contribution to disease progression and impact on drug response is not necessarily understood. In this review we discuss the progress, challenges, and limitations that occur at different stages of CNV analysis from the detection (using DNA microarrays and next-generation sequencing) and identification of recurrent CNVs to the association with phenotypes. We emphasize the importance of germline CNVs and propose strategies to aid clinicians to better interpret structural variations and assess their clinical implications.
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Affiliation(s)
- Armand Valsesia
- Genetics Core, Nestlé Institute of Health Sciences Lausanne, Switzerland
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