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Yang J, Li Z, Wu WKK, Yu S, Xu Z, Chu Q, Zhang Q. Deep learning identifies explainable reasoning paths of mechanism of action for drug repurposing from multilayer biological network. Brief Bioinform 2022; 23:6809964. [PMID: 36347526 DOI: 10.1093/bib/bbac469] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 09/07/2022] [Accepted: 09/29/2022] [Indexed: 11/11/2022] Open
Abstract
The discovery and repurposing of drugs require a deep understanding of the mechanism of drug action (MODA). Existing computational methods mainly model MODA with the protein-protein interaction (PPI) network. However, the molecular interactions of drugs in the human body are far beyond PPIs. Additionally, the lack of interpretability of these models hinders their practicability. We propose an interpretable deep learning-based path-reasoning framework (iDPath) for drug discovery and repurposing by capturing MODA on by far the most comprehensive multilayer biological network consisting of the complex high-dimensional molecular interactions between genes, proteins and chemicals. Experiments show that iDPath outperforms state-of-the-art machine learning methods on a general drug repurposing task. Further investigations demonstrate that iDPath can identify explicit critical paths that are consistent with clinical evidence. To demonstrate the practical value of iDPath, we apply it to the identification of potential drugs for treating prostate cancer and hypertension. Results show that iDPath can discover new FDA-approved drugs. This research provides a novel interpretable artificial intelligence perspective on drug discovery.
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Affiliation(s)
- Jiannan Yang
- School of Data Science, City University of Hong Kong, Hong Kong SAR, China
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - William Ka Kei Wu
- Department of Anaesthesia and Intensive Care, Chinese University of Hong Kong, Hong Kong SAR, China
| | - Shi Yu
- The USC Norris Center for Cancer Drug Development, University of Southern California, Los Angeles, CA, USA.,Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Zhongzhi Xu
- School of Data Science, City University of Hong Kong, Hong Kong SAR, China
| | - Qian Chu
- Department of Thoracic Oncology, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China
| | - Qingpeng Zhang
- School of Data Science, City University of Hong Kong, Hong Kong SAR, China
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2
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Xu Q, Zhang S, Wu C, Xiong Y, Niu J, Li F, Zhu J, Shen L, Zhu B, Xing Q, He L, Chen L, Li M, Li H, Ge J, Qin S. Genetic Associations With Stable Warfarin Dose Requirements in Han Chinese Patients. J Cardiovasc Pharmacol 2021; 78:e105-e111. [PMID: 33958549 DOI: 10.1097/fjc.0000000000001048] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 04/06/2021] [Indexed: 11/26/2022]
Abstract
ABSTRACT Warfarin is a commonly prescribed anticoagulant for valvular heart disease that plays an important role in clinical management to prevent thrombotic events. In this study, we aim to perform a comprehensive study to investigate the genetic biomarkers of stable warfarin dose in the Han Chinese population. We performed an integrative study on 211 Han Chinese patients with valvular heart disease. A total of 40 single nucleotide polymorphisms (SNPs) in 10 important genes (CYP2C9, VKORC1, ABCB1, CYP4F2, APOE, PROC, GGCX, EPHX1, CALU, and SETD1A) which are involved in the warfarin metabolic pathway and equilibrium of coagulation and anticoagulation were selected. We applied MassARRAY technology to genotype the 40 SNPs identified in these Han Chinese patients. Our results showed that 13 SNPs on 6 genes (CYP2C9, VKORC1, ABCB1, PROC, EPHX1, and SETD1A) were associated with the individual stable warfarin dose. Two VKORC1 SNPs (rs9934438 and rs2359612) were the strongest genetic factors determining warfarin dose requirements (P = 8 × 10-6 and 9 × 10-6, respectively). Rs4889599 in SETD1A was first reported to be associated with warfarin dose at a significant level of 0.001 in our study (Padjust = 0.040 after Bonferroni correction). We discovered that genetic variants in CYP2C9, VKORC1, ABCB1, PROC, EPHX1, and SETD1A may affect the stable warfarin dose requirement in Han Chinese patients with valvular disease. The discovery of these potential genetic markers will facilitate the development of advanced personalized anticoagulation therapy in Han Chinese patients.
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Affiliation(s)
- Qingqing Xu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Suli Zhang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
| | - Chaoneng Wu
- Shanghai Institute of Cardiovascular Diseases, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yuyu Xiong
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
| | - Jiamin Niu
- Laiwu People's Hospital, Shandong, China
| | | | - Jinhang Zhu
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
| | - Lu Shen
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
| | - Bin Zhu
- Shanghai Baio Technology Co, Ltd Shanghai, China
| | | | - Lin He
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
| | - Luan Chen
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
| | - Mo Li
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
| | - Hua Li
- Shanghai Institute of Cardiovascular Diseases, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Junbo Ge
- Shanghai Institute of Cardiovascular Diseases, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Shengying Qin
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
- The Third Affiliated Hospital, Guangzhou Medical University, Guangzhou, China
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3
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Parra-Galindo MA, Soto-Sedano JC, Mosquera-Vásquez T, Roda F. Pathway-based analysis of anthocyanin diversity in diploid potato. PLoS One 2021; 16:e0250861. [PMID: 33914830 PMCID: PMC8084248 DOI: 10.1371/journal.pone.0250861] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 04/14/2021] [Indexed: 12/21/2022] Open
Abstract
Anthocyanin biosynthesis is one of the most studied pathways in plants due to the important ecological role played by these compounds and the potential health benefits of anthocyanin consumption. Given the interest in identifying new genetic factors underlying anthocyanin content we studied a diverse collection of diploid potatoes by combining a genome-wide association study and pathway-based analyses. By using an expanded SNP dataset, we identified candidate genes that had not been associated with anthocyanin variation in potatoes, namely a Myb transcription factor, a Leucoanthocyanidin dioxygenase gene and a vacuolar membrane protein. Importantly, a genomic region in chromosome 10 harbored the SNPs with strongest associations with anthocyanin content in GWAS. Some of these SNPs were associated with multiple anthocyanin compounds and therefore could underline the existence of pleiotropic genes or anthocyanin biosynthetic clusters. We identified multiple anthocyanin homologs in this genomic region, including four transcription factors and five enzymes that could be governing anthocyanin variation. For instance, a SNP linked to the phenylalanine ammonia-lyase gene, encoding the first enzyme in the phenylpropanoid biosynthetic pathway, was associated with all of the five anthocyanins measured. Finally, we combined a pathway analysis and GWAS of other agronomic traits to identify pathways related to anthocyanin biosynthesis in potatoes. We found that methionine metabolism and the production of sugars and hydroxycinnamic acids are genetically correlated to anthocyanin biosynthesis. The results contribute to the understanding of anthocyanins regulation in potatoes and can be used in future breeding programs focused on nutraceutical food.
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Affiliation(s)
| | - Johana Carolina Soto-Sedano
- Departamento de Biología, Facultad de Ciencias, Universidad Nacional de Colombia, Sede Bogotá, Bogotá, Colombia
| | - Teresa Mosquera-Vásquez
- Facultad de Ciencias Agrarias, Universidad Nacional de Colombia, Sede Bogotá, Bogotá, Colombia
| | - Federico Roda
- Max Planck Tandem Group, Facultad de Ciencias, Universidad Nacional de Colombia, Sede Bogotá, Bogotá, Colombia
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4
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Li J, Chen F, Zhang Q, Meng X, Yao X, Risacher SL, Yan J, Saykin AJ, Liang H, Shen L. Genome-wide Network-assisted Association and Enrichment Study of Amyloid Imaging Phenotype in Alzheimer's Disease. Curr Alzheimer Res 2020; 16:1163-1174. [PMID: 31755389 DOI: 10.2174/1567205016666191121142558] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Revised: 11/19/2019] [Accepted: 11/21/2019] [Indexed: 11/22/2022]
Abstract
BACKGROUND The etiology of Alzheimer's disease remains poorly understood at the mechanistic level, and genome-wide network-based genetics have the potential to provide new insights into the disease mechanisms. OBJECTIVE The study aimed to explore the collective effects of multiple genetic association signals on an AV-45 PET measure, which is a well-known Alzheimer's disease biomarker, by employing a network assisted strategy. METHODS First, we took advantage of a dense module search algorithm to identify modules enriched by genetic association signals in a protein-protein interaction network. Next, we performed statistical evaluation to the modules identified by dense module search, including a normalization process to adjust the topological bias in the network, a replication test to ensure the modules were not found randomly , and a permutation test to evaluate unbiased associations between the modules and amyloid imaging phenotype. Finally, topological analysis, module similarity tests and functional enrichment analysis were performed for the identified modules. RESULTS We identified 24 consensus modules enriched by robust genetic signals in a genome-wide association analysis. The results not only validated several previously reported AD genes (APOE, APP, TOMM40, DDAH1, PARK2, ATP5C1, PVRL2, ELAVL1, ACTN1 and NRF1), but also nominated a few novel genes (ABL1, ABLIM2) that have not been studied in Alzheimer's disease but have shown associations with other neurodegenerative diseases. CONCLUSION The identified genes, consensus modules and enriched pathways may provide important clues to future research on the neurobiology of Alzheimer's disease and suggest potential therapeutic targets.
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Affiliation(s)
- Jin Li
- College of Automation, Harbin Engineering University, Harbin, China
| | - Feng Chen
- College of Automation, Harbin Engineering University, Harbin, China
| | - Qiushi Zhang
- College of Information Engineering, Northeast Dianli University, Jilin, China
| | - Xianglian Meng
- College of Automation, Harbin Engineering University, Harbin, China
| | - Xiaohui Yao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Shannon L Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, PA, United States
| | - Jingwen Yan
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, PA, United States
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, PA, United States
| | - Hong Liang
- College of Automation, Harbin Engineering University, Harbin, China
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
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Gorji AE, Roudbari Z, Alizadeh A, Sadeghi B. Investigation of systemic lupus erythematosus (SLE) with integrating transcriptomics and genome wide association information. Gene 2019; 706:181-187. [PMID: 31082500 DOI: 10.1016/j.gene.2019.05.004] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Revised: 03/10/2019] [Accepted: 05/03/2019] [Indexed: 02/06/2023]
Abstract
Systemic lupus erythematous (SEL) is a heterogeneous, systemic autoimmune disorder which is defined by its autoantibody pattern. Transcriptomic data analysis has shown pathways and immune system responses associated with SLE. Eight up-regulated genes (SOCE, MMP9, CXCL8, JUN, IL1B, NFKBIA, TNF and FOS) have been examined with four interactions among different pathways. These genes are associated with SNPs which have been identified through two datasets from SLE genome-wide association studies (GWAS). In this investigation, the GWAS results were integrated with pathway analysis of transcriptomes and several genes were detected with known SLE-related variations (TYK2, C5, SH2B, IRF5, IL2RA, STAT4, FCGR2A, IL7R, LYN, HLA-DRB and TNFAIP3). Pathway-based analysis on the Wikipathway Human Collection allowed the identification of prioritized variants in the relevant pathways, such as thymic stromal lymphopoietin (TSLP) signaling pathway linked to LYN, IL7R, STAT4 and rs7574865. Analysis of existing transcriptomes and GWAS data identified eight up-regulated candidate genes with more than four relationships among the different pathways associated with SNPs to pinpoint the relevant loci linked to SLE. The results of this investigation have expanded the number of candidate genes related to SLE and have highlighted possible pathways and GWAS-based methods for gene detection. Identification of the fundamental genes would assist in revealing the mechanisms responsible for SLE.
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Affiliation(s)
- Abdolvahab Ebrahimpour Gorji
- Department of Fisheries, Faculty of Animal Sciences and Fisheries, Sari Agricultural and Natural Resources University, Sari, Iran
| | - Zahra Roudbari
- Department of Animal Science, Faculty of Agriculture, University of Jiroft, Jiroft, Iran.
| | - Akram Alizadeh
- Department of Tissue Engineering and Applied Cell Sciences, Faculty of Medicine, Semnan University of Medical Sciences, Semnan, Iran
| | - Balal Sadeghi
- Food Hygiene and Public Health Department, Faculty of Veterinary Medicine, Shahid Bahonar University of Kerman, Kerman, Iran.
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Myrum C, Nikolaienko O, Bramham CR, Haavik J, Zayats T. Implication of the APP Gene in Intellectual Abilities. J Alzheimers Dis 2018; 59:723-735. [PMID: 28671113 PMCID: PMC5523840 DOI: 10.3233/jad-170049] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Background: Cognitive functions are highly heritable and polygenic, though the source of this genetic influence is unclear. On the neurobiological level, these functions rely on effective neuroplasticity, in which the activity-regulated cytoskeleton associated protein (ARC) plays an essential role. Objectives: To examine whether the ARC gene complex may contribute to the genetic components of intellectual function given the crucial role of ARC in brain plasticity and memory formation. Methods: The ARC complex was tested for association with intelligence (IQ) in children from the Avon Longitudinal Study of Parents and Children (ALSPAC, N = 5,165). As Alzheimer’s disease (AD) shares genetics with cognitive functioning, the association was followed up in an AD sample (17,008 cases, 37,154 controls). Results: The ARC complex revealed association with verbal and total IQ (empirical p = 0.027 and 0.041, respectively) in the ALSPAC. The strongest single variant signal (rs2830077; empirical p = 0.018), within the APP gene, was confirmed in the AD sample (p = 2.76E-03). Functional analyses of this variant showed its preferential binding to the transcription factor CP2. Discussion: This study implicates APP in childhood IQ. While follow-up studies are needed, this observation could help elucidate the etiology of disorders associated with cognitive dysfunction, such as AD.
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Affiliation(s)
- Craig Myrum
- K.G. Jebsen Centre for Neuropsychiatric Disorders, Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Oleksii Nikolaienko
- K.G. Jebsen Centre for Neuropsychiatric Disorders, Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Clive R Bramham
- K.G. Jebsen Centre for Neuropsychiatric Disorders, Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Jan Haavik
- K.G. Jebsen Centre for Neuropsychiatric Disorders, Department of Biomedicine, University of Bergen, Bergen, Norway.,Department of Psychiatry, Haukeland University Hospital, Bergen, Norway
| | - Tetyana Zayats
- K.G. Jebsen Centre for Neuropsychiatric Disorders, Department of Biomedicine, University of Bergen, Bergen, Norway
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7
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Kim M, Tagkopoulos I. Data integration and predictive modeling methods for multi-omics datasets. Mol Omics 2018; 14:8-25. [DOI: 10.1039/c7mo00051k] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
We provide an overview of opportunities and challenges in multi-omics predictive analytics with particular emphasis on data integration and machine learning methods.
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Affiliation(s)
- Minseung Kim
- Department of Computer Science
- University of California
- Davis
- USA
- Genome Center
| | - Ilias Tagkopoulos
- Department of Computer Science
- University of California
- Davis
- USA
- Genome Center
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8
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Hall MA, Moore JH, Ritchie MD. Embracing Complex Associations in Common Traits: Critical Considerations for Precision Medicine. Trends Genet 2017; 32:470-484. [PMID: 27392675 DOI: 10.1016/j.tig.2016.06.001] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2016] [Revised: 06/01/2016] [Accepted: 06/02/2016] [Indexed: 10/21/2022]
Abstract
Genome-wide association studies (GWAS) have identified numerous loci associated with human phenotypes. This approach, however, does not consider the richly diverse and complex environment with which humans interact throughout the life course, nor does it allow for interrelationships between genetic loci and across traits. As we move toward making precision medicine a reality, whereby we make predictions about disease risk based on genomic profiles, we need to identify improved predictive models of the relationship between genome and phenome. Methods that embrace pleiotropy (the effect of one locus on more than one trait), and gene-environment (G×E) and gene-gene (G×G) interactions, will further unveil the impact of alterations in biological pathways and identify genes that are only involved with disease in the context of the environment. This valuable information can be used to assess personal risk and choose the most appropriate medical interventions based on the genotype and environment of an individual, the whole premise of precision medicine.
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Affiliation(s)
- Molly A Hall
- Institute for Biomedical Informatics, Departments of Genetics and Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, 3535 Market Street, Philadelphia, PA 19104, USA
| | - Jason H Moore
- Institute for Biomedical Informatics, Departments of Genetics and Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, 3535 Market Street, Philadelphia, PA 19104, USA
| | - Marylyn D Ritchie
- Biomedical and Translational Informatics, Geisinger Health System, Danville, PA, USA; Department of Biochemistry and Molecular Biology, Center for Systems Genomics, Eberly College of Science, The Pennsylvania State University, University Park, PA, USA.
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9
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Garland J. Unravelling the complexity of signalling networks in cancer: A review of the increasing role for computational modelling. Crit Rev Oncol Hematol 2017; 117:73-113. [PMID: 28807238 DOI: 10.1016/j.critrevonc.2017.06.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2016] [Revised: 06/01/2017] [Accepted: 06/08/2017] [Indexed: 02/06/2023] Open
Abstract
Cancer induction is a highly complex process involving hundreds of different inducers but whose eventual outcome is the same. Clearly, it is essential to understand how signalling pathways and networks generated by these inducers interact to regulate cell behaviour and create the cancer phenotype. While enormous strides have been made in identifying key networking profiles, the amount of data generated far exceeds our ability to understand how it all "fits together". The number of potential interactions is astronomically large and requires novel approaches and extreme computation methods to dissect them out. However, such methodologies have high intrinsic mathematical and conceptual content which is difficult to follow. This review explains how computation modelling is progressively finding solutions and also revealing unexpected and unpredictable nano-scale molecular behaviours extremely relevant to how signalling and networking are coherently integrated. It is divided into linked sections illustrated by numerous figures from the literature describing different approaches and offering visual portrayals of networking and major conceptual advances in the field. First, the problem of signalling complexity and data collection is illustrated for only a small selection of known oncogenes. Next, new concepts from biophysics, molecular behaviours, kinetics, organisation at the nano level and predictive models are presented. These areas include: visual representations of networking, Energy Landscapes and energy transfer/dissemination (entropy); diffusion, percolation; molecular crowding; protein allostery; quinary structure and fractal distributions; energy management, metabolism and re-examination of the Warburg effect. The importance of unravelling complex network interactions is then illustrated for some widely-used drugs in cancer therapy whose interactions are very extensive. Finally, use of computational modelling to develop micro- and nano- functional models ("bottom-up" research) is highlighted. The review concludes that computational modelling is an essential part of cancer research and is vital to understanding network formation and molecular behaviours that are associated with it. Its role is increasingly essential because it is unravelling the huge complexity of cancer induction otherwise unattainable by any other approach.
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Affiliation(s)
- John Garland
- Manchester Interdisciplinary Biocentre, Manchester University, Manchester, UK.
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10
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Lima LDA, Feio-dos-Santos AC, Belangero SI, Gadelha A, Bressan RA, Salum GA, Pan PM, Moriyama TS, Graeff-Martins AS, Tamanaha AC, Alvarenga P, Krieger FV, Fleitlich-Bilyk B, Jackowski AP, Brietzke E, Sato JR, Polanczyk GV, Mari JDJ, Manfro GG, do Rosário MC, Miguel EC, Puga RD, Tahira AC, Souza VN, Chile T, Gouveia GR, Simões SN, Chang X, Pellegrino R, Tian L, Glessner JT, Hashimoto RF, Rohde LA, Sleiman PMA, Hakonarson H, Brentani H. An integrative approach to investigate the respective roles of single-nucleotide variants and copy-number variants in Attention-Deficit/Hyperactivity Disorder. Sci Rep 2016; 6:22851. [PMID: 26947246 PMCID: PMC4780010 DOI: 10.1038/srep22851] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2015] [Accepted: 02/23/2016] [Indexed: 02/07/2023] Open
Abstract
Many studies have attempted to investigate the genetic susceptibility of Attention-Deficit/Hyperactivity Disorder (ADHD), but without much success. The present study aimed to analyze both single-nucleotide and copy-number variants contributing to the genetic architecture of ADHD. We generated exome data from 30 Brazilian trios with sporadic ADHD. We also analyzed a Brazilian sample of 503 children/adolescent controls from a High Risk Cohort Study for the Development of Childhood Psychiatric Disorders, and also previously published results of five CNV studies and one GWAS meta-analysis of ADHD involving children/adolescents. The results from the Brazilian trios showed that cases with de novo SNVs tend not to have de novo CNVs and vice-versa. Although the sample size is small, we could also see that various comorbidities are more frequent in cases with only inherited variants. Moreover, using only genes expressed in brain, we constructed two "in silico" protein-protein interaction networks, one with genes from any analysis, and other with genes with hits in two analyses. Topological and functional analyses of genes in this network uncovered genes related to synapse, cell adhesion, glutamatergic and serotoninergic pathways, both confirming findings of previous studies and capturing new genes and genetic variants in these pathways.
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Affiliation(s)
- Leandro de Araújo Lima
- Inter-institutional Grad Program on Bioinformatics, University of São Paulo, São Paulo, SP, Brazil.,Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | | | - Sintia Iole Belangero
- National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, SP, Brazil.,Department of Psychiatry, Federal University of São Paulo, São Paulo, SP, Brazil
| | - Ary Gadelha
- National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, SP, Brazil.,Department of Psychiatry, Federal University of São Paulo, São Paulo, SP, Brazil
| | - Rodrigo Affonseca Bressan
- National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, SP, Brazil.,Department of Psychiatry, Federal University of São Paulo, São Paulo, SP, Brazil
| | - Giovanni Abrahão Salum
- National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, SP, Brazil.,Department of Psychiatry, Hospital de Clínicas de Porto Alegre, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Pedro Mario Pan
- National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, SP, Brazil.,Department of Psychiatry, Federal University of São Paulo, São Paulo, SP, Brazil
| | - Tais Silveira Moriyama
- Department &Institute of Psychiatry, University of São Paulo Medical School, São Paulo, SP, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, SP, Brazil.,Department of Psychiatry, Federal University of São Paulo, São Paulo, SP, Brazil
| | - Ana Soledade Graeff-Martins
- Department &Institute of Psychiatry, University of São Paulo Medical School, São Paulo, SP, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, SP, Brazil
| | - Ana Carina Tamanaha
- National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, SP, Brazil.,Department of Psychiatry, Federal University of São Paulo, São Paulo, SP, Brazil
| | - Pedro Alvarenga
- Department &Institute of Psychiatry, University of São Paulo Medical School, São Paulo, SP, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, SP, Brazil
| | - Fernanda Valle Krieger
- Department &Institute of Psychiatry, University of São Paulo Medical School, São Paulo, SP, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, SP, Brazil
| | - Bacy Fleitlich-Bilyk
- Department &Institute of Psychiatry, University of São Paulo Medical School, São Paulo, SP, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, SP, Brazil
| | - Andrea Parolin Jackowski
- National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, SP, Brazil.,Department of Psychiatry, Federal University of São Paulo, São Paulo, SP, Brazil
| | - Elisa Brietzke
- National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, SP, Brazil.,Department of Psychiatry, Federal University of São Paulo, São Paulo, SP, Brazil
| | - João Ricardo Sato
- National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, SP, Brazil.,Center of Mathematics, Computation and Cognition. Universidade Federal do ABC, Santo André, Brazil
| | - Guilherme Vanoni Polanczyk
- Department &Institute of Psychiatry, University of São Paulo Medical School, São Paulo, SP, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, SP, Brazil
| | - Jair de Jesus Mari
- National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, SP, Brazil.,Department of Psychiatry, Federal University of São Paulo, São Paulo, SP, Brazil
| | - Gisele Gus Manfro
- National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, SP, Brazil.,Department of Psychiatry, Hospital de Clínicas de Porto Alegre, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Maria Conceição do Rosário
- National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, SP, Brazil.,Department of Psychiatry, Federal University of São Paulo, São Paulo, SP, Brazil
| | - Eurípedes Constantino Miguel
- Department &Institute of Psychiatry, University of São Paulo Medical School, São Paulo, SP, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, SP, Brazil
| | - Renato David Puga
- Hospital Israelita Albert Einstein, Clinical Research, São Paulo, SP, Brazil
| | - Ana Carolina Tahira
- Department &Institute of Psychiatry, University of São Paulo Medical School, São Paulo, SP, Brazil
| | - Viviane Neri Souza
- Department &Institute of Psychiatry, University of São Paulo Medical School, São Paulo, SP, Brazil
| | - Thais Chile
- Department &Institute of Psychiatry, University of São Paulo Medical School, São Paulo, SP, Brazil
| | - Gisele Rodrigues Gouveia
- Department &Institute of Psychiatry, University of São Paulo Medical School, São Paulo, SP, Brazil
| | - Sérgio Nery Simões
- Inter-institutional Grad Program on Bioinformatics, University of São Paulo, São Paulo, SP, Brazil.,Federal Institute of Espírito Santo, Serra, ES, Brazil
| | - Xiao Chang
- Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Renata Pellegrino
- Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Lifeng Tian
- Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Joseph T Glessner
- Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Ronaldo Fumio Hashimoto
- Inter-institutional Grad Program on Bioinformatics, University of São Paulo, São Paulo, SP, Brazil.,Mathematics &Statistics Institute, University of São Paulo, São Paulo, SP, Brazil
| | - Luis Augusto Rohde
- Department &Institute of Psychiatry, University of São Paulo Medical School, São Paulo, SP, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, SP, Brazil.,Department of Psychiatry, Hospital de Clínicas de Porto Alegre, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Patrick M A Sleiman
- Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania Philadelphia, PA, USA
| | - Hakon Hakonarson
- Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania Philadelphia, PA, USA
| | - Helena Brentani
- Inter-institutional Grad Program on Bioinformatics, University of São Paulo, São Paulo, SP, Brazil.,Department &Institute of Psychiatry, University of São Paulo Medical School, São Paulo, SP, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents (INCT-CNPq), São Paulo, SP, Brazil
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11
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Eising E, Huisman SMH, Mahfouz A, Vijfhuizen LS, Anttila V, Winsvold BS, Kurth T, Ikram MA, Freilinger T, Kaprio J, Boomsma DI, van Duijn CM, Järvelin MRR, Zwart JA, Quaye L, Strachan DP, Kubisch C, Dichgans M, Davey Smith G, Stefansson K, Palotie A, Chasman DI, Ferrari MD, Terwindt GM, de Vries B, Nyholt DR, Lelieveldt BPF, van den Maagdenberg AMJM, Reinders MJT. Gene co-expression analysis identifies brain regions and cell types involved in migraine pathophysiology: a GWAS-based study using the Allen Human Brain Atlas. Hum Genet 2016; 135:425-439. [PMID: 26899160 PMCID: PMC4796339 DOI: 10.1007/s00439-016-1638-x] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2015] [Accepted: 01/16/2016] [Indexed: 01/03/2023]
Abstract
Migraine is a common disabling neurovascular brain disorder typically characterised by attacks of severe headache and associated with autonomic and neurological symptoms. Migraine is caused by an interplay of genetic and environmental factors. Genome-wide association studies (GWAS) have identified over a dozen genetic loci associated with migraine. Here, we integrated migraine GWAS data with high-resolution spatial gene expression data of normal adult brains from the Allen Human Brain Atlas to identify specific brain regions and molecular pathways that are possibly involved in migraine pathophysiology. To this end, we used two complementary methods. In GWAS data from 23,285 migraine cases and 95,425 controls, we first studied modules of co-expressed genes that were calculated based on human brain expression data for enrichment of genes that showed association with migraine. Enrichment of a migraine GWAS signal was found for five modules that suggest involvement in migraine pathophysiology of: (i) neurotransmission, protein catabolism and mitochondria in the cortex; (ii) transcription regulation in the cortex and cerebellum; and (iii) oligodendrocytes and mitochondria in subcortical areas. Second, we used the high-confidence genes from the migraine GWAS as a basis to construct local migraine-related co-expression gene networks. Signatures of all brain regions and pathways that were prominent in the first method also surfaced in the second method, thus providing support that these brain regions and pathways are indeed involved in migraine pathophysiology.
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Affiliation(s)
- Else Eising
- Department of Human Genetics, Leiden University Medical Center, 2333 ZC, Leiden, The Netherlands
| | - Sjoerd M H Huisman
- Delft Bioinformatics Lab, Department of Intelligent Systems, Delft University of Technology, 2628 CD, Delft, The Netherlands.,Division of Image Processing, Department of Radiology, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
| | - Ahmed Mahfouz
- Delft Bioinformatics Lab, Department of Intelligent Systems, Delft University of Technology, 2628 CD, Delft, The Netherlands.,Division of Image Processing, Department of Radiology, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
| | - Lisanne S Vijfhuizen
- Department of Human Genetics, Leiden University Medical Center, 2333 ZC, Leiden, The Netherlands
| | - Verneri Anttila
- Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA.,Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Bendik S Winsvold
- FORMI and Department of Neurology, Oslo University Hospital and University of Oslo, 0424, Oslo, Norway
| | - Tobias Kurth
- Institute of Public Health, Charité - Universitätsmedizin Berlin, 10117, Berlin, Germany.,Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02215-1204, USA
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus University Medical Centre, 3015 CE, Rotterdam, The Netherlands.,Department of Radiology, Erasmus University Medical Centre, 3015 CE, Rotterdam, The Netherlands.,Department of Neurology, Erasmus University Medical Centre, 3015 CE, Rotterdam, The Netherlands
| | - Tobias Freilinger
- Department of Neurology and Epileptology and Hertie-Institute for Clinical Brain Research, University of Tübingen, 72076, Tübingen, Germany.,Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximillians-Universität, 81377, Munich, Germany
| | - Jaakko Kaprio
- Department of Public Health, University of Helsinki, 00014, Helsinki, Finland.,Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00290, Helsinki, Finland
| | - Dorret I Boomsma
- Department of Biological Psychology, VU University, 1081 HV, Amsterdam, The Netherlands
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus University Medical Centre, 3015 CE, Rotterdam, The Netherlands
| | - Marjo-Riitta R Järvelin
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, W2 1PG, UK.,Center for Life-Course Health Research and Northern Finland Cohort Center, Faculty of Medicine, University of Oulu, P.O. Box 5000, 90014, Oulu, Finland.,Biocenter Oulu, University of Oulu, Aapistie 5A, P.O. Box 5000, 90014, Oulu, Finland.,Unit of Primary Care, Oulu University Hospital, Kajaanintie 50, 90029 OYS, P.O. Box 20, 90220, Oulu, Finland
| | - John-Anker Zwart
- FORMI and Department of Neurology, Oslo University Hospital and University of Oslo, 0424, Oslo, Norway
| | - Lydia Quaye
- Department of Twin Research and Genetic Epidemiology, King's College London, London, SE1 7EH, UK
| | - David P Strachan
- Population Health Research Institute, St George's, University of London, London, SW17 0RE, UK
| | - Christian Kubisch
- Institute of Human Genetics, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany
| | - Martin Dichgans
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximillians-Universität, 81377, Munich, Germany.,Munich Cluster for Systems Neurology (SyNergy), 81377, Munich, Germany
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit (IEU), School of Social and Community Medicine, University of Bristol, Bristol, BS8 2PS, UK
| | - Kari Stefansson
- deCODE Genetics, 101, Reykjavik, Iceland.,School of Medicine, University of Iceland, 101, Reykjavik, Iceland
| | - Aarno Palotie
- Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA.,Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.,Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00290, Helsinki, Finland
| | - Daniel I Chasman
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02215-1204, USA
| | - Michel D Ferrari
- Department of Neurology, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
| | - Gisela M Terwindt
- Department of Neurology, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
| | - Boukje de Vries
- Department of Human Genetics, Leiden University Medical Center, 2333 ZC, Leiden, The Netherlands
| | - Dale R Nyholt
- Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, QLD, Brisbane, QLD, 4059, Australia.,Queensland Institute of Medical Research (QIMR) Berghofer Medical Research Institute, Brisbane, QLD, 4006, Australia
| | - Boudewijn P F Lelieveldt
- Delft Bioinformatics Lab, Department of Intelligent Systems, Delft University of Technology, 2628 CD, Delft, The Netherlands.,Division of Image Processing, Department of Radiology, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands
| | - Arn M J M van den Maagdenberg
- Department of Human Genetics, Leiden University Medical Center, 2333 ZC, Leiden, The Netherlands. .,Department of Neurology, Leiden University Medical Center, 2333 ZA, Leiden, The Netherlands.
| | - Marcel J T Reinders
- Delft Bioinformatics Lab, Department of Intelligent Systems, Delft University of Technology, 2628 CD, Delft, The Netherlands.
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12
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Cutforth T, DeMille MM, Agalliu I, Agalliu D. CNS autoimmune disease after Streptococcus pyogenes infections: animal models, cellular mechanisms and genetic factors. FUTURE NEUROLOGY 2016; 11:63-76. [PMID: 27110222 DOI: 10.2217/fnl.16.4] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Streptococcus pyogenes infections have been associated with two autoimmune diseases of the CNS: Sydenham's chorea (SC) and Pediatric Autoimmune Neuropsychiatric Disorders Associated with Streptococcus infections (PANDAS). Despite the high frequency of pharyngeal streptococcus infections among children, only a small fraction develops SC or PANDAS. This suggests that several factors in combination are necessary to trigger autoimmune complications: specific S. pyogenes strains that induce a strong immune response toward the host nervous system; genetic susceptibility that predispose children toward an autoimmune response involving movement or tic symptoms; and multiple infections of the throat or tonsils that lead to a robust Th17 cellular and humoral immune response when untreated. In this review, we summarize the evidence for each factor and propose that all must be met for the requisite neurovascular pathology and behavioral deficits found in SC/PANDAS.
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Affiliation(s)
- Tyler Cutforth
- Department of Neurology, Columbia University Medical Center, 650 West 168 Street, Room 310E, New York, NY 10032, USA
| | - Mellissa Mc DeMille
- Department of Pediatrics, Yale Child Health Research Center, Yale University School of Medicine, 464 Congress Avenue, Suite S208, New Haven, CT 06519, USA
| | - Ilir Agalliu
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, 1300 Morris Park Ave., Room 1315-B, Bronx, NY 10461, USA
| | - Dritan Agalliu
- Department of Neurology, Columbia University Medical Center, 650 West 168 Street, Room 310E, New York, NY 10032, USA; Departments of Pathology & Cell Biology & Pharmacology, Columbia University Medical Center, 650 West 168 Street, Room 310E, New York, NY 10032, USA
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13
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Evidence for Association of Cell Adhesion Molecules Pathway and NLGN1 Polymorphisms with Schizophrenia in Chinese Han Population. PLoS One 2015; 10:e0144719. [PMID: 26674772 PMCID: PMC4682938 DOI: 10.1371/journal.pone.0144719] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2015] [Accepted: 11/23/2015] [Indexed: 01/22/2023] Open
Abstract
Multiple risk variants of schizophrenia have been identified by Genome-wide association studies (GWAS). As a complement for GWAS, previous pathway-based analysis has indicated that cell adhesion molecules (CAMs) pathway might be involved in the pathogenesis of schizophrenia. However, less replication studies have been reported. Our objective was to investigate the association between CAMs pathway and schizophrenia in the Chinese Han population. We first performed a pathway analysis utilizing our previous GWAS data. The CAMs pathway (hsa04514) was significantly associated with schizophrenia using hybrid gene set-based test (P = 1.03×10−10) and hypergeometric test (P = 5.04×10−6). Moreover, 12 genes (HLA-A, HLA-C, HLA-DOB, HLA-DPB1, HLA-DQA2, HLA-DRB1, MPZ, CD276, NLGN1, NRCAM, CLDN1 and ICAM3) were modestly significantly associated with schizophrenia (P<0.01). Then, we selected one promising gene neuroligin 1 (NLGN1) to further investigate the association between eight significant SNPs and schizophrenia in an independent sample (1814 schizophrenia cases and 1487 healthy controls). Our study showed that seven SNPs of NLGN1 and two haplotype blocks were significantly associated with schizophrenia. This association was confirmed by the results of combined analysis. Among them, SNP rs9835385 had the most significant association with schizophrenia (P = 2.83×10−7). Furthermore, in silico analysis we demonstrated that NLGN1 is preferentially expressed in human brain and SNP rs1488547 was related to the expression level. We validated the association of CAMs pathway with schizophrenia in pathway-level and identified one susceptibility gene NLGN1. Further investigation of the roles of CAMs pathway in the pathogenesis of schizophrenia is warranted.
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Association studies of genomic variants with treatment response to risperidone, clozapine, quetiapine and chlorpromazine in the Chinese Han population. THE PHARMACOGENOMICS JOURNAL 2015; 16:357-65. [PMID: 26282453 DOI: 10.1038/tpj.2015.61] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Received: 04/13/2015] [Revised: 06/08/2015] [Accepted: 07/14/2015] [Indexed: 01/01/2023]
Abstract
Schizophrenia is a widespread mental disease with a prevalence of about 1% in the world population. Continuous long-term treatment is required to maintain social functioning and prevent symptom relapse of schizophrenia patients. However, there are considerable individual differences in response to the antipsychotic drugs. There is a pressing need to identify more drug-response-related markers. But most pharmacogenomics of schizophrenia have typically focused on a few candidate genes in small sample size. In this study, 995 subjects were selected for discovering the drug-response-related markers. A total of 77 single-nucleotide polymorphisms of 25 genes have been investigated for four commonly used antipsychotic drugs in China: risperidone, clozapine, quetiapine, and chlorpromazine. Significant associations with treatment response for several genes, such as CYP2D6, CYP2C19, COMT, ABCB1, DRD3 and HTR2C have been verified in our study. Also, we found several new candidate genes (TNIK, RELN, NOTCH4 and SLC6A2) and combinations (haplotype rs1544325-rs5993883-rs6269-rs4818 in COMT) that are associated with treatment response to the four drugs. Also, multivariate interactions analysis demonstrated the combination of rs6269 in COMT and rs3813929 in HTR2C may work as a predictor to improve the clinical antipsychotic response. So our study is of great significance to improve current knowledge on the pharmacogenomics of schizophrenia, thus promoting the implementation of personalized medicine in schizophrenia.The Pharmacogenomics Journal advance online publication, 18 August 2015; doi:10.1038/tpj.2015.61.
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15
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Wang Y, Li D, Wei P. Powerful Tukey's One Degree-of-Freedom Test for Detecting Gene-Gene and Gene-Environment Interactions. Cancer Inform 2015; 14:209-18. [PMID: 26064040 PMCID: PMC4459566 DOI: 10.4137/cin.s17305] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2014] [Revised: 04/20/2015] [Accepted: 04/28/2015] [Indexed: 12/17/2022] Open
Abstract
Genome-wide association studies (GWASs) have identified thousands of single nucleotide polymorphisms (SNPs) robustly associated with hundreds of complex human diseases including cancers. However, the large number of GWAS-identified genetic loci only explains a small proportion of the disease heritability. This “missing heritability” problem has been partly attributed to the yet-to-be-identified gene–gene (G × G) and gene–environment (G × E) interactions. In spite of the important roles of G × G and G × E interactions in understanding disease mechanisms and filling in the missing heritability, straightforward GWAS scanning for such interactions has very limited statistical power, leading to few successes. Here we propose a two-step statistical approach to test G × G/G × E interactions: the first step is to perform principal component analysis (PCA) on the multiple SNPs within a gene region, and the second step is to perform Tukey’s one degree-of-freedom (1-df) test on the leading PCs. We derive a score test that is computationally fast and numerically stable for the proposed Tukey’s 1-df interaction test. Using extensive simulations we show that the proposed approach, which combines the two parsimonious models, namely, the PCA and Tukey’s 1-df form of interaction, outperforms other state-of-the-art methods. We also demonstrate the utility and efficiency gains of the proposed method with applications to testing G × G interactions for Crohn’s disease using the Wellcome Trust Case Control Consortium (WTCCC) GWAS data and testing G × E interaction using data from a case–control study of pancreatic cancer.
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Affiliation(s)
- Yaping Wang
- Department of Biostatistics, School of Public Health, University of Texas Health Science Center
| | - Donghui Li
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center
| | - Peng Wei
- Department of Biostatistics, School of Public Health, University of Texas Health Science Center ; Human Genetics Center, School of Public Health, University of Texas Health Science Center, Houston, TX, USA
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16
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Abstract
PURPOSE OF REVIEW Idiosyncratic drug-induced liver injury (iDILI) is a relatively rare condition, but can have serious consequences for the individual patient, public health, regulatory agencies and the pharmaceutical industry. Despite increased awareness of iDILI, its underlying mechanism is still not fully understood. This review summarizes the current understanding of the molecular mechanism behind iDILI. RECENT FINDINGS Genetic variations in drug metabolizing genes are in line with proposed mechanisms based on acetaminophen hepatotoxicity, whereby reactive metabolites covalently bind to cellular proteins and disturb the redox balance. In addition, immune-mediated effects have been reported for flucloxacillin hepatotoxicity, demonstrating both haptenization and direct binding between the drug and immune receptors. SUMMARY Idiosyncratic DILI development is believed to be orchestrated by multiple events, such as reactive metabolite formations, oxidative stress and signalling pathway inductions, with the mitochondria taking centre stage. Evidence also points towards the immune system (innate and adaptive responses) as important components in iDILI. Interindividual differences in one or more of these events, due to genetic variations and environmental factors, are likely to contribute to the idiosyncratic nature of this condition and subsequently distinguish between patient susceptibility and tolerance.
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17
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Talwar P, Silla Y, Grover S, Gupta M, Grewal GK, Kukreti R. Systems Pharmacology and Pharmacogenomics for Drug Discovery and Development. SYSTEMS AND SYNTHETIC BIOLOGY 2015. [DOI: 10.1007/978-94-017-9514-2_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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18
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Pharmacogenetic predictor of extrapyramidal symptoms induced by antipsychotics: multilocus interaction in the mTOR pathway. Eur Neuropsychopharmacol 2015; 25:51-9. [PMID: 25499605 DOI: 10.1016/j.euroneuro.2014.11.011] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2013] [Revised: 09/22/2014] [Accepted: 11/20/2014] [Indexed: 12/31/2022]
Abstract
Antipsychotic (AP) treatment-emergent extrapyramidal symptoms (EPS) are acute adverse reactions of APs. The aim of the present study is to analyze gene-gene interactions in nine genes related to the mTOR pathway, in order to develop genetic predictors of the appearance of EPS. 243 subjects (78 presenting EPS: 165 not) from three cohorts participated in the present study: Cohort 1, patients treated with risperidone, (n=114); Cohort 2, patients treated with APs other than risperidone (n=102); Cohort 3, AP-naïve patients with first-episode psychosis treated with risperidone, paliperidone or amisulpride, n=27. We analyzed gene-gene interactions by multifactor dimensionality reduction assay (MDR). In Cohort 1, we identified a four-way interaction, including the rs1130214 (AKT1), rs456998 (FCHSD1), rs7211818 (Raptor) and rs1053639 (DDIT4), that correctly predicted 97 of the 114 patients (85% accuracy). We validated the predictive power of the four-way interaction in Cohort 2 and in Cohort 3 with 86% and 88% accuracy respectively. We develop and validate a powerful pharmacogenetic predictor of AP-induced EPS. For the first time, the mTOR pathway has been related to EPS susceptibility and AP response. However, validation in larger and independent populations will be necessary for optimal generalization.
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19
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He H, Zhang L, Li J, Wang YP, Zhang JG, Shen J, Guo YF, Deng HW. Integrative analysis of GWASs, human protein interaction, and gene expression identified gene modules associated with BMDs. J Clin Endocrinol Metab 2014; 99:E2392-9. [PMID: 25119315 PMCID: PMC4223444 DOI: 10.1210/jc.2014-2563] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
CONTEXT To date, few systems genetics studies in the bone field have been performed. We designed our study from a systems-level perspective by integrating genome-wide association studies (GWASs), human protein-protein interaction (PPI) network, and gene expression to identify gene modules contributing to osteoporosis risk. METHODS First we searched for modules significantly enriched with bone mineral density (BMD)-associated genes in human PPI network by using 2 large meta-analysis GWAS datasets through a dense module search algorithm. One included 7 individual GWAS samples (Meta7). The other was from the Genetic Factors for Osteoporosis Consortium (GEFOS2). One was assigned as a discovery dataset and the other as an evaluation dataset, and vice versa. RESULTS In total, 42 modules and 129 modules were identified significantly in both Meta7 and GEFOS2 datasets for femoral neck and spine BMD, respectively. There were 3340 modules identified for hip BMD only in Meta7. As candidate modules, they were assessed for the biological relevance to BMD by gene set enrichment analysis in 2 expression profiles generated from circulating monocytes in subjects with low versus high BMD values. Interestingly, there were 2 modules significantly enriched in monocytes from the low BMD group in both gene expression datasets (nominal P value <.05). Two modules had 16 nonredundant genes. Functional enrichment analysis revealed that both modules were enriched for genes involved in Wnt receptor signaling and osteoblast differentiation. CONCLUSION We highlighted 2 modules and novel genes playing important roles in the regulation of bone mass, providing important clues for therapeutic approaches for osteoporosis.
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Affiliation(s)
- Hao He
- Center of Genomics and Bioinformatics and Department of Biostatistics and Bioinformatics (H.H., L.Z., J.L., Y.-P.W., J.-G.Z., H.-W.D.), Tulane University, New Orleans, Louisiana 70112; Biomedical Engineering Department (Y.-P.W.), Tulane University, New Orleans, Louisiana 70118; and Third Affiliated Hospital (J.S., Y.-F.G., H.-W.D.), China Southern Medical University, Guang Zhou 510000, People's Republic of China
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20
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Levovitz C, Chen D, Ivansson E, Gyllensten U, Finnigan JP, Alshawish S, Zhang W, Schadt EE, Posner MR, Genden EM, Boffetta P, Sikora AG. TGFβ receptor 1: an immune susceptibility gene in HPV-associated cancer. Cancer Res 2014; 74:6833-44. [PMID: 25273091 DOI: 10.1158/0008-5472.can-14-0602-t] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Only a minority of those exposed to human papillomavirus (HPV) develop HPV-related cervical and oropharyngeal cancer. Because host immunity affects infection and progression to cancer, we tested the hypothesis that genetic variation in immune-related genes is a determinant of susceptibility to oropharyngeal cancer and other HPV-associated cancers by performing a multitier integrative computational analysis with oropharyngeal cancer data from a head and neck cancer genome-wide association study (GWAS). Independent analyses, including single-gene, gene-interconnectivity, protein-protein interaction, gene expression, and pathway analysis, identified immune genes and pathways significantly associated with oropharyngeal cancer. TGFβR1, which intersected all tiers of analysis and thus selected for validation, replicated significantly in the head and neck cancer GWAS limited to HPV-seropositive cases and an independent cervical cancer GWAS. The TGFβR1 containing p38-MAPK pathway was significantly associated with oropharyngeal cancer and cervical cancer, and TGFβR1 was overexpressed in oropharyngeal cancer, cervical cancer, and HPV(+) head and neck cancer tumors. These concordant analyses implicate TGFβR1 signaling as a process dysregulated across HPV-related cancers. This study demonstrates that genetic variation in immune-related genes is associated with susceptibility to oropharyngeal cancer and implicates TGFβR1/TGFβ signaling in the development of both oropharyngeal cancer and cervical cancer. Better understanding of the immunogenetic basis of susceptibility to HPV-associated cancers may provide insight into host/virus interactions and immune processes dysregulated in the minority of HPV-exposed individuals who progress to cancer.
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Affiliation(s)
- Chaya Levovitz
- The Icahn School of Medicine at Mount Sinai, New York, New York. Department of Immunology, Icahn School of Medicine at Mount Sinai, New York, New York. Institute for Translational Epidemiology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Dan Chen
- SciLifeLab Uppsala, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden
| | - Emma Ivansson
- SciLifeLab Uppsala, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden
| | - Ulf Gyllensten
- SciLifeLab Uppsala, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden
| | - John P Finnigan
- The Icahn School of Medicine at Mount Sinai, New York, New York
| | - Sara Alshawish
- The Icahn School of Medicine at Mount Sinai, New York, New York
| | - Weijia Zhang
- Mount Sinai Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Eric E Schadt
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, New York. Mount Sinai Institute for Genomics and Multiscale Biology, New York, New York
| | - Marshal R Posner
- The Icahn School of Medicine at Mount Sinai, New York, New York. Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Eric M Genden
- The Icahn School of Medicine at Mount Sinai, New York, New York. Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, New York. Department of Immunology, Genetics and Pathology, Tisch Cancer Institute, New York, New York
| | - Paolo Boffetta
- The Icahn School of Medicine at Mount Sinai, New York, New York. Institute for Translational Epidemiology, Icahn School of Medicine at Mount Sinai, New York, New York. Department of Immunology, Genetics and Pathology, Tisch Cancer Institute, New York, New York
| | - Andrew G Sikora
- The Icahn School of Medicine at Mount Sinai, New York, New York. Department of Immunology, Icahn School of Medicine at Mount Sinai, New York, New York. Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, New York. Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, New York. Department of Immunology, Genetics and Pathology, Tisch Cancer Institute, New York, New York.
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McDonald MLN, Mattheisen M, Cho MH, Liu YY, Harshfield B, Hersh CP, Bakke P, Gulsvik A, Lange C, Beaty TH, Silverman EK. Beyond GWAS in COPD: probing the landscape between gene-set associations, genome-wide associations and protein-protein interaction networks. Hum Hered 2014; 78:131-9. [PMID: 25171373 PMCID: PMC4415367 DOI: 10.1159/000365589] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2014] [Accepted: 07/01/2014] [Indexed: 11/19/2022] Open
Abstract
OBJECTIVES To use a systems biology approach to integrate genotype and protein-protein interaction (PPI) data to identify disease network modules associated with chronic obstructive pulmonary disease (COPD) and to perform traditional pathway analysis. METHODS We utilized a standard gene-set association approach (FORGE) using gene-based association analysis and gene-set definitions from the molecular signatures database (MSigDB). As a discovery step, we analyzed GWAS results from 2 well-characterized COPD cohorts: COPDGene and GenKOLS. We used a third well-characterized COPD case-control cohort for replication: ECLIPSE. Next, we used dmGWAS, a method that integrates GWAS results with PPI, to identify COPD disease modules. RESULTS No gene-sets reached experiment-wide significance in either discovery population. We identified a consensus network of 10 genes identified in modules by integrating GWAS results with PPI that replicated in COPDGene, GenKOLS, and ECLIPSE. Members of 4 gene-sets were enriched among these 10 genes: (i) lung adenocarcinoma tumor-sequencing genes, (ii) IL-7 pathway genes, (iii) kidney cell response to arsenic, and (iv) CD4 T-cell responses. Further, several genes have also been associated with pathophysiology relevant to COPD including KCNK3, NEDD4L, and RIN3. In particular, KCNK3 has been associated with pulmonary arterial hypertension, a common complication in advanced COPD. CONCLUSION We report a set of new genes that may influence the etiology of COPD that would not have been identified using traditional GWAS and pathway analyses alone.
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Affiliation(s)
- Merry-Lynn Noelle McDonald
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Manuel Mattheisen
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biomedicine and Centre for integrative Sequencing (iSEQ), Aarhus University, Aarhus, Denmark
| | - Michael H. Cho
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Yang-Yu Liu
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Benjamin Harshfield
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Craig P. Hersh
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Per Bakke
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Amund Gulsvik
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | | | - Terri H. Beaty
- Johns Hopkins School of Public Health, Baltimore, MD, USA
| | - Edwin K. Silverman
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
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Genetics of the human metabolome, what is next? Biochim Biophys Acta Mol Basis Dis 2014; 1842:1923-1931. [PMID: 24905732 DOI: 10.1016/j.bbadis.2014.05.030] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2013] [Revised: 05/04/2014] [Accepted: 05/28/2014] [Indexed: 11/23/2022]
Abstract
Increases in throughput and decreases in costs have facilitated large scale metabolomics studies, the simultaneous measurement of large numbers of biochemical components in biological samples. Initial large scale studies focused on biomarker discovery for disease or disease progression and helped to understand biochemical pathways underlying disease. The first population-based studies that combined metabolomics and genome wide association studies (mGWAS) have increased our understanding of the (genetic) regulation of biochemical conversions. Measurements of metabolites as intermediate phenotypes are a potentially very powerful approach to uncover how genetic variation affects disease susceptibility and progression. However, we still face many hurdles in the interpretation of mGWAS data. Due to the composite nature of many metabolites, single enzymes may affect the levels of multiple metabolites and, conversely, levels of single metabolites may be affected by multiple enzymes. Here, we will provide a global review of the current status of mGWAS. We will specifically discuss the application of prior biological knowledge present in databases to the interpretation of mGWAS results and discuss the potential of mathematical models. As the technology continuously improves to detect metabolites and to measure genetic variation, it is clear that comprehensive systems biology based approaches are required to further our insight in the association between genes, metabolites and disease. This article is part of a Special Issue entitled: From Genome to Function.
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A hypothesis-driven pathway analysis reveals myelin-related pathways that contribute to the risk of schizophrenia and bipolar disorder. Prog Neuropsychopharmacol Biol Psychiatry 2014; 51:140-5. [PMID: 24447946 DOI: 10.1016/j.pnpbp.2014.01.006] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2013] [Revised: 01/03/2014] [Accepted: 01/06/2014] [Indexed: 11/23/2022]
Abstract
Schizophrenia (SZ) and bipolar disorder (BD) are both severe neuropsychiatric disorders with a strong and potential overlapping genetic background. Multiple lines of evidence, including genetic studies, gene expression studies and neuroimaging studies, have suggested that both disorders are closely related to myelin and oligodendrocyte dysfunctions. In the current study, we hypothesized that the holistic effect of the myelin-related pathway contributes to the genetic susceptibility to both SZ and BD. We extracted pathway data from the canonical pathway database, Gene Ontology (GO), and selected a 'compiled' pathway based on previous literature. We then performed hypothesis-driven pathway analysis on GWAS data from the Psychiatric Genomics Consortium (PGC). As a result, we identified three myelin-related pathways with a joint effect significantly associated with both disorders: 'Myelin sheath' pathway (P(SZ) = 2.45E-7, P(BD) = 1.22E-3), 'Myelination' pathway (P(SZ) = 2.10E-4, P(BD) = 2.53E-24), and 'Compiled' pathway (P(SZ) = 4.57E-8, P(BD) = 2.61E-9). In comparing the SNPs and genes in these three pathways across the two diseases, we identified a substantial overlap in nominally associated SNPs and genes, which could be susceptibility SNPs and genes for both disorders. From these observations, we propose that myelin-related pathways may be involved in the etiologies of both SZ and BD.
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Stevens A, Meyer S, Hanson D, Clayton P, Donn RP. Network analysis identifies protein clusters of functional importance in juvenile idiopathic arthritis. Arthritis Res Ther 2014; 16:R109. [PMID: 24886659 PMCID: PMC4062926 DOI: 10.1186/ar4559] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2014] [Accepted: 04/29/2014] [Indexed: 02/06/2023] Open
Abstract
Introduction Our objective was to utilise network analysis to identify protein clusters of greatest potential functional relevance in the pathogenesis of oligoarticular and rheumatoid factor negative (RF-ve) polyarticular juvenile idiopathic arthritis (JIA). Methods JIA genetic association data were used to build an interactome network model in BioGRID 3.2.99. The top 10% of this protein:protein JIA Interactome was used to generate a minimal essential network (MEN). Reactome FI Cytoscape 2.83 Plugin and the Disease Association Protein-Protein Link Evaluator (Dapple) algorithm were used to assess the functionality of the biological pathways within the MEN and to statistically rank the proteins. JIA gene expression data were integrated with the MEN and clusters of functionally important proteins derived using MCODE. Results A JIA interactome of 2,479 proteins was built from 348 JIA associated genes. The MEN, representing the most functionally related components of the network, comprised of seven clusters, with distinct functional characteristics. Four gene expression datasets from peripheral blood mononuclear cells (PBMC), neutrophils and synovial fluid monocytes, were mapped onto the MEN and a list of genes enriched for functional significance identified. This analysis revealed the genes of greatest potential functional importance to be PTPN2 and STAT1 for oligoarticular JIA and KSR1 for RF-ve polyarticular JIA. Clusters of 23 and 14 related proteins were derived for oligoarticular and RF-ve polyarticular JIA respectively. Conclusions This first report of the application of network biology to JIA, integrating genetic association findings and gene expression data, has prioritised protein clusters for functional validation and identified new pathways for targeted pharmacological intervention.
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Tragante V, Moore JH, Asselbergs FW. The ENCODE project and perspectives on pathways. Genet Epidemiol 2014; 38:275-80. [PMID: 24723339 DOI: 10.1002/gepi.21802] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2013] [Revised: 01/22/2014] [Accepted: 03/04/2014] [Indexed: 12/22/2022]
Abstract
The recently completed ENCODE project is a new source of information on metabolic activity, unveiling knowledge about evolution and similarities among species, refuting the myth that most DNA is "junk" and has no actual function. With this expansive resource comes a challenge: integrating these new layers of information into our current knowledge of single-nucleotide polymorphisms and previously described metabolic pathways with the aim of discovering new genes and pathways related to human diseases and traits. Further, we must determine which computational methods will be most useful in this pursuit. In this paper, we speculate over the possible methods that will emerge in this new, challenging field.
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Affiliation(s)
- Vinicius Tragante
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, GA Utrecht, The Netherlands; Department of Medical Genetics, Biomedical Genetics, University Medical Center Utrecht, CX Utrecht, The Netherlands
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FMRI and fcMRI phenotypes map the genomic effect of chromosome 13 in Brown Norway and Dahl salt-sensitive rats. Neuroimage 2014; 90:403-12. [DOI: 10.1016/j.neuroimage.2013.09.049] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2013] [Revised: 09/16/2013] [Accepted: 09/19/2013] [Indexed: 01/13/2023] Open
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Ma L, Ballantyne C, Brautbar A, Keinan A. Analysis of multiple association studies provides evidence of an expression QTL hub in gene-gene interaction network affecting HDL cholesterol levels. PLoS One 2014; 9:e92469. [PMID: 24651390 PMCID: PMC3961362 DOI: 10.1371/journal.pone.0092469] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2014] [Accepted: 02/21/2014] [Indexed: 11/18/2022] Open
Abstract
Epistasis has been suggested to underlie part of the missing heritability in genome-wide association studies. In this study, we first report an analysis of gene-gene interactions affecting HDL cholesterol (HDL-C) levels in a candidate gene study of 2,091 individuals with mixed dyslipidemia from a clinical trial. Two additional studies, the Atherosclerosis Risk in Communities study (ARIC; n = 9,713) and the Multi-Ethnic Study of Atherosclerosis (MESA; n = 2,685), were considered for replication. We identified a gene-gene interaction between rs1532085 and rs12980554 (P = 7.1×10−7) in their effect on HDL-C levels, which is significant after Bonferroni correction (Pc = 0.017) for the number of SNP pairs tested. The interaction successfully replicated in the ARIC study (P = 7.0×10−4; Pc = 0.02). Rs1532085, an expression QTL (eQTL) of LIPC, is one of the two SNPs involved in another, well-replicated gene-gene interaction underlying HDL-C levels. To further investigate the role of this eQTL SNP in gene-gene interactions affecting HDL-C, we tested in the ARIC study for interaction between this SNP and any other SNP genome-wide. We found the eQTL to be involved in a few suggestive interactions, one of which significantly replicated in MESA. Importantly, these gene-gene interactions, involving only rs1532085, explain an additional 1.4% variation of HDL-C, on top of the 0.65% explained by rs1532085 alone. LIPC plays a key role in the lipid metabolism pathway and it, and rs1532085 in particular, has been associated with HDL-C and other lipid levels. Collectively, we discovered several novel gene-gene interactions, all involving an eQTL of LIPC, thus suggesting a hub role of LIPC in the gene-gene interaction network that regulates HDL-C levels, which in turn raises the hypothesis that LIPC's contribution is largely via interactions with other lipid metabolism related genes.
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Affiliation(s)
- Li Ma
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, United States of America
| | - Christie Ballantyne
- Section of Cardiovascular Research, Department of Medicine, Baylor College of Medicine, Houston, Texas, United States of America
| | - Ariel Brautbar
- Section of Cardiovascular Research, Department of Medicine, Baylor College of Medicine, Houston, Texas, United States of America
- Department of Medical Genetics, Marshfield Clinic, Marshfield, Wisconsin, United States of America
- * E-mail: (AK); (AB)
| | - Alon Keinan
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, United States of America
- * E-mail: (AK); (AB)
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Yu H, Bi W, Liu C, Zhao Y, Zhang JF, Zhang D, Yue W. Protein-interaction-network-based analysis for genome-wide association analysis of schizophrenia in Han Chinese population. J Psychiatr Res 2014; 50:73-8. [PMID: 24365204 DOI: 10.1016/j.jpsychires.2013.11.014] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2013] [Revised: 11/10/2013] [Accepted: 11/27/2013] [Indexed: 01/26/2023]
Abstract
Schizophrenia is a severe neuropsychiatric disorder with a strong and complex genetic background. Recent genome-wide association studies (GWAS) have successfully identified several susceptibility loci of schizophrenia. In order to interpret the functional role of the genetic variants and detect the combined effects of some of these genes on schizophrenia, protein-interaction-network-based analysis (PINBA) has emerged as an effective approach. In the current study, we conducted a PINBA of our previous GWAS data taken from the Han Chinese population. In order to do so, we used dense module search (DMS), a method that locates densely connected modules for complex diseases by integrating the association signal from GWAS datasets into the human protein-protein interaction (PPI) network. As a result, we identified one gene set with a joint effect significantly associated with schizophrenia and gene expression profiling analysis suggested that they were mainly neuro- and immune-related genes, such as glutamatergic gene (GRM5), GABAergic genes (GABRB1, GABARAP) and genes located in the MHC region (HLA-C, TAP2, HIST1H1B). Further pathway enrichment analysis suggested that these genes are involved in processes related to neuronal and immune systems, such as the Adherens junction pathway, the Neurotrophin signaling pathway and the Toll-like receptor signaling pathway. In our study, we identified a set of susceptibility genes that had been missed in single-marker GWAS, and our findings could promote the study of the genetic mechanisms in schizophrenia.
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Affiliation(s)
- Hao Yu
- Institute of Mental Health, Peking University, 51 Hua Yuan Bei Road, Beijing 100191, China; Key Laboratory of Mental Health, Ministry of Health, Institute of Mental Health, The Sixth Hospital, Peking University, China
| | - Wenjian Bi
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
| | - Chenxing Liu
- Institute of Mental Health, Peking University, 51 Hua Yuan Bei Road, Beijing 100191, China; Key Laboratory of Mental Health, Ministry of Health, Institute of Mental Health, The Sixth Hospital, Peking University, China
| | - Yanlong Zhao
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
| | - Ji-Feng Zhang
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
| | - Dai Zhang
- Institute of Mental Health, Peking University, 51 Hua Yuan Bei Road, Beijing 100191, China; Key Laboratory of Mental Health, Ministry of Health, Institute of Mental Health, The Sixth Hospital, Peking University, China; Peking-Tsinghua Center for Life Sciences, Beijing, PR China; PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China.
| | - Weihua Yue
- Institute of Mental Health, Peking University, 51 Hua Yuan Bei Road, Beijing 100191, China; Key Laboratory of Mental Health, Ministry of Health, Institute of Mental Health, The Sixth Hospital, Peking University, China.
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Jia P, Zhao Z. Network.assisted analysis to prioritize GWAS results: principles, methods and perspectives. Hum Genet 2014; 133:125-38. [PMID: 24122152 PMCID: PMC3943795 DOI: 10.1007/s00439-013-1377-1] [Citation(s) in RCA: 73] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2012] [Accepted: 10/03/2013] [Indexed: 01/24/2023]
Abstract
Genome-wide association studies (GWAS) have rapidly become a powerful tool in genetic studies of complex diseases and traits. Traditionally, single marker-based tests have been used prevalently in GWAS and have uncovered tens of thousands of disease-associated SNPs. Network-assisted analysis (NAA) of GWAS data is an emerging area in which network-related approaches are developed and utilized to perform advanced analyses of GWAS data in order to study various human diseases or traits. Progress has been made in both methodology development and applications of NAA in GWAS data, and it has already been demonstrated that NAA results may enhance our interpretation and prioritization of candidate genes and markers. Inspired by the strong interest in and high demand for advanced GWAS data analysis, in this review article, we discuss the methodologies and strategies that have been reported for the NAA of GWAS data. Many NAA approaches search for subnetworks and assess the combined effects of multiple genes participating in the resultant subnetworks through a gene set analysis. With no restriction to pre-defined canonical pathways, NAA has the advantage of defining subnetworks with the guidance of the GWAS data under investigation. In addition, some NAA methods prioritize genes from GWAS data based on their interconnections in the reference network. Here, we summarize NAA applications to various diseases and discuss the available options and potential caveats related to their practical usage. Additionally, we provide perspectives regarding this rapidly growing research area.
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Lu TP, Chuang EY, Chen JJ. Identification of reproducible gene expression signatures in lung adenocarcinoma. BMC Bioinformatics 2013; 14:371. [PMID: 24369726 PMCID: PMC3877965 DOI: 10.1186/1471-2105-14-371] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2013] [Accepted: 12/20/2013] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Lung cancer is the leading cause of cancer-related death worldwide. Tremendous research efforts have been devoted to improving treatment procedures, but the average five-year overall survival rates are still less than 20%. Many biomarkers have been identified for predicting survival; challenges arise, however, in translating the findings into clinical practice due to their inconsistency and irreproducibility. In this study, we proposed an approach by identifying predictive genes through pathways. RESULTS The microarrays from Shedden et al. were used as the training set, and the log-rank test was performed to select potential signature genes. We focused on 24 cancer-related pathways from 4 biological databases. A scoring scheme was developed by the Cox hazard regression model, and patients were divided into two groups based on the medians. Subsequently, their predictability and generalizability were evaluated by the 2-fold cross-validation and a resampling test in 4 independent datasets, respectively. A set of 16 genes related to apoptosis execution was demonstrated to have good predictability as well as generalizability in more than 700 lung adenocarcinoma patients and was reproducible in 4 independent datasets. This signature set was shown to have superior performances compared to 6 other published signatures. Furthermore, the corresponding risk scores derived from the set were found to associate with the efficacy of the anti-cancer drug ZD-6474 targeting EGFR. CONCLUSIONS In summary, we presented a new approach to identify reproducible survival predictors for lung adenocarcinoma, and the identified genes may serve as both prognostic and predictive biomarkers in the future.
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Affiliation(s)
| | | | - James J Chen
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, Food and Drug Administration Jefferson, Little Rock, Arkansas, USA.
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Verschuren JJW, Trompet S, Sampietro ML, Heijmans BT, Koch W, Kastrati A, Houwing-Duistermaat JJ, Slagboom PE, Quax PHA, Jukema JW. Pathway analysis using genome-wide association study data for coronary restenosis--a potential role for the PARVB gene. PLoS One 2013; 8:e70676. [PMID: 23950981 PMCID: PMC3739784 DOI: 10.1371/journal.pone.0070676] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2013] [Accepted: 06/21/2013] [Indexed: 12/20/2022] Open
Abstract
Background Coronary restenosis after percutaneous coronary intervention (PCI) still remains a significant limitation of the procedure. The causative mechanisms of restenosis have not yet been fully identified. The goal of the current study was to perform gene-set analysis of biological pathways related to inflammation, proliferation, vascular function and transcriptional regulation on coronary restenosis to identify novel genes and pathways related to this condition. Methods The GENetic DEterminants of Restenosis (GENDER) databank contains genotypic data of 556,099SNPs of 295 cases with restenosis and 571 matched controls. Fifty-four pathways, related to known restenosis-related processes, were selected. Gene-set analysis was performed using PLINK, GRASS and ALIGATOR software. Pathways with a p<0.01 were fine-mapped and significantly associated SNPs were analyzed in an independent replication cohort. Results Six pathways (cell-extracellular matrix (ECM) interactions pathway, IL2 signaling pathway, IL6 signaling pathway, platelet derived growth factor pathway, vitamin D receptor pathway and the mitochondria pathway) were significantly associated in one or two of the software packages. Two SNPs in the cell-ECM interactions pathway were replicated in an independent restenosis cohort. No replication was obtained for the other pathways. Conclusion With these results we demonstrate a potential role of the cell-ECM interactions pathway in the development of coronary restenosis. These findings contribute to the increasing knowledge of the genetic etiology of restenosis formation and could serve as a hypothesis-generating effort for further functional studies.
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Affiliation(s)
| | - Stella Trompet
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
| | - M. Lourdes Sampietro
- Department Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Interuniversity Cardiology Institute of the Netherlands (ICIN), Utrecht, The Netherlands
| | - Bastiaan T. Heijmans
- Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Netherlands Consortium for Healthy Ageing, Leiden University Medical Center, Leiden, The Netherlands
| | - Werner Koch
- Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
| | - Adnan Kastrati
- Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
| | | | - P. Eline Slagboom
- Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Netherlands Consortium for Healthy Ageing, Leiden University Medical Center, Leiden, The Netherlands
| | - Paul H. A. Quax
- Department of Vascular Surgery, Leiden University Medical Center, Leiden, The Netherlands
| | - J. Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
- Netherlands Consortium for Healthy Ageing, Leiden University Medical Center, Leiden, The Netherlands
- Durrer Center for Cardiogenetic Research, Amsterdam, The Netherlands
- Interuniversity Cardiology Institute of the Netherlands (ICIN), Utrecht, The Netherlands
- * E-mail:
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Wu D, Lee YCG, Liu HC, Yuan RY, Chiou HY, Hung CH, Hu CJ. Identification of TLR downstream pathways in stroke patients. Clin Biochem 2013; 46:1058-1064. [DOI: 10.1016/j.clinbiochem.2013.05.059] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2013] [Accepted: 05/14/2013] [Indexed: 12/21/2022]
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Olivier JDA, Vinkers CH, Olivier B. The role of the serotonergic and GABA system in translational approaches in drug discovery for anxiety disorders. Front Pharmacol 2013; 4:74. [PMID: 23781201 PMCID: PMC3677985 DOI: 10.3389/fphar.2013.00074] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2013] [Accepted: 05/21/2013] [Indexed: 12/13/2022] Open
Abstract
There is ample evidence that genetic factors play an important role in anxiety disorders. In support, human genome-wide association studies have implicated several novel candidate genes. However, illumination of such genetic factors involved in anxiety disorders has not resulted in novel drugs over the past decades. A complicating factor is the heterogeneous classification of anxiety disorders in the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) and diverging operationalization of anxiety used in preclinical and clinical studies. Currently, there is an increasing focus on the gene × environment (G × E) interaction in anxiety as genes do not operate in isolation and environmental factors have been found to significantly contribute to the development of anxiety disorders in at-risk individuals. Nevertheless, extensive research on G × E mechanisms in anxiety has not resulted in major breakthroughs in drug discovery. Modification of individual genes in rodent models has enabled the specific study of anxiety in preclinical studies. In this context, two extensively studied neurotransmitters involved in anxiety are the gamma-aminobutyric acid (GABA) and 5-HT (5-hydroxytryptamine) system. In this review, we illustrate the complex interplay between genes and environment in anxiety processes by reviewing preclinical and clinical studies on the serotonin transporter (5-HTT), 5-HT1A receptor, 5-HT2 receptor, and GABAA receptor. Even though targets from the serotonin and GABA system have yielded drugs with known anxiolytic efficacy, the relation between the genetic background of these targets and anxiety symptoms and development of anxiety disorders is largely unknown. The aim of this review is to show the vast complexity of genetic and environmental factors in anxiety disorders. In light of the difficulty with which common genetic variants are identified in anxiety disorders, animal models with translational validity may aid in elucidating the neurobiological background of these genes and their possible role in anxiety. We argue that, in addition to human genetic studies, translational models are essential to map anxiety-related genes and to enhance our understanding of anxiety disorders in order to develop potentially novel treatment strategies.
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Affiliation(s)
- Jocelien D A Olivier
- Department of, Women's and Children's Health, Uppsala University Uppsala, Sweden ; Center for Gender Medicine, Karolinska Institutet Stockholm, Sweden
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Abstract
Determining the genetic architecture of liability for complex neuropsychiatric disorders like autism spectrum disorders and schizophrenia poses a tremendous challenge for contemporary biomedical research. Here we discuss how genetic studies first tested, and rejected, the hypothesis that common variants with large effects account for the prevalence of these disorders. We then explore how the discovery of structural variation has contributed to our understanding of the etiology of these disorders. The rise of fast and inexpensive oligonucleotide sequencing and methods of targeted enrichment and their influence on the search for rare genetic variation contributing to complex neuropsychiatric disorders is the next focus of our article. Finally, we consider the technical challenges and future prospects for the use of next-generation sequencing to reveal the complex genetic architecture of complex neuropsychiatric disorders in both research and the clinical settings.
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New Directions in Networks and Systems Approaches to Cardiovascular Disease. CURRENT GENETIC MEDICINE REPORTS 2013. [DOI: 10.1007/s40142-012-0005-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Affiliation(s)
- Andreas Ziegler
- Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig–Holstein, Campus Lübeck, Maria-Goeppert-Str. 1, 23562 Lübeck, Germany
| | - Yan V. Sun
- Department of Epidemiology, Department of Biomedical Informatics, Emory University, Atlanta, GA, USA
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