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Salim C, Batsaikhan E, Kan AK, Chen H, Jee C. Nicotine Motivated Behavior in C. elegans. Int J Mol Sci 2024; 25:1634. [PMID: 38338915 PMCID: PMC10855306 DOI: 10.3390/ijms25031634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 01/20/2024] [Accepted: 01/22/2024] [Indexed: 02/12/2024] Open
Abstract
To maximize the advantages offered by Caenorhabditis elegans as a high-throughput (HTP) model for nicotine dependence studies, utilizing its well-defined neuroconnectome as a robust platform, and to unravel the genetic basis of nicotine-motivated behaviors, we established the nicotine conditioned cue preference (CCP) paradigm. Nicotine CCP enables the assessment of nicotine preference and seeking, revealing a parallel to fundamental aspects of nicotine-dependent behaviors observed in mammals. We demonstrated that nicotine-elicited cue preference in worms is mediated by nicotinic acetylcholine receptors and requires dopamine for CCP development. Subsequently, we pinpointed nAChR subunits associated with nicotine preference and validated human GWAS candidates linked to nicotine dependence involved in nAChRs. Functional validation involves assessing the loss-of-function strain of the CACNA2D3 ortholog and the knock-out (KO) strain of the CACNA2D2 ortholog, closely related to CACNA2D3 and sharing human smoking phenotypes. Our orthogonal approach substantiates the functional conservation of the α2δ subunit of the calcium channel in nicotine-motivated behavior. Nicotine CCP in C. elegans serves as a potent affirmation of the cross-species functional relevance of GWAS candidate genes involved in nicotine seeking associated with tobacco abuse, providing a streamlined yet comprehensive system for investigating intricate behavioral paradigms within a simplified and reliable framework.
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Affiliation(s)
| | | | | | | | - Changhoon Jee
- Department of Pharmacology, Addiction Science and Toxicology, College of Medicine, University of Tennessee Health Science Center, Memphis, TN 38163, USA; (C.S.)
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2
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Hessenberger M, Haddad S, Obermair GJ. Pathophysiological Roles of Auxiliary Calcium Channel α 2δ Subunits. Handb Exp Pharmacol 2023; 279:289-316. [PMID: 36598609 DOI: 10.1007/164_2022_630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
α2δ proteins serve as auxiliary subunits of voltage-gated calcium channels, which are essential components of excitable cells such as skeletal and heart muscles, nerve cells of the brain and the peripheral nervous system, as well as endocrine cells. Over the recent years, α2δ proteins have been identified as critical regulators of synaptic functions, including the formation and differentiation of synapses. These functions require signalling mechanisms which are partly independent of calcium channels. Hence, in light of these features it is not surprising that the genes encoding for the four α2δ isoforms have recently been linked to neurological and neurodevelopmental disorders including epilepsy, autism spectrum disorders, schizophrenia, and depressive and bipolar disorders. Despite the increasing number of identified disease-associated mutations, the underlying pathophysiological mechanisms are only beginning to emerge. However, a thorough understanding of the pathophysiological role of α2δ proteins ideally serves two purposes: first, it will contribute to our understanding of general pathological mechanisms in synaptic disorders. Second, it may support the future development of novel and specific treatments for brain disorders. In this context, it is noteworthy that the antiepileptic and anti-allodynic drugs gabapentin and pregabalin both act via binding to α2δ proteins and are among the top sold drugs for treating neuropathic pain. In this book chapter, we will discuss recent developments in our understanding of the functions of α2δ proteins, both as calcium channel subunits and as independent regulatory entities. Furthermore, we present and summarize recently identified and likely pathogenic mutations in the genes encoding α2δ proteins and discuss potential underlying pathophysiological consequences at the molecular and structural level.
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Affiliation(s)
- Manuel Hessenberger
- Division Physiology, Department Pharmacology, Physiology and Microbiology, Karl Landsteiner University of Health Sciences, Krems, Austria
| | - Sabrin Haddad
- Division Physiology, Department Pharmacology, Physiology and Microbiology, Karl Landsteiner University of Health Sciences, Krems, Austria
- Institute of Physiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Gerald J Obermair
- Division Physiology, Department Pharmacology, Physiology and Microbiology, Karl Landsteiner University of Health Sciences, Krems, Austria.
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3
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Kimbrel NA, Ashley-Koch AE, Qin XJ, Lindquist JH, Garrett ME, Dennis MF, Hair LP, Huffman JE, Jacobson DA, Madduri RK, Trafton JA, Coon H, Docherty AR, Kang J, Mullins N, Ruderfer DM, Harvey PD, McMahon BH, Oslin DW, Hauser ER, Hauser MA, Beckham JC. A genome-wide association study of suicide attempts in the million veterans program identifies evidence of pan-ancestry and ancestry-specific risk loci. Mol Psychiatry 2022; 27:2264-2272. [PMID: 35347246 PMCID: PMC9910180 DOI: 10.1038/s41380-022-01472-3] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 12/16/2021] [Accepted: 02/02/2022] [Indexed: 12/30/2022]
Abstract
To identify pan-ancestry and ancestry-specific loci associated with attempting suicide among veterans, we conducted a genome-wide association study (GWAS) of suicide attempts within a large, multi-ancestry cohort of U.S. veterans enrolled in the Million Veterans Program (MVP). Cases were defined as veterans with a documented history of suicide attempts in the electronic health record (EHR; N = 14,089) and controls were defined as veterans with no documented history of suicidal thoughts or behaviors in the EHR (N = 395,064). GWAS was performed separately in each ancestry group, controlling for sex, age and genetic substructure. Pan-ancestry risk loci were identified through meta-analysis and included two genome-wide significant loci on chromosomes 20 (p = 3.64 × 10-9) and 1 (p = 3.69 × 10-8). A strong pan-ancestry signal at the Dopamine Receptor D2 locus (p = 1.77 × 10-7) was also identified and subsequently replicated in a large, independent international civilian cohort (p = 7.97 × 10-4). Additionally, ancestry-specific genome-wide significant loci were also detected in African-Americans, European-Americans, Asian-Americans, and Hispanic-Americans. Pathway analyses suggested over-representation of many biological pathways with high clinical significance, including oxytocin signaling, glutamatergic synapse, cortisol synthesis and secretion, dopaminergic synapse, and circadian rhythm. These findings confirm that the genetic architecture underlying suicide attempt risk is complex and includes both pan-ancestry and ancestry-specific risk loci. Moreover, pathway analyses suggested many commonly impacted biological pathways that could inform development of improved therapeutics for suicide prevention.
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Affiliation(s)
- Nathan A Kimbrel
- Durham Veterans Affairs (VA) Health Care System, Durham, NC, USA.
- VA Mid-Atlantic Mental Illness Research, Education and Clinical Center, Durham, NC, USA.
- VA Health Services Research and Development Center of Innovation to Accelerate Discovery and Practice Transformation, Durham, NC, USA.
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA.
| | - Allison E Ashley-Koch
- Duke Molecular Physiology Institute, Durham, NC, USA
- Department of Medicine, Duke University Health System, Durham, NC, USA
| | - Xue J Qin
- Durham Veterans Affairs (VA) Health Care System, Durham, NC, USA
- Duke Molecular Physiology Institute, Durham, NC, USA
| | - Jennifer H Lindquist
- VA Health Services Research and Development Center of Innovation to Accelerate Discovery and Practice Transformation, Durham, NC, USA
| | | | - Michelle F Dennis
- Durham Veterans Affairs (VA) Health Care System, Durham, NC, USA
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Lauren P Hair
- Durham Veterans Affairs (VA) Health Care System, Durham, NC, USA
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Jennifer E Huffman
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Daniel A Jacobson
- Biosciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee Knoxville, Knoxville, TN, USA
- Department of Psychology, NeuroNet Research Center, University of Tennessee Knoxville, Knoxville, TN, USA
| | - Ravi K Madduri
- Consortium for Advanced Science and Engineering, The University of Chicago, Chicago, IL, USA
- Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA
| | - Jodie A Trafton
- Program Evaluation and Resource Center, Office of Mental Health and Suicide Prevention, VA Palo Alto Health Care System, Menlo Park, CA, USA
| | - Hilary Coon
- Department of Psychiatry, Huntsman Mental Health Institute, University of Utah School of Medicine, Salt Lake City, UT, US
- Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT, US
| | - Anna R Docherty
- Department of Psychiatry, Huntsman Mental Health Institute, University of Utah School of Medicine, Salt Lake City, UT, US
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, US
| | - Jooeun Kang
- Division of Genetic Medicine, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, US
| | - Niamh Mullins
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, US
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, US
| | - Douglas M Ruderfer
- Division of Genetic Medicine, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, US
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, US
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, US
| | - Philip D Harvey
- Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, Miami, FL, USA
- Research Service Bruce W. Carter VA Medical Center, Miami, FL, USA
| | - Benjamin H McMahon
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - David W Oslin
- VISN 4 Mental Illness Research, Education, and Clinical Center, Center of Excellence, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth R Hauser
- Durham Veterans Affairs (VA) Health Care System, Durham, NC, USA
- Duke Molecular Physiology Institute, Durham, NC, USA
| | - Michael A Hauser
- Duke Molecular Physiology Institute, Durham, NC, USA
- Department of Medicine, Duke University Health System, Durham, NC, USA
| | - Jean C Beckham
- Durham Veterans Affairs (VA) Health Care System, Durham, NC, USA
- VA Mid-Atlantic Mental Illness Research, Education and Clinical Center, Durham, NC, USA
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA
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4
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Ewing AD, Cheetham SW, McGill JJ, Sharkey M, Walker R, West JA, West MJ, Summers KM. Microdeletion of 9q22.3: A patient with minimal deletion size associated with a severe phenotype. Am J Med Genet A 2021; 185:2070-2083. [PMID: 33960642 PMCID: PMC8251932 DOI: 10.1002/ajmg.a.62224] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 03/17/2021] [Accepted: 04/02/2021] [Indexed: 01/20/2023]
Abstract
Basal cell nevus syndrome (also known as Gorlin Syndrome; MIM109400) is an autosomal dominant disorder characterized by recurrent pathological features such as basal cell carcinomas and odontogenic keratocysts as well as skeletal abnormalities. Most affected individuals have point mutations or small insertions or deletions within the PTCH1 gene on human chromosome 9, but there are some cases with more extensive deletion of the region, usually including the neighboring FANCC and/or ERCC6L2 genes. We report a 16‐year‐old patient with a deletion of approximately 400,000 bases which removes only PTCH1 and some non‐coding RNA genes but leaves FANCC and ERCC6L2 intact. In spite of the small amount of DNA for which he is haploid, his phenotype is more extreme than many individuals with longer deletions in the region. This includes early presentation with a large number of basal cell nevi and other skin lesions, multiple jaw keratocysts, and macrosomia. We found that the deletion was in the paternal chromosome, in common with other macrosomia cases. Using public databases, we have examined possible interactions between sequences within and outside the deletion and speculate that a regulatory relationship exists with flanking genes, which is unbalanced by the deletion, resulting in abnormal activation or repression of the target genes and hence the severity of the phenotype.
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Affiliation(s)
- Adam D Ewing
- Mater Research Institute-University of Queensland, Translational Research Institute, Woolloongabba, Queensland, Australia
| | - Seth W Cheetham
- Mater Research Institute-University of Queensland, Translational Research Institute, Woolloongabba, Queensland, Australia
| | - James J McGill
- Department of Chemical Pathology, Royal Brisbane and Women's Hospital, Herston, Queensland, Australia
| | - Michael Sharkey
- Paddington Dermatology Specialist Clinic, Paddington, Queensland, Australia
| | - Rick Walker
- QLD Youth Cancer Service, Queensland Children's Hospital, South Brisbane, Queensland, Australia.,School of Clinical Medicine, The University of Queensland, Herston, Queensland, Australia
| | - Jennifer A West
- Northside Clinical School, Prince Charles Hospital, The University of Queensland, Chermside, Queensland, Australia
| | - Malcolm J West
- Northside Clinical School, Prince Charles Hospital, The University of Queensland, Chermside, Queensland, Australia
| | - Kim M Summers
- Mater Research Institute-University of Queensland, Translational Research Institute, Woolloongabba, Queensland, Australia
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5
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Ablinger C, Geisler SM, Stanika RI, Klein CT, Obermair GJ. Neuronal α 2δ proteins and brain disorders. Pflugers Arch 2020; 472:845-863. [PMID: 32607809 PMCID: PMC7351808 DOI: 10.1007/s00424-020-02420-2] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 06/08/2020] [Accepted: 06/12/2020] [Indexed: 01/31/2023]
Abstract
α2δ proteins are membrane-anchored extracellular glycoproteins which are abundantly expressed in the brain and the peripheral nervous system. They serve as regulatory subunits of voltage-gated calcium channels and, particularly in nerve cells, regulate presynaptic and postsynaptic functions independently from their role as channel subunits. α2δ proteins are the targets of the widely prescribed anti-epileptic and anti-allodynic drugs gabapentin and pregabalin, particularly for the treatment of neuropathic pain conditions. Recently, the human genes (CACNA2D1-4) encoding for the four known α2δ proteins (isoforms α2δ-1 to α2δ-4) have been linked to a large variety of neurological and neuropsychiatric disorders including epilepsy, autism spectrum disorders, bipolar disorders, schizophrenia, and depressive disorders. Here, we provide an overview of the hitherto identified disease associations of all known α2δ genes, hypothesize on the pathophysiological mechanisms considering their known physiological roles, and discuss the most immanent future research questions. Elucidating their specific physiological and pathophysiological mechanisms may open the way for developing entirely novel therapeutic paradigms for treating brain disorders.
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Affiliation(s)
- Cornelia Ablinger
- Institute of Physiology, Medical University Innsbruck, 6020, Innsbruck, Austria
| | - Stefanie M Geisler
- Department of Pharmacology and Toxicology, University of Innsbruck, 6020, Innsbruck, Austria
| | - Ruslan I Stanika
- Division Physiology, Karl Landsteiner University of Health Sciences, 3500, Krems, Austria
| | - Christian T Klein
- Department of Life Sciences, IMC University of Applied Sciences, 3500, Krems, Austria
| | - Gerald J Obermair
- Institute of Physiology, Medical University Innsbruck, 6020, Innsbruck, Austria.
- Division Physiology, Karl Landsteiner University of Health Sciences, 3500, Krems, Austria.
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6
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Risher WC, Eroglu C. Emerging roles for α2δ subunits in calcium channel function and synaptic connectivity. Curr Opin Neurobiol 2020; 63:162-169. [PMID: 32521436 DOI: 10.1016/j.conb.2020.04.007] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 04/29/2020] [Indexed: 12/17/2022]
Abstract
Central nervous system function requires the proper formation and function of synapses. The α2δ auxiliary subunits of voltage-gated calcium channels have emerged as regulators of a number of critical events associated with regulation of synaptic function, including channel trafficking and localization, as well as the initial establishment of synaptic structures. In this review, we will discuss some of these recent studies which have uncovered novel mechanisms for α2δ function at the synapse, including the regulation of calcium channel α1 subunit specificity and the promotion of dendritic spine growth. Moreover, we will cover recent advances that have been made in understanding the consequences of aberrant α2δ signaling in injury and disease.
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Affiliation(s)
- William Christopher Risher
- Department of Biomedical Sciences, Joan C. Edwards School of Medicine at Marshall University, Huntington, WV 25705, United States.
| | - Cagla Eroglu
- Department of Cell Biology, Duke University Medical Center, Durham, NC 27710, United States; Department of Neurobiology, Duke University Medical Center, Durham, NC 27710, United States; Duke Institute for Brain Sciences (DIBS), Durham, NC 27710, United States; Regeneration Next Initiative, Duke University, Durham, NC 27710, United States
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7
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Xu Y, Cao L, Zhao X, Yao Y, Liu Q, Zhang B, Wang Y, Mao Y, Ma Y, Ma JZ, Payne TJ, Li MD, Li L. Prediction of Smoking Behavior From Single Nucleotide Polymorphisms With Machine Learning Approaches. Front Psychiatry 2020; 11:416. [PMID: 32477189 PMCID: PMC7241440 DOI: 10.3389/fpsyt.2020.00416] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2019] [Accepted: 04/23/2020] [Indexed: 12/22/2022] Open
Abstract
Smoking is a complex behavior with a heritability as high as 50%. Given such a large genetic contribution, it provides an opportunity to prevent those individuals who are susceptible to smoking dependence from ever starting to smoke by predicting their inherited predisposition with their genomic profiles. Although previous studies have identified many susceptibility variants for smoking, they have limited power to predict smoking behavior. We applied the support vector machine (SVM) and random forest (RF) methods to build prediction models for smoking behavior. We first used 1,431 smokers and 1,503 non-smokers of African origin for model building with a 10-fold cross-validation and then tested the prediction models on an independent dataset consisting of 213 smokers and 224 non-smokers. The SVM model with 500 top single nucleotide polymorphisms (SNPs) selected using logistic regression (p<0.01) as the feature selection method achieved an area under the curve (AUC) of 0.691, 0.721, and 0.720 for the training, test, and independent test samples, respectively. The RF model with 500 top SNPs selected using logistic regression (p<0.01) achieved AUCs of 0.671, 0.665, and 0.667 for the training, test, and independent test samples, respectively. Finally, we used the combined logistic (p<0.01) and LASSO (λ=10-3) regression to select features and the SVM algorithm for model building. The SVM model with 500 top SNPs achieved AUCs of 0.756, 0.776, and 0.897 for the training, test, and independent test samples, respectively. We conclude that machine learning methods are promising means to build predictive models for smoking.
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Affiliation(s)
- Yi Xu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Liyu Cao
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xinyi Zhao
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yinghao Yao
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Qiang Liu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Bin Zhang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yan Wang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ying Mao
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yunlong Ma
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jennie Z Ma
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, United States
| | - Thomas J Payne
- Department of Otolaryngology and Communicative Sciences, University of Mississippi Medical Center, Jackson, MS, United States
| | - Ming D Li
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Research Center for Air Pollution and Health, Zhejiang University, Hangzhou, China
| | - Lanjuan Li
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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Zhao Y, Peng S, Jiang H, Du J, Yu S, Zhao M. Variants in GABBR1 Gene Are Associated with Methamphetamine Dependence and Two Years' Relapse after Drug Rehabilitation. J Neuroimmune Pharmacol 2018; 13:523-531. [PMID: 30143926 DOI: 10.1007/s11481-018-9802-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Accepted: 07/30/2018] [Indexed: 01/18/2023]
Abstract
Methamphetamine (MA) use disorder is a growing global health challenge marked by a steady increase worldwide. GABAergic system plays an important role in the mechanism of drug dependence, however few studies about the association between methamphetamine use disorder and genes in GABAergic system. Concerning GABBR1 gene which encoding the GABAB receptor subunit 1 is an important regulator in the GABAergic system. The aim of the study is to explore whether GABBR1 gene play a role in methamphetamine dependence and relapse after rehabilitation. Three single nucleotide polymorphisms (SNPs, rs2076483, rs29221, rs715044) of the GABBR1 gene were genotyped in 791 participants with MA use disorder and 448 healthy controls. The distribution of genotypes and alleles of the three SNPs between the two groups and their subgroups (dependence and abuse) was been analyzed. The multivariate logistic model was used to identify factors associate with relapse of MA use disorder during the following 2 years after drug rehabilitation. It was found that the C allele frequency of rs715044 of the GABBR1 gene was associated with MA use disorder and MA dependence. The CGA (rs2076483- rs29221- rs715044) was negatively associated with MA use disorder. The drug use years and rs29221 GG genotype were associated with relapse during the following 2 years after drug rehabilitation. GABBR1 gene may be associated with the susceptibility for MA use disorder and relapse and it indicates that the GABAergic system may play a role in the MA use disorder. Graphical Abstract GABBR1 gene may be associated with the susceptibility for MA use disorder and relapse and it indicates that the GABAergic system may play a role in the MA use disorder.
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Affiliation(s)
- Yan Zhao
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Sufang Peng
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Haifeng Jiang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Jiang Du
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Shunying Yu
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Min Zhao
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China.
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai, 200030, China.
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9
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Sharp BM, Chen H. Neurogenetic determinants and mechanisms of addiction to nicotine and smoked tobacco. Eur J Neurosci 2018; 50:2164-2179. [DOI: 10.1111/ejn.14171] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Revised: 08/31/2018] [Accepted: 09/18/2018] [Indexed: 12/18/2022]
Affiliation(s)
- Burt M. Sharp
- Department of Genetics, Genomics and Informatics College of Medicine University of Tennessee Health Science Center 19 S. Manassas, CRB #220 Memphis TN 38103 USA
| | - Hao Chen
- Department of Pharmacology University of Tennessee Health Science Center Memphis TN USA
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10
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Jiang Y, Chen S, McGuire D, Chen F, Liu M, Iacono WG, Hewitt JK, Hokanson JE, Krauter K, Laakso M, Li KW, Lutz SM, McGue M, Pandit A, Zajac GJM, Boehnke M, Abecasis GR, Vrieze SI, Zhan X, Jiang B, Liu DJ. Proper conditional analysis in the presence of missing data: Application to large scale meta-analysis of tobacco use phenotypes. PLoS Genet 2018; 14:e1007452. [PMID: 30016313 PMCID: PMC6063450 DOI: 10.1371/journal.pgen.1007452] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2017] [Revised: 07/27/2018] [Accepted: 05/25/2018] [Indexed: 11/19/2022] Open
Abstract
Meta-analysis of genetic association studies increases sample size and the power for mapping complex traits. Existing methods are mostly developed for datasets without missing values, i.e. the summary association statistics are measured for all variants in contributing studies. In practice, genotype imputation is not always effective. This may be the case when targeted genotyping/sequencing assays are used or when the un-typed genetic variant is rare. Therefore, contributed summary statistics often contain missing values. Existing methods for imputing missing summary association statistics and using imputed values in meta-analysis, approximate conditional analysis, or simple strategies such as complete case analysis all have theoretical limitations. Applying these approaches can bias genetic effect estimates and lead to seriously inflated type-I or type-II errors in conditional analysis, which is a critical tool for identifying independently associated variants. To address this challenge and complement imputation methods, we developed a method to combine summary statistics across participating studies and consistently estimate joint effects, even when the contributed summary statistics contain large amounts of missing values. Based on this estimator, we proposed a score statistic called PCBS (partial correlation based score statistic) for conditional analysis of single-variant and gene-level associations. Through extensive analysis of simulated and real data, we showed that the new method produces well-calibrated type-I errors and is substantially more powerful than existing approaches. We applied the proposed approach to one of the largest meta-analyses to date for the cigarettes-per-day phenotype. Using the new method, we identified multiple novel independently associated variants at known loci for tobacco use, which were otherwise missed by alternative methods. Together, the phenotypic variance explained by these variants was 1.1%, improving that of previously reported associations by 71%. These findings illustrate the extent of locus allelic heterogeneity and can help pinpoint causal variants. It is of great interest to estimate the joint effects of multiple variants from large scale meta-analyses, in order to fine-map causal variants and understand the genetic architecture for complex traits. The summary association statistics from participating studies in a meta-analysis often contain missing values at some variant sites, as the imputation methods may not work well and the variants with low imputation quality will be filtered out. Missingness is especially likely when the underlying genetic variant is rare or the participating studies use targeted genotyping array that is not suitable for imputation. Existing methods for conditional meta-analysis do not properly handle missing data, and can incorrectly estimate correlations between score statistics. As a result, they can produce highly inflated type-I errors for conditional analysis, which will result in overestimated phenotypic variance explained and incorrect identification of causal variants. We systematically evaluated this bias and proposed a novel partial correlation based score statistic. The new statistic has valid type-I errors for conditional analysis and much higher power than the existing methods, even when the contributed summary statistics contain a large fraction of missing values. We expect this method to be highly useful in the sequencing age for complex trait genetics.
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Affiliation(s)
- Yu Jiang
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania, United States of America
| | - Sai Chen
- Center of Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Daniel McGuire
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania, United States of America
| | - Fang Chen
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania, United States of America
| | - Mengzhen Liu
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - William G. Iacono
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - John K. Hewitt
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado, United States of America
| | - John E. Hokanson
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Kenneth Krauter
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado, United States of America
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Kevin W. Li
- Center of Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Sharon M. Lutz
- Department of Biostatistics and Informatics, University of Colorado, Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Matthew McGue
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Anita Pandit
- Center of Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Gregory J. M. Zajac
- Center of Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Michael Boehnke
- Center of Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Goncalo R. Abecasis
- Center of Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Scott I. Vrieze
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Xiaowei Zhan
- Department of Clinical Science, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
| | - Bibo Jiang
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania, United States of America
- * E-mail: (DJL); (BJ)
| | - Dajiang J. Liu
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania, United States of America
- * E-mail: (DJL); (BJ)
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11
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Abstract
PURPOSE OF REVIEW With the advent of the genome-wide association study (GWAS), our understanding of the genetics of addiction has made significant strides forward. Here, we summarize genetic loci containing variants identified at genome-wide statistical significance (P < 5 × 10-8) and independently replicated, review evidence of functional or regulatory effects for GWAS-identified variants, and outline multi-omics approaches to enhance discovery and characterize addiction loci. RECENT FINDINGS Replicable GWAS findings span 11 genetic loci for smoking, eight loci for alcohol, and two loci for illicit drugs combined and include missense functional variants and noncoding variants with regulatory effects in human brain tissues traditionally viewed as addiction-relevant (e.g., prefrontal cortex [PFC]) and, more recently, tissues often overlooked (e.g., cerebellum). GWAS analyses have discovered several novel, replicable variants contributing to addiction. Using larger sample sizes from harmonized datasets and new approaches to integrate GWAS with multiple 'omics data across human brain tissues holds great promise to significantly advance our understanding of the biology underlying addiction.
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Affiliation(s)
- Dana B Hancock
- Behavioral and Urban Health Program, Behavioral Health and Criminal Justice Division, RTI International, 3040 East Cornwallis Road, P. O. Box 12194, Research Triangle Park, NC, 27709, USA.
| | - Christina A Markunas
- Behavioral and Urban Health Program, Behavioral Health and Criminal Justice Division, RTI International, 3040 East Cornwallis Road, P. O. Box 12194, Research Triangle Park, NC, 27709, USA
| | - Laura J Bierut
- Department of Psychiatry, Washington University, St. Louis, MO, USA
| | - Eric O Johnson
- Fellow Program and Behavioral Health and Criminal Justice Division, RTI International, Research Triangle Park, NC, USA
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