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Elam KK, Bountress KE, Ha T, Shaw DS, Wilson MN, Aliev F, Dick DM, Lemery-Chalfant K. Developmental genetic effects on externalizing behavior and alcohol use: Examination across two longitudinal samples. Dev Psychopathol 2024; 36:82-91. [PMID: 35983793 PMCID: PMC9938843 DOI: 10.1017/s0954579422000980] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Externalizing behavior in early adolescence is associated with alcohol use in adolescence and early adulthood and these behaviors often emerge as part of a developmental sequence. This pattern can be the result of heterotypic continuity, in which different behaviors emerge over time based on an underlying shared etiology. In particular, there is largely a shared genetic etiology underlying externalizing and substance use behaviors. We examined whether polygenic risk for alcohol use disorder predicted (1) externalizing behavior in early adolescence and alcohol use in adolescence in the Early Steps Multisite sample and (2) externalizing behavior in adolescence and alcohol use in early adulthood in the Project Alliance 1 (PAL1) sample. We examined associations separately for African Americans and European Americans. When examining European Americans in the Early Steps sample, greater polygenic risk was associated with externalizing behavior in early adolescence. In European Americans in PAL1, we found greater polygenic risk was associated with alcohol use in early adulthood. Effects were largely absent in African Americans in both samples. Results imply that genetic predisposition for alcohol use disorder may increase risk for externalizing and alcohol use as these behaviors emerge developmentally.
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Affiliation(s)
- Kit K. Elam
- Department of Applied Health Science, Indiana University, 1025 E. 7 St., Suite 116, Bloomington, IN 47405
| | - Kaitlin E. Bountress
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University
| | - Thao Ha
- Department of Psychology, Arizona State University
| | | | | | - Fazil Aliev
- Department of Psychiatry, Rutgers Robert Wood Johnson Medical School
| | - Danielle M. Dick
- Department of Psychiatry, Rutgers Robert Wood Johnson Medical School
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2
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Tiego J, Verdejo-Garcia A, Anderson A, Koutoulogenis J, Bellgrove MA. Mechanisms of cognitive disinhibition explain individual differences in adult attention deficit hyperactivity disorder traits. Cortex 2023; 167:178-196. [PMID: 37567053 DOI: 10.1016/j.cortex.2023.06.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 04/12/2023] [Accepted: 06/08/2023] [Indexed: 08/13/2023]
Abstract
BACKGROUND Attention deficit hyperactivity disorder (ADHD) in adults is strongly associated with psychiatric comorbidity and functional impairment. Here, we aimed to use a newly developed online cognitive battery with strong psychometric properties for measuring individual differences in three cognitive mechanisms proposed to underlie ADHD traits in adults: 1) attentional control - the ability to mobilize cognitive resources to stop a prepotent motor response; 2) information sampling/gathering - adequate sampling of information in a stimulus detection task prior to making a decision; and 3) shifting - the ability to adapt behavior in response to positive and negative contingencies. METHODS This cross-sectional and correlational study recruited 650 adults (330 males) aged 18-69 years (M = 33.06; MD = 31.00; SD = 10.50), with previously diagnosed ADHD (n = 329) and those from the general community without a history of ADHD (n = 321). Self-report measures of ADHD traits (i.e., inattention/disorganization, impulsivity, hyperactivity) and the cognitive battery were completed online. RESULTS Latent class analysis, exploratory structural equation modeling and factor mixture modeling revealed self-reported ADHD traits formed a unidimensional and approximately normally distributed phenotype. Bayesian structural equation modeling demonstrated that all three mechanisms measured by the cognitive battery, explained unique, incremental variance in ADHD traits, with a total of 15.9% explained in the ADHD trait factor. CONCLUSIONS Attentional control and shifting, as well as the less researched cognitive process of information gathering, explain individual difference variance in self-reported ADHD traits with potential to yield genetic and neurobiological insights into adult ADHD.
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Affiliation(s)
- Jeggan Tiego
- School of Psychological Sciences and Turner Institute for Brain and Mental Health, Level 5, 18 Innovation Walk, Monash University, Clayton, Victoria, Australia 3800.
| | - Antonio Verdejo-Garcia
- School of Psychological Sciences and Turner Institute for Brain and Mental Health, Level 5, 18 Innovation Walk, Monash University, Clayton, Victoria, Australia 3800.
| | - Alexandra Anderson
- School of Psychological Sciences and Turner Institute for Brain and Mental Health, Level 5, 18 Innovation Walk, Monash University, Clayton, Victoria, Australia 3800.
| | - Julia Koutoulogenis
- School of Psychological Sciences and Turner Institute for Brain and Mental Health, Level 5, 18 Innovation Walk, Monash University, Clayton, Victoria, Australia 3800.
| | - Mark A Bellgrove
- School of Psychological Sciences and Turner Institute for Brain and Mental Health, Level 5, 18 Innovation Walk, Monash University, Clayton, Victoria, Australia 3800.
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3
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Wang J, Wu J, Sun Q, Wu Q, Li Y, Duan S, Yang L, Wu W, Wang Z, Liu Y, Tang R, Yang J, Wang C, Liu C, Xu J, Wang M, He G. Extensive genetic admixture between Tai-Kadai-speaking people and their neighbours in the northeastern region of the Yungui Plateau inferred from genome-wide variations. BMC Genomics 2023; 24:317. [PMID: 37308851 DOI: 10.1186/s12864-023-09412-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 05/27/2023] [Indexed: 06/14/2023] Open
Abstract
BACKGROUND Yungui Plateau in Southwest China is characterized by multi-language and multi-ethnic communities and is one of the regions with the wealthiest ethnolinguistic, cultural and genetic diversity in East Asia. There are numerous Tai-Kadai (TK)-speaking populations, but their detailed evolutionary history and biological adaptations are still unclear. RESULTS Here, we genotyped genome-wide SNP data of 77 unrelated TK-speaking Zhuang and Dong individuals from the Yungui Plateau and explored their detailed admixture history and adaptive features using clustering patterns, allele frequency differentiation and sharing haplotype patterns. TK-speaking Zhuang and Dong people in Guizhou are closely related to geographically close TK and Hmong-Mien (HM)-speaking populations. Besides, we identified that Guizhou TK-speaking people have a close genetic relationship with Austronesian (AN)-speaking Atayal and Paiwan people, which is supported by the common origin of the ancient Baiyue tribe. We additionally found subtle genetic differences among the newly studied TK people and previously reported Dais via the fine-scale genetic substructure analysis based on the shared haplotype chunks. Finally, we identified specific selection candidate signatures associated with several essential human immune systems and neurological disorders, which could provide evolutionary evidence for the allele frequency distribution pattern of genetic risk loci. CONCLUSIONS Our comprehensive genetic characterization of TK people suggested the strong genetic affinity within TK groups and extensive gene flow with geographically close HM and Han people. We also provided genetic evidence that supported the common origin hypothesis of TK and AN people. The best-fitted admixture models further suggested that ancestral sources from northern millet farmers and southern inland and coastal people contributed to the formation of the gene pool of the Zhuang and Dong people.
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Affiliation(s)
- Jiawen Wang
- School of Forensic Medicine, Guizhou Medical University, Guiyang, 550004, China.
| | - Jun Wu
- School of Forensic Medicine, Guizhou Medical University, Guiyang, 550004, China
| | - Qiuxia Sun
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, 610000, China
- Department of Forensic Medicine, College of Basic Medicine, Chongqing Medical University, Chongqing, 400331, China
| | - Qian Wu
- Qiannan Prefecture People's Hospital, Buyi and Miao Autonomous Prefecture of QianNan, Buyi and Miao Autonomous Prefecture of QianNan, 558000, China
| | - Youjing Li
- Congjiang People's Hospital, Congjiang, 557499, China
| | - Shuhan Duan
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, 610000, China
- School of Basic Medical Sciences, North Sichuan Medical College, Nanchong, 637000, China
| | - Lin Yang
- School of Forensic Medicine, Guizhou Medical University, Guiyang, 550004, China
| | - Wenxin Wu
- School of Forensic Medicine, Guizhou Medical University, Guiyang, 550004, China
| | - Zheng Wang
- Institute of Forensic Medicine, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, China
| | - Yan Liu
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, 610000, China
- School of Basic Medical Sciences, North Sichuan Medical College, Nanchong, 637000, China
| | - Renkuan Tang
- Department of Forensic Medicine, College of Basic Medicine, Chongqing Medical University, Chongqing, 400331, China
| | - Junbao Yang
- School of Basic Medical Sciences, North Sichuan Medical College, Nanchong, 637000, China
| | - Chuanchao Wang
- Department of Anthropology and Ethnology, School of Sociology and Anthropology, Institute of Anthropology, Xiamen University, Xiamen, 361000, China
| | - Chao Liu
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510275, China
| | - Jianwei Xu
- Department of Pharmacology, School of Basic Medicine, Guizhou Medical University, Guiyang, 550004, China.
| | - Mengge Wang
- Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510275, China.
| | - Guanglin He
- Institute of Rare Diseases, West China Hospital of Sichuan University, Sichuan University, Chengdu, 610000, China.
- Center for Archaeological Science, Sichuan University, Chengdu, 610000, China.
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4
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Maitusong B, Laguzzi F, Strawbridge RJ, Baldassarre D, Veglia F, Humphries SE, Savonen K, Kurl S, Pirro M, Smit AJ, Giral P, Silveira A, Tremoli E, Hamsten A, de Faire U, Gigante B, Leander K. Cross-Sectional Gene-Smoking Interaction Analysis in Relation to Subclinical Atherosclerosis-Results From the IMPROVE Study. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2023; 16:236-247. [PMID: 37021583 PMCID: PMC10284137 DOI: 10.1161/circgen.122.003710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 01/29/2023] [Indexed: 04/07/2023]
Abstract
BACKGROUND Smoking is associated with carotid intima-media thickness (C-IMT). However, knowledge about how genetics may influence this association is limited. We aimed to perform nonhypothesis driven gene-smoking interaction analyses to identify potential genetic variants, among those included in immune and metabolic platforms, that may modify the effect of smoking on carotid intima-media thickness. METHODS We used baseline data from 1551 men and 1700 women, aged 55 to 79, included in a European multi-center study. Carotid intima-media thickness maximum, the maximum of values measured at different locations of the carotid tree, was dichotomized with cut point values ≥75, respectively. Genetic data were retrieved through use of the Illumina Cardio-Metabo- and Immuno- Chips. Gene-smoking interactions were evaluated through calculations of Synergy index (S). After adjustments for multiple testing, P values of <2.4×10-7 for S were considered significant. The models were adjusted for age, sex, education, physical activity, type of diet, and population stratification. RESULTS Our screening of 207 586 SNPs available for analysis, resulted in the identification of 47 significant gene-smoking synergistic interactions in relation to carotid intima-media thickness maximum. Among the significant SNPs, 28 were in protein coding genes, 2 in noncoding RNA and the remaining 17 in intergenic regions. CONCLUSIONS Through nonhypothesis-driven analyses of gene-smoking interactions, several significant results were observed. These may stimulate further research on the role of specific genes in the process that determines the effect of smoking habits on the development of carotid atherosclerosis.
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Affiliation(s)
- Buamina Maitusong
- Department of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China (B.M.)
| | - Federica Laguzzi
- Unit of Cardiovascular & Nutritional Epidemiology, Institute of Environmental Medicine (F.L., U.d.F., K.L.), Karolinska Institutet, Stockholm, Sweden
| | - Rona J. Strawbridge
- Cardiovascular Medicine Unit, Department of Medicine Solna (R.J.S., B.G.), Karolinska Institutet, Stockholm, Sweden
- Mental Health & Wellbeing, Institute of Mental Health & Wellbeing, University of Glasgow (R.J.S.)
- Health Data Research, United Kingdom (R.J.S.)
| | - Damiano Baldassarre
- Department of Medical Biotechnology & Translational Medicine, Università degli Studi di Milano (D.B.)
- Centro Cardiologico Monzino, IRCCS, Milan, Italy (D.B., F.V., E.T.)
| | - Fabrizio Veglia
- Centro Cardiologico Monzino, IRCCS, Milan, Italy (D.B., F.V., E.T.)
| | - Steve E. Humphries
- Cardiovascular Genetics, Institute Cardiovascular Science, University College London, United Kingdom (S.E.H.)
| | - Kai Savonen
- Foundation for Research in Health Exercise & Nutrition, Kuopio & Research Institute of Exercise Medicine, Kuopio, Finland (K.S.)
- Department of Clinical Physiology & Nuclear Medicine, Kuopio University Hospital (K.S.)
| | - Sudhir Kurl
- Institute of Public Health & Clinical Nutrition, University of Eastern Finland, Kuopio (S.K.)
| | - Matteo Pirro
- Unit of Internal Medicine, Angiology & Arteriosclerosis Diseases, Department of Medicine, University of Perugia, Italy (M.P.)
| | - Andries J. Smit
- Department of Medicine, University Medical Center Groningen, the Netherlands (A.J.S.)
| | - Philippe Giral
- Unités de Prévention Cardiovasculaire, Assistance Publique-Hôpitaux de Paris, Service Endocrinologie-Métabolisme, Groupe Hospitalier Pitié-Salpétrière, France (P.G.)
| | - Angela Silveira
- Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institutet & Karolinska Hospital, Stockholm, Sweden (A.S., A.H.)
| | - Elena Tremoli
- Centro Cardiologico Monzino, IRCCS, Milan, Italy (D.B., F.V., E.T.)
| | - Anders Hamsten
- Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institutet & Karolinska Hospital, Stockholm, Sweden (A.S., A.H.)
| | - Ulf de Faire
- Unit of Cardiovascular & Nutritional Epidemiology, Institute of Environmental Medicine (F.L., U.d.F., K.L.), Karolinska Institutet, Stockholm, Sweden
| | - Bruna Gigante
- Cardiovascular Medicine Unit, Department of Medicine Solna (R.J.S., B.G.), Karolinska Institutet, Stockholm, Sweden
| | - Karin Leander
- Unit of Cardiovascular & Nutritional Epidemiology, Institute of Environmental Medicine (F.L., U.d.F., K.L.), Karolinska Institutet, Stockholm, Sweden
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5
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Majumdar S, Basu S, McGue M, Chatterjee S. Simultaneous selection of multiple important single nucleotide polymorphisms in familial genome wide association studies data. Sci Rep 2023; 13:8476. [PMID: 37231056 PMCID: PMC10213008 DOI: 10.1038/s41598-023-35379-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 05/17/2023] [Indexed: 05/27/2023] Open
Abstract
We propose a resampling-based fast variable selection technique for detecting relevant single nucleotide polymorphisms (SNP) in a multi-marker mixed effect model. Due to computational complexity, current practice primarily involves testing the effect of one SNP at a time, commonly termed as 'single SNP association analysis'. Joint modeling of genetic variants within a gene or pathway may have better power to detect associated genetic variants, especially the ones with weak effects. In this paper, we propose a computationally efficient model selection approach-based on the e-values framework-for single SNP detection in families while utilizing information on multiple SNPs simultaneously. To overcome computational bottleneck of traditional model selection methods, our method trains one single model, and utilizes a fast and scalable bootstrap procedure. We illustrate through numerical studies that our proposed method is more effective in detecting SNPs associated with a trait than either single-marker analysis using family data or model selection methods that ignore the familial dependency structure. Further, we perform gene-level analysis in Minnesota Center for Twin and Family Research (MCTFR) dataset using our method to detect several SNPs using this that have been implicated to be associated with alcohol consumption.
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Affiliation(s)
- Subhabrata Majumdar
- University of Minnesota Twin Cities, Minneapolis, USA.
- AI Risk and Vulnerability Alliance, Seattle, USA.
| | - Saonli Basu
- University of Minnesota Twin Cities, Minneapolis, USA
| | - Matt McGue
- University of Minnesota Twin Cities, Minneapolis, USA
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6
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Kong YF, Li SZ, Wang KW, Zhu B, Yuan YX, Li MK, Zhou JY. An Efficient Bayesian Method for Estimating the Degree of the Skewness of X Chromosome Inactivation Based on the Mixture of General Pedigrees and Unrelated Females. Biomolecules 2023; 13:biom13030543. [PMID: 36979477 PMCID: PMC10046098 DOI: 10.3390/biom13030543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 03/13/2023] [Accepted: 03/14/2023] [Indexed: 03/19/2023] Open
Abstract
Skewed X chromosome inactivation (XCI-S) has been reported to be associated with some X-linked diseases. Several methods have been proposed to estimate the degree of XCI-S (denoted as γ) for quantitative and qualitative traits based on unrelated females. However, there is no method available for estimating γ based on general pedigrees. Therefore, in this paper, we propose a Bayesian method to obtain the point estimate and the credible interval of γ based on the mixture of general pedigrees and unrelated females (called mixed data for brevity), which is also suitable for only general pedigrees. We consider the truncated normal prior and the uniform prior for γ. Further, we apply the eigenvalue decomposition and Cholesky decomposition to our proposed methods to accelerate the computation speed. We conduct extensive simulation studies to compare the performances of our proposed methods and two existing Bayesian methods which are only applicable to unrelated females. The simulation results show that the incorporation of general pedigrees can improve the efficiency of the point estimation and the precision and the accuracy of the interval estimation of γ. Finally, we apply the proposed methods to the Minnesota Center for Twin and Family Research data for their practical use.
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Affiliation(s)
- Yi-Fan Kong
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou 510006, China
| | - Shi-Zhu Li
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou 510006, China
| | - Kai-Wen Wang
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou 510006, China
| | - Bin Zhu
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou 510006, China
| | - Yu-Xin Yuan
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou 510006, China
| | - Meng-Kai Li
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou 510006, China
| | - Ji-Yuan Zhou
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China
- Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou 510006, China
- Correspondence:
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7
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Dattilo V, Ulivi S, Minelli A, La Bianca M, Giacopuzzi E, Bortolomasi M, Bignotti S, Gennarelli M, Gasparini P, Concas MP. Genome-wide association studies on Northern Italy isolated populations provide further support concerning genetic susceptibility for major depressive disorder. World J Biol Psychiatry 2023; 24:135-148. [PMID: 35615967 DOI: 10.1080/15622975.2022.2082523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
OBJECTIVES Major depressive disorder (MDD) is a psychiatric disorder with pathogenesis influenced by both genetic and environmental factors. To date, the molecular-level understanding of its aetiology remains unclear. Thus, we aimed to identify genetic variants and susceptibility genes for MDD with a genome-wide association study (GWAS) approach. METHODS We performed a meta-analysis of GWASs and a gene-based analysis on two Northern Italy isolated populations (cases/controls n = 166/472 and 33/320), followed by replication and polygenic risk score (PRS) analyses in Italian independent samples (cases n = 464, controls n = 339). RESULTS We identified two novel MDD-associated genes, KCNQ5 (lead SNP rs867262, p = 3.82 × 10-9) and CTNNA2 (rs6729523, p = 1.25 × 10-8). The gene-based analysis revealed another six genes (p < 2.703 × 10-6): GRM7, CTNT4, SNRK, SRGAP3, TRAPPC9, and FHIT. No replication of the genome-wide significant SNPs was found in the independent cohort, even if 14 SNPs around CTNNA2 showed association with MDD and related phenotypes at the nominal level of p (<0.05). Furthermore, the PRS model developed in the discovery cohort discriminated cases and controls in the replication cohort. CONCLUSIONS Our work suggests new possible genes associated with MDD, and the PRS analysis confirms the polygenic nature of this disorder. Future studies are required to better understand the role of these findings in MDD.
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Affiliation(s)
- Vincenzo Dattilo
- Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Sheila Ulivi
- Institute for Maternal and Child Health-IRCCS Burlo Garofolo, Trieste, Italy
| | - Alessandra Minelli
- Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.,Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Martina La Bianca
- Institute for Maternal and Child Health-IRCCS Burlo Garofolo, Trieste, Italy
| | - Edoardo Giacopuzzi
- Wellcome Centre for Human Genetics, Oxford University, Oxford, UK.,NIHR Oxford Biomedical Research Centre, Oxford, UK
| | | | - Stefano Bignotti
- Unit of Psychiatry, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Massimo Gennarelli
- Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy.,Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Paolo Gasparini
- Institute for Maternal and Child Health-IRCCS Burlo Garofolo, Trieste, Italy.,Department of Medicine, Surgery and Health Science, University of Trieste, Trieste, Italy
| | - Maria Pina Concas
- Institute for Maternal and Child Health-IRCCS Burlo Garofolo, Trieste, Italy
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8
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Odintsova VV, Hagenbeek FA, van der Laan CM, van de Weijer S, Boomsma DI. Genetics and epigenetics of human aggression. HANDBOOK OF CLINICAL NEUROLOGY 2023; 197:13-44. [PMID: 37633706 DOI: 10.1016/b978-0-12-821375-9.00005-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/28/2023]
Abstract
There is substantial variation between humans in aggressive behavior, with its biological etiology and molecular genetic basis mostly unknown. This review chapter offers an overview of genomic and omics studies revealing the genetic contribution to aggression and first insights into associations with epigenetic and other omics (e.g., metabolomics) profiles. We allowed for a broad phenotype definition including studies on "aggression," "aggressive behavior," or "aggression-related traits," "antisocial behavior," "conduct disorder," and "oppositional defiant disorder." Heritability estimates based on family and twin studies in children and adults of this broadly defined phenotype of aggression are around 50%, with relatively small fluctuations around this estimate. Next, we review the genome-wide association studies (GWAS) which search for associations with alleles and also allow for gene-based tests and epigenome-wide association studies (EWAS) which seek to identify associations with differently methylated regions across the genome. Both GWAS and EWAS allow for construction of Polygenic and DNA methylation scores at an individual level. Currently, these predict a small percentage of variance in aggression. We expect that increases in sample size will lead to additional discoveries in GWAS and EWAS, and that multiomics approaches will lead to a more comprehensive understanding of the molecular underpinnings of aggression.
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Affiliation(s)
- Veronika V Odintsova
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Amsterdam Reproduction and Development (AR&D) Research Institute, Amsterdam, The Netherlands; Mental Health Division, Amsterdam Public Health (APH) Research Institute, Amsterdam, The Netherlands
| | - Fiona A Hagenbeek
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Mental Health Division, Amsterdam Public Health (APH) Research Institute, Amsterdam, The Netherlands
| | - Camiel M van der Laan
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Netherlands Institute for the Study of Crime and Law Enforcement (NSCR), Amsterdam, The Netherlands
| | - Steve van de Weijer
- Netherlands Institute for the Study of Crime and Law Enforcement (NSCR), Amsterdam, The Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Amsterdam Reproduction and Development (AR&D) Research Institute, Amsterdam, The Netherlands.
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9
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A Century of Behavioral Genetics at the University of Minnesota. Twin Res Hum Genet 2022; 25:211-225. [PMID: 36734056 DOI: 10.1017/thg.2023.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The University of Minnesota has played an important role in the resurgence and eventual mainstreaming of human behavioral genetics in psychology and psychiatry. We describe this history in the context of three major movements in behavioral genetics: (1) radical eugenics in the early 20th century, (2) resurgence of human behavioral genetics in the 1960s, largely using twin and adoption designs to obtain more precise estimates of genetic and environmental influences on individual differences in behavior; and (3) use of measured genotypes to understand behavior. University of Minnesota scientists made significant contributions especially in (2) and (3) in the domains of cognitive ability, drug abuse and mental health, and endophenotypes. These contributions are illustrated through a historical perspective of major figures and events in behavioral genetics.
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10
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Chang XW, Sun Y, Muhai JN, Li YY, Chen Y, Lu L, Chang SH, Shi J. Common and distinguishing genetic factors for substance use behavior and disorder: an integrated analysis of genomic and transcriptomic studies from both human and animal studies. Addiction 2022; 117:2515-2529. [PMID: 35491750 DOI: 10.1111/add.15908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Accepted: 04/04/2022] [Indexed: 11/30/2022]
Abstract
BACKGROUND AND AIMS Genomic and transcriptomic findings greatly broaden the biological knowledge regarding substance use. However, systematic convergence and comparison evidence of genome-wide findings is lacking for substance use. Here, we combined all the genome-wide findings from both substance use behavior and disorder (SUBD) and identified common and distinguishing genetic factors for different SUBDs. METHODS Systemic literature search for genome-wide association (GWAS) and RNA-seq studies of alcohol/nicotine/drug use behavior (partially meets or not reported diagnostic criteria) and alcohol use behavior and disorder (AUBD), nicotine use behavior and disorder (NUBD) and drug use behavior and disorder (DUBD) was performed using PubMed and the GWAS catalog. Drug use was focused upon cannabis, opioid, cocaine and methamphetamine use. GWAS studies required case-control or case/cohort samples. RNA-seq studies were based on brain tissues. The genes which contained significant single nucleotide polymorphism (P ≤ 1 × 10-6 ) in GWAS and reported as significant in RNA-seq studies were extracted. Pathway enrichment was performed by using Metascape. Gene interaction networks were identified by using the Protein Interaction Network Analysis database. RESULTS Total SUBD-related 2910 genes were extracted from 75 GWAS studies (2 773 889 participants) and 17 RNA-seq studies. By overlapping the genes and pathways of AUBD, NUBD and DUBD, four shared genes (CACNB2, GRIN2B, PLXDC2 and PKNOX2), four shared pathways [two Gene Ontology (GO) terms of 'modulation of chemical synaptic transmission', 'regulation of trans-synaptic signaling', two Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of 'dopaminergic synapse', 'cocaine addiction'] were identified (significantly higher than random, P < 1 × 10-5 ). The top shared KEGG pathways (Benjamini-Hochberg-corrected P-value < 0.05) in the pairwise comparison of AUBD versus DUBD, NUBD versus DUBD, AUBD versus NUBD were 'Epstein-Barr virus infection', 'protein processing in endoplasmic reticulum' and 'neuroactive ligand-receptor interaction', respectively. We also identified substance-specific genetic factors: i.e. ADH1B and ALDH2 were unique for AUBD, while CHRNA3 and CHRNA4 were unique for NUBD. CONCLUSIONS This systematic review identifies the shared and unique genes and pathways for alcohol, nicotine and drug use behaviors and disorders at the genome-wide level and highlights critical biological processes for the common and distinguishing vulnerability of substance use behaviors and disorders.
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Affiliation(s)
- Xiang-Wen Chang
- Department of Pharmacology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China.,National Institute on Drug Dependence, Peking University, Beijing, China
| | - Yan Sun
- Department of Pharmacology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China.,National Institute on Drug Dependence, Peking University, Beijing, China
| | - Jia-Na Muhai
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Yang-Yang Li
- Department of Pharmacology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China.,National Institute on Drug Dependence, Peking University, Beijing, China
| | - Yun Chen
- Department of Pharmacology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China.,National Institute on Drug Dependence, Peking University, Beijing, China
| | - Lin Lu
- National Institute on Drug Dependence, Peking University, Beijing, China.,Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Su-Hua Chang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Jie Shi
- National Institute on Drug Dependence, Peking University, Beijing, China.,Beijing Key Laboratory of Drug Dependence Research, Peking University, Beijing, China.,The State Key Laboratory of Natural and Biomimetic Drugs, Peking University, Beijing, China.,The Key Laboratory for Neuroscience of the Ministry of Education and Health, Peking University, Beijing, China
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11
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Beierle JA, Yao EJ, Goldstein SI, Lynch WB, Scotellaro JL, Shah AA, Sena KD, Wong AL, Linnertz CL, Averin O, Moody DE, Reilly CA, Peltz G, Emili A, Ferris MT, Bryant CD. Zhx2 Is a Candidate Gene Underlying Oxymorphone Metabolite Brain Concentration Associated with State-Dependent Oxycodone Reward. J Pharmacol Exp Ther 2022; 382:167-180. [PMID: 35688478 PMCID: PMC9341249 DOI: 10.1124/jpet.122.001217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 05/16/2022] [Indexed: 11/22/2022] Open
Abstract
Understanding the pharmacogenomics of opioid metabolism and behavior is vital to therapeutic success, as mutations can dramatically alter therapeutic efficacy and addiction liability. We found robust, sex-dependent BALB/c substrain differences in oxycodone behaviors and whole brain concentration of oxycodone metabolites. BALB/cJ females showed robust state-dependent oxycodone reward learning as measured via conditioned place preference when compared with the closely related BALB/cByJ substrain. Accordingly, BALB/cJ females also showed a robust increase in brain concentration of the inactive metabolite noroxycodone and the active metabolite oxymorphone compared with BALB/cByJ mice. Oxymorphone is a highly potent, full agonist at the mu opioid receptor that could enhance drug-induced interoception and state-dependent oxycodone reward learning. Quantitative trait locus (QTL) mapping in a BALB/c F2 reduced complexity cross revealed one major QTL on chromosome 15 underlying brain oxymorphone concentration that explained 32% of the female variance. BALB/cJ and BALB/cByJ differ by fewer than 10,000 variants, which can greatly facilitate candidate gene/variant identification. Hippocampal and striatal cis-expression QTL (eQTL) and exon-level eQTL analysis identified Zhx2, a candidate gene coding for a transcriptional repressor with a private BALB/cJ retroviral insertion that reduces Zhx2 expression and sex-dependent dysregulation of cytochrome P450 enzymes. Whole brain proteomics corroborated the Zhx2 eQTL and identified upregulated CYP2D11 that could increase brain oxymorphone in BALB/cJ females. To summarize, Zhx2 is a highly promising candidate gene underlying brain oxycodone metabolite levels. Future studies will validate Zhx2 and its site of action using reciprocal gene editing and tissue-specific viral manipulations in BALB/c substrains. SIGNIFICANCE STATEMENT: Our findings show that genetic variation can result in sex-specific alterations in whole brain concentration of a bioactive opioid metabolite after oxycodone administration, reinforcing the need for sex as a biological factor in pharmacogenomic studies. The cooccurrence of female-specific increased oxymorphone and state-dependent reward learning suggests that this minor yet potent and efficacious metabolite of oxycodone could increase opioid interoception and drug-cue associative learning of opioid reward, which has implications for cue-induced relapse of drug-seeking behavior and for precision pharmacogenetics.
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Affiliation(s)
- Jacob A Beierle
- Ph.D. Program in Biomolecular Pharmacology (J.A.B., S.I.G.), Laboratory of Addiction Genetics, Department of Pharmacology and Experimental Therapeutics and Psychiatry (J.A.B., E.J.Y., W.B.L., J.L.S., A.A.S., K.D.S., A.L.W., C.D.B.), Department of Biology and Biochemistry, Center for Network Systems Biology (S.I.G., A.E.), and Graduate Program in Neuroscience (W.B.L), Boston University School of Medicine, Boston, Massachusetts; Transformative Training Program in Addiction Science (TTPAS) (J.A.B., W.B.L.) and Undergraduate Research Opportunity Program (J.L.S., K.D.S.), Boston University, Boston, Massachusetts; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (C.L.L., M.T.F.); Department of Pharmacology and Toxicity, Center for Human Toxicology, University of Utah, Salt Lake City, Utah (O.A., D.E.M., C.A.R.); and Department of Anesthesiology, Pain, and Preoperative Medicine Stanford University School of Medicine, Stanford, California (G.P.)
| | - Emily J Yao
- Ph.D. Program in Biomolecular Pharmacology (J.A.B., S.I.G.), Laboratory of Addiction Genetics, Department of Pharmacology and Experimental Therapeutics and Psychiatry (J.A.B., E.J.Y., W.B.L., J.L.S., A.A.S., K.D.S., A.L.W., C.D.B.), Department of Biology and Biochemistry, Center for Network Systems Biology (S.I.G., A.E.), and Graduate Program in Neuroscience (W.B.L), Boston University School of Medicine, Boston, Massachusetts; Transformative Training Program in Addiction Science (TTPAS) (J.A.B., W.B.L.) and Undergraduate Research Opportunity Program (J.L.S., K.D.S.), Boston University, Boston, Massachusetts; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (C.L.L., M.T.F.); Department of Pharmacology and Toxicity, Center for Human Toxicology, University of Utah, Salt Lake City, Utah (O.A., D.E.M., C.A.R.); and Department of Anesthesiology, Pain, and Preoperative Medicine Stanford University School of Medicine, Stanford, California (G.P.)
| | - Stanley I Goldstein
- Ph.D. Program in Biomolecular Pharmacology (J.A.B., S.I.G.), Laboratory of Addiction Genetics, Department of Pharmacology and Experimental Therapeutics and Psychiatry (J.A.B., E.J.Y., W.B.L., J.L.S., A.A.S., K.D.S., A.L.W., C.D.B.), Department of Biology and Biochemistry, Center for Network Systems Biology (S.I.G., A.E.), and Graduate Program in Neuroscience (W.B.L), Boston University School of Medicine, Boston, Massachusetts; Transformative Training Program in Addiction Science (TTPAS) (J.A.B., W.B.L.) and Undergraduate Research Opportunity Program (J.L.S., K.D.S.), Boston University, Boston, Massachusetts; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (C.L.L., M.T.F.); Department of Pharmacology and Toxicity, Center for Human Toxicology, University of Utah, Salt Lake City, Utah (O.A., D.E.M., C.A.R.); and Department of Anesthesiology, Pain, and Preoperative Medicine Stanford University School of Medicine, Stanford, California (G.P.)
| | - William B Lynch
- Ph.D. Program in Biomolecular Pharmacology (J.A.B., S.I.G.), Laboratory of Addiction Genetics, Department of Pharmacology and Experimental Therapeutics and Psychiatry (J.A.B., E.J.Y., W.B.L., J.L.S., A.A.S., K.D.S., A.L.W., C.D.B.), Department of Biology and Biochemistry, Center for Network Systems Biology (S.I.G., A.E.), and Graduate Program in Neuroscience (W.B.L), Boston University School of Medicine, Boston, Massachusetts; Transformative Training Program in Addiction Science (TTPAS) (J.A.B., W.B.L.) and Undergraduate Research Opportunity Program (J.L.S., K.D.S.), Boston University, Boston, Massachusetts; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (C.L.L., M.T.F.); Department of Pharmacology and Toxicity, Center for Human Toxicology, University of Utah, Salt Lake City, Utah (O.A., D.E.M., C.A.R.); and Department of Anesthesiology, Pain, and Preoperative Medicine Stanford University School of Medicine, Stanford, California (G.P.)
| | - Julia L Scotellaro
- Ph.D. Program in Biomolecular Pharmacology (J.A.B., S.I.G.), Laboratory of Addiction Genetics, Department of Pharmacology and Experimental Therapeutics and Psychiatry (J.A.B., E.J.Y., W.B.L., J.L.S., A.A.S., K.D.S., A.L.W., C.D.B.), Department of Biology and Biochemistry, Center for Network Systems Biology (S.I.G., A.E.), and Graduate Program in Neuroscience (W.B.L), Boston University School of Medicine, Boston, Massachusetts; Transformative Training Program in Addiction Science (TTPAS) (J.A.B., W.B.L.) and Undergraduate Research Opportunity Program (J.L.S., K.D.S.), Boston University, Boston, Massachusetts; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (C.L.L., M.T.F.); Department of Pharmacology and Toxicity, Center for Human Toxicology, University of Utah, Salt Lake City, Utah (O.A., D.E.M., C.A.R.); and Department of Anesthesiology, Pain, and Preoperative Medicine Stanford University School of Medicine, Stanford, California (G.P.)
| | - Anyaa A Shah
- Ph.D. Program in Biomolecular Pharmacology (J.A.B., S.I.G.), Laboratory of Addiction Genetics, Department of Pharmacology and Experimental Therapeutics and Psychiatry (J.A.B., E.J.Y., W.B.L., J.L.S., A.A.S., K.D.S., A.L.W., C.D.B.), Department of Biology and Biochemistry, Center for Network Systems Biology (S.I.G., A.E.), and Graduate Program in Neuroscience (W.B.L), Boston University School of Medicine, Boston, Massachusetts; Transformative Training Program in Addiction Science (TTPAS) (J.A.B., W.B.L.) and Undergraduate Research Opportunity Program (J.L.S., K.D.S.), Boston University, Boston, Massachusetts; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (C.L.L., M.T.F.); Department of Pharmacology and Toxicity, Center for Human Toxicology, University of Utah, Salt Lake City, Utah (O.A., D.E.M., C.A.R.); and Department of Anesthesiology, Pain, and Preoperative Medicine Stanford University School of Medicine, Stanford, California (G.P.)
| | - Katherine D Sena
- Ph.D. Program in Biomolecular Pharmacology (J.A.B., S.I.G.), Laboratory of Addiction Genetics, Department of Pharmacology and Experimental Therapeutics and Psychiatry (J.A.B., E.J.Y., W.B.L., J.L.S., A.A.S., K.D.S., A.L.W., C.D.B.), Department of Biology and Biochemistry, Center for Network Systems Biology (S.I.G., A.E.), and Graduate Program in Neuroscience (W.B.L), Boston University School of Medicine, Boston, Massachusetts; Transformative Training Program in Addiction Science (TTPAS) (J.A.B., W.B.L.) and Undergraduate Research Opportunity Program (J.L.S., K.D.S.), Boston University, Boston, Massachusetts; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (C.L.L., M.T.F.); Department of Pharmacology and Toxicity, Center for Human Toxicology, University of Utah, Salt Lake City, Utah (O.A., D.E.M., C.A.R.); and Department of Anesthesiology, Pain, and Preoperative Medicine Stanford University School of Medicine, Stanford, California (G.P.)
| | - Alyssa L Wong
- Ph.D. Program in Biomolecular Pharmacology (J.A.B., S.I.G.), Laboratory of Addiction Genetics, Department of Pharmacology and Experimental Therapeutics and Psychiatry (J.A.B., E.J.Y., W.B.L., J.L.S., A.A.S., K.D.S., A.L.W., C.D.B.), Department of Biology and Biochemistry, Center for Network Systems Biology (S.I.G., A.E.), and Graduate Program in Neuroscience (W.B.L), Boston University School of Medicine, Boston, Massachusetts; Transformative Training Program in Addiction Science (TTPAS) (J.A.B., W.B.L.) and Undergraduate Research Opportunity Program (J.L.S., K.D.S.), Boston University, Boston, Massachusetts; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (C.L.L., M.T.F.); Department of Pharmacology and Toxicity, Center for Human Toxicology, University of Utah, Salt Lake City, Utah (O.A., D.E.M., C.A.R.); and Department of Anesthesiology, Pain, and Preoperative Medicine Stanford University School of Medicine, Stanford, California (G.P.)
| | - Colton L Linnertz
- Ph.D. Program in Biomolecular Pharmacology (J.A.B., S.I.G.), Laboratory of Addiction Genetics, Department of Pharmacology and Experimental Therapeutics and Psychiatry (J.A.B., E.J.Y., W.B.L., J.L.S., A.A.S., K.D.S., A.L.W., C.D.B.), Department of Biology and Biochemistry, Center for Network Systems Biology (S.I.G., A.E.), and Graduate Program in Neuroscience (W.B.L), Boston University School of Medicine, Boston, Massachusetts; Transformative Training Program in Addiction Science (TTPAS) (J.A.B., W.B.L.) and Undergraduate Research Opportunity Program (J.L.S., K.D.S.), Boston University, Boston, Massachusetts; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (C.L.L., M.T.F.); Department of Pharmacology and Toxicity, Center for Human Toxicology, University of Utah, Salt Lake City, Utah (O.A., D.E.M., C.A.R.); and Department of Anesthesiology, Pain, and Preoperative Medicine Stanford University School of Medicine, Stanford, California (G.P.)
| | - Olga Averin
- Ph.D. Program in Biomolecular Pharmacology (J.A.B., S.I.G.), Laboratory of Addiction Genetics, Department of Pharmacology and Experimental Therapeutics and Psychiatry (J.A.B., E.J.Y., W.B.L., J.L.S., A.A.S., K.D.S., A.L.W., C.D.B.), Department of Biology and Biochemistry, Center for Network Systems Biology (S.I.G., A.E.), and Graduate Program in Neuroscience (W.B.L), Boston University School of Medicine, Boston, Massachusetts; Transformative Training Program in Addiction Science (TTPAS) (J.A.B., W.B.L.) and Undergraduate Research Opportunity Program (J.L.S., K.D.S.), Boston University, Boston, Massachusetts; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (C.L.L., M.T.F.); Department of Pharmacology and Toxicity, Center for Human Toxicology, University of Utah, Salt Lake City, Utah (O.A., D.E.M., C.A.R.); and Department of Anesthesiology, Pain, and Preoperative Medicine Stanford University School of Medicine, Stanford, California (G.P.)
| | - David E Moody
- Ph.D. Program in Biomolecular Pharmacology (J.A.B., S.I.G.), Laboratory of Addiction Genetics, Department of Pharmacology and Experimental Therapeutics and Psychiatry (J.A.B., E.J.Y., W.B.L., J.L.S., A.A.S., K.D.S., A.L.W., C.D.B.), Department of Biology and Biochemistry, Center for Network Systems Biology (S.I.G., A.E.), and Graduate Program in Neuroscience (W.B.L), Boston University School of Medicine, Boston, Massachusetts; Transformative Training Program in Addiction Science (TTPAS) (J.A.B., W.B.L.) and Undergraduate Research Opportunity Program (J.L.S., K.D.S.), Boston University, Boston, Massachusetts; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (C.L.L., M.T.F.); Department of Pharmacology and Toxicity, Center for Human Toxicology, University of Utah, Salt Lake City, Utah (O.A., D.E.M., C.A.R.); and Department of Anesthesiology, Pain, and Preoperative Medicine Stanford University School of Medicine, Stanford, California (G.P.)
| | - Christopher A Reilly
- Ph.D. Program in Biomolecular Pharmacology (J.A.B., S.I.G.), Laboratory of Addiction Genetics, Department of Pharmacology and Experimental Therapeutics and Psychiatry (J.A.B., E.J.Y., W.B.L., J.L.S., A.A.S., K.D.S., A.L.W., C.D.B.), Department of Biology and Biochemistry, Center for Network Systems Biology (S.I.G., A.E.), and Graduate Program in Neuroscience (W.B.L), Boston University School of Medicine, Boston, Massachusetts; Transformative Training Program in Addiction Science (TTPAS) (J.A.B., W.B.L.) and Undergraduate Research Opportunity Program (J.L.S., K.D.S.), Boston University, Boston, Massachusetts; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (C.L.L., M.T.F.); Department of Pharmacology and Toxicity, Center for Human Toxicology, University of Utah, Salt Lake City, Utah (O.A., D.E.M., C.A.R.); and Department of Anesthesiology, Pain, and Preoperative Medicine Stanford University School of Medicine, Stanford, California (G.P.)
| | - Gary Peltz
- Ph.D. Program in Biomolecular Pharmacology (J.A.B., S.I.G.), Laboratory of Addiction Genetics, Department of Pharmacology and Experimental Therapeutics and Psychiatry (J.A.B., E.J.Y., W.B.L., J.L.S., A.A.S., K.D.S., A.L.W., C.D.B.), Department of Biology and Biochemistry, Center for Network Systems Biology (S.I.G., A.E.), and Graduate Program in Neuroscience (W.B.L), Boston University School of Medicine, Boston, Massachusetts; Transformative Training Program in Addiction Science (TTPAS) (J.A.B., W.B.L.) and Undergraduate Research Opportunity Program (J.L.S., K.D.S.), Boston University, Boston, Massachusetts; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (C.L.L., M.T.F.); Department of Pharmacology and Toxicity, Center for Human Toxicology, University of Utah, Salt Lake City, Utah (O.A., D.E.M., C.A.R.); and Department of Anesthesiology, Pain, and Preoperative Medicine Stanford University School of Medicine, Stanford, California (G.P.)
| | - Andrew Emili
- Ph.D. Program in Biomolecular Pharmacology (J.A.B., S.I.G.), Laboratory of Addiction Genetics, Department of Pharmacology and Experimental Therapeutics and Psychiatry (J.A.B., E.J.Y., W.B.L., J.L.S., A.A.S., K.D.S., A.L.W., C.D.B.), Department of Biology and Biochemistry, Center for Network Systems Biology (S.I.G., A.E.), and Graduate Program in Neuroscience (W.B.L), Boston University School of Medicine, Boston, Massachusetts; Transformative Training Program in Addiction Science (TTPAS) (J.A.B., W.B.L.) and Undergraduate Research Opportunity Program (J.L.S., K.D.S.), Boston University, Boston, Massachusetts; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (C.L.L., M.T.F.); Department of Pharmacology and Toxicity, Center for Human Toxicology, University of Utah, Salt Lake City, Utah (O.A., D.E.M., C.A.R.); and Department of Anesthesiology, Pain, and Preoperative Medicine Stanford University School of Medicine, Stanford, California (G.P.)
| | - Martin T Ferris
- Ph.D. Program in Biomolecular Pharmacology (J.A.B., S.I.G.), Laboratory of Addiction Genetics, Department of Pharmacology and Experimental Therapeutics and Psychiatry (J.A.B., E.J.Y., W.B.L., J.L.S., A.A.S., K.D.S., A.L.W., C.D.B.), Department of Biology and Biochemistry, Center for Network Systems Biology (S.I.G., A.E.), and Graduate Program in Neuroscience (W.B.L), Boston University School of Medicine, Boston, Massachusetts; Transformative Training Program in Addiction Science (TTPAS) (J.A.B., W.B.L.) and Undergraduate Research Opportunity Program (J.L.S., K.D.S.), Boston University, Boston, Massachusetts; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (C.L.L., M.T.F.); Department of Pharmacology and Toxicity, Center for Human Toxicology, University of Utah, Salt Lake City, Utah (O.A., D.E.M., C.A.R.); and Department of Anesthesiology, Pain, and Preoperative Medicine Stanford University School of Medicine, Stanford, California (G.P.)
| | - Camron D Bryant
- Ph.D. Program in Biomolecular Pharmacology (J.A.B., S.I.G.), Laboratory of Addiction Genetics, Department of Pharmacology and Experimental Therapeutics and Psychiatry (J.A.B., E.J.Y., W.B.L., J.L.S., A.A.S., K.D.S., A.L.W., C.D.B.), Department of Biology and Biochemistry, Center for Network Systems Biology (S.I.G., A.E.), and Graduate Program in Neuroscience (W.B.L), Boston University School of Medicine, Boston, Massachusetts; Transformative Training Program in Addiction Science (TTPAS) (J.A.B., W.B.L.) and Undergraduate Research Opportunity Program (J.L.S., K.D.S.), Boston University, Boston, Massachusetts; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (C.L.L., M.T.F.); Department of Pharmacology and Toxicity, Center for Human Toxicology, University of Utah, Salt Lake City, Utah (O.A., D.E.M., C.A.R.); and Department of Anesthesiology, Pain, and Preoperative Medicine Stanford University School of Medicine, Stanford, California (G.P.)
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Kong YF, Li MK, Yuan YX, Yang ZY, Yu WY, Zhao PZ, Zhou JY. Detection of Parent-of-Origin Effects for the Variants Associated With Behavioral Disinhibition in the MCTFR Data. Front Genet 2022; 13:831685. [PMID: 35559008 PMCID: PMC9086303 DOI: 10.3389/fgene.2022.831685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 03/28/2022] [Indexed: 11/13/2022] Open
Abstract
Behavioral disinhibition is one of the important characteristics of many mental diseases. It has been reported in literature that serious behavioral disinhibition will affect people's health and greatly reduce people's quality of life. Meanwhile, behavioral disinhibition can easily lead to illegal drug abuse and violent crimes, etc., which will bring great harm to the society. At present, large-scale genome-wide association analysis has identified many loci associated with behavioral disinhibition. However, these studies have not incorporated the parent-of-origin effects (POE) into analysis, which may ignore or underestimate the genetic effects of loci on behavioral disinhibition. Therefore, in this article, we analyzed the five phenotypes related to behavioral disinhibition in the Minnesota Center for Twin and Family Research data (nicotine, alcohol consumption, alcohol dependence, illicit drugs, and non-substance use related behavioral disinhibition), to further explore the POE of variants on behavioral disinhibition. We applied a linear mixed model to test for the POE at a genome-wide scale on five transformed phenotypes, and found nine SNPs with statistically significant POE at the significance level of 5 × 10-8. Among them, SNPs rs4141854, rs9394515, and rs4711553 have been reported to be associated with two neurological disorders (restless legs syndrome and Tourette's syndrome) which are related to behavioral disinhibition; SNPs rs12960235 and rs715351 have been found to be associated with head and neck squamous cell carcinoma, skin cancer and type I diabetes, while both SNPs have not been identified to be related to behavioral disinhibition in literature; SNPs rs704833, rs6837925, rs1863548, and rs11067062 are novel loci identified in this article, and their function annotations have not been reported in literature. Follow-up study in molecular genetics is needed to verify whether they are surely related to behavioral disinhibition.
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Affiliation(s)
- Yi-Fan Kong
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, China.,Guangdong-Hong Hong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
| | - Meng-Kai Li
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, China.,Guangdong-Hong Hong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
| | - Yu-Xin Yuan
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, China
| | - Zi-Ying Yang
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, China
| | - Wen-Yi Yu
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, China
| | - Pei-Zhen Zhao
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, China
| | - Ji-Yuan Zhou
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, China.,Guangdong-Hong Hong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou, China
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13
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Li MK, Yuan YX, Zhu B, Wang KW, Fung WK, Zhou JY. Gene-Based Methods for Estimating the Degree of the Skewness of X Chromosome Inactivation. Genes (Basel) 2022; 13:genes13050827. [PMID: 35627212 PMCID: PMC9140558 DOI: 10.3390/genes13050827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 05/01/2022] [Accepted: 05/02/2022] [Indexed: 11/16/2022] Open
Abstract
Skewed X chromosome inactivation (XCI-S) has been reported to be associated with some X-linked diseases, and currently several methods have been proposed to estimate the degree of the XCI-S (denoted as γ) for a single locus. However, no method has been available to estimate γ for genes. Therefore, in this paper, we first propose the point estimate and the penalized point estimate of γ for genes, and then derive its confidence intervals based on the Fieller’s and penalized Fieller’s methods, respectively. Further, we consider the constraint condition of γ∈[0, 2] and propose the Bayesian methods to obtain the point estimates and the credible intervals of γ, where a truncated normal prior and a uniform prior are respectively used (denoted as GBN and GBU). The simulation results show that the Bayesian methods can avoid the extreme point estimates (0 or 2), the empty sets, the noninformative intervals ([0, 2]) and the discontinuous intervals to occur. GBN performs best in both the point estimation and the interval estimation. Finally, we apply the proposed methods to the Minnesota Center for Twin and Family Research data for their practical use. In summary, in practical applications, we recommend using GBN to estimate γ of genes.
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Affiliation(s)
- Meng-Kai Li
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China; (M.-K.L.); (Y.-X.Y.); (B.Z.); (K.-W.W.)
- Guangdong-Hong Hong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou 510006, China
| | - Yu-Xin Yuan
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China; (M.-K.L.); (Y.-X.Y.); (B.Z.); (K.-W.W.)
- Guangdong-Hong Hong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou 510006, China
| | - Bin Zhu
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China; (M.-K.L.); (Y.-X.Y.); (B.Z.); (K.-W.W.)
- Guangdong-Hong Hong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou 510006, China
| | - Kai-Wen Wang
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China; (M.-K.L.); (Y.-X.Y.); (B.Z.); (K.-W.W.)
- Guangdong-Hong Hong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou 510006, China
| | - Wing Kam Fung
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, China;
| | - Ji-Yuan Zhou
- Department of Biostatistics, State Key Laboratory of Organ Failure Research, Ministry of Education, and Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou 510515, China; (M.-K.L.); (Y.-X.Y.); (B.Z.); (K.-W.W.)
- Guangdong-Hong Hong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangzhou 510006, China
- Correspondence:
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Deak JD, Clark DA, Liu M, Schaefer JD, Jang SK, Durbin CE, Iacono WG, McGue M, Vrieze S, Hicks BM. Alcohol and nicotine polygenic scores are associated with the development of alcohol and nicotine use problems from adolescence to young adulthood. Addiction 2022; 117:1117-1127. [PMID: 34590376 PMCID: PMC8931861 DOI: 10.1111/add.15697] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 09/10/2021] [Indexed: 02/05/2023]
Abstract
BACKGROUND AND AIMS Molecular genetic studies of alcohol and nicotine use have identified many genome-wide association study (GWAS) loci. We measured associations between drinking and smoking polygenic scores (PGS) and trajectories of alcohol and nicotine use outcomes from late childhood to early adulthood, substance-specific versus broader-liability PGS effects, and if PGS performance varied for consumption versus problematic substance use. DESIGN, SETTING, PARTICIPANTS AND MEASUREMENTS We fitted latent growth curve models with structured residuals to scores on measures of alcohol and nicotine use and problems from ages 14 to 34 years. We then estimated associations between the intercept (initial status) and slope (rate of change) parameters and PGSs for drinks per week (DPW), problematic alcohol use (PAU), cigarettes per day (CPD) and ever being a regular smoker (SMK), controlling for sex and genetic principal components. All data were analyzed in the United States. PGSs were calculated for participants of the Minnesota Twin Family Study (n = 3225) using results from the largest GWAS of alcohol and nicotine consumption and problematic use to date. FINDINGS Each PGS was associated with trajectories of use for their respective substances [i.e. DPW (βmean = 0.08; βrange = 0.02-0.12) and PAU (βmean = 0.12; βrange = -0.02 to 0.31) for alcohol; CPD (βmean = 0.08; βrange = 0.04-0.14) and SMK (βmean = 0.18; βrange = 0.05-0.36) for nicotine]. The PAU and SMK PGSs also exhibited cross-substance associations (i.e. PAU for nicotine-specific intercepts and SMK for alcohol intercepts and slope). All identified SMK PGS effects remained as significant predictors of nicotine and alcohol trajectories (βmean = 0.15; βrange = 0.02-0.33), even after adjusting for the respective effects of all other PGSs. CONCLUSIONS Substance use-related polygenic scores (PGSs) vary in the strength and generality versus specificity of their associations with substance use and problems over time. The regular smoking PGS appears to be a robust predictor of substance use trajectories and seems to measure both nicotine-specific and non-specific genetic liability for substance use, and potentially externalizing problems in general.
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Affiliation(s)
- Joseph D. Deak
- Yale University, New Haven, CT, USA
- VA CT Healthcare System, West Haven, CT, USA
| | | | | | | | | | | | | | - Matt McGue
- University of Minnesota, Minneapolis, MN, USA
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15
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Kakhkharova ZI, Zharkov DO, Grin IR. A Low-Activity Polymorphic Variant of Human NEIL2 DNA Glycosylase. Int J Mol Sci 2022; 23:ijms23042212. [PMID: 35216329 PMCID: PMC8879280 DOI: 10.3390/ijms23042212] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 02/14/2022] [Accepted: 02/14/2022] [Indexed: 01/05/2023] Open
Abstract
Human NEIL2 DNA glycosylase (hNEIL2) is a base excision repair protein that removes oxidative lesions from DNA. A distinctive feature of hNEIL2 is its preference for the lesions in bubbles and other non-canonical DNA structures. Although a number of associations of polymorphisms in the hNEIL2 gene were reported, there is little data on the functionality of the encoded protein variants, as follows: only hNEIL2 R103Q was described as unaffected, and R257L, as less proficient in supporting the repair in a reconstituted system. Here, we report the biochemical characterization of two hNEIL2 variants found as polymorphisms in the general population, R103W and P304T. Arg103 is located in a long disordered segment within the N-terminal domain of hNEIL2, while Pro304 occupies a position in the β-turn of the DNA-binding zinc finger motif. Similar to the wild-type protein, both of the variants could catalyze base excision and nick DNA by β-elimination but demonstrated a lower affinity for DNA. Steady-state kinetics indicates that the P304T variant has its catalytic efficiency (in terms of kcat/KM) reduced ~5-fold compared with the wild-type hNEIL2, whereas the R103W enzyme is much less affected. The P304T variant was also less proficient than the wild-type, or R103W hNEIL2, in the removal of damaged bases from single-stranded and bubble-containing DNA. Overall, hNEIL2 P304T could be worthy of a detailed epidemiological analysis as a possible cancer risk modifier.
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Affiliation(s)
- Zarina I. Kakhkharova
- SB RAS Institute of Chemical Biology and Fundamental Medicine, 630090 Novosibirsk, Russia;
| | - Dmitry O. Zharkov
- SB RAS Institute of Chemical Biology and Fundamental Medicine, 630090 Novosibirsk, Russia;
- Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
- Correspondence: (D.O.Z.); (I.R.G.)
| | - Inga R. Grin
- SB RAS Institute of Chemical Biology and Fundamental Medicine, 630090 Novosibirsk, Russia;
- Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
- Correspondence: (D.O.Z.); (I.R.G.)
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Fernàndez-Castillo N, Cabana-Domínguez J, Corominas R, Cormand B. Molecular genetics of cocaine use disorders in humans. Mol Psychiatry 2022; 27:624-639. [PMID: 34453125 PMCID: PMC8960411 DOI: 10.1038/s41380-021-01256-1] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 07/01/2021] [Accepted: 07/30/2021] [Indexed: 12/11/2022]
Abstract
Drug addiction, one of the major health problems worldwide, is characterized by the loss of control in drug intake, craving, and withdrawal. At the individual level, drugs of abuse produce serious consequences on health and have a negative impact on the family environment and on interpersonal and work relationships. At a wider scale, they have significant socio-economic and public health consequences and they cause delinquency and citizen insecurity. Cocaine, a psychostimulant substance, is one of the most used illicit drugs, especially in America, Western Europe, and Australia. Cocaine use disorders (CUD) are complex multifactorial conditions driven by both genetic and environmental influences. Importantly, not all people who use cocaine develop CUD, and this is due, at least in part, to biological factors that are encoded in the genome of individuals. Acute and repeated use of cocaine induces epigenetic and gene expression changes responsible for the neuronal adaptations and the remodeling of brain circuits that lead to the transition from use to abuse or dependence. The purpose of this review is to delineate such factors, which should eventually help to understand the inter-individual variability in the susceptibility to cocaine addiction. Heritability estimates for CUD are high and genetic risk factors for cocaine addiction have been investigated by candidate gene association studies (CGAS) and genome-wide association studies (GWAS), reviewed here. Also, the high comorbidity that exists between CUD and several other psychiatric disorders is well known and includes phenotypes like schizophrenia, aggression, antisocial or risk-taking behaviors. Such comorbidities are associated with a worse lifetime trajectory, and here we report shared genetic factors that may contribute to them. Gene expression changes and epigenetic modifications induced by cocaine use and chronic abuse in humans are addressed by reviewing transcriptomic studies performed on neuronal cells and on postmortem brains. We report some genes which expression is altered by cocaine that also bear genetic risk variants for the disorder. Finally, we have a glance to the pharmacogenetics of CUD treatments, still in early stages. A better understanding of the genetic underpinnings of CUD will foster the search of effective treatments and help to move forward to personalized medicine.
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Affiliation(s)
- Noèlia Fernàndez-Castillo
- Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona, Barcelona, Catalonia, Spain. .,Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain. .,Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Catalonia, Spain. .,Institut de Recerca Sant Joan de Déu (IR-SJD), Esplugues de Llobregat, Catalonia, Spain.
| | - Judit Cabana-Domínguez
- grid.5841.80000 0004 1937 0247Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona, Barcelona, Catalonia Spain ,grid.452372.50000 0004 1791 1185Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain ,grid.5841.80000 0004 1937 0247Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Catalonia Spain ,grid.411160.30000 0001 0663 8628Institut de Recerca Sant Joan de Déu (IR-SJD), Esplugues de Llobregat, Catalonia Spain
| | - Roser Corominas
- grid.5841.80000 0004 1937 0247Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona, Barcelona, Catalonia Spain ,grid.452372.50000 0004 1791 1185Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain ,grid.5841.80000 0004 1937 0247Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Catalonia Spain ,grid.411160.30000 0001 0663 8628Institut de Recerca Sant Joan de Déu (IR-SJD), Esplugues de Llobregat, Catalonia Spain
| | - Bru Cormand
- Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona, Barcelona, Catalonia, Spain. .,Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid, Spain. .,Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Catalonia, Spain. .,Institut de Recerca Sant Joan de Déu (IR-SJD), Esplugues de Llobregat, Catalonia, Spain.
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Elam KK, Mun CJ, Kutzner J, Ha T. Polygenic Risk for Aggression Predicts Adult Substance Use Disorder Diagnoses via Substance Use Offending in Emerging Adulthood and is Moderated by a Family-Centered Intervention. Behav Genet 2021; 51:607-618. [PMID: 34117582 PMCID: PMC8404142 DOI: 10.1007/s10519-021-10070-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 05/28/2021] [Indexed: 12/31/2022]
Abstract
A substance use offense reflects an encounter with law enforcement and the court system in response to breaking the law which may increase risk for substance use problems later in life. Individuals may also be at risk for substance use offending and substance use problems based on genetic predisposition. We examined a mediation model in which polygenic risk for aggression predicted adult substance use disorder diagnoses (SUD) via substance use offending in emerging adulthood. In addition, we explored for potential attenuation of genetic influences on these outcomes by a family-based intervention, the Family Check-Up (FCU). Secondary data analyses based upon the Project Alliance 1 sample was conducted among those with genetic data (n = 631; 322 from control and 309 from FCU intervention). The sample was ethnically diverse (30% African American, 44% European American, 6% Latinx, 4% Asian American, 3% Native American, and 13% Other). Greater polygenic risk for aggression was found to increase risk for substance use violations (age 19-23), which in turn was associated with greater likelihood of being diagnosed with SUD at age 27. A gene-by-intervention effect was found in which individuals in the control group had greater risk for SUD with increasing polygenic risk for aggression. Some convergence in results was found when replicating analyses in African American and European American subgroups. Results imply that genetic predisposition may increase risk for problematic substance use later in life via antisocial behavior, such as substance use offending, and that this can be attenuated by a family-centered intervention.
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Affiliation(s)
- Kit K Elam
- Department of Applied Health Science, Indiana University, 1025 E. 7th St., Suite 116, Bloomington, IN, 47405, USA.
| | - Chung Jung Mun
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, USA
- Edson College of Nursing and Health Innovation, Arizona State University, Tempe, USA
| | - Jodi Kutzner
- Department of Applied Health Science, Indiana University, 1025 E. 7th St., Suite 116, Bloomington, IN, 47405, USA
| | - Thao Ha
- Department of Psychology, Arizona State University, Tempe, USA
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Brislin SJ, Clark DA, Heitzeg MM, Samek DR, Iacono WG, McGue M, Hicks BM. Co-development of alcohol use problems and antisocial peer affiliation from ages 11 to 34: selection, socialization and genetic and environmental influences. Addiction 2021; 116:1999-2007. [PMID: 33405277 PMCID: PMC8254734 DOI: 10.1111/add.15402] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 09/02/2020] [Accepted: 12/23/2020] [Indexed: 11/28/2022]
Abstract
BACKGROUND AND AIMS Social context is an important factor in determining the developmental trajectory of alcohol use. We examined the co-development between alcohol use problems and antisocial peer affiliation. We also estimated the genetic and environmental influences on alcohol use problems, antisocial peer affiliation and their co-development over time. DESIGN Longitudinal study using bivariate latent basis models with structured residuals (LBM-SR). A biometric model was then fitted to estimate the genetic and environmental influences on the growth factors and their covariances. SETTING The United States mid-west region. PARTICIPANTS Members of the Minnesota Twin Family Study (MTFS), an ongoing, longitudinal study of 3762 (52% female) twins (1881 pairs). MEASUREMENTS Alcohol use problems were assessed using a composite measure of average number of drinks per occasion in the past 12 months, maximum number of drinks in 24 hours and DSM-III-R symptoms of alcohol abuse and dependence. Antisocial peer affiliation was measured by self-report of the proportion of one's friends who exhibited types of antisocial behaviors. FINDINGS The LBM-SR model revealed that there was a large correlation between the growth factors for alcohol use problems and antisocial peer affiliation [r = 0.78, 95% confidence interval (CI) = 0.76, 0.80] and cross-lagged effects consistent with both selection and socialization effects. Additionally, antisocial peer affiliation in adolescence was associated with greater increases in alcohol use problems over time (r = 0.57, 95% CI = 0.54, 0.60). Genetic influences largely accounted for the association between antisocial peer affiliation in pre-adolescence and growth in alcohol use problems, while shared environmental influences accounted for the correlation between antisocial peer affiliation and alcohol use problems growth factors. CONCLUSIONS Antisocial peer affiliation in adolescence appears to be a salient, genetically influenced risk factor for early alcohol use and increase in alcohol use from adolescence to young adulthood.
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An Introduction to the Special Issue: Developmental Behavior Genetics and Externalizing Psychopathology. Behav Genet 2021; 51:443-447. [PMID: 34304323 DOI: 10.1007/s10519-021-10078-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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20
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A statistical measure for the skewness of X chromosome inactivation for quantitative traits and its application to the MCTFR data. BMC Genom Data 2021; 22:24. [PMID: 34215184 PMCID: PMC8254321 DOI: 10.1186/s12863-021-00978-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 06/17/2021] [Indexed: 11/24/2022] Open
Abstract
Background X chromosome inactivation (XCI) is that one of two chromosomes in mammalian females is silenced during early development of embryos. There has been a statistical measure for the degree of the skewness of XCI for qualitative traits. However, no method is available for such task at quantitative trait loci. Results In this article, we extend the existing statistical measure for the skewness of XCI for qualitative traits, and the likelihood ratio, Fieller’s and delta methods for constructing the corresponding confidence intervals, and make them accommodate quantitative traits. The proposed measure is a ratio of two linear regression coefficients when association exists. Noting that XCI may cause variance heterogeneity of the traits across different genotypes in females, we obtain the point estimate and confidence intervals of the measure by incorporating such information. The hypothesis testing of the proposed methods is also investigated. We conduct extensive simulation studies to assess the performance of the proposed methods. Simulation results demonstrate that the median of the point estimates of the measure is very close to the pre-specified true value. The likelihood ratio and Fieller’s methods control the size well, and have the similar test power and accurate coverage probability, which perform better than the delta method. So far, we are not aware of any association study for the X-chromosomal loci in the Minnesota Center for Twin and Family Research data. So, we apply our proposed methods to these data for their practical use and find that only the rs792959 locus, which is simultaneously associated with the illicit drug composite score and behavioral disinhibition composite score, may undergo XCI skewing. However, this needs to be confirmed by molecular genetics. Conclusions We recommend the Fieller’s method in practical use because it is a non-iterative procedure and has the similar performance to the likelihood ratio method. Supplementary Information The online version contains supplementary material available at 10.1186/s12863-021-00978-z.
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21
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Genetic underpinnings of affective temperaments: a pilot GWAS investigation identifies a new genome-wide significant SNP for anxious temperament in ADGRB3 gene. Transl Psychiatry 2021; 11:337. [PMID: 34075027 PMCID: PMC8169753 DOI: 10.1038/s41398-021-01436-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 04/29/2021] [Accepted: 05/05/2021] [Indexed: 12/22/2022] Open
Abstract
Although recently a large-sample GWASs identified significant loci in the background of depression, the heterogeneity of the depressive phenotype and the lack of accurate phenotyping hinders applicability of findings. We carried out a pilot GWAS with in-depth phenotyping of affective temperaments, considered as subclinical manifestations and high-risk states for affective disorders, in a general population sample of European origin. Affective temperaments were measured by TEMPS-A. SNP-level association was assessed by linear regression models, assuming an additive genetic effect, using PLINK1.9. Gender, age, the first ten principal components (PCs) and the other four temperaments were included in the regression models as covariates. SNP-level relevances (p-values) were aggregated to gene level using the PEGASUS method1. In SNP-based tests, a Bonferroni-corrected significance threshold of p ≤ 5.0 × 10-8 and a suggestive significance threshold of p ≤ 1.0 × 10-5, whereas in gene-based tests a Bonferroni-corrected significance of 2.0 × 10-6 and a suggestive significance of p ≤ 4.0 × 10-4 was established. To explore known functional effects of the most significant SNPs, FUMA v1.3.5 was used. We identified 1 significant and 21 suggestively significant SNPs in ADGRB3, expressed in the brain, for anxious temperament. Several other brain-relevant SNPs and genes emerged at suggestive significance for the other temperaments. Functional analyses reflecting effect on gene expression and participation in chromatin interactions also pointed to several genes expressed in the brain with potentially relevant phenotypes regulated by our top SNPs. Our findings need to be tested in larger GWA studies and candidate gene analyses in well-phenotyped samples in relation to affective disorders and related phenotypes.
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McGuire D, Jiang Y, Liu M, Weissenkampen JD, Eckert S, Yang L, Chen F, Berg A, Vrieze S, Jiang B, Li Q, Liu DJ. Model-based assessment of replicability for genome-wide association meta-analysis. Nat Commun 2021; 12:1964. [PMID: 33785739 PMCID: PMC8009871 DOI: 10.1038/s41467-021-21226-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Accepted: 01/07/2021] [Indexed: 01/17/2023] Open
Abstract
Genome-wide association meta-analysis (GWAMA) is an effective approach to enlarge sample sizes and empower the discovery of novel associations between genotype and phenotype. Independent replication has been used as a gold-standard for validating genetic associations. However, as current GWAMA often seeks to aggregate all available datasets, it becomes impossible to find a large enough independent dataset to replicate new discoveries. Here we introduce a method, MAMBA (Meta-Analysis Model-based Assessment of replicability), for assessing the "posterior-probability-of-replicability" for identified associations by leveraging the strength and consistency of association signals between contributing studies. We demonstrate using simulations that MAMBA is more powerful and robust than existing methods, and produces more accurate genetic effects estimates. We apply MAMBA to a large-scale meta-analysis of addiction phenotypes with 1.2 million individuals. In addition to accurately identifying replicable common variant associations, MAMBA also pinpoints novel replicable rare variant associations from imputation-based GWAMA and hence greatly expands the set of analyzable variants.
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Affiliation(s)
- Daniel McGuire
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA
| | - Yu Jiang
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA
| | - Mengzhen Liu
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - J Dylan Weissenkampen
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA
| | - Scott Eckert
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA
| | - Lina Yang
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA
| | - Fang Chen
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA
| | | | - Arthur Berg
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA
| | - Scott Vrieze
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Bibo Jiang
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA.
| | - Qunhua Li
- Department of Statistics, Penn State University, University Park, PA, USA.
| | - Dajiang J Liu
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA.
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Li W, Thygesen JH, O'Brien NL, Heydtmann M, Smith I, Degenhardt F, Nöthen MM, Morgan MY, Bass NJ, McQuillin A. The influence of regression models on genome-wide association studies of alcohol dependence: a comparison of binary and quantitative analyses. Psychiatr Genet 2021; 31:13-20. [PMID: 33290381 DOI: 10.1097/ypg.0000000000000268] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
INTRODUCTION Genome-wide association studies (GWAS) of alcohol dependence syndrome (ADS) offer a platform to detect genetic risk loci. However, the majority of the ADS GWAS undertaken, to date, have utilized a case-control design and have failed to identify consistently replicable loci with the exception of protective variants within the alcohol metabolizing genes, notably ADH1B. The ADS phenotype shows considerable variability which means that the use of quantitative variables as a proxy for the severity of ADS has the potential to facilitate identification of risk loci by increasing statistical power. The current study aims to examine the influences of using binary and adjusted quantitative measures of ADS on GWAS outcomes and on calculated polygenic risk scores (PRS). METHODS A GWAS was performed in 1251 healthy controls with no history of excess alcohol use and 739 patients with ADS classified using binary DMS-IV criteria. Two additional GWAS were undertaken using a quantitative score based on DSM-IV criteria, which were applied assuming both normal and non-normal distributions of the phenotypic variables. PRS analyses were performed utilizing the data from the binary and the quantitative trait analyses. RESULTS No associations were identified at genome-wide significance in any of the individual GWAS; results were comparable in all three. The top associated single nucleotide polymorphism was located on the alcohol dehydrogenase gene cluster on chromosome 4, consistent with previous ADS GWAS. The quantitative trait analysis adjusted for the distribution of the criterion score and the associated PRS had the smallest standard errors and thus the greatest precision. CONCLUSION Further exploitation of the use of qualitative trait analysis in GWAS in ADS is warranted.
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Affiliation(s)
- Wenqianglong Li
- Molecular Psychiatry Laboratory, Division of Psychiatry, University College London, London
| | - Johan Hilge Thygesen
- Molecular Psychiatry Laboratory, Division of Psychiatry, University College London, London
| | - Niamh Louise O'Brien
- Molecular Psychiatry Laboratory, Division of Psychiatry, University College London, London
| | - Mathis Heydtmann
- Royal Alexandria Hospital, NHS Greater Glasgow and Clyde, Paisley
| | - Iain Smith
- Glasgow Addiction Services, NHS Greater Glasgow and Clyde, Glasgow, UK
| | - Franziska Degenhardt
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn
- Department of Child and Adolescent Psychiatry, University of Duisburg-Essen, Essen, Germany
| | - Markus Maria Nöthen
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn
| | - Marsha Yvonne Morgan
- UCL Institute for Liver & Digestive Health, Division of Medicine, Royal Free Campus, University College London, London, UK
| | - Nicholas James Bass
- Molecular Psychiatry Laboratory, Division of Psychiatry, University College London, London
| | - Andrew McQuillin
- Molecular Psychiatry Laboratory, Division of Psychiatry, University College London, London
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Arbet J, McGue M, Basu S. A robust and unified framework for estimating heritability in twin studies using generalized estimating equations. Stat Med 2020; 39:3897-3913. [PMID: 32449216 DOI: 10.1002/sim.8564] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Revised: 03/13/2020] [Accepted: 04/10/2020] [Indexed: 11/11/2022]
Abstract
The 'heritability' of a phenotype measures the proportion of trait variance due to genetic factors in a population. In the past 50 years, studies with monozygotic and dizygotic twins have estimated heritability for 17,804 traits;1 thus twin studies are popular for estimating heritability. Researchers are often interested in estimating heritability for non-normally distributed outcomes such as binary, counts, skewed or heavy-tailed continuous traits. In these settings, the traditional normal ACE model (NACE) and Falconer's method can produce poor coverage of the true heritability. Therefore, we propose a robust generalized estimating equations (GEE2) framework for estimating the heritability of non-normally distributed outcomes. The traditional NACE and Falconer's method are derived within this unified GEE2 framework, which additionally provides robust standard errors. Although the traditional Falconer's method cannot adjust for covariates, the corresponding 'GEE2-Falconer' can incorporate mean and variance-level covariate effects (e.g. let heritability vary by sex or age). Given a non-normally distributed outcome, the GEE2 models are shown to attain better coverage of the true heritability compared to traditional methods. Finally, a scenario is demonstrated where NACE produces biased estimates of heritability while Falconer remains unbiased. Therefore, we recommend GEE2-Falconer for estimating the heritability of non-normally distributed outcomes in twin studies.
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Affiliation(s)
- Jaron Arbet
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Matt McGue
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Saonli Basu
- Department of Biostatistics, University of Minnesota, Minneapolis, Minnesota, USA
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Heritability of overlapping impulsivity and compulsivity dimensional phenotypes. Sci Rep 2020; 10:14378. [PMID: 32873811 PMCID: PMC7463011 DOI: 10.1038/s41598-020-71013-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 08/06/2020] [Indexed: 12/22/2022] Open
Abstract
Impulsivity and compulsivity are traits relevant to a range of mental health problems and have traditionally been conceptualised as distinct constructs. Here, we reconceptualised impulsivity and compulsivity as partially overlapping phenotypes using a bifactor modelling approach and estimated heritability for their shared and unique phenotypic variance within a classical twin design. Adult twin pairs (N = 173) completed self-report questionnaires measuring psychological processes related to impulsivity and compulsivity. We fitted variance components models to three uncorrelated phenotypic dimensions: a general impulsive-compulsive dimension; and two narrower phenotypes related to impulsivity and obsessiveness.There was evidence of moderate heritability for impulsivity (A2 = 0.33), modest additive genetic or common environmental effects for obsessiveness (A2 = 0.25; C2 = 0.23), and moderate effects of common environment (C2 = 0.36) for the general dimension, This general impulsive-compulsive phenotype may reflect a quantitative liability to related mental health disorders that indexes exposure to potentially modifiable environmental risk factors.
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26
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Clark DA, Durbin CE, Heitzeg MM, Iacono WG, McGue M, Hicks BM. Sexual Development in Adolescence: An Examination of Genetic and Environmental Influences. JOURNAL OF RESEARCH ON ADOLESCENCE : THE OFFICIAL JOURNAL OF THE SOCIETY FOR RESEARCH ON ADOLESCENCE 2020; 30:502-520. [PMID: 31782223 PMCID: PMC7253320 DOI: 10.1111/jora.12540] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Sexual development entails many experiences and is a major feature of adolescence. Most relevant behavioral genetic studies, however, focus primarily on sexual behaviors associated with health risks. We took a more normative, developmental perspective by examining genetic and environmental influences on five sexual behaviors ranging from dating to pregnancy in middle (Mage = 14.90 years) and late adolescence (Mage = 17.85 years) in a sample of twins (N = 3,762). Overall, behaviors that are more common and socially sanctioned (e.g., dating) were more heritable than behaviors that are less common and socially acceptable (e.g., sexual intercourse). That the etiology of different sexual behaviors is tied to their normativeness highlights the importance of considering the broader developmental context when studying sexual development.
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27
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Barr PB, Salvatore JE, Wetherill L, Anokhin A, Chan G, Edenberg HJ, Kuperman S, Meyers J, Nurnberger J, Porjesz B, Schuckit M, Dick DM. A Family-Based Genome Wide Association Study of Externalizing Behaviors. Behav Genet 2020; 50:175-183. [PMID: 32239439 PMCID: PMC7243710 DOI: 10.1007/s10519-020-09999-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Accepted: 03/23/2020] [Indexed: 01/12/2023]
Abstract
Shared genetic factors contribute to the high degree of comorbidity among externalizing problems (e.g. substance use and antisocial behavior). We leverage this common genetic etiology to identify genetic influences externalizing problems in participants from the Collaborative Study on the Genetics of Alcoholism (European ancestry = 7568; African ancestry = 3274). We performed a family-based genome-wide association study (GWAS) on externalizing scores derived from criterion counts of five DSM disorders (alcohol dependence, alcohol abuse, illicit drug dependence, illicit drug abuse, and either antisocial personality disorder or conduct disorder). We meta analyzed these results with a similar measure of externalizing in an independent sample, Spit for Science (combined sample N = 15,112). We did not discover any robust genome-wide significant signals. Polygenic scores derived from the ancestry-specific GWAS summary statistics predicted externalizing problems in an independent European ancestry sample, but not in those of African ancestry. However, these PRS were no longer significant after adjusting for multiple testing. Larger samples with deep phenotyping are necessary for the discovery of SNPs related to externalizing problems.
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Affiliation(s)
- Peter B Barr
- Department of Psychology, Virginia Commonwealth University, 8 North Harrison St, Richmond, VA, 23284, USA.
| | - Jessica E Salvatore
- Department of Psychology, Virginia Commonwealth University, 8 North Harrison St, Richmond, VA, 23284, USA
- Virginia Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - Leah Wetherill
- Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, IN, USA
| | - Andrey Anokhin
- Department of Psychiatry, School of Medicine, Washington University in St. Louis, St Louis, MO, USA
| | - Grace Chan
- Department of Psychiatry, School of Medicine, University of Connecticut, Farmington, CT, USA
| | - Howard J Edenberg
- Department of Biochemistry and Molecular Biology, School of Medicine, Indiana University, Indianapolis, IN, USA
| | - Samuel Kuperman
- Department of Psychiatry, Carver College of Medicine, University of Iowa, Iowa City, IA, USA
| | - Jacquelyn Meyers
- Department of Psychiatry & Behavioral Sciences, SUNY Down State Medical Center, SUNY Downstate, Brooklyn, NY, USA
| | - John Nurnberger
- Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, IN, USA
| | - Bernice Porjesz
- Department of Psychiatry & Behavioral Sciences, SUNY Down State Medical Center, SUNY Downstate, Brooklyn, NY, USA
| | - Mark Schuckit
- Department of Psychiatry, School of Medicine, University of California, San Diego, San Diego, CA, USA
| | - Danielle M Dick
- Department of Psychology, Virginia Commonwealth University, 8 North Harrison St, Richmond, VA, 23284, USA
- Department of Human and Molecular Genetics, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA
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28
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Genomics of human aggression: current state of genome-wide studies and an automated systematic review tool. Psychiatr Genet 2020; 29:170-190. [PMID: 31464998 DOI: 10.1097/ypg.0000000000000239] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
There are substantial differences, or variation, between humans in aggression, with its molecular genetic basis mostly unknown. This review summarizes knowledge on the genetic contribution to variation in aggression with the following three foci: (1) a comprehensive overview of reviews on the genetics of human aggression, (2) a systematic review of genome-wide association studies (GWASs), and (3) an automated tool for the selection of literature based on supervised machine learning. The phenotype definition 'aggression' (or 'aggressive behaviour', or 'aggression-related traits') included anger, antisocial behaviour, conduct disorder, and oppositional defiant disorder. The literature search was performed in multiple databases, manually and using a novel automated selection tool, resulting in 18 reviews and 17 GWASs of aggression. Heritability estimates of aggression in children and adults are around 50%, with relatively small fluctuations around this estimate. In 17 GWASs, 817 variants were reported as suggestive (P ≤ 1.0E), including 10 significant associations (P ≤ 5.0E). Nominal associations (P ≤ 1E) were found in gene-based tests for genes involved in immune, endocrine, and nervous systems. Associations were not replicated across GWASs. A complete list of variants and their position in genes and chromosomes are available online. The automated literature search tool produced literature not found by regular search strategies. Aggression in humans is heritable, but its genetic basis remains to be uncovered. No sufficiently large GWASs have been carried out yet. With increases in sample size, we expect aggression to behave like other complex human traits for which GWAS has been successful.
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29
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Waszczuk MA, Eaton NR, Krueger RF, Shackman AJ, Waldman ID, Zald DH, Lahey BB, Patrick CJ, Conway CC, Ormel J, Hyman SE, Fried EI, Forbes MK, Docherty AR, Althoff RR, Bach B, Chmielewski M, DeYoung CG, Forbush KT, Hallquist M, Hopwood CJ, Ivanova MY, Jonas KG, Latzman RD, Markon KE, Mullins-Sweatt SN, Pincus AL, Reininghaus U, South SC, Tackett JL, Watson D, Wright AGC, Kotov R. Redefining phenotypes to advance psychiatric genetics: Implications from hierarchical taxonomy of psychopathology. JOURNAL OF ABNORMAL PSYCHOLOGY 2020; 129:143-161. [PMID: 31804095 PMCID: PMC6980897 DOI: 10.1037/abn0000486] [Citation(s) in RCA: 71] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Genetic discovery in psychiatry and clinical psychology is hindered by suboptimal phenotypic definitions. We argue that the hierarchical, dimensional, and data-driven classification system proposed by the Hierarchical Taxonomy of Psychopathology (HiTOP) consortium provides a more effective approach to identifying genes that underlie mental disorders, and to studying psychiatric etiology, than current diagnostic categories. Specifically, genes are expected to operate at different levels of the HiTOP hierarchy, with some highly pleiotropic genes influencing higher order psychopathology (e.g., the general factor), whereas other genes conferring more specific risk for individual spectra (e.g., internalizing), subfactors (e.g., fear disorders), or narrow symptoms (e.g., mood instability). We propose that the HiTOP model aligns well with the current understanding of the higher order genetic structure of psychopathology that has emerged from a large body of family and twin studies. We also discuss the convergence between the HiTOP model and findings from recent molecular studies of psychopathology indicating broad genetic pleiotropy, such as cross-disorder SNP-based shared genetic covariance and polygenic risk scores, and we highlight molecular genetic studies that have successfully redefined phenotypes to enhance precision and statistical power. Finally, we suggest how to integrate a HiTOP approach into future molecular genetic research, including quantitative and hierarchical assessment tools for future data-collection and recommendations concerning phenotypic analyses. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Bo Bach
- Centre of Excellence on Personality Disorder
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30
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Nieto SJ, Grodin EN, Ray LA. On the path towards personalized medicine: Implications of pharmacogenetic studies of alcohol use disorder medications. EXPERT REVIEW OF PRECISION MEDICINE AND DRUG DEVELOPMENT 2020; 5:43-54. [PMID: 34291172 DOI: 10.1080/23808993.2020.1724510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Introduction The heritability of alcohol use disorder (AUD) is estimated to be ~50%; however, the genetic basis of the disease is still poorly understood. The genetic variants identified thus far only explain a small percentage of AUD phenotypic variability. While genome-wide association studies (GWAS) are impacted by technical and methodological limitations, genetic variants that have been identified independently of GWAS findings can moderate the efficacy of AUD medications. Areas Covered This review discusses findings from clinical pharmacogenetic studies of AUD medications. While the pharmacogenetic studies reviewed involve several genetic variants in the major neurotransmitter systems, genetic loci in the opioid system have garnered the most attention. Expert Opinion The clinical utility of pharmacogenetics in AUD populations is uncertain at this time. There are several ongoing prospective clinical trials that will enhance knowledge regarding the applicability of pharmacogenetics in clinical populations. We recommend that future work in this area consider reverse translating from genotype to phenotype, mapping genes to stages of the addiction cycle, mapping genes to neural circuits, and harnessing large population-based cohorts.
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Affiliation(s)
- Steven J Nieto
- University of California Los Angeles, Department of Psychology, Los Angeles, CA, USA
| | - Erica N Grodin
- University of California Los Angeles, Department of Psychology, Los Angeles, CA, USA
| | - Lara A Ray
- University of California Los Angeles, Department of Psychology, Los Angeles, CA, USA.,University of California, Los Angeles, Department of Psychiatry and Biobehavioral Sciences, Los Angeles, CA, USA.,University of California Los Angeles, Brain Research Institute, Los Angeles, CA, USA
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31
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Wehby GL, Shane D. Genetic variation in health insurance coverage. INTERNATIONAL JOURNAL OF HEALTH ECONOMICS AND MANAGEMENT 2019; 19:301-316. [PMID: 30421388 DOI: 10.1007/s10754-018-9255-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2017] [Accepted: 11/03/2018] [Indexed: 06/09/2023]
Abstract
We provide the first investigation into whether and how much genes explain having health insurance coverage or not and possible mechanisms for genetic variation. Using a twin-design that compares identical and non-identical twins from a national sample of US twins from the National Survey of Midlife Development in the United States, we find that genetic effects explain over 40% of the variation in whether a person has any health coverage versus not, and nearly 50% of the variation in whether individuals younger than 65 have private coverage versus whether they have no coverage at all. Nearly one third of the genetic variation in being uninsured versus having private coverage is explained by employment industry, self-employment status, and income, and together with education, they explain over 40% of the genetic influence. Marital status, number of children, and available measures of health status, risk preferences, and prevention effort do not appear to be important channels for genetic effects. That genes have meaningful effects on the insurance status suggests an important source of heterogeneity in insurance take up.
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Affiliation(s)
- George L Wehby
- Department of Health Management and Policy, University of Iowa, 145 N. Riverside Dr., 100 College of Public Health Bldg., Room N250, Iowa City, IA, 52242-2007, USA.
- Department of Economics, University of Iowa, Iowa City, IA, USA.
- Department of Preventive and Community Dentistry, University of Iowa, Iowa City, IA, USA.
- Public Policy Center, University of Iowa, Iowa City, IA, USA.
- National Bureau of Economic Research, Cambridge, MA, USA.
| | - Dan Shane
- Department of Health Management and Policy, University of Iowa, 145 N. Riverside Dr., 100 College of Public Health Bldg., Room N250, Iowa City, IA, 52242-2007, USA
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32
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Fairchild G, Hawes DJ, Frick PJ, Copeland WE, Odgers CL, Franke B, Freitag CM, De Brito SA. Conduct disorder. Nat Rev Dis Primers 2019; 5:43. [PMID: 31249310 DOI: 10.1038/s41572-019-0095-y] [Citation(s) in RCA: 161] [Impact Index Per Article: 32.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/17/2019] [Indexed: 02/06/2023]
Abstract
Conduct disorder (CD) is a common and highly impairing psychiatric disorder that usually emerges in childhood or adolescence and is characterized by severe antisocial and aggressive behaviour. It frequently co-occurs with attention-deficit/hyperactivity disorder (ADHD) and often leads to antisocial personality disorder in adulthood. CD affects ~3% of school-aged children and is twice as prevalent in males than in females. This disorder can be subtyped according to age at onset (childhood-onset versus adolescent-onset) and the presence or absence of callous-unemotional traits (deficits in empathy and guilt). The aetiology of CD is complex, with contributions of both genetic and environmental risk factors and different forms of interplay among the two (gene-environment interaction and correlation). In addition, CD is associated with neurocognitive impairments; smaller grey matter volume in limbic regions such as the amygdala, insula and orbitofrontal cortex, and functional abnormalities in overlapping brain circuits responsible for emotion processing, emotion regulation and reinforcement-based decision-making have been reported. Lower hypothalamic-pituitary-adrenal axis and autonomic reactivity to stress has also been reported. Management of CD primarily involves parent-based or family-based psychosocial interventions, although stimulants and atypical antipsychotics are sometimes used, especially in individuals with comorbid ADHD.
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Affiliation(s)
| | - David J Hawes
- School of Psychology, University of Sydney, Sydney, New South Wales, Australia
| | - Paul J Frick
- Department of Psychology, Louisiana State University, Baton Rouge, LA, USA and Institute for Learning Science and Teacher Education, Australian Catholic University, Brisbane, Queensland, Australia
| | | | - Candice L Odgers
- Department of Psychological Science, School of Social Ecology, University of California, Irvine, CA, USA
| | - Barbara Franke
- Departments of Human Genetics and Psychiatry, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Christine M Freitag
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Stephane A De Brito
- School of Psychology and Centre for Human Brain Health, University of Birmingham, Birmingham, UK
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33
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Conway CC, Forbes MK, Forbush KT, Fried EI, Hallquist MN, Kotov R, Mullins-Sweatt SN, Shackman AJ, Skodol AE, South SC, Sunderland M, Waszczuk MA, Zald DH, Afzali MH, Bornovalova MA, Carragher N, Docherty AR, Jonas KG, Krueger RF, Patalay P, Pincus AL, Tackett JL, Reininghaus U, Waldman ID, Wright AG, Zimmermann J, Bach B, Bagby RM, Chmielewski M, Cicero DC, Clark LA, Dalgleish T, DeYoung CG, Hopwood CJ, Ivanova MY, Latzman RD, Patrick CJ, Ruggero CJ, Samuel DB, Watson D, Eaton NR. A Hierarchical Taxonomy of Psychopathology Can Transform Mental Health Research. PERSPECTIVES ON PSYCHOLOGICAL SCIENCE 2019; 14:419-436. [PMID: 30844330 PMCID: PMC6497550 DOI: 10.1177/1745691618810696] [Citation(s) in RCA: 190] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
For more than a century, research on psychopathology has focused on categorical diagnoses. Although this work has produced major discoveries, growing evidence points to the superiority of a dimensional approach to the science of mental illness. Here we outline one such dimensional system-the Hierarchical Taxonomy of Psychopathology (HiTOP)-that is based on empirical patterns of co-occurrence among psychological symptoms. We highlight key ways in which this framework can advance mental-health research, and we provide some heuristics for using HiTOP to test theories of psychopathology. We then review emerging evidence that supports the value of a hierarchical, dimensional model of mental illness across diverse research areas in psychological science. These new data suggest that the HiTOP system has the potential to accelerate and improve research on mental-health problems as well as efforts to more effectively assess, prevent, and treat mental illness.
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Affiliation(s)
- Christopher C. Conway
- Department of Psychological Sciences, College of William & Mary, Williamsburg, VA, USA
| | - Miriam K. Forbes
- Centre for Emotional Health, Department of Psychology, Macquarie University, Sydney, Australia
| | | | - Eiko I. Fried
- Department of Psychology, University of Amsterdam, Amsterdam, Netherlands
| | - Michael N. Hallquist
- Department of Psychology, The Pennsylvania State University, State College, PA, USA
| | - Roman Kotov
- Department of Psychiatry, State University of New York, Stony Brook, NY, USA
| | | | - Alexander J. Shackman
- Department of Psychology and Neuroscience and Cognitive Science Program, University of Maryland, College Park, MD, USA
| | - Andrew E. Skodol
- Department of Psychiatry, University of Arizona, Tucson, AZ, USA
| | - Susan C. South
- Purdue University, Department of Psychological Sciences, West Lafayette, IN, USA
| | - Matthew Sunderland
- NHMRC Centre for Research Excellence in Mental Health and Substance Use, National Drug and Alcohol Research Centre, University of New South Wales, Sydney, Australia
| | - Monika A. Waszczuk
- Department of Psychiatry, State University of New York, Stony Brook, NY, USA
| | - David H. Zald
- Department of Psychology, Vanderbilt University, Nashville, TN, USA
| | | | | | - Natacha Carragher
- Medical Education and Student Office, Faculty of Medicine, University of New South Wales Australia, Sydney, New South Wales, Australia
| | - Anna R. Docherty
- Department of Psychiatry, University of Utah, Salt Lake City, UT, USA
| | - Katherine G. Jonas
- Department of Psychiatry, State University of New York, Stony Brook, NY, USA
| | - Robert F. Krueger
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Praveetha Patalay
- Institute of Psychology, Health and Society, University of Liverpool, Liverpool, UK
| | - Aaron L. Pincus
- Department of Psychology, The Pennsylvania State University, State College, PA, USA
| | | | - Ulrich Reininghaus
- Department of Psychiatry and Psychology, School for Mental Health and Neuroscience, Maastricht University, The Netherlands
- Centre for Epidemiology and Public Health, Health Service and Population Research Department, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | | | - Aidan G.C. Wright
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Bo Bach
- Psychiatric Research Unit, Slagelse Psychiatric Hospital, Slagelse, Denmark
| | - R. Michael Bagby
- Departments of Psychology and Psychiatry, University of Toronto, Toronto, Canada
| | | | - David C. Cicero
- Department of Psychology, University of Hawaii at Manoa, HI, USA
| | - Lee Anna Clark
- Department of Psychology, University of Notre Dame, Notre Dame, IN, USA
| | - Tim Dalgleish
- Medical Research Council Cognition and Brain Sciences Unit, Cambridge, UK
| | - Colin G. DeYoung
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | | | - Masha Y. Ivanova
- Department of Psychiatry, University of Vermont, Burlington, VT, USA
| | - Robert D. Latzman
- Department of Psychology, Georgia State University, Atlanta, GA, USA
| | | | - Camilo J. Ruggero
- Department of Psychology, University of North Texas, Denton, TX, USA
| | - Douglas B. Samuel
- Purdue University, Department of Psychological Sciences, West Lafayette, IN, USA
| | - David Watson
- Department of Psychology, University of Notre Dame, Notre Dame, IN, USA
| | - Nicholas R. Eaton
- Department of Psychology, Stony Brook University, Stony Brook, NY, USA
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34
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Cornelis MC. Genetic determinants of beverage consumption: Implications for nutrition and health. ADVANCES IN FOOD AND NUTRITION RESEARCH 2019; 89:1-52. [PMID: 31351524 PMCID: PMC7047661 DOI: 10.1016/bs.afnr.2019.03.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Beverages make important contributions to nutritional intake and their role in health has received much attention. This review focuses on the genetic determinants of common beverage consumption and how research in this field is contributing insight to what and how much we consume and why this genetic knowledge matters from a research and public health perspective. The earliest efforts in gene-beverage behavior mapping involved genetic linkage and candidate gene analysis but these approaches have been largely replaced by genome-wide association studies (GWAS). GWAS have identified biologically plausible loci underlying alcohol and coffee drinking behavior. No GWAS has identified variants specifically associated with consumption of tea, juice, soda, wine, beer, milk or any other common beverage. Thus far, GWAS highlight an important behavior-reward component (as opposed to taste) to beverage consumption which may serve as a potential barrier to dietary interventions. Loci identified have been used in Mendelian randomization and gene×beverage interaction analysis of disease but results have been mixed. This research is necessary as it informs the clinical relevance of SNP-beverage associations and thus genotype-based personalized nutrition, which is gaining interest in the commercial and public health sectors.
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Affiliation(s)
- Marilyn C Cornelis
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States.
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35
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Gonzalez S, Villa E, Rodriguez M, Ramirez M, Zavala J, Armas R, Dassori A, Contreras J, Raventós H, Flores D, Jerez A, Ontiveros A, Nicolini H, Escamilla M. Fine-mapping scan of bipolar disorder susceptibility loci in Latino pedigrees. Am J Med Genet B Neuropsychiatr Genet 2019; 180:213-222. [PMID: 30779416 DOI: 10.1002/ajmg.b.32715] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Revised: 12/27/2018] [Accepted: 01/28/2019] [Indexed: 11/11/2022]
Abstract
We previously identified bipolar disorder (BD) susceptibility loci on 8q24, 14q32, and 2q12-14 in a genome-wide nonparametric linkage screen in a Latino cohort. We now perform a fine mapping analysis using a dense map of additional SNPs to identify BD susceptibility genes within these regions. One thousand nine hundred and thirty-eight individuals with Latino ancestry (880 individuals with BD Type I or Schizoaffective, Bipolar Type) from 416 Latino pedigrees from the United States, Mexico, Costa Rica, and Guatemala were genotyped with 3,074 SNPs to provide dense coverage of the 8q24 (11.5 cM), 14q32 (7.5 cM), and 2q12-14 (6.5 cM) chromosomal loci. Single-marker association tests in the presence of linkage were performed using the LAMP software. The top linkage peak (rs7834818; LOD = 5.08, p = 3.30E - 5) and associated single marker (rs2280915, p = 2.70E - 12) were located within FBXO32 on 8q24. On chromosome 2, the top linkage peak (rs6750326; LOD = 5.06, p = 3.50E - 5) and associated single marker (rs11887088, p = 2.90E - 6) were located in intragenic regions near ACTR3 and DPP10. None of the additional markers in the region around chromosome 14q32 met significance levels for linkage or association. We identified six SNPs on 2q12-q14 and one SNP in FBXO32 on 8q24 that were significantly associated with BD in this Latino cohort.
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Affiliation(s)
- Suzanne Gonzalez
- Center of Emphasis in Neurosciences, Department of Biomedical Sciences, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, Texas.,Departments of Psychiatry and Pharmacology, Penn State College of Medicine, Hershey, Pennsylvania
| | - Erika Villa
- Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Marco Rodriguez
- Center of Emphasis in Neurosciences, Department of Biomedical Sciences, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, Texas
| | - Mercedes Ramirez
- Center of Emphasis in Neurosciences, Department of Biomedical Sciences, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, Texas.,Department of Psychiatry, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, Texas
| | - Juan Zavala
- Center of Emphasis in Neurosciences, Department of Biomedical Sciences, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, Texas.,Department of Psychiatry, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, Texas
| | - Regina Armas
- Langley Porter Psychiatric Institute, University of California at San Francisco, San Francisco, California
| | - Albana Dassori
- Department of Psychiatry, University of Texas Health Science Center at San Antonio, San Antonio, Texas.,Department of Psychiatry, South Texas Veterans Health Care System, San Antonio, Texas
| | - Javier Contreras
- Centro de Investigación en Biología Celular y Molecular y Escuela de Biologia, Universidad de Costa Rica, San Jose, Costa Rica
| | - Henriette Raventós
- Centro de Investigación en Biología Celular y Molecular y Escuela de Biologia, Universidad de Costa Rica, San Jose, Costa Rica
| | - Deborah Flores
- Los Angeles Biomedical Research Center at Harbor, University of California Los Angeles Medical Center, Torrance, California
| | - Alvaro Jerez
- Centro Internacional de Trastornos Afectivos y de la Conducta Adictiva, Guatemala City, Guatemala
| | - Alfonso Ontiveros
- Departamento de Psiquiatria, Hospital Universitario UANL, Monterrey, Nuevo Leon, Mexico
| | - Humberto Nicolini
- Grupo de Estudios Médicos y Familiares Carracci S.C., México, Distrito Federal, Mexico.,Laboratorio de Enfermedades Psychiatricas y Neurodegenerativas, Instituto Nacional de Medicina Genómica, México, Distrito Federal, Mexico
| | - Michael Escamilla
- Center of Emphasis in Neurosciences, Department of Biomedical Sciences, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, Texas.,Department of Psychiatry, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, Texas
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36
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Genetic loci for alcohol-related life events and substance-induced affective symptoms: indexing the "dark side" of addiction. Transl Psychiatry 2019; 9:71. [PMID: 30718457 PMCID: PMC6362044 DOI: 10.1038/s41398-019-0397-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Accepted: 01/17/2019] [Indexed: 12/24/2022] Open
Abstract
A limited number of genetic variants have been identified in traditional GWAS as risk or protective factors for alcohol use disorders (AUD) and related phenotypes. We herein report whole-genome association and rare-variant analyses on AUD traits in American Indians (AI) and European Americans (EA). We evaluated 742 AIs and 1711 EAs using low-coverage whole-genome sequencing. Phenotypes included: (1) a metric based on the occurrence of 36 alcohol-related life events that reflect AUD severity; (2) two alcohol-induced affective symptoms that accompany severe AUDs. We identified two new loci for alcohol-related life events with converging evidence from both cohorts: rare variants of K2P channel gene KCNK2, and rare missense and splice-site variants in pro-inflammatory mediator gene PDE4C. A NAF1-FSTL5 intergenic variant and an FSTL5 variant were respectively associated with alcohol-related life events in AI and EA. PRKG2 of serine/threonine protein kinase family, and rare variants in interleukin subunit gene EBI3 (IL-27B) were uniquely associated with alcohol-induced affective symptoms in AI. LncRNA LINC02347 on 12q24.32 was uniquely associated with alcohol-induced depression in EA. The top GWAS findings were primarily rare/low-frequency variants in AI, and common variants in EA. Adrenal gland was the most enriched in tissue-specific gene expression analysis for alcohol-related life events, and nucleus accumbens was the most enriched for alcohol-induced affective states in AI. Prefrontal cortex was the most enriched in EA for both traits. These studies suggest that whole-genome sequencing can identify novel, especially uncommon, variants associated with severe AUD phenotypes although the findings may be population specific.
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37
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Alblooshi H, Al Safar H, Fisher HF, Cordell HJ, El Kashef A, Al Ghaferi H, Shawky M, Reece S, Hulse GK, Tay GK. A case-control genome wide association study of substance use disorder (SUD) identifies novel variants on chromosome 7p14.1 in patients from the United Arab Emirates (UAE). Am J Med Genet B Neuropsychiatr Genet 2019; 180:68-79. [PMID: 30556296 DOI: 10.1002/ajmg.b.32708] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Revised: 10/17/2018] [Accepted: 11/21/2018] [Indexed: 12/11/2022]
Abstract
Genome wide association studies (GWASs) have provided insights into the molecular basis of the disorder in different population. This study presents the first GWAS of substance use disorder (SUD) in patients from the United Arab Emirates (UAE). The aim was to identify genetic association(s) that may provide insights into the molecular basis of the disorder. The GWAS discovery cohort consisted of 512 (250 cases and 262 controls) male participants from the UAE. Controls with no prior history of SUD were available from the Emirates family registry. The replication cohort consisted of 520 (415 cases and 105 controls) Australian male Caucasian participants. The GWAS discovery samples were genotyped for 4.6 million single nucleotide polymorphism (SNP). The replication cohort was genotyped using TaqMan assay. The GWAS association analysis identified three potential SNPs rs118129027 (p-value = 6.24 × 10-8 ), rs74477937 (p-value = 8.56 × 10-8 ) and rs78707086 (p-value = 8.55 × 10-8 ) on ch7p14.1, that did not meet the GWAS significance threshold but were highly suggestive. In the replication cohort, the association of the three top SNPs did not reach statistical significance. In a meta-analysis of the discovery and the replication cohorts, there were no strengthen evidence for association of the three SNPs. The top identified rs118129027 overlaps with a regulatory factor (enhancer) region that targets three neighboring genes LOC105375237, LOC105375240, and YAE1D1. The YAE1D1, which represents a potential locus that is involved in regulating translation initiation pathway. Novel associations that require further confirmation were identified, suggesting a new insight to the genetic basis of SUD.
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Affiliation(s)
- Hiba Alblooshi
- School of Psychiatry and Clinical Neurosciences, The University of Western Australia, Crawley, Western Australia, Australia.,School of Human Science, The University of Western Australia, Crawley, Western Australia, Australia
| | - Habiba Al Safar
- Center of Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.,Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Holly F Fisher
- Institute of Genetic Medicine, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Heather J Cordell
- Institute of Genetic Medicine, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - Ahmed El Kashef
- National Rehabilitation Center, Abu Dhabi, United Arab Emirates
| | | | - Mansour Shawky
- National Rehabilitation Center, Abu Dhabi, United Arab Emirates
| | - Stuart Reece
- School of Psychiatry and Clinical Neurosciences, The University of Western Australia, Crawley, Western Australia, Australia
| | - Gary K Hulse
- School of Psychiatry and Clinical Neurosciences, The University of Western Australia, Crawley, Western Australia, Australia.,School of Medical Science, Edith Cowan University, Joondalup, Western Australia, Australia
| | - Guan K Tay
- School of Psychiatry and Clinical Neurosciences, The University of Western Australia, Crawley, Western Australia, Australia.,Center of Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.,Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.,School of Medical Science, Edith Cowan University, Joondalup, Western Australia, Australia
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38
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Coombes BJ, Basu S, McGue M. A linear mixed model framework for gene-based gene-environment interaction tests in twin studies. Genet Epidemiol 2018; 42:648-663. [PMID: 30203856 DOI: 10.1002/gepi.22150] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Revised: 04/25/2018] [Accepted: 04/30/2018] [Indexed: 02/03/2023]
Abstract
Interaction between genes and environments (G×E) can be well investigated in families due to the shared genes and environment among family members. However, the majority of the current tests of G×E interaction between a set of variants and an environment are only suitable for studies with unrelated subjects. In this paper, we extend several G×E interaction tests to a linear mixed model framework to study interaction between a set of correlated environments and a candidate gene in families. The correlated environments can either be modeled separately or jointly in one model. We demonstrate theoretically that the tests developed by modeling correlated environments separately are valid and present a computationally fast alternative to detect G×E interaction in families. For either strategy, we propose treating the genetic main effects as a random effect to reduce the number of main-effect parameters and thus improve the power to detect interactions. Additionally, we propose a generalization of a test of interaction that adaptively sums the interactions using a sequential algorithm. This generalized set of tests, referred to as the sequential algorithm for the sum of powered score (Seq-SPU) family of tests, can be expressed as a weighted version of the SPU. We find that the adaptive version of our test, Seq-aSPU, can outperform aSPU in cases where the interactions effects are in opposite directions. We applied these methods to the Minnesota Center for Twin and Family Research data set and found one significant gene in interaction with four psychosocial environmental factors affecting the alcohol consumption among the twins.
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Affiliation(s)
- Brandon J Coombes
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota
| | - Saonli Basu
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota
| | - Matt McGue
- Department of Psychology, School of Public Health, University of Minnesota, Minneapolis, Minnesota
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39
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Feitosa MF, Kraja AT, Chasman DI, Sung YJ, Winkler TW, Ntalla I, Guo X, Franceschini N, Cheng CY, Sim X, Vojinovic D, Marten J, Musani SK, Li C, Bentley AR, Brown MR, Schwander K, Richard MA, Noordam R, Aschard H, Bartz TM, Bielak LF, Dorajoo R, Fisher V, Hartwig FP, Horimoto ARVR, Lohman KK, Manning AK, Rankinen T, Smith AV, Tajuddin SM, Wojczynski MK, Alver M, Boissel M, Cai Q, Campbell A, Chai JF, Chen X, Divers J, Gao C, Goel A, Hagemeijer Y, Harris SE, He M, Hsu FC, Jackson AU, Kähönen M, Kasturiratne A, Komulainen P, Kühnel B, Laguzzi F, Luan J, Matoba N, Nolte IM, Padmanabhan S, Riaz M, Rueedi R, Robino A, Said MA, Scott RA, Sofer T, Stančáková A, Takeuchi F, Tayo BO, van der Most PJ, Varga TV, Vitart V, Wang Y, Ware EB, Warren HR, Weiss S, Wen W, Yanek LR, Zhang W, Zhao JH, Afaq S, Amin N, Amini M, Arking DE, Aung T, Boerwinkle E, Borecki I, Broeckel U, Brown M, Brumat M, Burke GL, Canouil M, Chakravarti A, Charumathi S, Ida Chen YD, Connell JM, Correa A, de las Fuentes L, de Mutsert R, de Silva HJ, Deng X, Ding J, Duan Q, Eaton CB, Ehret G, Eppinga RN, Evangelou E, Faul JD, Felix SB, Forouhi NG, Forrester T, Franco OH, Friedlander Y, Gandin I, Gao H, Ghanbari M, Gigante B, Gu CC, Gu D, Hagenaars SP, Hallmans G, Harris TB, He J, Heikkinen S, Heng CK, Hirata M, Howard BV, Ikram MA, John U, Katsuya T, Khor CC, Kilpeläinen TO, Koh WP, Krieger JE, Kritchevsky SB, Kubo M, Kuusisto J, Lakka TA, Langefeld CD, Langenberg C, Launer LJ, Lehne B, Lewis CE, Li Y, Lin S, Liu J, Liu J, Loh M, Louie T, Mägi R, McKenzie CA, Meitinger T, Metspalu A, Milaneschi Y, Milani L, Mohlke KL, Momozawa Y, Nalls MA, Nelson CP, Sotoodehnia N, Norris JM, O'Connell JR, Palmer ND, Perls T, Pedersen NL, Peters A, Peyser PA, Poulter N, Raffel LJ, Raitakari OT, Roll K, Rose LM, Rosendaal FR, Rotter JI, Schmidt CO, Schreiner PJ, Schupf N, Scott WR, Sever PS, Shi Y, Sidney S, Sims M, Sitlani CM, Smith JA, Snieder H, Starr JM, Strauch K, Stringham HM, Tan NYQ, Tang H, Taylor KD, Teo YY, Tham YC, Turner ST, Uitterlinden AG, Vollenweider P, Waldenberger M, Wang L, Wang YX, Wei WB, Williams C, Yao J, Yu C, Yuan JM, Zhao W, Zonderman AB, Becker DM, Boehnke M, Bowden DW, Chambers JC, Deary IJ, Esko T, Farrall M, Franks PW, Freedman BI, Froguel P, Gasparini P, Gieger C, Jonas JB, Kamatani Y, Kato N, Kooner JS, Kutalik Z, Laakso M, Laurie CC, Leander K, Lehtimäki T, Study LC, Magnusson PKE, Oldehinkel AJ, Penninx BWJH, Polasek O, Porteous DJ, Rauramaa R, Samani NJ, Scott J, Shu XO, van der Harst P, Wagenknecht LE, Wareham NJ, Watkins H, Weir DR, Wickremasinghe AR, Wu T, Zheng W, Bouchard C, Christensen K, Evans MK, Gudnason V, Horta BL, Kardia SLR, Liu Y, Pereira AC, Psaty BM, Ridker PM, van Dam RM, Gauderman WJ, Zhu X, Mook-Kanamori DO, Fornage M, Rotimi CN, Cupples LA, Kelly TN, Fox ER, Hayward C, van Duijn CM, Tai ES, Wong TY, Kooperberg C, Palmas W, Rice K, Morrison AC, Elliott P, Caulfield MJ, Munroe PB, Rao DC, Province MA, Levy D. Novel genetic associations for blood pressure identified via gene-alcohol interaction in up to 570K individuals across multiple ancestries. PLoS One 2018; 13:e0198166. [PMID: 29912962 PMCID: PMC6005576 DOI: 10.1371/journal.pone.0198166] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2018] [Accepted: 05/15/2018] [Indexed: 01/01/2023] Open
Abstract
Heavy alcohol consumption is an established risk factor for hypertension; the mechanism by which alcohol consumption impact blood pressure (BP) regulation remains unknown. We hypothesized that a genome-wide association study accounting for gene-alcohol consumption interaction for BP might identify additional BP loci and contribute to the understanding of alcohol-related BP regulation. We conducted a large two-stage investigation incorporating joint testing of main genetic effects and single nucleotide variant (SNV)-alcohol consumption interactions. In Stage 1, genome-wide discovery meta-analyses in ≈131K individuals across several ancestry groups yielded 3,514 SNVs (245 loci) with suggestive evidence of association (P < 1.0 x 10-5). In Stage 2, these SNVs were tested for independent external replication in ≈440K individuals across multiple ancestries. We identified and replicated (at Bonferroni correction threshold) five novel BP loci (380 SNVs in 21 genes) and 49 previously reported BP loci (2,159 SNVs in 109 genes) in European ancestry, and in multi-ancestry meta-analyses (P < 5.0 x 10-8). For African ancestry samples, we detected 18 potentially novel BP loci (P < 5.0 x 10-8) in Stage 1 that warrant further replication. Additionally, correlated meta-analysis identified eight novel BP loci (11 genes). Several genes in these loci (e.g., PINX1, GATA4, BLK, FTO and GABBR2) have been previously reported to be associated with alcohol consumption. These findings provide insights into the role of alcohol consumption in the genetic architecture of hypertension.
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Affiliation(s)
- Mary F. Feitosa
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Aldi T. Kraja
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Daniel I. Chasman
- Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Yun J. Sung
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Thomas W. Winkler
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Ioanna Ntalla
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Xiuqing Guo
- Genomic Outcomes, Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, California, United States of America
| | - Nora Franceschini
- Epidemiology, University of North Carolina Gilling School of Global Public Health, Chapel Hill, North Carolina, United States of America
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, Singapore
| | - Dina Vojinovic
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Jonathan Marten
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
| | - Solomon K. Musani
- Jackson Heart Study, Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
| | - Changwei Li
- Epidemiology and Biostatistics, University of Georgia at Athens College of Public Health, Athens, Georgia, United States of America
| | - Amy R. Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Michael R. Brown
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Karen Schwander
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Melissa A. Richard
- Brown Foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Raymond Noordam
- Internal Medicine, Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands
| | - Hugues Aschard
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America
- Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur, Paris, France
| | - Traci M. Bartz
- Cardiovascular Health Research Unit, Biostatistics and Medicine, University of Washington, Seattle, Washington, United States of America
| | - Lawrence F. Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Rajkumar Dorajoo
- Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore
| | - Virginia Fisher
- Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America
| | - Fernando P. Hartwig
- Postgraduate Programme in Epidemiology, Federal University of Pelotas, Pelotas, RS, Brazil
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
| | - Andrea R. V. R. Horimoto
- Laboratory of Genetics and Molecular Cardiology, Heart Institute (InCor), University of São Paulo Medical School, São Paulo, SP, Brazil
| | - Kurt K. Lohman
- Biostatistical Sciences, Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Alisa K. Manning
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Tuomo Rankinen
- Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, Louisiana, United States of America
| | - Albert V. Smith
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Salman M. Tajuddin
- Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States of America
| | - Mary K. Wojczynski
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Maris Alver
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Mathilde Boissel
- CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, University of Lille, Lille, France
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
| | - Archie Campbell
- Centre for Genomic & Experimental Medicine, Institute of Genetics & Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
| | - Jin Fang Chai
- Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, Singapore
| | - Xu Chen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Stockholm, Sweden
| | - Jasmin Divers
- Biostatistical Sciences, Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Chuan Gao
- Molecular Genetics and Genomics Program, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Anuj Goel
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, Oxfordshire, United Kingdom
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, Oxfordshire, United Kingdom
| | - Yanick Hagemeijer
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Sarah E. Harris
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, United Kingdom
- Medical Genetics Section, Centre for Genomic and Experimental Medicine and MRC Institute of Genetics and Molecular Medicine, The University of Edinburgh, Edinburgh, United Kingdom
| | - Meian He
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health for Incubating, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Fang-Chi Hsu
- Biostatistical Sciences, Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Anne U. Jackson
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland
- University of Tampere, Tampere, Finland
| | | | - Pirjo Komulainen
- Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
| | - Brigitte Kühnel
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Federica Laguzzi
- Unit of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Jian'an Luan
- MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Nana Matoba
- Laboratory for Statistical Analysis, Center for Integrative Medical Sciences, RIKEN, Yokohama, Japan
| | - Ilja M. Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Sandosh Padmanabhan
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom
| | - Muhammad Riaz
- Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Rico Rueedi
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Instititute of Bioinformatics, Lausanne, Switzerland
| | - Antonietta Robino
- Institute for Maternal and Child Health—IRCCS "Burlo Garofolo", Trieste, Italy
| | - M. Abdullah Said
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Robert A. Scott
- MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Tamar Sofer
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, United States of America
| | - Alena Stančáková
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - Fumihiko Takeuchi
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Bamidele O. Tayo
- Department of Public Health Sciences, Loyola University Chicago, Maywood, Illinois, United States of America
| | - Peter J. van der Most
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Tibor V. Varga
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Skåne University Hospital, Malmö, Sweden
| | - Veronique Vitart
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
| | - Yajuan Wang
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - Erin B. Ware
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Helen R. Warren
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
- NIHR Barts Cardiovascular Biomedical Research Unit, Queen Mary University of London, London, London, United Kingdom
| | - Stefan Weiss
- Interfaculty Institute for Genetics and Functional genomics, University Medicine Ernst Moritz Arndt University Greifsald, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
| | - Wanqing Wen
- Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
| | - Lisa R. Yanek
- Division of General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
- Department of Cardiology, Ealing Hospital, Middlesex, United Kingdom
| | - Jing Hua Zhao
- MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Saima Afaq
- Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
| | - Najaf Amin
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Marzyeh Amini
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Dan E. Arking
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Tin Aung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas School of Public Health, Houston, Texas, United States of America
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, United States of America
| | - Ingrid Borecki
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Ulrich Broeckel
- Section of Genomic Pediatrics, Department of Pediatrics, Medicine and Physiology, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Morris Brown
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
- NIHR Barts Cardiovascular Biomedical Research Unit, Queen Mary University of London, London, London, United Kingdom
| | - Marco Brumat
- Department of Medical Sciences, University of Trieste, Trieste, Italy
| | - Gregory L. Burke
- Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Mickaël Canouil
- CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, University of Lille, Lille, France
| | - Aravinda Chakravarti
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Sabanayagam Charumathi
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Yii-Der Ida Chen
- Genomic Outcomes, Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, California, United States of America
| | - John M. Connell
- Ninewells Hospital & Medical School, University of Dundee, Dundee, Scotland, United Kingdom
| | - Adolfo Correa
- Jackson Heart Study, Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
| | - Lisa de las Fuentes
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, United States of America
- Cardiovascular Division, Department of Medicine, Washington University, St. Louis, Missouri, United States of America
| | - Renée de Mutsert
- Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - H. Janaka de Silva
- Department of Medicine, Faculty of Medicine, University of Kelaniya, Ragama, Sri Lanka
| | - Xuan Deng
- Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America
| | - Jingzhong Ding
- Center on Diabetes, Obesity, and Metabolism, Gerontology and Geriatric Medicine, Wake Forest University Health Sciences, Winston-Salem, North Carolina, United States of America
| | - Qing Duan
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Charles B. Eaton
- Department of Family Medicine and Epidemiology, Alpert Medical School of Brown University, Providence, Rhode Island, United States of America
| | - Georg Ehret
- Cardiology, Geneva University Hospital, Geneva, Switzerland
| | - Ruben N. Eppinga
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Evangelos Evangelou
- Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Jessica D. Faul
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Stephan B. Felix
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
| | - Nita G. Forouhi
- MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Terrence Forrester
- The Caribbean Institute for Health Research (CAIHR), University of the West Indies, Mona, Jamaica
| | - Oscar H. Franco
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Yechiel Friedlander
- Braun School of Public Health, Hebrew University-Hadassah Medical Center, Jerusalem, Israel
| | - Ilaria Gandin
- Department of Medical Sciences, University of Trieste, Trieste, Italy
| | - He Gao
- Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Genetics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Bruna Gigante
- Unit of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - C. Charles Gu
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Dongfeng Gu
- Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Saskia P. Hagenaars
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, United Kingdom
- Psychology, The University of Edinburgh, Edinburgh, United Kingdom
| | - Göran Hallmans
- Department of Public Health and Clinical Medicine, Nutritional Research, Umeå University, Umeå, Västerbotten, Sweden
| | - Tamara B. Harris
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Jiang He
- Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America
- Medicine, Tulane University School of Medicine, New Orleans, Louisiana, United States of America
| | - Sami Heikkinen
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
- Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio Campus, Finland
| | - Chew-Kiat Heng
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Khoo Teck Puat–National University Children's Medical Institute, National University Health System, Singapore
| | - Makoto Hirata
- Laboratory of Genome Technology, Human Genome Center, Institute of Medical Science, The University of Tokyo, Minato-ku, Japan
| | - Barbara V. Howard
- MedStar Health Research Institute, Hyattsville, Maryland, United States of America
- Center for Clinical and Translational Sciences and Department of Medicine, Georgetown-Howard Universities, Washington, DC, United States of America
| | - M. Arfan Ikram
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Neurology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | | | - Ulrich John
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
- Institute of Social Medicine and Prevention, University Medicine Greifswald, Greifswald, Germany
| | - Tomohiro Katsuya
- Department of Clinical Gene Therapy, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Geriatric Medicine and Nephrology, Osaka University Graduate School of Medicine, Suita, Japan
| | - Chiea Chuen Khor
- Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore
- Department of Biochemistry, National University of Singapore, Singapore, Singapore
| | - Tuomas O. Kilpeläinen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Environmental Medicine and Public Health, The Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Woon-Puay Koh
- Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - José E. Krieger
- Laboratory of Genetics and Molecular Cardiology, Heart Institute (InCor), University of São Paulo Medical School, São Paulo, SP, Brazil
| | - Stephen B. Kritchevsky
- Sticht Center for Healthy Aging and Alzheimer's Prevention, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Michiaki Kubo
- Center for Integrative Medical Sciences, RIKEN, Yokohama, Japan
| | - Johanna Kuusisto
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - Timo A. Lakka
- Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
- Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio Campus, Finland
- Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
| | - Carl D. Langefeld
- Biostatistical Sciences, Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Lenore J. Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Benjamin Lehne
- Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
| | - Cora E. Lewis
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States of America
| | - Yize Li
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Shiow Lin
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Jianjun Liu
- Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, Singapore
- Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore
| | - Jingmin Liu
- WHI CCC, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Marie Loh
- Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
- Translational Laboratory in Genetic Medicine, Agency for Science, Technology and Research, Singapore
| | - Tin Louie
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
| | - Reedik Mägi
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Colin A. McKenzie
- The Caribbean Institute for Health Research (CAIHR), University of the West Indies, Mona, Jamaica
| | - Thomas Meitinger
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, Technische Universität München, Munich, Germany
| | | | - Yuri Milaneschi
- Department of Psychiatry, Amsterdam Neuroscience and Amsterdam Public Health Research Institute, VU University Medical Center, Amsterdam, The Netherlands
| | - Lili Milani
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Karen L. Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Yukihide Momozawa
- Laboratory for Genotyping Development, Center for Integrative Medical Sciences, RIKEN, Yokohama, Japan
| | - Mike A. Nalls
- Data Tecnica International, Glen Echo, Maryland, United States of America
- Laboratory of Neurogenetics, National Institute on Aging, Bethesda, Maryland, United States of America
| | - Christopher P. Nelson
- Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Nona Sotoodehnia
- Cardiovascular Health Research Unit, Division of Cardiology, University of Washington, Seattle, Washington, United States of America
| | - Jill M. Norris
- Department of Epidemiology, Colorado School of Public Health, Aurora, Colorado, United States of America
| | - Jeff R. O'Connell
- Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
- Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Nicholette D. Palmer
- Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Thomas Perls
- Geriatrics Section, Boston University Medical Center, Boston, Massachusetts, United States of America
| | - Nancy L. Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Stockholm, Sweden
| | - Annette Peters
- Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Neuherberg, Germany
| | - Patricia A. Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Neil Poulter
- School of Public Health, Imperial College London, London, London, United Kingdom
| | - Leslie J. Raffel
- Division of Genetic and Genomic Medicine, Department of Pediatrics, University of California, Irvine, California, United States of America
| | - Olli T. Raitakari
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
| | - Kathryn Roll
- Genomic Outcomes, Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, California, United States of America
| | - Lynda M. Rose
- Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
| | - Frits R. Rosendaal
- Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Jerome I. Rotter
- Genomic Outcomes, Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, California, United States of America
| | - Carsten O. Schmidt
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Pamela J. Schreiner
- Epidemiology & Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Nicole Schupf
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Medical Center, New York, New York, United States of America
| | - William R. Scott
- Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Peter S. Sever
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Yuan Shi
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Stephen Sidney
- Division of Research, Kaiser Permanente of Northern California, Oakland, California, United States of America
| | - Mario Sims
- Jackson Heart Study, Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
| | - Colleen M. Sitlani
- Cardiovascular Health Research Unit, Medicine, University of Washington, Seattle, Washington, United States of America
| | - Jennifer A. Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - John M. Starr
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, United Kingdom
- Alzheimer Scotland Dementia Research Centre, The University of Edinburgh, Edinburgh, United Kingdom
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU, Munich, Germany
| | - Heather M. Stringham
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Nicholas Y. Q. Tan
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Hua Tang
- Department of Genetics, Stanford University, Stanford, California, United States of America
| | - Kent D. Taylor
- Genomic Outcomes, Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, California, United States of America
| | - Yik Ying Teo
- Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, Singapore
- Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore
- Life Sciences Institute, National University of Singapore, Singapore, Singapore
- NUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore, Singapore
- Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore
| | - Yih Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Stephen T. Turner
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, United States of America
| | - André G. Uitterlinden
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Peter Vollenweider
- Service of Internal Medicine, Department of Internal Medicine, University Hospital, Lausanne, Switzerland
| | - Melanie Waldenberger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Lihua Wang
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Ya Xing Wang
- Beijing Institute of Ophthalmology, Beijing Ophthalmology and Visual Science Key Lab, Beijing Tongren Eye Center, Capital Medical University, Beijing, China
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Wen Bin Wei
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Christine Williams
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Jie Yao
- Genomic Outcomes, Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, California, United States of America
| | - Caizheng Yu
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health for Incubating, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jian-Min Yuan
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Division of Cancer Control and Population Sciences, UPMC Hillman Cancer, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Alan B. Zonderman
- Behavioral Epidemiology Section, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States of America
| | - Diane M. Becker
- Division of General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Donald W. Bowden
- Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - John C. Chambers
- Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
- Department of Cardiology, Ealing Hospital, Middlesex, United Kingdom
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- Imperial College Healthcare NHS Trust, London, United Kingdom
- MRC-PHE Centre for Environment and Health, Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Ian J. Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh, United Kingdom
- Psychology, The University of Edinburgh, Edinburgh, United Kingdom
| | - Tõnu Esko
- Estonian Genome Center, University of Tartu, Tartu, Estonia
- Broad Institute of the Massachusetts Institute of Technology and Harvard University, Boston, Massachusetts, United States of America
| | - Martin Farrall
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, Oxfordshire, United Kingdom
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, Oxfordshire, United Kingdom
| | - Paul W. Franks
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Skåne University Hospital, Malmö, Sweden
- Harvard T. H. Chan School of Public Health, Department of Nutrition, Harvard University, Boston, Massachusetts, United States of America
| | - Barry I. Freedman
- Nephrology, Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Philippe Froguel
- CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, University of Lille, Lille, France
- Department of Genomics of Common Disease, Imperial College London, London, United Kingdom
| | - Paolo Gasparini
- Institute for Maternal and Child Health—IRCCS "Burlo Garofolo", Trieste, Italy
- Department of Medical Sciences, University of Trieste, Trieste, Italy
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany
| | - Jost Bruno Jonas
- Beijing Institute of Ophthalmology, Beijing Ophthalmology and Visual Science Key Lab, Beijing Tongren Eye Center, Capital Medical University, Beijing, China
- Department of Ophthalmology, Medical Faculty Mannheim, University Heidelberg, Mannheim, Germany, Germany
| | - Yoichiro Kamatani
- Laboratory for Statistical Analysis, Center for Integrative Medical Sciences, RIKEN, Yokohama, Japan
| | - Norihiro Kato
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Jaspal S. Kooner
- Department of Cardiology, Ealing Hospital, Middlesex, United Kingdom
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Imperial College Healthcare NHS Trust, London, United Kingdom
- MRC-PHE Centre for Environment and Health, Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Zoltán Kutalik
- Swiss Instititute of Bioinformatics, Lausanne, Switzerland
- Institute of Social and Preventive Medicine, Lausanne University Hospital, Lausanne, Switzerland
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - Cathy C. Laurie
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
| | - Karin Leander
- Unit of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center—Tampere, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland
| | | | - Patrik K. E. Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Stockholm, Sweden
| | - Albertine J. Oldehinkel
- Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Brenda W. J. H. Penninx
- Department of Psychiatry, Amsterdam Neuroscience and Amsterdam Public Health Research Institute, VU University Medical Center, Amsterdam, The Netherlands
| | - Ozren Polasek
- Department of Public Health, Department of Medicine, University of Split, Split, Croatia
- Psychiatric Hospital "Sveti Ivan", Zagreb, Croatia
- Gen-info Ltd, Zagreb, Croatia
| | - David J. Porteous
- Centre for Genomic & Experimental Medicine, Institute of Genetics & Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
| | - Rainer Rauramaa
- Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
| | - Nilesh J. Samani
- Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - James Scott
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
| | - Pim van der Harst
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Lynne E. Wagenknecht
- Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | | | - Hugh Watkins
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, Oxfordshire, United Kingdom
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, Oxfordshire, United Kingdom
| | - David R. Weir
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, Michigan, United States of America
| | | | - Tangchun Wu
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health for Incubating, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
| | - Claude Bouchard
- Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, Louisiana, United States of America
| | - Kaare Christensen
- The Danish Aging Research Center, Institute of Public Health, University of Southern Denmark, Odense, Denmark
| | - Michele K. Evans
- Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States of America
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Bernardo L. Horta
- Postgraduate Programme in Epidemiology, Federal University of Pelotas, Pelotas, RS, Brazil
| | - Sharon L. R. Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Yongmei Liu
- Public Health Sciences, Epidemiology and Prevention, Wake Forest University Health Sciences, Winston-Salem, North Carolina, United States of America
| | - Alexandre C. Pereira
- Laboratory of Genetics and Molecular Cardiology, Heart Institute (InCor), University of São Paulo Medical School, São Paulo, SP, Brazil
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit, Epidemiology, Medicine and Health Services, University of Washington, Seattle, Washington, United States of America
- Kaiser Permanente Washington, Health Research Institute, Seattle, Washington, United States of America
| | - Paul M. Ridker
- Preventive Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Rob M. van Dam
- Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - W. James Gauderman
- Biostatistics, Preventive Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Xiaofeng Zhu
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - Dennis O. Mook-Kanamori
- Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands
| | - Myriam Fornage
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
- Brown Foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Charles N. Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - L. Adrienne Cupples
- Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America
- The Framingham Heart Study, Framingham, Massachusetts, United States of America
| | - Tanika N. Kelly
- Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America
| | - Ervin R. Fox
- Cardiology, Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
| | - Caroline Hayward
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
| | - Cornelia M. van Duijn
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - E Shyong Tai
- Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Charles Kooperberg
- Fred Hutchinson Cancer Research Center, University of Washington School of Public Health, Seattle, Washington, United States of America
| | - Walter Palmas
- Medicine, Columbia University Medical Center, New York, New York, United States of America
| | - Kenneth Rice
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
| | - Alanna C. Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Paul Elliott
- MRC-PHE Centre for Environment and Health, Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Mark J. Caulfield
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
- NIHR Barts Cardiovascular Biomedical Research Unit, Queen Mary University of London, London, London, United Kingdom
| | - Patricia B. Munroe
- Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
- NIHR Barts Cardiovascular Biomedical Research Unit, Queen Mary University of London, London, London, United Kingdom
| | - Dabeeru C. Rao
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Michael A. Province
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Daniel Levy
- The Framingham Heart Study, Framingham, Massachusetts, United States of America
- The Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, United States of America
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Koulentaki M, Kouroumalis E. GABA A receptor polymorphisms in alcohol use disorder in the GWAS era. Psychopharmacology (Berl) 2018; 235:1845-1865. [PMID: 29721579 DOI: 10.1007/s00213-018-4918-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Accepted: 04/18/2018] [Indexed: 12/11/2022]
Abstract
Alcohol use disorder (AUD) is a chronic, relapsing, neuro-psychiatric illness of high prevalence and with a serious public health impact worldwide. It is complex and polygenic, with a heritability of about 50%, and influenced by environmental causal heterogeneity. Risk factors associated with its etiology have a genetic component. GABA (γ-aminobutyric acid) is a major inhibitory neurotransmitter in mammalian brain. GABAA receptors are believed to mediate some of the physiological and behavioral actions of alcohol. In this critical review, relevant genetic terms and type and methodology of the genetic studies are briefly explained. Postulated candidate genes that encode subunits of GABAA receptors, with all the reported SNPs, are presented. Genetic studies and meta-analyses examining polymorphisms of the GABAA receptor and their association with AUD predisposition are presented. The data are critically examined with reference to recent GWAS studies that failed to show relations between GABAA receptors and AUD. Restrictions and perspectives of the different findings are discussed.
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Affiliation(s)
- Mairi Koulentaki
- Alcohology Research Laboratory, Medical School, University of Crete, 71500, Heraklion, Crete, Greece.,Department of Gastroenterology, University Hospital Heraklion, 71500, Heraklion, Crete, Greece
| | - Elias Kouroumalis
- Department of Gastroenterology, University Hospital Heraklion, 71500, Heraklion, Crete, Greece.
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Pineda-Cirera L, Cabana-Domínguez J, Roncero C, Cozar M, Grau-López L, Abad AC, Martínez-Luna N, Robles-Martínez M, Sánchez-Mora C, Ramos-Quiroga JA, Casas M, Ribasés M, Fernàndez-Castillo N, Cormand B. Evaluation of previous substance dependence genome-wide significant findings in a Spanish sample. Drug Alcohol Depend 2018; 187:358-362. [PMID: 29715653 DOI: 10.1016/j.drugalcdep.2018.03.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2017] [Revised: 03/02/2018] [Accepted: 03/08/2018] [Indexed: 11/19/2022]
Abstract
BACKGROUND Substance dependence is a chronic and relapsing disorder explained by genetic and environmental risk factors. The aim of our study is to replicate previous genome-wide significant (GWS) hits identified in substance dependence in general or in cocaine dependence in particular using an independent sample from Spain. METHODS We evaluated, in a Spanish sample of 1711 subjects with substance dependence (1011 of them cocaine dependent) and 1719 control individuals, three SNPs identified as GWS in previous studies: rs1868152 and rs2952621 (located near LINC02052 and LINC01854, respectively), associated with substance dependence, and rs2629540 (in the first intron of FAM53B), associated with cocaine dependence. RESULTS We replicated the association between rs2952621 and substance dependence under the dominant model (P = 0.020), with the risk allele (T) being the same in our sample and in those two reported previously. We then performed a meta-analysis of the two samples used in the original study that reported the association of rs2952621 with substance dependence (Collaborative Studies on Genetics of Alcoholism (COGA) and Study of Addiction: Genetics and Environment (SAGE)) together with our Spanish sample. The meta-analysis of 3747 cases and 4043 controls confirmed the association (OR = 1.26, 95% CI = 1.15-1.39). CONCLUSIONS The rs2952621 variant, located downstream from the yet uncharacterized gene LINC01854, is associated with substance dependence in our Spanish sample. Further research is needed to understand its contribution to the susceptibility to substance dependence.
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Affiliation(s)
- Laura Pineda-Cirera
- Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona, Av. Diagonal 643, 08028 Barcelona, Catalonia, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Av. Monforte de Lemos 3-5, Pabellón 11, 280029, Madrid, Spain; Institut de Biomedicina de la Universitat de Barcelona (IBUB), Av. Diagonal 643, 08028 Barcelona, Catalonia, Spain; Institut de Recerca Sant Joan de Déu (IR-SJD), Passeig de Sant Joan de Déu 2, 08950 Esplugues de Llobregat, Barcelona, Catalonia, Spain
| | - Judit Cabana-Domínguez
- Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona, Av. Diagonal 643, 08028 Barcelona, Catalonia, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Av. Monforte de Lemos 3-5, Pabellón 11, 280029, Madrid, Spain; Institut de Biomedicina de la Universitat de Barcelona (IBUB), Av. Diagonal 643, 08028 Barcelona, Catalonia, Spain; Institut de Recerca Sant Joan de Déu (IR-SJD), Passeig de Sant Joan de Déu 2, 08950 Esplugues de Llobregat, Barcelona, Catalonia, Spain
| | - Carlos Roncero
- Psychiatry Department, Universitat Autònoma de Barcelona, 08193 Bellaterra, Catalonia, Spain; Addiction and Dual Diagnosis Unit, Psychiatric Services, Hospital Universitari Vall d'Hebron-ASPB, Passeig Vall d'Hebron 119-129, Edifici Mediterrània, 08035 Barcelona, Catalonia, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Av. Monforte de Lemos 3-5, Pabellón 11, 280029 Madrid, Spain; Department of Psychiatry, Hospital Universitari Vall d'Hebron, Passeig Vall d'Hebron 119-129, Edifici Mediterrània, 08035 Barcelona, Catalonia, Spain; Psychiatric Service, University of Salamanca Health Care Complex and Institute of Biomedicine of Salamanca, University of Salamanca, Castile and Leon, Spain
| | - Mònica Cozar
- Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona, Av. Diagonal 643, 08028 Barcelona, Catalonia, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Av. Monforte de Lemos 3-5, Pabellón 11, 280029, Madrid, Spain; Institut de Biomedicina de la Universitat de Barcelona (IBUB), Av. Diagonal 643, 08028 Barcelona, Catalonia, Spain; Institut de Recerca Sant Joan de Déu (IR-SJD), Passeig de Sant Joan de Déu 2, 08950 Esplugues de Llobregat, Barcelona, Catalonia, Spain
| | - Lara Grau-López
- Psychiatry Department, Universitat Autònoma de Barcelona, 08193 Bellaterra, Catalonia, Spain; Addiction and Dual Diagnosis Unit, Psychiatric Services, Hospital Universitari Vall d'Hebron-ASPB, Passeig Vall d'Hebron 119-129, Edifici Mediterrània, 08035 Barcelona, Catalonia, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Av. Monforte de Lemos 3-5, Pabellón 11, 280029 Madrid, Spain; Department of Psychiatry, Hospital Universitari Vall d'Hebron, Passeig Vall d'Hebron 119-129, Edifici Mediterrània, 08035 Barcelona, Catalonia, Spain
| | - Alfonso C Abad
- Addiction and Dual Diagnosis Unit, Psychiatric Services, Hospital Universitari Vall d'Hebron-ASPB, Passeig Vall d'Hebron 119-129, Edifici Mediterrània, 08035 Barcelona, Catalonia, Spain; Department of Psychiatry, Hospital Universitari Vall d'Hebron, Passeig Vall d'Hebron 119-129, Edifici Mediterrània, 08035 Barcelona, Catalonia, Spain
| | - Nieves Martínez-Luna
- Psychiatry Department, Universitat Autònoma de Barcelona, 08193 Bellaterra, Catalonia, Spain; Addiction and Dual Diagnosis Unit, Psychiatric Services, Hospital Universitari Vall d'Hebron-ASPB, Passeig Vall d'Hebron 119-129, Edifici Mediterrània, 08035 Barcelona, Catalonia, Spain; Department of Psychiatry, Hospital Universitari Vall d'Hebron, Passeig Vall d'Hebron 119-129, Edifici Mediterrània, 08035 Barcelona, Catalonia, Spain
| | - María Robles-Martínez
- Addiction and Dual Diagnosis Unit, Psychiatric Services, Hospital Universitari Vall d'Hebron-ASPB, Passeig Vall d'Hebron 119-129, Edifici Mediterrània, 08035 Barcelona, Catalonia, Spain; Department of Psychiatry, Hospital Universitari Vall d'Hebron, Passeig Vall d'Hebron 119-129, Edifici Mediterrània, 08035 Barcelona, Catalonia, Spain
| | - Cristina Sánchez-Mora
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Av. Monforte de Lemos 3-5, Pabellón 11, 280029 Madrid, Spain; Department of Psychiatry, Hospital Universitari Vall d'Hebron, Passeig Vall d'Hebron 119-129, Edifici Mediterrània, 08035 Barcelona, Catalonia, Spain; Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, 08193 Bellaterra, Catalonia, Spain
| | - Josep Antoni Ramos-Quiroga
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Av. Monforte de Lemos 3-5, Pabellón 11, 280029 Madrid, Spain; Department of Psychiatry, Hospital Universitari Vall d'Hebron, Passeig Vall d'Hebron 119-129, Edifici Mediterrània, 08035 Barcelona, Catalonia, Spain; Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, 08193 Bellaterra, Catalonia, Spain; Department of Psychiatry and Legal Medicine, Universitat Autònoma de Barcelona, 08193 Bellaterra, Catalonia, Spain
| | - Miquel Casas
- Psychiatry Department, Universitat Autònoma de Barcelona, 08193 Bellaterra, Catalonia, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Av. Monforte de Lemos 3-5, Pabellón 11, 280029 Madrid, Spain; Department of Psychiatry, Hospital Universitari Vall d'Hebron, Passeig Vall d'Hebron 119-129, Edifici Mediterrània, 08035 Barcelona, Catalonia, Spain
| | - Marta Ribasés
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Av. Monforte de Lemos 3-5, Pabellón 11, 280029 Madrid, Spain; Department of Psychiatry, Hospital Universitari Vall d'Hebron, Passeig Vall d'Hebron 119-129, Edifici Mediterrània, 08035 Barcelona, Catalonia, Spain; Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, 08193 Bellaterra, Catalonia, Spain
| | - Noèlia Fernàndez-Castillo
- Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona, Av. Diagonal 643, 08028 Barcelona, Catalonia, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Av. Monforte de Lemos 3-5, Pabellón 11, 280029, Madrid, Spain; Institut de Biomedicina de la Universitat de Barcelona (IBUB), Av. Diagonal 643, 08028 Barcelona, Catalonia, Spain; Institut de Recerca Sant Joan de Déu (IR-SJD), Passeig de Sant Joan de Déu 2, 08950 Esplugues de Llobregat, Barcelona, Catalonia, Spain.
| | - Bru Cormand
- Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona, Av. Diagonal 643, 08028 Barcelona, Catalonia, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Av. Monforte de Lemos 3-5, Pabellón 11, 280029, Madrid, Spain; Institut de Biomedicina de la Universitat de Barcelona (IBUB), Av. Diagonal 643, 08028 Barcelona, Catalonia, Spain; Institut de Recerca Sant Joan de Déu (IR-SJD), Passeig de Sant Joan de Déu 2, 08950 Esplugues de Llobregat, Barcelona, Catalonia, Spain.
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Huckins LM, Hatzikotoulas K, Southam L, Thornton LM, Steinberg J, Aguilera-McKay F, Treasure J, Schmidt U, Gunasinghe C, Romero A, Curtis C, Rhodes D, Moens J, Kalsi G, Dempster D, Leung R, Keohane A, Burghardt R, Ehrlich S, Hebebrand J, Hinney A, Ludolph A, Walton E, Deloukas P, Hofman A, Palotie A, Palta P, van Rooij FJA, Stirrups K, Adan R, Boni C, Cone R, Dedoussis G, van Furth E, Gonidakis F, Gorwood P, Hudson J, Kaprio J, Kas M, Keski-Rahonen A, Kiezebrink K, Knudsen GP, Slof-Op 't Landt MCT, Maj M, Monteleone AM, Monteleone P, Raevuori AH, Reichborn-Kjennerud T, Tozzi F, Tsitsika A, van Elburg A, Collier DA, Sullivan PF, Breen G, Bulik CM, Zeggini E. Investigation of common, low-frequency and rare genome-wide variation in anorexia nervosa. Mol Psychiatry 2018; 23:1169-1180. [PMID: 29155802 PMCID: PMC5828108 DOI: 10.1038/mp.2017.88] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2016] [Revised: 02/16/2017] [Accepted: 02/17/2017] [Indexed: 12/12/2022]
Abstract
Anorexia nervosa (AN) is a complex neuropsychiatric disorder presenting with dangerously low body weight, and a deep and persistent fear of gaining weight. To date, only one genome-wide significant locus associated with AN has been identified. We performed an exome-chip based genome-wide association studies (GWAS) in 2158 cases from nine populations of European origin and 15 485 ancestrally matched controls. Unlike previous studies, this GWAS also probed association in low-frequency and rare variants. Sixteen independent variants were taken forward for in silico and de novo replication (11 common and 5 rare). No findings reached genome-wide significance. Two notable common variants were identified: rs10791286, an intronic variant in OPCML (P=9.89 × 10-6), and rs7700147, an intergenic variant (P=2.93 × 10-5). No low-frequency variant associations were identified at genome-wide significance, although the study was well-powered to detect low-frequency variants with large effect sizes, suggesting that there may be no AN loci in this genomic search space with large effect sizes.
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Affiliation(s)
- L M Huckins
- Department of Human Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
- Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - K Hatzikotoulas
- Department of Human Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - L Southam
- Department of Human Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - L M Thornton
- Department of Psychiatry and Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - J Steinberg
- Department of Human Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - F Aguilera-McKay
- Department of Human Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - J Treasure
- Section of Eating Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - U Schmidt
- Section of Eating Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - C Gunasinghe
- Section of Eating Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- NIHR BRC SLaM BioResource for Mental Health, SGDP Centre & Centre for Neuroimaging Sciences, Section of Eating Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - A Romero
- Section of Eating Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- NIHR BRC SLaM BioResource for Mental Health, SGDP Centre & Centre for Neuroimaging Sciences, Section of Eating Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - C Curtis
- Section of Eating Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- NIHR BRC SLaM BioResource for Mental Health, SGDP Centre & Centre for Neuroimaging Sciences, Section of Eating Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - D Rhodes
- Section of Eating Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- NIHR BRC SLaM BioResource for Mental Health, SGDP Centre & Centre for Neuroimaging Sciences, Section of Eating Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - J Moens
- Section of Eating Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- NIHR BRC SLaM BioResource for Mental Health, SGDP Centre & Centre for Neuroimaging Sciences, Section of Eating Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - G Kalsi
- Section of Eating Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- NIHR BRC SLaM BioResource for Mental Health, SGDP Centre & Centre for Neuroimaging Sciences, Section of Eating Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - D Dempster
- Section of Eating Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- NIHR BRC SLaM BioResource for Mental Health, SGDP Centre & Centre for Neuroimaging Sciences, Section of Eating Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - R Leung
- Section of Eating Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- NIHR BRC SLaM BioResource for Mental Health, SGDP Centre & Centre for Neuroimaging Sciences, Section of Eating Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - A Keohane
- Section of Eating Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- NIHR BRC SLaM BioResource for Mental Health, SGDP Centre & Centre for Neuroimaging Sciences, Section of Eating Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - R Burghardt
- Klinik für Kinder- und Jugendpsychiatrie, Psychotherapie und Psychosomatik Klinikum Frankfurt, Frankfurt, Germany
| | - S Ehrlich
- Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, TU Dresden, Dresden, Germany
- Eating Disorders Research and Treatment Center, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Dresden, Germany
| | - J Hebebrand
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - A Hinney
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - A Ludolph
- Helmholtz Zentrum München, Deutsches Forschungszentrum für Gesundheit und Umwelt, Neuherberg, Germany
| | - E Walton
- Division of Psychological & Social Medicine and Developmental Neurosciences, Technische Universität Dresden, Faculty of Medicine, University Hospital C.G. Carus, Dresden, Germany
- Department of Psychology, Georgia State University, Atlanta, GA, USA
| | - P Deloukas
- Department of Human Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - A Hofman
- Erasmus University Medical Center, Rotterdam, The Netherlands
| | - A Palotie
- Center for Human Genome Research at the Massachusetts General Hospital, Boston, MA, USA
- Department of Public Health & Institute for Molecular Medicine FIMM, University of Helsinki, Helsinki, Finland
| | - P Palta
- Department of Public Health & Institute for Molecular Medicine FIMM, University of Helsinki, Helsinki, Finland
| | - F J A van Rooij
- Erasmus University Medical Center, Rotterdam, The Netherlands
| | - K Stirrups
- Department of Human Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - R Adan
- Brain Center Rudolf Magnus, Department of Neuroscience and Pharmacology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - C Boni
- INSERM U984, Centre of Psychiatry and Neuroscience, Paris, France
| | - R Cone
- Mary Sue Coleman Director, Life Sciences Institute, Professor of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, USA
| | - G Dedoussis
- Department of Dietetics-Nutrition, Harokopio University, Athens, Greece
| | - E van Furth
- Rivierduinen Eating Disorders Ursula, Leiden, Zuid-Holland, The Netherlands
| | - F Gonidakis
- Eating Disorders Unit, 1st Department of Psychiatry, National and Kapodistrian University of Athens, Medical School, Athens, Greece
| | - P Gorwood
- INSERM U984, Centre of Psychiatry and Neuroscience, Paris, France
| | - J Hudson
- Department of Psychiatry, McLean Hospital/Harvard Medical School, Belmont, MA, USA
| | - J Kaprio
- Department of Public Health & Institute for Molecular Medicine FIMM, University of Helsinki, Helsinki, Finland
| | - M Kas
- Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands
| | - A Keski-Rahonen
- Department of Public Health, Clinicum, University of Helsinki, Helsinki, Finland
| | - K Kiezebrink
- Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
| | - G-P Knudsen
- Health Data and Digitalisation, Norwegian Institute of Public Health, Oslo, Norway
| | | | - M Maj
- Department of Psychiatry, University of Naples SUN, Naples, Italy
| | - A M Monteleone
- Department of Psychiatry, University of Naples SUN, Naples, Italy
| | - P Monteleone
- Department of Medicine and Surgery, Section of Neurosciences, University of Salerno, Salerno, Italy
| | - A H Raevuori
- Department of Public Health, Clinicum, University of Helsinki, Helsinki, Finland
| | - T Reichborn-Kjennerud
- Department of Genetics, Environment and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - F Tozzi
- eHealth Lab-Computer Science Department, University of Cyprus, Nicosia, Cyprus
| | - A Tsitsika
- Adolescent Health Unit (A.H.U.), 2nd Department of Pediatrics – Medical School, University of Athens "P. & A. Kyriakou" Children's Hospital, Athens, Greece
| | - A van Elburg
- Center for Eating Disorders Rintveld, University of Utrecht, Utrecht, The Netherlands
| | - D A Collier
- Eli Lilly and Company, Erl Wood Manor, Windlesham, UK
| | - P F Sullivan
- Departments of Genetics and Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Medical Epidemiology and Biostatistics, Karolinksa Institutet, Stockholm, Sweden
| | - G Breen
- Social Genetic and Developmental Psychiatry, King's College London, London, UK
| | - C M Bulik
- Department of Psychiatry and Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Medical Epidemiology and Biostatistics, Karolinksa Institutet, Stockholm, Sweden
| | - E Zeggini
- Department of Human Genetics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
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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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44
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Li MJ, Zhang J, Liang Q, Xuan C, Wu J, Jiang P, Li W, Zhu Y, Wang P, Fernandez D, Shen Y, Chen Y, Kocher JPA, Yu Y, Sham PC, Wang J, Liu JS, Liu XS. Exploring genetic associations with ceRNA regulation in the human genome. Nucleic Acids Res 2017; 45:5653-5665. [PMID: 28472449 PMCID: PMC5449616 DOI: 10.1093/nar/gkx331] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2015] [Accepted: 04/26/2017] [Indexed: 01/01/2023] Open
Abstract
Competing endogenous RNAs (ceRNAs) are RNA molecules that sequester shared microRNAs (miRNAs) thereby affecting the expression of other targets of the miRNAs. Whether genetic variants in ceRNA can affect its biological function and disease development is still an open question. Here we identified a large number of genetic variants that are associated with ceRNA's function using Geuvaids RNA-seq data for 462 individuals from the 1000 Genomes Project. We call these loci competing endogenous RNA expression quantitative trait loci or 'cerQTL', and found that a large number of them were unexplored in conventional eQTL mapping. We identified many cerQTLs that have undergone recent positive selection in different human populations, and showed that single nucleotide polymorphisms in gene 3΄UTRs at the miRNA seed binding regions can simultaneously regulate gene expression changes in both cis and trans by the ceRNA mechanism. We also discovered that cerQTLs are significantly enriched in traits/diseases associated variants reported from genome-wide association studies in the miRNA binding sites, suggesting that disease susceptibilities could be attributed to ceRNA regulation. Further in vitro functional experiments demonstrated that a cerQTL rs11540855 can regulate ceRNA function. These results provide a comprehensive catalog of functional non-coding regulatory variants that may be responsible for ceRNA crosstalk at the post-transcriptional level.
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Affiliation(s)
- Mulin Jun Li
- Department of pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China.,Department of Statistics, Harvard University, Cambridge, MA 02138, USA.,Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR 999077, China
| | - Jian Zhang
- Department of pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Qian Liang
- Department of pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Chenghao Xuan
- Department of pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Jiexing Wu
- Department of Statistics, Harvard University, Cambridge, MA 02138, USA
| | - Peng Jiang
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard T.H.Chan School of Public Health, Boston, MA 02215, USA
| | - Wei Li
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard T.H.Chan School of Public Health, Boston, MA 02215, USA
| | - Yun Zhu
- Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR 999077, China.,School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR 999077, China
| | - Panwen Wang
- Department of Health Sciences Research & Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ 85259, USA
| | - Daniel Fernandez
- Department of Statistics, Harvard University, Cambridge, MA 02138, USA
| | - Yujun Shen
- Department of pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Yiwen Chen
- Department of Bioinformatics and Computational Biology, Division of Quantitative Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jean-Pierre A Kocher
- Department of Health Sciences Research & Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ 85259, USA
| | - Ying Yu
- Department of pharmacology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Pak Chung Sham
- Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR 999077, China.,Department of Psychiatry, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR 999077, China
| | - Junwen Wang
- Department of Health Sciences Research & Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ 85259, USA.,Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ 85259, USA
| | - Jun S Liu
- Department of Statistics, Harvard University, Cambridge, MA 02138, USA
| | - X Shirley Liu
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard T.H.Chan School of Public Health, Boston, MA 02215, USA
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45
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The utility of twins in developmental cognitive neuroscience research: How twins strengthen the ABCD research design. Dev Cogn Neurosci 2017; 32:30-42. [PMID: 29107609 PMCID: PMC5847422 DOI: 10.1016/j.dcn.2017.09.001] [Citation(s) in RCA: 59] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2017] [Revised: 08/31/2017] [Accepted: 09/05/2017] [Indexed: 02/01/2023] Open
Abstract
The ABCD twin study will elucidate the genetic and environmental contributions to a wide range of mental and physical health outcomes in children, including substance use, brain and behavioral development, and their interrelationship. Comparisons within and between monozygotic and dizygotic twin pairs, further powered by multiple assessments, provide information about genetic and environmental contributions to developmental associations, and enable stronger tests of causal hypotheses, than do comparisons involving unrelated children. Thus a sub-study of 800 pairs of same-sex twins was embedded within the overall Adolescent Brain and Cognitive Development (ABCD) design. The ABCD Twin Hub comprises four leading centers for twin research in Minnesota, Colorado, Virginia, and Missouri. Each site is enrolling 200 twin pairs, as well as singletons. The twins are recruited from registries of all twin births in each State during 2006-2008. Singletons at each site are recruited following the same school-based procedures as the rest of the ABCD study. This paper describes the background and rationale for the ABCD twin study, the ascertainment of twin pairs and implementation strategy at each site, and the details of the proposed analytic strategies to quantify genetic and environmental influences and test hypotheses critical to the aims of the ABCD study.
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Yang JJ, Williams LK, Buu A. Identifying pleiotropic genes in genome-wide association studies from related subjects using the linear mixed model and Fisher combination function. BMC Bioinformatics 2017; 18:376. [PMID: 28836938 PMCID: PMC5571642 DOI: 10.1186/s12859-017-1791-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2016] [Accepted: 08/15/2017] [Indexed: 11/11/2022] Open
Abstract
Background A multivariate genome-wide association test is proposed for analyzing data on multivariate quantitative phenotypes collected from related subjects. The proposed method is a two-step approach. The first step models the association between the genotype and marginal phenotype using a linear mixed model. The second step uses the correlation between residuals of the linear mixed model to estimate the null distribution of the Fisher combination test statistic. Results The simulation results show that the proposed method controls the type I error rate and is more powerful than the marginal tests across different population structures (admixed or non-admixed) and relatedness (related or independent). The statistical analysis on the database of the Study of Addiction: Genetics and Environment (SAGE) demonstrates that applying the multivariate association test may facilitate identification of the pleiotropic genes contributing to the risk for alcohol dependence commonly expressed by four correlated phenotypes. Conclusions This study proposes a multivariate method for identifying pleiotropic genes while adjusting for cryptic relatedness and population structure between subjects. The two-step approach is not only powerful but also computationally efficient even when the number of subjects and the number of phenotypes are both very large.
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Affiliation(s)
- James J Yang
- School of Nursing, University of Michigan, Ann Arbor, 48104, Michigan, USA.
| | - L Keoki Williams
- Department of Internal Medicine, Henry Ford Health System, Detroit, 48202, Michigan, USA.,The Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, 48202, Michigan, USA
| | - Anne Buu
- Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, 48104, Michigan, USA
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47
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Arbet J, McGue M, Chatterjee S, Basu S. Resampling-based tests for Lasso in genome-wide association studies. BMC Genet 2017; 18:70. [PMID: 28738830 PMCID: PMC5525347 DOI: 10.1186/s12863-017-0533-3] [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] [Received: 12/05/2016] [Accepted: 06/30/2017] [Indexed: 01/08/2023] Open
Abstract
Background Genome-wide association studies involve detecting association between millions of genetic variants and a trait, which typically use univariate regression to test association between each single variant and the phenotype. Alternatively, Lasso penalized regression allows one to jointly model the relationship between all genetic variants and the phenotype. However, it is unclear how to best conduct inference on the individual Lasso coefficients, especially in high-dimensional settings. Methods We consider six methods for testing the Lasso coefficients: two permutation (Lasso-Ayers, Lasso-PL) and one analytic approach (Lasso-AL) to select the penalty parameter for type-1-error control, residual bootstrap (Lasso-RB), modified residual bootstrap (Lasso-MRB), and a permutation test (Lasso-PT). Methods are compared via simulations and application to the Minnesota Center for Twins and Family Study. Results We show that for finite sample sizes with increasing number of null predictors, Lasso-RB, Lasso-MRB, and Lasso-PT fail to be viable methods of inference. However, Lasso-PL and Lasso-AL remain fast and powerful tools for conducting inference with the Lasso, even in high-dimensions. Conclusion Our results suggest that the proposed permutation selection procedure (Lasso-PL) and the analytic selection method (Lasso-AL) are fast and powerful alternatives to the standard univariate analysis in genome-wide association studies.
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Affiliation(s)
- Jaron Arbet
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, 55455, USA
| | - Matt McGue
- Department of Psychology, University of Minnesota, Minneapolis, 55455, USA
| | | | - Saonli Basu
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, 55455, USA.
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48
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Cho SB, Aliev F, Clark SL, Adkins AE, Edenberg HJ, Bucholz KK, Porjesz B, Dick DM. Using Patterns of Genetic Association to Elucidate Shared Genetic Etiologies Across Psychiatric Disorders. Behav Genet 2017; 47:405-415. [PMID: 28343281 PMCID: PMC5996973 DOI: 10.1007/s10519-017-9844-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2016] [Accepted: 03/09/2017] [Indexed: 11/28/2022]
Abstract
Twin studies indicate that latent genetic factors overlap across comorbid psychiatric disorders. In this study, we used a novel approach to elucidate shared genetic factors across psychiatric outcomes by clustering single nucleotide polymorphisms based on their genome-wide association patterns. We applied latent profile analysis (LPA) to p-values resulting from genome-wide association studies across three phenotypes: symptom counts of alcohol dependence (AD), antisocial personality disorder (ASP), and major depression (MD), using the European-American case-control genome-wide association study subsample of the collaborative study on the genetics of alcoholism (N = 1399). In the 3-class model, classes were characterized by overall low associations (85.6% of SNPs), relatively stronger association only with MD (6.8%), and stronger associations with AD and ASP but not with MD (7.6%), respectively. These results parallel the genetic factor structure identified in twin studies. The findings suggest that applying LPA to association results across multiple disorders may be a promising approach to identify the specific genetic etiologies underlying shared genetic variance.
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Affiliation(s)
- Seung Bin Cho
- Department of Psychology, Virginia Commonwealth University, PO Box 842018, 817W. Franklin Street, Richmond, VA, 23284-2018, USA.
- College Behavioral and Emotional Health Institute, Virginia Commonwealth University, Richmond, VA, USA.
| | - Fazil Aliev
- Department of Psychology, Virginia Commonwealth University, PO Box 842018, 817W. Franklin Street, Richmond, VA, 23284-2018, USA
- College Behavioral and Emotional Health Institute, Virginia Commonwealth University, Richmond, VA, USA
- Faculty of Business, Karabuk University, Karabuk, Turkey
| | - Shaunna L Clark
- Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - Amy E Adkins
- Department of Psychology, Virginia Commonwealth University, PO Box 842018, 817W. Franklin Street, Richmond, VA, 23284-2018, USA
- College Behavioral and Emotional Health Institute, Virginia Commonwealth University, Richmond, VA, USA
| | - Howard J Edenberg
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
| | | | - Bernice Porjesz
- Department of Psychiatry and Behavioral Sciences, State University of New York Downstate Medical Center, Brooklyn, NY, USA
| | - Danielle M Dick
- Department of Psychology, Virginia Commonwealth University, PO Box 842018, 817W. Franklin Street, Richmond, VA, 23284-2018, USA
- College Behavioral and Emotional Health Institute, Virginia Commonwealth University, Richmond, VA, USA
- Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA, USA
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49
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Wills AG, Evans LM, Hopfer C. Phenotypic and Genetic Relationship Between BMI and Drinking in a Sample of UK Adults. Behav Genet 2017; 47:290-297. [PMID: 28238197 PMCID: PMC5546738 DOI: 10.1007/s10519-017-9838-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2016] [Accepted: 02/10/2017] [Indexed: 10/20/2022]
Abstract
The health impairments derived from both alcoholism and obesity are well known. However, reports that relate increased alcohol use with increased measures of obesity have been mixed in their findings, especially with respect to genetic factors that could potentially link these two behaviors. Here, using a large sample of adults from the UK (n ≈ 113,000), we report both the observed and genetic correlations between BMI (kg/m2) and two measures of alcohol use: reported quantity (drinks per week) and frequency of use (from never to daily). Overall, both observationally and genetically, alcohol intake is negatively correlated with BMI. Phenotypic correlations ranged from -0.01 to -0.17, and genetic correlations ranged from -0.1 to -0.4. Genetic correlations tended to be stronger than the phenotypic correlations, and these correlations were stronger in females and between BMI and, specifically, frequency of use. Though the mechanisms driving these relationships are yet to be identified, we can conclude that the genetic factors related to drinking both more and more often are shared with those responsible for lower BMI.
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Affiliation(s)
- Amanda G Wills
- Department of Pharmacology, University of Colorado School of Medicine, 12800 E. 19th Ave., Aurora, CO, 80045, USA.
- Institute for Behavioral Genetics, University of Colorado Boulder, 1480 30th Street, Boulder, CO, 80301, USA.
- Department of Pharmacology, University of Colorado Denver, Research North Tower, 12800 East 19th Ave., Aurora, CO, 80010, USA.
| | - Luke M Evans
- Institute for Behavioral Genetics, University of Colorado Boulder, 1480 30th Street, Boulder, CO, 80301, USA
| | - Christian Hopfer
- Institute for Behavioral Genetics, University of Colorado Boulder, 1480 30th Street, Boulder, CO, 80301, USA
- Division of Substance Dependence, Department of Psychiatry, University of Colorado School of Medicine, 12469 E 17th Place, Bldg 400, Mail Stop F478, Aurora, CO, 80045, USA
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50
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Onuki R, Yamaguchi R, Shibuya T, Kanehisa M, Goto S. Revealing phenotype-associated functional differences by genome-wide scan of ancient haplotype blocks. PLoS One 2017; 12:e0176530. [PMID: 28445522 PMCID: PMC5406033 DOI: 10.1371/journal.pone.0176530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2016] [Accepted: 04/12/2017] [Indexed: 11/18/2022] Open
Abstract
Genome-wide scans for positive selection have become important for genomic medicine, and many studies aim to find genomic regions affected by positive selection that are associated with risk allele variations among populations. Most such studies are designed to detect recent positive selection. However, we hypothesize that ancient positive selection is also important for adaptation to pathogens, and has affected current immune-mediated common diseases. Based on this hypothesis, we developed a novel linkage disequilibrium-based pipeline, which aims to detect regions associated with ancient positive selection across populations from single nucleotide polymorphism (SNP) data. By applying this pipeline to the genotypes in the International HapMap project database, we show that genes in the detected regions are enriched in pathways related to the immune system and infectious diseases. The detected regions also contain SNPs reported to be associated with cancers and metabolic diseases, obesity-related traits, type 2 diabetes, and allergic sensitization. These SNPs were further mapped to biological pathways to determine the associations between phenotypes and molecular functions. Assessments of candidate regions to identify functions associated with variations in incidence rates of these diseases are needed in the future.
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Affiliation(s)
- Ritsuko Onuki
- Bioinformatics Team, Advanced Analysis Center, National Agriculture and Food Research Organization (NARO), 2-1-2 Kannondai, Tsukuba, Ibaraki, Japan
| | - Rui Yamaguchi
- Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, Japan
| | - Tetsuo Shibuya
- Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, Japan
| | - Minoru Kanehisa
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, Japan
| | - Susumu Goto
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, Japan
- * E-mail:
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