Molecular genetics of addiction and related heritable phenotypes: genome-wide association approaches identify "connectivity constellation" and drug target genes with pleiotropic effects.

paper Cited Public

Authors: Uhl, George R; Drgon, Tomas; Johnson, Catherine; Li, Chuan-Yun; Contoreggi, Carlo; Hess, Judith; Naiman, Daniel; Liu, Qing-Rong
Year: 2008
Journal: Annals of the New York Academy of Sciences
PMID: 18991966
DOI: 10.1196/annals.1441.018
PMCID: PMC3922196

Figure 1

Pie graph model for the genetic architecture of human vulnerability to dependence on addictive substancesPolygenic additive genetic influences and environmental influences that are largely those that are not shared between members of sibships are depicted. Potential roles for g × g and g × e interactions are not depicted here.

Figure 2

Venn diagram of overlapping genetic contributions to several of the phenotypes discussed here based on genome wide association datasets of about 500000 SNPsNote that the area of overlap in the figure does not necessarily represent the area of the overlap in the datasets. See text for more details.

Figure 3

Validation of SNP genotyping in DNA pools (From [12])The relationship (r = 0.95) between individual and pooled genotyping using 500k SNP Affymetrix arrays provides an opportunity to assess the sensitivity of pooled genotyping. Since these validation experiments were the first ones performed with new array sets, these data provide a lower limit. Current results from 1M SNP arrays (6.0) provide relationships ca 0.98 (Drgon et al, in preparation).

Figure 4

Power of genome wide association as assessed using Gene DetectiveSimulation of power with 620,000 diallelic markers for samples with n = 400 case and n = 400 controls with nominal 0.05 α levels. Note the striking relationship between power and effect size. Power to detect effects that would produce odds ratios of less than 1.2 –fold is modest, while power to detect effects as high as 1.7 fold is relatively good.

Figure 5

Power of genome wide association as assessed using Gene DetectiveSimulation of power with 1,000,000 diallelic markers for samples with n = 2000 case and n = 2000 controls with nominal 0.05 α levels. Note that the striking relationship between power and effect size is retained.

Figure 6

Distributions of data mapped onto cartoons of human chromosomesChromosomes 1– 8 (top row), 9 – 16 (second row), 17–21 (third row), 22 (fourth row).Red triangles to the left of the (black) main axis for each chromosome mark locations where genes are identified by clustered positive SNPs from Samples 1 & 2, and support is provided from at least one other sample.Green triangles to the left of the main axis mark locations where the gene is also identified by clustered positive SNPs from both Methanphetamine Samples 4 & 5.Blue triangles to the left of the main axis mark locations where the gene is also identified by clustered positive SNPs from 2 of the 3 Nicotine Abstinance Samples 11–14.Nave squares to the right of the main axis where the gene is also identified by clustered positive SNPs from 2 of the 3 Bipolar Disease Samples 7–9.Purple squares to the right of the main axis mark locations where the gene is also identified by clustered positive SNPs from both Cognitive Function Samples 15 & 16.Grey squares to the right of the main axis mark locations where the gene is also identified by clustered positive SNPs from both Alzheimer’s Disease Samples 17 & 18.Yellow squares to the right of the main axis axis mark locations where the gene is also identified by clustered positive SNPs from the NHLBI Frontal Brain Volume Sample 10.Chromosomal positions are based on National Center for Biotechnology Information MAPVIEWER Build 36.1 coordinates and supplemental data from NETAFFX.

#	Section	Preview
0	I. Introduction	Genome wide association is now increasingly the method of choice for identifying allelic variants…
1	I. Introduction	No single approach to designing genome wide association studies or to analyzing genome wide…
2	I. Introduction	A number of samples provide the bases for these analyses. In analyzing data from these samples, we…
3	I. Introduction	We focus here on clinical phenotypes that co-occur with addiction and a structural brain phenotype,…
4	I. Introduction	A number of the genes identified here encode classical “druggable” targets for pharmacological…
5	I. Introduction	A. What is genome wide association and why might it be useful for studies of the molecular bases of…
6	I. Introduction	Such inconsistent linkage data is consistent with the idea that most of the genetic architecture for…
7	I. Introduction	power as densities of genomic markers increase. Association identifies much smaller chromosomal…
8	I. Introduction	B. A likely underlying genetic architecture for addiction (and other common heritable brain-based…
9	I. Introduction	differences between “disease” and “control” samples, as noted above, 2) uneven distribution…
10	I. Introduction	C. Family, adoption and twin data that each support substantial polygenic heritability for…
11	I. Introduction	Support for the idea that vulnerability to addictions is a complex trait with strong genetic…
12	I. Introduction	D. Twin data document that most of this heritable influence is not substance-specific, but provides…
13	I. Introduction	Elsewhere [32] we have suggested levels of analysis for pharmacogenomics and pharmacogenetics: 1)…
14	I. Introduction	E. Failure to document evidence for substance dependence genes of major effect in most populations…
15	I. Introduction	Quantity-frequency data for smoking also provide evidence for a replicable “secondary”…
16	I. Introduction	Linkage-based analyses for addiction vulnerabilities would be expected to reproducibly identify many…
17	I. Introduction	F. Current models for the genetic architecture of human dependence on legal and illegal addictive…
18	I. Introduction	Analyses of twin data for vulnerability to develop dependence on a substance fit with large additive…
19	I. Introduction	E correlation occurs when an individual’s genotype provides different reactions from the…

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In this knowledge base

Title	Year	PMID
Implications of genome wide association studies for addiction: are our a priori assumptions all wrong?	2013	23872493
Genome-wide association study of comorbid depressive syndrome and alcohol dependence.	2012	22064162
Genomic regions identified by overlapping clusters of nominally-positive SNPs from genome-wide studies of alcohol and illegal substance dependence.	2011	21818250
A genomewide association study of nicotine and alcohol dependence in Australian and Dutch populations.	2010	20158304
Genome wide association for addiction: replicated results and comparisons of two analytic approaches.	2010	20098672
Genome-wide association studies in ADHD.	2009	19384554
New insights into the genetics of addiction.	2009	19238175
Genome wide association for substance dependence: convergent results from epidemiologic and research volunteer samples.	2008	19094236

External

Title	Authors	Journal	Year	Link
Alzheimer's disease multi-ancestry genome-wide interaction and stratified study with smoking.	Dacey R et al.	—	2025	→
Generalized genetic liability to substance use disorders.	Miller AP et al.	—	2024	→
Low Dopamine D2 Receptor Expression Drives Gene Networks Related to GABA, cAMP, Growth and Neuroinflammation in Striatal Indirect Pathway Neurons.	Guerri L et al.	—	2023	→
Application of optogenetics and in vivo imaging approaches for elucidating the neurobiology of addiction.	Vickstrom CR et al.	—	2022	→
Cadherin Expression and EMT: A Focus on Gliomas.	Noronha C et al.	—	2021	→
Common genetic substrates of alcohol and substance use disorder severity revealed by pleiotropy detection against GWAS catalog in two populations.	Peng Q et al.	—	2021	→
The role of repetitive transcranial magnetic stimulation (rTMS) in the treatment of behavioral addictions: Two case reports and review of the literature.	Cuppone D et al.	—	2021	→
Attention-deficit/hyperactivity disorder and lifetime cannabis use: genetic overlap and causality.	Soler Artigas M et al.	—	2020	→
Selective breeding for high alcohol preference is associated with increased sensitivity to cannabinoid reward within the nucleus accumbens shell.	Hauser SR et al.	—	2020	→
A multi-ancestry genome-wide study incorporating gene-smoking interactions identifies multiple new loci for pulse pressure and mean arterial pressure.	Sung YJ et al.	—	2019	→
ANCO-GeneDB: annotations and comprehensive analysis of candidate genes for alcohol, nicotine, cocaine and opioid dependence.	Hu R et al.	—	2018	→
Exploring the Validity of Proposed Transgenic Animal Models of Attention-Deficit Hyperactivity Disorder (ADHD).	de la Peña JB et al.	—	2018	→
Selective breeding for high alcohol consumption and response to nicotine: locomotor activity, dopaminergic in the mesolimbic system, and innate genetic differences in male and female alcohol-preferring, non-preferring, and replicate lines of high-alcohol drinking and low-alcohol drinking rats.	Deehan GA et al.	—	2018	→
The Role of Cell Adhesion Molecule Genes Regulating Neuroplasticity in Addiction.	Muskiewicz DE et al.	—	2018	→
Alpha-1 antitrypsin inhibits RANKL-induced osteoclast formation and functions.	Akbar MA et al.	—	2017	→
Cadherin-13 Deficiency Increases Dorsal Raphe 5-HT Neuron Density and Prefrontal Cortex Innervation in the Mouse Brain.	Forero A et al.	—	2017	→
Cadherin 13: human <i>cis</i>-regulation and selectively-altered addiction phenotypes and cerebral cortical dopamine in knockout mice.	Drgonova J et al.	—	2016	→
Modeling Multiple Responses via Bootstrapping Margins with an Application to Genetic Association Testing.	Zhao J et al.	—	2016	→
The role of ASTN2 variants in childhood and adult ADHD, comorbid disorders and associated personality traits.	Freitag CM et al.	—	2016	→
Altered CSMD1 Expression Alters Cocaine-Conditioned Place Preference: Mutual Support for a Complex Locus from Human and Mouse Models.	Drgonova J et al.	—	2015	→
Cadherin-13, a risk gene for ADHD and comorbid disorders, impacts GABAergic function in hippocampus and cognition.	Rivero O et al.	—	2015	→
Chronic methamphetamine abuse and corticostriatal deficits revealed by neuroimaging.	London ED et al.	—	2015	→
Gene-smoking interactions identify several novel blood pressure loci in the Framingham Heart Study.	Sung YJ et al.	—	2015	→
Human cell adhesion molecules: annotated functional subtypes and overrepresentation of addiction-associated genes.	Zhong X et al.	—	2015	→
Mouse Model for Protein Tyrosine Phosphatase D (<i>PTPRD</i>) Associations with Restless Leg Syndrome or Willis-Ekbom Disease and Addiction: Reduced Expression Alters Locomotion, Sleep Behaviors and Cocaine-Conditioned Place Preference.	Drgonova J et al.	—	2015	→
Quantitative trait locus mapping of acute functional tolerance in the LXS recombinant inbred strains.	Bennett B et al.	—	2015	→
Understanding Addiction as a Developmental Disorder: An Argument for a Developmentally Informed Multilevel Approach.	McCrory EJ et al.	—	2015	→
Allostasis as a conceptual framework linking bipolar disorder and addiction.	Pettorruso M et al.	—	2014	→
Association of the HTR2A gene with alcohol and heroin abuse.	Cao J et al.	—	2014	→
Bipolar disorder and gambling disorder comorbidity: current evidence and implications for pharmacological treatment.	Di Nicola M et al.	—	2014	→
Cell adhesion molecules: druggable targets for modulating the connectome and brain disorders?	Uhl GR et al.	—	2014	→
Curious cases: Altered dose-response relationships in addiction genetics.	Uhl GR et al.	—	2014	→
Psychometric properties of the Icelandic NEO-FFI in a general population sample compared to a sample recruited for a study on the genetics of addiction.	Bjornsdottir G et al.	—	2014	→
TARV: tree-based analysis of rare variants identifying risk modifying variants in CTNNA2 and CNTNAP2 for alcohol addiction.	Song C et al.	—	2014	→
Associations of the 5-hydroxytryptamine (serotonin) receptor 1B gene (HTR1B) with alcohol, cocaine, and heroin abuse.	Cao J et al.	—	2013	→
Cocaine self-administration in mice with forebrain knock-down of trpc5 ion channels.	Pomrenze MB et al.	—	2013	→
Four factors for the initiation of substance use by young adulthood: a 10-year follow-up twin and sibling study of marital conflict, monitoring, siblings, and peers.	Neiderhiser JM et al.	—	2013	→
Genetic matters: thirty years of progress using mouse models in nicotinic research.	Marks MJ	—	2013	→
Impact of the ADHD-susceptibility gene CDH13 on development and function of brain networks.	Rivero O et al.	—	2013	→
Implications of genome wide association studies for addiction: are our a priori assumptions all wrong?	Hall FS et al.	—	2013	→
Multi-cultural association of the serotonin transporter gene (SLC6A4) with substance use disorder.	Cao J et al.	—	2013	→
The synaptic adhesion molecule SynCAM 1 contributes to cocaine effects on synapse structure and psychostimulant behavior.	Giza JI et al.	—	2013	→
Externalizing behaviors are associated with SNPs in the CHRNA5/CHRNA3/CHRNB4 gene cluster.	Stephens SH et al.	—	2012	→
Genome-wide association study of comorbid depressive syndrome and alcohol dependence.	Edwards AC et al.	—	2012	→
Mouse models for studying genetic influences on factors determining smoking cessation success in humans.	Hall FS et al.	—	2012	→
Stress-sensitive neurosignalling in depression: an integrated network biology approach to candidate gene selection for genetic association analysis.	van Eekelen JA et al.	—	2012	→
The genetics of addiction.	Volkow ND et al.	—	2012	→
Use of next generation sequencing technologies in research and beyond: are participants with mental health disorders fully protected?	Groisman IJ et al.	—	2012	→
Alcohol-preferring (P) rats are more sensitive than Wistar rats to the reinforcing effects of cocaine self-administered directly into the nucleus accumbens shell.	Katner SN et al.	—	2011	→
Confirmation of prior evidence of genetic susceptibility to alcoholism in a genome-wide association study of comorbid alcoholism and bipolar disorder.	Lydall GJ et al.	—	2011	→
Elevated levels of DNA methylation at the OPRM1 promoter in blood and sperm from male opioid addicts.	Chorbov VM et al.	—	2011	→
Genomic regions identified by overlapping clusters of nominally-positive SNPs from genome-wide studies of alcohol and illegal substance dependence.	Johnson C et al.	—	2011	→
"Replicated" genome wide association for dependence on illegal substances: genomic regions identified by overlapping clusters of nominally positive SNPs.	Drgon T et al.	—	2011	→
Translating glutamate: from pathophysiology to treatment.	Javitt DC et al.	—	2011	→
Understanding the Global Problem of Drug Addiction is a Challenge for IDARS Scientists.	Ali SF et al.	—	2011	→
A genomewide association study of nicotine and alcohol dependence in Australian and Dutch populations.	Lind PA et al.	—	2010	→
Fitting the pieces together: current research on the genetic basis of attention-deficit/hyperactivity disorder (ADHD).	Stergiakouli E et al.	—	2010	→
Genome wide association for addiction: replicated results and comparisons of two analytic approaches.	Drgon T et al.	—	2010	→
Genome-wide association for smoking cessation success in a trial of precessation nicotine replacement.	Uhl GR et al.	—	2010	→
Genome-wide association for smoking cessation success: participants in the Patch in Practice trial of nicotine replacement.	Uhl GR et al.	—	2010	→
Molecular genetics of attention-deficit/hyperactivity disorder: an overview.	Banaschewski T et al.	—	2010	→
Addiction genetics and pleiotropic effects of common haplotypes that make polygenic contributions to vulnerability to substance dependence.	Uhl GR et al.	—	2009	→
Genome-wide association for nicotine dependence and smoking cessation success in NIH research volunteers.	Drgon T et al.	—	2009	→
Genome-wide association for smoking cessation success: participants in a trial with adjunctive denicotinized cigarettes.	Drgon T et al.	—	2009	→
Genome-wide association studies in ADHD.	Franke B et al.	—	2009	→
New insights into the genetics of addiction.	Li MD et al.	—	2009	→
Reward processing by the opioid system in the brain.	Le Merrer J et al.	—	2009	→
Selected summaries from the XVI World Congress of Psychiatric Genetics, Osaka, Japan, 11-15 October 2008.	Bergen S et al.	—	2009	→
Smoking and smoking cessation in disadvantaged women: assessing genetic contributions.	Uhl GR et al.	—	2009	→
Substance use disorders in schizophrenia--clinical implications of comorbidity.	Volkow ND	—	2009	→
Genome wide association for substance dependence: convergent results from epidemiologic and research volunteer samples.	Johnson C et al.	—	2008	→