Genome-wide association study of d-amphetamine response in healthy volunteers identifies putative associations, including cadherin 13 (CDH13).
- Authors
- Hart, Amy B; Engelhardt, Barbara E; Wardle, Margaret C; Sokoloff, Greta; Stephens, Matthew; de Wit, Harriet; Palmer, Abraham A
- Year
- 2012
- Journal
- PloS one
- PMID
- 22952603
- DOI
- 10.1371/journal.pone.0042646
- PMCID
- PMC3429486
Both the subjective response to d-amphetamine and the risk for amphetamine addiction are known to be heritable traits. Because subjective responses to drugs may predict drug addiction, identifying alleles that influence acute response may also provide insight into the genetic risk factors for drug abuse. We performed a Genome Wide Association Study (GWAS) for the subjective responses to amphetamine in 381 non-drug abusing healthy volunteers. Responses to amphetamine were measured using a double-blind, placebo-controlled, within-subjects design. We used sparse factor analysis to reduce the dimensionality of the data to ten factors. We identified several putative associations; the strongest was between a positive subjective drug-response factor and a SNP (rs3784943) in the 8(th) intron of cadherin 13 (CDH13; Pβ=β4.58Γ10(-8)), a gene previously associated with a number of psychiatric traits including methamphetamine dependence. Additionally, we observed a putative association between a factor representing the degree of positive affect at baseline and a SNP (rs472402) in the 1(st) intron of steroid-5-alpha-reductase-Ξ±-polypeptide-1 (SRD5A1; Pβ=β2.53Γ10(-7)), a gene whose protein product catalyzes the rate-limiting step in synthesis of the neurosteroid allopregnanolone. This SNP belongs to an LD-block that has been previously associated with the expression of SRD5A1 and differences in SRD5A1 enzymatic activity. The purpose of this study was to begin to explore the genetic basis of subjective responses to stimulant drugs using a GWAS approach in a modestly sized sample. Our approach provides a case study for analysis of high-dimensional intermediate pharmacogenomic phenotypes, which may be more tractable than clinical diagnoses.
Elation (POMS) example phenotype.Panel A shows the mean (SEM) scores on the POMS Elation scale at each time point before and after administration of placebo or d-amphetamine (10 and 20 mg). The box identifies the values shown in greater detail in Panel B. Panel B shows a boxplot of the Elation phenotype at 90 minutes after placebo, 10 or 20 mg d-amphetamine sessions. Panel C shows the Pearson correlation coefficient for the Elation phenotypes at all six time points for the 20 mg session: darker blue indicates high correlation and white indicates low correlation.
LLM interpretation
This figure presents POMS Elation scores across three panels. Panel A is a line graph showing mean scores (Β±SEM) over 180 minutes, where d-amphetamine (10 and 20 mg) leads to higher elation scores compared to placebo. Panel B is a boxplot comparing elation scores at the 90-minute mark, showing an increase in median scores from placebo to 10 mg and 20 mg doses. Panel C is a heatmap displaying Pearson correlation coefficients between time points for the 20 mg session, with darker blue indicating higher correlations.
Potential association of rs3784943 (CDH13) with an amphetamine response factor.Panel A shows the factor loadings for factor F1; abbreviations of outcome measures are defined in the text. Panel B shows the raw phenotype scores as a function of dose (denoted by color coded vertically stacked panels) and time (x-axis within each panel) for individuals in the upper and lower deciles of this factor. Panel C shows the association between factor F1 and each SNP (expressed as βlog10 P), the most significant association was on chromosome 16 at rs3784943 (P = 4.58Γ10β8). The red horizontal line indicates 5Γ10β8, which is often used as a threshold for significance. The blue horizontal line indicates 1Γ10β5, which could be considered a threshold for suggestive evidence. Panel D shows a Q-Q plot of observed βlog10 P versus the average βlog10 P from ten random permutations. Panel E shows a boxplot of the values for factor F1 stratified by genotype. The width of each box corresponds to number of observations at the corresponding genotype. Panel F shows the raw phenotype scores stratified by genotypes as a function of dose and time. Asterisks indicate a significant DrugΓtimeΓgenotype interaction in a 3Γ3Γ6 repeated measures ANOVA using age, sex, BMI, and the first two principal components as covariates. * Pβ€0.05; ** Pβ€0.01; *** Pβ€0.0001; **** Pβ€10β4.
LLM interpretation
This figure analyzes the association between the rs3784943 (CDH13) SNP and an amphetamine response factor (F1). It includes factor loadings (A), phenotype scores by factor deciles (B), a Manhattan plot showing a significant association at rs3784943 (C), a Q-Q plot (D), and a boxplot of factor values by genotype (E). Panel F displays mean phenotype scores across doses and time stratified by genotype, with asterisks indicating significant DrugΓtimeΓgenotype interactions.
Potential association of rs472402 (SRD5A1) with the positive affect baseline factor.Panel A shows the factor loadings for factor F2; abbreviations of outcome measures are defined in the text. Panel B shows the raw phenotype scores as a function of dose (denoted by color coded vertically stacked panels) and time (x-axis within each panel) for individuals in the upper and lower deciles of this factor. Panel C shows the association between factor F1 and each SNP (expressed as βlog10 P), the most significant association was on chromosome 5 at rs472402 (P = 2.53Γ10β7). The red horizontal line indicates 5Γ10β8, which is often used as a threshold for significance. The blue horizontal line indicates 1Γ10β5, which could be considered a threshold for suggestive evidence. Panel D shows a Q-Q plot of observed βlog10 P versus the average βlog10 P from ten random permutations. Panel E shows a boxplot of the values for factor F1 stratified by genotype. The width of each box corresponds to number of observations at the corresponding genotype. Panel F shows the raw phenotype scores stratified by genotypes as a function of dose and time. Asterisks indicate a significant main effect of genotype in a 3Γ3Γ6 repeated measures ANOVA using age, sex, BMI, and the first two principal components as covariates. * Pβ€0.05; **** Pβ€10β4.
LLM interpretation
This figure analyzes the association between the rs472402 (SRD5A1) SNP and a positive affect baseline factor (F1/F2). It includes factor loadings (A), phenotype scores by dose and time for factor deciles (B), a Manhattan plot showing a significant association at rs472402 (C), a Q-Q plot (D), and a boxplot of factor values by genotype (E). Panel F displays raw phenotype scores stratified by genotype across dose and time, with asterisks indicating significant main effects of genotype (e.g., **** Pβ€10β»β΄ for SystolicBP and POMS measures).
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In this knowledge base
External
| Title | Authors | Journal | Year | Link |
|---|---|---|---|---|
| Selecting the appropriate hurdles and endpoints for pentilludin, a novel antiaddiction pharmacotherapeutic targeting the receptor type protein tyrosine phosphatase D. | Uhl GR | β | 2023 | β |
| Genetic characterization of outbred Sprague Dawley rats and utility for genome-wide association studies. | Gileta AF et al. | β | 2022 | β |
| Methamphetamine, amphetamine, and aggression in humans: A systematic review of drug administration studies. | O'Malley KY et al. | β | 2022 | β |
| Structure-activity studies of PTPRD phosphatase inhibitors identify a 7-cyclopentymethoxy illudalic acid analog candidate for development. | Henderson IM et al. | β | 2022 | β |
| A Common <i>CDH13</i> Variant Is Associated with Low Agreeableness and Neural Responses to Working Memory Tasks in ADHD. | Ziegler GC et al. | β | 2021 | β |
| Genome-wide analyses of smoking behaviors in schizophrenia: Findings from the Psychiatric Genomics Consortium. | Peterson RE et al. | β | 2021 | β |
| Reduced PTPRD expression differentially alters brain phosphotyrosine phosphoproteomic profiles of 2 and 12 month-old mice | Uhl GR et al. | β | 2021 | β |
| Serotonin-specific neurons differentiated from human iPSCs form distinct subtypes with synaptic protein assembly. | Jansch C et al. | β | 2021 | β |
| Subjective responses predict d-amphetamine choice in healthy volunteers. | Murray CH et al. | β | 2021 | β |
| No Association Between AKT1 Polymorphisms and Methamphetamine Addiction in Iranian Population. | Ghafouri-Fard S et al. | β | 2020 | β |
| The Streetlight Effect: Reappraising the Study of Addiction in Light of the Findings of Genome-wide Association Studies. | Hall FS et al. | β | 2020 | β |
| Translational Molecular Approaches in Substance Abuse Research. | Fulton SL et al. | β | 2020 | β |
| ADGRL3 (LPHN3) variants predict substance use disorder. | Arcos-Burgos M et al. | β | 2019 | β |
| Case-only Methods Identified Genetic Loci Predicting a Subgroup of Men with Reduced Risk of High-grade Prostate Cancer by Finasteride. | Dai JY et al. | β | 2019 | β |
| Genetic loci for alcohol-related life events and substance-induced affective symptoms: indexing the "dark side" of addiction. | Peng Q et al. | β | 2019 | β |
| Genome-wide by environment interaction studies of depressive symptoms and psychosocial stress in UK Biobank and Generation Scotland. | Arnau-Soler A et al. | β | 2019 | β |
| PTPRD: neurobiology, genetics, and initial pharmacology of a pleiotropic contributor to brain phenotypes. | Uhl GR et al. | β | 2019 | β |
| Trace amine-associated receptor gene polymorphism increases drug craving in individuals with methamphetamine dependence. | Loftis JM et al. | β | 2019 | β |
| Unravelling the genetic basis of schizophrenia and bipolar disorder with GWAS: A systematic review. | Prata DP et al. | β | 2019 | β |
| Cocaine reward is reduced by decreased expression of receptor-type protein tyrosine phosphatase D (PTPRD) and by a novel PTPRD antagonist. | Uhl GR et al. | β | 2018 | β |
| Genome-wide meta-analysis of copy number variations with alcohol dependence. | Sulovari A et al. | β | 2018 | β |
| Human Genetics of Addiction: New Insights and Future Directions. | Hancock DB et al. | β | 2018 | β |
| The Role of Cell Adhesion Molecule Genes Regulating Neuroplasticity in Addiction. | Muskiewicz DE et al. | β | 2018 | β |
| Allopregnanolone levels and depressive symptoms during pregnancy in relation to single nucleotide polymorphisms in the allopregnanolone synthesis pathway. | Hellgren C et al. | β | 2017 | β |
| Alpha-1 antitrypsin inhibits RANKL-induced osteoclast formation and functions. | Akbar MA et al. | β | 2017 | β |
| Ancestry-specific and sex-specific risk alleles identified in a genome-wide gene-by-alcohol dependence interaction study of risky sexual behaviors. | Polimanti R et al. | β | 2017 | β |
| Cdh13 and AdipoQ gene knockout alter instrumental and Pavlovian drug conditioning. | King CP et al. | β | 2017 | β |
| Pharmacogenetics of stimulant abuse liability: association of CDH13 variant with amphetamine response in a racially-heterogeneous sample of healthy young adults. | Leventhal AM et al. | β | 2017 | β |
| Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset Alzheimer's disease. | Lu Q et al. | β | 2017 | β |
| Whole exome sequencing in thrombophilic pedigrees to identify genetic risk factors for venous thromboembolism. | Cunha MLR et al. | β | 2017 | β |
| Amphetamine increases activity but not exploration in humans and mice. | Minassian A et al. | β | 2016 | β |
| A Review of Genome-Wide Association Studies of Stimulant and Opioid Use Disorders. | Jensen KP | β | 2016 | β |
| Behavioral and transcriptomic profiling of mice null for Lphn3, a gene implicated in ADHD and addiction. | Orsini CA et al. | β | 2016 | β |
| Cadherin 13: human <i>cis</i>-regulation and selectively-altered addiction phenotypes and cerebral cortical dopamine in knockout mice. | Drgonova J et al. | β | 2016 | β |
| Cerebellum Transcriptome of Mice Bred for High Voluntary Activity Offers Insights into Locomotor Control and Reward-Dependent Behaviors. | Caetano-AnollΓ©s K et al. | β | 2016 | β |
| Emotional traits predict individual differences in amphetamine-induced positive mood in healthy volunteers. | Kirkpatrick MG et al. | β | 2016 | β |
| Individual differences in timing of peak positive subjective responses to d-amphetamine: Relationship to pharmacokinetics and physiology. | Smith CT et al. | β | 2016 | β |
| RNA-binding proteins, neural development and the addictions. | Bryant CD et al. | β | 2016 | β |
| Single nucleotide polymorphism near CREB1, rs7591784, is associated with pretreatment methamphetamine use frequency and outcome of outpatient treatment for methamphetamine use disorder. | Heinzerling KG et al. | β | 2016 | β |
| Summaries of plenary, symposia, and oral sessions at the XXII World Congress of Psychiatric Genetics, Copenhagen, Denmark, 12-16 October 2014. | Aas M et al. | β | 2016 | β |
| A gene-based association method for mapping traits using reference transcriptome data. | Gamazon ER et al. | β | 2015 | β |
| Alcohol Dependence Genetics: Lessons Learned From Genome-Wide Association Studies (GWAS) and Post-GWAS Analyses. | Hart AB et al. | β | 2015 | β |
| An animal model of differential genetic risk for methamphetamine intake. | Phillips TJ et al. | β | 2015 | β |
| A Preliminary Investigation of Individual Differences in Subjective Responses to D-Amphetamine, Alcohol, and Delta-9-Tetrahydrocannabinol Using a Within-Subjects Randomized Trial. | Wardle MC et al. | β | 2015 | β |
| Cadherin-13, a risk gene for ADHD and comorbid disorders, impacts GABAergic function in hippocampus and cognition. | Rivero O et al. | β | 2015 | β |
| Cadherin-based transsynaptic networks in establishing and modifying neural connectivity. | Friedman LG et al. | β | 2015 | β |
| CDH13 and HCRTR2 May Be Associated with Hypersomnia Symptom of Bipolar Depression: A Genome-Wide Functional Enrichment Pathway Analysis. | Cho CH et al. | β | 2015 | β |
| Genetic background of extreme violent behavior. | Tiihonen J et al. | β | 2015 | β |
| Genome-wide association studies in Africans and African Americans: expanding the framework of the genomics of human traits and disease. | Peprah E et al. | β | 2015 | β |
| Multivariate analysis of subjective responses to d-amphetamine in healthy volunteers finds novel genetic pathway associations. | Yarosh HL et al. | β | 2015 | β |
| Neuropsychiatric Adverse Effects of Amphetamine and Methamphetamine. | Harro J | β | 2015 | β |
| Addiction science: Uncovering neurobiological complexity. | Volkow ND et al. | β | 2014 | β |
| Curious cases: Altered dose-response relationships in addiction genetics. | Uhl GR et al. | β | 2014 | β |
| Genetic variation associated with euphorigenic effects of d-amphetamine is associated with diminished risk for schizophrenia and attention deficit hyperactivity disorder. | Hart AB et al. | β | 2014 | β |
| Genome-wide association study of cocaine dependence and related traits: FAM53B identified as a risk gene. | Gelernter J et al. | β | 2014 | β |
| Interrogation of allelic chromatin states in human cells by high-density ChIP-genotyping. | Light N et al. | β | 2014 | β |
| Schizophrenia gene expression profile reverted to normal levels by antipsychotics. | Crespo-Facorro B et al. | β | 2014 | β |
| A common copy number variation (CNV) polymorphism in the CNTNAP4 gene: association with aging in females. | Iakoubov L et al. | β | 2013 | β |
| A proximity-based method to identify genomic regions correlated with a continuously varying environmental variable. | Di Gaetano C et al. | β | 2013 | β |
| Candidate gene studies of a promising intermediate phenotype: failure to replicate. | Hart AB et al. | β | 2013 | β |
| Does COMT genotype influence the effects of d-amphetamine on executive functioning? | Wardle MC et al. | β | 2013 | β |
| Inattention, impulsive action, and subjective response to D-amphetamine. | Weafer J et al. | β | 2013 | β |