Unraveling the molecular relevance of brain phenotypes: A comparative analysis of null models and test statistics

Correlating transcriptional profiles with imaging-derived phenotypes has the potential to reveal possible molecular architectures associated with cognitive functions, brain development and disorders. Competitive null models built by resampling genes and self-contained null models built by spinning brain regions, along with varying test statistics, have been used to determine the significance of transcriptional associations. However, there has been no systematic evaluation of their performance in imaging transcriptomics analyses. Here, we evaluated the performance of eight different test statistics (mean, mean absolute value, mean squared value, max mean, median, Kolmogorov-Smirnov (KS), Weighted KS and the number of significant correlations) in both competitive null models and self-contained null models. Simulated brain maps (n = 1,000) and gene sets (n = 500) were used to calculate the probability of significance (Psig) for each statistical test. Our results suggested that competitive null models may result in false positive results driven by co-expression within gene sets. Furthermore, we demonstrated that the self-contained null models may fail to account for distribution characteristics (e.g., bimodality) of correlations between all available genes and brain phenotypes, leading to false positives. These two confounding factors interacted differently with test statistics, resulting in varying outcomes. Specifically, the sign-sensitive test statistics (i.e., mean, median, KS, Weighted KS) were influenced by co-expression bias in the competitive null models, while median and sign-insensitive test statistics were sensitive to the bimodality bias in the self-contained null models. Additionally, KS-based statistics produced conservative results in the self-contained null models, which increased the risk of false negatives. Comprehensive supplementary analyses with various configurations, including realistic scenarios, supported the results. These findings suggest utilizing sign-insensitive test statistics such as mean absolute value, max mean in the competitive null models and the mean as the test statistic for the self-contained null models. Additionally, adopting the confounder-matched (e.g., coexpression-matched) null models as an alternative to standard null models can be a viable strategy. Overall, the present study offers insights into the selection of statistical tests for imaging transcriptomics studies, highlighting areas for further investigation and refinement in the evaluation of novel and commonly used tests.

A phenome-wide association and Mendelian randomisation study of alcohol use variants in a diverse cohort comprising over 3 million individuals

BACKGROUND

Alcohol consumption is associated with numerous negative social and health outcomes. These associations may be direct consequences of drinking, or they may reflect common genetic factors that influence both alcohol consumption and other outcomes.

METHODS

We performed exploratory phenome-wide association studies (PheWAS) of three of the best studied protective single nucleotide polymorphisms (SNPs) in genes encoding ethanol metabolising enzymes (ADH1B: rs1229984-T, rs2066702-A; ADH1C: rs698-T) using up to 1109 health outcomes across 28 phenotypic categories (e.g., substance-use, mental health, sleep, immune, cardiovascular, metabolic) from a diverse 23andMe cohort, including European (N ≤ 2,619,939), Latin American (N ≤ 446,646) and African American (N ≤ 146,776) populations to uncover new and perhaps unexpected associations. These SNPs have been consistently implicated by both candidate gene studies and genome-wide association studies of alcohol-related behaviours but have not been investigated in detail for other relevant phenotypes in a hypothesis-free approach in such a large cohort of multiple ancestries. To provide insight into potential causal effects of alcohol consumption on the outcomes significant in the PheWAS, we performed univariable two-sample and one-sample Mendelian randomisation (MR) analyses.

FINDINGS

The minor allele rs1229984-T, which is protective against alcohol behaviours, showed the highest number of PheWAS associations across the three cohorts (N = 232, European; N = 29, Latin American; N = 7, African American). rs1229984-T influenced multiple domains of health. We replicated associations with alcohol-related behaviours, mental and sleep conditions, and cardio-metabolic health. We also found associations with understudied traits related to neurological (migraines, epilepsy), immune (allergies), musculoskeletal (fibromyalgia), and reproductive health (preeclampsia). MR analyses identified evidence of causal effects of alcohol consumption on liability for 35 of these outcomes in the European cohort.

INTERPRETATION

Our work demonstrates that polymorphisms in genes encoding alcohol metabolising enzymes affect multiple domains of health beyond alcohol-related behaviours. Understanding the underlying mechanisms of these effects could have implications for treatments and preventative medicine.

FUNDING

MVJ, NCK, SBB, SSR and AAP were supported by T32IR5226 and 28IR-0070. SSR was also supported by NIDA DP1DA054394. NCK and RBC were also supported by R25MH081482. ASH was supported by funds from NIAAA K01AA030083. JLMO was supported by VA 1IK2CX002095. JLMO and JJMM were also supported by NIDA R21DA050160. JJMM was also supported by the Kavli Postdoctoral Award for Academic Diversity. EGA was supported by K01MH121659 from the NIMH/NIH, the Caroline Wiess Law Fund for Research in Molecular Medicine and the ARCO Foundation Young Teacher-Investigator Fund at Baylor College of Medicine. MSA was supported by the Instituto de Salud Carlos III and co-funded by the European Union Found: Fondo Social Europeo Plus (FSE+) (P19/01224, PI22/00464 and CP22/00128).

Brain structural covariance network features are robust markers of early heavy alcohol use

BACKGROUND AND AIMS

Recently, we demonstrated that a distinct pattern of structural covariance networks (SCN) from magnetic resonance imaging (MRI)-derived measurements of brain cortical thickness characterized young adults with alcohol use disorder (AUD) and predicted current and future problematic drinking in adolescents relative to controls. Here, we establish the robustness and value of SCN for identifying heavy alcohol users in three additional independent studies.

DESIGN AND SETTING

Cross-sectional and longitudinal studies using data from the Pediatric Imaging, Neurocognition and Genetics (PING) study (n = 400, age range = 14-22 years), the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) (n = 272, age range = 17-22 years) and the Human Connectome Project (HCP) (n = 375, age range = 22-37 years).

CASES

Cases were defined based on heavy alcohol use patterns or former alcohol use disorder (AUD) diagnoses: 50, 68 and 61 cases were identified. Controls had none or low alcohol use or absence of AUD: 350, 204 and 314 controls were selected.

MEASUREMENTS

Graph theory metrics of segregation and integration were used to summarize SCN.

FINDINGS

Mirroring our prior findings, and across the three data sets, cases had a lower clustering coefficient [area under the curve (AUC) = -0.029, P = 0.002], lower modularity (AUC = -0.14, P = 0.004), lower average shortest path length (AUC = -0.078, P = 0.017) and higher global efficiency (AUC = 0.007, P = 0.010). Local efficiency differences were marginal (AUC = -0.017, P = 0.052). That is, cases exhibited lower network segregation and higher integration, suggesting that adjacent nodes (i.e. brain regions) were less similar in thickness whereas spatially distant nodes were more similar.

CONCLUSION

Structural covariance network (SCN) differences in the brain appear to constitute an early marker of heavy alcohol use in three new data sets and, more generally, demonstrate the utility of SCN-derived metrics to detect brain-related psychopathology.

Recalibrating single-study effect sizes using hierarchical Bayesian models

Introduction

There are growing concerns about commonly inflated effect sizes in small neuroimaging studies, yet no study has addressed recalibrating effect size estimates for small samples. To tackle this issue, we propose a hierarchical Bayesian model to adjust the magnitude of single-study effect sizes while incorporating a tailored estimation of sampling variance.

Methods

We estimated the effect sizes of case-control differences on brain structural features between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis and non-dependent participants for 21 individual studies (Total cases: 903; Total controls: 996). Then, the study-specific effect sizes were modeled using a hierarchical Bayesian approach in which the parameters of the study-specific effect size distributions were sampled from a higher-order overarching distribution. The posterior distribution of the overarching and study-specific parameters was approximated using the Gibbs sampling method.

Results

The results showed shrinkage of the posterior distribution of the study-specific estimates toward the overarching estimates given the original effect sizes observed in individual studies. Differences between the original effect sizes (i.e., Cohen's d) and the point estimate of the posterior distribution ranged from 0 to 0.97. The magnitude of adjustment was negatively correlated with the sample size (r = -0.27, p < 0.001) and positively correlated with empirically estimated sampling variance (r = 0.40, p < 0.001), suggesting studies with smaller samples and larger sampling variance tended to have greater adjustments.

Discussion

Our findings demonstrate the utility of the hierarchical Bayesian model in recalibrating single-study effect sizes using information from similar studies. This suggests that Bayesian utilization of existing knowledge can be an effective alternative approach to improve the effect size estimation in individual studies, particularly for those with smaller samples.

Association Between Brain Structure and Alcohol Use Behaviors in Adults: A Mendelian Randomization and Multiomics Study

IMPORTANCE

Past studies have identified associations between brain macrostructure and alcohol use behaviors. However, identifying directional associations between these phenotypes is difficult due to the limitations of observational studies.

OBJECTIVE

To use mendelian randomization (MR) to identify directional associations between brain structure and alcohol use and elucidate the transcriptomic and cellular underpinnings of identified associations.

DESIGN, SETTING, AND PARTICIPANTS

The main source data comprised summary statistics from population-based and case-control genome-wide association studies (GWAS) of neuroimaging, behavioral, and clinical phenotypes (N = 763 874). Using these data, bidirectional and multivariable MR was performed analyzing associations between brain macrostructure and alcohol use. Downstream transcriptome-wide association studies (TWAS) and cell-type enrichment analyses investigated the biology underlying identified associations. The study approach was data driven and did not test any a priori hypotheses. Data were analyzed August 2021 to May 2022.

MAIN OUTCOMES AND MEASURES

Brain structure phenotypes (global cortical thickness [GCT] and global cortical surface area [GCSA] in 33 709 individuals and left-right subcortical volumes in 19 629 individuals) and alcohol use behaviors (alcoholic drinks per week [DPW] in 537 349 individuals, binge drinking frequency in 143 685 individuals, and alcohol use disorder in 8845 individuals vs 20 657 control individuals [total of 29 502]).

RESULTS

The main bidirectional MR analyses were performed in samples totaling 763 874 individuals, among whom more than 94% were of European ancestry, 52% to 54% were female, and the mean cohort ages were 40 to 63 years. Negative associations were identified between genetically predicted GCT and binge drinking (β, -2.52; 95% CI, -4.13 to -0.91) and DPW (β, -0.88; 95% CI, -1.37 to -0.40) at a false discovery rate (FDR) of 0.05. These associations remained significant in multivariable MR models that accounted for neuropsychiatric phenotypes, substance use, trauma, and neurodegeneration. TWAS of GCT and alcohol use behaviors identified 5 genes at the 17q21.31 locus oppositely associated with GCT and binge drinking or DPW (FDR = 0.05). Cell-type enrichment analyses implicated glutamatergic cortical neurons in alcohol use behaviors.

CONCLUSIONS AND RELEVANCE

The findings in this study show that the associations between GCT and alcohol use may reflect a predispositional influence of GCT and that 17q21.31 genes and glutamatergic cortical neurons may play a role in this association. While replication studies are needed, these findings should enhance the understanding of associations between brain structure and alcohol use.

Bayesian causal network modeling suggests adolescent cannabis use accelerates prefrontal cortical thinning

While there is substantial evidence that cannabis use is associated with differences in human brain development, most of this evidence is correlational in nature. Bayesian causal network (BCN) modeling attempts to identify probable causal relationships in correlational data using conditional probabilities to estimate directional associations between a set of interrelated variables. In this study, we employed BCN modeling in 637 adolescents from the IMAGEN study who were cannabis naïve at age 14 to provide evidence that the accelerated prefrontal cortical thinning found previously in adolescent cannabis users by Albaugh et al. [1] is a result of cannabis use causally affecting neurodevelopment. BCNs incorporated data on cannabis use, prefrontal cortical thickness, and other factors related to both brain development and cannabis use, including demographics, psychopathology, childhood adversity, and other substance use. All BCN algorithms strongly suggested a directional relationship from adolescent cannabis use to accelerated cortical thinning. While BCN modeling alone does not prove a causal relationship, these results are consistent with a body of animal and human research suggesting that adolescent cannabis use adversely affects brain development.

White matter microstructure differences in individuals with dependence on cocaine, methamphetamine, and nicotine: Findings from the ENIGMA-Addiction working group

BACKGROUND

Nicotine and illicit stimulants are very addictive substances. Although associations between grey matter and dependence on stimulants have been frequently reported, white matter correlates have received less attention.

METHODS

Eleven international sites ascribed to the ENIGMA-Addiction consortium contributed data from individuals with dependence on cocaine (n = 147), methamphetamine (n = 132) and nicotine (n = 189), as well as non-dependent controls (n = 333). We compared the fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) of 20 bilateral tracts. Also, we compared the performance of various machine learning algorithms in deriving brain-based classifications on stimulant dependence.

RESULTS

The cocaine and methamphetamine groups had lower regional FA and higher RD in several association, commissural, and projection white matter tracts. The methamphetamine dependent group additionally showed lower regional AD. The nicotine group had lower FA and higher RD limited to the anterior limb of the internal capsule. The best performing machine learning algorithm was the support vector machine (SVM). The SVM successfully classified individuals with dependence on cocaine (AUC = 0.70, p < 0.001) and methamphetamine (AUC = 0.71, p < 0.001) relative to non-dependent controls. Classifications related to nicotine dependence proved modest (AUC = 0.62, p = 0.014).

CONCLUSIONS

Stimulant dependence was related to FA disturbances within tracts consistent with a role in addiction. The multivariate pattern of white matter differences proved sufficient to identify individuals with stimulant dependence, particularly for cocaine and methamphetamine.

Characterizing reward system neural trajectories from adolescence to young adulthood

Mixed findings exist in studies comparing brain responses to reward in adolescents and adults. Here we examined the trajectories of brain response, functional connectivity and task-modulated network properties during reward processing with a large-sample longitudinal design. Participants from the IMAGEN study performed a Monetary Incentive Delay task during fMRI at timepoint 1 (T1; n = 1304, mean age=14.44 years old) and timepoint 2 (T2; n = 1241, mean age=19.09 years). The Alcohol Use Disorders Identification Test (AUDIT) was administrated at both T1 and T2 to assess a participant’s alcohol use during the past year. Voxel-wise linear mixed effect models were used to compare whole brain response as well as functional connectivity of the ventral striatum (VS) during reward anticipation (large reward vs no-reward cue) between T1 and T2. In addition, task-modulated networks were constructed using generalized psychophysiological interaction analysis and summarized with graph theory metrics. To explore alcohol use in relation to development, participants with no/low alcohol use at T1 but increased alcohol use to hazardous use level at T2 (i.e., participants with AUDIT≤2 at T1 and ≥8 at T2) were compared against those with consistently low scores (i.e., participants with AUDIT≤2 at T1 and ≤7 at T2). Across the whole sample, lower brain response during reward anticipation was observed at T2 compared with T1 in bilateral caudate nucleus, VS, thalamus, midbrain, dorsal anterior cingulate as well as left precentral and postcentral gyrus. Conversely, greater response was observed bilaterally in the inferior and middle frontal gyrus and right precentral and postcentral gyrus at T2 (vs. T1). Increased functional connectivity with VS was found in frontal, temporal, parietal and occipital regions at T2. Graph theory metrics of the task-modulated network showed higher inter-regional connectivity and topological efficiency at T2. Interactive effects between time (T1 vs. T2) and alcohol use group (low vs. high) on the functional connectivity were observed between left middle temporal gyrus and right VS and the characteristic shortest path length of the task-modulated networks. Collectively, these results demonstrate the utility of the MID task as a probe of typical brain response and network properties during development and of differences in these features related to adolescent drinking, a reward-related behaviour associated with heightened risk for future negative health outcomes.

•

Imaging data during reward anticipation at T1 (age 14) and T2 (age 19) was compared.

•

Brain response decreased in subcortical areas and increased in cortical areas at T2.

•

Functional connectivity (FC) with the ventral striatum increased at T2.

•

Topological efficiency of task-modulated network increased at T2.

•

The developmental pattern was altered in those who increased drinking most at T2.

Analysis of structural brain asymmetries in attention-deficit/hyperactivity disorder in 39 datasets

OBJECTIVE

Some studies have suggested alterations of structural brain asymmetry in attention-deficit/hyperactivity disorder (ADHD), but findings have been contradictory and based on small samples. Here, we performed the largest ever analysis of brain left-right asymmetry in ADHD, using 39 datasets of the ENIGMA consortium.

METHODS

We analyzed asymmetry of subcortical and cerebral cortical structures in up to 1,933 people with ADHD and 1,829 unaffected controls. Asymmetry Indexes (AIs) were calculated per participant for each bilaterally paired measure, and linear mixed effects modeling was applied separately in children, adolescents, adults, and the total sample, to test exhaustively for potential associations of ADHD with structural brain asymmetries.

RESULTS

There was no evidence for altered caudate nucleus asymmetry in ADHD, in contrast to prior literature. In children, there was less rightward asymmetry of the total hemispheric surface area compared to controls (t = 2.1, p = .04). Lower rightward asymmetry of medial orbitofrontal cortex surface area in ADHD (t = 2.7, p = .01) was similar to a recent finding for autism spectrum disorder. There were also some differences in cortical thickness asymmetry across age groups. In adults with ADHD, globus pallidus asymmetry was altered compared to those without ADHD. However, all effects were small (Cohen's d from -0.18 to 0.18) and would not survive study-wide correction for multiple testing.

CONCLUSION

Prior studies of altered structural brain asymmetry in ADHD were likely underpowered to detect the small effects reported here. Altered structural asymmetry is unlikely to provide a useful biomarker for ADHD, but may provide neurobiological insights into the trait.

Mapping cortical and subcortical asymmetries in substance dependence: Findings from the ENIGMA Addiction Working Group

Brain asymmetry reflects left-right hemispheric differentiation, which is a quantitative brain phenotype that develops with age and can vary with psychiatric diagnoses. Previous studies have shown that substance dependence is associated with altered brain structure and function. However, it is unknown whether structural brain asymmetries are different in individuals with substance dependence compared with nondependent participants. Here, a mega-analysis was performed using a collection of 22 structural brain MRI datasets from the ENIGMA Addiction Working Group. Structural asymmetries of cortical and subcortical regions were compared between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis (n = 1,796) and nondependent participants (n = 996). Substance-general and substance-specific effects on structural asymmetry were examined using separate models. We found that substance dependence was significantly associated with differences in volume asymmetry of the nucleus accumbens (NAcc; less rightward; Cohen's d = 0.15). This effect was driven by differences from controls in individuals with alcohol dependence (less rightward; Cohen's d = 0.10) and nicotine dependence (less rightward; Cohen's d = 0.11). These findings suggest that disrupted structural asymmetry in the NAcc may be a characteristic of substance dependence.

Virtual Histology of Cortical Thickness and Shared Neurobiology in 6 Psychiatric Disorders

IMPORTANCE

Large-scale neuroimaging studies have revealed group differences in cortical thickness across many psychiatric disorders. The underlying neurobiology behind these differences is not well understood.

OBJECTIVE

To determine neurobiologic correlates of group differences in cortical thickness between cases and controls in 6 disorders: attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), and schizophrenia.

DESIGN, SETTING, AND PARTICIPANTS

Profiles of group differences in cortical thickness between cases and controls were generated using T1-weighted magnetic resonance images. Similarity between interregional profiles of cell-specific gene expression and those in the group differences in cortical thickness were investigated in each disorder. Next, principal component analysis was used to reveal a shared profile of group difference in thickness across the disorders. Analysis for gene coexpression, clustering, and enrichment for genes associated with these disorders were conducted. Data analysis was conducted between June and December 2019. The analysis included 145 cohorts across 6 psychiatric disorders drawn from the ENIGMA consortium. The numbers of cases and controls in each of the 6 disorders were as follows: ADHD: 1814 and 1602; ASD: 1748 and 1770; BD: 1547 and 3405; MDD: 2658 and 3572; OCD: 2266 and 2007; and schizophrenia: 2688 and 3244.

MAIN OUTCOMES AND MEASURES

Interregional profiles of group difference in cortical thickness between cases and controls.

RESULTS

A total of 12 721 cases and 15 600 controls, ranging from ages 2 to 89 years, were included in this study. Interregional profiles of group differences in cortical thickness for each of the 6 psychiatric disorders were associated with profiles of gene expression specific to pyramidal (CA1) cells, astrocytes (except for BD), and microglia (except for OCD); collectively, gene-expression profiles of the 3 cell types explain between 25% and 54% of variance in interregional profiles of group differences in cortical thickness. Principal component analysis revealed a shared profile of difference in cortical thickness across the 6 disorders (48% variance explained); interregional profile of this principal component 1 was associated with that of the pyramidal-cell gene expression (explaining 56% of interregional variation). Coexpression analyses of these genes revealed 2 clusters: (1) a prenatal cluster enriched with genes involved in neurodevelopmental (axon guidance) processes and (2) a postnatal cluster enriched with genes involved in synaptic activity and plasticity-related processes. These clusters were enriched with genes associated with all 6 psychiatric disorders.

CONCLUSIONS AND RELEVANCE

In this study, shared neurobiologic processes were associated with differences in cortical thickness across multiple psychiatric disorders. These processes implicate a common role of prenatal development and postnatal functioning of the cerebral cortex in these disorders.

The association between SYT1-rs2251214 and cocaine use disorder further supports its role in psychiatry

Synaptotagmin-1 is an essential regulator of synaptic vesicle exocytosis, and its encoding gene (SYT1) is a genome and transcriptome-wide association hit in cognitive performance, personality and cocaine use disorder (CUD) studies. Additionally, in candidate gene studies the specific variant rs2251214 has been associated with attention-deficit/hyperactivity disorder (ADHD), antisocial personality disorder and other externalizing phenotypes in adults with ADHD, as well as with response to methylphenidate (MPH) treatment. In this context, we sought to evaluate, in an independent sample, the association of this variant with CUD, a phenotype that shares common biological underpinnings with the previously associated traits. We tested the association between SYT1-rs2251214 and CUD susceptibility and severity (addiction severity index) in a sample composed by 315 patients addicted to smoked cocaine and 769 non-addicted volunteers. SYT1-rs2251214 was significantly associated with susceptibility to CUD, where the G allele presented increased risk for the disorder in the genetic models tested (P = 0.0021, OR = 1.44, allelic; P = 0.0012, OR = 1.48, additive; P = 0.0127, OR = 1.41, dominant). This is the same allele that was associated with increased risk for ADHD and other externalizing behaviors, as well as poor response to MPH treatment in previous studies. These findings suggest that the neurotransmitter exocytosis pathway might play a critical role in the liability for psychiatric disorders, especially externalizing behaviors and CUD.

Effects of DRD2 splicing-regulatory polymorphism and DRD4 48 bp VNTR on crack cocaine addiction

There is evidence that dopamine receptors D2 (DRD2) and D4 (DRD4) polymorphisms may influence substance use disorders (SUD) susceptibility both individually and through their influence in the formation of DRD2-DRD4 heteromers. The dopaminergic role on the vulnerability to addiction appears to be influenced by sex. A cross-sectional study with 307 crack cocaine addicts and 770 controls was conducted. The influence of DRD2 rs2283265 and DRD4 48 bp VNTR in exon 3 variants, as well as their interaction on crack cocaine addiction susceptibility and severity were evaluated in women and men separately. An association between the DRD2 T allele and crack cocaine addiction was found in women. In this same group, interaction analysis demonstrated that the presence of DRD2-T allele and concomitant absence of DRD4-7R allele were associated with risk for crack cocaine addiction. No influence of DRD2 and DRD4 variants was observed in men regarding addiction severity. This study reinforces the role of dopaminergic genes in externalizing behaviors, especially the influence of DRD2-DRD4 interaction on SUD. This is the fourth sample that independently associated the DRD2-DRD4 interaction with SUD itself or related disorders. In addition, our findings point out to a potential difference of dopaminergic neurotransmission across sex influencing addiction susceptibility.

Evidence of sexual dimorphism of HTR1B gene on major adult ADHD comorbidities

Attention-deficit/hyperactivity disorder (ADHD) is a very common psychiatric disorder across the life cycle and frequently presents comorbidities. Since ADHD is highly heritable, several studies have focused in the underlying genetic factors involved in its etiology. One of the major challenges in this search is the phenotypic heterogeneity, which could be partly attributable to the sexual dimorphism frequently seen in psychiatric disorders. Taking into account the well-known sexual dimorphic effect observed in serotonergic system characteristics, we differentially tested the influence of HTR1B SNPs (rs11568817, rs130058, rs6296 and rs13212041) on ADHD susceptibility and on its major comorbidities according to sex. The sample comprised 564 adults with ADHD diagnosed according to DSM-IV criteria and 635 controls. There was no association of any HTR1B SNPs tested in relation to ADHD susceptibility. As for the comorbidities evaluated, after correction for multiple tests, significant associations were observed for both rs11568817 and rs130058 with substance use disorders (P_corr = 0.009 and P_corr = 0.018, respectively) and for rs11568817 with nicotine dependence (P_corr = 0.025) in men with ADHD. In women with ADHD, the same rs11568817 was associated with generalized anxiety disorder (P_corr = 0.031). The observed effects of rs11568817 G allele presence conferring risk to either substance use disorders or generalized anxiety disorder according to sex, suggest an overall scenario where a higher transcriptional activity of HTR1B, resulting from the presence of this allele, is related to externalizing behaviors in men and internalizing behaviors in women. These results are consistent with and expand previous evidence of sexual dimorphism of the serotoninergic system.

Pleiotropic effects of Chr15q25 nicotinic gene cluster and the relationship between smoking, cognition and ADHD

Polymorphisms in the CHRNA5-CHRNA3-CHRNB4 gene cluster (Chr15q25) have been robustly associated with nicotine dependence, including genome-wide studies, as well as with cognitive and neuropsychological measures. In addition, cognitive processes can be influenced by nicotine use through nicotinic acetylcholine receptors (nAChRs). Here, we evaluated the effect of polymorphisms in CHRNA5-CHRNA3-CHRNB4 gene cluster and their interaction with tobacco smoking status on cognition in patients with Attention Deficit/Hyperactivity Disorder (ADHD). Eight SNPs from the CHRNA5-CHRNA3-CHRNB4 gene cluster were evaluated on a clinical sample of 403 adults with ADHD. Cognitive performance was assessed using the Wechsler Adult Intelligence Scale-Revised (WAIS-R). Analyses of covariance were used to assess the influence of single markers and their interaction with smoking status in the Vocabulary and Block Design subtests of WAIS-R. Correction for multiple comparisons was applied. Lifetime smoking was associated to Vocabulary subtest. The TT genotypes of CHRNA5 SNPs rs588765 and rs514743 showed a trend towards association with, respectively, higher and lower scores on the Vocabulary subtest. There was a significant interaction between intergenic SNP rs8023462 and smoking on Vocabulary scores. Our results are consistent with an influence of variants in the CHRNA5-CHRNA3-CHRNB4 gene cluster on cognitive measures. The overall scenario suggests a pleiotropic role of Chr15q25 nicotinic gene cluster with complex influences in ADHD, tobacco smoking and cognitive performance, characteristics that can be partially interdependent and may share underlying genetic factors.

MR and GR functional SNPs may modulate tobacco smoking susceptibility

A number of studies have demonstrated that stress is involved in all aspects of smoking behavior, including initiation, maintenance and relapse. The mineralocorticoid (MR) and glucocorticoid (GR) receptors are expressed in several brain areas and play a key role in negative feedback of the hypothalamic-pituitary-adrenal (HPA) axis. As nicotine increases the activation of the HPA axis, we wondered if functional SNPs (single nucleotide polymorphisms) in MR and GR coding genes (NR3C2 rs5522 and NR3C1 rs6198, respectively) may be involved in smoking susceptibility. The sample included 627 volunteers, of which 514 were never-smokers and 113 lifetime smokers. We report an interaction effect between rs5522 and rs6198 SNPs. The odds ratio (OR) for the presence of the NR3C2 rs5522 Val allele in NR3C1 rs6198 G carriers was 0.18 (P = 0.007), while in rs6198 G noncarriers the OR was 1.83 (P = 0.027). We also found main effects of the NR3C1 rs6198 G allele on number of cigarettes smoked per day (P = 0.027) and in total score of the Fagerström Test for Nicotine Dependence (P = 0.007). These findings are consistent with a possible link between NR3C2 and NR3C1 polymorphisms and smoking behavior and provide a first partial replication for a nominally significant GWAS finding between NR3C2 and tobacco smoking.