Shared genetic etiology between cortical brain morphology and tobacco, alcohol, and cannabis use

ABSTRACTGenome-wide association studies (GWAS) have independently identified hundreds of genomic regions associated with brain morphology and substance use. However, the genetic overlap between brain structure and substance use has not been characterized. Here we leverage GWAS summary data of 71 brain imaging measures and alcohol, tobacco, and cannabis use to investigate their genetic overlap using LD score regression. We also used genomic structural equation modeling to model a ‘substance use common genetic factor’ and examined its genetic overlap with brain structure. After accounting for multiple testing, we identified eight significant negative genetic correlations, including between alcoholic drinks per week and average cortical thickness and intracranial volume with the age of smoking initiation; and five positive genetic correlations, including between insula surface area and lifetime cannabis use, and between the common factor with pericalcarine surface area. Our findings highlight a shared genetic etiology between variation in cortical brain morphology and substance use.

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Shared Genetic Etiology between Cortical Brain Morphology and Tobacco, Alcohol, and Cannabis Use

Cerebral Cortex ◽

10.1093/cercor/bhab243 ◽

2021 ◽

Author(s):

Jill A Rabinowitz ◽

Adrian I Campos ◽

Jue-Sheng Ong ◽

Luis M García-Marín ◽

Sarael Alcauter ◽

...

Keyword(s):

Surface Area ◽

Brain Structure ◽

Genetic Variants ◽

Genetic Factor ◽

Genetic Correlations ◽

Cannabis Use ◽

Brain Morphology ◽

Genetic Etiology ◽

Genetic Overlap ◽

Shared Genetic

Abstract Genome-wide association studies (GWAS) have identified genetic variants associated with brain morphology and substance use behaviors (SUB). However, the genetic overlap between brain structure and SUB has not been well characterized. We leveraged GWAS summary data of 71 brain imaging measures and alcohol, tobacco, and cannabis use to investigate their genetic overlap using linkage disequilibrium score regression. We used genomic structural equation modeling to model a “common SUB genetic factor” and investigated its genetic overlap with brain structure. Furthermore, we estimated SUB polygenic risk scores (PRS) and examined whether they predicted brain imaging traits using the Adolescent Behavior and Cognitive Development (ABCD) study. We identified 8 significant negative genetic correlations, including between (1) alcoholic drinks per week and average cortical thickness, and (2) intracranial volume with age of smoking initiation. We observed 5 positive genetic correlations, including those between (1) insula surface area and lifetime cannabis use, and (2) the common SUB genetic factor and pericalcarine surface area. SUB PRS were associated with brain structure variation in ABCD. Our findings highlight a shared genetic etiology between cortical brain morphology and SUB and suggest that genetic variants associated with SUB may be causally related to brain structure differences.

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Shared genetic etiology of age of menarche and socioeconomic variables: No evidence for genetic overlap with psychiatric traits

10.1101/2020.05.01.072348 ◽

2020 ◽

Author(s):

Martin Steppan

Keyword(s):

Mental Health ◽

Socioeconomic Status ◽

Pubertal Timing ◽

Genetic Correlations ◽

Poor Mental Health ◽

Mr Studies ◽

Genetic Etiology ◽

Genetic Overlap ◽

Age Of Menarche ◽

Shared Genetic

AbstractEarlier research has shown observational associations of early pubertal timing and poor mental health. Mendelian randomization (MR) studies demonstrated a transient effect of pubertal timing on mental health during adolescence, but not later in life. MR studies also showed that there is a likely causal association of pubertal timing with life history traits. However, the strongest causal effects and genetic correlations with age of menarche have been found for Body Mass Index (BMI). As high BMI is associated with lower socioeconomic status and with poor mental health, the shared genetic etiology of socioeconomic status, BMI and poor mental health is not yet fully understood. BMI correlates negatively with socioeconomic status and several mental health outcomes. Despite their substantial genetic overlap, the underlying genetic etiology of these phenotypes remains unclear. In this study we applied Linkage Disequi-librium score regression to test genetic correlations of age of menarche with 33 socioeconomic, life history, social interaction, personality and psychiatric traits, and BMI. We further applied spectral decomposition and hierarchical clustering to the genetic correlation matrix. After controlling for multiple testing, we could only identify significant genetic correlations with BMI and three socioeconomic traits (household income, deprivation and parental longevity). The results suggest that genome-wide association studies on age of menarche also contain socioeconomic information. Future MR studies aiming to test the unconfounded effect of pubertal timing should make sure that genetic instruments have no pleiotropic effect on socioeconomic variables, or (if possible) also control for socioeconomic status on the observational level.

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Genetic overlap between Alzheimer’s disease and depression mapped onto the brain

10.1101/2021.01.23.21250016 ◽

2021 ◽

Author(s):

Jennifer Monereo Sánchez ◽

Miranda T. Schram ◽

Oleksandr Frei ◽

Kevin O’Connell ◽

Alexey A. Shadrin ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Genome Wide Association Study ◽

Brain Mri ◽

Genetic Correlations ◽

Gaussian Mixture ◽

Brain Morphology ◽

Uk Biobank ◽

Genetic Overlap ◽

The Uk

ABSTRACTBackgroundAlzheimer’s disease (AD) and depression are debilitating brain disorders that are often comorbid. Shared brain mechanisms have been implicated, yet findings are inconsistent, reflecting the complexity of the underlying pathophysiology. As both disorders are (partly) heritable, characterizing their genetic overlap may provide etiological clues. While previous studies have indicated negligible genetic correlations, this study aims to expose the genetic overlap that may remain hidden due to mixed directions of effects.MethodsWe applied Gaussian mixture modelling, through MiXeR, and conjunctional false discovery rate (cFDR) analysis, through pleioFDR, to genome-wide association study (GWAS) summary statistics of AD (n=79,145) and depression (n=450,619). The effects of identified overlapping loci on AD and depression were tested in 403,029 participants of the UK Biobank (mean age 57.21 52.0% female), and mapped onto brain morphology in 30,699 individuals with brain MRI data.ResultsMiXer estimated 98 causal genetic variants overlapping between the two disorders, with 0.44 concordant directions of effects. Through pleioFDR, we identified a SNP in the TMEM106B gene, which was significantly associated with AD (B=-0.002, p=9.1×10−4) and depression (B=0.007, p=3.2×10−9) in the UK Biobank. This SNP was also associated with several regions of the corpus callosum volume anterior (B>0.024, p<8.6×10−4), third ventricle volume ventricle (B=-0.025, p=5.0×10−6), and inferior temporal gyrus surface area (B=0.017, p=5.3×10−4).DiscussionOur results indicate there is substantial genetic overlap, with mixed directions of effects, between AD and depression. These findings illustrate the value of biostatistical tools that capture such overlap, providing insight into the genetic architectures of these disorders.

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Genetic Overlap Between Alzheimer’s Disease and Depression Mapped Onto the Brain

Frontiers in Neuroscience ◽

10.3389/fnins.2021.653130 ◽

2021 ◽

Vol 15 ◽

Author(s):

Jennifer Monereo-Sánchez ◽

Miranda T. Schram ◽

Oleksandr Frei ◽

Kevin O’Connell ◽

Alexey A. Shadrin ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Genome Wide Association Study ◽

Third Ventricle ◽

Brain Mri ◽

Genetic Correlations ◽

Gaussian Mixture ◽

Brain Morphology ◽

Genetic Overlap ◽

Ventricle Volume

Background: Alzheimer’s disease (AD) and depression are debilitating brain disorders that are often comorbid. Shared brain mechanisms have been implicated, yet findings are inconsistent, reflecting the complexity of the underlying pathophysiology. As both disorders are (partly) heritable, characterising their genetic overlap may provide aetiological clues. While previous studies have indicated negligible genetic correlations, this study aims to expose the genetic overlap that may remain hidden due to mixed directions of effects.Methods: We applied Gaussian mixture modelling, through MiXeR, and conjunctional false discovery rate (cFDR) analysis, through pleioFDR, to genome-wide association study (GWAS) summary statistics of AD (n = 79,145) and depression (n = 450,619). The effects of identified overlapping loci on AD and depression were tested in 403,029 participants of the UK Biobank (UKB) (mean age 57.21, 52.0% female), and mapped onto brain morphology in 30,699 individuals with brain MRI data.Results: MiXer estimated 98 causal genetic variants overlapping between the 2 disorders, with 0.44 concordant directions of effects. Through pleioFDR, we identified a SNP in the TMEM106B gene, which was significantly associated with AD (B = −0.002, p = 9.1 × 10–4) and depression (B = 0.007, p = 3.2 × 10–9) in the UKB. This SNP was also associated with several regions of the corpus callosum volume anterior (B > 0.024, p < 8.6 × 10–4), third ventricle volume ventricle (B = −0.025, p = 5.0 × 10–6), and inferior temporal gyrus surface area (B = 0.017, p = 5.3 × 10–4).Discussion: Our results indicate there is substantial genetic overlap, with mixed directions of effects, between AD and depression. These findings illustrate the value of biostatistical tools that capture such overlap, providing insight into the genetic architectures of these disorders.

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Quantifying the Polygenic Architecture of the Human Cerebral Cortex: Extensive Genetic Overlap between Cortical Thickness and Surface Area

Cerebral Cortex ◽

10.1093/cercor/bhaa146 ◽

2020 ◽

Vol 30 (10) ◽

pp. 5597-5603 ◽

Cited By ~ 3

Author(s):

Dennis van der Meer ◽

Oleksandr Frei ◽

Tobias Kaufmann ◽

Chi-Hua Chen ◽

Wesley K Thompson ◽

...

Keyword(s):

Surface Area ◽

Cortical Thickness ◽

Large Scale ◽

Gaussian Mixture ◽

Total Surface Area ◽

Brain Morphology ◽

Genetic Determinants ◽

Genetic Predictors ◽

Genetic Overlap ◽

Causal Variants

Abstract The thickness of the cerebral cortical sheet and its surface area are highly heritable traits thought to have largely distinct polygenic architectures. Despite large-scale efforts, the majority of their genetic determinants remain unknown. Our ability to identify causal genetic variants can be improved by employing brain measures that better map onto the biology we seek to understand. Such measures may have fewer variants but with larger effects, that is, lower polygenicity and higher discoverability. Using Gaussian mixture modeling, we estimated the number of causal variants shared between mean cortical thickness and total surface area, as well as the polygenicity and discoverability of regional measures. We made use of UK Biobank data from 30 880 healthy White European individuals (mean age 64.3, standard deviation 7.5, 52.1% female). We found large genetic overlap between total surface area and mean thickness, sharing 4016 out of 7941 causal variants. Regional surface area was more discoverable (P = 2.6 × 10−6) and less polygenic (P = 0.004) than regional thickness measures. These findings may serve as a roadmap for improved future GWAS studies; knowledge of which measures are most discoverable may be used to boost identification of genetic predictors and thereby gain a better understanding of brain morphology.

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Exploring Phenotypic and Genetic Overlap Between Cannabis Use and Schizotypy

Twin Research and Human Genetics ◽

10.1017/thg.2020.68 ◽

2020 ◽

Vol 23 (4) ◽

pp. 221-227 ◽

Cited By ~ 1

Author(s):

James Vaissiere ◽

Jackson G. Thorp ◽

Jue-Sheng Ong ◽

Alfredo Ortega-Alonso ◽

Eske M. Derks

Keyword(s):

Multiple Testing ◽

Association Studies ◽

Genetic Correlations ◽

Cannabis Use ◽

Exact Nature ◽

Social Anhedonia ◽

Multiple Testing Correction ◽

Phenotypic Correlations ◽

Genetic Overlap ◽

Heritability Estimates

AbstractThere is a well-established relationship between cannabis use and psychosis, although the exact nature of this relationship is not fully understood. Recent studies have observed significant genetic overlap between a diagnosis of schizophrenia and lifetime cannabis use. Expanding on this work, the current study aimed to examine whether genetic overlap also occurs for subclinical psychosis (schizotypy) and cannabis use, as well as examining the phenotypic association between the traits. Phenotypic correlations were calculated for a variety of schizotypy and cannabis phenotypes in the UK Biobank (UKB), and single nucleotide polymorphism (SNP)-based heritability estimates and genetic correlations were calculated for these UKB phenotypes as well as for several other variables taken from recent genomewide association studies. Positive phenotypic correlations were observed between 11 out of 12 pairs of the cannabis use and schizotypy phenotypes (correlation range .05–.18), indicating a robust association between increased symptoms of schizotypy and cannabis use. SNP-based heritability estimates for two schizotypy phenotypes remained significant after multiple testing correction: social anhedonia (h2SNP = .08, SE = .02, N = 4025) and ever seen an unreal vision (h2SNP = .35, SE = .10, N = 150,717). Finally, one significant genetic correlation was observed between schizotypy and cannabis use, a negative correlation between social anhedonia and number of times used cannabis (rg = −.30, p = .012). The current study suggests the relationship between cannabis use and psychosis is also seen in subclinical symptoms of psychosis, but further research with larger samples is needed to determine the biological mechanisms underlying this association.

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Quantifying the polygenic architecture of the human cerebral cortex: Extensive genetic overlap between cortical thickness and surface area

10.1101/868307 ◽

2019 ◽

Author(s):

Dennis van der Meer ◽

Oleksandr Frei ◽

Tobias Kaufmann ◽

Chi-Hua Chen ◽

Wesley K. Thompson ◽

...

Keyword(s):

Surface Area ◽

Cortical Thickness ◽

Large Scale ◽

Gaussian Mixture ◽

Total Surface Area ◽

Brain Morphology ◽

Genetic Determinants ◽

Genetic Predictors ◽

Genetic Overlap ◽

Causal Variants

ABSTRACTIntroductionThe thickness of the cerebral cortical sheet and its surface area are highly heritable traits thought to have largely distinct polygenic architectures. Despite large-scale efforts, the majority of their genetic determinants remains unknown. Our ability to identify causal genetic variants can be improved by employing better delineated, less noisy brain measures that better map onto the biology we seek to understand. Such measures may have fewer variants but with larger effects, i.e. lower polygenicity and higher discoverability.MethodsUsing Gaussian mixture modeling, we estimated the number of causal variants shared between mean cortical thickness and total surface area. We further determined the polygenicity and discoverability of regional cortical measures from five often-employed parcellation schemes. We made use of UK Biobank data from 31,312 healthy White European individuals (mean age 55.5, standard deviation (SD) 7.4, 52.1% female).ResultsContrary to previous reports, we found large genetic overlap between total surface area and mean thickness, sharing 4427 out of 7150 causal variants. Regional surface area was more discoverable (p=4.1×10−6) and less polygenic (p=.007) than regional thickness measures. We further found that genetically-informed and less granular parcellation schemes had highest discoverability, with no differences in polygenicity.ConclusionsThese findings may serve as a roadmap for improved future GWAS studies; Knowledge of which measures or parcellations are most discoverable, as well as the genetic overlap between these measures, may be used to boost identification of genetic predictors and thereby gain a better understanding of brain morphology.

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Common Genes Contribute to Depressive Symptoms and Heart Rate Variability: The Twins Heart Study

Twin Research and Human Genetics ◽

10.1375/twin.13.1.1 ◽

2010 ◽

Vol 13 (1) ◽

pp. 1-9 ◽

Cited By ~ 26

Author(s):

Shaoyong Su ◽

Rachel Lampert ◽

Forrester Lee ◽

J. Douglas Bremner ◽

Harold Snieder ◽

...

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Depressive Symptoms ◽

Structural Equation ◽

Genetic Correlations ◽

Low Frequency ◽

Total Power ◽

Equation Modeling ◽

Genetic Components ◽

Shared Genetic

AbstractDepression and reduced heart rate variability (HRV) are predictors of coronary artery disease (CAD), and highly correlated with each other. However, little is known to what extend this correlation can be explained by common genetic components. We examined 198 middle-aged male twins (121 monozygotic and 77 dizygotic) from the Vietnam Era Twin Registry. Current depressive symptoms were assessed using the Beck Depression Inventory-II and HRV was assessed on 24-hour electrocardiographic Holter recordings. Five frequency domain variables were used, including ultra low frequency (ULF), very low frequency (VLF), low frequency (LF), high frequency (HF) and total power (TPow). Structural equation modeling was used to estimate shared genetic effects for depressive symptoms and the HRV frequency domains. Both depressive symptoms (h2=.5) and all measurements of HRV showed high heritability (h2=.43-.63). A significant inverse correlation was found between depressive symptoms and all HRV indices except LF and HF, with the highest coefficient (r) for TPow (r = −.24,P= .01) and ULF (r = −.24,P= .01). Bivariate genetic modeling revealed significant genetic correlations between depressive symptoms and TPow (rA= −.21,P= .04), as well as ULF (rA= −.23,P= .02). Of the total covariance between depressive symptoms and these two HRV indices, over 80% was due to the same genetic factors. In conclusion, depressive symptoms are associated with decreased HRV and this association is due, in large part, to a shared genetic effect. These results suggest that a common neurobiological dysfunction links depression and autonomic dysregulation.

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Shared Genetic Vulnerability for Disordered Gambling and Alcohol Use Disorder in Men and Women: Evidence from a National Community-Based Australian Twin Study

Twin Research and Human Genetics ◽

10.1017/thg.2013.11 ◽

2013 ◽

Vol 16 (2) ◽

pp. 525-534 ◽

Cited By ~ 50

Author(s):

Wendy S. Slutske ◽

Jarrod M. Ellingson ◽

Leah S. Richmond-Rakerd ◽

Gu Zhu ◽

Nicholas G. Martin

Keyword(s):

Substance Use ◽

Substance Use Disorders ◽

Alcohol Use Disorder ◽

Disordered Gambling ◽

Community Based ◽

Men And Women ◽

National Community ◽

Dsm 5 ◽

Genetic Overlap ◽

Shared Genetic

Disordered gambling (DG) will soon be included along with the substance use disorders in a revised diagnostic category of the Diagnostic and Statistical Manual of Mental Disorders DSM-5 called ‘Substance Use and Addictive Disorders’. This was premised in part on the common etiologies of DG and the substance use disorders. Using data from the national community-based Australian Twin Registry, we used biometric model fitting to examine the extent to which the genetic liabilities for DG and alcohol use disorder (AUD) were shared, and whether this differed for men and women. The effect of using categorical versus dimensional DG and AUD phenotypes was explored, as was the effect of using diagnoses based on the DSM-IV and the proposed DSM-5 diagnostic criteria. The genetic correlations between DG and AUD ranged from 0.29 to 0.44. There was a significantly larger genetic correlation between DG and AUD among men than women when using dimensional phenotypes. Overall, about one-half to two-thirds of the association between DG and AUD was due to a shared genetic vulnerability. This study represents one of the few empirical demonstrations of an overlap in the genetic risk for DG and another substance-related addictive disorder. More research is needed on the genetic overlap between DG and other substance use disorders, as well as the genetic overlap between DG and other (non-substance-related) psychiatric disorders.

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Dissecting the shared genetic architecture of suicide attempt, psychiatric disorders and known risk factors

10.1101/2020.12.01.20241281 ◽

2020 ◽

Author(s):

Niamh Mullins ◽

Jooeun Kang ◽

Adrian I Campos ◽

Jonathan R I Coleman ◽

Alexis C Edwards ◽

...

Keyword(s):

Risk Factors ◽

Suicide Attempt ◽

Psychiatric Disorders ◽

Risk Taking ◽

Sleep Disturbances ◽

Genetic Correlations ◽

Genetic Etiology ◽

Genome Wide ◽

A Genome ◽

Shared Genetic

AbstractSuicide is a leading cause of death worldwide and non-fatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both are known to have a substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium and conditioned the results on psychiatric disorders using GWAS summary statistics, to investigate their shared and divergent genetic architectures. Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, which remained associated after conditioning and has previously been implicated in risk-taking, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, lower socioeconomic status, pain, lower educational attainment, reproductive traits, risk-taking, sleep disturbances, and poorer overall general health. After conditioning, the genetic correlations between SA and psychiatric disorders decreased, whereas those with non-psychiatric traits remained largely unchanged. Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest the existence of a shared genetic etiology between SA and known risk factors that is not mediated by psychiatric disorders.

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