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

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.

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

2021 ◽  
Author(s):  
Jill A. Rabinowitz ◽  
Adrian I. Campos ◽  
Jue-Sheng Ong ◽  
Luis M. García-Marín ◽  
Sarael Alcauter ◽  
...  
Keyword(s):  
Substance Use ◽  
Surface Area ◽  
Brain Structure ◽  
Genetic Correlations ◽  
Cannabis Use ◽  
Brain Morphology ◽  
Equation Modeling ◽  
Genetic Etiology ◽  
Genetic Overlap ◽  
Shared Genetic

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.

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

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.

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

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.

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

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 &gt; 0.024, p &lt; 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.

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

2020 ◽  
Vol 30 (10) ◽  
pp. 5597-5603 ◽  
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.

scholarly journals Exploring Phenotypic and Genetic Overlap Between Cannabis Use and Schizotypy

2020 ◽  
Vol 23 (4) ◽  
pp. 221-227 ◽  
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.

scholarly journals Evidence for shared genetic risk factors between lymphangioleiomyomatosis and pulmonary function

2021 ◽  
pp. 00375-2021
Author(s):  
Xavier Farré ◽  
Roderic Espín ◽  
Alexandra Baiges ◽  
Eline Blommaert ◽  
Wonji Kim ◽  
...  
Keyword(s):  
Pulmonary Function ◽  
Genetic Variants ◽  
Genetic Basis ◽  
Association Studies ◽  
Genetic Correlations ◽  
Forced Expiratory Volume ◽  
Low Grade ◽  
Genome Wide Association Studies ◽  
Cell Of Origin ◽  
Shared Genetic

IntroductionLymphangioleiomyomatosis (LAM) is a rare low-grade metastasising disease characterised by cystic lung destruction. The genetic basis of LAM remains incompletely determined, and the disease cell-of-origin is uncertain. We analysed the possibility of a shared genetic basis between LAM and cancer, and LAM and pulmonary function.MethodsThe results of genome-wide association studies (GWASs) of LAM, 17 cancer types, and spirometry measures (forced expiratory volume in 1-second (FEV1), forced vital capacity (FVC), FEV1/FVC ratio, and peak expiratory flow (PEF)) were analysed for genetic correlations, shared genetic variants, and causality. Genomic and transcriptomic data were examined, and immunodetection assays were performed to evaluate pleiotropic genes.ResultsThere were no significant overall genetic correlations between LAM and cancer, but LAM correlated negatively with FVC and PEF, and a trend in the same direction was observed for FEV1. Twenty-two shared genetic variants were uncovered between LAM and pulmonary function, while seven shared variants were identified between LAM and cancer. The LAM-pulmonary function shared genetics identified four pleiotropic genes previously recognised in LAM single-cell transcriptomes: ADAM12, BNC2, NR2F2, and SP5. We had previously associated NR2F2 variants with LAM, and we identified its functional partner NR3C1 as another pleotropic factor. NR3C1 expression was confirmed in LAM lung lesions. Another candidate pleiotropic factor, CNTN2, was found more abundant in plasma of LAM patients than that of healthy women.ConclusionsThis study suggests the existence of a common genetic aetiology between LAM and pulmonary function.

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

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.

scholarly journals Dynamics of Brain Structure and its Genetic Architecture over the Lifespan

2020 ◽  
Author(s):  
Rachel M. Brouwer ◽  
Marieke Klein ◽  
Katrina L. Grasby ◽  
Hugo G. Schnack ◽  
Neda Jahanshad ◽  
...  
Keyword(s):  
Brain Development ◽  
Brain Structure ◽  
Genetic Variants ◽  
Imaging Genetics ◽  
Adult Brain ◽  
Brain Morphology ◽  
Brain Growth ◽  
Genome Wide ◽  
Common Genetic Variants ◽  
Age Dependent

AbstractHuman brain structure changes throughout our lives. Altered brain growth or rates of decline are implicated in a vast range of psychiatric, developmental, and neurodegenerative diseases. While heritable, specific loci in the genome that influence these rates are largely unknown. Here, we sought to find common genetic variants that affect rates of brain growth or atrophy, in the first genome-wide association analysis of longitudinal changes in brain morphology across the lifespan. Longitudinal magnetic resonance imaging data from 10,163 individuals aged 4 to 99 years, on average 3.5 years apart, were used to compute rates of morphological change for 15 brain structures. We discovered 5 genome-wide significant loci and 15 genes associated with brain structural changes. Most individual variants exerted age-dependent effects. All identified genes are expressed in fetal and adult brain tissue, and some exhibit developmentally regulated expression across the lifespan. We demonstrate genetic overlap with depression, schizophrenia, cognitive functioning, height, body mass index and smoking. Several of the discovered loci are implicated in early brain development and point to involvement of metabolic processes. Gene-set findings also implicate immune processes in the rates of brain changes. Taken together, in the world’s largest longitudinal imaging genetics dataset we identified genetic variants that alter age-dependent brain growth and atrophy throughout our lives.One-sentence summaryWe identified common genetic variants associated with the rate of brain development and aging, in longitudinal MRI scans worldwide.

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

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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