Genetic correlations between subcortical brain volumes and psychiatric disorders

2020 ◽  
Vol 216 (5) ◽  
pp. 280-283
Author(s):  
Kazutaka Ohi ◽  
Takamitsu Shimada ◽  
Yuzuru Kataoka ◽  
Toshiki Yasuyama ◽  
Yasuhiro Kawasaki ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Genetic Variants ◽  
Large Scale ◽  
Association Studies ◽  
Genetic Correlations ◽  
Intracranial Volume ◽  
Genome Wide Association Studies ◽  
Brain Volumes ◽  
Subcortical Brain ◽  
Genome Wide

SummaryPsychiatric disorders as well as subcortical brain volumes are highly heritable. Large-scale genome-wide association studies (GWASs) for these traits have been performed. We investigated the genetic correlations between five psychiatric disorders and the seven subcortical brain volumes and the intracranial volume from large-scale GWASs by linkage disequilibrium score regression. We revealed weak overlaps between the genetic variants associated with psychiatric disorders and subcortical brain and intracranial volumes, such as in schizophrenia and the hippocampus and bipolar disorder and the accumbens. We confirmed shared aetiology and polygenic architecture across the psychiatric disorders and the specific subcortical brain and intracranial volume.

scholarly journals GWAS of 19,629 individuals identifies novel genetic variants for regional brain volumes and refines their genetic co-architecture with cognitive and mental health traits

2019 ◽  
Author(s):  
Bingxin Zhao ◽  
Tianyou Luo ◽  
Tengfei Li ◽  
Yun Li ◽  
Jingwen Zhang ◽  
...  
Keyword(s):  
Mental Health ◽  
Sample Size ◽  
Genetic Variants ◽  
Association Studies ◽  
Genetic Correlations ◽  
Joint Analysis ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies ◽  
Brain Volumes ◽  
Genome Wide

AbstractVolumetric variations of human brain are heritable and are associated with many brain-related complex traits. Here we performed genome-wide association studies (GWAS) and post-GWAS analyses of 101 brain volumetric phenotypes using the UK Biobank (UKB) sample including 19,629 participants. GWAS identified 287 independent SNPs exceeding genome-wide significance threshold of 4.9*10−10, adjusted for testing multiple phenotypes. Gene-based association study found 142 associated genes (113 new) and functional gene mapping analysis linked 122 more genes. Many of the discovered genetic variants have previously been implicated with cognitive and mental health traits (such as cognitive performance, education, mental disease/disorders), and significant genetic correlations were detected for 29 pairs of traits. The significant SNPs discovered in the UKB sample were supported by a joint analysis with other four independent studies (total sample size 2,192), and we performed a meta-analysis of five samples to provide GWAS summary statistics with sample size larger than 20,000. Using genome-wide polygenic risk scores prediction, up to 4.36% of phenotypic variance (p-value=2.97*10−22) in the four independent studies can be explained by the UKB GWAS results. In conclusion, our study identifies many new genetic variants at SNP, locus and gene levels and advances our understanding of the pleiotropy and genetic co-architecture between brain volumes and other traits.

scholarly journals Comment on ‘Large-Scale Cognitive GWAS Meta-Analysis Reveals Tissue-Specific Neural Expression and Potential Nootropic Drug Targets’ by Lam et al.

2018 ◽  
Vol 21 (2) ◽  
pp. 84-88 ◽  
Author(s):  
W. David Hill
Keyword(s):  
Genetic Information ◽  
Drug Targets ◽  
Large Scale ◽  
Association Studies ◽  
Meta Analysis ◽  
Genetic Correlations ◽  
Data Sets ◽  
Genome Wide Association Studies ◽  
Nootropic Drug ◽  
Genome Wide

Intelligence and educational attainment are strongly genetically correlated. This relationship can be exploited by Multi-Trait Analysis of GWAS (MTAG) to add power to Genome-wide Association Studies (GWAS) of intelligence. MTAG allows the user to meta-analyze GWASs of different phenotypes, based on their genetic correlations, to identify association's specific to the trait of choice. An MTAG analysis using GWAS data sets on intelligence and education was conducted by Lam et al. (2017). Lam et al. (2017) reported 70 loci that they described as ‘trait specific’ to intelligence. This article examines whether the analysis conducted by Lam et al. (2017) has resulted in genetic information about a phenotype that is more similar to education than intelligence.

scholarly journals Analysis of shared heritability in common disorders of the brain

Science ◽  
2018 ◽  
Vol 360 (6395) ◽  
pp. eaap8757 ◽  
Author(s):  
◽  
Verneri Anttila ◽  
Brendan Bulik-Sullivan ◽  
Hilary K. Finucane ◽  
Raymond K. Walters ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Neurological Disorders ◽  
Statistical Power ◽  
Association Studies ◽  
Genetic Correlations ◽  
Genome Wide Association Studies ◽  
Brain Disorders ◽  
Cognitive Measures ◽  
Genome Wide ◽  
The Brain

Disorders of the brain can exhibit considerable epidemiological comorbidity and often share symptoms, provoking debate about their etiologic overlap. We quantified the genetic sharing of 25 brain disorders from genome-wide association studies of 265,218 patients and 784,643 control participants and assessed their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders share common variant risk, whereas neurological disorders appear more distinct from one another and from the psychiatric disorders. We also identified significant sharing between disorders and a number of brain phenotypes, including cognitive measures. Further, we conducted simulations to explore how statistical power, diagnostic misclassification, and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a risk factor for brain disorders and the value of heritability-based methods in understanding their etiology.

scholarly journals circVAR database: genome-wide archive of genetic variants for human circular RNAs

2020 ◽  
Author(s):  
Min Zhao ◽  
Hong Qu
Keyword(s):  
Genetic Variants ◽  
Complex Traits ◽  
Large Scale ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Association Studies ◽  
Chromosome 17 ◽  
Circular Rnas ◽  
Genome Wide Association Studies ◽  
Genome Wide

Abstract Background: Circular RNAs (circRNAs) play important roles in regulating gene expression through binding miRNAs and RNA binding proteins. Genetic variation of circRNAs may affect complex traits/diseases by changing their binding efficiency to target miRNAs and proteins. There is a growing demand for investigations of the functions of genetic changes using large-scale experimental evidence. However, there is no online genetic resource for circRNA genes. Results: We performed extensive genetic annotation of 295,526 circRNAs integrated from circBase, circNet and circRNAdb. All pre-computed genetic variants were presented at our online resource, circVAR, with data browsing and search functionality. We explored the chromosome-based distribution of circRNAs and their associated variants. We found that, based on mapping to the 1000 Genomes and ClinVAR databases, chromosome 17 has a relatively large number of circRNAs and associated common and health-related genetic variants. Following the annotation of genome wide association studies (GWAS)-based circRNA variants, we found many non-coding variants within circRNAs, suggesting novel mechanisms for common diseases reported from GWAS studies. For cancer-based somatic variants, we found that chromosome 7 has many highly complex mutations that have been overlooked in previous research. Conclusion: We used the circVAR database to collect SNPs and small insertions and deletions (INDELs) in putative circRNA regions and to identify their potential phenotypic information. To provide a reusable resource for the circRNA research community, we have published all the pre-computed genetic data concerning circRNAs and associated genes together with data query and browsing functions at http://soft.bioinfo-minzhao.org/circvar .

scholarly journals Genetic analysis identifies molecular systems and biological pathways associated with household income

2019 ◽  
Author(s):  
W. David Hill ◽  
Neil M. Davies ◽  
Stuart J. Ritchie ◽  
Nathan G. Skene ◽  
Julien Bryois ◽  
...  
Keyword(s):  
Cognitive Ability ◽  
Genetic Variants ◽  
Household Income ◽  
Association Studies ◽  
Genetic Correlations ◽  
Functional Enrichment ◽  
Genome Wide Association Studies ◽  
Income Differences ◽  
Genome Wide ◽  
Common Genetic Variants

AbstractSocio-economic position (SEP) is a multi-dimensional construct reflecting (and influencing) multiple socio-cultural, physical, and environmental factors. Previous genome-wide association studies (GWAS) using household income as a marker of SEP have shown that common genetic variants account for 11% of its variation. Here, in a sample of 286,301 participants from UK Biobank, we identified 30 independent genome-wide significant loci, 29 novel, that are associated with household income. Using a recently-developed method to meta-analyze data that leverages power from genetically-correlated traits, we identified an additional 120 income-associated loci. These loci showed clear evidence of functional enrichment, with transcriptional differences identified across multiple cortical tissues, in addition to links with GABAergic and serotonergic neurotransmission. We identified neurogenesis and the components of the synapse as candidate biological systems that are linked with income. By combining our GWAS on income with data from eQTL studies and chromatin interactions, 24 genes were prioritized for follow up, 18 of which were previously associated with cognitive ability. Using Mendelian Randomization, we identified cognitive ability as one of the causal, partly-heritable phenotypes that bridges the gap between molecular genetic inheritance and phenotypic consequence in terms of income differences. Significant differences between genetic correlations indicated that, the genetic variants associated with income are related to better mental health than those linked to educational attainment (another commonly-used marker of SEP). Finally, we were able to predict 2.5% of income differences using genetic data alone in an independent sample. These results are important for understanding the observed socioeconomic inequalities in Great Britain today.

Genetic contributions to anxiety disorders: where we are and where we are heading

2021 ◽  
pp. 1-16
Author(s):  
Helga Ask ◽  
Rosa Cheesman ◽  
Eshim S. Jami ◽  
Daniel F. Levey ◽  
Kirstin L. Purves ◽  
...  
Keyword(s):  
Anxiety Disorders ◽  
Psychiatric Disorders ◽  
Large Scale ◽  
Current Knowledge ◽  
Association Studies ◽  
Twin Studies ◽  
Future Research ◽  
Specific Gene ◽  
Genome Wide Association Studies ◽  
Genome Wide

Abstract Anxiety disorders are among the most common psychiatric disorders worldwide. They often onset early in life, with symptoms and consequences that can persist for decades. This makes anxiety disorders some of the most debilitating and costly disorders of our time. Although much is known about the synaptic and circuit mechanisms of fear and anxiety, research on the underlying genetics has lagged behind that of other psychiatric disorders. However, alongside the formation of the Psychiatric Genomic Consortium Anxiety workgroup, progress is rapidly advancing, offering opportunities for future research. Here we review current knowledge about the genetics of anxiety across the lifespan from genetically informative designs (i.e. twin studies and molecular genetics). We include studies of specific anxiety disorders (e.g. panic disorder, generalised anxiety disorder) as well as those using dimensional measures of trait anxiety. We particularly address findings from large-scale genome-wide association studies and show how such discoveries may provide opportunities for translation into improved or new therapeutics for affected individuals. Finally, we describe how discoveries in anxiety genetics open the door to numerous new research possibilities, such as the investigation of specific gene–environment interactions and the disentangling of causal associations with related traits and disorders. We discuss how the field of anxiety genetics is expected to move forward. In addition to the obvious need for larger sample sizes in genome-wide studies, we highlight the need for studies among young people, focusing on specific underlying dimensional traits or components of anxiety.

scholarly journals Evidence of shared genetic influences underlying schizophrenia and alcohol use disorder, but not alcohol consumption

2020 ◽  
Author(s):  
Emma C. Johnson ◽  
Manav Kapoor ◽  
Alexander S. Hatoum ◽  
Hang Zhou ◽  
Renato Polimanti ◽  
...  
Keyword(s):  
Alcohol Consumption ◽  
Alcohol Use ◽  
Genetic Variants ◽  
Alcohol Use Disorder ◽  
Association Studies ◽  
Genetic Correlations ◽  
Genome Wide Association Studies ◽  
Genetic Covariance ◽  
Genome Wide ◽  
Brain Tissues

AbstractBackgroundAlcohol use disorder (AUD) and schizophrenia (SCZ) frequently co-occur, and recent genome-wide association studies (GWAS) have identified significant genetic correlations between them. In parallel, mounting evidence from GWAS suggests that alcohol consumption is only weakly genetically correlated with SCZ, but this has not yet been systematically investigated.MethodsWe used the largest published GWAS for AUD (total cases = 77,822) and SCZ (total cases = 46,827) to systematically identify genetic variants that influence both disorders (in either the same or opposite direction of effect) as well as disorder-specific loci, and contrast our findings with GWAS data for drinks per week (DPW; N = 537,349) as a measure of alcohol consumption.ResultsWe identified 55 independent genome-wide significant SNPs with the same direction of effect on AUD and SCZ, 9 with robust opposite effects, and 99 with disorder-specific effects. We also found evidence for 12 genes whose pleiotropic associations with AUD and SCZ are consistent with mediation via gene expression in the prefrontal cortex. The genetic covariance between AUD and SCZ was concentrated in genomic regions functional in brain tissues (p = 0.001). The genetic correlation between DPW and SCZ (rg = 0.102, SE = 0.022) was significantly lower than that for AUD and SCZ (rg = 0.392, SE = 0.029; p-value of the difference = 9.3e-18), and the genetic covariance between DPW and SCZ was not enriched for any meaningful tissue-specific categories.ConclusionsOur findings provide a detailed view of genetic loci that influence risk of both AUD and SCZ, suggest that biological commonalities underlying genetic variants with an effect on both disorders are manifested in brain tissues, and provide further evidence that SCZ shares meaningful genetic overlap with AUD and not merely alcohol consumption.

scholarly journals EnigmaVis: Online Interactive Visualization of Genome-Wide Association Studies of the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium

2012 ◽  
Vol 15 (3) ◽  
pp. 414-418 ◽  
Author(s):  
Nic M. Novak ◽  
Jason L. Stein ◽  
Sarah E. Medland ◽  
Derrek P. Hibar ◽  
Paul M. Thompson ◽  
...  
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Meta Analysis ◽  
Genome Wide Association ◽  
Intracranial Volume ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Genome Wide ◽  
Neuroimaging Data ◽  
Neuroimaging Genetics

In an attempt to increase power to detect genetic associations with brain phenotypes derived from human neuroimaging data, we recently conducted a large-scale, genome-wide association meta-analysis of hippocampal, brain, and intracranial volume through the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium. Here, we present a freely available online interactive tool, EnigmaVis, which makes it easy to visualize the association results generated by the consortium alongside allele frequency, genes, and functional annotations. EnigmaVis runs natively within the web browser, and generates plots that show the level of association between brain phenotypes at user-specified genomic positions. Uniquely, EnigmaVis is dynamic; users can interact with elements on the plot in real time. This software will be useful when exploring the effect on brain structure of particular genetic variants influencing neuropsychiatric illness and cognitive function. Future projects of the consortium and updates to EnigmaVis will also be displayed on the site. EnigmaVis is freely available online at http://enigma.loni.ucla.edu/enigma-vis/

Shared genetic etiology between anxiety disorders and psychiatric and related intermediate phenotypes

2019 ◽  
Vol 50 (4) ◽  
pp. 692-704 ◽  
Author(s):  
Kazutaka Ohi ◽  
Takeshi Otowa ◽  
Mihoko Shimada ◽  
Tsukasa Sasaki ◽  
Hisashi Tanii
Keyword(s):  
Anxiety Disorders ◽  
Psychiatric Disorders ◽  
Genetic Variants ◽  
Large Scale ◽  
Genetic Correlations ◽  
Well Being ◽  
Subjective Well Being ◽  
Intermediate Phenotypes ◽  
Genome Wide ◽  
Common Genetic Variants

AbstractBackgroundPsychiatric disorders and related intermediate phenotypes are highly heritable and have a complex, overlapping polygenic architecture. A large-scale genome-wide association study (GWAS) of anxiety disorders identified genetic variants that are significant on a genome-wide. The current study investigated the genetic etiological overlaps between anxiety disorders and frequently cooccurring psychiatric disorders and intermediate phenotypes.MethodsUsing case–control and factor score models, we investigated the genetic correlations of anxiety disorders with eight psychiatric disorders and intermediate phenotypes [the volumes of seven subcortical brain regions, childhood cognition, general cognitive ability and personality traits (subjective well-being, loneliness, neuroticism and extraversion)] from large-scale GWASs (n= 7556–298 420) by linkage disequilibrium score regression.ResultsAmong psychiatric disorders, the risk of anxiety disorders was positively genetically correlated with the risks of major depressive disorder (MDD) (rg± standard error = 0.83 ± 0.16,p= 1.97 × 10−7), schizophrenia (SCZ) (0.28 ± 0.09,p= 1.10 × 10−3) and attention-deficit/hyperactivity disorder (ADHD) (0.34 ± 0.13,p= 8.40 × 10−3). Among intermediate phenotypes, significant genetic correlations existed between the risk of anxiety disorders and neuroticism (0.81 ± 0.17,p= 1.30 × 10−6), subjective well-being (−0.73 ± 0.18,p= 4.89 × 10−5), general cognitive ability (−0.23 ± 0.08,p= 4.70 × 10−3) and putamen volume (−0.50 ± 0.18,p= 5.00 × 10−3). No other significant genetic correlations between anxiety disorders and psychiatric or intermediate phenotypes were observed (p> 0.05). The case–control model yielded stronger genetic effect sizes than the factor score model.ConclusionsOur findings suggest that common genetic variants underlying the risk of anxiety disorders contribute to elevated risks of MDD, SCZ, ADHD and neuroticism and reduced quality of life, putamen volume and cognitive performance. We suggest that the comorbidity of anxiety disorders is partly explained by common genetic variants.

scholarly journals circVAR Database: Genome-Wide Archive of Genetic Variants for Human Circular RNAs

2020 ◽  
Author(s):  
Min Zhao ◽  
Hong Qu
Keyword(s):  
Genetic Variants ◽  
Complex Traits ◽  
Large Scale ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Association Studies ◽  
Chromosome 17 ◽  
Circular Rnas ◽  
Genome Wide Association Studies ◽  
Genome Wide

Abstract Background: Circular RNAs (circRNAs) play important roles in regulating gene expression through binding miRNAs and RNA binding proteins. Genetic variation of circRNAs may affect complex traits/diseases by changing their binding efficiency to target miRNAs and proteins. There is a growing demand for investigations of the functions of genetic changes using large-scale experimental evidence. However, there is no online genetic resource for circRNA genes. Results: We performed extensive genetic annotation of 295,526 circRNAs integrated from circBase, circNet and circRNAdb. All pre-computed genetic variants were presented at our online resource, circVAR, with data browsing and search functionality. We explored the chromosome-based distribution of circRNAs and their associated variants. We found that, based on mapping to the 1000 Genomes and ClinVAR databases, chromosome 17 has a relatively large number of circRNAs and associated common and health-related genetic variants. Following the annotation of genome wide association studies (GWAS)-based circRNA variants, we found many non-coding variants within circRNAs, suggesting novel mechanisms for common diseases reported from GWAS studies. For cancer-based somatic variants, we found that chromosome 7 has many highly complex mutations that have been overlooked in previous research.Conclusion: We used the circVAR database to collect SNPs and small insertions and deletions (INDELs) in putative circRNA regions and to identify their potential phenotypic information. To provide a reusable resource for the circRNA research community, we have published all the pre-computed genetic data concerning circRNAs and associated genes together with data query and browsing functions at http://soft.bioinfo-minzhao.org/circvar.

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