scholarly journals Analysis of Shared Heritability in Common Disorders of the Brain

2016 ◽  
Author(s):  
V Anttila ◽  
B Bulik-Sullivan ◽  
H Finucane ◽  
R Walters ◽  
J Bras ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Neurological Disorders ◽  
Association Studies ◽  
Genetic Correlations ◽  
Personality Types ◽  
Genome Wide Association Studies ◽  
Brain Disorders ◽  
Cognitive Measures ◽  
Genome Wide ◽  
The Brain

AbstractDisorders of the brain exhibit considerable epidemiological comorbidity and frequently share symptoms, provoking debate about the extent of their etiologic overlap. We quantified the genetic sharing of 25 brain disorders based on summary statistics from genome-wide association studies of 215,683 patients and 657,164 controls, and their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders show substantial sharing of common variant risk, while neurological disorders appear more distinct from one another. We observe limited evidence of sharing between neurological and psychiatric disorders, but do identify robust sharing between disorders and several cognitive measures, as well as disorders and personality types. We also performed extensive simulations to explore how power, diagnostic misclassification and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a source of risk for brain disorders and the value of heritability-based methods in understanding their etiology.

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.

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 Genetic correlations among psychiatric and immune-related phenotypes based on genome-wide association data

2016 ◽  
Author(s):  
Daniel S. Tylee ◽  
Jiayin Sun ◽  
Jonathan L. Hess ◽  
Muhammad A. Tahir ◽  
Esha Sharma ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Lupus Erythematosus ◽  
Association Studies ◽  
Genetic Correlations ◽  
Genome Wide Association ◽  
Biliary Cirrhosis ◽  
Genome Wide Association Studies ◽  
Compulsive Disorder ◽  
Genome Wide ◽  
Heritability Estimation

AbstractIndividuals with psychiatric disorders have elevated rates of autoimmune comorbidity and altered immune signaling. It is unclear whether these altered immunological states have a shared genetic basis with those psychiatric disorders. The present study sought to use existing summary-level data from previous genome-wide association studies (GWASs) to determine if commonly varying single nucleotide polymorphisms (SNPs) are shared between psychiatric and immune-related phenotypes. We estimated heritability and examined pair-wise genetic correlations using the linkage disequilibrium score regression (LDSC) and heritability estimation from summary statistics (HESS) methods. Using LDSC, we observed significant genetic correlations between immune-related disorders and several psychiatric disorders, including anorexia nervosa, attention deficit-hyperactivity disorder, bipolar disorder, major depression, obsessive compulsive disorder, schizophrenia, smoking behavior, and Tourette syndrome. Loci significantly mediating genetic correlations were identified for schizophrenia when analytically paired with Crohn’s disease, primary biliary cirrhosis, systemic lupus erythematosus, and ulcerative colitis. We report significantly correlated loci and highlight those containing genome-wide associations and candidate genes for respective disorders. We also used the LDSC method to characterize genetic correlations amongst the immune-related phenotypes. We discuss our findings in the context of relevant genetic and epidemiological literature, as well as the limitations and caveats of the study.

A systematic analysis of genetically regulated differences in gene expression and the role of co-expression networks across 16 psychiatric disorders and substance use phenotypes

2021 ◽  
Author(s):  
Zachary F Gerring ◽  
Jackson G Thorp ◽  
Eric R Gamazon ◽  
Eske M Derks
Keyword(s):  
Gene Expression ◽  
Mental Health ◽  
Substance Use ◽  
Prefrontal Cortex ◽  
Psychiatric Disorders ◽  
Developmental Disorders ◽  
Association Studies ◽  
Genetic Correlations ◽  
Autism Spectrum ◽  
Genome Wide Association Studies

ABSTRACTGenome-wide association studies (GWASs) have identified thousands of risk loci for many psychiatric and substance use phenotypes, however the biological consequences of these loci remain largely unknown. We performed a transcriptome-wide association study of 10 psychiatric disorders and 6 substance use phenotypes (collectively termed “mental health phenotypes”) using expression quantitative trait loci data from 532 prefrontal cortex samples. We estimated the correlation due to predicted genetically regulated expression between pairs of mental health phenotypes, and compared the results with the genetic correlations. We identified 1,645 genes with at least one significant trait association, comprising 2,176 significant associations across the 16 mental health phenotypes of which 572 (26%) are novel. Overall, the transcriptomic correlations for phenotype pairs were significantly higher than the respective genetic correlations. For example, attention deficit hyperactivity disorder and autism spectrum disorder, both childhood developmental disorders, showed a much higher transcriptomic correlation (r=0.84) than genetic correlation (r=0.35). Finally, we tested the enrichment of phenotype-associated genes in gene co-expression networks built from prefrontal cortex. Phenotype-associated genes were enriched in multiple gene co-expression modules and the implicated modules contained genes involved in mRNA splicing and glutamatergic receptors, among others. Together, our results highlight the utility of gene expression data in the understanding of functional gene mechanisms underlying psychiatric disorders and substance use phenotypes.

scholarly journals Insights into the genetic basis of retinal detachment

2019 ◽  
Vol 29 (4) ◽  
pp. 689-702 ◽  
Author(s):  
Thibaud S Boutin ◽  
David G Charteris ◽  
Aman Chandra ◽  
Susan Campbell ◽  
Caroline Hayward ◽  
...  
Keyword(s):  
Retinal Detachment ◽  
Association Studies ◽  
Genetic Correlations ◽  
Self Report ◽  
Cataract Operation ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Genetic Associations ◽  
Data Set ◽  
Genome Wide

Abstract Retinal detachment (RD) is a serious and common condition, but genetic studies to date have been hampered by the small size of the assembled cohorts. In the UK Biobank data set, where RD was ascertained by self-report or hospital records, genetic correlations between RD and high myopia or cataract operation were, respectively, 0.46 (SE = 0.08) and 0.44 (SE = 0.07). These correlations are consistent with known epidemiological associations. Through meta-analysis of genome-wide association studies using UK Biobank RD cases (N = 3 977) and two cohorts, each comprising ~1 000 clinically ascertained rhegmatogenous RD patients, we uncovered 11 genome-wide significant association signals. These are near or within ZC3H11B, BMP3, COL22A1, DLG5, PLCE1, EFEMP2, TYR, FAT3, TRIM29, COL2A1 and LOXL1. Replication in the 23andMe data set, where RD is self-reported by participants, firmly establishes six RD risk loci: FAT3, COL22A1, TYR, BMP3, ZC3H11B and PLCE1. Based on the genetic associations with eye traits described to date, the first two specifically impact risk of a RD, whereas the last four point to shared aetiologies with macular condition, myopia and glaucoma. Fine-mapping prioritized the lead common missense variant (TYR S192Y) as causal variant at the TYR locus and a small set of credible causal variants at the FAT3 locus. The larger study size presented here, enabled by resources linked to health records or self-report, provides novel insights into RD aetiology and underlying pathological pathways.

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 TWAS pathway method greatly enhances the number of leads for uncovering the molecular underpinnings of psychiatric disorders

2018 ◽  
Author(s):  
Chris Chatzinakos ◽  
Donghyung Lee ◽  
Na Cai ◽  
Vladimir I. Vladimirov ◽  
Anna Docherty ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Computational Burden ◽  
Gene Sets ◽  
Genome Wide ◽  
Genetic Signal ◽  
Meta Analyses ◽  
Combine Information ◽  
Or Genes

ABSTRACTGenetic signal detection in genome-wide association studies (GWAS) is enhanced by pooling small signals from multiple Single Nucleotide Polymorphism (SNP), e.g. across genes and pathways. Because genes are believed to influence traits via gene expression, it is of interest to combine information from expression Quantitative Trait Loci (eQTLs) in a gene or genes in the same pathway. Such methods, widely referred as transcriptomic wide association analysis (TWAS), already exist for gene analysis. Due to the possibility of eliminating most of the confounding effect of linkage disequilibrium (LD) from TWAS gene statistics, pathway TWAS methods would be very useful in uncovering the true molecular bases of psychiatric disorders. However, such methods are not yet available for arbitrarily large pathways/gene sets. This is possibly due to it quadratic (in the number of SNPs) computational burden for computing LD across large regions. To overcome this obstacle, we propose JEPEGMIX2-P, a novel TWAS pathway method that i) has a linear computational burden, ii) uses a large and diverse reference panel (33K subjects), iii) is competitive (adjusts for background enrichment in gene TWAS statistics) and iv) is applicable as-is to ethnically mixed cohorts. To underline its potential for increasing the power to uncover genetic signals over the state-of-the-art and commonly used non-transcriptomics methods, e.g. MAGMA, we applied JEPEGMIX2-P to summary statistics of most large meta-analyses from Psychiatric Genetics Consortium (PGC). While our work is just the very first step toward clinical translation of psychiatric disorders, PGC anorexia results suggest a possible avenue for treatment.

scholarly journals Functional classes of SNPs related to psychiatric disorders and behavioral traits contrast with those related to neurological disorders

2021 ◽  
Author(s):  
Mark A. Reimers ◽  
Kenneth S. Kendler
Keyword(s):  
Psychiatric Disorders ◽  
Neurological Disorders ◽  
Association Studies ◽  
Dynamic Responses ◽  
Ctcf Binding ◽  
Genome Wide Association Studies ◽  
Behavioral Traits ◽  
Functional Classes ◽  
Genomic Regions ◽  
Regulatory Sites

AbstractWe investigated the functional classes of genomic regions containing SNPS contributing most to the SNP-heritability of important psychiatric and neurological disorders and behavioral traits, as determined from recent genome-wide association studies. We employed linkage-disequilibrium score regression with several brain-specific genomic annotations not previously used. The classes of genomic annotations conferring substantial SNP-heritability for the psychiatric disorders and behavioral traits differed systematically from the classes associated with neurological disorders, and both differed from the classes enriched for height, a biometric trait used here as a control outgroup. The SNPs implicated in these psychiatric disorders and behavioral traits were highly enriched in CTCF binding sites, in conserved regions likely to be enhancers, and in brain-specific promoters, regulatory sites likely to affect dynamic responses. The SNPs relevant for neurological disorders were highly enriched in constitutive coding regions and splice regulatory sites. We suggest that our results provide a bridge between genetics and the well-known effects of life history and recent stressful experiences on risk of psychiatric illness.

scholarly journals A combined analysis of genetically correlated traits identifies 107 loci associated with intelligence

2017 ◽  
Author(s):  
W. D. Hill ◽  
G. Davies ◽  
A. M. McIntosh ◽  
C. R. Gale ◽  
I. J. Deary
Keyword(s):  
Household Income ◽  
Association Studies ◽  
Meta Analysis ◽  
Genetic Correlations ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Correlated Traits ◽  
Single Measure ◽  
Combined Analysis ◽  
Genome Wide

AbstractIntelligence, or general cognitive function, is phenotypically and genetically correlated with many traits, including many physical and mental health variables. Both education and household income are strongly genetically correlated with intelligence, at rg =0.73 and rg =0.70 respectively. This allowed us to utilize a novel approach, Multi-Trait Analysis of Genome-wide association studies (MTAG; Turley et al. 2017), to combine two large genome-wide association studies (GWASs) of education and household income to increase power in the largest GWAS on intelligence so far (Sniekers et al. 2017). This study had four goals: firstly, to facilitate the discovery of new genetic loci associated with intelligence; secondly, to add to our understanding of the biology of intelligence differences; thirdly, to examine whether combining genetically correlated traits in this way produces results consistent with the primary phenotype of intelligence; and, finally, to test how well this new meta-analytic data sample on intelligence predict phenotypic intelligence variance in an independent sample. We apply MTAG to three large GWAS: Sniekers et al (2017) on intelligence, Okbay et al. (2016) on Educational attainment, and Hill et al. (2016) on household income. By combining these three samples our functional sample size increased from 78 308 participants to 147 194. We found 107 independent loci associated with intelligence, implicating 233 genes, using both SNP-based and gene-based GWAS. We find evidence that neurogenesis may explain some of the biological differences in intelligence as well as genes expressed in the synapse and those involved in the regulation of the nervous system. We show that the results of our combined analysis demonstrate the same pattern of genetic correlations as a single measure/the simple measure of intelligence, providing support for the meta-analysis of these genetically-related phenotypes. We find that our MTAG meta-analysis of intelligence shows similar genetic correlations to 26 other phenotypes when compared with a GWAS consisting solely of cognitive tests. Finally, using an independent sample of 6 844 individuals we were able to predict 7% of intelligence using SNP data alone.

Sign in / Sign up

Export Citation Format

Share Document