scholarly journals Genome-wide association meta-analysis of childhood and adolescent internalising symptoms

2020 ◽  
Author(s):  
Eshim S Jami ◽  
Anke R Hammerschlag ◽  
Hill F Ip ◽  
Andrea G Allegrini ◽  
Beben Benyamin ◽  
...  
Keyword(s):  
Confidence Intervals ◽  
Meta Analysis ◽  
Genetic Correlations ◽  
Genetic Effects ◽  
Childhood Aggression ◽  
Genome Wide Association ◽  
Childhood And Adolescence ◽  
Obsessive Compulsive ◽  
Compulsive Disorder ◽  
Genome Wide

Internalising symptoms in childhood and adolescence are as heritable as adult depression and anxiety, yet little is known of their molecular basis. This genome-wide association meta-analysis of internalising symptoms included repeated observations from 64,641 individuals, aged between 3 and 18. The N-weighted meta-analysis of overall internalising symptoms (INToverall) detected no genome-wide significant hits and showed low SNP heritability (1.66%, 95% confidence intervals 0.84-2.48%, Neffective=132,260). Stratified analyses showed rater-based heterogeneity in genetic effects, with self-reported internalising symptoms showing the highest heritability (5.63%, 95% confidence intervals 3.08-8.18%). Additive genetic effects on internalising symptoms appeared stable over age, with overlapping estimates of SNP heritability from early-childhood to adolescence. Gene-based analyses showed significant associations with three genes: WNT3 (p=1.13×10-06), CCL26 (p=1.88×10-06), and CENPO (p=2.54×10-06). Of these, WNT3 was previously associated with neuroticism, with which INToverall also shared a strong genetic correlation (rg=0.76). Genetic correlations were also observed with adult anxiety, depression, and the wellbeing spectrum (|rg|> 0.70), as well as with insomnia, loneliness, attention-deficit hyperactivity disorder, autism, and childhood aggression (range |rg|=0.42-0.60), whereas there were no robust associations with schizophrenia, bipolar disorder, obsessive-compulsive disorder, or anorexia nervosa. Overall, childhood and adolescent internalising symptoms share substantial genetic vulnerabilities with adult internalising disorders and other childhood psychiatric traits, which could explain both the persistence of internalising symptoms over time, and the high comorbidity amongst childhood psychiatric traits. Reducing phenotypic heterogeneity in childhood samples will be key in paving the way to future GWAS success.

scholarly journals Genetic Association Study of Childhood Aggression across raters, instruments and age

2019 ◽  
Author(s):  
Hill F. Ip ◽  
Camiel M. van der Laan ◽  
Eva M. L. Krapohl ◽  
Isabell Brikell ◽  
Cristina Sánchez-Mora ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Genetic Correlations ◽  
Childhood Aggression ◽  
Age At First Birth ◽  
Nucleotide Polymorphisms ◽  
Obsessive Compulsive ◽  
Psychological Traits ◽  
Compulsive Disorder ◽  
Genome Wide ◽  
Polygenic Scores

AbstractBackgroundHuman aggressive behavior (AGG) has a substantial genetic component. Here we present a large genome-wide association meta-analysis (GWAMA) of childhood AGG.MethodsWe analyzed assessments of AGG for a total of 328,935 observations from 87,485 children (aged 1.5 – 18 years), from multiple assessors, instruments, and ages, while accounting for sample overlap. We performed an overall analysis and meta-analyzed subsets of the data within rater, instrument, and age.ResultsHeritability based on the overall meta-analysis (AGGall) that could be attributed to Single Nucleotide Polymorphisms (SNPs) was 3.31% (SE=0.0038). No single SNP reached genome-wide significance, but gene-based analysis returned three significant genes: ST3GAL3 (P=1.6E-06), PCDH7 (P=2.0E-06) and IPO13 (P=2.5E-06). All three genes have previously been associated with educational traits. Polygenic scores based on our GWAMA significantly predicted aggression in a holdout sample of children and in retrospectively assessed childhood aggression. We obtained moderate-to-strong genetic correlations (rg‘s) with selected phenotypes from multiple domains, but hardly with any of the classical biomarkers thought to be associated with AGG. Significant genetic correlations were observed with most psychiatric and psychological traits (range |rg|: 0.19 –.1.00), except for obsessive-compulsive disorder. Aggression had a negative genetic correlation (rg =∼-0.5) with cognitive traits and age at first birth. Aggression was strongly genetically correlated with smoking phenotypes (range |rg|: 0.46 – 0.60). Genetic correlations between AGG and psychiatric disorders were strongest for mother- and self-reported AGG.ConclusionsThe current GWAMA of childhood aggression provides a powerful tool to interrogate the genetic etiology of AGG by creating individual polygenic scores and genetic correlations with psychiatric traits.

scholarly journals Genetic association study of childhood aggression across raters, instruments, and age

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hill F. Ip ◽  
Camiel M. van der Laan ◽  
Eva M. L. Krapohl ◽  
Isabell Brikell ◽  
Cristina Sánchez-Mora ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Genetic Correlations ◽  
Teacher Assessment ◽  
Childhood Aggression ◽  
Age At First Birth ◽  
Psychological Traits ◽  
Compulsive Disorder ◽  
Genome Wide ◽  
A Genome ◽  
Variance Explained

AbstractChildhood aggressive behavior (AGG) has a substantial heritability of around 50%. Here we present a genome-wide association meta-analysis (GWAMA) of childhood AGG, in which all phenotype measures across childhood ages from multiple assessors were included. We analyzed phenotype assessments for a total of 328 935 observations from 87 485 children aged between 1.5 and 18 years, while accounting for sample overlap. We also meta-analyzed within subsets of the data, i.e., within rater, instrument and age. SNP-heritability for the overall meta-analysis (AGGoverall) was 3.31% (SE = 0.0038). We found no genome-wide significant SNPs for AGGoverall. The gene-based analysis returned three significant genes: ST3GAL3 (P = 1.6E–06), PCDH7 (P = 2.0E–06), and IPO13 (P = 2.5E–06). All three genes have previously been associated with educational traits. Polygenic scores based on our GWAMA significantly predicted aggression in a holdout sample of children (variance explained = 0.44%) and in retrospectively assessed childhood aggression (variance explained = 0.20%). Genetic correlations (rg) among rater-specific assessment of AGG ranged from rg = 0.46 between self- and teacher-assessment to rg = 0.81 between mother- and teacher-assessment. We obtained moderate-to-strong rgs with selected phenotypes from multiple domains, but hardly with any of the classical biomarkers thought to be associated with AGG. Significant genetic correlations were observed with most psychiatric and psychological traits (range $$\left| {r_g} \right|$$ r g : 0.19–1.00), except for obsessive-compulsive disorder. Aggression had a negative genetic correlation (rg = ~−0.5) with cognitive traits and age at first birth. Aggression was strongly genetically correlated with smoking phenotypes (range $$\left| {r_g} \right|$$ r g : 0.46–0.60). The genetic correlations between aggression and psychiatric disorders were weaker for teacher-reported AGG than for mother- and self-reported AGG. The current GWAMA of childhood aggression provides a powerful tool to interrogate the rater-specific genetic etiology of AGG.

scholarly journals Genetic meta-analysis of obsessive-compulsive disorder and self-report compulsive symptoms

2019 ◽  
Author(s):  
Dirk J.A. Smit ◽  
Danielle Cath ◽  
Nuno R. Zilhão ◽  
Hill F. Ip ◽  
Damiaan Denys ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Genetic Correlations ◽  
Significant Snps ◽  
Population Based ◽  
Case Control ◽  
Reward Processing ◽  
Self Report ◽  
Obsessive Compulsive ◽  
Compulsive Disorder ◽  
Genome Wide

AbstractWe investigated whether obsessive compulsive (OC) symptoms from a population-based sample could be analyzed to detect genetic variants influencing OCD. We performed a GWAS on the obsession (rumination and impulsions) and compulsion (checking, washing, and ordering/precision) subscales of an abbreviated version of the Padua Inventory (N=8267 with genome-wide genotyping and phenotyping). The compulsion subscale showed a substantial and significant positive genetic correlation with an OCD case-control GWAS (rG=0.61, p=0.017) previously published by the Psychiatric Genomics Consortium (PGC-OCD). The obsession subscale and the total Padua score showed no significant genetic correlations (rG=–0.02 and rG=0.42, respectively). A meta-analysis of the compulsive symptoms GWAS with the PGC-OCD revealed no genome-wide significant SNPs (combined N=17992, indicating that the power is still low for individual SNP effects). A gene-based association analysis, however, yielded two novel genes (WDR7 and ADCK1). The top 250 genes in the gene-based test also showed significant increase in enrichment for psychiatric and brain-expressed genes. S-Predixcan testing showed that for genes expressed in hippocampus, amygdala, and caudate nucleus significance increased in the meta-analysis with compulsive symptoms compared to the original PGC-OCD GWAS. Thus, inclusion of dimensional symptom data in genome-wide association on clinical case-control GWAS of OCD may be useful to find genes for OCD if the data are based on quantitative indices of compulsive behavior. SNP-level power increases were limited, but aggregate, gene-level analyses showed increased enrichment for brain-expressed genes related to psychiatric disorders, and increased association with gene-expression in brain tissues with known emotional, reward processing, memory, and fear-formation functions.

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.

scholarly journals Common Genetic Variation Indicates Separate Causes for Periventricular and Deep White Matter Hyperintensities

Stroke ◽  
2020 ◽  
Vol 51 (7) ◽  
pp. 2111-2121 ◽  
Author(s):  
Nicola J. Armstrong ◽  
Karen A. Mather ◽  
Muralidharan Sargurupremraj ◽  
Maria J. Knol ◽  
Rainer Malik ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
White Matter ◽  
White Matter Hyperintensities ◽  
Meta Analysis ◽  
Genetic Correlations ◽  
Imaging Genetics ◽  
Genome Wide Association ◽  
Association Analyses ◽  
New Genes ◽  
Genome Wide

Background and Purpose: Periventricular white matter hyperintensities (WMH; PVWMH) and deep WMH (DWMH) are regional classifications of WMH and reflect proposed differences in cause. In the first study, to date, we undertook genome-wide association analyses of DWMH and PVWMH to show that these phenotypes have different genetic underpinnings. Methods: Participants were aged 45 years and older, free of stroke and dementia. We conducted genome-wide association analyses of PVWMH and DWMH in 26,654 participants from CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology), ENIGMA (Enhancing Neuro-Imaging Genetics Through Meta-Analysis), and the UKB (UK Biobank). Regional correlations were investigated using the genome-wide association analyses -pairwise method. Cross-trait genetic correlations between PVWMH, DWMH, stroke, and dementia were estimated using LDSC. Results: In the discovery and replication analysis, for PVWMH only, we found associations on chromosomes 2 ( NBEAL ), 10q23.1 ( TSPAN14/FAM231A ), and 10q24.33 ( SH3PXD2A). In the much larger combined meta-analysis of all cohorts, we identified ten significant regions for PVWMH: chromosomes 2 (3 regions), 6, 7, 10 (2 regions), 13, 16, and 17q23.1. New loci of interest include 7q36.1 ( NOS3 ) and 16q24.2. In both the discovery/replication and combined analysis, we found genome-wide significant associations for the 17q25.1 locus for both DWMH and PVWMH. Using gene-based association analysis, 19 genes across all regions were identified for PVWMH only, including the new genes: CALCRL (2q32.1), KLHL24 (3q27.1), VCAN (5q27.1), and POLR2F (22q13.1). Thirteen genes in the 17q25.1 locus were significant for both phenotypes. More extensive genetic correlations were observed for PVWMH with small vessel ischemic stroke. There were no associations with dementia for either phenotype. Conclusions: Our study confirms these phenotypes have distinct and also shared genetic architectures. Genetic analyses indicated PVWMH was more associated with ischemic stroke whilst DWMH loci were implicated in vascular, astrocyte, and neuronal function. Our study confirms these phenotypes are distinct neuroimaging classifications and identifies new candidate genes associated with PVWMH only.

scholarly journals Genome-wide association study in obsessive-compulsive disorder: results from the OCGAS

2014 ◽  
Vol 20 (3) ◽  
pp. 337-344 ◽  
Author(s):  
M Mattheisen ◽  
J F Samuels ◽  
Y Wang ◽  
B D Greenberg ◽  
A J Fyer ◽  
...  
Keyword(s):  
Association Study ◽  
Obsessive Compulsive Disorder ◽  
Genome Wide Association Study ◽  
Genome Wide Association ◽  
Obsessive Compulsive ◽  
Compulsive Disorder ◽  
Genome Wide

scholarly journals Genome-wide association study of pediatric obsessive-compulsive traits: shared genetic risk between traits and disorder

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Christie L. Burton ◽  
◽  
Mathieu Lemire ◽  
Bowei Xiao ◽  
Elizabeth C. Corfield ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Genetic Risk ◽  
Meta Analysis ◽  
Community Sample ◽  
Case Control ◽  
Genome Wide Association ◽  
Obsessive Compulsive ◽  
Polygenic Risk ◽  
Genome Wide ◽  
Shared Genetic

Abstract Using a novel trait-based measure, we examined genetic variants associated with obsessive-compulsive (OC) traits and tested whether OC traits and obsessive-compulsive disorder (OCD) shared genetic risk. We conducted a genome-wide association analysis (GWAS) of OC traits using the Toronto Obsessive-Compulsive Scale (TOCS) in 5018 unrelated Caucasian children and adolescents from the community (Spit for Science sample). We tested the hypothesis that genetic variants associated with OC traits from the community would be associated with clinical OCD using a meta-analysis of all currently available OCD cases. Shared genetic risk was examined between OC traits and OCD in the respective samples using polygenic risk score and genetic correlation analyses. A locus tagged by rs7856850 in an intron of PTPRD (protein tyrosine phosphatase δ) was significantly associated with OC traits at the genome-wide significance level (p = 2.48 × 10−8). rs7856850 was also associated with OCD in a meta-analysis of OCD case/control genome-wide datasets (p = 0.0069). The direction of effect was the same as in the community sample. Polygenic risk scores from OC traits were significantly associated with OCD in case/control datasets and vice versa (p’s < 0.01). OC traits were highly, but not significantly, genetically correlated with OCD (rg = 0.71, p = 0.062). We report the first validated genome-wide significant variant for OC traits in PTPRD, downstream of the most significant locus in a previous OCD GWAS. OC traits measured in the community sample shared genetic risk with OCD case/control status. Our results demonstrate the feasibility and power of using trait-based approaches in community samples for genetic discovery.

scholarly journals Novel genome-wide associations for anhedonia, genetic correlation with psychiatric disorders, and polygenic association with brain structure

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Joey Ward ◽  
Laura M. Lyall ◽  
Richard A. I. Bethlehem ◽  
Amy Ferguson ◽  
Rona J. Strawbridge ◽  
...  
Keyword(s):  
White Matter ◽  
Psychiatric Disorders ◽  
Brain Structure ◽  
Genetic Correlations ◽  
Grey Matter Volume ◽  
Obsessive Compulsive ◽  
Major Depressive ◽  
Compulsive Disorder ◽  
Matter Volume ◽  
Genome Wide

AbstractAnhedonia is a core symptom of several psychiatric disorders but its biological underpinnings are poorly understood. We performed a genome-wide association study of state anhedonia in 375,275 UK Biobank participants and assessed for genetic correlation between anhedonia and neuropsychiatric conditions (major depressive disorder, schizophrenia, bipolar disorder, obsessive compulsive disorder and Parkinson’s Disease). We then used a polygenic risk score approach to test for association between genetic loading for anhedonia and both brain structure and brain function. This included: magnetic resonance imaging (MRI) assessments of total grey matter volume, white matter volume, cerebrospinal fluid volume, and 15 cortical/subcortical regions of interest; diffusion tensor imaging (DTI) measures of white matter tract integrity; and functional MRI activity during an emotion processing task. We identified 11 novel loci associated at genome-wide significance with anhedonia, with a SNP heritability estimate (h2SNP) of 5.6%. Strong positive genetic correlations were found between anhedonia and major depressive disorder, schizophrenia and bipolar disorder; but not with obsessive compulsive disorder or Parkinson’s Disease. Polygenic risk for anhedonia was associated with poorer brain white matter integrity, smaller total grey matter volume, and smaller volumes of brain regions linked to reward and pleasure processing, including orbito-frontal cortex. In summary, the identification of novel anhedonia-associated loci substantially expands our current understanding of the biological basis of state anhedonia and genetic correlations with several psychiatric disorders confirm the utility of this phenotype as a transdiagnostic marker of vulnerability to mental illness. We also provide the first evidence that genetic risk for state anhedonia influences brain structure, including in regions associated with reward and pleasure processing.

scholarly journals Exon-focused genome-wide association study of obsessive-compulsive disorder and shared polygenic risk with schizophrenia

2016 ◽  
Vol 6 (3) ◽  
pp. e768-e768 ◽  
Author(s):  
J Costas ◽  
N Carrera ◽  
P Alonso ◽  
X Gurriarán ◽  
C Segalàs ◽  
...  
Keyword(s):  
Association Study ◽  
Obsessive Compulsive Disorder ◽  
Genome Wide Association Study ◽  
Genome Wide Association ◽  
Obsessive Compulsive ◽  
Polygenic Risk ◽  
Compulsive Disorder ◽  
Genome Wide
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