Publications by authors named "Hill F Ip"

12 Publications

  • Page 1 of 1

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

Transl Psychiatry 2021 07 30;11(1):413. Epub 2021 Jul 30.

Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.

Childhood 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 (AGG) was 3.31% (SE = 0.0038). We found no genome-wide significant SNPs for AGG. 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 (r) among rater-specific assessment of AGG ranged from r = 0.46 between self- and teacher-assessment to r = 0.81 between mother- and teacher-assessment. We obtained moderate-to-strong rs 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 [Formula: see text]: 0.19-1.00), except for obsessive-compulsive disorder. Aggression had a negative genetic correlation (r = ~-0.5) with cognitive traits and age at first birth. Aggression was strongly genetically correlated with smoking phenotypes (range [Formula: see text]: 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.
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http://dx.doi.org/10.1038/s41398-021-01480-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8324785PMC
July 2021

DNA methylation signatures of aggression and closely related constructs: A meta-analysis of epigenome-wide studies across the lifespan.

Mol Psychiatry 2021 Jun 8;26(6):2148-2162. Epub 2021 Jan 8.

Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, 33520, Finland.

DNA methylation profiles of aggressive behavior may capture lifetime cumulative effects of genetic, stochastic, and environmental influences associated with aggression. Here, we report the first large meta-analysis of epigenome-wide association studies (EWAS) of aggressive behavior (N = 15,324 participants). In peripheral blood samples of 14,434 participants from 18 cohorts with mean ages ranging from 7 to 68 years, 13 methylation sites were significantly associated with aggression (alpha = 1.2 × 10; Bonferroni correction). In cord blood samples of 2425 children from five cohorts with aggression assessed at mean ages ranging from 4 to 7 years, 83% of these sites showed the same direction of association with childhood aggression (r = 0.74, p = 0.006) but no epigenome-wide significant sites were found. Top-sites (48 at a false discovery rate of 5% in the peripheral blood meta-analysis or in a combined meta-analysis of peripheral blood and cord blood) have been associated with chemical exposures, smoking, cognition, metabolic traits, and genetic variation (mQTLs). Three genes whose expression levels were associated with top-sites were previously linked to schizophrenia and general risk tolerance. At six CpGs, DNA methylation variation in blood mirrors variation in the brain. On average 44% (range = 3-82%) of the aggression-methylation association was explained by current and former smoking and BMI. These findings point at loci that are sensitive to chemical exposures with potential implications for neuronal functions. We hope these results to be a starting point for studies leading to applications as peripheral biomarkers and to reveal causal relationships with aggression and related traits.
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http://dx.doi.org/10.1038/s41380-020-00987-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8263810PMC
June 2021

Investigating the genetic architecture of noncognitive skills using GWAS-by-subtraction.

Nat Genet 2021 01 7;53(1):35-44. Epub 2021 Jan 7.

Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.

Little is known about the genetic architecture of traits affecting educational attainment other than cognitive ability. We used genomic structural equation modeling and prior genome-wide association studies (GWASs) of educational attainment (n = 1,131,881) and cognitive test performance (n = 257,841) to estimate SNP associations with educational attainment variation that is independent of cognitive ability. We identified 157 genome-wide-significant loci and a polygenic architecture accounting for 57% of genetic variance in educational attainment. Noncognitive genetics were enriched in the same brain tissues and cell types as cognitive performance, but showed different associations with gray-matter brain volumes. Noncognitive genetics were further distinguished by associations with personality traits, less risky behavior and increased risk for certain psychiatric disorders. For socioeconomic success and longevity, noncognitive and cognitive-performance genetics demonstrated associations of similar magnitude. By conducting a GWAS of a phenotype that was not directly measured, we offer a view of genetic architecture of noncognitive skills influencing educational success.
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http://dx.doi.org/10.1038/s41588-020-00754-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7116735PMC
January 2021

Content, diagnostic, correlational, and genetic similarities between common measures of childhood aggressive behaviors and related psychiatric traits.

J Child Psychol Psychiatry 2020 12 20;61(12):1328-1338. Epub 2020 Feb 20.

Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

Background: Given the role of childhood aggressive behavior (AGG) in everyday child development, precise and accurate measurement is critical in clinical practice and research. This study aims to quantify agreement among widely used measures of childhood AGG regarding item content, clinical concordance, correlation, and underlying genetic construct.

Methods: We analyzed data from 1254 Dutch twin pairs (age 8-10 years, 51.1% boys) from a general population sample for whom both parents completed the A-TAC, CBCL, and SDQ at the same occasion.

Results: There was substantial variation in item content among AGG measures, ranging from .00 (i.e., mutually exclusive) to .50 (moderate agreement). Clinical concordance (i.e., do the same children score above a clinical threshold among AGG measures) was very weak to moderate with estimates ranging between .01 and .43 for mother-reports and between .12 and .42 for father-reports. Correlations among scales were weak to strong, ranging from .32 to .70 for mother-reports and from .32 to .64 for father-reports. We found weak to very strong genetic correlations among the measures, with estimates between .65 and .84 for mother-reports and between .30 and .87 for father-reports.

Conclusions: Our results demonstrated that degree of agreement between measures of AGG depends on the type (i.e., item content, clinical concordance, correlation, genetic correlation) of agreement considered. Because agreement was higher for correlations compared to clinical concordance (i.e., above or below a clinical cutoff), we propose the use of continuous scores to assess AGG, especially for combining data with different measures. Although item content can be different and agreement among observed measures may not be high, the genetic correlations indicate that the underlying genetic liability for childhood AGG is consistent across measures.
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http://dx.doi.org/10.1111/jcpp.13218DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7754303PMC
December 2020

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

Am J Med Genet B Neuropsychiatr Genet 2020 06 31;183(4):208-216. Epub 2019 Dec 31.

Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit, Amsterdam, The Netherlands.

We investigated whether obsessive-compulsive (OC) symptoms from a population-based sample could be analyzed to detect genetic variants influencing obsessive-compulsive disorder (OCD). We performed a genome-wide association studies (GWAS) on the obsession (rumination and impulsions) and compulsion (checking, washing, and ordering/precision) subscales of an abbreviated version of the Padua Inventory (N = 8,267 with genome-wide genotyping and phenotyping). The compulsion subscale showed a substantial and significant positive genetic correlation with an OCD case-control GWAS (r = 0.61, p = .017) previously published by the Psychiatric Genomics Consortium (PGC-OCD). The obsession subscale and the total Padua score showed no significant genetic correlations (r = -0.02 and r = 0.42, respectively). A meta-analysis of the compulsive symptoms GWAS with the PGC-OCD revealed no genome-wide significant Single-Nucleotide Polymorphisms (SNPs combined N = 17,992, 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 a 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, the 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.
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http://dx.doi.org/10.1002/ajmg.b.32777DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7317414PMC
June 2020

Genomics of human aggression: current state of genome-wide studies and an automated systematic review tool.

Psychiatr Genet 2019 10;29(5):170-190

Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands VI Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology Federal Research Institute for Health Organization and Informatics, Moscow, Russia Leiden Institute for Brain and Cognition, Leiden University Curium-LUMC, Department of Child and Adolescent Psychiatry, Leiden University Medical Center, Leiden Netherlands Institute for the Study of Crime and Law Enforcement, Amsterdam, The Netherlands.

There are substantial differences, or variation, between humans in aggression, with its molecular genetic basis mostly unknown. This review summarizes knowledge on the genetic contribution to variation in aggression with the following three foci: (1) a comprehensive overview of reviews on the genetics of human aggression, (2) a systematic review of genome-wide association studies (GWASs), and (3) an automated tool for the selection of literature based on supervised machine learning. The phenotype definition 'aggression' (or 'aggressive behaviour', or 'aggression-related traits') included anger, antisocial behaviour, conduct disorder, and oppositional defiant disorder. The literature search was performed in multiple databases, manually and using a novel automated selection tool, resulting in 18 reviews and 17 GWASs of aggression. Heritability estimates of aggression in children and adults are around 50%, with relatively small fluctuations around this estimate. In 17 GWASs, 817 variants were reported as suggestive (P ≤ 1.0E), including 10 significant associations (P ≤ 5.0E). Nominal associations (P ≤ 1E) were found in gene-based tests for genes involved in immune, endocrine, and nervous systems. Associations were not replicated across GWASs. A complete list of variants and their position in genes and chromosomes are available online. The automated literature search tool produced literature not found by regular search strategies. Aggression in humans is heritable, but its genetic basis remains to be uncovered. No sufficiently large GWASs have been carried out yet. With increases in sample size, we expect aggression to behave like other complex human traits for which GWAS has been successful.
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http://dx.doi.org/10.1097/YPG.0000000000000239DOI Listing
October 2019

Author Correction: GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal effect of schizophrenia liability.

Nat Neurosci 2019 Jul;22(7):1196

Department of Youth and Family, Utrecht University, Utrecht, the Netherlands.

Several occurrences of the word 'schizophrenia' have been re-worded as 'liability to schizophrenia' or 'schizophrenia risk', including in the title, which should have been "GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal effect of schizophrenia liability," as well as in Supplementary Figures 1-10 and Supplementary Tables 7-10, to more accurately reflect the findings of the work.
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http://dx.doi.org/10.1038/s41593-019-0402-7DOI Listing
July 2019

Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits.

Nat Hum Behav 2019 05 8;3(5):513-525. Epub 2019 Apr 8.

Department of Psychology, University of Texas at Austin, Austin, TX, USA.

Genetic correlations estimated from genome-wide association studies (GWASs) reveal pervasive pleiotropy across a wide variety of phenotypes. We introduce genomic structural equation modelling (genomic SEM): a multivariate method for analysing the joint genetic architecture of complex traits. Genomic SEM synthesizes genetic correlations and single-nucleotide polymorphism heritabilities inferred from GWAS summary statistics of individual traits from samples with varying and unknown degrees of overlap. Genomic SEM can be used to model multivariate genetic associations among phenotypes, identify variants with effects on general dimensions of cross-trait liability, calculate more predictive polygenic scores and identify loci that cause divergence between traits. We demonstrate several applications of genomic SEM, including a joint analysis of summary statistics from five psychiatric traits. We identify 27 independent single-nucleotide polymorphisms not previously identified in the contributing univariate GWASs. Polygenic scores from genomic SEM consistently outperform those from univariate GWASs. Genomic SEM is flexible and open ended, and allows for continuous innovation in multivariate genetic analysis.
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http://dx.doi.org/10.1038/s41562-019-0566-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6520146PMC
May 2019

Biological insights into multiple birth: genetic findings from UK Biobank.

Eur J Hum Genet 2019 06 13;27(6):970-979. Epub 2019 Feb 13.

Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit, Amsterdam, The Netherlands.

The tendency to conceive spontaneous dizygotic (DZ) twins is a complex trait with important contributions from both environmental factors and genetic disposition. In earlier work, we identified the first two genes as maternal susceptibility loci for DZ twinning. The aim of this study was to identify genetic variants influencing multiple births and to genetically correlate the findings across a broad range of traits. We performed a genome-wide association study (GWAS) in 8962 participants with Caucasian ancestry from UK Biobank who reported being part of a multiple birth, and 409,591 singleton controls. We replicated the association between FSHB, SMAD3 and twinning in the gene-based (but not SNP-based) test, which had been established in previous genome-wide association analyses in mothers with dizygotic twin offspring. Additionally, we report a novel genetic variant associated with multiple birth, rs428022 at 15q23 (p = 2.84 × 10) close to two genes: PIAS1 and SKOR1. Finally, we identified meaningful genetic correlations between being part of a multiple birth and other phenotypes (anthropometric traits, health-related traits, and fertility-related measures). The outcomes of this study provide important new insights into the genetic aetiology of multiple births and fertility, and open up novel directions for fertility and reproduction research.
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http://dx.doi.org/10.1038/s41431-019-0355-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6777609PMC
June 2019

Multivariate genome-wide analyses of the well-being spectrum.

Nat Genet 2019 03 14;51(3):445-451. Epub 2019 Jan 14.

Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.

We introduce two novel methods for multivariate genome-wide-association meta-analysis (GWAMA) of related traits that correct for sample overlap. A broad range of simulation scenarios supports the added value of our multivariate methods relative to univariate GWAMA. We applied the novel methods to life satisfaction, positive affect, neuroticism, and depressive symptoms, collectively referred to as the well-being spectrum (N = 2,370,390), and found 304 significant independent signals. Our multivariate approaches resulted in a 26% increase in the number of independent signals relative to the four univariate GWAMAs and in an ~57% increase in the predictive power of polygenic risk scores. Supporting transcriptome- and methylome-wide analyses (TWAS and MWAS, respectively) uncovered an additional 17 and 75 independent loci, respectively. Bioinformatic analyses, based on gene expression in brain tissues and cells, showed that genes differentially expressed in the subiculum and GABAergic interneurons are enriched in their effect on the well-being spectrum.
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http://dx.doi.org/10.1038/s41588-018-0320-8DOI Listing
March 2019

GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal influence of schizophrenia.

Nat Neurosci 2018 09 27;21(9):1161-1170. Epub 2018 Aug 27.

Department of Youth and Family, Utrecht University, Utrecht, the Netherlands.

Cannabis use is a heritable trait that has been associated with adverse mental health outcomes. In the largest genome-wide association study (GWAS) for lifetime cannabis use to date (N = 184,765), we identified eight genome-wide significant independent single nucleotide polymorphisms in six regions. All measured genetic variants combined explained 11% of the variance. Gene-based tests revealed 35 significant genes in 16 regions, and S-PrediXcan analyses showed that 21 genes had different expression levels for cannabis users versus nonusers. The strongest finding across the different analyses was CADM2, which has been associated with substance use and risk-taking. Significant genetic correlations were found with 14 of 25 tested substance use and mental health-related traits, including smoking, alcohol use, schizophrenia and risk-taking. Mendelian randomization analysis showed evidence for a causal positive influence of schizophrenia risk on cannabis use. Overall, our study provides new insights into the etiology of cannabis use and its relation with mental health.
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http://dx.doi.org/10.1038/s41593-018-0206-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6386176PMC
September 2018

Characterizing the Relation Between Expression QTLs and Complex Traits: Exploring the Role of Tissue Specificity.

Behav Genet 2018 09 20;48(5):374-385. Epub 2018 Jul 20.

Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

Measurement of gene expression levels and detection of eQTLs (expression quantitative trait loci) are difficult in tissues with limited sample availability, such as the brain. However, eQTL overlap between tissues might be high, which would allow for inference of eQTL functioning in the brain via eQTLs detected in readily accessible tissues, e.g. whole blood. Applying Stratified Linkage Disequilibrium Score Regression (SLDSR), we quantified the enrichment in polygenic signal of blood and brain eQTLs in genome-wide association studies (GWAS) of 11 complex traits. We looked at eQTLs discovered in 44 tissues by the Genotype-Tissue Expression (GTEx) consortium and two other large representative studies, and found no tissue-specific eQTL effects. Next, we integrated the GTEx eQTLs with regions associated with tissue-specific histone modifiers, and interrogated their effect on rheumatoid arthritis and schizophrenia. We observed substantially enriched effects of eQTLs located inside regions bearing modification H3K4me1 on schizophrenia, but not rheumatoid arthritis, and not tissue-specific. Finally, we extracted eQTLs associated with tissue-specific differentially expressed genes and determined their effects on rheumatoid arthritis and schizophrenia, these analysis revealed limited enrichment of eQTLs associated with gene specifically expressed in specific tissues. Our results pointed to strong enrichment of eQTLs in their effect on complex traits, without evidence for tissue-specific effects. Lack of tissue-specificity can be either due to a lack of statistical power or due to the true absence of tissue-specific effects. We conclude that eQTLs are strongly enriched in GWAS signal and that the enrichment is not specific to the eQTL discovery tissue. Until sample sizes for eQTL discovery grow sufficiently large, working with relatively accessible tissues as proxy for eQTL discovery is sensible and restricting lookups for GWAS hits to a specific tissue for which limited samples are available might not be advisable.
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http://dx.doi.org/10.1007/s10519-018-9914-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6097736PMC
September 2018
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