Publications by authors named "Anke R Hammerschlag"

28 Publications

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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

Systematic Review: Molecular Studies of Common Genetic Variation in Child and Adolescent Psychiatric Disorders.

J Am Acad Child Adolesc Psychiatry 2021 Apr 28. Epub 2021 Apr 28.

Ms. Akingbuwa, Dr. Hammerschlag, and Profs. Bartels and Middeldorp are with Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Dr. Hammerschlag and Prof. Middeldorp are also with the Child Health Research Centre, the University of Queensland, Brisbane, Queensland, Australia; Prof. Middeldorp is also with the Child and Youth Mental Health Service, Children's Health Queensland Hospital and Health Services, Brisbane, Queensland, Australia.

Objective: A systematic review of studies using molecular genetics and statistical approaches to investigate the role of common genetic variation in the development, persistence, and comorbidity of childhood psychiatric traits was conducted.

Method: A literature review was performed using the PubMed database, following the Preferred Reporting Items for Meta-Analyses guidelines. There were 131 studies meeting inclusion criteria, having investigated at least one type of childhood-onset or childhood-measured psychiatric disorder or trait with the aim of identifying trait-associated common genetic variants, estimating the contribution of single nucleotide polymorphisms (SNPs) to the amount of variance explained (SNP-based heritability), investigating genetic overlap between psychiatric traits, or investigating whether the stability in traits or the association with adult traits is explained by genetic factors.

Results: The first robustly associated genetic variants have started to be identified for childhood psychiatric traits. There were substantial contributions of common genetic variants to many traits, with variation in single nucleotide polymorphism heritability estimates depending on age and raters. Moreover, genetic variants also appeared to explain comorbidity as well as stability across a range of psychiatric traits in childhood and across the life span.

Conclusion: Common genetic variation plays a substantial role in childhood psychiatric traits. Increased sample sizes will lead to increased power to identify genetic variants and to understand genetic architecture, which will ultimately be beneficial to targeted and prevention strategies. This can be achieved by harmonizing phenotype measurements, as is already proposed by large international consortia and by including the collection of genetic material in every study.
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http://dx.doi.org/10.1016/j.jaac.2021.03.020DOI Listing
April 2021

Parental characteristics and offspring mental health and related outcomes: a systematic review of genetically informative literature.

Transl Psychiatry 2021 04 1;11(1):197. Epub 2021 Apr 1.

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

Various parental characteristics, including psychiatric disorders and parenting behaviours, are associated with offspring mental health and related outcomes in observational studies. The application of genetically informative designs is crucial to disentangle the role of genetic and environmental factors (as well as gene-environment correlation) underlying these observations, as parents provide not only the rearing environment but also transmit 50% of their genes to their offspring. This article first provides an overview of behavioural genetics, matched-pair, and molecular genetics designs that can be applied to investigate parent-offspring associations, whilst modelling or accounting for genetic effects. We then present a systematic literature review of genetically informative studies investigating associations between parental characteristics and offspring mental health and related outcomes, published since 2014. The reviewed studies provide reliable evidence of genetic transmission of depression, criminal behaviour, educational attainment, and substance use. These results highlight that studies that do not use genetically informative designs are likely to misinterpret the mechanisms underlying these parent-offspring associations. After accounting for genetic effects, several parental characteristics, including parental psychiatric traits and parenting behaviours, were associated with offspring internalising problems, externalising problems, educational attainment, substance use, and personality through environmental pathways. Overall, genetically informative designs to study intergenerational transmission prove valuable for the understanding of individual differences in offspring mental health and related outcomes, and mechanisms of transmission within families.
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http://dx.doi.org/10.1038/s41398-021-01300-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8016911PMC
April 2021

Overview of CAPICE-Childhood and Adolescence Psychopathology: unravelling the complex etiology by a large Interdisciplinary Collaboration in Europe-an EU Marie Skłodowska-Curie International Training Network.

Eur Child Adolesc Psychiatry 2021 Jan 20. Epub 2021 Jan 20.

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

The Roadmap for Mental Health and Wellbeing Research in Europe (ROAMER) identified child and adolescent mental illness as a priority area for research. CAPICE (Childhood and Adolescence Psychopathology: unravelling the complex etiology by a large Interdisciplinary Collaboration in Europe) is a European Union (EU) funded training network aimed at investigating the causes of individual differences in common childhood and adolescent psychopathology, especially depression, anxiety, and attention deficit hyperactivity disorder. CAPICE brings together eight birth and childhood cohorts as well as other cohorts from the EArly Genetics and Life course Epidemiology (EAGLE) consortium, including twin cohorts, with unique longitudinal data on environmental exposures and mental health problems, and genetic data on participants. Here we describe the objectives, summarize the methodological approaches and initial results, and present the dissemination strategy of the CAPICE network. Besides identifying genetic and epigenetic variants associated with these phenotypes, analyses have been performed to shed light on the role of genetic factors and the interplay with the environment in influencing the persistence of symptoms across the lifespan. Data harmonization and building an advanced data catalogue are also part of the work plan. Findings will be disseminated to non-academic parties, in close collaboration with the Global Alliance of Mental Illness Advocacy Networks-Europe (GAMIAN-Europe).
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http://dx.doi.org/10.1007/s00787-020-01713-2DOI Listing
January 2021

Refining Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder Genetic Loci by Integrating Summary Data From Genome-wide Association, Gene Expression, and DNA Methylation Studies.

Biol Psychiatry 2020 09 16;88(6):470-479. Epub 2020 May 16.

Child Health Research Centre, University of Queensland, Brisbane, Queensland, Australia; Child and Youth Mental Health Service, Children's Health Queensland Hospital and Health Services, Brisbane, Queensland, Australia; Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands. Electronic address:

Background: Recent genome-wide association studies (GWASs) identified the first genetic loci associated with attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). The next step is to use these results to increase our understanding of the biological mechanisms involved. Most of the identified variants likely influence gene regulation. The aim of the current study is to shed light on the mechanisms underlying the genetic signals and prioritize genes by integrating GWAS results with gene expression and DNA methylation (DNAm) levels.

Methods: We applied summary-data-based Mendelian randomization to integrate ADHD and ASD GWAS data with fetal brain expression and methylation quantitative trait loci, given the early onset of these disorders. We also analyzed expression and methylation quantitative trait loci datasets of adult brain and blood, as these provide increased statistical power. We subsequently used summary-data-based Mendelian randomization to investigate if the same variant influences both DNAm and gene expression levels.

Results: We identified multiple gene expression and DNAm levels in fetal brain at chromosomes 1 and 17 that were associated with ADHD and ASD, respectively, through pleiotropy at shared genetic variants. The analyses in brain and blood showed additional associated gene expression and DNAm levels at the same and additional loci, likely because of increased statistical power. Several of the associated genes have not been identified in ADHD and ASD GWASs before.

Conclusions: Our findings identified the genetic variants associated with ADHD and ASD that likely act through gene regulation. This facilitates prioritization of candidate genes for functional follow-up studies.
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http://dx.doi.org/10.1016/j.biopsych.2020.05.002DOI Listing
September 2020

Maternal and paternal effects on offspring internalizing problems: Results from genetic and family-based analyses.

Am J Med Genet B Neuropsychiatr Genet 2020 07 1;183(5):258-267. Epub 2020 May 1.

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

It is unclear to what extent parental influences on the development of internalizing problems in offspring are explained by indirect genetic effects, reflected in the environment provided by the parent, in addition to the genes transmitted from parent to child. In this study, these effects were investigated using two innovative methods in a large birth cohort. Using maternal-effects genome complex trait analysis (M-GCTA), the effects of offspring genotype, maternal or paternal genotypes, and their covariance on offspring internalizing problems were estimated in 3,801 mother-father-child genotyped trios. Next, estimated genetic correlations within pedigree data, including 10,688 children, were used to estimate additive genetic effects, maternal and paternal genetic effects, and a shared family effect using linear mixed effects modeling. There were no significant maternal or paternal genetic effects on offspring anxiety or depressive symptoms at age 8, beyond the effects transmitted via the genetic pathway between parents and children. However, indirect maternal genetic effects explained a small, but nonsignificant, proportion of variance in childhood depressive symptoms in both the M-GCTA (~4%) and pedigree (~8%) analyses. Our results suggest that parental effects on offspring internalizing problems are predominantly due to transmitted genetic variants, rather than the indirect effect of parental genes via the environment.
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http://dx.doi.org/10.1002/ajmg.b.32784DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7317352PMC
July 2020

Genetic Associations Between Childhood Psychopathology and Adult Depression and Associated Traits in 42 998 Individuals: A Meta-analysis.

JAMA Psychiatry 2020 07;77(7):715-728

University of Bristol School of Psychological Science, Bristol, United Kingdom.

Importance: Adult mood disorders are often preceded by behavioral and emotional problems in childhood. It is yet unclear what explains the associations between childhood psychopathology and adult traits.

Objective: To investigate whether genetic risk for adult mood disorders and associated traits is associated with childhood disorders.

Design, Setting, And Participants: This meta-analysis examined data from 7 ongoing longitudinal birth and childhood cohorts from the UK, the Netherlands, Sweden, Norway, and Finland. Starting points of data collection ranged from July 1985 to April 2002. Participants were repeatedly assessed for childhood psychopathology from ages 6 to 17 years. Data analysis occurred from September 2017 to May 2019.

Exposures: Individual polygenic scores (PGS) were constructed in children based on genome-wide association studies of adult major depression, bipolar disorder, subjective well-being, neuroticism, insomnia, educational attainment, and body mass index (BMI).

Main Outcomes And Measures: Regression meta-analyses were used to test associations between PGS and attention-deficit/hyperactivity disorder (ADHD) symptoms and internalizing and social problems measured repeatedly across childhood and adolescence and whether these associations depended on childhood phenotype, age, and rater.

Results: The sample included 42 998 participants aged 6 to 17 years. Male participants varied from 43.0% (1040 of 2417 participants) to 53.1% (2434 of 4583 participants) by age and across all cohorts. The PGS of adult major depression, neuroticism, BMI, and insomnia were positively associated with childhood psychopathology (β estimate range, 0.023-0.042 [95% CI, 0.017-0.049]), while associations with PGS of subjective well-being and educational attainment were negative (β, -0.026 to -0.046 [95% CI, -0.020 to -0.057]). There was no moderation of age, type of childhood phenotype, or rater with the associations. The exceptions were stronger associations between educational attainment PGS and ADHD compared with internalizing problems (Δβ, 0.0561 [Δ95% CI, 0.0318-0.0804]; ΔSE, 0.0124) and social problems (Δβ, 0.0528 [Δ95% CI, 0.0282-0.0775]; ΔSE, 0.0126), and between BMI PGS and ADHD and social problems (Δβ, -0.0001 [Δ95% CI, -0.0102 to 0.0100]; ΔSE, 0.0052), compared with internalizing problems (Δβ, -0.0310 [Δ95% CI, -0.0456 to -0.0164]; ΔSE, 0.0074). Furthermore, the association between educational attainment PGS and ADHD increased with age (Δβ, -0.0032 [Δ 95% CI, -0.0048 to -0.0017]; ΔSE, 0.0008).

Conclusions And Relevance: Results from this study suggest the existence of a set of genetic factors influencing a range of traits across the life span with stable associations present throughout childhood. Knowledge of underlying mechanisms may affect treatment and long-term outcomes of individuals with psychopathology.
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http://dx.doi.org/10.1001/jamapsychiatry.2020.0527DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7160753PMC
July 2020

Synaptic and brain-expressed gene sets relate to the shared genetic risk across five psychiatric disorders.

Psychol Med 2020 07 22;50(10):1695-1705. Epub 2019 Jul 22.

Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

Background: Mounting evidence shows genetic overlap between multiple psychiatric disorders. However, the biological underpinnings of shared risk for psychiatric disorders are not yet fully uncovered. The identification of underlying biological mechanisms is crucial for the progress in the treatment of these disorders.

Methods: We applied gene-set analysis including 7372 gene sets, and 53 tissue-type specific gene-expression profiles to identify sets of genes that are involved in the etiology of multiple psychiatric disorders. We included genome-wide meta-association data of the five psychiatric disorders schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorder, and attention-deficit/hyperactivity disorder. The total dataset contained 159 219 cases and 262 481 controls.

Results: We identified 19 gene sets that were significantly associated with the five psychiatric disorders combined, of which we excluded five sets because their associations were likely driven by schizophrenia only. Conditional analyses showed independent effects of several gene sets that in particular relate to the synapse. In addition, we found independent effects of gene expression levels in the cerebellum and frontal cortex.

Conclusions: We obtained novel evidence for shared biological mechanisms that act across psychiatric disorders and we showed that several gene sets that have been related to individual disorders play a role in a broader range of psychiatric disorders.
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http://dx.doi.org/10.1017/S0033291719001776DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7408577PMC
July 2020

Genome-wide analysis of insomnia in 1,331,010 individuals identifies new risk loci and functional pathways.

Nat Genet 2019 03 25;51(3):394-403. Epub 2019 Feb 25.

Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, the Netherlands.

Insomnia is the second most prevalent mental disorder, with no sufficient treatment available. Despite substantial heritability, insight into the associated genes and neurobiological pathways remains limited. Here, we use a large genetic association sample (n = 1,331,010) to detect novel loci and gain insight into the pathways, tissue and cell types involved in insomnia complaints. We identify 202 loci implicating 956 genes through positional, expression quantitative trait loci, and chromatin mapping. The meta-analysis explained 2.6% of the variance. We show gene set enrichments for the axonal part of neurons, cortical and subcortical tissues, and specific cell types, including striatal, hypothalamic, and claustrum neurons. We found considerable genetic correlations with psychiatric traits and sleep duration, and modest correlations with other sleep-related traits. Mendelian randomization identified the causal effects of insomnia on depression, diabetes, and cardiovascular disease, and the protective effects of educational attainment and intracranial volume. Our findings highlight key brain areas and cell types implicated in insomnia, and provide new treatment targets.
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http://dx.doi.org/10.1038/s41588-018-0333-3DOI Listing
March 2019

Exome Chip Meta-analysis Fine Maps Causal Variants and Elucidates the Genetic Architecture of Rare Coding Variants in Smoking and Alcohol Use.

Biol Psychiatry 2019 06 6;85(11):946-955. Epub 2018 Dec 6.

Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.

Background: Smoking and alcohol use have been associated with common genetic variants in multiple loci. Rare variants within these loci hold promise in the identification of biological mechanisms in substance use. Exome arrays and genotype imputation can now efficiently genotype rare nonsynonymous and loss of function variants. Such variants are expected to have deleterious functional consequences and to contribute to disease risk.

Methods: We analyzed ∼250,000 rare variants from 16 independent studies genotyped with exome arrays and augmented this dataset with imputed data from the UK Biobank. Associations were tested for five phenotypes: cigarettes per day, pack-years, smoking initiation, age of smoking initiation, and alcoholic drinks per week. We conducted stratified heritability analyses, single-variant tests, and gene-based burden tests of nonsynonymous/loss-of-function coding variants. We performed a novel fine-mapping analysis to winnow the number of putative causal variants within associated loci.

Results: Meta-analytic sample sizes ranged from 152,348 to 433,216, depending on the phenotype. Rare coding variation explained 1.1% to 2.2% of phenotypic variance, reflecting 11% to 18% of the total single nucleotide polymorphism heritability of these phenotypes. We identified 171 genome-wide associated loci across all phenotypes. Fine mapping identified putative causal variants with double base-pair resolution at 24 of these loci, and between three and 10 variants for 65 loci. Twenty loci contained rare coding variants in the 95% credible intervals.

Conclusions: Rare coding variation significantly contributes to the heritability of smoking and alcohol use. Fine-mapping genome-wide association study loci identifies specific variants contributing to the biological etiology of substance use behavior.
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http://dx.doi.org/10.1016/j.biopsych.2018.11.024DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6534468PMC
June 2019

Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences.

Nat Genet 2019 02 14;51(2):245-257. Epub 2019 Jan 14.

Team Loyalty BV, Hoofddorp, the Netherlands.

Humans vary substantially in their willingness to take risks. In a combined sample of over 1 million individuals, we conducted genome-wide association studies (GWAS) of general risk tolerance, adventurousness, and risky behaviors in the driving, drinking, smoking, and sexual domains. Across all GWAS, we identified hundreds of associated loci, including 99 loci associated with general risk tolerance. We report evidence of substantial shared genetic influences across risk tolerance and the risky behaviors: 46 of the 99 general risk tolerance loci contain a lead SNP for at least one of our other GWAS, and general risk tolerance is genetically correlated ([Formula: see text] ~ 0.25 to 0.50) with a range of risky behaviors. Bioinformatics analyses imply that genes near SNPs associated with general risk tolerance are highly expressed in brain tissues and point to a role for glutamatergic and GABAergic neurotransmission. We found no evidence of enrichment for genes previously hypothesized to relate to risk tolerance.
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http://dx.doi.org/10.1038/s41588-018-0309-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6713272PMC
February 2019

Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci.

Mol Psychiatry 2020 10 7;25(10):2392-2409. Epub 2019 Jan 7.

Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, Netherlands.

Smoking is a major heritable and modifiable risk factor for many diseases, including cancer, common respiratory disorders and cardiovascular diseases. Fourteen genetic loci have previously been associated with smoking behaviour-related traits. We tested up to 235,116 single nucleotide variants (SNVs) on the exome-array for association with smoking initiation, cigarettes per day, pack-years, and smoking cessation in a fixed effects meta-analysis of up to 61 studies (up to 346,813 participants). In a subset of 112,811 participants, a further one million SNVs were also genotyped and tested for association with the four smoking behaviour traits. SNV-trait associations with P < 5 × 10 in either analysis were taken forward for replication in up to 275,596 independent participants from UK Biobank. Lastly, a meta-analysis of the discovery and replication studies was performed. Sixteen SNVs were associated with at least one of the smoking behaviour traits (P < 5 × 10) in the discovery samples. Ten novel SNVs, including rs12616219 near TMEM182, were followed-up and five of them (rs462779 in REV3L, rs12780116 in CNNM2, rs1190736 in GPR101, rs11539157 in PJA1, and rs12616219 near TMEM182) replicated at a Bonferroni significance threshold (P < 4.5 × 10) with consistent direction of effect. A further 35 SNVs were associated with smoking behaviour traits in the discovery plus replication meta-analysis (up to 622,409 participants) including a rare SNV, rs150493199, in CCDC141 and two low-frequency SNVs in CEP350 and HDGFRP2. Functional follow-up implied that decreased expression of REV3L may lower the probability of smoking initiation. The novel loci will facilitate understanding the genetic aetiology of smoking behaviour and may lead to the identification of potential drug targets for smoking prevention and/or cessation.
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http://dx.doi.org/10.1038/s41380-018-0313-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515840PMC
October 2020

Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence.

Nat Genet 2018 07 25;50(7):912-919. Epub 2018 Jun 25.

Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden.

Intelligence is highly heritable and a major determinant of human health and well-being. Recent genome-wide meta-analyses have identified 24 genomic loci linked to variation in intelligence, but much about its genetic underpinnings remains to be discovered. Here, we present a large-scale genetic association study of intelligence (n = 269,867), identifying 205 associated genomic loci (190 new) and 1,016 genes (939 new) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and associations with 146 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain, specifically in striatal medium spiny neurons and hippocampal pyramidal neurons. Gene set analyses implicate pathways related to nervous system development and synaptic structure. We confirm previous strong genetic correlations with multiple health-related outcomes, and Mendelian randomization analysis results suggest protective effects of intelligence for Alzheimer's disease and ADHD and bidirectional causation with pleiotropic effects for schizophrenia. These results are a major step forward in understanding the neurobiology of cognitive function as well as genetically related neurological and psychiatric disorders.
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http://dx.doi.org/10.1038/s41588-018-0152-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6411041PMC
July 2018

Meta-analysis of genome-wide association studies for neuroticism in 449,484 individuals identifies novel genetic loci and pathways.

Nat Genet 2018 07 25;50(7):920-927. Epub 2018 Jun 25.

Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.

Neuroticism is an important risk factor for psychiatric traits, including depression, anxiety, and schizophrenia. At the time of analysis, previous genome-wide association studies (GWAS) reported 16 genomic loci associated to neuroticism. Here we conducted a large GWAS meta-analysis (n = 449,484) of neuroticism and identified 136 independent genome-wide significant loci (124 new at the time of analysis), which implicate 599 genes. Functional follow-up analyses showed enrichment in several brain regions and involvement of specific cell types, including dopaminergic neuroblasts (P = 3.49 × 10), medium spiny neurons (P = 4.23 × 10), and serotonergic neurons (P = 1.37 × 10). Gene set analyses implicated three specific pathways: neurogenesis (P = 4.43 × 10), behavioral response to cocaine processes (P = 1.84 × 10), and axon part (P = 5.26 × 10). We show that neuroticism's genetic signal partly originates in two genetically distinguishable subclusters ('depressed affect' and 'worry'), suggesting distinct causal mechanisms for subtypes of individuals. Mendelian randomization analysis showed unidirectional and bidirectional effects between neuroticism and multiple psychiatric traits. These results enhance neurobiological understanding of neuroticism and provide specific leads for functional follow-up experiments.
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http://dx.doi.org/10.1038/s41588-018-0151-7DOI Listing
July 2018

Exploring the role of low-frequency and rare exonic variants in alcohol and tobacco use.

Drug Alcohol Depend 2018 07 25;188:94-101. Epub 2018 Apr 25.

Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

Background: Alcohol and tobacco use are heritable phenotypes. However, only a small number of common genetic variants have been identified, and common variants account for a modest proportion of the heritability. Therefore, this study aims to investigate the role of low-frequency and rare variants in alcohol and tobacco use.

Methods: We meta-analyzed ExomeChip association results from eight discovery cohorts and included 12,466 subjects and 7432 smokers in the analysis of alcohol consumption and tobacco use, respectively. The ExomeChip interrogates low-frequency and rare exonic variants, and in addition a small pool of common variants. We investigated top variants in an independent sample in which ICD-9 diagnoses of "alcoholism" (N = 25,508) and "tobacco use disorder" (N = 27,068) had been assessed. In addition to the single variant analysis, we performed gene-based, polygenic risk score (PRS), and pathway analyses.

Results: The meta-analysis did not yield exome-wide significant results. When we jointly analyzed our top results with the independent sample, no low-frequency or rare variants reached significance for alcohol consumption or tobacco use. However, two common variants that were present on the ExomeChip, rs16969968 (p = 2.39 × 10) and rs8034191 (p = 6.31 × 10) located in CHRNA5 and AGPHD1 at 15q25.1, showed evidence for association with tobacco use.

Discussion: Low-frequency and rare exonic variants with large effects do not play a major role in alcohol and tobacco use, nor does the aggregate effect of ExomeChip variants. However, our results confirmed the role of the CHRNA5-CHRNA3-CHRNB4 cluster of nicotinic acetylcholine receptor subunit genes in tobacco use.
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http://dx.doi.org/10.1016/j.drugalcdep.2018.03.026DOI Listing
July 2018

Publisher Correction: Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity.

Nat Genet 2018 05;50(5):766-767

Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany.

In the version of this article originally published, one of the two authors with the name Wei Zhao was omitted from the author list and the affiliations for both authors were assigned to the single Wei Zhao in the author list. In addition, the ORCID for Wei Zhao (Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA) was incorrectly assigned to author Wei Zhou. The errors have been corrected in the HTML and PDF versions of the article.
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http://dx.doi.org/10.1038/s41588-018-0082-3DOI Listing
May 2018

Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity.

Nat Genet 2018 01 22;50(1):26-41. Epub 2017 Dec 22.

Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany.

Genome-wide association studies (GWAS) have identified >250 loci for body mass index (BMI), implicating pathways related to neuronal biology. Most GWAS loci represent clusters of common, noncoding variants from which pinpointing causal genes remains challenging. Here we combined data from 718,734 individuals to discover rare and low-frequency (minor allele frequency (MAF) < 5%) coding variants associated with BMI. We identified 14 coding variants in 13 genes, of which 8 variants were in genes (ZBTB7B, ACHE, RAPGEF3, RAB21, ZFHX3, ENTPD6, ZFR2 and ZNF169) newly implicated in human obesity, 2 variants were in genes (MC4R and KSR2) previously observed to be mutated in extreme obesity and 2 variants were in GIPR. The effect sizes of rare variants are ~10 times larger than those of common variants, with the largest effect observed in carriers of an MC4R mutation introducing a stop codon (p.Tyr35Ter, MAF = 0.01%), who weighed ~7 kg more than non-carriers. Pathway analyses based on the variants associated with BMI confirm enrichment of neuronal genes and provide new evidence for adipocyte and energy expenditure biology, widening the potential of genetically supported therapeutic targets in obesity.
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http://dx.doi.org/10.1038/s41588-017-0011-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5945951PMC
January 2018

Genome-Wide Association Studies of a Broad Spectrum of Antisocial Behavior.

JAMA Psychiatry 2017 12;74(12):1242-1250

Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.

Importance: Antisocial behavior (ASB) places a large burden on perpetrators, survivors, and society. Twin studies indicate that half of the variation in this trait is genetic. Specific causal genetic variants have, however, not been identified.

Objectives: To estimate the single-nucleotide polymorphism-based heritability of ASB; to identify novel genetic risk variants, genes, or biological pathways; to test for pleiotropic associations with other psychiatric traits; and to reevaluate the candidate gene era data through the Broad Antisocial Behavior Consortium.

Design, Setting, And Participants: Genome-wide association data from 5 large population-based cohorts and 3 target samples with genome-wide genotype and ASB data were used for meta-analysis from March 1, 2014, to May 1, 2016. All data sets used quantitative phenotypes, except for the Finnish Crime Study, which applied a case-control design (370 patients and 5850 control individuals).

Main Outcome And Measures: This study adopted relatively broad inclusion criteria to achieve a quantitative measure of ASB derived from multiple measures, maximizing the sample size over different age ranges.

Results: The discovery samples comprised 16 400 individuals, whereas the target samples consisted of 9381 individuals (all individuals were of European descent), including child and adult samples (mean age range, 6.7-56.1 years). Three promising loci with sex-discordant associations were found (8535 female individuals, chromosome 1: rs2764450, chromosome 11: rs11215217; 7772 male individuals, chromosome X, rs41456347). Polygenic risk score analyses showed prognostication of antisocial phenotypes in an independent Finnish Crime Study (2536 male individuals and 3684 female individuals) and shared genetic origin with conduct problems in a population-based sample (394 male individuals and 431 female individuals) but not with conduct disorder in a substance-dependent sample (950 male individuals and 1386 female individuals) (R2 = 0.0017 in the most optimal model, P = 0.03). Significant inverse genetic correlation of ASB with educational attainment (r = -0.52, P = .005) was detected.

Conclusions And Relevance: The Broad Antisocial Behavior Consortium entails the largest collaboration to date on the genetic architecture of ASB, and the first results suggest that ASB may be highly polygenic and has potential heterogeneous genetic effects across sex.
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http://dx.doi.org/10.1001/jamapsychiatry.2017.3069DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6309228PMC
December 2017

Genome-wide association analysis of insomnia complaints identifies risk genes and genetic overlap with psychiatric and metabolic traits.

Nat Genet 2017 Nov 12;49(11):1584-1592. Epub 2017 Jun 12.

Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.

Persistent insomnia is among the most frequent complaints in general practice. To identify genetic factors for insomnia complaints, we performed a genome-wide association study (GWAS) and a genome-wide gene-based association study (GWGAS) in 113,006 individuals. We identify three loci and seven genes associated with insomnia complaints, with the associations for one locus and five genes supported by joint analysis with an independent sample (n = 7,565). Our top association (MEIS1, P < 5 × 10) has previously been implicated in restless legs syndrome (RLS). Additional analyses favor the hypothesis that MEIS1 exhibits pleiotropy for insomnia and RLS and show that the observed association with insomnia complaints cannot be explained only by the presence of an RLS subgroup within the cases. Sex-specific analyses suggest that there are different genetic architectures between the sexes in addition to shared genetic factors. We show substantial positive genetic correlation of insomnia complaints with internalizing personality traits and metabolic traits and negative correlation with subjective well-being and educational attainment. These findings provide new insight into the genetic architecture of insomnia.
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http://dx.doi.org/10.1038/ng.3888DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5600256PMC
November 2017

Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence.

Nat Genet 2017 Jul 22;49(7):1107-1112. Epub 2017 May 22.

Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, the Netherlands.

Intelligence is associated with important economic and health-related life outcomes. Despite intelligence having substantial heritability (0.54) and a confirmed polygenic nature, initial genetic studies were mostly underpowered. Here we report a meta-analysis for intelligence of 78,308 individuals. We identify 336 associated SNPs (METAL P < 5 × 10) in 18 genomic loci, of which 15 are new. Around half of the SNPs are located inside a gene, implicating 22 genes, of which 11 are new findings. Gene-based analyses identified an additional 30 genes (MAGMA P < 2.73 × 10), of which all but one had not been implicated previously. We show that the identified genes are predominantly expressed in brain tissue, and pathway analysis indicates the involvement of genes regulating cell development (MAGMA competitive P = 3.5 × 10). Despite the well-known difference in twin-based heritability for intelligence in childhood (0.45) and adulthood (0.80), we show substantial genetic correlation (r = 0.89, LD score regression P = 5.4 × 10). These findings provide new insight into the genetic architecture of intelligence.
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http://dx.doi.org/10.1038/ng.3869DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5665562PMC
July 2017

A Genome-Wide Association Meta-Analysis of Attention-Deficit/Hyperactivity Disorder Symptoms in Population-Based Pediatric Cohorts.

J Am Acad Child Adolesc Psychiatry 2016 10 5;55(10):896-905.e6. Epub 2016 Aug 5.

Dr. Middeldorp is with Biological Psychology, Neuroscience Campus Amsterdam, VU University Amsterdam, and GGZinGeest/ VU University Medical Center, Amsterdam. Ms. Hammerschlag is with the Generation R Study Group, Erasmus MC Rotterdam, the Netherlands, and Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, VU University Amsterdam. Mr. Ouwens and Dr. Groen-Blokhuis are with Biological Psychology, VU University Amsterdam, and the EMGO+ Institute for Health and Care Research, VU University Medical Center. Dr. St. Pourcain is with MRC Integrative Epidemiology Unit (MRC IEU), University of Bristol, UK, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands, and School of Experimental Psychology, University of Bristol. Dr. Greven is with Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Karakter, Child and Adolescent Psychiatry University Center, Radboud University Nijmegen, and MRC Social Genetic and Developmental Psychiatry Centre, King's College London. Dr. Pappa is with Generation R Study Group, and Pedagogical and Education Science, Erasmus University Rotterdam, The Netherlands. Drs. Tiesler and Thiering are with Institute of Epidemiology I, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany and the Division of Metabolic and Nutritional Medicine, Munich, and Dr. von Hauner Children's Hospital, University of Munich Medical Center, Germany. Mr. Ang, Ms. Wang, and Dr. Pennell are with School of Women's and Infants' Health, University of Western Australia, Perth. Dr. Nolte is with University of Groningen, University Medical Center Groningen, The Netherlands. Ms. Vilor-Tejedor is with Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Universitat Pompeu Fabra (UPF), Barcelona, and CIBER Epidemiology and Public Health (CIBERESP), Madrid. Mr. Bacelis is with Gothenburg University, Sweden. Drs. Ebejer, Martin, and Medland are with QIMR Berghofer Medical Research Institute, Brisbane, Australia. Drs. Zhao and Nyholt are with Institute of Health and Biomedical Innovation, Queensland University of Technology, Queensland, Australia. Drs. Davies and Ehli are with Avera Institute for Human Genetics, SD. Drs. Evans, Kemp, and Ring are with MRC IEU, School of Social and Community Medicine, and School of Social and Community Medicine, University of Bristol, and Diamantina Institute, Translational Research Institute, University of Queensland, Brisbane. Ms. Fedko is with Biological Psychology, VU University Amsterdam. Dr. Guxens is with CREAL, UPF, CIBERESP, and Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center-Sophia Children´s Hospital, The Netherlands. Dr. Hottenga is with Biological Psychology, VU University, and EMGO+ Institute for Health and Care Research, VU University Medical Center. Dr. Hudziak is with Vermont Center for Children, Youth and Families and College of Medicine, University of Vermont, Burlington, and Child and Adolescent Psychiatry, Erasmus Medical Center. Drs. Jugessur, Myhre, and Stoltenberg are with the Norwegian Institute of Public Health, Oslo. Ms. Krapohl and Drs. Trzaskowski and Plomin are with MRC Social, Genetic and Developmental Psychiatry Centre, King's College London. Mr. Murcia is with CIBERESP, and FISABIO-Universitat Jaume I-Universitat de València Joint Research Unit of Epidemiology and Environmental Health, Valencia, Spain. Drs. Ormel and Hartman are with the Interdisciplinary Center Psychopathology and Emotion regulation (ICPE), University Medical Center Groningen. Drs. Standl and Heinrich are with Institute of Epidemiology I, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany. Drs. Stergiakouli and Timpson are with MRC IEU; Dr. Timpson is also with School of Social and Community Medicine, University of Bristol. Dr. van der Most is with University of Groningen and University Medical Center Groningen. Dr. Neale is with Program in Medical and Population Genetics and Stanley Center for Psychiatric Genetics, Broad Institute of Massachusetts Institute of Technology, Boston, Analytic and Translation Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, and Harvard University, Cambridge, MA. Dr. Jacobsson is with Obstetrics and Gynecology, Gothenburg University, and the Norwegian Institute of Public Health. Dr. Sunyer is with CREAL, IMIM (Hospital del Mar Medical Research Institute), Barcelona, UPF, and CIBERESP. Dr. Whitehouse is with Telethon Kids Institute, University of Western Australia, Perth. Dr. Davey Smith is with MRC IEU, and School of Social and Community Medicine. Dr. Tiemeier is with Epidemiology, Child and Adolescent Psychiatry, and Psychiatry, Erasmus Medical Center. Dr. Posthuma is with the Generation R Study Group, Erasmus MC Rotterdam, the Netherlands, Child and Adolescent Psychiatry, Erasmus Medical Center, Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, VU University, and Clinical Genetics, VU University Medical Center. Dr. Boomsma is with Biological Psychology, VU University, Neuroscience Campus Amsterdam, VU University, and EMGO+ Institute for Health and Care Research, VU University Medical Center.

Objective: The aims of this study were to elucidate the influence of common genetic variants on childhood attention-deficit/hyperactivity disorder (ADHD) symptoms, to identify genetic variants that explain its high heritability, and to investigate the genetic overlap of ADHD symptom scores with ADHD diagnosis.

Method: Within the EArly Genetics and Lifecourse Epidemiology (EAGLE) consortium, genome-wide single nucleotide polymorphisms (SNPs) and ADHD symptom scores were available for 17,666 children (<13 years of age) from nine population-based cohorts. SNP-based heritability was estimated in data from the three largest cohorts. Meta-analysis based on genome-wide association (GWA) analyses with SNPs was followed by gene-based association tests, and the overlap in results with a meta-analysis in the Psychiatric Genomics Consortium (PGC) case-control ADHD study was investigated.

Results: SNP-based heritability ranged from 5% to 34%, indicating that variation in common genetic variants influences ADHD symptom scores. The meta-analysis did not detect genome-wide significant SNPs, but three genes, lying close to each other with SNPs in high linkage disequilibrium (LD), showed a gene-wide significant association (p values between 1.46 × 10(-6) and 2.66 × 10(-6)). One gene, WASL, is involved in neuronal development. Both SNP- and gene-based analyses indicated overlap with the PGC meta-analysis results with the genetic correlation estimated at 0.96.

Conclusion: The SNP-based heritability for ADHD symptom scores indicates a polygenic architecture, and genes involved in neurite outgrowth are possibly involved. Continuous and dichotomous measures of ADHD appear to assess a genetically common phenotype. A next step is to combine data from population-based and case-control cohorts in genetic association studies to increase sample size and to improve statistical power for identifying genetic variants.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5068552PMC
http://dx.doi.org/10.1016/j.jaac.2016.05.025DOI Listing
October 2016

A Population-Based Imaging Genetics Study of Inattention/Hyperactivity: Basal Ganglia and Genetic Pathways.

J Am Acad Child Adolesc Psychiatry 2015 Sep 17;54(9):745-52. Epub 2015 Jun 17.

Erasmus Medical Center, Rotterdam, the Netherlands; VU University, Amsterdam, the Netherlands; VU Medical Center, Amsterdam, The Netherlands. Electronic address:

Objective: Although attention-deficit/hyperactivity disorder (ADHD) is 1 of the most common neurodevelopmental disorders, little is known about the neurobiology. Clinical studies suggest basal ganglia morphology plays a role. Furthermore, hyperactivity/impulsivity symptoms have recently been linked to genetic pathways involved in dopamine/norepinephrine and serotonin neurotransmission and neuritic outgrowth. We aimed to assess the association between ADHD symptoms, basal ganglia volume, and the 3 proposed genetic pathways in a pediatric population-based sample. With this, we aimed to investigate the generalizability of earlier clinical findings to the general population.

Method: This study included a population-based sample of 1,871 children with data on ADHD symptoms and genetic data, and 344 children with additional neuroimaging data. Regression analyses between ADHD symptom severity and volumetric data of the basal ganglia were performed. Also, gene-set analyses investigating the association between both ADHD symptom severity and basal ganglia volume with the dopamine/norepinephrine, serotonin, and neuritic outgrowth pathways were performed.

Results: More inattention and hyperactivity/impulsivity symptoms were associated with a smaller volume of the putamen (β = -0.13, p = .034), which was regarded as trend-level after correction for multiple testing. Stratified analyses showed a stronger putamen-hyperactivity association in children with clinical scores, although a similar trend was visible in the nonclinical subsample. The genetic pathways were not related to either ADHD symptoms or basal ganglia volume.

Conclusion: ADHD symptoms were marginally related to putamen volume in our population-based sample. We found no evidence for a role of dopamine/norepinephrine, serotonin, or neuritic outgrowth genetic pathways in ADHD symptom severity.
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http://dx.doi.org/10.1016/j.jaac.2015.05.018DOI Listing
September 2015

Functional gene-set analysis does not support a major role for synaptic function in attention deficit/hyperactivity disorder (ADHD).

Genes (Basel) 2014 Jul 22;5(3):604-14. Epub 2014 Jul 22.

Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, VU University Amsterdam, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands.

Attention Deficit/Hyperactivity Disorder (ADHD) is one of the most common childhood-onset neuropsychiatric disorders. Despite high heritability estimates, genome-wide association studies (GWAS) have failed to find significant genetic associations, likely due to the polygenic character of ADHD. Nevertheless, genetic studies suggested the involvement of several processes important for synaptic function. Therefore, we applied a functional gene-set analysis to formally test whether synaptic functions are associated with ADHD. Gene-set analysis tests the joint effect of multiple genetic variants in groups of functionally related genes. This method provides increased statistical power compared to conventional GWAS. We used data from the Psychiatric Genomics Consortium including 896 ADHD cases and 2455 controls, and 2064 parent-affected offspring trios, providing sufficient statistical power to detect gene sets representing a genotype relative risk of at least 1.17. Although all synaptic genes together showed a significant association with ADHD, this association was not stronger than that of randomly generated gene sets matched for same number of genes. Further analyses showed no association of specific synaptic function categories with ADHD after correction for multiple testing. Given current sample size and gene sets based on current knowledge of genes related to synaptic function, our results do not support a major role for common genetic variants in synaptic genes in the etiology of ADHD.
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http://dx.doi.org/10.3390/genes5030604DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4198920PMC
July 2014

Structural and functional brain connectivity in presymptomatic familial frontotemporal dementia.

Neurology 2014 Jul;83(2):e19-26

From the Departments of Neurology (E.G.P.D., L.C.J., T.d.H., H.S., J.C.v.S.), Epidemiology (T.d.H.), and Neuropsychology (J.R.A.d.G., I.d.K.), Erasmus Medical Center, Rotterdam; Department of Radiology (E.G.P.D., S.A.R.B.R., I.M.V., M.A.v.B.), Leiden University Medical Center; Departments of Neurology (E.G.P.D.) and Clinical Genetics (A.R.H., P.R., J.C.v.S.), VU Medical Center, Amsterdam; Leiden Institute for Brain and Cognition (S.A.R.B.R., I.M.V., M.A.v.B.) and Institute of Psychology (S.A.R.B.R., I.M.V.), Leiden University; Department of Neurology (T.d.H.), Sint Franciscus Gasthuis, Rotterdam, the Netherlands; and Department of Neurology (W.W.S.) and Memory and Aging Center (W.W.S.), University of California, San Francisco.

Objective: We aimed to investigate whether cognitive deficits and structural and functional connectivity changes can be detected before symptom onset in a large cohort of carriers of MAPT (microtubule-associated protein tau) or GRN (progranulin) mutations.

Methods: In this case-control study, 75 healthy individuals (aged 20-70 years) with 50% risk of frontotemporal dementia (FTD) underwent DNA screening, neuropsychological assessment, structural MRI, and fMRI. We used voxel-based morphometry and tract-based spatial statistics for voxel-wise analyses of gray matter volume and diffusion tensor imaging measures. Using resting-state fMRI scans, we assessed whole-brain functional connectivity to frontoinsular, anterior midcingulate, and posterior cingulate cortices.

Results: Carriers (n = 39) and noncarriers (n = 36) had similar neuropsychological performance, except for lower Letter Digit Substitution Test scores in carriers. Worse performance on Stroop III, Rivermead Behavioral Memory Test, and Happé Cartoons correlated with higher age in carriers, but not controls. Reduced fractional anisotropy in the right uncinate fasciculus was found in carriers compared with controls. Reductions in functional connectivity between anterior midcingulate cortex and frontoinsula and several other brain regions were found in carriers compared with controls and correlated with higher age in carriers, but not controls. We found no significant differences or age correlations in posterior cingulate cortex connectivity. No differences in regional gray matter volume were found, except for a small cluster of higher volume in the precentral gyrus in carriers.

Conclusions: This study demonstrates that alterations in structural and functional connectivity develop before the first symptoms of FTD arise. These findings suggest that diffusion tensor imaging and resting-state fMRI may have the potential to become sensitive biomarkers for early FTD in future clinical trials.
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http://dx.doi.org/10.1212/WNL.0000000000000583DOI Listing
July 2014

Structural and functional brain connectivity in presymptomatic familial frontotemporal dementia.

Neurology 2013 Feb 6;80(9):814-23. Epub 2013 Feb 6.

Department of Neurology, Erasmus Medical Center, Rotterdam, the Netherlands.

Objective: We aimed to investigate whether cognitive deficits and structural and functional connectivity changes can be detected before symptom onset in a large cohort of carriers of microtubule-associated protein tau and progranulin mutations.

Methods: In this case-control study, 75 healthy individuals (aged 20-70 years) with 50% risk for frontotemporal dementia (FTD) underwent DNA screening, neuropsychological assessment, and structural and functional MRI. We used voxel-based morphometry and tract-based spatial statistics for voxelwise analyses of gray matter volume and diffusion tensor imaging measures. Using resting-state fMRI scans, we assessed whole-brain functional connectivity to frontoinsula, anterior midcingulate cortex (aMCC), and posterior cingulate cortex.

Results: Although carriers (n = 37) and noncarriers (n = 38) had similar neuropsychological performance, worse performance on Stroop III, Ekman faces, and Happé cartoons correlated with higher age in carriers, but not controls. Reduced fractional anisotropy and increased radial diffusivity throughout frontotemporal white matter tracts were found in carriers and correlated with higher age. Reductions in functional aMCC connectivity were found in carriers compared with controls, and connectivity between frontoinsula and aMCC seeds and several brain regions significantly decreased with higher age in carriers but not controls. We found no significant differences or age correlations in posterior cingulate cortex connectivity. No differences in regional gray matter volume were found.

Conclusions: This study convincingly demonstrates that alterations in structural and functional connectivity develop before the first symptoms of FTD arise. These findings suggest that diffusion tensor imaging and resting-state fMRI may have the potential to become sensitive biomarkers for early FTD in future clinical trials.
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http://dx.doi.org/10.1212/WNL.0b013e31828407bcDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3598452PMC
February 2013
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