Publications by authors named "Christiaan A de Leeuw"

22 Publications

  • Page 1 of 1

Systematic assessment of variability in the proteome of iPSC derivatives.

Stem Cell Res 2021 Aug 20;56:102512. Epub 2021 Aug 20.

Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, The Netherlands; Department of Child and Youth Psychiatry, Emma Children's Hospital, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, The Netherlands. Electronic address:

The use of induced pluripotent stem cells (iPSC) to model human complex diseases is gaining popularity as it allows investigation of human cells that are otherwise sparsely available. However, due to its laborious and cost intensive nature, iPSC research is often plagued by limited sample size and putative large variability between clones, decreasing statistical power for detecting experimental effects. Here, we investigate the source and magnitude of variability in the proteome of parallel differentiated astrocytes using mass spectrometry. We compare three possible sources of variability: inter-donor variability, inter- and intra-clonal variability, at different stages of maturation. We show that the interclonal variability is significantly smaller than the inter-donor variability, and that including more donors has a much larger influence on statistical power than adding more clones per donor. Our results provide insight into the sources of variability at protein level between iPSC samples derived in parallel and will aid in optimizing iPSC studies.
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http://dx.doi.org/10.1016/j.scr.2021.102512DOI Listing
August 2021

Correction to: Genome-wide gene-environment interactions in neuroticism: an exploratory study across 25 environments.

Transl Psychiatry 2021 Apr 8;11(1):207. Epub 2021 Apr 8.

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

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http://dx.doi.org/10.1038/s41398-021-01334-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8032730PMC
April 2021

Genome-wide gene-environment interactions in neuroticism: an exploratory study across 25 environments.

Transl Psychiatry 2021 03 22;11(1):180. Epub 2021 Mar 22.

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

Gene-environment interactions (GxE) are often suggested to play an important role in the aetiology of psychiatric phenotypes, yet so far, only a handful of genome-wide environment interaction studies (GWEIS) of psychiatric phenotypes have been conducted. Representing the most comprehensive effort of its kind to date, we used data from the UK Biobank to perform a series of GWEIS for neuroticism across 25 broadly conceptualised environmental risk factors (trauma, social support, drug use, physical health). We investigated interactions on the level of SNPs, genes, and gene-sets, and computed interaction-based polygenic risk scores (PRS) to predict neuroticism in an independent sample subset (N = 10,000). We found that the predictive ability of the interaction-based PRSs did not significantly improve beyond that of a traditional PRS based on SNP main effects from GWAS, but detected one variant and two gene-sets showing significant interaction signal after correction for the number of analysed environments. This study illustrates the possibilities and limitations of a comprehensive GWEIS in currently available sample sizes.
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http://dx.doi.org/10.1038/s41398-021-01288-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7985503PMC
March 2021

Genome-wide meta-analysis of brain volume identifies genomic loci and genes shared with intelligence.

Nat Commun 2020 11 5;11(1):5606. Epub 2020 Nov 5.

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

The phenotypic correlation between human intelligence and brain volume (BV) is considerable (r ≈ 0.40), and has been shown to be due to shared genetic factors. To further examine specific genetic factors driving this correlation, we present genomic analyses of the genetic overlap between intelligence and BV using genome-wide association study (GWAS) results. First, we conduct a large BV GWAS meta-analysis (N = 47,316 individuals), followed by functional annotation and gene-mapping. We identify 18 genomic loci (14 not previously associated), implicating 343 genes (270 not previously associated) and 18 biological pathways for BV. Second, we use an existing GWAS for intelligence (N = 269,867 individuals), and estimate the genetic correlation (r) between BV and intelligence to be 0.24. We show that the r is partly attributable to physical overlap of GWAS hits in 5 genomic loci. We identify 92 shared genes between BV and intelligence, which are mainly involved in signaling pathways regulating cell growth. Out of these 92, we prioritize 32 that are most likely to have functional impact. These results provide information on the genetics of BV and provide biological insight into BV's shared genetic etiology with intelligence.
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http://dx.doi.org/10.1038/s41467-020-19378-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7644755PMC
November 2020

Author Correction: Genetic mapping of cell type specificity for complex traits.

Nat Commun 2020 Apr 1;11(1):1718. Epub 2020 Apr 1.

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

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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http://dx.doi.org/10.1038/s41467-020-15365-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7113291PMC
April 2020

Recognizing peripheral ecosystems in marine protected areas: A case study of golden jellyfish lakes in Raja Ampat, Indonesia.

Mar Pollut Bull 2020 Feb 29;151:110700. Epub 2020 Jan 29.

Department of Marine Animal Ecology, Wageningen University & Research, Droevendaalsesteeg 1, 6708PB Wageningen, the Netherlands; Wageningen Marine Research, Ankerpark 27, 1781AG Den Helder, the Netherlands. Electronic address:

Peripheral marine ecosystems can harbor endemic diversity and attract tourism attention, yet are generally not included in conservation management plans due to their remoteness or inland positioning. A case study in Raja Ampat of seven landlocked marine lakes containing golden jellyfish (Mastigias spp.) was conducted to address the lack of fundamental insights into evolutionary, ecological and social contexts of these ecosystems. An interdisciplinary approach was taken towards identifying the jellyfish lakes as distinct management units in order to incorporate them into existing Marine Protected Areas. Mastigias papua populations showed strong genetic (ϕ: 0.30-0.86) and morphological (F = 28.62, p-value = 0.001) structure among lakes, with putative new subspecies. Risks arising from rapid increase in tourism to Raja Ampat (30-fold since 2007) warrant restrictions on jellyfish lake use. Recommendations are provided for adaptive management and science-based conservation policies for jellyfish lakes across Indonesia.
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http://dx.doi.org/10.1016/j.marpolbul.2019.110700DOI Listing
February 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

Genetic mapping of cell type specificity for complex traits.

Nat Commun 2019 07 19;10(1):3222. Epub 2019 Jul 19.

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

Single-cell RNA sequencing (scRNA-seq) data allows to create cell type specific transcriptome profiles. Such profiles can be aligned with genome-wide association studies (GWASs) to implicate cell type specificity of the traits. Current methods typically rely only on a small subset of available scRNA-seq datasets, and integrating multiple datasets is hampered by complex batch effects. Here we collated 43 publicly available scRNA-seq datasets. We propose a 3-step workflow with conditional analyses within and between datasets, circumventing batch effects, to uncover associations of traits with cell types. Applying this method to 26 traits, we identify independent associations of multiple cell types. These results lead to starting points for follow-up functional studies aimed at gaining a mechanistic understanding of these traits. The proposed framework as well as the curated scRNA-seq datasets are made available via an online platform, FUMA, to facilitate rapid evaluation of cell type specificity by other researchers.
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http://dx.doi.org/10.1038/s41467-019-11181-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6642112PMC
July 2019

Genome-wide association study identifies 30 loci associated with bipolar disorder.

Nat Genet 2019 05 1;51(5):793-803. Epub 2019 May 1.

Department of Psychiatry, Weill Cornell Medical College, New York, NY, USA.

Bipolar disorder is a highly heritable psychiatric disorder. We performed a genome-wide association study (GWAS) including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P < 1 × 10 in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (P < 5 × 10) in the discovery GWAS were not genome-wide significant in the combined analysis, consistent with small effect sizes and limited power but also with genetic heterogeneity. In the combined analysis, 30 loci were genome-wide significant, including 20 newly identified loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene sets, including regulation of insulin secretion and endocannabinoid signaling. Bipolar I disorder is strongly genetically correlated with schizophrenia, driven by psychosis, whereas bipolar II disorder is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential biological mechanisms for bipolar disorder.
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http://dx.doi.org/10.1038/s41588-019-0397-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6956732PMC
May 2019

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

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

Integrative functional genomic analysis of human brain development and neuropsychiatric risks.

Science 2018 12;362(6420)

To broaden our understanding of human neurodevelopment, we profiled transcriptomic and epigenomic landscapes across brain regions and/or cell types for the entire span of prenatal and postnatal development. Integrative analysis revealed temporal, regional, sex, and cell type-specific dynamics. We observed a global transcriptomic cup-shaped pattern, characterized by a late fetal transition associated with sharply decreased regional differences and changes in cellular composition and maturation, followed by a reversal in childhood-adolescence, and accompanied by epigenomic reorganizations. Analysis of gene coexpression modules revealed relationships with epigenomic regulation and neurodevelopmental processes. Genes with genetic associations to brain-based traits and neuropsychiatric disorders (including , , , , and ) converged in a small number of modules and distinct cell types, revealing insights into neurodevelopment and the genomic basis of neuropsychiatric risks.
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http://dx.doi.org/10.1126/science.aat7615DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6413317PMC
December 2018

Conditional and interaction gene-set analysis reveals novel functional pathways for blood pressure.

Nat Commun 2018 09 14;9(1):3768. Epub 2018 Sep 14.

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

Gene-set analysis provides insight into which functional and biological properties of genes are aetiologically relevant for a particular phenotype. But genes have multiple properties, and these properties are often correlated across genes. This can cause confounding in a gene-set analysis, because one property may be statistically associated even if biologically irrelevant to the phenotype, by being correlated with gene properties that are relevant. To address this issue we present a novel conditional and interaction gene-set analysis approach, which attains considerable functional refinement of its conclusions compared to traditional gene-set analysis. We applied our approach to blood pressure phenotypes in the UK Biobank data (N = 360,243), the results of which we report here. We confirm and further refine several associations with multiple processes involved in heart and blood vessel formation but also identify novel interactions, among others with cardiovascular tissues involved in regulatory pathways of blood pressure homoeostasis.
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http://dx.doi.org/10.1038/s41467-018-06022-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6138636PMC
September 2018

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

No Evidence That Schizophrenia Candidate Genes Are More Associated With Schizophrenia Than Noncandidate Genes.

Biol Psychiatry 2017 Nov 13;82(10):702-708. Epub 2017 Jul 13.

Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, Colorado; Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado.

Background: A recent analysis of 25 historical candidate gene polymorphisms for schizophrenia in the largest genome-wide association study conducted to date suggested that these commonly studied variants were no more associated with the disorder than would be expected by chance. However, the same study identified other variants within those candidate genes that demonstrated genome-wide significant associations with schizophrenia. As such, it is possible that variants within historic schizophrenia candidate genes are associated with schizophrenia at levels above those expected by chance, even if the most-studied specific polymorphisms are not.

Methods: The present study used association statistics from the largest schizophrenia genome-wide association study conducted to date as input to a gene set analysis to investigate whether variants within schizophrenia candidate genes are enriched for association with schizophrenia.

Results: As a group, variants in the most-studied candidate genes were no more associated with schizophrenia than were variants in control sets of noncandidate genes. While a small subset of candidate genes did appear to be significantly associated with schizophrenia, these genes were not particularly noteworthy given the large number of more strongly associated noncandidate genes.

Conclusions: The history of schizophrenia research should serve as a cautionary tale to candidate gene investigators examining other phenotypes: our findings indicate that the most investigated candidate gene hypotheses of schizophrenia are not well supported by genome-wide association studies, and it is likely that this will be the case for other complex traits as well.
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http://dx.doi.org/10.1016/j.biopsych.2017.06.033DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5643230PMC
November 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

Highly divergent mussel lineages in isolated Indonesian marine lakes.

PeerJ 2016 13;4:e2496. Epub 2016 Oct 13.

Department of Marine Biodiversity, Naturalis Biodiversity Center, Leiden, The Netherlands; Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, The Netherlands.

Marine lakes, with populations in landlocked seawater and clearly delineated contours, have the potential to provide a unique model to study early stages of evolution in coastal marine taxa. Here we ask whether populations of the mussel from marine lakes in Berau, East Kalimantan (Indonesia) are isolated from each other and from the coastal mangrove systems. We analyzed sequence data of one mitochondrial marker (Cytochrome Oxidase I (COI)), and two nuclear markers (18S and 28S). In addition, we examined shell shape using a geometric morphometric approach. The Indonesian populations of spp. harbored four deeply diverged lineages (14-75% COI corrected net sequence divergence), two of which correspond to previously recorded lineages from marine lakes in Palau, 1,900 km away. These four lineages also showed significant differences in shell shape and constitute a species complex of at least four undescribed species. Each lake harbored a different lineage despite the fact that the lakes are separated from each other by only 2-6 km, while the two mangrove populations, at 20 km distance from each other, harbored the same lineage and shared haplotypes. Marine lakes thus represent isolated habitats. As each lake contained unique within lineage diversity (0.1-0.2%), we suggest that this may have resulted from divergence due to isolation of founder populations after the formation of the lakes (6,000-12,000 years before present). Combined effects of stochastic processes, local adaptation and increased evolutionary rates could produce high levels of differentiation in small populations such as in marine lake environments. Such short-term isolation at small spatial scales may be an important contributing factor to the high marine biodiversity that is found in the Indo-Australian Archipelago.
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http://dx.doi.org/10.7717/peerj.2496DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5068364PMC
October 2016

The statistical properties of gene-set analysis.

Nat Rev Genet 2016 04;17(6):353-64

Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research/VU University Amsterdam, Amsterdam 1081 HV, Netherlands.

The rapid increase in loci discovered in genome-wide association studies has created a need to understand the biological implications of these results. Gene-set analysis provides a means of gaining such understanding, but the statistical properties of gene-set analysis are not well understood, which compromises our ability to interpret its results. In this Analysis article, we provide an extensive statistical evaluation of the core structure that is inherent to all gene- set analyses and we examine current implementations in available tools. We show which factors affect valid and successful detection of gene sets and which provide a solid foundation for performing and interpreting gene-set analysis.
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http://dx.doi.org/10.1038/nrg.2016.29DOI Listing
April 2016

Meta-analysis of the heritability of human traits based on fifty years of twin studies.

Nat Genet 2015 Jul 18;47(7):702-9. Epub 2015 May 18.

1] Department of Complex Trait Genetics, VU University, Center for Neurogenomics and Cognitive Research, Amsterdam, the Netherlands. [2] Department of Clinical Genetics, VU University Medical Center, Neuroscience Campus Amsterdam, Amsterdam, the Netherlands.

Despite a century of research on complex traits in humans, the relative importance and specific nature of the influences of genes and environment on human traits remain controversial. We report a meta-analysis of twin correlations and reported variance components for 17,804 traits from 2,748 publications including 14,558,903 partly dependent twin pairs, virtually all published twin studies of complex traits. Estimates of heritability cluster strongly within functional domains, and across all traits the reported heritability is 49%. For a majority (69%) of traits, the observed twin correlations are consistent with a simple and parsimonious model where twin resemblance is solely due to additive genetic variation. The data are inconsistent with substantial influences from shared environment or non-additive genetic variation. This study provides the most comprehensive analysis of the causes of individual differences in human traits thus far and will guide future gene-mapping efforts. All the results can be visualized using the MaTCH webtool.
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http://dx.doi.org/10.1038/ng.3285DOI Listing
July 2015

MAGMA: generalized gene-set analysis of GWAS data.

PLoS Comput Biol 2015 Apr 17;11(4):e1004219. Epub 2015 Apr 17.

Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, VU University Amsterdam, Amsterdam, The Netherlands; Department of Clinical Genetics, VU University Medical Centre Amsterdam, Neuroscience Campus Amsterdam, The Netherlands.

By aggregating data for complex traits in a biologically meaningful way, gene and gene-set analysis constitute a valuable addition to single-marker analysis. However, although various methods for gene and gene-set analysis currently exist, they generally suffer from a number of issues. Statistical power for most methods is strongly affected by linkage disequilibrium between markers, multi-marker associations are often hard to detect, and the reliance on permutation to compute p-values tends to make the analysis computationally very expensive. To address these issues we have developed MAGMA, a novel tool for gene and gene-set analysis. The gene analysis is based on a multiple regression model, to provide better statistical performance. The gene-set analysis is built as a separate layer around the gene analysis for additional flexibility. This gene-set analysis also uses a regression structure to allow generalization to analysis of continuous properties of genes and simultaneous analysis of multiple gene sets and other gene properties. Simulations and an analysis of Crohn's Disease data are used to evaluate the performance of MAGMA and to compare it to a number of other gene and gene-set analysis tools. The results show that MAGMA has significantly more power than other tools for both the gene and the gene-set analysis, identifying more genes and gene sets associated with Crohn's Disease while maintaining a correct type 1 error rate. Moreover, the MAGMA analysis of the Crohn's Disease data was found to be considerably faster as well.
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http://dx.doi.org/10.1371/journal.pcbi.1004219DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4401657PMC
April 2015
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