53 research outputs found

    Associations of sleep with psychological problems and well-being in adolescence: causality or common genetic predispositions?

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    Background: Whereas short and problematic sleep are associated with psychological problems in adolescence, causality remains to be elucidated. This study therefore utilized the discordant monozygotic cotwin design and cross-lagged models to investigate how short and problematic sleep affect psychological functioning. Methods: Adolescent twins (N = 12,803, 13–20 years, 42% male) completed questionnaires on sleep and psychological functioning repeatedly over a two-year interval. Monozygotic twin pairs were classified as concordant or discordant for sleep duration and trouble sleeping. Resulting subgroups were compared regarding internalizing problems, externalizing problems, and subjective well-being. Results: Cross-sectional analyses indicated associations of worse psychological functioning with both short sleep and problematic sleep, and cross-lagged models indicate bidirectional associations. Longitudinal analyses showed that an increase in sleep problems experienced selectively by one individual of an identical twin pair was accompanied by an increase of 52% in internalizing problem scores and 25% in externalizing problem scores. These changes were significantly different from the within-subject changes in cotwins with unchanged sleep quality (respectively, 3% increase and 5% decrease). Psychological functioning did, however, not worsen with decreasing sleep duration. Conclusions: The findings suggest that sleep quality, rather than sleep duration, should be the primary target for prevention and intervention, with possible effect on psychological functioning in adolescents

    Resequencing three candidate genes for major depressive disorder in a Dutch cohort

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    Major depressive disorder (MDD) is a psychiatric disorder, characterized by periods of low mood of more than two weeks, loss of interest in normally enjoyable activities and behavioral changes. MDD is a complex disorder and does not have a single genetic cause. In 2009 a genome wide association study (GWAS) was performed on the Dutch GAIN-MDD cohort. Many of the top signals of this GWAS mapped to a region spanning the gene PCLO, and the non-synonymous coding single nucleotide polymorphism (SNP) rs2522833 in the PCLO gene became genome wide significant after post-hoc analysis. We performed resequencing of PCLO, GRM7, and SLC6A4 in 50 control samples from the GAIN-MDD cohort, to detect new genomic variants. Subsequently, we genotyped these variants in the entire GAIN-MDD cohort and performed association analysis to investigate if rs2522833 is the causal variant or simply in linkage disequilibrium with a more associated variant. GRM7 and SLC6A4 are both candidate genes for MDD from literature. We aimed to gather more evidence that rs2522833 is indeed the causal variant in the GAIN-MDD cohort or to find a previously undetected common variant in either PCLO, GRM7, or SLC6A4 with a higher association in this cohort. After next generation sequencing and association analysis we excluded the possibility of an undetected common variant to be more associated. For neither PCLO nor GRM7 we found a more associated variant. For SLC6A4, we found a new SNP that showed a lower P-value (P = 0.07) than in the GAIN-MDD GWAS (P = 0.09). However, no evidence for genome-wide significance was found. Although we did not take into account rare variants, we conclude that our results provide further support for the hypothesis that the non-synonymous coding SNP rs2522833 in the PCLO gene is indeed likely to be the causal variant in the GAIN-MDD cohort

    Genome-wide identification of genes regulating DNA methylation using genetic anchors for causal inference

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    BACKGROUND: DNA methylation is a key epigenetic modification in human development and disease, yet there is limited understanding of its highly coordinated regulation. Here, we identify 818 genes that affect DNA methylation patterns in blood using large-scale population genomics data. RESULTS: By employing genetic instruments as causal anchors, we establish directed associations between gene expression and distant DNA methylation levels, while ensuring specificity of the associations by correcting for linkage disequilibrium and pleiotropy among neighboring genes. The identified genes are enriched for transcription factors, of which many consistently increased or decreased DNA methylation levels at multiple CpG sites. In addition, we show that a substantial number of transcription factors affected DNA methylation at their experimentally determined binding sites. We also observe genes encoding proteins with heterogenous functions that have widespread effects on DNA methylation, e.g., NFKBIE, CDCA7(L), and NLRC5, and for several examples, we suggest plausible mechanisms underlying their effect on DNA methylation. CONCLUSION: We report hundreds of genes that affect DNA methylation and provide key insights in the principles underlying epigenetic regulation

    Estimation of Genetic Relationships Between Individuals Across Cohorts and Platforms: Application to Childhood Height

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    Combining genotype data across cohorts increases power to estimate the heritability due to common single nucleotide polymorphisms (SNPs), based on analyzing a Genetic Relationship Matrix (GRM). However, the combination of SNP data across multiple cohorts may lead to stratification, when for example, different genotyping platforms are used. In the current study, we address issues of combining SNP data from different cohorts, the Netherlands Twin Register (NTR) and the Generation R (GENR) study. Both cohorts include children of Northern European Dutch background (N = 3102 + 2826, respectively) who were genotyped on different platforms. We explore imputation and phasing as a tool and compare three GRM-building strategies, when data from two cohorts are (1) just comb

    Gene co-expression analysis identifies brain regions and cell types involved in migraine pathophysiology

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    Migraine is a common disabling neurovascular brain disorder typically characterised by attacks of severe headache and associated with autonomic and neurological symptoms. Migraine is caused by an interplay of genetic and environmental factors. Genome-wide association studies (GWAS) have identified over a dozen genetic loci associated with migraine. Here, we integrated migraine GWAS data with high-resolution spatial gene expression data of normal adult brains from the Allen Human Brain Atlas to identify specific brain regions and molecular pathways that are possibly involved in migraine pathophysiology. To this end, we used two complementary methods. In GWAS data from 23,285 migraine cases and 95,425 controls, we first studied modules of co-expressed genes that were calculated based on human brain expression data for enrichment of genes that showed association with migraine. Enrichment of a migraine GWAS signal was found for five modules that suggest involvement in migraine pathophysiology of: (i) neurotransmission, protein catabolism and mitochondria in the cortex; (ii) transcription regulation in the cortex and cerebellum; and (iii) oligodendrocytes and mitochondria in subcortical areas. Second, we used the high-confidence genes from the migraine GWAS as a basis to construct local migraine-related co-expression gene networks. Signatures of all brain regions and pathways that were prominent in the first method also surfaced in the second method, thus providing support that these brain regions and pathways are indeed involved in migraine pathophysiology

    Bayesian evidence synthesis in case of multi-cohort datasets: An illustration by multi-informant differences in self-control

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    The trend toward large-scale collaborative studies gives rise to the challenge of combining data from different sources efficiently. Here, we demonstrate how Bayesian evidence synthesis can be used to quantify and compare support for competing hypotheses and to aggregate this support over studies. We applied this method to study the ordering of multi-informant scores on the ASEBA Self Control Scale (ASCS), employing a multi-cohort design with data from four Dutch cohorts. Self-control reports were collected from mothers, fathers, teachers and children themselves. The available set of reporters differed between cohorts, so in each cohort varying components of the overarching hypotheses were evaluated. We found consistent support for the partial hypothesis that parents reported more self-control problems than teachers. Furthermore, the aggregated results indicate most support for the combined hypothesis that children report most problem behaviors, followed by their mothers and fathers, and that teachers report the fewest problems. However, there was considerable inconsistency across cohorts regarding the rank order of children's reports. This article illustrates Bayesian evidence synthesis as a method when some of the cohorts only have data to ev

    Age-related accrual of methylomic variability is linked to fundamental ageing mechanisms

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    Background: Epigenetic change is a hallmark of ageing but its link to ageing mechanisms in humans remains poorly understood. While DNA methylation at many CpG sites closely tracks chronological age, DNA methylation changes relevant to biological age are expected to gradually dissociate from chronological age, mirroring the increased heterogeneity in health status at older ages. Results: Here, we report on the large-scale identification of 6366 age-related variably methylated positions (aVMPs) identified in 3295 whole blood DNA methylation profiles, 2044 of which have a matching RNA-seq gene expression profile. aVMPs are enriched at polycomb repressed regions and, accordingly, methylation at those positions is associated with the expression of genes encoding components of polycomb repressive complex 2 (PRC2) in trans. Further analysis revealed trans-associations for 1816 aVMPs with an additional 854 genes. These trans-associated aVMPs are characterized by either an age-related

    Harmonising and linking biomedical and clinical data across disparate data archives to enable integrative cross-biobank research

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    A wealth of biospecimen samples are stored in modern globally distributed biobanks. Biomedical researchers worldwide need to be able to combine the available resources to improve the power of large-scale studies. A prerequisite for this effort is to be able to search and access phenotypic, clinical and other information about samples that are currently stored at biobanks in an integrated manner. However, privacy issues together with heterogeneous information systems and the lack of agreed-upon vocabularies have made specimen searching across multiple biobanks extremely challenging. We describe three case studies where we have linked samples and sample descriptions in order to facilitate global searching of available samples for research. The use cases include the ENGAGE (European Network for Genetic and Genomic Epidemiology) consortium comprising at least 39 cohorts, the SUMMIT (surrogate markers for micro- and macro-vascular hard endpoints for innovative diabetes tools) consortium and a pilot for data integration between a Swedish clinical health registry and a biobank. We used the Sample avAILability (SAIL) method for data linking: first, created harmonised variables and then annotated and made searchable information on the number of specimens available in individual biobanks for various phenotypic categories. By operating on this categorised availability data we sidestep many obstacles related to privacy that arise when handling real values and show that harmonised and annotated records about data availability across disparate biomedical archives provide a key methodological advance in pre-analysis exchange of information between biobanks, that is, during the project planning phase
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