The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

Integrative analysis of 111 reference human epigenomes

Chromatin state annotation using combinations of chromatin modification patterns has emerged as a powerful approach for discovering regulatory regions and their cell type specific activity patterns, and for interpreting disease-association studies1-5. However, the computational challenge of learning chromatin state models from large numbers of chromatin modification datasets in multiple cell types still requires extensive bioinformatics expertise making it inaccessible to the wider scientific community. To address this challenge, we have developed ChromHMM, an automated computational system for learning chromatin states, characterizing their biological functions and correlations with large-scale functional datasets, and visualizing the resulting genome-wide maps of chromatin state annotations.

ChromHMM: automating chromatin-state discovery and characterization

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

/pdf/genomic-structural-equation-modelling-provides-insights-into-3f0ww78svw.pdf

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

Physical activity and sleep duration are established risk factors for many diseases, but their aetiology is poorly understood, partly due to relying on self-reported evidence. Here we report a genome-wide association study (GWAS) of device-measured physical activity and sleep duration in 91,105 UK Biobank participants, finding 14 significant loci (7 novel). These loci account for 0.06% of activity and 0.39% of sleep duration variation. Genome-wide estimates of ~ 15% phenotypic variation indicate high polygenicity. Heritability is higher in women than men for overall activity (23 vs. 20%, p = 1.5 × 10−4) and sedentary behaviours (18 vs. 15%, p = 9.7 × 10−4). Heritability partitioning, enrichment and pathway analyses indicate the central nervous system plays a role in activity behaviours. Two-sample Mendelian randomisation suggests that increased activity might causally lower diastolic blood pressure (beta mmHg/SD: −0.91, SE = 0.18, p = 8.2 × 10−7), and odds of hypertension (Odds ratio/SD: 0.84, SE = 0.03, p = 4.9 × 10−8). Our results advocate the value of physical activity for reducing blood pressure. Studying the genetic underpinnings of physical activity and sleep duration can be confounded by self-reporting. Here, Doherty et al. use data from 91,105 UK Biobank participants, whose activity had been monitored for a week by a wearable device, for genome-wide association analysis and identify 14 loci.

/pdf/gwas-identifies-14-loci-for-device-measured-physical-23pwthvmm0.pdf

GWAS identifies 14 loci for device-measured physical activity and sleep duration.

Insomnia is a common disorder linked with adverse long-term medical and psychiatric outcomes. The underlying pathophysiological processes and causal relationships of insomnia with disease are poorly understood. Here we identified 57 loci for self-reported insomnia symptoms in the UK Biobank (n = 453,379) and confirmed their effects on self-reported insomnia symptoms in the HUNT Study (n = 14,923 cases and 47,610 controls), physician-diagnosed insomnia in the Partners Biobank (n = 2,217 cases and 14,240 controls), and accelerometer-derived measures of sleep efficiency and sleep duration in the UK Biobank (n = 83,726). Our results suggest enrichment of genes involved in ubiquitin-mediated proteolysis and of genes expressed in multiple brain regions, skeletal muscle, and adrenal glands. Evidence of shared genetic factors was found between frequent insomnia symptoms and restless legs syndrome, aging, and cardiometabolic, behavioral, psychiatric, and reproductive traits. Evidence was found for a possible causal link between insomnia symptoms and coronary artery disease, depressive symptoms, and subjective well-being.

/pdf/biological-and-clinical-insights-from-genetics-of-insomnia-15sav8zzfe.pdf

Biological and clinical insights from genetics of insomnia symptoms

Insomnia is the second-most prevalent mental disorder, with no sufficient treatment available. Despite a substantial role of genetic factors, only a handful of genes have been implicated and insight into the associated neurobiological pathways remains limited. Here, we use an unprecedented large genetic association sample (N=1,331,010) to allow detection of a substantial number of genetic variants and gain insight into biological functions, cell types and tissues involved in insomnia. We identify 202 genome-wide significant loci implicating 956 genes through positional, eQTL and chromatin interaction mapping. We show involvement of the axonal part of neurons, of specific cortical and subcortical tissues, and of two specific cell-types in insomnia: striatal medium spiny neurons and hypothalamic neurons. These cell-types have been implicated previously in the regulation of reward processing, sleep and arousal in animal studies, but have never been genetically linked to insomnia in humans. We found weak genetic correlations with other sleep-related traits, but strong genetic correlations with psychiatric and metabolic traits. Mendelian randomization identified causal effects of insomnia on specific psychiatric and metabolic traits. Our findings reveal key brain areas and cells implicated in the neurobiology of insomnia and its related disorders, and provide novel targets for treatment.

/pdf/genome-wide-analysis-of-insomnia-n-1-331-010-identifies-2pxkosjba2.pdf

Genome-wide Analysis of Insomnia (N=1,331,010) Identifies Novel Loci and Functional Pathways

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Genome-wide Analysis of Insomnia (N=1,331,010) Identifies Novel Loci and Functional Pathways