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      Characterizing sleep spindles in 11,630 individuals from the National Sleep Research Resource

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          Abstract

          Sleep spindles are characteristic electroencephalogram (EEG) signatures of stage 2 non-rapid eye movement sleep. Implicated in sleep regulation and cognitive functioning, spindles may represent heritable biomarkers of neuropsychiatric disease. Here we characterize spindles in 11,630 individuals aged 4 to 97 years, as a prelude to future genetic studies. Spindle properties are highly reliable but exhibit distinct developmental trajectories. Across the night, we observe complex patterns of age- and frequency-dependent dynamics, including signatures of circadian modulation. We identify previously unappreciated correlates of spindle activity, including confounding by body mass index mediated by cardiac interference in the EEG. After taking account of these confounds, genetic factors significantly contribute to spindle and spectral sleep traits. Finally, we consider topographical differences and critical measurement issues. Taken together, our findings will lead to an increased understanding of the genetic architecture of sleep spindles and their relation to behavioural and health outcomes, including neuropsychiatric disorders.

          Abstract

          Sleep patterns vary and are associated with health and disease. Here Purcell et al characterize sleep spindle activity in 11,630 individuals and describe age-related changes, genetic influences, and possible confounding effects, serving as a resource for further understanding the physiology of sleep.

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          PLINK: a tool set for whole-genome association and population-based linkage analyses.

          Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
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            A global reference for human genetic variation

            The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.
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              Biological Insights From 108 Schizophrenia-Associated Genetic Loci

              Summary Schizophrenia is a highly heritable disorder. Genetic risk is conferred by a large number of alleles, including common alleles of small effect that might be detected by genome-wide association studies. Here, we report a multi-stage schizophrenia genome-wide association study of up to 36,989 cases and 113,075 controls. We identify 128 independent associations spanning 108 conservatively defined loci that meet genome-wide significance, 83 of which have not been previously reported. Associations were enriched among genes expressed in brain providing biological plausibility for the findings. Many findings have the potential to provide entirely novel insights into aetiology, but associations at DRD2 and multiple genes involved in glutamatergic neurotransmission highlight molecules of known and potential therapeutic relevance to schizophrenia, and are consistent with leading pathophysiological hypotheses. Independent of genes expressed in brain, associations were enriched among genes expressed in tissues that play important roles in immunity, providing support for the hypothesized link between the immune system and schizophrenia.
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                Author and article information

                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group
                2041-1723
                26 June 2017
                2017
                : 8
                : 15930
                Affiliations
                [1 ]Department of Psychiatry, Brigham and Women’s Hospital , Boston, Massachusetts 02115, USA
                [2 ]Harvard Medical School , Boston, Massachusetts 02115, USA
                [3 ]Department of Psychiatry, Icahn School of Medicine at Mount Sinai , New York, New York 10029, USA
                [4 ]Department of Psychiatry, Massachusetts General Hospital , Boston, Massachusetts 02114, USA
                [5 ]Athinoula A. Martinos Center for Biomedical Imaging , Charlestown, Massachusetts 02129, USA
                [6 ]Division of Sleep and Circadian Disorders, Brigham and Women's Hospital , Boston, Massachusetts 02115, USA
                [7 ]Division of Sleep Medicine, Harvard Medical School , Boston, Massachusetts 02115, USA
                [8 ]Department of Psychiatry, Beth Israel Deaconess Medical Center , Boston, Massachusetts 02215, USA
                [9 ]Center for Human Genetic Research, Massachusetts General Hospital , Boston, Massachusetts 02114, USA
                [10 ]Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital , Boston, Massachusetts 02114, USA
                [11 ]Program in Medical and Population Genetics, Broad Institute , Cambridge, Massachusetts 02142, USA
                [12 ]Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT , Cambridge, Massachusetts 02142, USA
                [13 ]Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital , Boston, Massachusetts 02114, USA
                Author notes
                [*]

                These authors contributed equally to this work.

                Article
                ncomms15930
                10.1038/ncomms15930
                5490197
                28649997
                c6cb054f-6312-46b7-b4e1-7f4e954f7256
                Copyright © 2017, The Author(s)

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 12 August 2016
                : 12 May 2017
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