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      Large genome-wide association study identifies three novel risk variants for restless legs syndrome

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      1 , 2 , 2 , 3 , 1 , 4 , 5 , 6 , 7 , 8 , 1 , 9 , 10 , 1 , 11 , 4 , 5 , 6 , 4 , 12 , 13 , 14 , 14 , 14 , 2 , 2 , 2 , 3 , 15 , 3 , 16 , 16 , 17 , 4 , 12 , 13 , 4 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 4 , 5 , 6 , 13 , 4 , 5 , 6 , 2 , 1 , , 2
      Communications Biology
      Nature Publishing Group UK
      Sleep disorders, Gene expression, Genome-wide association studies

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          Abstract

          Restless legs syndrome (RLS) is a common neurological sensorimotor disorder often described as an unpleasant sensation associated with an urge to move the legs. Here we report findings from a meta-analysis of genome-wide association studies of RLS including 480,982 Caucasians (cases = 10,257) and a follow up sample of 24,977 (cases = 6,651). We confirm 19 of the 20 previously reported RLS sequence variants at 19 loci and report three novel RLS associations; rs112716420-G (OR = 1.25, P = 1.5 × 10 −18), rs10068599-T (OR = 1.09, P = 6.9 × 10 −10) and rs10769894-A (OR = 0.90, P = 9.4 × 10 −14). At four of the 22 RLS loci, cis-eQTL analysis indicates a causal impact on gene expression. Through polygenic risk score for RLS we extended prior epidemiological findings implicating obesity, smoking and high alcohol intake as risk factors for RLS. To improve our understanding, with the purpose of seeking better treatments, more genetics studies yielding deeper insights into the disease biology are needed.

          Abstract

          Didriksen, Nawaz, et al. identify three novel genetic risk variants for restless legs syndrome and confirm 19 out of 20 previously reported variants through a genome-wide association meta-analysis including nearly half a million individuals. Using expression QTL analysis, they also find that a subset of these loci may have a causal effect on nearby gene expression.

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          The Molecular Signatures Database (MSigDB) hallmark gene set collection.

          The Molecular Signatures Database (MSigDB) is one of the most widely used and comprehensive databases of gene sets for performing gene set enrichment analysis. Since its creation, MSigDB has grown beyond its roots in metabolic disease and cancer to include >10,000 gene sets. These better represent a wider range of biological processes and diseases, but the utility of the database is reduced by increased redundancy across, and heterogeneity within, gene sets. To address this challenge, here we use a combination of automated approaches and expert curation to develop a collection of "hallmark" gene sets as part of MSigDB. Each hallmark in this collection consists of a "refined" gene set, derived from multiple "founder" sets, that conveys a specific biological state or process and displays coherent expression. The hallmarks effectively summarize most of the relevant information of the original founder sets and, by reducing both variation and redundancy, provide more refined and concise inputs for gene set enrichment analysis.
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            The UK Biobank resource with deep phenotyping and genomic data

            The UK Biobank project is a prospective cohort study with deep genetic and phenotypic data collected on approximately 500,000 individuals from across the United Kingdom, aged between 40 and 69 at recruitment. The open resource is unique in its size and scope. A rich variety of phenotypic and health-related information is available on each participant, including biological measurements, lifestyle indicators, biomarkers in blood and urine, and imaging of the body and brain. Follow-up information is provided by linking health and medical records. Genome-wide genotype data have been collected on all participants, providing many opportunities for the discovery of new genetic associations and the genetic bases of complex traits. Here we describe the centralized analysis of the genetic data, including genotype quality, properties of population structure and relatedness of the genetic data, and efficient phasing and genotype imputation that increases the number of testable variants to around 96 million. Classical allelic variation at 11 human leukocyte antigen genes was imputed, resulting in the recovery of signals with known associations between human leukocyte antigen alleles and many diseases.
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              LD Score regression distinguishes confounding from polygenicity in genome-wide association studies.

              Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size.
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                Author and article information

                Contributors
                henrik.ullum@regionh.dk
                Journal
                Commun Biol
                Commun Biol
                Communications Biology
                Nature Publishing Group UK (London )
                2399-3642
                25 November 2020
                25 November 2020
                2020
                : 3
                : 703
                Affiliations
                [1 ]GRID grid.475435.4, Department of Clinical Immunology, , Copenhagen University Hospital, Rigshospitalet, ; 2100 Copenhagen, Denmark
                [2 ]GRID grid.421812.c, ISNI 0000 0004 0618 6889, deCODE Genetics, ; 101 Reykjavik, Iceland
                [3 ]GRID grid.14013.37, ISNI 0000 0004 0640 0021, Faculty of Medicine, , University of Iceland, ; 101 Reykjavik, Iceland
                [4 ]GRID grid.5335.0, ISNI 0000000121885934, The National Institute for Health Research Blood and Transplant Unit in Donor Health and Genomics, , University of Cambridge, ; Cambridge, CB1 8RN UK
                [5 ]GRID grid.5335.0, ISNI 0000000121885934, British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, , University of Cambridge, ; Cambridge, CB1 8RN UK
                [6 ]GRID grid.120073.7, ISNI 0000 0004 0622 5016, British Heart Foundation Centre of Research Excellence, Division of Cardiovascular Medicine, Addenbrooke’s Hospital, ; Cambridge, CB2 0QQ UK
                [7 ]GRID grid.154185.c, ISNI 0000 0004 0512 597X, Department of Clinical Immunology, , Aarhus University Hospital, ; Aarhus, Denmark
                [8 ]Department of Clinical Immunology, Nastved Sygehus, Nastved, Denmark
                [9 ]GRID grid.475435.4, Department of Clinical Neurophysiology, Danish Center for Sleep Medicine, , Copenhagen University Hospital, Rigshospitalet, ; Glostrup, Denmark
                [10 ]GRID grid.5254.6, ISNI 0000 0001 0674 042X, Faculty of Health, , University of Copenhagen, ; Copenhagen, Denmark
                [11 ]GRID grid.5335.0, ISNI 0000000121885934, Faculty of Human, Social and Political Sciences, , University of Cambridge, ; Cambridge, CB1 8RN UK
                [12 ]GRID grid.5335.0, ISNI 0000000121885934, Department of Haematology, , University of Cambridge, Cambridge Biomedical Campus, ; Cambridge, CB2 0PT UK
                [13 ]GRID grid.10306.34, ISNI 0000 0004 0606 5382, Department of Human Genetics, , The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, ; Cambridge, CB10 1HH UK
                [14 ]GRID grid.189967.8, ISNI 0000 0001 0941 6502, Department of Neurology and Program in Sleep, , Emory University, ; Atlanta, GA USA
                [15 ]GRID grid.416950.f, ISNI 0000 0004 0627 3771, Department of Psychiatry, , Telemark Hospital Trust, ; Skien, Norway
                [16 ]GRID grid.417732.4, ISNI 0000 0001 2234 6887, Department of Donor Studies, , Sanquin Research, ; 1066 CX Amsterdam, The Netherlands
                [17 ]GRID grid.7177.6, ISNI 0000000084992262, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam UMC, , University of Amsterdam, ; Amsterdam, The Netherlands
                [18 ]National Health Service (NHS) Blood and Transplant and Radcliffe Department of Medicine, NIHR Oxford Biomedical Research Centre, University of Oxford, John Radcliffe Hospital, Oxford, UK
                [19 ]GRID grid.415719.f, ISNI 0000 0004 0488 9484, BRC Haematology Theme and Department of Haematology, Churchill Hospital, ; Oxford, UK
                [20 ]GRID grid.62562.35, ISNI 0000000100301493, RTI International, Research Triangle Park, ; Durham, NC USA
                [21 ]Vitalant Research Institute, San Francisco, CA USA
                [22 ]GRID grid.267103.1, ISNI 0000 0004 0461 8879, Department of Laboratory Medicine, , University of San Francisco, ; San Francisco, CA USA
                [23 ]GRID grid.280427.b, ISNI 0000 0004 0434 015X, Blood Research Institute, Versiti, ; Milwaukee, WI USA
                [24 ]GRID grid.62562.35, ISNI 0000000100301493, RTI International, ; Atlanta, GA USA
                Author information
                http://orcid.org/0000-0002-4856-496X
                http://orcid.org/0000-0002-5576-9007
                http://orcid.org/0000-0001-5381-2633
                http://orcid.org/0000-0001-6774-3149
                http://orcid.org/0000-0001-6551-6647
                http://orcid.org/0000-0003-2312-5976
                http://orcid.org/0000-0002-8243-937X
                http://orcid.org/0000-0002-6915-9015
                http://orcid.org/0000-0001-9828-4459
                http://orcid.org/0000-0002-7744-1790
                http://orcid.org/0000-0001-6576-1319
                http://orcid.org/0000-0002-1446-125X
                http://orcid.org/0000-0003-2582-3786
                http://orcid.org/0000-0002-9331-6666
                http://orcid.org/0000-0003-1676-864X
                Article
                1430
                10.1038/s42003-020-01430-1
                7689502
                33239738
                32d71517-ffb2-4ccb-9764-2c425e454b2c
                © The Author(s) 2020

                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
                : 26 May 2020
                : 21 October 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100000274, British Heart Foundation (BHF);
                Award ID: RG/13/13/30194
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100012095, Scottish Government;
                Funded by: FundRef https://doi.org/10.13039/501100000289, Cancer Research UK (CRUK);
                Categories
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                © The Author(s) 2020

                sleep disorders,gene expression,genome-wide association studies

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