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      Genome-wide analysis of 102,084 migraine cases identifies 123 risk loci and subtype-specific risk alleles

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      1 , 2 , 3 , 4 , 1 , 5 , 6 , 7 , 8 , 3 , 9 , 10 , 11 , 12 , 1 , 13 , 14 , 15 , 16 , 17 , 3 , 18 , 19 , 20 , 8 , 6 , 21 , 22 , 23 , 14 , 3 , 24 , 25 , 26 , 1 , 17 , 27 , 3 , 28 , 3 , 29 , 30 , 31 , 30 , 32 , 33 , 34 , 35 , 17 , 5 , 21 , 36 , 6 , 37 , 38 , 39 , 19 , 20 , 40 , 16 , 3 , 27 , 5 , 18 , 7 , 1 , 7 , 41 , International Headache Genetics Consortium, HUNT All-in Headache, Danish Blood Donor Study Genomic Cohort, 42 , 39 , 43 , 44 , 24 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 6 , 14 , 1 , 52 , 8 , 19 , 20 , 53 , 5 , 5 , 6 , 7 , 8 , 15 , 1 , 54 , 55 , 2 , 3 , 29 , 1 , 56 , 57 , 1 , 55 , 58 ,
      Nature Genetics
      Nature Publishing Group US
      Migraine, Genome-wide association studies

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          Abstract

          Migraine affects over a billion individuals worldwide but its genetic underpinning remains largely unknown. Here, we performed a genome-wide association study of 102,084 migraine cases and 771,257 controls and identified 123 loci, of which 86 are previously unknown. These loci provide an opportunity to evaluate shared and distinct genetic components in the two main migraine subtypes: migraine with aura and migraine without aura. Stratification of the risk loci using 29,679 cases with subtype information indicated three risk variants that seem specific for migraine with aura (in HMOX2, CACNA1A and MPPED2), two that seem specific for migraine without aura (near SPINK2 and near FECH) and nine that increase susceptibility for migraine regardless of subtype. The new risk loci include genes encoding recent migraine-specific drug targets, namely calcitonin gene-related peptide ( CALCA/CALCB) and serotonin 1F receptor ( HTR1F). Overall, genomic annotations among migraine-associated variants were enriched in both vascular and central nervous system tissue/cell types, supporting unequivocally that neurovascular mechanisms underlie migraine pathophysiology.

          Abstract

          Genome-wide association analyses identify 123 susceptibility loci for migraine and implicate neurovascular mechanisms in its pathophysiology. Subtype analyses highlight risk loci specific for migraine with or without aura in addition to shared risk variants.

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          Most cited references103

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

            Summary Background In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990–2010 time period, with the greatest annualised rate of decline occurring in the 0–9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10–24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10–24 years were also in the top ten in the 25–49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50–74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and development investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. Funding Bill & Melinda Gates Foundation.
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              Second-generation PLINK: rising to the challenge of larger and richer datasets

              PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for even faster and more scalable implementations of key functions. In addition, GWAS and population-genetic data now frequently contain probabilistic calls, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1's primary data format. To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, O(sqrt(n))-time/constant-space Hardy-Weinberg equilibrium and Fisher's exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. This will be followed by PLINK 2.0, which will introduce (a) a new data format capable of efficiently representing probabilities, phase, and multiallelic variants, and (b) extensions of many functions to account for the new types of information. The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.
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                Author and article information

                Contributors
                matti.pirinen@helsinki.fi
                Journal
                Nat Genet
                Nat Genet
                Nature Genetics
                Nature Publishing Group US (New York )
                1061-4036
                1546-1718
                3 February 2022
                3 February 2022
                2022
                : 54
                : 2
                : 152-160
                Affiliations
                [1 ]GRID grid.7737.4, ISNI 0000 0004 0410 2071, Institute for Molecular Medicine Finland (FIMM), , Helsinki Institute of Life Science (HiLIFE), University of Helsinki, ; Helsinki, Finland
                [2 ]GRID grid.55325.34, ISNI 0000 0004 0389 8485, Department of Research, Innovation and Education, Division of Clinical Neuroscience, , Oslo University Hospital, ; Oslo, Norway
                [3 ]GRID grid.5947.f, ISNI 0000 0001 1516 2393, K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, , Norwegian University of Science and Technology, ; Trondheim, Norway
                [4 ]GRID grid.55325.34, ISNI 0000 0004 0389 8485, Department of Neurology, , Oslo University Hospital, ; Oslo, Norway
                [5 ]GRID grid.421812.c, ISNI 0000 0004 0618 6889, deCODE genetics/Amgen Inc., ; Reykjavik, Iceland
                [6 ]GRID grid.10419.3d, ISNI 0000000089452978, Department of Neurology, , Leiden University Medical Center, ; Leiden, the Netherlands
                [7 ]GRID grid.10419.3d, ISNI 0000000089452978, Department of Human Genetics, , Leiden University Medical Center, ; Leiden, the Netherlands
                [8 ]GRID grid.4973.9, ISNI 0000 0004 0646 7373, Danish Headache Center, Department of Neurology, , Copenhagen University Hospital, ; Copenhagen, Denmark
                [9 ]GRID grid.5947.f, ISNI 0000 0001 1516 2393, Department of Clinical and Molecular Medicine, , Norwegian University of Science and Technology, ; Trondheim, Norway
                [10 ]GRID grid.5947.f, ISNI 0000 0001 1516 2393, BioCore - Bioinformatics Core Facility, , Norwegian University of Science and Technology, ; Trondheim, Norway
                [11 ]GRID grid.52522.32, ISNI 0000 0004 0627 3560, Clinic of Laboratory Medicine, St. Olavs Hospital, , Trondheim University Hospital, ; Trondheim, Norway
                [12 ]GRID grid.10419.3d, ISNI 0000000089452978, Department of Internal Medicine, Section of Gerontology and Geriatrics, , Leiden University Medical Center, ; Leiden, the Netherlands
                [13 ]GRID grid.418019.5, ISNI 0000 0004 0393 4335, GlaxoSmithKline, ; Cambridge, MA USA
                [14 ]GRID grid.15485.3d, ISNI 0000 0000 9950 5666, Department of Neurology, , Helsinki University Central Hospital, ; Helsinki, Finland
                [15 ]GRID grid.5254.6, ISNI 0000 0001 0674 042X, Novo Nordic Foundation Center for Protein Research, , Copenhagen University, ; Copenhagen, Denmark
                [16 ]Neurology Private Practice, Laeknasetrid, Reykjavik, Iceland
                [17 ]GRID grid.12380.38, ISNI 0000 0004 1754 9227, Netherlands Twin Register, Department of Biological Psychology, , Vrije Universiteit, ; Amsterdam, the Netherlands
                [18 ]GRID grid.4973.9, ISNI 0000 0004 0646 7373, Department of Clinical Immunology, , Copenhagen University Hospital, ; Rigshospitalet Copenhagen, Denmark
                [19 ]GRID grid.62560.37, ISNI 0000 0004 0378 8294, Division of Preventive Medicine, , Brigham and Women’s Hospital, ; Boston, MA USA
                [20 ]GRID grid.38142.3c, ISNI 000000041936754X, Harvard Medical School, ; Boston, MA USA
                [21 ]GRID grid.5252.0, ISNI 0000 0004 1936 973X, Institute for Stroke and Dementia Research, , University Hospital, LMU Munich, ; Munich, Germany
                [22 ]GRID grid.452617.3, Munich Cluster for Systems Neurology (Synergy), ; Munich, Germany
                [23 ]GRID grid.154185.c, ISNI 0000 0004 0512 597X, Department of Clinical Immunology, , Aarhus University Hospital, ; Aarhus, Denmark
                [24 ]GRID grid.5645.2, ISNI 000000040459992X, Department of Epidemiology, , Erasmus University Medical Center, ; Rotterdam, the Netherlands
                [25 ]GRID grid.5947.f, ISNI 0000 0001 1516 2393, Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, , Norwegian University of Science and Technology (NTNU), ; Trondheim, Norway
                [26 ]GRID grid.52522.32, ISNI 0000 0004 0627 3560, Clinical Research Unit Central Norway, St. Olavs University Hospital, ; Trondheim, Norway
                [27 ]GRID grid.410540.4, ISNI 0000 0000 9894 0842, Landspitali University Hospital, ; Reykjavik, Iceland
                [28 ]GRID grid.5947.f, ISNI 0000 0001 1516 2393, HUNT Research Center, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, , Norwegian University of Science and Technology, ; Trondheim, Norway
                [29 ]GRID grid.5510.1, ISNI 0000 0004 1936 8921, Institute of Clinical Medicine, Faculty of Medicine, , University of Oslo, ; Oslo, Norway
                [30 ]GRID grid.55325.34, ISNI 0000 0004 0389 8485, Research and Communication Unit for Musculoskeletal Health (FORMI), Department of Research, Innovation and Education, Division of Clinical Neuroscience, , Oslo University Hospital, ; Oslo, Norway
                [31 ]GRID grid.502801.e, ISNI 0000 0001 2314 6254, Department of Clinical Physiology, Tampere University Hospital, and Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, , Tampere University, ; Tampere, Finland
                [32 ]GRID grid.5510.1, ISNI 0000 0004 1936 8921, Department of General Practice, Institute of Health and Society, , University of Oslo, ; Oslo, Norway
                [33 ]GRID grid.411279.8, ISNI 0000 0000 9637 455X, Department of Neurology, , Akershus University Hospital, ; Lørenskog, Norway
                [34 ]GRID grid.6363.0, ISNI 0000 0001 2218 4662, Institute of Public Health, , Charité – Universitätsmedizin Berlin, ; Berlin, Germany
                [35 ]GRID grid.502801.e, ISNI 0000 0001 2314 6254, Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, , Tampere University, ; Tampere, Finland
                [36 ]GRID grid.512923.e, ISNI 0000 0004 7402 8188, Department of Clinical Immunology, , Zealand University Hospital, ; Køge, Denmark
                [37 ]GRID grid.16872.3a, ISNI 0000 0004 0435 165X, Department of Psychiatry, , Amsterdam UMC, Vrije Universiteit, Amsterdam Public Health Research Institute, ; Amsterdam, the Netherlands
                [38 ]GRID grid.420193.d, ISNI 0000 0004 0546 0540, GGZ inGeest Specialized Mental Health Care, ; Amsterdam, the Netherlands
                [39 ]GRID grid.4714.6, ISNI 0000 0004 1937 0626, Department of Neuroscience, Karolinska Institutet, ; Stockholm, Sweden
                [40 ]GRID grid.10419.3d, ISNI 0000000089452978, Department of Clinical Epidemiology, , Leiden University Medical Center, ; Leiden, the Netherlands
                [41 ]GRID grid.10419.3d, ISNI 0000000089452978, Department of Internal Medicine, Division of Endocrinology, , Leiden University Medical Center, ; Leiden, the Netherlands
                [42 ]GRID grid.14758.3f, ISNI 0000 0001 1013 0499, National Public Health Institute (Finnish Institute for Health and Welfare - THL), ; Helsinki, Finland
                [43 ]GRID grid.506534.1, ISNI 0000 0000 9259 167X, Klinikum Passau, , Department of Neurology, ; Passau, Germany
                [44 ]GRID grid.428620.a, Centre of Neurology, , Hertie Institute for Clinical Brain Research, ; Tuebingen, Germany
                [45 ]GRID grid.7445.2, ISNI 0000 0001 2113 8111, Department of Epidemiology and Biostatistics, , MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, ; London, UK
                [46 ]GRID grid.10858.34, ISNI 0000 0001 0941 4873, Center for Life Course Health Research, Faculty of Medicine, , University of Oulu, ; Oulu, Finland
                [47 ]GRID grid.412326.0, ISNI 0000 0004 4685 4917, Unit of Primary Health Care, , Oulu University Hospital, ; Oulu, Finland
                [48 ]GRID grid.7728.a, ISNI 0000 0001 0724 6933, Department of Life Sciences, College of Health and Life Sciences, , Brunel University London, ; London, UK
                [49 ]GRID grid.1374.1, ISNI 0000 0001 2097 1371, Centre for Population Health Research, , University of Turku and Turku University Hospital, ; Turku, Finland
                [50 ]GRID grid.1374.1, ISNI 0000 0001 2097 1371, Research Centre of Applied and Preventive Cardiovascular Medicine, , University of Turku, ; Turku, Finland
                [51 ]GRID grid.410552.7, ISNI 0000 0004 0628 215X, Department of Clinical Physiology and Nuclear Medicine, , Turku University Hospital, ; Turku, Finland
                [52 ]GRID grid.428673.c, ISNI 0000 0004 0409 6302, Folkhälsan Research Center, ; Helsinki, Finland
                [53 ]GRID grid.1024.7, ISNI 0000000089150953, School of Biomedical Sciences and Centre for Genomics and Personalised Health, Faculty of Health, , Queensland University of Technology, ; Brisbane, QLD Australia
                [54 ]GRID grid.66859.34, ISNI 0000 0004 0546 1623, Broad Institute of MIT and Harvard, ; Cambridge, MA USA
                [55 ]GRID grid.7737.4, ISNI 0000 0004 0410 2071, Department of Public Health, , University of Helsinki, ; Helsinki, Finland
                [56 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Analytic and Translational Genetics Unit, Department of Medicine, Department of Neurology and Department of Psychiatry, , Massachusetts General Hospital, ; Boston, MA USA
                [57 ]GRID grid.66859.34, ISNI 0000 0004 0546 1623, The Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, ; Cambridge, MA USA
                [58 ]GRID grid.7737.4, ISNI 0000 0004 0410 2071, Department of Mathematics and Statistics, , University of Helsinki, ; Helsinki, Finland
                Author information
                http://orcid.org/0000-0002-5585-6856
                http://orcid.org/0000-0003-4171-8919
                http://orcid.org/0000-0002-1631-4069
                http://orcid.org/0000-0002-5477-7232
                http://orcid.org/0000-0001-9782-7810
                http://orcid.org/0000-0003-0548-2486
                http://orcid.org/0000-0001-7801-809X
                http://orcid.org/0000-0003-2489-2499
                http://orcid.org/0000-0002-7099-7972
                http://orcid.org/0000-0002-3058-1059
                http://orcid.org/0000-0003-4609-1895
                http://orcid.org/0000-0002-7261-762X
                http://orcid.org/0000-0002-0654-387X
                http://orcid.org/0000-0001-6551-6647
                http://orcid.org/0000-0002-9476-7143
                http://orcid.org/0000-0002-5668-2368
                http://orcid.org/0000-0002-8999-5424
                http://orcid.org/0000-0001-7169-2620
                http://orcid.org/0000-0002-2555-4427
                http://orcid.org/0000-0002-6570-3319
                http://orcid.org/0000-0001-6669-3071
                http://orcid.org/0000-0003-2312-5976
                http://orcid.org/0000-0003-1763-5443
                http://orcid.org/0000-0003-0239-9871
                http://orcid.org/0000-0001-7466-0405
                http://orcid.org/0000-0002-2172-7394
                http://orcid.org/0000-0001-6668-3172
                http://orcid.org/0000-0003-0372-8585
                http://orcid.org/0000-0002-2149-0630
                http://orcid.org/0000-0002-6712-2702
                http://orcid.org/0000-0003-3357-0862
                http://orcid.org/0000-0001-7159-3040
                http://orcid.org/0000-0002-9331-6666
                http://orcid.org/0000-0003-1676-864X
                http://orcid.org/0000-0001-6703-7762
                http://orcid.org/0000-0002-0504-1202
                http://orcid.org/0000-0001-5721-0154
                http://orcid.org/0000-0002-2527-5874
                http://orcid.org/0000-0002-1664-1350
                Article
                990
                10.1038/s41588-021-00990-0
                8837554
                35115687
                e9134216-7f3e-410b-956c-613aa4752e40
                © The Author(s) 2022

                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 January 2021
                : 22 November 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100006306, Sigrid Juséliuksen Säätiö (Sigrid Jusélius Foundation);
                Funded by: FundRef https://doi.org/10.13039/501100002341, Academy of Finland (Suomen Akatemia);
                Award ID: 288509, 312076, 336825
                Award ID: 312062
                Award Recipient :
                Funded by: South-Eastern Norway Regional Health Authority (grant no. 2020034).
                Funded by: FundRef https://doi.org/10.13039/501100009708, Novo Nordisk Fonden (Novo Nordisk Foundation);
                Award ID: NNF14CC0001, NNF17OC0027594
                Award ID: NNF14CC0001, NNF17OC0027594
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000065, U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS);
                Award ID: R21NS09296, R21NS104398
                Award Recipient :
                Funded by: CANDY foundation (CEHEAD)
                Funded by: FundRef https://doi.org/10.13039/501100005633, Sydäntutkimussäätiö (Finnish Foundation for Cardiovascular Research);
                Funded by: University of Helsinki HiLIFE Fellow and Grand Challenge grants
                Funded by: FundRef https://doi.org/10.13039/100010135, Finska Läkaresällskapet (Medical Society of Finland);
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                Custom metadata
                © The Author(s), under exclusive licence to Springer Nature America, Inc. 2022

                Genetics
                migraine,genome-wide association studies
                Genetics
                migraine, genome-wide association studies

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