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      Meta-analysis of 542,934 subjects of European ancestry identifies new genes and mechanisms predisposing to refractive error and myopia

      research-article
      1 , 2 , 3 , * , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 4 , 2 , 1 , 1 , 5 , 4 , 3 , 12 , 13 , 9 , 10 , 11 , 14 , 15 , 16 , 6 , 17 , 17 , 18 , 9 , 10 , 19 , 20 , 21 , The Consortium for Refractive Error and Myopia 22 , 5 , 5 , 23 , The UK Eye and Vision Consortium 22 , 24 , 23andMe Inc. 22 , 3 , 5 , 12 , 25 , 4 , 1 , 2
      Nature genetics

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          Abstract

          Refractive errors, in particular myopia, are a leading cause of morbidity and disability world-wide. Genetic investigation can improve understanding of the molecular mechanisms underlying abnormal eye development and impaired vision. We conducted a meta-analysis of genome-wide association studies involving 542,934 European participants and identified 336 novel genetic loci associated with refractive error. Collectively, all associated genetic variants explain 18.4% of heritability and improve the accuracy of myopia prediction (AUC=0.75). Our results suggest that refractive error is genetically heterogeneous, driven by genes participating in the development of every anatomical component of the eye. In addition, our analyses suggest that genetic factors controlling circadian rhythm and pigmentation are also involved in the development of myopia and refractive error. These results may make possible predicting refractive error and the development of personalized myopia prevention strategies in the future.

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          Most cited references79

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          Is Open Access

          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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            Is Open Access

            Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016

            Summary Background As mortality rates decline, life expectancy increases, and populations age, non-fatal outcomes of diseases and injuries are becoming a larger component of the global burden of disease. The Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) provides a comprehensive assessment of prevalence, incidence, and years lived with disability (YLDs) for 328 causes in 195 countries and territories from 1990 to 2016. Methods We estimated prevalence and incidence for 328 diseases and injuries and 2982 sequelae, their non-fatal consequences. We used DisMod-MR 2.1, a Bayesian meta-regression tool, as the main method of estimation, ensuring consistency between incidence, prevalence, remission, and cause of death rates for each condition. For some causes, we used alternative modelling strategies if incidence or prevalence needed to be derived from other data. YLDs were estimated as the product of prevalence and a disability weight for all mutually exclusive sequelae, corrected for comorbidity and aggregated to cause level. We updated the Socio-demographic Index (SDI), a summary indicator of income per capita, years of schooling, and total fertility rate. GBD 2016 complies with the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER). Findings Globally, low back pain, migraine, age-related and other hearing loss, iron-deficiency anaemia, and major depressive disorder were the five leading causes of YLDs in 2016, contributing 57·6 million (95% uncertainty interval [UI] 40·8–75·9 million [7·2%, 6·0–8·3]), 45·1 million (29·0–62·8 million [5·6%, 4·0–7·2]), 36·3 million (25·3–50·9 million [4·5%, 3·8–5·3]), 34·7 million (23·0–49·6 million [4·3%, 3·5–5·2]), and 34·1 million (23·5–46·0 million [4·2%, 3·2–5·3]) of total YLDs, respectively. Age-standardised rates of YLDs for all causes combined decreased between 1990 and 2016 by 2·7% (95% UI 2·3–3·1). Despite mostly stagnant age-standardised rates, the absolute number of YLDs from non-communicable diseases has been growing rapidly across all SDI quintiles, partly because of population growth, but also the ageing of populations. The largest absolute increases in total numbers of YLDs globally were between the ages of 40 and 69 years. Age-standardised YLD rates for all conditions combined were 10·4% (95% UI 9·0–11·8) higher in women than in men. Iron-deficiency anaemia, migraine, Alzheimer’s disease and other dementias, major depressive disorder, anxiety, and all musculoskeletal disorders apart from gout were the main conditions contributing to higher YLD rates in women. Men had higher age-standardised rates of substance use disorders, diabetes, cardiovascular diseases, cancers, and all injuries apart from sexual violence. Globally, we noted much less geographical variation in disability than has been documented for premature mortality. In 2016, there was a less than two times difference in age-standardised YLD rates for all causes between the location with the lowest rate (China, 9201 YLDs per 100 000, 95% UI 6862–11943) and highest rate (Yemen, 14 774 YLDs per 100 000, 11 018–19 228). Interpretation The decrease in death rates since 1990 for most causes has not been matched by a similar decline in age-standardised YLD rates. For many large causes, YLD rates have either been stagnant or have increased for some causes, such as diabetes. As populations are ageing, and the prevalence of disabling disease generally increases steeply with age, health systems will face increasing demand for services that are generally costlier than the interventions that have led to declines in mortality in childhood or for the major causes of mortality in adults. Up-to-date information about the trends of disease and how this varies between countries is essential to plan for an adequate health-system response.
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              GCTA: a tool for genome-wide complex trait analysis.

              For most human complex diseases and traits, SNPs identified by genome-wide association studies (GWAS) explain only a small fraction of the heritability. Here we report a user-friendly software tool called genome-wide complex trait analysis (GCTA), which was developed based on a method we recently developed to address the "missing heritability" problem. GCTA estimates the variance explained by all the SNPs on a chromosome or on the whole genome for a complex trait rather than testing the association of any particular SNP to the trait. We introduce GCTA's five main functions: data management, estimation of the genetic relationships from SNPs, mixed linear model analysis of variance explained by the SNPs, estimation of the linkage disequilibrium structure, and GWAS simulation. We focus on the function of estimating the variance explained by all the SNPs on the X chromosome and testing the hypotheses of dosage compensation. The GCTA software is a versatile tool to estimate and partition complex trait variation with large GWAS data sets.

                Author and article information

                Journal
                9216904
                Nat Genet
                Nat. Genet.
                Nature genetics
                1061-4036
                1546-1718
                25 February 2020
                30 March 2020
                April 2020
                30 September 2020
                : 52
                : 4
                : 401-407
                Affiliations
                [1 ]King’s College London, Section of Ophthalmology, School of Life Course Sciences, London, UK
                [2 ]King’s College London, Department of Twin Research and Genetic Epidemiology, London, UK
                [3 ]University College London, GOSH Institute of Child Health, London, UK
                [4 ]Division of Research, Kaiser Permanente Northern California, Oakland, California, USA
                [5 ]NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
                [6 ]Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge School of Clinical Medicine, Cambridge, UK
                [7 ]Department of Biophysics, Johns Hopkins University, Baltimore, MD, USA
                [8 ]Wilmer Eye Institute, Johns Hopkins School of Medicine, Baltimore, MD, USA
                [9 ]Department of Ophthalmology, Erasmus Medical Center, Rotterdam, The Netherlands
                [10 ]Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
                [11 ]Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, The Netherlands
                [12 ]Ulverscroft Vision Research Group, UCL Great Ormond Street Institute of Child Health, University College London, UK
                [13 ]Kaiser Permanente Northern California, Department of Ophthalmology, Redwood City, CA, USA
                [14 ]MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, The University of Edinburgh, United Kingdom
                [15 ]Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear, Boston, MA, USA
                [16 ]Department of Ophthalmology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
                [17 ]Department of Ophthalmology, Royal Hobart Hospital, Hobart, Tasmania
                [18 ]Centre for Ophthalmology and Visual Science, University of Western Australia, Lions Eye Institute, Perth, WA, WA 6009, Australia
                [19 ]Department of Ophthalmology, Radboud University Medical Center, Rotterdam
                [20 ]Institute of Molecular and Clinical Ophthalmology Basel, Switzerland
                [21 ]QIMR Berghofer Medical Research Institute, Brisbane, Australia
                [22 ]Names and affiliations of the consortium members are listed in the Supplementary Note
                [23 ]Division of Genetics and Epidemiology, UCL Institute of Ophthalmology, London, UK
                [24 ]Cardiff University, School of Optometry & Vision Sciences, UK
                [25 ]Department of Ophthalmology and NIHR Biomedical Research Centre Great Ormond Street Hospital NHS Foundation Trust
                Author notes
                [†]

                These authors jointly led this work

                [*]

                These authors jointly supervised this work

                Article
                EMS85881
                10.1038/s41588-020-0599-0
                7145443
                32231278
                9f8e5760-6030-4050-8cee-f8533d4ccb2e

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