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      Genome-wide assessment of genetic risk for systemic lupus erythematosus and disease severity

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          Abstract

          Using three European and two Chinese genome-wide association studies (GWAS), we investigated the performance of genetic risk scores (GRSs) for predicting the susceptibility and severity of systemic lupus erythematosus (SLE), using renal disease as a proxy for severity. We used four GWASs to test the performance of GRS both cross validating within the European population and between European and Chinese populations. The performance of GRS in SLE risk prediction was evaluated by receiver operating characteristic (ROC) curves. We then analyzed the polygenic nature of SLE statistically. We also partitioned patients according to their age-of-onset and evaluated the predictability of GRS in disease severity in each age group. We found consistently that the best GRS in the prediction of SLE used SNPs associated at the level of P < 1e−05 in all GWAS data sets and that SNPs with P-values above 0.2 were inflated for SLE true positive signals. The GRS results in an area under the ROC curve ranging between 0.64 and 0.72, within European and between the European and Chinese populations. We further showed a significant positive correlation between a GRS and renal disease in two independent European GWAS ( P cohort1  = 2.44e−08; P cohort2  = 0.00205) and a significant negative correlation with age of SLE onset ( P cohort1  = 1.76e−12; P cohort2  = 0.00384). We found that the GRS performed better in the prediction of renal disease in the ‘later onset’ compared with the ‘earlier onset’ group. The GRS predicts SLE in both European and Chinese populations and correlates with poorer prognostic factors: young age-of-onset and lupus nephritis.

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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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            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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              Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach

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                Author and article information

                Journal
                Hum Mol Genet
                Hum. Mol. Genet
                hmg
                Human Molecular Genetics
                Oxford University Press
                0964-6906
                1460-2083
                27 June 2020
                20 February 2020
                20 February 2020
                : 29
                : 10
                : 1745-1756
                Affiliations
                [1 ] Department of Medical and Molecular Genetics , King’s College London, London, UK
                [2 ] MRC/BHF Cardiovascular Epidemiology Unit , University of Cambridge, Cambridge, UK
                [3 ] Department of Paediatrics and Adolescent Medicine , LKS Faculty of Medicine, The University of Hong Kong, Hong Kong
                [4 ] Department of Dermatology , NO. 1 Hospital, Anhui Medical University, Hefei, Anhui, China
                [5 ] Key Laboratory of Dermatology , Ministry of Education, Anhui Medical University, Hefei, Anhui, China
                [6 ] Department of Dermatology , Huashan Hospital of Fudan University, Shanghai, China
                [7 ] Department of Twin Research and Genetic Epidemiology , King’s College London, London, UK
                Author notes
                To whom correspondence should be addressed at: Immunogenetics Group, Department of Medical and Molecular Genetics, King’s College London, School of Medicine, 8th Floor, Tower Wing, Guy’s Hospital, Great Maze Pond, London SE1 9RT, UK. Tel: +44(0)2078488504; Fax: +44(0)2071882585; Email: david.l.morris@ 123456kcl.ac.uk
                Author information
                http://orcid.org/0000-0002-1260-6291
                http://orcid.org/0000-0002-5704-9249
                http://orcid.org/0000-0003-1123-1464
                http://orcid.org/0000-0002-1754-8932
                Article
                ddaa030
                10.1093/hmg/ddaa030
                7322569
                32077931
                251d1b2c-df83-44fb-9233-1e9410ea7487
                © The Author(s) 2020. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 13 December 2019
                : 30 January 2020
                : 17 February 2020
                : 11 February 2020
                Page count
                Pages: 12
                Funding
                Funded by: National Institute for Health Research Biomedical Research Centre;
                Funded by: Versus Arthritis, DOI 10.13039/501100012041;
                Award ID: 20332
                Award ID: 20265
                Award ID: 20580
                Funded by: Medical Research Council, DOI 10.13039/501100000265;
                Award ID: M01665X/1
                Funded by: National Science Foundation of China, DOI 10.13039/501100001809;
                Funded by: China Scholarship Council, DOI 10.13039/501100004543;
                Award ID: 81801636
                Categories
                AcademicSubjects/SCI01140
                General Article

                Genetics
                Genetics

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