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      A multi-ancestry polygenic risk score improves risk prediction for coronary artery disease


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          Identification of individuals at highest risk of coronary artery disease (CAD)—ideally before onset—remains an important public health need. Prior studies have developed genome-wide polygenic scores to enable risk stratification, reflecting the substantial inherited component to CAD risk. Here we develop a new and significantly improved polygenic score for CAD, termed GPS Mult, that incorporates genome-wide association data across five ancestries for CAD (>269,000 cases and >1,178,000 controls) and ten CAD risk factors. GPS Mult strongly associated with prevalent CAD (odds ratio per standard deviation 2.14, 95% confidence interval 2.10–2.19, P < 0.001) in UK Biobank participants of European ancestry, identifying 20.0% of the population with 3-fold increased risk and conversely 13.9% with 3-fold decreased risk as compared with those in the middle quintile. GPS Mult was also associated with incident CAD events (hazard ratio per standard deviation 1.73, 95% confidence interval 1.70–1.76, P < 0.001), identifying 3% of healthy individuals with risk of future CAD events equivalent to those with existing disease and significantly improving risk discrimination and reclassification. Across multiethnic, external validation datasets inclusive of 33,096, 124,467, 16,433 and 16,874 participants of African, European, Hispanic and South Asian ancestry, respectively, GPS Mult demonstrated increased strength of associations across all ancestries and outperformed all available previously published CAD polygenic scores. These data contribute a new GPS Mult for CAD to the field and provide a generalizable framework for how large-scale integration of genetic association data for CAD and related traits from diverse populations can meaningfully improve polygenic risk prediction.


          A polygenic risk score for coronary artery disease developed using data from individuals of five different ancestries has increased accuracy across diverse populations.

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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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            Is Open Access

            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.

                Author and article information

                Nat Med
                Nat Med
                Nature Medicine
                Nature Publishing Group US (New York )
                6 July 2023
                6 July 2023
                : 29
                : 7
                : 1793-1803
                [1 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Division of Cardiology, Department of Medicine, , Massachusetts General Hospital, ; Boston, MA USA
                [2 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Center for Genomic Medicine, Department of Medicine, , Massachusetts General Hospital, ; Boston, MA USA
                [3 ]GRID grid.66859.34, ISNI 0000 0004 0546 1623, Cardiovascular Disease Initiative, , Broad Institute of MIT and Harvard, ; Cambridge, MA USA
                [4 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Medicine, , Harvard Medical School, ; Boston, MA USA
                [5 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Cardiovascular Research Center, , Massachusetts General Hospital, ; Boston, MA USA
                [6 ]GRID grid.9227.e, ISNI 0000000119573309, CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, , Chinese Academy of Sciences and China National Center for Bioinformation, ; Beijing, China
                [7 ]Veteran Affairs Boston Healthcare System, Boston, MA USA
                [8 ]GRID grid.168010.e, ISNI 0000000419368956, Stanford University School of Medicine, ; Palo Alto, CA USA
                [9 ]GRID grid.280747.e, ISNI 0000 0004 0419 2556, Veterans Affairs Palo Alto Healthcare System, ; Palo Alto, CA USA
                [10 ]GRID grid.249878.8, ISNI 0000 0004 0572 7110, Gladstone Institutes, ; San Francisco, CA USA
                [11 ]GRID grid.16753.36, ISNI 0000 0001 2299 3507, Feinberg School of Medicine, , Northwestern University, ; Chicago, IL USA
                [12 ]Veteran Affairs Atlanta Healthcare System, Decatur, GA USA
                [13 ]GRID grid.4868.2, ISNI 0000 0001 2171 1133, Blizard Institute, Barts and the London School of Medicine and Dentistry, , Queen Mary University of London, ; London, UK
                [14 ]GRID grid.5335.0, ISNI 0000000121885934, British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, and Centre of Research Excellence, , University of Cambridge, ; Cambridge, UK
                [15 ]GRID grid.511023.4, Verve Therapeutics, ; Boston, MA USA
                Author information
                © The Author(s) 2023

                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/.

                : 26 August 2022
                : 30 May 2023
                Funded by: FundRef https://doi.org/10.13039/100000051, U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI);
                Award ID: 1K08HG010155
                Award ID: 1U01HG011719
                Award ID: 1U01HG011719
                Award ID: 1U01HG011719
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100005294, Massachusetts General Hospital (MGH);
                Funded by: FundRef https://doi.org/10.13039/100013114, Broad Institute;
                Funded by: FundRef https://doi.org/10.13039/100007299, Harvard Catalyst (Harvard Clinical and Translational Science Center);
                Funded by: FundRef https://doi.org/10.13039/100000050, U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI);
                Award ID: 1K08HL161448
                Award ID: 1RO1HL092577
                Award ID: 1R01HL157635
                Award ID: 1R01HL157635
                Award ID: 1K08HL153937
                Award ID: R01HL1427
                Award ID: R01HL148565
                Award ID: R01HL148050
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000968, American Heart Association (American Heart Association, Inc.);
                Award ID: 18SFRN34110082
                Award ID: 17IFUNP3384001
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000738, U.S. Department of Veterans Affairs (Department of Veterans Affairs);
                Award ID: 01BX003362
                Award ID: I01-BX004821
                Award ID: I01-BX004821
                Award Recipient :
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                © Springer Nature America, Inc. 2023

                risk factors,myocardial infarction,genetics research
                risk factors, myocardial infarction, genetics research


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