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      Genetic architecture of heart failure with preserved versus reduced ejection fraction

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          Abstract

          Pharmacologic clinical trials for heart failure with preserved ejection fraction have been largely unsuccessful as compared to those for heart failure with reduced ejection fraction. Whether differences in the genetic underpinnings of these major heart failure subtypes may provide insights into the disparate outcomes of clinical trials remains unknown. We utilize a large, uniformly phenotyped, single cohort of heart failure sub-classified into heart failure with reduced and with preserved ejection fractions based on current clinical definitions, to conduct detailed genetic analyses of the two heart failure sub-types. We find different genetic architectures and distinct genetic association profiles between heart failure with reduced and with preserved ejection fraction suggesting differences in underlying pathobiology. The modest genetic discovery for heart failure with preserved ejection fraction (one locus) compared to heart failure with reduced ejection fraction (13 loci) despite comparable sample sizes indicates that clinically defined heart failure with preserved ejection fraction likely represents the amalgamation of several, distinct pathobiological entities. Development of consensus sub-phenotyping of heart failure with preserved ejection fraction is paramount to better dissect the underlying genetic signals and contributors to this highly prevalent condition.

          Abstract

          While the genetic basis of heart failure has been explored by genetic studies, the differences between subtypes are not well understood. Here, the authors performed genetic analyses on the two major subtypes of heart failure in a large biobank with genetic and health record data, finding unique genetic architecture for each subtype.

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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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              Heart Disease and Stroke Statistics—2020 Update

              Circulation
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                Author and article information

                Contributors
                jacob.joseph@va.gov
                yan.v.sun@emory.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                14 December 2022
                14 December 2022
                2022
                : 13
                : 7753
                Affiliations
                [1 ]GRID grid.410370.1, ISNI 0000 0004 4657 1992, Massachusetts Veterans Epidemiology Research and Information Center, , VA Boston Healthcare System, ; Boston, MA USA
                [2 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Medicine, , Brigham and Women’s Hospital, Harvard Medical School, ; Boston, MA USA
                [3 ]Cardiology Section (111A), VA Providence Healthcare System, 830 Chalkstone Avenue, Providence, RI 02908 USA
                [4 ]GRID grid.189967.8, ISNI 0000 0001 0941 6502, Emory University Rollins School of Public Health, ; Atlanta, GA USA
                [5 ]GRID grid.484294.7, Atlanta VA Health Care System, ; Decatur, GA USA
                [6 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Massachusetts General Hospital, ; Boston, MA USA
                [7 ]GRID grid.66859.34, ISNI 0000 0004 0546 1623, Broad Institute of Harvard and MIT, ; Cambridge, MA USA
                [8 ]GRID grid.94365.3d, ISNI 0000 0001 2297 5165, Center for Precision Health Research, National Human Genome Research Institute, , National Institutes of Health, ; Bethesda, MD USA
                [9 ]GRID grid.412807.8, ISNI 0000 0004 1936 9916, Division of Epidemiology, Department of Medicine, , Vanderbilt University Medical Center, ; Nashville, TN USA
                [10 ]GRID grid.412807.8, ISNI 0000 0004 1936 9916, Division of Epidemiology, Department of Medicine, Vanderbilt Genetics Institute, , Vanderbilt University Medical Center, ; Nashville, TN USA
                [11 ]GRID grid.189504.1, ISNI 0000 0004 1936 7558, Boston University School of Medicine, ; Boston, MA USA
                [12 ]GRID grid.189967.8, ISNI 0000 0001 0941 6502, Emory University School of Medicine, ; Atlanta, GA USA
                Author information
                http://orcid.org/0000-0002-7279-4896
                http://orcid.org/0000-0002-8918-7224
                http://orcid.org/0000-0002-8421-3518
                http://orcid.org/0000-0003-3223-9131
                http://orcid.org/0000-0002-9672-2491
                http://orcid.org/0000-0003-4318-6119
                http://orcid.org/0000-0002-2667-8624
                http://orcid.org/0000-0002-2838-1824
                Article
                35323
                10.1038/s41467-022-35323-0
                9751124
                36517512
                cec483a3-3683-4bbf-8e1f-45db7e980fd6
                © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 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
                : 17 February 2022
                : 28 November 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000738, U.S. Department of Veterans Affairs (Department of Veterans Affairs);
                Award ID: I01CX001737
                Award ID: MVP037
                Award ID: I01-BX004821
                Award ID: I01-CX001737
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                medical genomics,genome-wide association studies,cardiovascular genetics
                Uncategorized
                medical genomics, genome-wide association studies, cardiovascular genetics

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