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      A genome-wide association study of serum proteins reveals shared loci with common diseases

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          Abstract

          With the growing number of genetic association studies, the genotype-phenotype atlas has become increasingly more complex, yet the functional consequences of most disease associated alleles is not understood. The measurement of protein level variation in solid tissues and biofluids integrated with genetic variants offers a path to deeper functional insights. Here we present a large-scale proteogenomic study in 5,368 individuals, revealing 4,035 independent associations between genetic variants and 2,091 serum proteins, of which 36% are previously unreported. The majority of both cis- and trans-acting genetic signals are unique for a single protein, although our results also highlight numerous highly pleiotropic genetic effects on protein levels and demonstrate that a protein’s genetic association profile reflects certain characteristics of the protein, including its location in protein networks, tissue specificity and intolerance to loss of function mutations. Integrating protein measurements with deep phenotyping of the cohort, we observe substantial enrichment of phenotype associations for serum proteins regulated by established GWAS loci, and offer new insights into the interplay between genetics, serum protein levels and complex disease.

          Abstract

          Circulating proteins have been linked to many conditions, and understanding their genetic control can lead to understanding of disease. Here, the authors associate common genetic variants with protein levels, finding overlap of genetic associations with circulating proteins and complex disease.

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          Most cited references59

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          Proteomics. Tissue-based map of the human proteome.

          Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. Copyright © 2015, American Association for the Advancement of Science.
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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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              Twelve years of SAMtools and BCFtools

              Abstract Background SAMtools and BCFtools are widely used programs for processing and analysing high-throughput sequencing data. They include tools for file format conversion and manipulation, sorting, querying, statistics, variant calling, and effect analysis amongst other methods. Findings The first version appeared online 12 years ago and has been maintained and further developed ever since, with many new features and improvements added over the years. The SAMtools and BCFtools packages represent a unique collection of tools that have been used in numerous other software projects and countless genomic pipelines. Conclusion Both SAMtools and BCFtools are freely available on GitHub under the permissive MIT licence, free for both non-commercial and commercial use. Both packages have been installed >1 million times via Bioconda. The source code and documentation are available from https://www.htslib.org.
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                Author and article information

                Contributors
                v.gudnason@hjarta.is
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                25 January 2022
                25 January 2022
                2022
                : 13
                : 480
                Affiliations
                [1 ]GRID grid.420802.c, ISNI 0000 0000 9458 5898, Icelandic Heart Association, ; Holtasmari 1, 201 Kopavogur, Iceland
                [2 ]GRID grid.14013.37, ISNI 0000 0004 0640 0021, Faculty of Medicine, , University of Iceland, ; 101 Reykjavik, Iceland
                [3 ]GRID grid.419475.a, ISNI 0000 0000 9372 4913, Laboratory of Epidemiology and Population Sciences, Intramural Research Program, , National Institute on Aging, ; Bethesda, MD 20892-9205 USA
                [4 ]GRID grid.418185.1, ISNI 0000 0004 0627 6737, GNF Novartis, ; 10675 John Jay Hopkins Drive, San Diego, CA 92121 USA
                [5 ]GRID grid.418424.f, ISNI 0000 0004 0439 2056, Novartis Institutes for Biomedical Research, ; 22 Windsor Street, Cambridge, MA 02139 USA
                Author information
                http://orcid.org/0000-0002-7459-1603
                http://orcid.org/0000-0002-7156-9080
                http://orcid.org/0000-0002-7661-4872
                http://orcid.org/0000-0002-3238-7612
                http://orcid.org/0000-0001-5130-8417
                http://orcid.org/0000-0002-7998-5433
                http://orcid.org/0000-0001-9982-0524
                http://orcid.org/0000-0001-5696-0084
                Article
                27850
                10.1038/s41467-021-27850-z
                8789779
                35078996
                39174185-c98d-4b28-b046-19a3ca874cf8
                © 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
                : 29 June 2021
                : 15 December 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000049, U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging);
                Award ID: HHSN271201200022C
                Award ID: 1R01AG065596-01A1
                Award Recipient :
                Funded by: U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)
                Funded by: FundRef https://doi.org/10.13039/501100001840, Icelandic Centre for Research (Rannsóknamiðstöð Íslands);
                Award ID: 195761-051, 184845-053 and 206692-051
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                proteomics,genomics,quantitative trait
                Uncategorized
                proteomics, genomics, quantitative trait

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