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      A phenome-wide approach to identify causal risk factors for deep vein thrombosis

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          Abstract

          Deep vein thrombosis (DVT) is the formation of a blood clot in a deep vein. DVT can lead to a venous thromboembolism (VTE), the combined term for DVT and pulmonary embolism, a leading cause of death and disability worldwide. Despite the prevalence and associated morbidity of DVT, the underlying causes are not well understood. Our aim was to leverage publicly available genetic summary association statistics to identify causal risk factors for DVT. We conducted a Mendelian randomization phenome-wide association study (MR-PheWAS) using genetic summary association statistics for 973 exposures and DVT (6,767 cases and 330,392 controls in UK Biobank). There was evidence for a causal effect of 57 exposures on DVT risk, including previously reported risk factors (e.g. body mass index—BMI and height) and novel risk factors (e.g. hyperthyroidism and varicose veins). As the majority of identified risk factors were adiposity-related, we explored the molecular link with DVT by undertaking a two-sample MR mediation analysis of BMI-associated circulating proteins on DVT risk. Our results indicate that circulating neurogenic locus notch homolog protein 1 (NOTCH1), inhibin beta C chain (INHBC) and plasminogen activator inhibitor 1 (PAI-1) influence DVT risk, with PAI-1 mediating the BMI-DVT relationship. Using a phenome-wide approach, we provide putative causal evidence that hyperthyroidism, varicose veins and BMI enhance the risk of DVT. Furthermore, the circulating protein PAI-1 has a causal role in DVT aetiology and is involved in mediating the BMI-DVT relationship.

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          The online version contains supplementary material available at 10.1186/s12920-023-01710-9.

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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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            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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              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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                Author and article information

                Contributors
                andrei.constantinescu@bristol.ac.uk
                Journal
                BMC Med Genomics
                BMC Med Genomics
                BMC Medical Genomics
                BioMed Central (London )
                1755-8794
                11 November 2023
                11 November 2023
                2023
                : 16
                : 284
                Affiliations
                [1 ]MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, ( https://ror.org/0524sp257) Bristol, UK
                [2 ]Bristol Medical School, Population Health Sciences, University of Bristol, Oakfield House, Oakfield Grove, ( https://ror.org/0524sp257) Bristol, UK
                [3 ]School of Translational Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, ( https://ror.org/0524sp257) Bristol, UK
                [4 ]GRID grid.507332.0, ISNI 0000 0004 9548 940X, Health Data Research UK. Registered Office, ; 215 Euston Road, London, NW1 2BE UK
                [5 ]School of Physiology, Pharmacology and Neuroscience, University of Bristol, ( https://ror.org/0524sp257) Bristol, UK
                [6 ]GRID grid.16821.3c, ISNI 0000 0004 0368 8293, Department of Endocrine and Metabolic Diseases, , Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, ; Shanghai, China
                [7 ]GRID grid.16821.3c, ISNI 0000 0004 0368 8293, Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, , Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, ; Shanghai, China
                [8 ]Our Future Health Ltd. Registered office: 2 New Bailey, 6 Stanley Street, Manchester, M3 5GS UK
                [9 ]UKRI Medical Research Council, ( https://ror.org/03x94j517) Swindon, UK
                Article
                1710
                10.1186/s12920-023-01710-9
                10640748
                37951941
                302dc5e3-9d8b-4885-a0a8-058519cc0797
                © 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 31 May 2023
                : 20 October 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: MR/N0137941/1
                Award ID: MC_UU_00011/1
                Award ID: MC_UU_00011/1
                Award ID: MC_UU_00011/1
                Award ID: MC_UU_00011/1
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000289, Cancer Research UK;
                Award ID: C18281/A29019
                Award ID: C18281/A29019
                Award ID: C18281/A29019
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000361, Diabetes UK;
                Award ID: 17/0005587
                Award ID: 17/0005587
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100013743, World Cancer Research Fund International;
                Award ID: IIG_2019_2009
                Award ID: IIG_2019_2009
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100023699, Health Data Research UK;
                Funded by: FundRef http://dx.doi.org/10.13039/501100000274, British Heart Foundation;
                Award ID: AA/18/1/34219
                Award ID: PG/16/3/31833
                Award ID: PG/16/3/31833
                Award Recipient :
                Funded by: Shanghai Thousand Talents Program and the National Health Commission of the PR China
                Award ID: SBF006\1117
                Award Recipient :
                Funded by: Our Future Health
                Funded by: FundRef http://dx.doi.org/10.13039/100010269, Wellcome Trust;
                Award ID: 217065/Z/19/Z
                Award ID: 204813/Z/16/Z
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100015250, NIHR Bristol Biomedical Research Centre;
                Award ID: BRC-1215-2001
                Award Recipient :
                Funded by: EPSRC Prostanoid Programme, United Kingdom
                Award ID: EP/M012530/1
                Award ID: EP/M012530/1
                Award Recipient :
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2023

                Genetics
                mendelian randomization,deep vein thrombosis,alspac,protein quantitative trait loci,genome-wide association study

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