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      The burden of rare protein-truncating genetic variants on human lifespan

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          Abstract

          Genetic predisposition has been shown to contribute substantially to the age at which we die. Genome-wide association studies (GWASs) have linked more than 20 loci to phenotypes related to human lifespan 1 . However, little is known about how lifespan is impacted by gene loss of function. Through whole-exome sequencing of 352,338 UK Biobank participants of European ancestry, we assessed the relevance of protein-truncating variant (PTV) gene burden on individual and parental survival. We identified four exome-wide significant ( P < 4.2 × 10 −7) human lifespan genes, BRCA1, BRCA2, ATM and TET2. Gene and gene-set, PTV-burden, phenome-wide association studies support known roles of these genes in cancer to impact lifespan at the population level. The TET2 PTV burden was associated with a lifespan through somatic mutation events presumably due to clonal hematopoiesis. The overlap between PTV burden and common variant-based lifespan GWASs was modest, underscoring the value of exome sequencing in well-powered biobank cohorts to complement GWASs for identifying genes underlying complex traits.

          Abstract

          This study demonstrates that the burden of protein-truncating variants identified through population-scale exome sequencing impacts how long we live. The authors report four distinct human lifespan genes with roles in cancer and clonal hematopoiesis.

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          Most cited references29

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          The mutational constraint spectrum quantified from variation in 141,456 humans

          Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes 1 . Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases.
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            UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age

            Cathie Sudlow and colleagues describe the UK Biobank, a large population-based prospective study, established to allow investigation of the genetic and non-genetic determinants of the diseases of middle and old age.
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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
                jimmy.z.liu@gmail.com
                heiko.runz@gmail.com
                Journal
                Nat Aging
                Nat Aging
                Nature Aging
                Nature Publishing Group US (New York )
                2662-8465
                3 March 2022
                3 March 2022
                2022
                : 2
                : 4
                : 289-294
                Affiliations
                GRID grid.417832.b, ISNI 0000 0004 0384 8146, Translational Biology, Research & Development, Biogen Inc., ; Cambridge, MA USA
                Author information
                http://orcid.org/0000-0002-8379-5480
                http://orcid.org/0000-0002-5625-1189
                http://orcid.org/0000-0003-0308-5583
                http://orcid.org/0000-0003-4293-8710
                http://orcid.org/0000-0002-2133-7345
                Article
                182
                10.1038/s43587-022-00182-3
                10154195
                37117740
                b5074f3d-5a04-40d5-b175-7b48207a2047
                © 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
                : 13 July 2021
                : 20 January 2022
                Categories
                Letter
                Custom metadata
                © The Author(s), under exclusive licence to Springer Nature America, Inc. 2022

                cancer models,rare variants,ageing
                cancer models, rare variants, ageing

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