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      A common NFKB1 variant detected through antibody analysis in UK Biobank predicts risk of infection and allergy

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          Summary

          Infectious agents contribute significantly to the global burden of diseases through both acute infection and their chronic sequelae. We leveraged the UK Biobank to identify genetic loci that influence humoral immune response to multiple infections. From 45 genome-wide association studies in 9,611 participants from UK Biobank, we identified NFKB1 as a locus associated with quantitative antibody responses to multiple pathogens, including those from the herpes, retro-, and polyoma-virus families. An insertion-deletion variant thought to affect NFKB1 expression (rs28362491), was mapped as the likely causal variant and could play a key role in regulation of the immune response. Using 121 infection- and inflammation-related traits in 487,297 UK Biobank participants, we show that the deletion allele was associated with an increased risk of infection from diverse pathogens but had a protective effect against allergic disease. We propose that altered expression of NFKB1, as a result of the deletion, modulates hematopoietic pathways and likely impacts cell survival, antibody production, and inflammation. Taken together, we show that disruptions to the tightly regulated immune processes may tip the balance between exacerbated immune responses and allergy, or increased risk of infection and impaired resolution of inflammation.

          Abstract

          Infectious agents contribute significantly to the global burden of diseases, through both acute infection and their chronic sequelae. We identified an insertion-deletion variant thought to affect NFKB1 expression as a causal variant—central to serological responses to diverse infectious agents, risk of infection, immune cell survival, antibody production, and inflammation.

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          Most cited references66

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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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              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
                Journal
                Am J Hum Genet
                Am J Hum Genet
                American Journal of Human Genetics
                Elsevier
                0002-9297
                1537-6605
                16 January 2024
                01 February 2024
                16 January 2024
                : 111
                : 2
                : 295-308
                Affiliations
                [1 ]The Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
                [2 ]Division of Infections and Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
                [3 ]Advanced Genomics Unit, National Laboratory of Genomics for Biodiversity (LANGEBIO), CINVESTAV, Irapuato, Mexico
                [4 ]Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
                [5 ]MRC-Population Health Research Unit, University of Oxford, Oxford, UK
                [6 ]Nuffield Department of Population Health, University of Oxford, Oxford, UK
                [7 ]Department of Paediatrics, University of Oxford, Oxford, UK
                [8 ]Department of Oncology, University of Oxford, Oxford, UK
                [9 ]NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
                [10 ]Global Health Institute, School of Life Sciences, EPFL, Lausanne, Switzerland
                [11 ]Swiss Institute of Bioinformatics, Lausanne, Switzerland
                [12 ]Precision Medicine Unit, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
                [13 ]The Jenner Institute, University of Oxford, Oxford, UK
                Author notes
                []Corresponding author amanda.chong@ 123456well.ox.ac.uk
                [∗∗ ]Corresponding author alexander.mentzer@ 123456ndm.ox.ac.uk
                [14]

                Senior author

                Article
                S0002-9297(23)00445-7
                10.1016/j.ajhg.2023.12.013
                10870136
                38232728
                c7e3f003-f4d6-4d76-9472-d9f52a329d5f
                © 2023 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 6 January 2023
                : 10 December 2023
                Categories
                Article

                Genetics
                infection,immunity,antibody,nfkb1,gwas,uk biobank,multiplex serology
                Genetics
                infection, immunity, antibody, nfkb1, gwas, uk biobank, multiplex serology

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