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      Function of multiple sclerosis-protective HLA class I alleles revealed by genome-wide protein-quantitative trait loci mapping of interferon signalling

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          Abstract

          Interferons (IFNs) are cytokines that are central to the host defence against viruses and other microorganisms. If not properly regulated, IFNs may contribute to the pathogenesis of inflammatory autoimmune, or infectious diseases. To identify genetic polymorphisms regulating the IFN system we performed an unbiased genome-wide protein-quantitative trait loci (pQTL) mapping of cell-type specific type I and type II IFN receptor levels and their responses in immune cells from 303 healthy individuals. Seven genome-wide significant (p < 5.0E-8) pQTLs were identified. Two independent SNPs that tagged the multiple sclerosis (MS)-protective HLA class I alleles A*02/A*68 and B*44, respectively, were associated with increased levels of IFNAR2 in B and T cells, with the most prominent effect in IgD CD27 + memory B cells. The increased IFNAR2 levels in B cells were replicated in cells from an independent set of healthy individuals and in MS patients. Despite increased IFNAR2 levels, B and T cells carrying the MS-protective alleles displayed a reduced response to type I IFN stimulation. Expression and methylation-QTL analysis demonstrated increased mRNA expression of the pseudogene HLA-J in B cells carrying the MS-protective class I alleles, possibly driven via methylation-dependent transcriptional regulation. Together these data suggest that the MS-protective effects of HLA class I alleles are unrelated to their antigen-presenting function, and propose a previously unappreciated function of type I IFN signalling in B and T cells in MS immune-pathogenesis.

          Author summary

          Genetic association studies have been very successful in identifying disease-associated single nucleotide polymorphisms (SNPs), but it has been challenging to define the molecular mechanisms underlying these associations. As interferons (IFNs) have a central role in the immune system, we hypothesized that some of the SNPs associated to immune-mediated diseases would affect the IFN system. By combining genetic data with characterization of interferon receptor levels and their responses on the protein level in immune cells from 303 genotyped healthy individuals, we show that two SNPs tagging the HLA class I alleles A*02/A*68 and B*44 are associated with a decreased response to type I IFN stimulation in B cells and T cells. Notably, both HLA-A*02 and HLA-B*44 confer protection from developing multiple sclerosis (MS), which is a chronic inflammatory neurologic disease. In addition to suggesting a pathogenic role of enhanced type I interferon signalling in B cells and T cells in MS, our data emphasize the fact that genetic associations in the HLA locus can affect functions not directly associated to antigen presentation, which conceptually may be important for other diseases genetically associated to the HLA locus.

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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 NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019

            Abstract The GWAS Catalog delivers a high-quality curated collection of all published genome-wide association studies enabling investigations to identify causal variants, understand disease mechanisms, and establish targets for novel therapies. The scope of the Catalog has also expanded to targeted and exome arrays with 1000 new associations added for these technologies. As of September 2018, the Catalog contains 5687 GWAS comprising 71673 variant-trait associations from 3567 publications. New content includes 284 full P-value summary statistics datasets for genome-wide and new targeted array studies, representing 6 × 109 individual variant-trait statistics. In the last 12 months, the Catalog's user interface was accessed by ∼90000 unique users who viewed >1 million pages. We have improved data access with the release of a new RESTful API to support high-throughput programmatic access, an improved web interface and a new summary statistics database. Summary statistics provision is supported by a new format proposed as a community standard for summary statistics data representation. This format was derived from our experience in standardizing heterogeneous submissions, mapping formats and in harmonizing content. Availability: https://www.ebi.ac.uk/gwas/.
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              Principal components analysis corrects for stratification in genome-wide association studies.

              Population stratification--allele frequency differences between cases and controls due to systematic ancestry differences-can cause spurious associations in disease studies. We describe a method that enables explicit detection and correction of population stratification on a genome-wide scale. Our method uses principal components analysis to explicitly model ancestry differences between cases and controls. The resulting correction is specific to a candidate marker's variation in frequency across ancestral populations, minimizing spurious associations while maximizing power to detect true associations. Our simple, efficient approach can easily be applied to disease studies with hundreds of thousands of markers.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: VisualizationRole: Writing – original draft
                Role: Data curationRole: Formal analysisRole: VisualizationRole: Writing – review & editing
                Role: ResourcesRole: Writing – review & editing
                Role: Formal analysisRole: ResourcesRole: Writing – review & editing
                Role: Project administrationRole: ResourcesRole: Writing – review & editing
                Role: ResourcesRole: Writing – review & editing
                Role: ResourcesRole: Writing – review & editing
                Role: Project administrationRole: ResourcesRole: Writing – review & editing
                Role: ResourcesRole: Writing – review & editing
                Role: Funding acquisitionRole: ResourcesRole: Writing – original draft
                Role: ConceptualizationRole: Funding acquisitionRole: Writing – original draft
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: ResourcesRole: VisualizationRole: Writing – original draft
                Role: Editor
                Journal
                PLoS Genet
                PLoS Genet
                plos
                plosgen
                PLoS Genetics
                Public Library of Science (San Francisco, CA USA )
                1553-7390
                1553-7404
                26 October 2020
                October 2020
                : 16
                : 10
                : e1009199
                Affiliations
                [1 ] Rheumatology and Science for Life Laboratories, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
                [2 ] Molecular Medicine and Science for Life Laboratory, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
                [3 ] Centre for Molecular Medicine, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
                The Jackson Laboratory, UNITED STATES
                Author notes

                I have read the journal's policy and the authors of this manuscript have the following competing interests: Tomas Olsson has received unrestricted MS research grants, and/or advisory board/lecture honoraria from AstraZeneca, Biogen, Novartis, Merck, Sanofi and Roche, none of which has been related to this work.

                Author information
                https://orcid.org/0000-0001-5872-4253
                https://orcid.org/0000-0003-3342-1373
                https://orcid.org/0000-0002-7230-8990
                https://orcid.org/0000-0002-1211-9821
                https://orcid.org/0000-0002-8454-1351
                https://orcid.org/0000-0002-3829-7431
                https://orcid.org/0000-0003-1382-2321
                https://orcid.org/0000-0002-0867-4726
                https://orcid.org/0000-0002-2938-1877
                https://orcid.org/0000-0001-9403-6503
                https://orcid.org/0000-0003-2064-2716
                Article
                PGENETICS-D-20-01001
                10.1371/journal.pgen.1009199
                7644105
                33104735
                545ed6a4-8363-43a0-bac5-42581b038887
                © 2020 Lundtoft et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 25 June 2020
                : 15 October 2020
                Page count
                Figures: 6, Tables: 1, Pages: 22
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100004359, Vetenskapsrådet;
                Award ID: D0283001
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100004359, Vetenskapsrådet;
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100007949, Reumatikerförbundet;
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100007949, Reumatikerförbundet;
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100007857, Stiftelsen Konung Gustaf V:s 80-årsfond;
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100007857, Stiftelsen Konung Gustaf V:s 80-årsfond;
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100007687, Svenska Läkaresällskapet;
                Award Recipient :
                Funded by: Erik, Karin och Gösta Selanders stiftelse
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100007435, Åke Wiberg Stiftelse;
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100004063, Knut och Alice Wallenbergs Stiftelse;
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100003792, Hjärnfonden;
                Award Recipient :
                Funded by: Margareta af Ugglas Stiftelse
                Award Recipient :
                This work was supported by grants from the Swedish Research Council for Medicine and Health to LR (D0283001) and TO ( www.vr.se), the Swedish Rheumatism Association to LR and NH, King Gustaf V’s 80-year Foundation to LR and NH, the Swedish Society of Medicine and the Ingegerd Johansson donation to LR ( www.sls.se), Erik, Karin and Gösta Selander’s foundation to NH and JIK, Åke Wiberg’s foundation to NH ( www.ake-wiberg.se), Knut and Alice Wallenberg Foundation to TO ( www.kaw.wallenberg.org), The Swedish Brain Foundation to TO ( www.hjarnfonden.se) and Margaretha af Ugglas Foundation to TO. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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                2020-11-05
                Flow cytometry data (experiment normalized geometric mean fluorescence intensities) and covariate data for each individual are provided in S1 Data. Summary statistics for all SNPs with p-values < 1.0E-4 are provided in S2 Data. The genotype data are not publically available due to them containing information that could compromise research participant's privacy and consent, but are available from SciLifeLab data centre ( https://scilifelab.figshare.com/, DOI: 10.17044/scilifelab.12901388) for researchers who meet the criteria for access to confidential data. Data underlying figures are provided in S3 Data. All other relevant data are within the manuscript and its Supporting Information files.

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