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      Systematic Prioritization of Candidate Genes in Disease Loci Identifies TRAFD1 as a Master Regulator of IFNγ Signaling in Celiac Disease

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          Abstract

          Celiac disease (CeD) is a complex T cell-mediated enteropathy induced by gluten. Although genome-wide association studies have identified numerous genomic regions associated with CeD, it is difficult to accurately pinpoint which genes in these loci are most likely to cause CeD. We used four different in silico approaches—Mendelian randomization inverse variance weighting, COLOC, LD overlap, and DEPICT—to integrate information gathered from a large transcriptomics dataset. This identified 118 prioritized genes across 50 CeD-associated regions. Co-expression and pathway analysis of these genes indicated an association with adaptive and innate cytokine signaling and T cell activation pathways. Fifty-one of these genes are targets of known drug compounds or likely druggable genes, suggesting that our methods can be used to pinpoint potential therapeutic targets. In addition, we detected 172 gene combinations that were affected by our CeD-prioritized genes in trans. Notably, 41 of these trans-mediated genes appear to be under control of one master regulator, TRAF-type zinc finger domain containing 1 ( TRAFD1), and were found to be involved in interferon (IFN)γ signaling and MHC I antigen processing/presentation. Finally, we performed in vitro experiments in a human monocytic cell line that validated the role of TRAFD1 as an immune regulator acting in trans. Our strategy confirmed the role of adaptive immunity in CeD and revealed a genetic link between CeD and IFNγ signaling as well as with MHC I antigen processing, both major players of immune activation and CeD pathogenesis.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            The Sequence Alignment/Map format and SAMtools

            Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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              HISAT: a fast spliced aligner with low memory requirements.

              HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.
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                Author and article information

                Contributors
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                25 January 2021
                2020
                : 11
                : 562434
                Affiliations
                [1] 1Department of Genetics, University Medical Center Groningen, University of Groningen , Groningen, Netherlands
                [2] 2Estonian Genome Center, Institute of Genomics, University of Tartu , Tartu, Estonia
                [3] 3Department of Immunology, K. G. Jebsen Coeliac Disease Research Centre, University of Oslo , Oslo, Norway
                [4] 4Deutsches Rheumaforschungszentrum Berlin (DRFZ), An Institute of the Leibniz Association , Berlin, Germany
                [5] 5Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Gastroenterology, Infectious Diseases and Rheumatology , Berlin, Germany
                [6] 6Department of Immunology, Leiden University , Leiden, Netherlands
                [7] 7Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center , Nijmegen, Netherlands
                [8] 8Department of Computational Biology for Individualised Infection Medicine, Centre for Individualised Infection Medicine, Helmholtz Centre for Infection Research, Hannover Medical School , Hanover, Germany
                [9] 9Istituto di Ricerca Genetica e Biomedica (IRGB) del Consiglio Nazionale delle Ricerche (CNR) , Monserrato, Italy
                Author notes

                Edited by: Yue-miao Zhang, Peking University People's Hospital, China

                Reviewed by: Jose Ramon Bilbao, University of the Basque Country, Spain; Jin Xu, Ningbo University, China

                *Correspondence: Serena Sanna serena.sanna@ 123456irgb.cnr.it

                This article was submitted to Genomic Medicine, a section of the journal Frontiers in Genetics

                †These authors have contributed equally to this work

                Article
                10.3389/fgene.2020.562434
                7868554
                33569077
                f94b2e6e-aa28-4c11-8d95-ca820eee73b8
                Copyright © 2021 van der Graaf, Zorro, Claringbould, Võsa, Aguirre-Gamboa, Li, Mooiweer, Ricaño-Ponce, Borek, Koning, Kooy-Winkelaar, Sollid, Qiao, Kumar, Li, Franke, Withoff, Wijmenga, Sanna, Jonkers and BIOS Consortium.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 10 July 2020
                : 16 December 2020
                Page count
                Figures: 4, Tables: 0, Equations: 0, References: 76, Pages: 16, Words: 14232
                Funding
                Funded by: European Research Council 10.13039/501100000781
                Funded by: Nederlandse Organisatie voor Wetenschappelijk Onderzoek 10.13039/501100003246
                Funded by: Rijksuniversiteit Groningen 10.13039/501100001721
                Funded by: Stiftelsen Kristian Gerhard Jebsen 10.13039/100007793
                Categories
                Genetics
                Original Research

                Genetics
                celiac disease,gene prioritization,expression quantitative trait locus (eqtl),trafd1,trans regulation

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