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      Tejaas: reverse regression increases power for detecting trans-eQTLs

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          Abstract

          Trans-acting expression quantitative trait loci ( trans-eQTLs) account for ≥70 % expression heritability and could therefore facilitate uncovering mechanisms underlying the origination of complex diseases. Identifying trans-eQTLs is challenging because of small effect sizes, tissue specificity, and a severe multiple-testing burden. Tejaas predicts trans-eQTLs by performing L2-regularized “reverse” multiple regression of each SNP on all genes, aggregating evidence from many small trans-effects while being unaffected by the strong expression correlations. Combined with a novel unsupervised k-nearest neighbor method to remove confounders, Tejaas predicts 18851 unique trans-eQTLs across 49 tissues from GTEx. They are enriched in open chromatin, enhancers, and other regulatory regions. Many overlap with disease-associated SNPs, pointing to tissue-specific transcriptional regulation mechanisms.

          Supplementary Information

          The online version contains supplementary material available at (10.1186/s13059-021-02361-8).

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          The variant call format and VCFtools

          Petr Danecek, Adam Auton, Gonçalo R Abecasis … (2011)
          Summary: The variant call format (VCF) is a generic format for storing DNA polymorphism data such as SNPs, insertions, deletions and structural variants, together with rich annotations. VCF is usually stored in a compressed manner and can be indexed for fast data retrieval of variants from a range of positions on the reference genome. The format was developed for the 1000 Genomes Project, and has also been adopted by other projects such as UK10K, dbSNP and the NHLBI Exome Project. VCFtools is a software suite that implements various utilities for processing VCF files, including validation, merging, comparing and also provides a general Perl API. Availability: http://vcftools.sourceforge.net Contact: rd@sanger.ac.uk
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            The Genotype-Tissue Expression (GTEx) project.

            John M. Lonsdale, Jeffrey Thomas, Mike Salvatore … (2014)
            Genome-wide association studies have identified thousands of loci for common diseases, but, for the majority of these, the mechanisms underlying disease susceptibility remain unknown. Most associated variants are not correlated with protein-coding changes, suggesting that polymorphisms in regulatory regions probably contribute to many disease phenotypes. Here we describe the Genotype-Tissue Expression (GTEx) project, which will establish a resource database and associated tissue bank for the scientific community to study the relationship between genetic variation and gene expression in human tissues.
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              The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019

              Annalisa Buniello, Jacqueline A L MacArthur, Maria Cerezo … (2018)
              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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                Author and article information

                Contributors
                Saikat Banerjee: bnrj.saikat@gmail.com
                Johannes Söding:
                ORCID: http://orcid.org/0000-0001-9642-8244
                soeding@mpibpc.mpg.de
                Journal
                Journal ID (nlm-ta): Genome Biol
                Journal ID (iso-abbrev): Genome Biol
                Title: Genome Biology
                Publisher: BioMed Central (London )
                ISSN (Print): 1474-7596
                ISSN (Electronic): 1474-760X
                Publication date (Electronic): 6 May 2021
                Publication date PMC-release: 6 May 2021
                Publication date Collection: 2021
                Volume: 22
                Electronic Location Identifier: 142
                Affiliations
                [1 ]GRID grid.418140.8, ISNI 0000 0001 2104 4211, Quantitative and Computational Biology, Max-Planck Institute for Biophysical Chemistry, ; Göttingen, 37077 Germany
                [2 ]GRID grid.7450.6, ISNI 0000 0001 2364 4210, Georg-August University, ; Göttingen, 37075 Germany
                [3 ]GRID grid.417965.8, ISNI 0000 0000 8702 0100, Indian Institute of Technology, ; Kanpur, India
                [4 ]GRID grid.7450.6, ISNI 0000 0001 2364 4210, Campus-Institut Data Science (CIDAS), University of Göttingen, ; Göttingen, 37073 Germany
                [5 ]GRID grid.7450.6, ISNI 0000 0001 2364 4210, Cluster of Excellence “Multiscale Bioimaging” (MBExC), University of Göttingen, ; Göttingen, 37075 Germany
                Author information
                http://orcid.org/0000-0001-9642-8244
                Article
                Publisher ID: 2361
                DOI: 10.1186/s13059-021-02361-8
                PMC ID: 8101255
                PubMed ID: 33957961
                SO-VID: 7420a1dc-4b08-43bb-8018-1c6e253771ff
                Copyright © © The Author(s) 2021
                License:

                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
                Date received : 9 September 2020
                Date accepted : 22 April 2021
                Funding
                Funded by: Bundesministerium f?r Bildung und Forschung (DE)
                Award ID: 01ZX1313A- 2014
                Funded by: Deutsche Forschungsgemeinschaft (DE)
                Award ID: EXC 2067/1-390729940
                Categories
                Subject: Method
                Custom metadata
                issue-copyright-statement © The Author(s) 2021

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

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