Judong Shen1, Kijoung Song2, Andrew J Slater3, Enrico Ferrero4, Matthew R Nelson2. 1. Biostatistics and Research Decision Sciences, Merck Research Laboratories, Rahway, NJ, USA 07065. 2. Target Sciences, GSK, King of Prussia, Philadelphia, PA 27513, USA. 3. OmicSoft Corporation, Cary, NC 27513, USA. 4. Target Sciences, GSK, Stevenage, SG1 2NY UK.
Abstract
SUMMARY: We developed the STOPGAP (Systematic Target OPportunity assessment by Genetic Association Predictions) database, an extensive catalog of human genetic associations mapped to effector gene candidates. STOPGAP draws on a variety of publicly available GWAS associations, linkage disequilibrium (LD) measures, functional genomic and variant annotation sources. Algorithms were developed to merge the association data, partition associations into non-overlapping LD clusters, map variants to genes and produce a variant-to-gene score used to rank the relative confidence among potential effector genes. This database can be used for a multitude of investigations into the genes and genetic mechanisms underlying inter-individual variation in human traits, as well as supporting drug discovery applications. AVAILABILITY AND IMPLEMENTATION: Shell, R, Perl and Python scripts and STOPGAP R data files (version 2.5.1 at publication) are available at https://github.com/StatGenPRD/STOPGAP . Some of the most useful STOPGAP fields can be queried through an R Shiny web application at http://stopgapwebapp.com . CONTACT: matthew.r.nelson@gsk.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
SUMMARY: We developed the STOPGAP (Systematic Target OPportunity assessment by Genetic Association Predictions) database, an extensive catalog of human genetic associations mapped to effector gene candidates. STOPGAP draws on a variety of publicly available GWAS associations, linkage disequilibrium (LD) measures, functional genomic and variant annotation sources. Algorithms were developed to merge the association data, partition associations into non-overlapping LD clusters, map variants to genes and produce a variant-to-gene score used to rank the relative confidence among potential effector genes. This database can be used for a multitude of investigations into the genes and genetic mechanisms underlying inter-individual variation in human traits, as well as supporting drug discovery applications. AVAILABILITY AND IMPLEMENTATION: Shell, R, Perl and Python scripts and STOPGAP R data files (version 2.5.1 at publication) are available at https://github.com/StatGenPRD/STOPGAP . Some of the most useful STOPGAP fields can be queried through an R Shiny web application at http://stopgapwebapp.com . CONTACT: matthew.r.nelson@gsk.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: David Zhang; Sebastian Guelfi; Sonia Garcia-Ruiz; Beatrice Costa; Regina H Reynolds; Karishma D'Sa; Wenfei Liu; Thomas Courtin; Amy Peterson; Andrew E Jaffe; John Hardy; Juan A Botía; Leonardo Collado-Torres; Mina Ryten Journal: Sci Adv Date: 2020-06-10 Impact factor: 14.136
Authors: Lynn D Condreay; Laura R Parham; Xiaoyan A Qu; Jonathan Steinfeld; Michael E Wechsler; Benjamin A Raby; Steven W Yancey; Soumitra Ghosh Journal: Rheumatol Int Date: 2020-02-03 Impact factor: 2.631
Authors: David Zhang; Sebastian Guelfi; Sonia Garcia-Ruiz; Beatrice Costa; Regina H Reynolds; Karishma D'Sa; Wenfei Liu; Thomas Courtin; Amy Peterson; Andrew E Jaffe; John Hardy; Juan A Botía; Leonardo Collado-Torres; Mina Ryten Journal: Sci Adv Date: 2020-06-10 Impact factor: 14.136
Authors: Ioanna Tachmazidou; Konstantinos Hatzikotoulas; Lorraine Southam; Jorge Esparza-Gordillo; Valeriia Haberland; Jie Zheng; Toby Johnson; Mine Koprulu; Eleni Zengini; Julia Steinberg; Jeremy M Wilkinson; Sahir Bhatnagar; Joshua D Hoffman; Natalie Buchan; Dániel Süveges; Laura Yerges-Armstrong; George Davey Smith; Tom R Gaunt; Robert A Scott; Linda C McCarthy; Eleftheria Zeggini Journal: Nat Genet Date: 2019-01-21 Impact factor: 38.330