Brian M Schilder1,2,3,4,5, Jack Humphrey1,2,3,4,5, Towfique Raj1,2,3,4,5. 1. Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America. 2. Ronald M. Loeb Center for Alzheimer's disease, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America. 3. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America. 4. Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America. 5. Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America.
Abstract
SUMMARY: echolocatoR integrates a diverse suite of statistical and functional fine-mapping tools in order to identify, test enrichment in, and visualize high-confidence causal consensus variants in any phenotype. It requires minimal input from users (a summary statistics file), can be run in a single R function, and provides extensive access to relevant datasets (e.g. reference linkage disequilibrium panels, quantitative trait loci, genome-wide annotations, cell-type-specific epigenomics), thereby enabling rapid, robust and scalable end-to-end fine-mapping investigations. AVAILABILITY AND IMPLEMENTATION: echolocatoR is an open-source R package available through GitHub under the MIT license: https://github.com/RajLabMSSM/echolocatoR. SUPPLEMENTARY INFORMATION: Installation instructions (with an optional conda environment to minimize dependency conflicts), vignettes, example data, documentation of all functions and annotation datasets, as well as source code can be found in the echolocatoR website:https://rajlabmssm.github.io/echolocatoR In addition, we have created the echolocatoR Fine-mapping Portal which allows anyone to interactively visualize and download echolocatoR fine-mapping results across multiple traits: https://rajlab.shinyapps.io/Fine_Mapping_Shiny.
SUMMARY: echolocatoR integrates a diverse suite of statistical and functional fine-mapping tools in order to identify, test enrichment in, and visualize high-confidence causal consensus variants in any phenotype. It requires minimal input from users (a summary statistics file), can be run in a single R function, and provides extensive access to relevant datasets (e.g. reference linkage disequilibrium panels, quantitative trait loci, genome-wide annotations, cell-type-specific epigenomics), thereby enabling rapid, robust and scalable end-to-end fine-mapping investigations. AVAILABILITY AND IMPLEMENTATION: echolocatoR is an open-source R package available through GitHub under the MIT license: https://github.com/RajLabMSSM/echolocatoR. SUPPLEMENTARY INFORMATION: Installation instructions (with an optional conda environment to minimize dependency conflicts), vignettes, example data, documentation of all functions and annotation datasets, as well as source code can be found in the echolocatoR website:https://rajlabmssm.github.io/echolocatoR In addition, we have created the echolocatoR Fine-mapping Portal which allows anyone to interactively visualize and download echolocatoR fine-mapping results across multiple traits: https://rajlab.shinyapps.io/Fine_Mapping_Shiny.
Authors: Elisa Navarro; Evan Udine; Katia de Paiva Lopes; Madison Parks; Giulietta Riboldi; Brian M Schilder; Jack Humphrey; Gijsje J L Snijders; Ricardo A Vialle; Maojuan Zhuang; Tamjeed Sikder; Charalambos Argyrou; Amanda Allan; Michael J Chao; Kurt Farrell; Brooklyn Henderson; Sarah Simon; Deborah Raymond; Sonya Elango; Roberto A Ortega; Vicki Shanker; Matthew Swan; Carolyn W Zhu; Ritesh Ramdhani; Ruth H Walker; Winona Tse; Mary Sano; Ana C Pereira; Tim Ahfeldt; Alison M Goate; Susan Bressman; John F Crary; Lotje de Witte; Steven Frucht; Rachel Saunders-Pullman; Towfique Raj Journal: Nat Aging Date: 2021-09-14
Authors: Bhuwan Khatri; Kandice L Tessneer; Astrid Rasmussen; Farhang Aghakhanian; Tove Ragna Reksten; Adam Adler; Ilias Alevizos; Juan-Manuel Anaya; Lara A Aqrawi; Eva Baecklund; Johan G Brun; Sara Magnusson Bucher; Maija-Leena Eloranta; Fiona Engelke; Helena Forsblad-d'Elia; Stuart B Glenn; Daniel Hammenfors; Juliana Imgenberg-Kreuz; Janicke Liaaen Jensen; Svein Joar Auglænd Johnsen; Malin V Jonsson; Marika Kvarnström; Jennifer A Kelly; He Li; Thomas Mandl; Javier Martín; Gaétane Nocturne; Katrine Brække Norheim; Øyvind Palm; Kathrine Skarstein; Anna M Stolarczyk; Kimberly E Taylor; Maria Teruel; Elke Theander; Swamy Venuturupalli; Daniel J Wallace; Kiely M Grundahl; Kimberly S Hefner; Lida Radfar; David M Lewis; Donald U Stone; C Erick Kaufman; Michael T Brennan; Joel M Guthridge; Judith A James; R Hal Scofield; Patrick M Gaffney; Lindsey A Criswell; Roland Jonsson; Per Eriksson; Simon J Bowman; Roald Omdal; Lars Rönnblom; Blake Warner; Maureen Rischmueller; Torsten Witte; A Darise Farris; Xavier Mariette; Marta E Alarcon-Riquelme; Caroline H Shiboski; Marie Wahren-Herlenius; Wan-Fai Ng; Kathy L Sivils; Indra Adrianto; Gunnel Nordmark; Christopher J Lessard Journal: Nat Commun Date: 2022-07-27 Impact factor: 17.694