ShinyGAStool: A user-friendly tool for candidate gene association studies
Thomas J. Hoffmann,
Christine Miaskowski,
Kord M. Kober
Affiliations
Thomas J. Hoffmann
Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, United States of America; Institute for Human Genetics, University of California, San Francisco, CA, United States of America; School of Nursing, University of California, San Francisco, CA, United States of America
Christine Miaskowski
School of Nursing, University of California, San Francisco, CA, United States of America; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, United States of America
Kord M. Kober
School of Nursing, University of California, San Francisco, CA, United States of America; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, United States of America; Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, United States of America; Corresponding author at: School of Nursing, University of California, San Francisco, CA, United States of America.
A major barrier to the analysis of genotyping and sequencing data is the relative complexity of the tools needed to perform the analysis. We developed shinyGAStool, an open source tool that enables the user to perform a candidate gene association analysis from large datasets in an easy to use tool from a web browser. With a 3-step workflow, shinyGAStool allows the user to (1) identify and explore distributions of the phenotype and covariates, (2) select genes and variants to evaluate, and (3) run an association analysis linking the two.
