Abstract
SummaryLocusZoom.js is a JavaScript library for creating interactive web-based visualizations of genetic association study results. It can display one or more traits in the context of relevant biological data (such as gene models and other genomic annotation), and allows interactive refinement of analysis models (by selecting linkage disequilibrium reference panels, identifying sets of likely causal variants, or comparisons to the GWAS catalog). It can be embedded in web pages to enable data sharing and exploration. Views can be customized and extended to display other data types such as phenome-wide association study (PheWAS) results, chromatin co-accessibility, or eQTL measurements. A new web upload service harmonizes datasets, adds annotations, and makes it easy to explore user-provided result sets.Availability and implementationLocusZoom.js is open-source software under a permissive MIT license. Code and documentation are available at: https://github.com/statgen/locuszoom/. Installable packages for all versions are also distributed via NPM. Additional features are provided as standalone libraries to promote reuse. Use with your own GWAS results at https://my.locuszoom.org/.Supplementary informationSupplementary data are available at Bioinformatics online.
Highlights
Genetic association studies are a workhorse technique in the study of complex disease genetics, and in recent years the numbers of reported studies and associations have risen dramatically. (Buniello et al, 2019) It has long been recognized that interpreting genetic association studies requires significant context, including linkage disequilibrium (LD) patterns, recombination rate, nearby genes, results for related traits, chromatin accessibility, and other information
Additional tracks show recombination rate, nearby genes, and relevant annotations such as variants for which associations have been reported in the EBI GWAS catalog (Buniello et al, 2019)
LocusZoom.js most commonly fetches data via REST APIs, and we provide an API server with access to standard datasets such as 1000 Genomes Project LD, GENCODE genes, HapMap-based recombination rate, and the EBI GWAS Catalog
Summary
Genetic association studies are a workhorse technique in the study of complex disease genetics, and in recent years the numbers of reported studies and associations have risen dramatically. (Buniello et al, 2019) It has long been recognized that interpreting genetic association studies requires significant context, including linkage disequilibrium (LD) patterns, recombination rate, nearby genes, results for related traits, chromatin accessibility, and other information. Use with your own GWAS results at https://my.locuszoom.org/. (Buniello et al, 2019) It has long been recognized that interpreting genetic association studies requires significant context, including linkage disequilibrium (LD) patterns, recombination rate, nearby genes, results for related traits, chromatin accessibility, and other information.
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