Abstract
SummaryOne of the key characteristics of any genetic variant is its geographic distribution. The geographic distribution can shed light on where an allele first arose, what populations it has spread to, and in turn on how migration, genetic drift, and natural selection have acted. The geographic distribution of a genetic variant can also be of great utility for medical/clinical geneticists and collectively many genetic variants can reveal population structure. Here we develop an interactive visualization tool for rapidly displaying the geographic distribution of genetic variants. Through a REST API and dynamic front-end, the Geography of Genetic Variants (GGV) browser (http://popgen.uchicago.edu/ggv/) provides maps of allele frequencies in populations distributed across the globe.Availability and ImplementationGGV is implemented as a website (http://popgen.uchicago.edu/ggv/) which employs an API to access frequency data (http://popgen.uchicago.edu/freq_api/). Python and javascript source code for the website and the API are available at: http://github.com/NovembreLab/ggv/ and http://github.com/NovembreLab/ggv-api/.Supplementary information Supplementary data are available at Bioinformatics online.
Highlights
Genetics researchers often face the problem that they have identified one or many genetic variants of interest using an approach such as a genome-wide association study and would like to know the geographic distribution of the variant
While many applications require inspection of the distribution of a specific variant, from our experience, it can be very helpful to view the geographic distribution of several randomly chosen variants to quickly gain a sense of structure in a dataset. We find this to be especially useful in teaching contexts, as it provides a highly visual way for learners to understand human genetic variation
By allowing rapid generation of allele frequency maps, we hope to facilitate the interpretation of variant function and history by practicing geneticists
Summary
Genetics researchers often face the problem that they have identified one or many genetic variants of interest using an approach such as a genome-wide association study and would like to know the geographic distribution of the variant. One common approach to visualizing such high-dimensional data is to compress the SNV dimensions down to a small number of latent factors, using a method such as principal components analysis (Price et al., 2006; Patterson et al, 2006), or a model-based clustering method such as STRUCTURE (Pritchard et al, 2000) or ADMIXTURE (Alexander et al, 2009). While these approaches are extremely valuable, researchers can use them too often without inspecting the underlying variant data in more detail. A natural approach to gaining more insight to the overall structure of a population genetic dataset is to visually inspect what geographic patterns arise in allele frequency maps
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