Published December 22, 2020
| Version v1
Journal article
Open
A variant-centric perspective on geographic patterns of human allele frequency variation
Description
A key challenge in human genetics is to understand the geographic distribution of human genetic variation. Often genetic variation is described by showing relationships among populations or individuals, drawing inferences over many variants. Here, we introduce an alternative representation of genetic variation that reveals the relative abundance of different allele frequency patterns. This approach allows viewers to easily see several features of human genetic structure: (1) most variants are rare and geographically localized, (2) variants that are common in a single geographic region are more likely to be shared across the globe than to be private to that region, and (3) where two individuals differ, it is most often due to variants that are found globally, regardless of whether the individuals are from the same region or different regions. Our variantcentric visualization clarifies the geographic patterns of human variation and can help address misconceptions about genetic differentiation among populations.
Data availability
The GeoVar assignments for each variant have been deposited to Dryad (https://doi.org/10.5061/dryad.rjdfn2z7v). The code for replicating the analyses is available at: https://github.com/aabiddanda/geovar_rep_paper (copy archived at https://archive.softwareheritage.org/swh:1:rev:db3ca8faeecf8697973f803bc05c5a3d0a187145/). A python package (https://aabiddanda.github.io/geovar/) allows users to make GeoVar plots from frequency tables or VCF files.
The following data sets were generated:
Biddanda ARice DPNovembre J (2020) Dryad Digital Repository Geographic allele frequency variation in the 1000 Genomes hg38 NYGC dataset. https://doi.org/10.5061/dryad.rjdfn2z7v
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Additional details
Identifiers
- DOI
- 10.7554/eLife.60107
- Other
- oai:uchicago.tind.io:9852
Funding
- National Institute of General Medical Sciences
- R01 GM132383
- Unknown funder
- Chicago Fellows Program of the University of Chicago
- National Institute of General Medical Sciences
- T32 GM07197