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GeneOnEarth: Fitting Genetic PC Plots on the Globe
July-Aug. 2013 (vol. 10 no. 4)
pp. 1009-1016
Sergio Torres-Sanchez, Dept. de Lenguajes y Sist. Informaticos, Univ. de Granada, Granada, Spain
Nuria Medina-Medina, Dept. de Lenguajes y Sist. Informaticos, Univ. de Granada, Granada, Spain
Chris Gignoux, Dept. of Med., Univ. of California San Francisco, San Francisco, CA, USA
Maria M. Abad-Grau, Dept. de Lenguajes y Sist. Informaticos, Univ. de Granada, Granada, Spain
Esteban Gonzalez-Burchard, Dept. of Med., Univ. of California San Francisco, San Francisco, CA, USA
Principal component (PC) plots have become widely used to summarize genetic variation of individuals in a sample. The similarity between genetic distance in PC plots and geographical distance has shown to be quite impressive. However, in most situations, individual ancestral origins are not precisely known or they are heterogeneously distributed; hence, they are hardly linked to a geographical area. We have developed GeneOnEarth, a user-friendly web-based tool to help geneticists to understand whether a linear isolation-by-distance model may apply to a genetic data set; thus, genetic distances among a set of individuals resemble geographical distances among their origins. Its main goal is to allow users to first apply a by-view Procrustes method to visually learn whether this model holds. To do that, the user can choose the exact geographical area from an on line 2D or 3D world map by using, respectively, Google Maps or Google Earth, and rotate, flip, and resize the images. GeneOnEarth can also compute the optimal rotation angle using Procrustes analysis and assess statistical evidence of similarity when a different rotation angle has been chosen by the user. An online version of GeneOnEarth is available for testing and using purposes at >http://bios.ugr.es/GeneOnEarth.
Index Terms:
statistical analysis,bioinformatics,genetics,genomics,Internet,learning (artificial intelligence),GeneOnEarth,Procrustes analysis,optimal rotation angle,Google Earth,Google Maps,learning,line 3D world map,line 2D world map,geographical area,genetic data set,linear isolation-by-distance model,user-friendly Web-based tool,geographical distance,genetic distance,genetic variation,genetic principal component plot fitting,Sociology,Statistics,Google,Earth,Genetics,Europe,Data models,admixture,Population genetics,SNPs,web-based application,PCA,procrustes analysis,population stratification
Citation:
Sergio Torres-Sanchez, Nuria Medina-Medina, Chris Gignoux, Maria M. Abad-Grau, Esteban Gonzalez-Burchard, "GeneOnEarth: Fitting Genetic PC Plots on the Globe," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. 4, pp. 1009-1016, July-Aug. 2013, doi:10.1109/TCBB.2013.81
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