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Abstract

Over the past two decades, microsatellite genotypes have provided the data for landmark studies of human population-genetic variation. However, the various microsatellite data sets have been prepared with different procedures and sets of markers, so that it has been difficult to synthesize available data for a comprehensive analysis. Here, we combine eight human population-genetic data sets at the 645 microsatellite loci they share in common, accounting for procedural differences in the production of the different data sets, to assemble a single data set containing 5795 individuals from 267 worldwide populations. We perform a systematic analysis of genetic relatedness, detecting 240 intra-population and 92 inter-population pairs of previously unidentified close relatives and proposing standardized subsets of unrelated individuals for use in future studies. We then augment the human data with a data set of 84 chimpanzees at the 246 loci they share in common with the human samples. Multidimensional scaling and neighbor-joining analyses of these data sets offer new insights into the structure of human populations and enable a comparison of genetic variation patterns in chimpanzees with those in humans. Our combined data sets are the largest of their kind reported to date and provide a resource for use in human population-genetic studies.

Details

Title
Population Structure in a Comprehensive Genomic Data Set on Human Microsatellite Variation
Author
Pemberton, Trevor J 1 ; DeGiorgio, Michael 2 ; Rosenberg, Noah A 3 

 Department of Biology, Stanford University, Stanford, California 94305; Department of Biology, Stanford University, Stanford, California 94305 
 Department of Integrative Biology, University of California, Berkeley, California 94720 
 Department of Biology, Stanford University, Stanford, California 94305 
Pages
891-907
Publication year
2013
Publication date
May 1, 2013
Publisher
Oxford University Press
e-ISSN
21601836
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3169748075
Copyright
© 2013 Pemberton et al..