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Genetic Population Structure Analysis in New Hampshire Reveals Eastern European Ancestry

Overview of attention for article published in PLOS ONE, September 2009
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Title
Genetic Population Structure Analysis in New Hampshire Reveals Eastern European Ancestry
Published in
PLOS ONE, September 2009
DOI 10.1371/journal.pone.0006928
Pubmed ID
Authors

Chantel D. Sloan, Angeline D. Andrew, Eric J. Duell, Scott M. Williams, Margaret R. Karagas, Jason H. Moore

Abstract

Genetic structure due to ancestry has been well documented among many divergent human populations. However, the ability to associate ancestry with genetic substructure without using supervised clustering has not been explored in more presumably homogeneous and admixed US populations. The goal of this study was to determine if genetic structure could be detected in a United States population from a single state where the individuals have mixed European ancestry. Using Bayesian clustering with a set of 960 single nucleotide polymorphisms (SNPs) we found evidence of population stratification in 864 individuals from New Hampshire that can be used to differentiate the population into six distinct genetic subgroups. We then correlated self-reported ancestry of the individuals with the Bayesian clustering results. Finnish and Russian/Polish/Lithuanian ancestries were most notably found to be associated with genetic substructure. The ancestral results were further explained and substantiated using New Hampshire census data from 1870 to 1930 when the largest waves of European immigrants came to the area. We also discerned distinct patterns of linkage disequilibrium (LD) between the genetic groups in the growth hormone receptor gene (GHR). To our knowledge, this is the first time such an investigation has uncovered a strong link between genetic structure and ancestry in what would otherwise be considered a homogenous US population.

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Geographical breakdown

Country Count As %
United Kingdom 2 7%
United States 2 7%
Ethiopia 1 3%
Unknown 25 83%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 30%
Student > Ph. D. Student 8 27%
Student > Master 3 10%
Student > Doctoral Student 2 7%
Professor 2 7%
Other 5 17%
Unknown 1 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 17 57%
Medicine and Dentistry 6 20%
Social Sciences 2 7%
Biochemistry, Genetics and Molecular Biology 1 3%
Environmental Science 1 3%
Other 1 3%
Unknown 2 7%