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Linkage Disequilibrium Decay and Past Population History in the Human Genome

Overview of attention for article published in PLOS ONE, October 2012
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Title
Linkage Disequilibrium Decay and Past Population History in the Human Genome
Published in
PLOS ONE, October 2012
DOI 10.1371/journal.pone.0046603
Pubmed ID
Authors

Leeyoung Park

Abstract

The fluctuation of population size has not been well studied in the previous studies of theoretical linkage disequilibrium (LD) expectation. In this study, an improved theoretical prediction of LD decay was derived to account for the effects of changes in effective population sizes. The equation was used to estimate effective population size (N(e)) assuming a constant N(e) and LD at equilibrium, and these N(e) estimates implied the past changes of N(e) for a certain number of generations until equilibrium, which differed based on recombination rate. As the influence of recent population history on the N(e) estimates is larger than old population history, recent changes in population size can be inferred more accurately than old changes. The theoretical predictions based on this improved expression showed accurate agreement with the simulated values. When applied to human genome data, the detailed recent history of human populations was obtained. The inferred past population history of each population showed good correspondence with historical studies. Specifically, four populations (three African ancestries and one Mexican ancestry) showed population growth that was significantly less than that of other populations, and two populations originated from China showed prominent exponential growth. During the examination of overall LD decay in the human genome, a selection pressure on chromosome 14, the gephyrin gene, was observed in all populations.

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Geographical breakdown

Country Count As %
United States 3 5%
Israel 1 2%
Germany 1 2%
Spain 1 2%
Romania 1 2%
Unknown 56 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 33%
Researcher 7 11%
Student > Master 7 11%
Student > Bachelor 4 6%
Professor > Associate Professor 4 6%
Other 13 21%
Unknown 7 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 33 52%
Biochemistry, Genetics and Molecular Biology 10 16%
Computer Science 4 6%
Medicine and Dentistry 3 5%
Linguistics 1 2%
Other 3 5%
Unknown 9 14%