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LDx: Estimation of Linkage Disequilibrium from High-Throughput Pooled Resequencing Data

Overview of attention for article published in PLOS ONE, November 2012
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Title
LDx: Estimation of Linkage Disequilibrium from High-Throughput Pooled Resequencing Data
Published in
PLOS ONE, November 2012
DOI 10.1371/journal.pone.0048588
Pubmed ID
Authors

Alison F. Feder, Dmitri A. Petrov, Alan O. Bergland

Abstract

High-throughput pooled resequencing offers significant potential for whole genome population sequencing. However, its main drawback is the loss of haplotype information. In order to regain some of this information, we present LDx, a computational tool for estimating linkage disequilibrium (LD) from pooled resequencing data. LDx uses an approximate maximum likelihood approach to estimate LD (r(2)) between pairs of SNPs that can be observed within and among single reads. LDx also reports r(2) estimates derived solely from observed genotype counts. We demonstrate that the LDx estimates are highly correlated with r(2) estimated from individually resequenced strains. We discuss the performance of LDx using more stringent quality conditions and infer via simulation the degree to which performance can improve based on read depth. Finally we demonstrate two possible uses of LDx with real and simulated pooled resequencing data. First, we use LDx to infer genomewide patterns of decay of LD with physical distance in D. melanogaster population resequencing data. Second, we demonstrate that r(2) estimates from LDx are capable of distinguishing alternative demographic models representing plausible demographic histories of D. melanogaster.

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The data shown below were compiled from readership statistics for 147 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 2%
Spain 2 1%
Austria 2 1%
Netherlands 1 <1%
Switzerland 1 <1%
Portugal 1 <1%
China 1 <1%
Germany 1 <1%
United Kingdom 1 <1%
Other 1 <1%
Unknown 133 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 52 35%
Researcher 36 24%
Student > Master 12 8%
Professor 8 5%
Student > Bachelor 6 4%
Other 20 14%
Unknown 13 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 96 65%
Biochemistry, Genetics and Molecular Biology 22 15%
Computer Science 3 2%
Environmental Science 2 1%
Mathematics 1 <1%
Other 6 4%
Unknown 17 12%