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emeraLD: rapid linkage disequilibrium estimation with massive datasets.

Citation
Quick, C., et al. “Emerald: Rapid Linkage Disequilibrium Estimation With Massive Datasets.”. Bioinformatics (Oxford, England), pp. 164-166.
Center University of Michigan
Author Corbin Quick, Christian Fuchsberger, Daniel Taliun, Goncalo Abecasis, Michael Boehnke, Hyun Min Kang
Abstract

Summary: Estimating linkage disequilibrium (LD) is essential for a wide range of summary statistics-based association methods for genome-wide association studies. Large genetic datasets, e.g. the TOPMed WGS project and UK Biobank, enable more accurate and comprehensive LD estimates, but increase the computational burden of LD estimation. Here, we describe emeraLD (Efficient Methods for Estimation and Random Access of LD), a computational tool that leverages sparsity and haplotype structure to estimate LD up to 2 orders of magnitude faster than current tools.

Availability and implementation: emeraLD is implemented in C++, and is open source under GPLv3. Source code and documentation are freely available at http://github.com/statgen/emeraLD.

Supplementary information: Supplementary data are available at Bioinformatics online.

Year of Publication
2019
Journal
Bioinformatics (Oxford, England)
Volume
35
Issue
1
Number of Pages
164-166
Date Published
12/2019
ISSN Number
1367-4811
DOI
10.1093/bioinformatics/bty547
Alternate Journal
Bioinformatics
PMID
30204848
PMCID
PMC6298049
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