emeraLD: rapid linkage disequilibrium estimation with massive datasets | Zendy

Corbin Quick | Zendy; Christian Fuchsberger | Zendy; Daniel Taliun | Zendy; Gonçalo R. Abecasis | Zendy; Michael Boehnke | Zendy; Hyun Min Kang | Zendy

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emeraLD: rapid linkage disequilibrium estimation with massive datasets

Author(s) -

Corbin Quick,

Christian Fuchsberger,

Daniel Taliun,

Gonçalo R. Abecasis,

Michael Boehnke,

Hyun Min Kang

Publication year - 2018

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bty547

Subject(s) - linkage disequilibrium , emerald , disequilibrium , linkage (software) , computer science , estimation , computational biology , statistics , biology , genetics , mathematics , medicine , haplotype , geology , allele , economics , mineralogy , management , ophthalmology , gene

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.

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