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ngsLD: evaluating linkage disequilibrium using genotype likelihoods.
Fox, Emma A; Wright, Alison E; Fumagalli, Matteo; Vieira, Filipe G.
Afiliação
  • Fox EA; Department of Life Sciences, Silwood Park Campus, Imperial College London, Ascot, UK.
  • Wright AE; Department of Animal and Plant Sciences, University of Sheffield, Sheffield, UK.
  • Fumagalli M; Department of Life Sciences, Silwood Park Campus, Imperial College London, Ascot, UK.
  • Vieira FG; Center for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Copenhagen, Denmark.
Bioinformatics ; 35(19): 3855-3856, 2019 10 01.
Article em En | MEDLINE | ID: mdl-30903149
ABSTRACT
MOTIVATION Linkage disequilibrium (LD) measures the correlation between genetic loci and is highly informative for association mapping and population genetics. As many studies rely on called genotypes for estimating LD, their results can be affected by data uncertainty, especially when employing a low read depth sequencing strategy. Furthermore, there is a manifest lack of tools for the analysis of large-scale, low-depth and short-read sequencing data from non-model organisms with limited sample sizes.

RESULTS:

ngsLD addresses these issues by estimating LD directly from genotype likelihoods in a fast, reliable and user-friendly implementation. This method makes use of the full information available from sequencing data and provides accurate estimates of linkage disequilibrium patterns compared with approaches based on genotype calling. We conducted a case study to investigate how LD decays over physical distance in two avian species. AVAILABILITY AND IMPLEMENTATION The methods presented in this work were implemented in C/C and are freely available for non-commercial use from https//github.com/fgvieira/ngsLD. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Assuntos

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Software Idioma: En Revista: Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Reino Unido

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Software Idioma: En Revista: Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Reino Unido