Fox, E.A., Wright, A.E., Fumagalli, M. et al. (1 more author) (2019) ngsLD: evaluating linkage disequilibrium using genotype likelihoods. Bioinformatics, 35 (19). pp. 3855-3856. ISSN 1367-4803
Abstract
MOTIVATION: Linkage disequilibrium 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 linkage disequilibrium, 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 linkage disequilibrium 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 to approaches based on genotype calling. We conducted a case study to investigate how linkage disequilibrium decays over physical distance in two avian species. AVAILABILITY: 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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 The Author(s). This is an author produced version of a paper subsequently published in Bioinformatics. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Biosciences (Sheffield) > Department of Animal and Plant Sciences (Sheffield) |
Funding Information: | Funder Grant number NATURAL ENVIRONMENT RESEARCH COUNCIL NE/N013948/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 02 Apr 2019 13:49 |
Last Modified: | 22 Nov 2021 12:17 |
Status: | Published |
Publisher: | Oxford University Press |
Refereed: | Yes |
Identification Number: | 10.1093/bioinformatics/btz200 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:144284 |