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hmmIBD: software to infer pairwise identity by descent between haploid genotypes

BACKGROUND: A number of recent malaria studies have used identity by descent (IBD) to study epidemiological processes relevant to malaria control. In this paper, a software package, hmmIBD, is introduced for estimating pairwise IBD between haploid genomes, such as those of the malaria parasite, samp...

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Autores principales: Schaffner, Stephen F., Taylor, Aimee R., Wong, Wesley, Wirth, Dyann F., Neafsey, Daniel E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5952413/
https://www.ncbi.nlm.nih.gov/pubmed/29764422
http://dx.doi.org/10.1186/s12936-018-2349-7
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author Schaffner, Stephen F.
Taylor, Aimee R.
Wong, Wesley
Wirth, Dyann F.
Neafsey, Daniel E.
author_facet Schaffner, Stephen F.
Taylor, Aimee R.
Wong, Wesley
Wirth, Dyann F.
Neafsey, Daniel E.
author_sort Schaffner, Stephen F.
collection PubMed
description BACKGROUND: A number of recent malaria studies have used identity by descent (IBD) to study epidemiological processes relevant to malaria control. In this paper, a software package, hmmIBD, is introduced for estimating pairwise IBD between haploid genomes, such as those of the malaria parasite, sampled from one or two populations. Source code is freely available. METHODS: The performance of hmmIBD was verified using simulated data and benchmarked against an existing method for detecting IBD within populations. Code for all tests is freely available. The utility of hmmIBD for detecting IBD across populations was demonstrated using Plasmodium falciparum data from Cambodia and Ghana. RESULTS: Alongside an existing method, hmmIBD was highly accurate, sensitive and specific. It is fast, requiring only 70 s on average to analyse 50 whole genome sequences on a laptop computer, and scales linearly in the number of pairwise comparisons. Treatment of different populations under hmmIBD improves detection of IBD across populations. CONCLUSION: Fast and accurate software for detecting IBD in malaria parasite genetic data sampled from one or two populations is presented. The latter will likely be a useful feature for malaria elimination efforts, since it could facilitate identification of imported malaria cases. Software is robust to possible misspecification of the genotyping error and the recombination rate. However, exclusion of data in regions whose rates vary greatly from their genome-wide average is recommended. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12936-018-2349-7) contains supplementary material, which is available to authorized users.
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spelling pubmed-59524132018-05-21 hmmIBD: software to infer pairwise identity by descent between haploid genotypes Schaffner, Stephen F. Taylor, Aimee R. Wong, Wesley Wirth, Dyann F. Neafsey, Daniel E. Malar J Methodology BACKGROUND: A number of recent malaria studies have used identity by descent (IBD) to study epidemiological processes relevant to malaria control. In this paper, a software package, hmmIBD, is introduced for estimating pairwise IBD between haploid genomes, such as those of the malaria parasite, sampled from one or two populations. Source code is freely available. METHODS: The performance of hmmIBD was verified using simulated data and benchmarked against an existing method for detecting IBD within populations. Code for all tests is freely available. The utility of hmmIBD for detecting IBD across populations was demonstrated using Plasmodium falciparum data from Cambodia and Ghana. RESULTS: Alongside an existing method, hmmIBD was highly accurate, sensitive and specific. It is fast, requiring only 70 s on average to analyse 50 whole genome sequences on a laptop computer, and scales linearly in the number of pairwise comparisons. Treatment of different populations under hmmIBD improves detection of IBD across populations. CONCLUSION: Fast and accurate software for detecting IBD in malaria parasite genetic data sampled from one or two populations is presented. The latter will likely be a useful feature for malaria elimination efforts, since it could facilitate identification of imported malaria cases. Software is robust to possible misspecification of the genotyping error and the recombination rate. However, exclusion of data in regions whose rates vary greatly from their genome-wide average is recommended. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12936-018-2349-7) contains supplementary material, which is available to authorized users. BioMed Central 2018-05-15 /pmc/articles/PMC5952413/ /pubmed/29764422 http://dx.doi.org/10.1186/s12936-018-2349-7 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology
Schaffner, Stephen F.
Taylor, Aimee R.
Wong, Wesley
Wirth, Dyann F.
Neafsey, Daniel E.
hmmIBD: software to infer pairwise identity by descent between haploid genotypes
title hmmIBD: software to infer pairwise identity by descent between haploid genotypes
title_full hmmIBD: software to infer pairwise identity by descent between haploid genotypes
title_fullStr hmmIBD: software to infer pairwise identity by descent between haploid genotypes
title_full_unstemmed hmmIBD: software to infer pairwise identity by descent between haploid genotypes
title_short hmmIBD: software to infer pairwise identity by descent between haploid genotypes
title_sort hmmibd: software to infer pairwise identity by descent between haploid genotypes
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5952413/
https://www.ncbi.nlm.nih.gov/pubmed/29764422
http://dx.doi.org/10.1186/s12936-018-2349-7
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