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01.12.2018 | Methodology | Ausgabe 1/2018 Open Access

Malaria Journal 1/2018

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

Zeitschrift:
Malaria Journal > Ausgabe 1/2018
Autoren:
Stephen F. Schaffner, Aimee R. Taylor, Wesley Wong, Dyann F. Wirth, Daniel E. Neafsey
Wichtige Hinweise

Electronic supplementary material

The online version of this article (https://​doi.​org/​10.​1186/​s12936-018-2349-7) contains supplementary material, which is available to authorized users.
Stephen F. Schaffner and Aimee R. Taylor are joint first authors

Abstract

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.
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