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

doi:10.1186/s12936-018-2349-7

. 2018 May 15;17(1):196.

doi: 10.1186/s12936-018-2349-7.

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

Stephen F Schaffner ^{1

2}, Aimee R Taylor ^{3

4}, Wesley Wong ^{5

6}, Dyann F Wirth ^{3

5}, Daniel E Neafsey ^{3

5}

Affiliations

¹ Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA. sfs@broadinstitute.org.
² Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA. sfs@broadinstitute.org.
³ Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
⁴ Department of Epidemiology and Center for Communicable Disease Dynamics, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA.
⁵ Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA.
⁶ Institute for Disease Modeling, Bellevue, WA, USA.

PMID: 29764422
PMCID: PMC5952413
DOI: 10.1186/s12936-018-2349-7

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

Stephen F Schaffner et al. Malar J. 2018.

. 2018 May 15;17(1):196.

doi: 10.1186/s12936-018-2349-7.

Authors

Stephen F Schaffner ^{1

2}, Aimee R Taylor ^{3

4}, Wesley Wong ^{5

6}, Dyann F Wirth ^{3

5}, Daniel E Neafsey ^{3

5}

Affiliations

¹ Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA. sfs@broadinstitute.org.
² Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA. sfs@broadinstitute.org.
³ Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
⁴ Department of Epidemiology and Center for Communicable Disease Dynamics, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA.
⁵ Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA.
⁶ Institute for Disease Modeling, Bellevue, WA, USA.

PMID: 29764422
PMCID: PMC5952413
DOI: 10.1186/s12936-018-2349-7

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.

Keywords: Haploid; Hidden Markov model; Identity by descent; Malaria; Plasmodium falciparum.

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Figures

Fig. 1

Fig. 1

The fraction of sample pairs that are IBD along chromosome 7, where one sample is from Ghana and the other from Cambodia. Blue curve: IBD as reconstructed by hmmIBD correctly treating the samples as coming from two populations; red curve: IBD as reconstructed from a single, averaged population. (See Additional file 2 for details.)

See this image and copyright information in PMC

References

1. Thompson EA. Identity by descent: variation in meiosis, across genomes, and in populations. Genetics. 2013;194:301–326. doi: 10.1534/genetics.112.148825. - DOI - PMC - PubMed
1. Browning SR, Browning BL. Identity by descent between distant relatives: detection and applications. Annu Rev Genet. 2012;46:617–633. doi: 10.1146/annurev-genet-110711-155534. - DOI - PubMed
1. Daniels RF, Schaffner SF, Wenger EA, Proctor JL, Chang H-H, Wong W, et al. Modeling malaria genomics reveals transmission decline and rebound in Senegal. Proc Natl Acad Sci USA. 2015;112:7067–7072. doi: 10.1073/pnas.1505691112. - DOI - PMC - PubMed
1. Wong W, Griggs AD, Daniels RF, Schaffner SF, Ndiaye D, Bei AK, et al. Genetic relatedness analysis reveals the cotransmission of genetically related Plasmodium falciparum parasites in Thiès, Senegal. Genome Med. 2017;9:5. doi: 10.1186/s13073-017-0398-0. - DOI - PMC - PubMed
1. Henden L, Lee S, Mueller I, Barry A, Bahlo M. Detecting selection signals in Plasmodium falciparum using identity-by-descent analysis. Preprint. bioRxiv. 2016. 10.1101/088039. Posted 16 November 2016.

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[1] Thompson EA. Identity by descent: variation in meiosis, across genomes, and in populations. Genetics. 2013;194:301–326. doi: 10.1534/genetics.112.148825. - DOI - PMC - PubMed

[2] Thompson EA. Identity by descent: variation in meiosis, across genomes, and in populations. Genetics. 2013;194:301–326. doi: 10.1534/genetics.112.148825. - DOI - PMC - PubMed

[3] Browning SR, Browning BL. Identity by descent between distant relatives: detection and applications. Annu Rev Genet. 2012;46:617–633. doi: 10.1146/annurev-genet-110711-155534. - DOI - PubMed

[4] Browning SR, Browning BL. Identity by descent between distant relatives: detection and applications. Annu Rev Genet. 2012;46:617–633. doi: 10.1146/annurev-genet-110711-155534. - DOI - PubMed

[5] Daniels RF, Schaffner SF, Wenger EA, Proctor JL, Chang H-H, Wong W, et al. Modeling malaria genomics reveals transmission decline and rebound in Senegal. Proc Natl Acad Sci USA. 2015;112:7067–7072. doi: 10.1073/pnas.1505691112. - DOI - PMC - PubMed

[6] Daniels RF, Schaffner SF, Wenger EA, Proctor JL, Chang H-H, Wong W, et al. Modeling malaria genomics reveals transmission decline and rebound in Senegal. Proc Natl Acad Sci USA. 2015;112:7067–7072. doi: 10.1073/pnas.1505691112. - DOI - PMC - PubMed

[7] Wong W, Griggs AD, Daniels RF, Schaffner SF, Ndiaye D, Bei AK, et al. Genetic relatedness analysis reveals the cotransmission of genetically related Plasmodium falciparum parasites in Thiès, Senegal. Genome Med. 2017;9:5. doi: 10.1186/s13073-017-0398-0. - DOI - PMC - PubMed

[8] Wong W, Griggs AD, Daniels RF, Schaffner SF, Ndiaye D, Bei AK, et al. Genetic relatedness analysis reveals the cotransmission of genetically related Plasmodium falciparum parasites in Thiès, Senegal. Genome Med. 2017;9:5. doi: 10.1186/s13073-017-0398-0. - DOI - PMC - PubMed

[9] Henden L, Lee S, Mueller I, Barry A, Bahlo M. Detecting selection signals in Plasmodium falciparum using identity-by-descent analysis. Preprint. bioRxiv. 2016. 10.1101/088039. Posted 16 November 2016.

[10] Henden L, Lee S, Mueller I, Barry A, Bahlo M. Detecting selection signals in Plasmodium falciparum using identity-by-descent analysis. Preprint. bioRxiv. 2016. 10.1101/088039. Posted 16 November 2016.

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

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

Authors

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Abstract

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References

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Full Text Sources

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