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Simultaneous population genomics of hosts and their parasites with selective whole genome amplification

Ellis, Vincenzo A. LU ; Theodosopoulos, Angela LU ; Sharma, Ishika ; Bardil, Amélie ; Stjernman, Martin LU orcid and Hellgren, Olof LU (2025) In Parasites and Vectors 18(1).
Abstract

Background: Generating parasite genomes is challenging when little of the DNA in infected host tissue is from the parasite. We used selective whole genome amplification (SWGA) to generate genomic data from wildlife samples of the avian haemosporidian Haemoproteus majoris (lineage PARUS1) and its host, the blue tit (Cyanistes caeruleus). Methods: We used SWGA to amplify the parasite DNA in nine avian blood samples collected between 1996 and 2021, and subsequently performed short-read sequencing and bioinformatically separated the host and parasite reads in each sample. Results: SWGA increased the percentage of parasite reads significantly. Sequencing to a depth of about 56 million reads (forward and reverse) per sample resulted on... (More)

Background: Generating parasite genomes is challenging when little of the DNA in infected host tissue is from the parasite. We used selective whole genome amplification (SWGA) to generate genomic data from wildlife samples of the avian haemosporidian Haemoproteus majoris (lineage PARUS1) and its host, the blue tit (Cyanistes caeruleus). Methods: We used SWGA to amplify the parasite DNA in nine avian blood samples collected between 1996 and 2021, and subsequently performed short-read sequencing and bioinformatically separated the host and parasite reads in each sample. Results: SWGA increased the percentage of parasite reads significantly. Sequencing to a depth of about 56 million reads (forward and reverse) per sample resulted on average (± standard error [SE]) in 11.3X ± 1.85 for the host genome and 1.17X ± 0.446 mean depth of coverage for the host and parasite, respectively, after SWGA. Furthermore, about 74% of the host genome (genome size approx. 1.2 Gb) and 33% of the parasite genome (approx. 23.9 Mb) had at least 1X coverage on average; two samples had 1X coverage of approximately 60% of the parasite genome. Parasite sequencing success was positively correlated with parasitemia. When comparing the parasite sequences in the four best samples, we identified 9895 sites (minimum 5X coverage) that varied among the infections. When filtering the full dataset to at least six samples per variant, we identified 14,512,339 and 7068 sites that varied among samples in the host and parasite populations, respectively, revealing variation among samples and years. Conclusions: SWGA facilitates dual host-parasite population genomics in this system and will greatly expand our understanding of host-parasite interactions over space and time.

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author
; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Avian malaria, Coevolution, Host genomics, Parasite evolution, Pathogen genomics
in
Parasites and Vectors
volume
18
issue
1
article number
448
publisher
BioMed Central (BMC)
external identifiers
  • scopus:105020993561
  • pmid:41194238
ISSN
1756-3305
DOI
10.1186/s13071-025-07087-1
language
English
LU publication?
yes
id
43e229f5-84d0-4efc-9f0c-fb2559ee486e
date added to LUP
2025-12-09 13:18:35
date last changed
2025-12-09 13:19:24
@article{43e229f5-84d0-4efc-9f0c-fb2559ee486e,
  abstract     = {{<p>Background: Generating parasite genomes is challenging when little of the DNA in infected host tissue is from the parasite. We used selective whole genome amplification (SWGA) to generate genomic data from wildlife samples of the avian haemosporidian Haemoproteus majoris (lineage PARUS1) and its host, the blue tit (Cyanistes caeruleus). Methods: We used SWGA to amplify the parasite DNA in nine avian blood samples collected between 1996 and 2021, and subsequently performed short-read sequencing and bioinformatically separated the host and parasite reads in each sample. Results: SWGA increased the percentage of parasite reads significantly. Sequencing to a depth of about 56 million reads (forward and reverse) per sample resulted on average (± standard error [SE]) in 11.3X ± 1.85 for the host genome and 1.17X ± 0.446 mean depth of coverage for the host and parasite, respectively, after SWGA. Furthermore, about 74% of the host genome (genome size approx. 1.2 Gb) and 33% of the parasite genome (approx. 23.9 Mb) had at least 1X coverage on average; two samples had 1X coverage of approximately 60% of the parasite genome. Parasite sequencing success was positively correlated with parasitemia. When comparing the parasite sequences in the four best samples, we identified 9895 sites (minimum 5X coverage) that varied among the infections. When filtering the full dataset to at least six samples per variant, we identified 14,512,339 and 7068 sites that varied among samples in the host and parasite populations, respectively, revealing variation among samples and years. Conclusions: SWGA facilitates dual host-parasite population genomics in this system and will greatly expand our understanding of host-parasite interactions over space and time.</p>}},
  author       = {{Ellis, Vincenzo A. and Theodosopoulos, Angela and Sharma, Ishika and Bardil, Amélie and Stjernman, Martin and Hellgren, Olof}},
  issn         = {{1756-3305}},
  keywords     = {{Avian malaria; Coevolution; Host genomics; Parasite evolution; Pathogen genomics}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{BioMed Central (BMC)}},
  series       = {{Parasites and Vectors}},
  title        = {{Simultaneous population genomics of hosts and their parasites with selective whole genome amplification}},
  url          = {{http://dx.doi.org/10.1186/s13071-025-07087-1}},
  doi          = {{10.1186/s13071-025-07087-1}},
  volume       = {{18}},
  year         = {{2025}},
}