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MalKinID: A Likelihood-Based Model for Identifying Malaria Parasite Genealogical Relationships Using

Wesley Wong1, Lea Wang2, Stephen S Schaffner3

  • 1Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA, USA.

Biorxiv : the Preprint Server for Biology
|July 29, 2024
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Summary
This summary is machine-generated.

MalKinID, a new model, identifies malaria parasite relationships using genome-wide identity-by-descent (IBD) data. It accurately tracks transmission history and aids in genetic studies, especially with outcrossing.

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Area of Science:

  • Genomics and Bioinformatics
  • Infectious Disease Epidemiology
  • Parasitology

Background:

  • Pathogen genomics is crucial for tracking infectious disease transmission dynamics.
  • Identity-by-descent (IBD) analysis is vital for assessing genetic relatedness in malaria parasites.
  • IBD aids in studying malaria transmission, importation, and reconstructing parasite genealogy.

Purpose of the Study:

  • To introduce MalKinID (Malaria Kinship Identifier), a novel likelihood-based classification model.
  • To identify genealogical relationships among malaria parasites using genome-wide IBD data.
  • To enable accurate reconstruction of transmission history and parasite separation for genetic studies.

Main Methods:

  • Developed MalKinID, a model analyzing IBD proportions and segment distributions.
  • Calibrated MalKinID using genomic data from three laboratory-based genetic crosses.
  • Evaluated model performance on parent-child and full-sibling comparisons, and simulated importations.

Main Results:

  • MalKinID achieved >80% sensitivity in identifying laboratory-generated F1 progeny.
  • Accurately reconstructed genealogy in simulated outcrossed importations (F1-scores > 0.84).
  • Performance declined with inbreeding (serial co-transmission), showing lower precision and sensitivity for relatedness inference.

Conclusions:

  • MalKinID provides a robust framework for inferring malaria parasite genealogy from IBD data.
  • Genealogical inference is most effective in outcrossing scenarios or with predefined pedigrees.
  • The model supports tracking parasite transmission history and facilitating quantitative-trait-locus experiments.