<i>MalKinID</i>: A classification model for identifying malaria parasite genealogical relationships using identity-by-descent
Pathogen genomics is a powerful tool for tracking infectious disease transmission. In malaria, identity-by-descent is used to assess the genetic relatedness between parasites and has been used to study transmission and importation. In theory, identity-by-descent can be used to distinguish genealogical relationships to reconstruct transmission history or identify parasites for QTL experiments. MalKinID (Malaria Kinship Identifier) is a new classification model designed to identify genealogical relationships among malaria parasites based on genome-wide identity-by-descent proportions and identity-by-descent segment distributions. MalKinID was calibrated to the genomic data from 3 laboratory-based genetic crosses (yielding 440 parent-child and 9060 full-sibling comparisons). MalKinID identified lab-generated F1 progeny with >80% sensitivity and showed that 0.39 (95% CI 0.28, 0.49) of the second-generation progeny of a NF54 and NHP4026 cross were F1s and 0.56 (0.45, 0.67) were backcrosses of an F1 with the parental NF54 strain. In simulated outcrossed importations, MalKinID reconstructs genealogy history with high precision and sensitivity, with F1-scores exceeding 0.84. However, when importation involves inbreeding, such as during serial co-transmission, the precision and sensitivity of MalKinID declined, with F1-scores (the harmonic mean of precision and sensitivity) of 0.76 (0.56, 0.92) and 0.23 (0.0, 0.4) for parent-child and full-sibling and <0.05 for second-degree and third-degree relatives. Disentangling inbred relationships required adapting MalKinID to perform multisample comparisons. Genealogical inference is most powered when (1) outcrossing is the norm or (2) multisample comparisons based on a predefined pedigree are used. MalKinID lays the foundations for using identity-by-descent to track parasite transmission history and for separating progeny for quantitative-trait-locus experiments.
Authors
- Dyann F. Wirth (ORCID: https://orcid.org/0000-0002-8604-4455)
- Ian H. Cheeseman (ORCID: https://orcid.org/0000-0003-4426-4027)
- Daniel L. Hartl (ORCID: https://orcid.org/0000-0002-0302-306X)
- S. F. Schaffner (ORCID: https://orcid.org/0000-0001-6699-3568)
- Ashley M. Vaughan (ORCID: https://orcid.org/0000-0001-5815-756X)
- Michael T. Ferdig (ORCID: https://orcid.org/0000-0003-3516-9829)
- Sarah K. Volkman (ORCID: https://orcid.org/0000-0003-0608-392X)
- Wesley Wong (ORCID: https://orcid.org/0000-0001-5664-7143)
- Xue Li (ORCID: https://orcid.org/0000-0002-7501-4445)
- Timothy J. C. Anderson (ORCID: https://orcid.org/0000-0002-0191-0204)
- Lea Wang
Institutions
- Broad Institute (US)
- University of Notre Dame (US)
- Texas Biomedical Research Institute (US)
- Center for Infectious Disease Research (US)
- Infectious Disease Research Institute (US)
- Harvard University (US)
- Simmons University (US)
- University of Washington (US)
- Harvard University Press (US)
Publication Details
- Journal
- Genetics
- Published
- 2024-11-23
- DOI
- https://doi.org/10.1093/genetics/iyae197
- Citations
- 5
- Primary Topic
- Malaria Research and Control
- Type
- article
- Field-Weighted Citation Impact
- 2.71
Funders
- Bill and Melinda Gates Foundation
- Texas Biomedical Research Institute
- National Institutes of Health
- National Center for Research Resources