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Updated: May 30, 2025
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Methods to Increase the Sensitivity of High Resolution Melting Single Nucleotide Polymorphism Genotyping in Malaria
Published on: November 10, 2015
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Systematic bias in malaria parasite relatedness estimation.
Somya Mehra1,2,3, Daniel E Neafsey1,2, Michael White4
1Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
G3 (Bethesda, Md.)
|January 30, 2025
Summary
Estimating malaria parasite relatedness using genetic data can be biased. Whole-genome sequencing with hidden Markov models can mitigate bias, offering more accurate relative or absolute relatedness measures.
Area of Science:
- Genetics
- Parasitology
- Bioinformatics
Background:
- Genetic studies of Plasmodium parasites increasingly rely on relatedness estimates.
- Current methods for malaria parasite relatedness estimation have limitations and biases that are not fully understood, particularly compared to diploid organisms.
Purpose of the Study:
- To characterize systematic bias in malaria parasite relatedness estimation.
- To compare relatedness estimates derived from whole-genome sequencing (WGS) versus sparser data types.
- To provide tools for practitioners to evaluate relatedness estimation methods.
Main Methods:
- Theoretical analysis under a non-ancestral statistical model.
- Numerical simulations using an ancestry model.
- Empirical analysis of parasite data from Guyana and Colombia.
Main Results:
- Allele frequency estimates inherently encode averaged relatedness, potentially leading to systematic underestimation in pairwise models.
- Underestimation can serve as a calibration for relative relatedness.
- Hidden Markov Models (HMMs) utilizing linkage disequilibrium in WGS data mitigate bias, unlike methods assuming marker independence.
Conclusions:
- Systematic underestimation is inherent when assuming marker independence for relatedness estimation.
- WGS data analyzed with HMMs offer improved accuracy for absolute relatedness estimation.
- Practitioners can choose between relative relatedness under independence or absolute relatedness under HMMs, with tools provided for case-by-case evaluation.
