Rare variation in malaria parasites biases population-genetic inference

Understanding how pathogens evolve is fundamental to disease control and is a basic question in evolutionary biology, yet pathogens with complex life cycles violate assumptions of classic evolutionary models. Genetic analyses of the malaria parasite <i>Plasmodium falciparum</i> have shown multiple surprising patterns. For example, empirical analyses produce effective population size estimates that vary by orders of magnitude depending on the method and an excess of genes with elevated nonsynonymous variation (measured as <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msub><mml:mi>π</mml:mi> <mml:mi>N</mml:mi></mml:msub> <mml:mo>/</mml:mo> <mml:msub><mml:mi>π</mml:mi> <mml:mi>S</mml:mi></mml:msub> </mml:mrow> </mml:math> ). Here, reanalyzing genomic data from 18 worldwide populations, I show that these observations directly follow from distributions of genetic variation enriched for rare variants. Multiple potential mechanisms may increase the proportion of rare variants, including host expansions, lifecycle dynamics, population structure, or selection. The observed genealogies are more consistent with a multiple-merger coalescent than a Kingman coalescent, causing common summary statistics to be biased in predictable directions. In particular, the abundance of rare variants interacts with the mathematical properties of ratio statistics to systematically inflate gene-level <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msub><mml:mi>π</mml:mi> <mml:mi>N</mml:mi></mml:msub> <mml:mo>/</mml:mo> <mml:msub><mml:mi>π</mml:mi> <mml:mi>S</mml:mi></mml:msub> </mml:mrow> </mml:math> estimates even in the absence of selection. Notably, filtering this rare variation reveals previously masked candidates for selection, including well-characterized antigens such as merozoite surface proteins. This framework provides a foundation for interpreting genomic data in pathogens with high variance in reproductive success.

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Journal
bioRxiv (Cold Spring Harbor Laboratory)
Published
2026-01-14
DOI
https://doi.org/10.64898/2026.01.09.698000
Primary Topic
Malaria Research and Control
Type
article
Field-Weighted Citation Impact
0.00

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article

Rare variation in malaria parasites biases population-genetic inference

Amy Goldberg
bioRxiv (Cold Spring Harbor Laboratory)
Malaria Research and Control
article

Rare variation in malaria parasites biases population-genetic inference

Amy Goldberg
article en

Abstract

Understanding how pathogens evolve is fundamental to disease control and is a basic question in evolutionary biology, yet pathogens with complex life cycles violate assumptions of classic evolutionary models. Genetic analyses of the malaria parasite <i>Plasmodium falciparum</i> have shown multiple surprising patterns. For example, empirical analyses produce effective population size estimates that vary by orders of magnitude depending on the method and an excess of genes with elevated nonsynonymous variation (measured as <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msub><mml:mi>π</mml:mi> <mml:mi>N</mml:mi></mml:msub> <mml:mo>/</mml:mo> <mml:msub><mml:mi>π</mml:mi> <mml:mi>S</mml:mi></mml:msub> </mml:mrow> </mml:math> ). Here, reanalyzing genomic data from 18 worldwide populations, I show that these observations directly follow from distributions of genetic variation enriched for rare variants. Multiple potential mechanisms may increase the proportion of rare variants, including host expansions, lifecycle dynamics, population structure, or selection. The observed genealogies are more consistent with a multiple-merger coalescent than a Kingman coalescent, causing common summary statistics to be biased in predictable directions. In particular, the abundance of rare variants interacts with the mathematical properties of ratio statistics to systematically inflate gene-level <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msub><mml:mi>π</mml:mi> <mml:mi>N</mml:mi></mml:msub> <mml:mo>/</mml:mo> <mml:msub><mml:mi>π</mml:mi> <mml:mi>S</mml:mi></mml:msub> </mml:mrow> </mml:math> estimates even in the absence of selection. Notably, filtering this rare variation reveals previously masked candidates for selection, including well-characterized antigens such as merozoite surface proteins. This framework provides a foundation for interpreting genomic data in pathogens with high variance in reproductive success.

bioRxiv (Cold Spring Harbor Laboratory)
University of California, Los Angeles (US)
National Institutes of Health
Zero hunger
Openalex Percentile: Top 88%
Malaria Research and Control
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Rare variation in malaria parasites biases population-genetic inference — Amy Goldberg · bioRxiv (Cold Spring Harbor Laboratory) (2026) | TGRS Research Map | TGRS