Aimee R. Taylor

Systematic bias in malaria parasite relatedness estimation

This paper identifies systematic biases in malaria parasite relatedness estimation and proposes practical tools to address them.

Distinguishing gene flow between malaria parasite populations

This paper evaluates the ability to distinguish gene flow levels between malaria parasite populations using real and simulated data, comparing estimators and determining marker and sample thresholds.

A cautionary note on the use of unsupervised machine learning algorithms to characterise malaria parasite population structure from genetic distance matrices

This paper warns against using unsupervised machine learning algorithms like PCoA and HAC to infer malaria parasite ancestry from genetic distance matrices, highlighting their limitations and providing guidance for more rigorous analysis.

Identity-by-descent relatedness estimates with uncertainty characterise departure from isolation-by-distance between Plasmodium falciparum populations on the Colombian-Pacific coast

The paper estimates IBD-relatedness with uncertainty between Plasmodium falciparum populations and finds connectivity between Buenaventura and Tumaco is higher than expected under isolation-by-distance.

Estimating relatedness between malaria parasites

This paper compares relatedness measures in malaria parasites using identity-by-state and identity-by-descent, recommending identity-by-descent for portability and providing marker requirements for reliable estimates.

hmmIBD: software to infer pairwise identity by descent between haploid genotypes

The paper introduces hmmIBD, a software tool for estimating pairwise identity by descent between haploid genomes, validated using simulated data and applied to Plasmodium falciparum samples.