Biology subjects

Python, A.

Publications and source records attributed to Python, A..

4 recordsLinked to original sources

bioRxiv2026

Beyond straight lines: migration costs considering geography enhance tracing human genetic ancestry

Reconstructing the spatio-temporal history of human genetic lineages is fundamental to understanding human evolution and population distribution. While succinct tree sequences and maximum parsimony reconstruction methods applied to large-scale genomic data have improved our ability to trace the geographic history of genetic ancestry, they have essentially relied on Euclidean distances, which ineluctably ignore opportunity costs that have shaped human mobility patterns since the earliest human migrations and settlement formations. Here we propose an approach to incorporate realistic geographical migration costs through a human movement friction surface. Using simulated data mimicking the dispersal process of human migration out of Africa, we found that, compared to the Euclidean-based benchmark (M0), the proposed friction-based model (Mf) leads to a more accurate estimation of the geographical origin (n = 346, accuracy M0 = 0.18, f = 0.27) and genetic flux (n = 30, MSE M0 = 0.20, Mf = 0.12) through the Mandeb corridor in the Horn of Africa. We further illustrate these findings in a case study, in which our model seems to better identify plausible human migration paths from Eurasia to the Americas by accounting for geographic factors affecting migration opportunity costs, such as the Alaska Range and Rocky Mountains that represent physical barriers that constraint migration. While important migration drivers such as climate change, technological advances, social organization, and culture remain omitted here, our work highlights the importance of explicitly accounting for geographic constraints to improve our ability to reconstruct past human mobility and, ultimately, understand the evolution of human populations.

evolutionary biology↗

bioRxiv2025

Geographical variation drives adaptive equilibrium of the P. falciparum sickle-associated mutations

The recent discovery of genetic mutations in Plasmodium falciparum--the most lethal malaria parasite--that enable it to overcome the protective effects of sickle cell trait, raises fundamental questions about the underlying biological and evolutionary interactions. Here we develop a geostatistical model to compare sickle haemoglobin genotype frequencies to the Plasmodium falciparum sickle-associated alleles across global populations, and find a robust association at multiple geographical scales, implying that sickle drives positive selection for these parasite mutations. A model of parasite evolution and an analysis of local haplotype patterns suggest that key features of these mutations - that they are polymorphic in all African populations and are mutually correlated despite lying in different genome regions - are caused by geographical variation in selection pressure, and that the alleles may have been maintained by balancing selection over timescales comparable to the age of the sickle mutation itself. The predicted impact of this host-parasite interaction on disease outcomes varies widely across populations, and functional data are needed to discover the biological mechanisms involved.

genetics↗

bioRxiv2019

Modelling geospatial distributions of the triatomine vectors of Trypanosoma cruzi in Latin America

Approximately 150 triatomine species are known to be infected with the Chagas parasite, Trypanosoma cruzi, but they differ in the risk they pose to human populations. The largest risk comes from species that have a domestic life cycle and these species have been targeted by indoor residual spraying campaigns, which have been successful in many locations. It is now important to consider residual transmission that may be linked to persistent populations of dominant vectors, or to secondary or minor vectors. The aim of this project was to define the geographical distributions of the community of triatomine species in Latin America. Presence-only data with over 12, 000 observations of triatomine vectors were extracted from a public database and target-group background data were generated to account for sampling bias in the presence data. Geostatistical regression was then applied to estimate species distributions and fine-scale distribution maps were generated for thirty triatomine vector species. The results for Panstrongylus geniculatus, P. megistus, Triatoma barberi, T. brasiliensis, and T. pseudomaculata are presented in detail and the model validation results for each of the 30 species are presented in full. The predictive maps for all species are made publicly available so that they can be used to assess the communities of vectors present within different regions of the endemic zone. The maps are presented alongside key indicators for the capacity of each species to transmit T. cruzi to humans. These indicators include infection prevalence, evidence for human blood meals, and colonisation or invasion of homes. A summary of these indicators shows that the majority of the 30 species mapped by this study have the potential to transmit T. cruzi to humans.\n\nAuthor summaryThe Pan American Health Organisations Strategy and Plan of Action for Chagas Disease Prevention, Control and Care highlights the importance of eliminating those triatomine vector species that colonise homes, and has had great success in many locations. Since indoor residual spraying campaigns have targeted these species, their importance relative to other vectors has diminished and their geographical distributions may also have changed. It is now vital to consider the full community of vector species, including previously dominant vectors as well as secondary or minor vector species, in order to target residual transmission to humans. Our aim was to define the geographical distributions of the most commonly reported triatomine species in Latin America. We extracted reports of triatomine vector species observed at specific locations from a public database and we used a geostatistical model to generate fine-scale predictive maps for thirty triatomine vector species. We present these maps alongside a summary of key indicators related to the capacity of each species to transmit the Chagas parasite to humans. We show that most of the 30 species that we have mapped pose a potential threat to human populations.

ecology↗

bioRxiv2019

Model ensembles with different response variables for base and meta models: malaria disaggregation regression combining prevalence and incidence data

Maps of infection risk are a vital tool for the elimination of malaria. Routine surveillance data of malaria case counts, often aggregated over administrative regions, is becoming more widely available and can better measure low malaria risk than prevalence surveys. However, aggregation of case counts over large, heterogeneous areas means that these data are often underpowered for learning relationships between the environment and malaria risk. A model that combines point surveys and aggregated surveillance data could have the benefits of both but must be able to account for the fact that these two data types are different malariometric units. Here, we train multiple machine learning models on point surveys and then combine the predictions from these with a geostatistical disaggregation model that uses routine surveillance data. We find that, in tests using data from Colombia and Madagascar, using a disaggregation regression model to combine predictions from machine learning models trained on point surveys improves model accuracy relative to using the environmental covariates directly.

epidemiology↗