Biology subjects

Pemberton, T.

Publications and source records attributed to Pemberton, T..

4 recordsLinked to original sources

bioRxiv2026

Characterising recent antimalarial resistance in West Africa: Insights from amplicon sequencing of 17,384 Plasmodium falciparum infection samples

Plasmodium falciparum (P. falciparum) infection remains a significant public health threat in West Africa, where chemoprevention and first-line therapies are key interventions against malaria. However, the development and spread of resistance to commonly used antimalarials poses a growing threat to the efficacy of these strategies. This study characterises the recent landscape of antimalarial resistance in West Africa by analysing targeted amplicon sequences from 17,384 P. falciparum infection samples. Across countries, the prevalence of the pyrimethamine resistance-associated dhfr triple mutant allele (51I/59R/108N) exceeded 80%, while its combination with the sulphadoxine resistance-associated dhps 437G exceeded 60% of infections. Unlike the parasite genotypes in East Africa, the prevalence of the dhps 540E mutant was low (1.5%), whereas dhps 436A was common (43.8%). The chloroquine resistance marker crt 76T showed greatest geographic heterogeneity, ranging from low prevalence in Ghana (1.3%) to very common in The Gambia (64.9%). Non-synonymous mutants of kelch13 were uncommon, most with unknown relevance to artemisinin resistance and observed for the first time in Africa. However, mutants that are artemisinin resistance-associated elsewhere were detected in three infection samples from Ghana (574L, 561H, 469Y), and one in Cameroon (538V). This large-scale genomic surveillance of P. falciparum infections highlights the need for ongoing monitoring of drug resistance and for data integration throughout the region.

genomics↗

bioRxiv2025

Targeted genomic surveillance of insecticide resistance in African malaria vectors

The emergence of insecticide resistance is threatening the efforts of malaria control programmes, which rely heavily on a limited arsenal of insecticidal tools, such as insecticide-treated bed nets. Importantly, genomic surveillance of malaria vectors can provide critical, policy-relevant insights into the presence and evolution of insecticide resistance, allowing us to maintain and extend the shelf life of these interventions. Yet the complex genetic architecture of resistance, combined with resource constraints in malaria-endemic settings, have thus far precluded the widespread use of genomics in routine surveillance. Meanwhile, stakeholders in sub-Saharan Africa are moving towards locally driven, decentralised generation of genomic data, underscoring the need for standardised and robust genomics workflows. To address this need, we demonstrate an approach to targeted genomic surveillance in Anopheles gambiae s.l with Illumina sequencing. We target 90 genomic loci in the Anopheles gambiae s.l genome, including 55 resistance-associated mutations and 35 ancestry informative markers. This protocol is coupled with advanced, automated software for accurate and reproducible variant analysis. We are able to elucidate population structure and ancestry in our cohorts and accurately identify most species in the An. gambiae species complex. We report frequencies of variants at insecticide-resistance loci and explore the continued evolution of the pyrethroid target site, the Voltage-gated sodium channel. Applying the platform to a recently established colony of field-caught resistant mosquitoes (Siaya, Kenya), we identified seven independent resistance-associated variants contributing to reduced efficacy of insecticide-treated nets in East Africa. Additionally, we leverage a machine learning algorithm (XGBoost) to demonstrate the possibility of predicting bioassay mortality using genotypes alone. This achieved high accuracy (75%), demonstrating the potential of targeted genomics to predictively monitor insecticide resistance. Together these tools provide a practical, scalable solution for resistance monitoring while advancing the goal of building local genomic surveillance capacity in sub-Saharan Africa.

genomics↗

bioRxiv2017

A Genome Scan for Genes Underlying Adult Body Size Differences between Central African Pygmies and their Non-Pygmy Neighbors

BackgroundCentral African hunter-gatherer Pygmy populations have reduced body size compared with their often much larger agricultural non-Pygmy neighbors, potentially reflecting adaptation to the anatomical and physiological constraints of their lifestyle in tropical rainforests. Earlier studies investigating the genetics of the pygmy phenotype have focused on standing height, one aspect of this complex phenotype that is itself a composite of skeletal components with different growth patterns. Here, we extend the investigations of standing height to the variability and genetic architecture of sitting height and subischial leg length as well as body mass index (BMI) in a sample of 406 unrelated West Central African Pygmies and non-Pygmies.\n\nResultsIn addition to their significantly reduced standing height compared with non-Pygmies, we find Pygmies to have significantly shorter sitting heights and subischial leg lengths as well as higher sitting/standing height ratios than non-Pygmies. However, while male Pygmies had significantly lower BMI compared with male non-Pygmies, the BMI of females were instead similar. Consistent with prior observations with standing height, sitting height and subischial leg length were strongly correlated with inferred levels of non-Pygmy genetic admixture while BMI was instead weakly correlated, likely reflecting the greater contribution of non-genetic factors to the determination of body weight compared with height. Using 196,725 SNPs on the Illumina Cardio-MetaboChip with genotypes on 358 Pygmy and 169 non-Pygmy individuals together with single-and multi-marker association approaches, we identified a single genomic region and seven genes associated with Pygmy/non-Pygmy categorization as well as 9, 10, 9, and 10 genes associated with standing and sitting height, sitting/standing height ratio, and subischial leg length, respectively. Many of the genes identified have putative functions consistent with a role in determining their associated trait as well as the complex Central African pygmy phenotype.\n\nConclusionsThese findings highlight the potential of modestly sized datasets of Pygmies and non-Pygmies to detect biologically meaningful associations with traits contributing to the Central African pygmy phenotype. Moreover, they provide new insights into the phenotypic and genetic bases of the complex pygmy phenotype and offer new opportunities to facilitate our understanding of its complex evolutionary origins.

genetics↗

bioRxiv2017

Weighted likelihood inference of genomic autozygosity patterns in dense genotype data

BackgroundGenomic regions of autozygosity (ROA) arise when an individual is homozygous for haplotypes inherited identical-by-descent from ancestors shared by both parents. Over the past decade, they have gained importance for understanding evolutionary history and the genetic basis of complex diseases and traits. However, methods to detect ROA in dense genotype data have not evolved in step with advances in genome technology that now enable us to rapidly create large high-resolution genotype datasets, limiting our ability to investigate their constituent ROA patterns.\n\nResultsWe report a weighted likelihood approach for identifying ROA in dense genotype data that accounts for autocorrelation among genotyped positions and the possibilities of unobserved mutation and recombination events, and variability in the confidence of individual genotype calls in whole genome sequence (WGS) data. Forward-time genetic simulations under two demographic scenarios that reflect situations where inbreeding and its effect on fitness are of interest suggest this approach is better powered than existing state-of-the-art methods to detect ROA at marker densities consistent with WGS and popular microarray genotyping platforms used in human and non-human studies. Moreover, we present evidence that suggests this approach is able to distinguish ROA arising via consanguinity from ROA arising via endogamy. Using subsets of The 1000 Genomes Project Phase 3 data we show that, relative to WGS, intermediate and long ROA are captured robustly with popular microarray platforms, while detection of short ROA is more variable and improves with marker density. Worldwide ROA patterns inferred from WGS data are found to accord well with those previously reported on the basis of microarray genotype data. Finally, we highlight the potential of this approach to detect genomic regions enriched for autozygosity signals in one group relative to another based upon comparisons of per-individual autozygosity likelihoods instead of inferred ROA frequencies.\n\nConclusionsThis weighted likelihood ROA detection approach can assist population- and disease-geneticists working with a wide variety of data types and species to explore ROA patterns and to identify genomic regions with differential ROA signals among groups, thereby advancing our understanding of evolutionary history and the role of recessive variation in phenotypic variation and disease.

genomics↗