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bioRxiv2026

Towards genetic indicators in ectomycorrhizal fungi: estimating the effective population size

Ectomycorrhizal (EM) fungi are vital to forest ecosystems, supporting tree growth and survival. However, their inclusion in conservation policy and action remains limited and little is known about the status of their genetic diversity, which is essential for their long-term survival and adaptation. The Global Biodiversity Framework adopted a genetic indicator based on the effective population size, Ne, to monitor genetic diversity in all species. To date, it is still uncertain how Ne, a key parameter, can be reliably assessed in species with complex life history traits. Ectomycorrhizal fungi are a highly diverse group of taxa displaying haplodiplontic life cycles with partially clonal reproduction. Here, we review the literature to understand how these life history traits might affect Ne and its estimation in six species of EM fungi. We estimated Ne in 19 populations using eight genetic and genomic datasets from selected studies. We compared Ne estimates using Linkage Disequilibrium (LD) and Sibship Frequency (SF) methods. We tested how Ne estimates change due to partial clonality and genetic structure gradients and whether the number of genetic markers influence the precision of the estimates. We show a systematic bias in Ne estimations when large clones are present and when populations are not correctly delimited. We found both methods are not robust to these factors, which makes them unreliable for conservation assessment purposes in EM fungi. This study provides new perspectives for further research into the links between life history traits and the effective population size of ectomycorrhizal fungi.

genetics↗

bioRxiv2026

MetaboXcan: A multiomic framework linking genetically predicted metabolites, gene expression, and complex traits

Genetically regulated molecular phenotypes, such as gene expression and metabolites, are widely thought to mediate the effect of disease-relevant genetic loci identified by genome-wide association studies (GWAS); however, molecular mechanisms connecting genetic variation to disease remain poorly understood. While transcriptome-wide association studies (TWAS) have identified disease-associated genes, strategies that integrate the metabolome remain underexplored. Here, we introduce MetaboXcan, a framework for predicting plasma metabolite levels from genetic data and associating them with complex traits using GWAS summary statistics. We trained lasso regression models on plasma metabolite and genotype data from the Metabolic Syndrome in Men Study (METSIM); these models outperform existing genetic metabolite predictors and generalize well across independent cohorts. MetaboXcan leverages these models to perform four complementary association analyses: (i) gene-to-trait (TWAS), (ii) gene-to-metabolite (M-TWAS), (iii) metabolite-to-trait (MWAS), and (iv) gene-expression-based metabolite-to-trait associations (g-MWAS). The framework further organizes results into metabolic pathways and gene-metabolite interaction networks to facilitate biological interpretation. Applied to chronic kidney disease (CKD), MetaboXcan identified known disease risk genes (PDILT/UMOD, SPATA5L1/GATM), as well as multiple CKD-relevant metabolites (e.g., glycine, homoarginine). Our integrated multiomic analysis enables biological interpretation by revealing glycine availability-centered biochemical axes spanning oxidative stress, cellular energetics, and vascular signaling, while nominating candidate mechanisms for downstream investigation. These results validate known disease-related biology with genetic evidence and generate new hypotheses for further investigation. MetaboXcan is publicly available and broadly applicable to any GWAS phenotype.

genetics↗

bioRxiv2026

How small is too small? Genetic signatures from naturally small populations of a large island vertebrate, the Turks and Caicos Rock Iguana

Small populations are increasingly common on the landscape, and many face rising probabilities of extinction. Demographic factors alone can drive a small population into an extinction vortex, but genetic factors such as inbreeding and genetic drift lead to reductions in genetic diversity and expression of deleterious alleles, further impacting fitness. Populations on islands are also impacted by founder effects, minimal gene flow, and habitat constraints, yet naturally small, island populations have persisted through many generations despite all these factors. This suggests that island biota may operate at smaller viable population sizes than their mainland counterparts while avoiding extinction vortices. Using 26 microsatellites, we describe the genetic diversity and population structure of 13 subpopulations of a large island vertebrate, the endangered Turks and Caicos rock iguana (Cyclura carinata), found on a range of small cays (0.151-374ha). Most subpopulations had decreased genetic variation but limited evidence of inbreeding despite small island size. Even with water barriers limiting gene flow, some subpopulations in geographical proximity are functioning as a metapopulation while others are not. These subpopulations are not in danger of extirpation, even though census size could be as low as 10 individuals. We believe this study shows that C. carinata has a low population size threshold for long-term persistence that can inform the species' future management, such as with conservation translocations. Furthermore, this project anecdotally adds more evidence to how island populations persist through selection despite genetic drift.

genetics↗

bioRxiv2026

Linkage disequilibrium moderates how evolving genetic background shapes variant-level architecture

Evolutionary theory offers a unifying explanation for the genetics of complex traits and diseases by modeling how a trait and its genetic basis are maintained by evolutionary forces. A growing body of evidence suggests that linkage disequilibrium (LD) couples a variant's evolution to that of the genetic background it resides on. However, widely used single-variant theories omit this coupling between variants and the background, or hold the background fixed. We close this gap by developing a model of linkage LD evolution accounting for stabilizing selection, recombination, and genetic drift. The model deduces the genetic architecture of individual variants subject to the co-evolving genetic background. More efficient selection in larger populations is partially offset by slower decay of existing LD and increased generation of new LD. Conversely, noisier LD in smaller populations exposes the variants to episodes of stronger selection. We ask whether ancestry differences in the relationship between effect-size magnitude and minor allele frequency require different selection strengths or can arise from differences in demographic history alone. Stabilizing selection needs to be more than three times stronger in Africans than in Europeans under the constant-size demography, whereas an Out-of-Africa demography alone can explain most of the observed frequency-dependence gap. As this example indicates, selection strength is in general not identifiable from the frequency--effect-size relationship alone, because different combinations of selection and demographic history can produce the same pattern in data with further redundancy added by LD.

genetics↗

bioRxiv2026

A comprehensive genetic study of the vaginal microbiota composition of Holstein cows

The vaginal microbiota is a potentially important component of the bovine reproductive tract and has been associated with fertility, health, and production-related traits in cattle. However, the impact of host genetics on the bovine vaginal microbiota and its genetic correlations with animal performance remain largely unexplored. To investigate this, we performed a comprehensive genetic study of the vaginal microbiota in Holstein cows. Vaginal swabs from 681 lactating cows across 18 herds in Northern France were collected, along with records of fertility, milk production, and health traits. The microbiota samples were sequenced for the 16S rRNA gene, generating count tables of amplicon sequence variants (ASV). We estimated genetic parameters for vaginal microbiota -diversity (Chao1, Shannon, and Simpson indices) and {beta}-diversity (Bray-Curtis dissimilarity matrix), as well as the centered log-ratio (CLR)-transformed abundances of taxa present in more than 50% of samples. Across the 173 analyzed taxa, heritability estimates ranged from low to moderate (h2 = 0.09 to 0.38) for five genera, three families, eight orders, three classes, and one phylum. Notably, the order Flavobacteriales exhibited the highest heritability estimate. The study also highlighted genetic correlations between heritable microbiota traits and host phenotypes related to fertility, production, and health. Sequence-based genome-wide association studies (GWAS) identified biologically relevant signals, including a quantitative trait locus (QTL) for the genus Alloprevotella on chromosome 6, with lead variants located within the NAF1 gene. Additionally, two signals on chromosome 17 for the Bacteroidales order and the Bacteroida class co-localized with IL15. Our findings show that host genetics contributes to variation in specific components of the bovine vaginal microbiota and suggest that microbiome-related traits may warrant further investigation as potential indicators in dairy cattle breeding and management research.

genetics↗

bioRxiv2026

Capturing Spatial Heterogeneity Using UAV-Derived Multispectral Phenotypes and Random Regression Models Improves Genetic Evaluation in Alfalfa Breeding Trials

Spatial heterogeneity is a major source of environmental noise in field trials, reducing genetic evaluation accuracy. Conventional spatial correction methods use field layout and plot proximity but often fail to capture in-season dynamics or leverage temporal data from high-throughput phenotyping. We hypothesized that longitudinal UAV-derived multispectral measurements could characterize field-scale environmental heterogeneity and improve genetic evaluation in breeding trials. Multispectral imagery was collected throughout the growing season across eight alfalfa trials in New York and New Mexico (20222023). Vegetation indices were evaluated for heritability and association with biomass yield using univariate and multi-trait models. Longitudinal measurements were modeled with random regression (RR) to estimate plot-specific permanent environmental effects (PEE), incorporated into genomic mixed models within a two-stage framework. Model performance was compared with conventional autoregressive (AR) and baseline (no correction) models using metrics of consistency and accuracy of estimated genetic effects across replicates. Vegetation indices showed moderate to high heritability, with GNDVI consistently outperforming NDVI and NDRE. Both univariate and multi-trait analyses revealed substantial spatial variability, particularly in New Mexico trials. RR-derived PEE captured field-scale spatial patterns and improved genetic signal recovery relative to baseline and AR models, with the largest gains under strong spatial heterogeneity and comparable performance where variability was limited. The RR framework improved reproducibility of genetic effect estimates across independent field subsets without compromising agreement with full-trial evaluations. These findings show that integrating UAV data via random regression enhances genetic signal recovery and selection accuracy, with broader potential for precision agriculture and biosensor-driven crop management.

genetics↗

bioRxiv2026

Understanding genetic and environmental complexity in phenotypic resemblance across relatedness in population-scale data

A fundamental feature of a trait's variation in a population is how its phenotypic similarity scales with genetic relatedness. Under a strictly additive model of genetic architecture, phenotypic similarity increases linearly with genetic relatedness, with a slope equal to the heritability; this underpins approaches to heritability estimation from sibling IBD regression to Haseman--Elston regression and GREML. Empirically, however, the relationship is not linear for many traits (Kemper et al., 2021). Biobank-scale measurements make fine-grain observations of the phenotypic covariance versus genetic relatedness relationship increasingly feasible, motivating new theory to understand these new empirical observations. Here we evaluate how three processes might introduce non-linearities to the relationship: imperfect tagging of causal variants, assortative mating, and multi-generational shared environments. Through theoretical modeling and simulation, we find each process acts within pedigree classes differently, introducing non-linearities through impacts on the local slope and intercept of phenotypic covariance on genetic relatedness: imperfect tagging lowers the slope among distant relatives, shared environments elevates the intercepts for closer relatives, and assortative mating changes both the slope and intercept. Within these models, we explore a variety of scenarios, finding cases where the local slope of covariance on relatedness values among unrelated pairs versus among siblings can reach nearly three times the raw heritability, and building insight to where heritability estimates are likely to be biased. We then examine these patterns across a range of traits in the UK Biobank, with a supplementary analysis in All of Us. The shape of the relationship varies widely across traits, with profiles exhibiting features found under each of the theoretical mechanisms we explored. Overall, we conclude disentangling these multiple overlaid processes will be necessary to fully understand how phenotypic similarity increases with genetic relatedness in large cohorts.

genetics↗

bioRxiv2014

Genetic drift opposes mutualism during spatial population expansion

Mutualistic interactions benefit both partners, promoting coexistence and genetic diversity. Spatial structure can promote cooperation, but spatial expansions may also make it hard for mutualistic partners to stay together, since genetic drift at the expansion front creates regions of low genetic and species diversity. To explore the antagonism between mutualism and genetic drift, we grew cross-feeding strains of the budding yeast S. cerevisiae on agar surfaces as a model for mutualists undergoing spatial expansions. By supplying varying amounts of the exchanged nutrients, we tuned strength and symmetry of the mutualistic interaction. Strong mutualism suppresses genetic demixing during spatial expansions and thereby maintains diversity, but weak or asymmetric mutualism is overwhelmed by genetic drift even when mutualism is still beneficial, slowing growth and reducing diversity. Theoretical modeling using experimentally measured parameters predicts the size of demixed regions and how strong mutualism must be to survive a spatial expansion.

Evolutionary Biology↗

bioRxiv2014

ResistanceGA: An R package for the optimization of resistance surfaces using genetic algorithms

O_LIUnderstanding how landscape features affect functional connectivity among populations is a cornerstone of landscape genetic analyses. However, parameterization of resistance surfaces that best describe connectivity is largely a subjective process that explores a limited parameter space.\nC_LIO_LIResistanceGA is a new R package that utilizes a genetic algorithm to optimize resistance surfaces based on pairwise genetic distances and either CIRCUITSCAPE resistance distances or cost distances calculated along least cost paths. Functions in this package allow for the optimization of both categorical and continuous resistance surfaces, as well as the simultaneous optimization of multiple resistance surfaces.\nC_LIO_LIThere is considerable controversy concerning the use of Mantel tests to accurately relate pairwise genetic distances with resistance distances. Optimization in uses linear mixed effects models with the maximum likelihood population effects parameterization to determine AICc, which is the fitness function for the genetic algorithm.\nC_LIO_LIResistanceGA fills a void in the landscape genetic toolbox, allowing for unbiased optimization of resistance surfaces and for the simultaneous optimization of multiple resistance surfaces to create a novel composite resistance surface.\nC_LI

Ecology↗

bioRxiv2014

Social evolution and genetic interactions in the short and long term

The evolution of social traits remains one of the most fascinating and feisty topics in evolutionary bi-ology even after half a century of theoretical research. W. D. Hamilton shaped much of the field initially with his 1964 papers that laid out the foundation for understanding the effect of genetic relatedness on the evolution of social behavior. Early theoretical investigations revealed two critical assumptions required for Hamiltons rule to hold in dynamical models: weak selection and additive genetic interactions. However, only recently have analytical approaches from population genetics and evolutionary game theory developed sufficiently so that social evolution can be studied under the joint action of selection, mutation, and genetic drift. We review how these approaches suggest two timescales for evolution under weak mutation: (i) a short-term timescale where evolution occurs between a finite set of alleles, and (ii) a long-term timescale where a continuum of alleles are possible and populations evolve continuously from one monomorphic trait to another. We show how Hamiltons rule emerges from the short-term analysis under additivity and how non-additive genetic interactions can be accounted for more generally. This short-term approach re-produces, synthesizes, and generalizes many previous results including the one-third law from evolutionary game theory and risk dominance from economic game theory. Using the long-term approach, we illustrate how trait evolution can be described with a diffusion equation that is a stochastic analogue of the canonical equation of adaptive dynamics. Peaks in the stationary distribution of the diffusion capture classic notions of convergence stability from evolutionary game theory and generally depend on the additive genetic in-teractions inherent in Hamiltons rule. Surprisingly, the peaks of the long-term stationary distribution can predict the effects of simple kinds of non-additive interactions. Additionally, the peaks capture both weak and strong effects of social payoffs in a manner difficult to replicate with the short-term approach. Together, the results from the short and long-term approaches suggest both how Hamiltons insight may be robust in unexpected ways and how current analytical approaches can expand our understanding of social evolution far beyond Hamiltons original work.

Evolutionary Biology↗

bioRxiv2014

The genetic architecture of local adaptation I: The genomic landscape of foxtail pine (Pinus balfouriana Grev. & Balf.) as revealed from a high-density linkage map

Explaining the origin and evolutionary dynamics of the genetic architecture of adaptation is a major research goal of evolutionary genetics. Despite controversy surrounding success of the attempts to accomplish this goal, a full understanding of adaptive genetic variation necessitates knowledge about the genomic location and patterns of dispersion for the genetic components affecting fitness-related phenotypic traits. Even with advances in next generation sequencing technologies, the production of full genome sequences for non-model species is often cost prohibitive, especially for tree species such as pines where genome size often exceeds 20 to 30 Gbp. We address this need by constructing a dense linkage map for foxtail pine (Pinus balfouriana Grev. & Balf.), with the ultimate goal of uncovering and explaining the origin and evolutionary dynamics of adaptive genetic variation in natural populations of this forest tree species. We utilized megagametophyte arrays (n = 76-95 megagametophytes/tree) from four maternal trees in combination with double-digestion restriction site associated DNA sequencing (ddRADseq) to produce a consensus linkage map covering 98.58% of the foxtail pine genome, which was estimated to be 1276 cM in length (95% CI: 1174 cM to 1378 cM). A novel bioinformatic approach using iterative rounds of marker ordering and imputation was employed to produce single-tree linkage maps (507-17 066 contigs/map; lengths: 1037.40- 1572.80 cM). These linkage maps were collinear across maternal trees, with highly correlated marker orderings (Spearmans{rho} > 0.95). A consensus linkage map derived from these single-tree linkage maps contained 12 linkage groups along which 20 655 contigs were non-randomly distributed across 901 unique positions (n = 23 contigs/position), with an average spacing of 1.34 cM between adjacent positions. Of the 20 655 contigs positioned on the consensus linkage map, 5627 had enough sequence similarity to contigs contained within the most recent build of the loblolly pine (P. taeda L.) genome to identify them as putative homologs containing both genic and non-genic loci. Importantly, all 901 unique positions on the consensus linkage map had at least one contig with putative homology to loblolly pine. When combined with the other biological signals that predominate in our data (e.g., correlations of recombination fractions across single trees), we show that dense linkage maps for non-model forest tree species can be efficiently constructed using next generation sequencing technologies. We subsequently discuss the usefulness of these maps as community-wide resources and as tools with which to test hypotheses about the genetic architecture of adaptation.

Evolutionary Biology↗

bioRxiv2015

Long live the alien: is high genetic diversity a pivotal aspect of crested porcupine (Hystrix cristata) long-lasting and successful invasion?

Studying the evolutionary dynamics of an alien species surviving and continuing to expand after several generations can provide fundamental information on the relevant features of clearly successful invasions. Here, we tackle this task by investigating the dynamics of the genetic diversity in invasive crested porcupine (Hystrix cristata) populations, introduced to Italy about 1500 years ago, which are still growing in size, distribution range and ecological niche. Using genome-wide RAD markers, we describe the structure of the genetic diversity and the demographic dynamics of the H. cristata invasive populations and compare their genetic diversity with that of native African populations of both H. cristata and its sister species, H. africaeaustralis. First, we demonstrate that genetic diversity is lower in both the invasive Italian and the North Africa source range relative to other native populations from Sub-Saharan and South Africa. Second, we find evidence of multiple introduction events in the invasive range followed by very limited gene flow. Through coalescence-based demographic reconstructions, we also show that the bottleneck at introduction was mild and did not affect the introduced genetic diversity. Finally, we reveal that the current spatial expansion at the northern boundary of the range is following a leading-edge model characterized by a general reduction of genetic diversity towards the edge of the expanding range. We conclude that the level of genome-wide diversity of H. cristata invasive populations is less important in explaining its successful invasion than species-specific life-history traits or the phylogeographic history in the native source range.

Evolutionary Biology↗

bioRxiv2015

Low but significant genetic differentiation underlies biologically meaningful phenotypic divergence in a large Atlantic salmon population

Despite decades of research assessing the genetic structure of natural populations, the biological meaning of low yet significant genetic divergence often remains unclear due to a lack of associated phenotypic and ecological information. At the same time, structured populations with low genetic divergence and overlapping boundaries can potentially provide excellent models to study adaptation and reproductive isolation in cases where high resolution genetic markers and relevant phenotypic and life history information are available. Here, we combined SNP-based population inference with extensive phenotypic and life history data to identify potential biological mechanisms driving fine scale sub-population differentiation in Atlantic salmon (Salmo salar) from the Teno River, a major salmon river in Europe. Two sympatrically occurring sub-populations had low but significant genetic differentiation (FST = 0.018) and displayed marked differences in the distribution of life history strategies, including variation in juvenile growth rate, age at maturity and size within age classes. Large, late-maturing individuals were virtually absent from one of the two sub-populations and there were significant differences in juvenile growth rates and size-at-age after oceanic migration between individuals in the respective sub-populations. Our findings suggest that different evolutionary processes affect each sub-population and that hybridization and subsequent selection may maintain low genetic differentiation without hindering adaptive divergence.

Evolutionary Biology↗

bioRxiv2015

MicrobiomeGWAS: a tool for identifying host genetic variants associated with microbiome composition

The microbiome is the collection of all microbial genes and can be investigated by sequencing highly variable regions of 16S ribosomal RNA (rRNA) genes. Evidence suggests that environmental factors and host genetics may interact to impact human microbiome composition. Identifying host genetic variants associated with human microbiome composition not only provides clues for characterizing microbiome variation but also helps to elucidate biological mechanisms of genetic associations, prioritize genetic variants, and improve genetic risk prediction. Since a microbiota functions as a community, it is best characterized by beta diversity, that is, a pairwise distance matrix. We develop a statistical framework and a computationally efficient software package, microbiomeGWAS, for identifying host genetic variants associated with microbiome beta diversity with or without interacting with an environmental factor. We show that score statistics have positive skewness and kurtosis due to the dependent nature of the pairwise data, which makes P-value approximations based on asymptotic distributions unacceptably liberal. By correcting for skewness and kurtosis, we develop accurate P-value approximations, whose accuracy was verified by extensive simulations. We exemplify our methods by analyzing a set of 147 genotyped subjects with 16S rRNA microbiome profiles from non-malignant lung tissues. Correcting for skewness and kurtosis eliminated the dramatic deviation in the quantile-quantile plots. We provided preliminary evidence that six established lung cancer risk SNPs were collectively associated with microbiome composition for both unweighted (P=0.0032) and weighted (P=0.011) UniFrac distance matrices. In summary, our methods will facilitate analyzing large-scale genome-wide association studies of the human microbiome.

Bioinformatics↗

bioRxiv2015

Marker-based estimates reveal significant non-additive effects in clonally propagated cassava (Manihot esculenta): implications for the prediction of total genetic value and the selection of varieties

In clonally propagated crops, non-additive genetic effects can be effectively exploited by the identification of superior genetic individuals as varieties. Cassava (Manihot esculenta Crantz) is a clonally propagated staple food crop that feeds hundreds of millions. We quantified the amount and nature of non-additive genetic variation for key traits in a breeding population of cassava from sub-Saharan Africa using additive and non-additive genome-wide marker-based relationship matrices. We then assessed the accuracy of genomic prediction of additive compared to total (additive plus non-additive) genetic value. We confirmed previous findings based on diallel populations, that non-additive genetic variation is significant, especially for yield traits. Further, we show that we total genetic value correlated more strongly to observed phenotypes than did additive value, although this is constrained by low broad-sense heritability and is not beneficial for traits with already high heritability. We address the implication of these results for cassava breeding and put our work in the context of previous results in cassava, and other plant and animal species.

Genomics↗

bioRxiv2016

Ecological factors rather than barriers to dispersal shape genetic structure of algal symbionts in horizontally-transmitting corals

Many reef-building corals acquire their algal symbionts (Symbiodinium sp.) from the local environment upon recruitment. This horizontal transmission strategy where hosts pair with locally available symbionts could serve to increase coral fitness across diverse environments, as long as hosts maintain high promiscuity and symbionts adapt locally. Here, we tested this hypothesis in two coral species by comparing host and symbiont genetic structures across different spatial scales in Micronesia. Each host species associated with two genetically distinct Symbiodinium lineages, confirming high promiscuity in broadly dispersing hosts. However, contrary to our initial expectation, symbiont genetic structure was independent of physical barriers to dispersal between islands, unlike genetic structure of their hosts that was nearly perfectly explained by ocean currents. Instead, Symbiodinium consistently demonstrated genetic divergence among local reefs and between the two host species at each island, although not necessarily between distant islands. These observations indicate that Symbiodinium lineages disperse much more broadly than previously thought and continuously adapt to specific hosts and reef environments across their range, following the classical Baas Beckings hypothesis: \"Everything is everywhere, but the environment selects\". Overall, our findings confirm that horizontal transmission could be a mechanism for broadly dispersing coral species to enhance their local fitness by associating with locally adapted symbionts. Dramatic differences in factors driving the genetic structures of horizontally-transmitting corals and their Symbiodinium imply that viewing their combined genomes as a single entity ( hologenome) would not be useful in the context of their evolution and adaptation.

Ecology↗

bioRxiv2016

Flexibility of co-evolutionary patterns in ectoparasite populations: genetic structure and diversity in Apodemus mice and their lice.

Host-parasite co-evolution belongs among the major processes governing evolution of biodiversity on the global scale. Numerous studies performed at inter-specific level revealed variety of patterns from strict co-speciation to lack of co-divergence and frequent host-switching, even in species tightly linked to their hosts. To explain these observations and formulate ecological hypotheses, we need to acquire better understanding to parasites population genetics and dynamics, and their main determinants. Here, we analyse the impact of co-evolutionary processes on genetic diversity and structure of parasite populations, using a model composed of the louse Polyplax serrata and its hosts, mice of the genus Apodemus, collected from several dozens of localities across Europe. We use mitochondrial DNA sequences and microsatellite data to describe the level of genealogical congruence between hosts and parasites and to assess genetic diversity of the populations. We also explore links between the genetic assignment of the parasite and its host affiliation, and test the prediction that populations of the parasite possessing narrower host specificity show deeper pattern of population structure and lower level of genetic diversity as a result of limited dispersal and smaller effective population size. We demonstrate an overall complexity of the co-evolutionary processes and their variability even among closely related lineages of the parasites. In the analysis of several sympatric parasite populations, we find strong evidence for the link between the width of host specificity and genetic diversity of parasites.

Evolutionary Biology↗

bioRxiv2016

Extensive genetic diversity among populations of the malaria mosquito Anopheles moucheti revealed by population genomics

Malaria vectors are exposed to intense selective pressures due to large-scale intervention programs that are underway in most African countries. One of the current priorities is therefore to clearly assess the adaptive potential of Anopheline populations, which is critical to understand and anticipate the response mosquitoes can elicit against such adaptive challenges. The development of genomic resources that will empower robust examinations of evolutionary changes in all vectors including currently understudied species is an inevitable step toward this goal. Here we constructed double-digest Restriction Associated DNA (ddRAD) libraries and generated 6461 Single Nucleotide Polymorphisms (SNPs) that we used to explore the population structure and demographic history of wild-caught Anopheles moucheti from Cameroon. The genome-wide distribution of allelic frequencies among samples best fitted that of an old population at equilibrium, characterized by a weak genetic structure and extensive genetic diversity, presumably due to a large long term effective population size. Estimates of FST and Linkage Disequilibrium (LD) across SNPs reveal a very low genetic differentiation throughout the genome and the absence of segregating LD blocks among populations, suggesting an overall lack of local adaptation. Our study provides the first investigation of the genetic structure and diversity in An. moucheti at the genomic scale. We conclude that, despite a weak genetic structure, this species has the potential to challenge current vector control measures and other rapid anthropogenic and environmental changes thanks to its great genetic diversity.

Evolutionary Biology↗