The research, led by Riccardo Piccinno and Anna Rodolfa Malacrida of the University of Pavia, together with colleagues at the Institut Pasteur in Paris and partners in Switzerland, Italy and the Balkans, set out to answer a deceptively simple question: do genetically distinct mosquito populations differ in their ability to transmit arboviruses? The answer, the authors report, is a clear yes, and the pattern they uncovered is striking. Neighboring mosquito populations with different genetic ancestries showed drastically different competence profiles for CHIKV and DENV, while geographically distant populations that share ancestry behaved similarly. In other words, for these viruses, where a mosquito’s genes came from matters more than where the mosquito lives.

To reach that conclusion, the team combined two complementary lines of evidence: population genetics and experimental vector competence assays. On the genetic side, they genotyped mosquitoes from an ancestral reference population in Guangzhou, China, and from ten adventive populations sampled across five Mediterranean regions, spanning Southern Switzerland, Northern and Central Italy, the Balkans and Greece. The markers of choice were highly polymorphic simple sequence repeats, or SSRs, which allow fine-scale discrimination of genetic lineages. Using the Bayesian clustering program STRUCTURE, the researchers assigned individuals to ancestry groups, and with the approximate Bayesian computation framework DIYABC-RF they reconstructed the demographic histories that produced the present-day genetic mosaic.

That history, the study finds, was anything but a single clean invasion wave. By combining historical records with demographic modeling, the authors show that the establishment of Aedes albopictus in the Mediterranean was driven by chaotic, discontinuous introductions of distinct lineages, followed by admixture events in which previously separated genetic backgrounds interbred. The result is high heterogeneity in both space and ancestry: populations separated by only a few hundred kilometers can carry markedly different genetic compositions, reflecting separate introduction routes, likely via the international trade in used tires and other commodities that has repeatedly ferried mosquito eggs across borders. This genetic patchwork, rather than a homogeneous expansion front, is the biological backdrop against which viral transmission now plays out.

The experimental half of the study measured vector competence using the standard trio of quantitative indices. Infection rate captures the proportion of mosquitoes in which the virus successfully establishes an infection in the midgut after an infectious blood meal; dissemination efficiency measures how many of those infected mosquitoes allow the virus to escape the midgut and spread to secondary organs, including the salivary glands; and transmission efficiency quantifies the fraction with virus present in the saliva, the prerequisite for onward transmission to a new host. Mosquitoes from each population were fed blood meals containing CHIKV strain 06.21, DENV-1, or ZIKV strain PE243, and these indices were scored at multiple days post-infection, specifically 7, 14 and 21 days, to track the temporal progression of infection.

Statistical analysis then linked the genetic and virological datasets. Univariate and multivariate models assessed whether population identity, ancestry group membership, and days post-infection were associated with each competence index. For CHIKV, the analyses identified day post-infection, population, and two ancestry components as significant factors associated with infection, with some ancestry fractions showing positive associations and others negative ones. For DENV, population and three ancestry components were significantly associated with infection rate, although the authors note that two of these lacked a biologically plausible monotonic trend across ancestry categories, meaning higher ancestry fractions did not simply translate into higher infection rates. For ZIKV, by contrast, no variable emerged as a significant predictor, suggesting that the genetic heterogeneity captured in this study does not translate into measurable variation in Zika competence among these populations.

The most consequential finding concerns CHIKV and DENV. Ancestry and population identity significantly influenced dissemination and transmission as well as infection, and the geographic pattern was counterintuitive: proximity did not predict similarity. Populations in neighboring geographical areas but with distinct ancestries exhibited drastically different competence profiles, whereas geographically distant populations sharing genetic ancestry displayed similar vector competence. This dissociation between geography and genetics implies that the introduction history of each local population, not local environmental conditions alone, has left a durable imprint on its capacity to transmit these viruses. It also suggests that the genetic variants governing viral infection and dissemination vary among source lineages, so that admixture has produced a mosaic of transmission potential across the region.

The public health implications are direct. Europe has experienced a rising trend of both imported and autochthonous arboviral infections in recent years, with chikungunya and dengue outbreaks recorded in Italy, France and elsewhere. If the mosquitoes buzzing in one province are substantially more competent vectors than those in the next, then uniform, region-wide control assumptions may misallocate resources. The authors argue that their results underscore the urgent need for localized, geographically tailored vector control strategies in Europe, in which surveillance and intervention intensity are calibrated to the actual transmission potential of local mosquito populations rather than to a generic regional estimate.

Methodologically, the study demonstrates the value of integrating fine-scale population genomics with standardized vector competence assays. The SSR-based genotyping, combined with STRUCTURE clustering and DIYABC-RF demographic inference, allowed the team to move beyond simple labels of origin and to quantify ancestry fractions within admixed populations. The logistic regression framework then made it possible to test the contribution of each factor while accounting for others, including the strong temporal effects of days post-infection that are well known to shape competence measurements. Supplementary analyses reported pairwise genetic differentiation among populations and detailed infection, dissemination and transmission rates stratified by virus, time point and population, providing a transparent record of the underlying data.

The work also carries broader lessons for invasion biology. Aedes albopictus is a textbook example of how global trade creates repeated, genetically diverse introductions, and this study shows that such demographic chaos can have functional consequences for disease transmission. As the climate warms and the mosquito’s suitable habitat expands northward, new introductions and further admixture are likely, potentially reshaping vector competence in currently lower-risk areas. Understanding the provenance of each new population, the authors suggest, should therefore become part of the risk-assessment toolkit, alongside entomological surveillance and case monitoring, if Europe is to anticipate rather than merely react to the next arboviral emergence.

Subject of Research: Vector competence and population genetics of invasive Aedes albopictus mosquitoes in the Mediterranean Basin

Article Title: Provenance and competence for CHIKV and DENV of Aedes albopictus populations

Article References: Piccinno, R., Madec, Y., Mariconti, M., Fiorenza, G., Carraretto, D., Forneris, F., Flacio, E., Coletti, S., Gasperi, G., Failloux, A.-B., & Malacrida, A. R. (2026). Provenance and competence for CHIKV and DENV of Aedes albopictus populations. Parasites & Vectors. https://doi.org/10.1186/s13071-026-07705-6

Image Credits: AI Generated

DOI: 10.1186/s13071-026-07705-6

Keywords: Aedes albopictus, Asian tiger mosquito, vector competence, chikungunya, dengue, Zika, population genetics, Mediterranean Basin, invasive species, arboviruses, genetic ancestry, vector control

The dispute began with Chiapperino and Paneni’s earlier paper, Why epigenetics is (not) a biosocial science and why that matters, which examined whether and how epigenetic research can genuinely capture the entanglement of biological and social processes in health and disease. Dupras responded with a critical assessment, reading their argument as a call for less reductionism and more holistic methods in the field. That reading, the two authors now insist, misrepresents their intent. They never claimed that epigenetics should be assimilated to nurture while genetics stands for nature, nor did they seek to draw a sharp line between biosocial epigenetics and a supposedly non-biosocial genetics.

At the heart of the reply is a reframing of the central question. The authors argue that the problem is not whether the processes of development, health differentiation, and evolution are either natural or social, either biosocial or not. Instead, they ask to what extent the methods, tools, and study designs of epigenetics can capture the interactions that necessarily occur between nature and nurture, between biological and social processes. This developmental version of epigenetics, which they align with work by researchers such as Robert Lickliter and David Witherington, is explicitly designed to move beyond the inherited dichotomies of the nature-nurture debate rather than reproduce them under a new molecular vocabulary.

The authors also reject the charge that they are demanding a wholesale turn to holism. They acknowledge that the full complexity of biosocial processes may be incommensurable with the methods of the biomedical sciences, yet they explicitly disavow any foundational endeavor toward an all-encompassing holistic biosocial science. Drawing on the history, philosophy, and social anthropology of science, including the work of Hans-Jörg Rheinberger, John Law, and Annemarie Mol, they contend that the reductionism-holism dichotomy is itself inadequate for understanding postgenomic scientific practice. The point, they write, is not whether epigenetic methods are reductionist, since they plainly are, but how they operate most prolifically at the fuzzy boundary between the trivial and the complex.

To make this abstract argument concrete, Chiapperino and Paneni turn to the measurement tools that dominate epigenetic studies of psychosocial environments: stress scales and allostatic load indices. These instruments, widely used to link adversity to molecular change, have drawn sustained criticism for lacking definition, standardization, and fidelity, and for what anthropologist Jörg Niewöhner has called the molecularization of biography and milieu. The authors concede that such methods are certainly reductionist. But they ask whether reductionism necessarily dooms these tools to a poor picture of the environmental embeddedness of health, and their answer is a qualified no.

The problem, they argue, lies less in the instruments than in the study designs that house them. Much epigenetic research reproduces a simplistic linearity: exposures, objectified through stress or allostatic load measurements, produce biological differences readable in the epigenome, which are in turn linked to disease through mechanistic or statistical associations. A different configuration employing the very same reductionist tools could avoid this pitfall. Repeated longitudinal measures of psychological and physiological scales, for example, could reveal the multidirectional effects of stress, its biological embodiment over time, and the biopsychosocial looping effects through which people respond to and reshape their own conditions. Such designs would leave the reductionist qualities of the methods untouched while dramatically complexifying the account of the phenomenon they can offer.

The reply also addresses Dupras’s defense of ambitious large-scale initiatives such as the International Human Epigenome Consortium. Chiapperino and Paneni are careful to state that they do not question the consortium’s importance for advancing standards, methods, reference knowledge, and a sense of global community among epigeneticists. Nor do they deny that the boundary between epigenetics and genetics is blurred, a definitional controversy that continues to occupy commentators such as Bernhard Horsthemke. Yet they maintain that a theoretically modest point remains highly relevant for any endeavor seeking to build reference epigenomes for the understanding of health and disease.

That point comes into sharp focus around ethnicity and DNA methylation. Few researchers hold that interindividual differences in the epigenome can be fully recapitulated by genetic variation, including variation across so-called genetic ancestry groups. The authors cite a study by James Galanter and colleagues showing that, across the genome, genetic ancestry accounts for roughly three-quarters of the association between ethnicity and methylation differences, with the remaining quarter attributed to shared environmental, social, and cultural factors within groups. In that residual quarter lies evidence that both ancestry and the social construction of ethnic difference shape epigenetically measurable disparities in disease prevalence and health trajectories. This finding, they argue, validates ongoing calls to diversify reference epigenomic maps and epigenome-wide association studies beyond their current overrepresentation of participants of European ancestry, but it also demands more than genetic imputation alone.

Here the authors pose a pointed question to the global epigenetics community: what is currently on the agenda to account for the share of health disparities across social groups that stems from differences in social and environmental exposures rather than genetic ancestry? They suggest that the biosocial dimensions of ethnic differences in the epigenome likely lack a fixed ontology, being neither purely biological nor purely social. For some traits, reference epigenomes may prove highly sensitive to genetic differences across ancestry groups; for others, they may be more open to varied developmental trajectories, local contexts, and culturally situated practices. They caution, following Maurizio Meloni and colleagues, that even simplistic cause-effect models can constitute a problematic form of biosocial determinism around epigenetic differences.

Ultimately, Chiapperino and Paneni frame their reply as a call to symmetry: a demand that the development of epigenetic tools, methods, and study designs refrain from overlooking either side of the biosocial continuum. They do not claim to offer a unique path, nor to certify whether epigenetics is or is not a holistic biosocial science. The simplistic stance, they suggest, belongs to those who feel compelled to pick a side in what is ultimately a fabricated dichotomy between the biological and social origins of epigenetic differences. In a field increasingly asked to explain how poverty, discrimination, and stress become embodied, that insistence on balance may prove more consequential than any single methodological prescription, reminding researchers that the most rigorous science of the epigenome may be the one that refuses to let nature and nurture compete for exclusive ownership of it.

Subject of Research: The role of reductionist and holistic methods in capturing biosocial interactions in epigenetic research

Article Title: In medio stat virtus? A reply to Dupras

Article References: Chiapperino, L., & Paneni, F. (2023). In medio stat virtus? A reply to Dupras. Epigenetics Communications, 3(1), Article 5. https://doi.org/10.1186/s43682-023-00017-1

Image Credits: AI Generated

DOI: 10.1186/s43682-023-00017-1

Keywords: epigenetics, biosocial science, reductionism, holism, DNA methylation, allostatic load, genetic ancestry, health disparities, International Human Epigenome Consortium, nature and nurture, study design, philosophy of science

The core difficulty that SPLENDID addresses stems from how human genetic variation is actually structured. Genetic ancestry does not come in neat, mutually exclusive boxes. Decades of population genetics have shown that human variation varies smoothly across geography and history, and many people, particularly those with recent admixture from multiple continental sources, sit at points in ancestry space that no pre-specified category can capture. When existing multi-ancestry prediction methods force every individual into a labeled group, they make two kinds of errors at once. Individuals whose ancestry blends several sources are assigned awkwardly to one group or another, discarding information, and the boundaries between groups themselves are arbitrary, changing with the reference panels and clustering algorithms used to define them. As the SPLENDID authors note, clinical decisions are rarely, if ever, made on the basis of an ancestry label, so a prediction framework that depends on such labels is poorly matched to real-world practice.

SPLENDID, whose name reflects its penalized-regression foundation, works directly with individual-level genotype data at biobank scale and treats ancestry as a continuum throughout the modeling pipeline. Rather than building separate ancestry-specific scores and then deciding which one to hand to each patient, the method fits a unified penalized regression model in which genetic effects are allowed to vary smoothly as a function of continuous ancestry information, typically captured through principal components of the genotype matrix. The framework uses modern sparse-regression machinery, including L0L1-type penalties that encourage the selection of a parsimonious set of variants, together with ensemble learning strategies that combine multiple candidate models tuned across the ancestry continuum. This produces one model, not many, and that model can be applied to any incoming individual without first asking which population bucket they belong to.

The mathematical machinery behind this idea is demanding, because biobank-scale genotyping data routinely contain hundreds of thousands of individuals and millions of variants. Fitting penalized models in which variant effects interact smoothly with ancestry dimensions requires efficient coordinate-descent and discrete-optimization algorithms that can operate on ultrahigh-dimensional data. The authors, including Tony Chen and Xihong Lin of Harvard T.H. Chan School of Public Health, Haoyu Zhang of the National Cancer Institute and Rahul Mazumder of MIT’s Sloan School of Management, built SPLENDID on computational tools of the kind developed for fast best-subset selection and large-scale sparse regression, making the method practical for datasets such as the UK Biobank and the All of Us Research Program. The software is implemented for R and released openly with tutorials, lowering the barrier for other groups to adopt the approach.

The empirical evaluation is extensive. In simulation studies designed to mimic the genetic architecture and demographic history of real human populations, SPLENDID outperformed a battery of existing polygenic prediction methods, including GWAS-based approaches and multi-ancestry tools such as PRS-CSx, CT-SLEB, PROSPER and iPGS. Notably, when the team forced other methods to produce a single pooled-ancestry score rather than ancestry-specific ones, prediction accuracy dropped sharply, particularly for the GWAS-based approaches. This comparison underscores the central argument of the paper: existing frameworks implicitly assume that ancestry labels are available and reliable, and when that assumption fails, their performance collapses in ways that SPLENDID’s continuum-based design avoids by construction.

The real-world tests were conducted on two of the largest and most demographically diverse genetic resources in existence. In the All of Us Research Program, whose more than 224,000 genotyped participants include substantial representation of African, Hispanic, Asian and Indigenous American ancestries, and in the UK Biobank, with roughly 340,000 analyzed participants, SPLENDID delivered significantly higher prediction accuracy than competing methods across nine continuous traits examined. The improvements were most pronounced for non-European and admixed individuals, exactly the groups for whom conventional scores perform worst. In admixed UK Biobank samples, identified through probabilistic classification against the 1000 Genomes Project reference panel, SPLENDID maintained accuracy where label-dependent methods faltered, validating the premise that treating ancestry as a continuum captures genuine genetic heterogeneity that discrete labels erase.

One striking illustration of why continuous modeling matters comes from the biology of blood cell traits. The paper examines individual-level effects of a variant, rs1213375, on mean corpuscular hemoglobin across the genetic ancestry spectrum, an example that connects to well-known ancestry-linked variation in hematological parameters such as the influence of Duffy status on neutrophil counts in people of African ancestry. Effect sizes of genetic variants are known from recent work to be broadly conserved across human groups, but fine-scale population structure and local ancestry still modulate how variants express themselves. A model that can represent these gradual shifts, rather than jumping between group-specific estimates at arbitrary boundaries, is better positioned to translate genomic discovery into accurate predictions for everyone.

The broader significance of this work lies in health equity. Polygenic risk scores are beginning to enter clinical workflows, with validated assays for conditions ranging from cardiovascular disease to breast cancer and with large consortia such as PRIMED dedicated to reducing disparities in polygenic risk assessment. If these tools systematically underperform in non-European populations, the benefits of genomic medicine will flow disproportionately to people already advantaged by existing research infrastructure, widening health disparities rather than narrowing them. Analyses spanning the Global Biobank Meta-analysis Initiative and studies of portability across nine ancestry groups have repeatedly documented the scope of this problem. SPLENDID’s contribution is to show that part of the solution does not require waiting for larger non-European datasets alone; it also requires statistical frameworks that use the full richness of ancestry information already present in biobanks.

There are practical implications as well. Because SPLENDID produces a single model, health systems deploying it do not need to maintain separate score pipelines for separate populations, nor do they need to assign patients an ancestry label before applying a score, a step that raises both logistical and ethical concerns given the fraught relationship between genetic ancestry and social identity. Scholars have cautioned repeatedly that genetic ancestry must be handled carefully in science and society, and a method that renders explicit labeling unnecessary sidesteps many of those pitfalls. The method’s demonstrated scalability also means it can be retrained as biobanks grow and diversify, and its dependence on individual-level data makes it well suited to trusted-access environments such as All of Us and the UK Biobank, where controlled analyses are the norm.

The path from a Nature Methods paper to routine clinical use is never short, and the authors are careful to frame SPLENDID as a tool for robust risk prediction across diverse populations rather than a finished clinical product. Validation for additional traits, disease endpoints and health systems will be needed, and continued investment in diversifying genomic datasets remains essential, because no statistical method can fully compensate for reference data that under-represent much of humanity. Still, the results reported across more than half a million participants in two flagship biobanks represent a meaningful advance: evidence that acknowledging the continuous, blended nature of human genetic ancestry, rather than forcing it into categories, yields measurably better predictions for the people who need them most. In a field where each incremental gain in accuracy can translate into earlier screening and better prevention, SPLENDID’s continuum-based approach offers a template for building genomic risk tools that work equitably across the full breadth of human diversity.

Subject of Research: A penalized regression framework, SPLENDID, that treats genetic ancestry as a continuum to improve polygenic risk prediction across diverse biobank populations.

Article Title: SPLENDID incorporates continuous genetic ancestry in biobank-scale data to improve polygenic risk prediction across diverse populations

Article References: Chen, T., Zhang, H., Mazumder, R., & Lin, X. (2026). SPLENDID incorporates continuous genetic ancestry in biobank-scale data to improve polygenic risk prediction across diverse populations. Nature Methods. https://doi.org/10.1038/s41592-026-03235-2

Image Credits: AI Generated

DOI: 10.1038/s41592-026-03235-2

Keywords: polygenic risk scores, genetic ancestry, SPLENDID, biobank-scale data, All of Us Research Program, UK Biobank, penalized regression, health disparities, admixed populations, Nature Methods, precision medicine, statistical genetics