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Greenbaum, G.

Publications and source records attributed to Greenbaum, G..

At least 19 recordsLinked to original sources
bioRxiv2026

Forensic investigative genetic genealogy match rate estimated from a nation-wide population register

Forensic investigative genetic genealogy (FIGG) is a revolutionary method in forensic genetics, whereby genetic relatives of an unknown target person are detected in direct-to-consumer genomic databases; the family trees of these relatives are then reconstructed to suggest candidates for the target person. Despite recent successes, the full potential of the technology has not yet been systematically evaluated at the scale of an entire country. To estimate the proportion of FIGG cases where a genetic relative is detected in the database (the "match rate"), we used the Israeli population register, covering all past and present citizens. After extensive quality control, the register included 12.4 million individuals, among them 10.4 million alive. We simulated genomic databases by randomly selecting subsets of predefined sizes of the live adult population. With a database covering 1% of the population, we estimate that FIGG would detect at least one relative of fifth degree (e.g., a second cousin) or closer for about 25% of the population, and at least two relatives for 9% of the population. A database covering 5% of the population would find at least one relative of third degree (e.g., a first cousin) or closer for half the population. More distant relatives are rarely identified in the register, likely due to its limited time depth. Our results provide the first country-wide direct estimates for the utility of FIGG in generating investigative leads.

genetics↗
bioRxiv2026

Drivers of immune-related genetic variation across human populations

Pathogen-mediated disease burden imposes some of the strongest selective pressures on human populations, shaping genetic variation in immune-related genomic regions. Because historical, cultural, ecological, and environmental factors can influence disease burden, associations between these factors and immune-related genomic signatures can provide insight into how pathogen-mediated selection varied among populations. To investigate these associations and identify factors potentially contributing to historical disease burden, we analyzed worldwide variation in the Major Histocompatibility Complex (MHC). We evaluated the relationships between global patterns of genetic variation and a comprehensive set of historical-cultural, ecological, and climatic variables compiled in DisECCO (Disease, Ecological, Cultural, and Climatic Origins dataset). This dataset includes factors potentially associated with historical disease burden, including the timing of major cultural transitions, historical climatic conditions, exposure to domesticated animals, and pre-industrial pathogen stress. Contrary to genome-wide expectations, MHC diversity and variation were not well explained by geographic distance from Africa, suggesting that neutral demographic processes play a limited role in shaping MHC variation. Instead, MHC genetic variation was significantly associated with climatic variables, domestication exposure, and the time lag between the onset of the Neolithic and urbanization, with the strongest explanatory models identified for MHC Class II. These results indicate that cultural transitions and environmental conditions have played a central role in shaping immune-related genetic variation, and may have contributed substantially to changes in disease burden. Overall, our findings show that integrating ecological and cultural-historical variables with genomic data can help explain global patterns of genetic variation in humans.

evolutionary biology↗
bioRxiv2026

Contrasting effects of forest fragmentation on the genetics and microbiomes of an endangered arboreal primate

Landscape fragmentation, one of the leading drivers of biodiversity loss, can reshape both the genetics and microbiomes of wild populations. Although fragmentation is generally expected to limit gene flow and erode genetic diversity, and to disrupt host-associated microbial communities, these responses arise via different pathways and may therefore diverge within the same population. To understand how fragmentation simultaneously shapes population genetics and gut microbiomes, we analyzed fecal-derived host genomic and microbiome data from endangered, arboreal black howler monkeys (Alouatta pigra) across a fragmentation gradient. We then integrated these data with measures of ecological connectivity, habitat quality, and demography to identify the drivers of genetic and microbiome variation and structure. Multivariate analyses indicated that genetic patterns were shaped by both connectivity and habitat quality, whereas microbiome variation was driven mainly by habitat quality. Contrary to expectations under reduced realized connectivity with increasing isolation, monkeys showed the strongest gene flow signal in the most fragmented region, and higher genetic diversity and lower inbreeding than monkeys in continuous forest. Relatedness and isolation-by-distance patterns suggested that fragmentation has sex-specific effects on movement, disrupting the usual pattern of short-range male dispersal in the most fragmented region. Gut microbiomes, however, showed predicted negative responses to fragmentation: individuals in highly fragmented habitat had lower microbial diversity and compositional shifts consistent with lower-quality diets and increased exposure to disturbed environments. These results show contrasting biological responses to fragmentation within a single population, with genetic patterns likely resulting from compensatory behavioral flexibility and microbiome patterns reflecting local habitat degradation. Our findings underscore the need for conservation assessments that integrate multiple dimensions of population health rather than relying on any single indicator of fragmentation impact.

ecology↗
bioRxiv2026

Optimal release of gene drives in population connectivity networks

Gene drives, genetic constructs that can spread deleterious alleles in wild populations, have the potential to address some of the major pressing challenges of the Anthropocene such as invasive species, spread of disease vectors, and agricultural pests. However, responsible and effective deployment of gene drive requires taking into account the complex nature of real-world population connectivity networks. In particular, it is unclear how the topological position of the deployment site affects the spread process and its final outcome. Here we develop a framework for modeling gene drive spread in population connectivity networks, and study the eco-evolutionary dynamics of gene drive spread under complex population structures. We investigated the relationship between the position of the deployment site in the topology of the network and whether the gene drive is eventually lost, fixed, or maintained at an intermediate frequency. We identified network centrality measures of deployment sites that are highly correlated with the outcome of deployment for different gene drive designs and across diverse network topologies. We also show that there is a trade-off between the time-to-fixation and the final outcome, implying that multiple centrality measures of the deployment site would need to be considered when aiming to achieve rapid and successful population control using gene drives.

ecology↗
bioRxiv2026

Mapping Gene Drive Dynamics onto Mendelian Models

CRISPR-based gene drives bias their own transmission and can spread even when deleterious, giving rise to evolutionary dynamics that can be substantially more complex than those governed by standard Mendelian inheritance. Identifying conditions under which gene-drive dynamics can be faithfully approximated by Mendelian models would therefore enable the extensive theoretical toolkit of classical population genetics to be applied to gene-drive systems. Here, we develop a general mapping framework that translates gene-drive models into dynamically equivalent Mendelian models, allowing their behavior to be analyzed using classical theory. By deriving both haploid and diploid effective-parameter mappings, we identify Mendelian models that closely reproduce allele-frequency trajectories of gene drives across a wide range of conversion rates, fitness costs, and dominance effects. We delineate the regions of the parameter space where a one-parameter haploid approximation provides an accurate first-order representation, and where incorporating dominance in a diploid mapping substantially improves fidelity and recovers internal equilibria and threshold behavior. Analytic approximations yield efficient mappings across most of the drive parameter space, while a trajectory-based grid search further improves accuracy near nonlinear regime boundaries. To demonstrate the utility of this framework, we apply it to predicting gene swamping in a two-deme migration-selection model and show that the mapped Mendelian system accurately forecasts transitions between fixation and loss under three relevant release scenarios: environmental variation in fitness, engineered fitness asymmetries, and environment-dependent conversion. Together, these results establish a theoretical bridge between non-Mendelian gene drives and classical population genetic models, providing an interpretable and computationally efficient foundation for predicting gene-drive outcomes and guiding the design of gene drive systems and deployment strategies.

evolutionary biology↗
bioRxiv2025

Coupled dynamics of predator group formation and prey populations

1A diverse array of social carnivores, such as orcas and wolves, also hunt cooperatively, but it is not well understood how prey availability feeds back with the size of social carnivore groups. Cooperative hunting may expand predators access to food by allowing them to hunt prey they could not catch alone, but many predator groups are much larger than the group size that would maximize individual food intake. One promising explanation is that if individuals can freely leave and join groups, then predators behaving adaptively should continue to join a group until the fitness that they achieve in the group is the same as that of being solitary. Here, we investigate the stable group sizes that form in a population of predator groups when predators hunt two types of prey: big prey that is best hunted in groups and small prey that is best hunted alone. In conjunction, we track the dynamics of the distribution of predator group sizes when individuals make decisions about whether to join or leave groups based on their own fitness. We find that if individual predators are able to freely leave groups and pair up with other individuals, starting new groups, then group sizes at equilibrium are much smaller than expected, generally staying at or below the size that maximizes individual fitness. Furthermore, accounting for flexible group sizes leads to ecological conditions that are not predicted if predator group sizes are held constant; in some regions of the parameter space, predators partition resources by forming a bimodal distribution of group sizes, and as a result, create situations where an increase in one prey type helps the other prey population grow. This result has applications to management of carnivore-livestock conflict, implying that increasing populations of wild prey may protect livestock.

ecology↗
bioRxiv2025

Integrating gene drives with established pest controls to mitigate spillover risk

Gene drives--artificial genetic constructs that can rapidly spread in a population due to biased inheritance--are a promising tool for controlling disease vectors and invasive species. While gene drives can eradicate target populations within several generations, their unintended spillover into non-target populations poses serious ecological concerns. Here, we examine how integrating gene drives with established pest control methods, such as pesticide application and sterile-male release, can mitigate spillover risk. We develop optimal control models to identify cost-effective treatment strategies that minimize total eradication costs, accounting for actual investments and potential damage from spillover. We show that pesticides are most cost-effective at the intermediate stage of eradication when gene drives have already slowed population growth but do not yet substantially suppress the population. For sterile-male release, however, we observe two distinct types of optimal strategies, the choice of which depends on parameter values: intensive early-stage releases, or mild later-stage releases. Using established pest controls also relaxes requirements to gene drive design, favoring milder suppressive effects as the treatment intensifies.

ecology↗
bioRxiv2025

Genomic signatures of increasing disease burden in recent prehistory

One of the strongest selection pressures experienced by human populations is that driven by diseases on immune-related genomic regions. While it has been hypothesized for some time that disease burdens increased with the shift to agricultural and urbanized lifestyles, direct evidence for this hypothesis is lacking. Here, we capitalize on the accumulation of ancient genomic data to study changes in disease burden in human populations over the past 12,000 years. We investigated changes in genetic diversity and balancing selection in two distinct geographical and cultural centers in Southwest Eurasia and Eastern Asia, and found that not only is the major histocompatibility complex (MHC) the genomic region with the most substantial increases in diversity in terms of enrichment of genes and rates of increase, but that the rates of changes for individual genes are highly correlated between the two cultural centers, indicating similar selection pressures. We identify periods of time with substantial peaks in MHC diversity increase that primarily correspond to periods with settlement intensification, increased connectivity, and the expansion of animal domestication, which suggest that the most intensive disease burden occurred following the transition to sedentary lifestyle but prior to urbanization. These findings demonstrate the potential of our approach in uncovering the interplay between cultural shifts and selection, and provide strong support to the hypothesis that the levels of disease burden have substantially increased in recent prehistory following changes in lifestyle, connectivity and the introduction of domesticated animals.

evolutionary biology↗
bioRxiv2025

Network-based genetic monitoring of landscape fragmentation

Habitat fragmentation is one of the most immediate and substantial threats to biodiversity, generating isolated populations with reduced genetic diversity. Genetic monitoring has become essential for detecting fragmentation and tracking its progress. However, the coherent interpretation of genetic monitoring data and understanding the genetic consequences of fragmentation require frameworks that accurately represent real-world complexity. Existing theoretical frameworks typically rely on simplified spatial structures and do not adequately capture the heterogeneous migration patterns of natural populations. Here, we integrate network theory and mathematical population genetics to develop a framework for studying the genetic consequences of fragmentation processes, explicitly accounting for heterogeneous connectivity and temporal dynamics. We apply this framework to examine how different fragmentation processes affect genetic measures commonly used in genetic monitoring. We find that different fragmentation scenarios produce substantially distinct trajectories in key genetic measures, sometimes exhibiting rapid transitional dynamics, suggesting that the interpretation of genetic monitoring data must be tailored to ecological contexts. Furthermore, fragmentation can cause deviations from classical theoretical expectations, such as the expected correlation between genetic and geographic distance (isolation-by-distance) or between genetic diversity and connectivity. Finally, we propose and demonstrate detectable early warning signals in genetic monitoring data that precede rapid transitional phases. Our framework thus provides a practical interpretation of genetic monitoring data, bridging the gap between idealized theoretical models and real-world connectivity dynamics.

genetics↗
bioRxiv2025

Latitudinal phylogenetic and diversity gradients are explained by a tropical-temperate transitional region

The distribution of ecological and evolutionary forces throughout space bring about the patterning of biodiversity. In large geographical areas, this causes the regionalization of biodiversity into structured units known as bioregions. In order to understand how such patterns emerge, a clear delineation of bioregions is required. We use tree species as model taxa in order to analyze the global distribution of biodiversity and understand how latitudinal gradients of biodiversity, specifically the latitudinal phylogenetic and diveristy gradients are formed. By compiling an extensive dataset of tree species distributions and their phylogenetic relationships, we use a data-driven approach to delineate global bioregions of similar evolutionary histories, termed phyloregions. Our analysis reveals the presence of a region between the tropical and temperate regions, coined bridge phylore-gion, with a unique evolutionary composition and characteristically weaker association to climatic and environmental parameters. Through simulations, we show that the pres-ence of latitudinal phylogenetic and diversity gradients are much more likely to emerge in the presence of an independent ecological region between tropical and temperate regions, suggesting that its role as a stepping-stone in colonization of species between distinct climatic zones has shaped latitudinal gradients. This study highlights that accurate de-lineation of evolutionary structures of biodiversity can reveal previously cryptic regions with fundamental evolutionary roles in the formation of biodiveristy patterns. 1 Significance StatementBiodiversity patterns are shaped by geography, forming distinctive units known as bioregions. At global scales there are important gradients in terms of species diversity and phylogenetic relations that are well documented, but the underlying processes that generate them are unclear. We comprised an extensive dataset of tree species distributions and phylogeny, and identified the bioregions from the dataset. We analyzed the diversity and the phylogenetic gradients and identified a previously cryptic bioregion between tropical and temperate zones. Though less climatically distinct, we show through simulations that the presence of this transition zone better explains latitudinal biodiversity patterns. Our findings refine the global dispersal dynamics of species between tropical and temperate regions and highlight the role of this intermediate region in shaping global biodiversity patterns.

evolutionary biology↗
bioRxiv2024

Predicting the direction of phenotypic difference

Predicting phenotypes from genomic data is a key goal in genetics, but for most complex phenotypes, predictions are hampered by incomplete genotype-to-phenotype mapping. Here, we describe a more attainable approach than quantitative predictions, which is aimed at qualitatively predicting phenotypic differences. Despite incomplete genotype-to-phenotype mapping, we show that it is relatively easy to determine which of two individuals has a greater phenotypic value. This question is central in many scenarios, e.g., comparing disease risk between individuals, the yield of crop strains, or the anatomy of extinct vs extant species. To evaluate prediction accuracy, i.e., the probability that the individual with the greater predicted phenotype indeed has a greater phenotypic value, we developed an estimator of the ratio between known and unknown effects on the phenotype. We evaluated prediction accuracy using human data from tens of thousands of individuals from either the same family or the same population, as well as data from different species. We found that, in many cases, even when only a small fraction of the loci affecting a phenotype is known, the individual with the greater phenotypic value can be identified with over 90% accuracy. Our approach also circumvents some of the limitations in transferring genetic association results across populations. Overall, we introduce an approach that enables accurate predictions of key information on phenotypes -- the direction of phenotypic difference -- and suggest that more phenotypic information can be extracted from genomic data than previously appreciated.

genetics↗
bioRxiv2024

Data-driven analyses of social complexity in bees reveal phenotypic diversification following a major evolutionary transition

How social complexity evolved is a long-standing enigma. In most animal groups, social complexity is typically classified into a few discrete classes. This approach is oversimplified and constrains our inference of social evolution to a narrow trajectory consisting of transitions between classes. This approach also limits quantitative studies on the molecular and environmental drivers of social complexity. However, the recent accumulation of relevant quantitative data has now set the stage to overcome these limitations. Here, we propose a data-driven approach for studying the full diversity of social phenotypes. We curated and analyzed a comprehensive dataset encompassing 17 social traits for 77 species and studied the evolution of social complexity in bees. We found that corbiculate bees -- honey bees, stingless bees, and bumble bees -- underwent a major evolutionary transition [~]70 mya, which is inconsistent with the stepwise progression of the social ladder conceptual framework. This major evolutionary transition was followed by a phase of substantial phenotypic diversification of social complexity. Non-corbiculate bee lineages display a continuum of social complexity, ranging from solitary to simple societies, but do not reach levels of social complexity comparable to those of corbiculate bees. Bee evolution provides a unique demonstration of a macroevolutionary process in which a major transition removed biological constraints and opened novel evolutionary opportunities, driving the exploration of the landscape of social phenotypes. Our approach can be extended to incorporate additional data types and readily applied to illuminate the evolution of social complexity in additional animal groups.

evolutionary biology↗
bioRxiv2024

DORA: an interactive map for the visualization and analysis of ancient human DNA and associated data

The ability to sequence ancient genomes has revolutionized the way in which we study evolutionary history by providing access to the most important aspect of evolution -- time. Until recently, studying human demography, ecology, biology, and history using population genomic inference relied on contemporary genomic datasets. Over the past decade, the availability of human ancient DNA (aDNA) has increased rapidly, almost doubling every year, opening the way for spatiotemporal studies of ancient human populations. However, the multidimensionality of aDNA, with genotypes having temporal, spatial and genomic coordinates, and the need to integrate multiple sources of data, poses a challenge for developing meta-analyses pipelines. To address this challenge, we developed a publicly-available interactive tool, DORA, which integrates multiple data types, genomic and non-genomic, in a unified interface. This web-based tool allows users to browse sample metadata along with additional layers of information, such as population structure, climatic data, and unpublished samples. Users can then perform analyses on genotypes of these samples, or export sample subsets for external analyses. DORA integrates analyses and visualizations in a single intuitive interface, resolving the technical issues of combining datasets from different sources and formats, and allowing researchers to focus on analysis and the scientific questions that can be addressed through analysis of aDNA datasets.

genomics↗
bioRxiv2023

Unraveling the Genomic Diversity and Evolutionary History of Captive Tigers in the United States

Genomic studies of rare and endangered species have focused broadly on describing diversity patterns and resolving phylogenetic relationships, with the overarching goal of informing conservation efforts. However, few studies have investigated the genomic diversity potentially housed in captive populations. For tigers (Panthera tigris) in particular, captive individuals vastly outnumber those in the wild, yet the diversity of the captive population remains largely unexplored. Here, we present the first large-scale genetic study of the private (non-zoo) captive tiger population in the United States (U.S.), also known as Generic tigers. We find that the U.S. Generic tiger population has an admixture fingerprint comprising all six extant wild tiger subspecies (P. t. altaica, Amur; P. t. tigris, Bengal; P. t. corbetti, Indochinese; P. t. jacksoni, Malayan; P. t. amoyensis, South China; P. t. sumatrae, Sumatran). We show that the Generic tiger population has a comparable amount of genetic diversity to most wild subspecies, relatively few private variants, and fewer deleterious mutations. We also observe inbreeding coefficients that are similar to wild populations, suggesting that inbreeding in captive populations is not pervasive, although there are some individuals within the Generic population that are substantially inbred. Our results elucidate the admixture history of the Generic tiger population in the U.S. Additionally, we develop a reference panel for tigers and show that it can be used with imputation to accurately distinguish individuals and assign ancestry even with ultra-low coverage (0.25x) data. The study and reference panel will provide a resource to assist in tiger conservation efforts.

evolutionary biology↗
bioRxiv2022

Rescue by gene swamping as a gene drive deployment strategy

Gene drives are genetic constructs that can spread deleterious alleles with potential application to population suppression of harmful species. Given that a gene drive can potentially spill over to other populations or even other species, control measures and fail-safes strategies must be considered. Gene drives are designed to generate a rapid demographic decline, while at the same time generating a dynamic change in the populations genetics. Since these evolutionary and demographic processes are linked and are expected to occur at a similar time-scale during gene drive spread, feedback between these processes may significantly affect the outcome of deployment. To study this feedback and to understand how it affects gene drive spillovers, we developed a gene drive model that combines evolutionary and demographic dynamics in a two-population setting. The model demonstrates how feedback between evolutionary and demographic dynamics can generate additional outcomes to those generated by the evolutionary dynamics alone. We identify an outcome of particular interest, where the short-term suppression of the target population is followed by gene swamping and loss of the gene drive. This outcome could be useful for designing gene drive deployments that temporarily suppress the population, but ultimately do not remain in the population. Using our model, we demonstrate the robustness of this outcome to spillover and to the evolution of resistance, and suggest that it could be used as a fail-safe strategy for gene drive deployment.

evolutionary biology↗
bioRxiv2022

modelRxiv: A platform for the distribution, computation and interactive display of models

Modeling the dynamics of biological processes is ubiquitous across the ecological and evolutionary disciplines. However, the increasing complexity of these models poses a significant challenge to the dissemination of model-derived results. With the existing requirements of scientific publishing, most often only a small subset of model results are generated, presented in static figures or tables, and made available to the scientific community. Further exploration of the parameter space of a model, investigation of possible variations of a model, and validation of the results in relation to model assumptions commonly rely on local deployment of code supplied by the authors. This can pose a technical challenge due to the diversity of frameworks and environments in which models are developed, and preclude model validation and exploration by readers and reviewers. To address this issue, we developed a platform that serves as an interactive repository of biological models, called modelRxiv. The platform provides a unified interface for the analysis of models that does not require any technical understanding of the model implementation. To facilitate adding models to modelRxiv, we utilize OpenAI large-language models (LLMs) to make code written in different programming languages compatible with modelRxiv, making the platform language-agnostic. modelRxiv is designed to serve as an interactive extension of published models, allowing users to regenerate model results under user-defined parameterizations of the model. By making published models accessible, this platform promises to significantly improve the accessibility, reproducibility, and validation of ecological and evolutionary models.

ecology↗
bioRxiv2019

Recent evolutionary history of tigers highlights contrasting roles of genetic drift and selection

Formulating strategies for species conservation requires knowledge of evolutionary and genetic history. Tigers are among the most charismatic of endangered species and garner significant conservation attention. However, the evolutionary history and genomic variation of tigers remain poorly known. With 70% of the worlds wild tigers living in India, such knowledge is critical for tiger conservation. We re-sequenced 65 individual tiger genomes across their extant geographic range, representing most extant subspecies with a specific focus on tigers from India. As suggested by earlier studies, we found strong genetic differentiation between the putative tiger subspecies. Despite high total genomic diversity in India, individual tigers host longer runs of homozygosity, potentially suggesting recent inbreeding, possibly because of small and fragmented protected areas. Surprisingly, demographic models suggest recent divergence (within the last 10,000 years) between populations, and strong population bottlenecks. Amur tiger genomes revealed the strongest signals of selection mainly related to metabolic adaptation to cold, while Sumatran tigers show evidence of evolving under weak selection for genes involved in body size regulation. Depending on conservation objectives, our results support the isolation of Amur and Sumatran tigers, while geneflow between Malayan and South Asian tigers may be considered. Further, the impacts of ongoing connectivity loss on the health and persistence of tigers in India should be closely monitored.

genomics↗
bioRxiv2019

Designing gene drives to limit spillover to non-target populations

The prospect of utilizing CRISPR-based gene-drive technology for controlling populations, such as invasive and disease-vector species, has generated much excitement. However, the potential for spillovers of gene drive alleles from the target population to non-target populations -- events that may be ecologically catastrophic -- has raised concerns. Here, using two-population mathematical models, we investigate the possibility of limiting spillovers and impact on non-target populations by designing differential-targeting gene drives, in which the expected equilibrium gene drive allele frequencies are high in the target population but low in the non-target population. We find that achieving differential targeting is possible with certain configurations of gene drive parameters. Most of these configurations ensure differential targeting only under relatively low migration rates between target and non-target populations. Under high migration, differential targeting is possible only in a narrow region of the parameter space. When migration is increased, differential-targeting states can sharply transition to states of global fixation or global loss of the gene drive. Because fixation of the gene drive in the non-target population could severely disrupt ecosystems, we outline possible ways to avoid this outcome. Our results emphasize that, although gene drive technology is promising, understanding the potential consequences for populations other than the targets requires detailed analysis of gene-drive spillovers, and that ways to limit the unintended effects of gene drives to non-target populations should be explored prior to the application of gene drives in natural settings.

evolutionary biology↗