Biology subjects

Kern, A. D.

Publications and source records attributed to Kern, A. D..

At least 19 recordsLinked to original sources

bioRxiv2026

This must be the place: deep learning local adaptation

Climate change is increasingly disrupting the relationship between locally adapted populations and the environments in which they evolved, creating an urgent need for tools that connect genomic variation to climate. Common-garden and provenance trials remain the gold standard for characterizing local adaptation, but their time and resource requirements limit how broadly they can be applied. Genomic approaches provide a complementary path. Genotype--environment association (GEA) methods identify environmentally associated loci. Machine-learning models have also shown that geographic origin can be predicted directly from genotypes. Here we introduce EcoLocator, a supervised deep neural network that jointly predicts geographic location and climate of origin from genotypes. Through extensive simulations we demonstrate that EcoLocator accurately recovers geographic location and environment of origin from genotype data, and, with SHAP-based feature attribution, identifies adaptive loci more reliably than benchmark GEA methods. We apply our method to coastal Douglas-fir (Pseudotsuga menziesii var. menziesii), where EcoLocator predicts geographic origin (R2=0.75--0.83) and climate of origin (R2=0.52--0.72) under leave-one-out cross-validation. Notably, we find that climate predicted directly from genotypes outperforms climate inferred by first predicting geographic origin, showing that EcoLocator captures genotype--climate signal that cannot be recovered from geography alone. Our prediction errors fall within the tolerances used in existing seed-transfer guidelines, demonstrating that EcoLocator's predictions are ready for practical application, and our approach is readily extendable to other species and conservation contexts.

evolutionary biology↗

bioRxiv2026

GPU accelerated population genetics statistics using pg_gpu

Population genetics summary statistics--diversity, divergence, linkage disequilibrium, selection scans, and dimensionality reduction--are fundamental across human, agricultural, and ecological genomics. As whole-genome sequencing datasets have grown to hundreds of thousands of individuals, the cost of computing these statistics on conventional CPU implementations has become a major bottleneck: windowed scans of a single chromosome arm can take hours to days, and computation of pairwise linkage-disequilibrium statistics useful for demographic inference scales as O(n2) in sample size, often exceeding wall-clock budgets entirely. We present pg_gpu, a Python library implementing a comprehensive catalog of population-genetics summary statistics as fused CUDA kernels on NVIDIA GPUs. pg_gpu covers eleven categories spanning diversity and neutrality tests, divergence, admixture, the site-frequency spectrum, linkage disequilibrium, haplotype-based selection scans, dimensionality reduction (PCA, randomized PCA, local PCA / lostruct), distance distributions, relatedness, resampling, and a generalized weighted-SFS framework for custom{omega} estimators. On the full Ag1000G Phase 3 chromosome 3R arm (2,940 haplotypes, 10.9 million variants) pg_gpu agrees with scikit-allel and PLINK2 to machine precision while delivering a median 139x and maximum 1,096x speedup. For the multi-population LD statistics used by moments for demographic inference, pg_gpu is a drop-in replacement that yields a ~1,750-fold speedup over the native implementation. Whole chromosome arm scans, lostruct screens, and calculation of LD statistics complete on a single NVIDIA A100 in seconds to a few minutes.

evolutionary biology↗

bioRxiv2026

Ecotypes, Wolbachia, and urbanization shape Culex pipiens population structure in a West Nile virus hotspot

Culex pipiens is a major vector of West Nile Virus (WNV) in Europe and has a complex evolutionary history that has been linked to the probability of WNV spillover to humans. Here, we present a population genomic analysis of Cx. pipiens from a WNV hotspot in southwestern Spain. Whole-genome sequencing of 217 individuals from 24 localities revealed that population structure is mainly shaped by the coexistence of pipiens and molestus ecotypes. Despite clustering, genome-wide divergence between ecotypes was low, consistent with shared ancestral variation. Furthermore, Wolbachia infection was heterogeneous across the study area, where some Culex pipiens pipiens remain uninfected. We also identified three new polymorphic chromosomal inversions on chromosomes 1 and 3 that segregate independently. Although inversions did not underlie ecotypic differentiation, they were enriched for genes involved in olfaction and insecticide resistance pathways, suggesting potential adaptive and epidemiological relevance. Distance-based redundancy analyses showed that ecotype identity explained the largest fraction of genetic variance, with additional contributions from Wolbachia infection, habitat type (urban versus rural) and geographic distance. These results reveal that partial ecotypic differentiation, symbiont infection, and habitat interact to shape population structure in Cx. pipiens, with implications for vector ecology and disease transmission.

genomics↗

bioRxiv2026

Diversity and divergence of two sympatric, sibling octopus species

Coleoid cephalopods have convergently evolved many traits shared with vertebrates, including camera-type eyes, large brain-to-body size ratios, and complex behaviors. Most evolutionary studies of cephalopods have compared individual genomes of taxa that diverged tens to hundreds of millions of years ago, yet very few have examined more recent evolution from a population genetics perspective. Here we present a comparative population genomic analysis of the sympatric sister species Octopus bimaculatus and Octopus bimaculoides using whole-genome resequencing. Despite similar morphologies, these species differ substantially in their life histories, ecologies, and geographic distributions. Using demographic inference, we estimated that the two species diverged approximately one million years ago and that O. bimaculatus has maintained a consistently larger effective population size since divergence. Consistent with these demographic histories, we found stronger signatures of positive selection in O. bimaculatus, including a positive correlation between recombination rate and nucleotide diversity, more selective sweeps, and a higher proportion of mutations fixed by adaptation--all consistent with more efficient natural selection in larger populations. Protein-coding genes overlapping with selective sweeps were enriched for various functions that included many related to brain and eye development, suggesting that traits characteristic of coleoid cephalopods continue to be shaped by positive selection on recent timescales in these species. Comparing coding-sequence divergence on the Z chromosome to the autosomes, we also find evidence for a female-biased mutation rate, consistent with an independent estimate from a deeper-timescale cephalopod comparison.

evolutionary biology↗

bioRxiv2026

The genome of the Pacific acorn barnacle provides insights into the evolution of extremely large populations

Population size is a key factor underlying the mode and tempo of evolution, particularly as it relates to the strength of selection and drift. While the mechanisms underlying the interactions between population size and selection have been studied in population genetics for over a century, empirical knowledge of these dynamics has been limited to species with small-to-moderate historical population sizes. This gap in knowledge highlights the need for empirical studies in systems with historically large population sizes and elevated diversity. The Pacific acorn barnacle (Balanus glandula) presents an exceptional model to study evolution in extremely large populations, exhibiting census sizes often exceeding the tens of thousands of individuals per square meter. We generated a new chromosome-level genome assembly for this species, which we leverage in one of the first large-scale genomic analyses in this system. This assembly reveals a highly polymorphic genome with over 3% heterozygosity. At a population level, B. glandula exhibits extreme levels of polymorphism, with nucleotide diversity surpassing 5% genome-wide in just a small collection of individuals. Across the genome, nucleotide diversity predictably decreases at functional elements, including both coding and non-coding sequences, likely reflecting strong purifying selection along the genome. At the same time, McDonald-Kreitman tests reveal that the majority of non-synonymous substitutions between barnacle species were driven by positive selection, consistent with the expected increase in the efficacy of selection in large populations. These remarkable levels of diversity set B. glandula as a unique model for the study of evolution at extreme demographic scales and highlights the importance of testing evolutionary theory across a wide variety of empirical systems.

evolutionary biology↗

bioRxiv2026

Protocol for genotyping cephalopod sex using a skin swab and quantitative PCR

The coleoid cephalopods (octopus, cuttlefish, and squid) are emerging model organisms for neuroscience, development, and evolutionary biology. Determining their sex early in life is critical for population management and controlled experiments. Here, we present a protocol to non-invasively determine the sex of multiple cephalopod species as young as 3 hours post-hatching using a skin swab and quantitative PCR (qPCR). We describe steps for designing qPCR primers, swabbing live animals, extracting DNA, running the qPCR, and analyzing the results. For complete details on the use and execution of this protocol, please refer to Rubino et al.1 HighlightsO_LISwab live cephalopods as early as 3 hours post-hatching C_LIO_LIExtract DNA from cephalopod skin swabs C_LIO_LIPerform qPCR-based sex determination C_LIO_LIDesign and validate qPCR primers for new species C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=190 SRC="FIGDIR/small/715692v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@10797f5org.highwire.dtl.DTLVardef@18961a2org.highwire.dtl.DTLVardef@690df0org.highwire.dtl.DTLVardef@1122899_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2026

Assessing the potential of bee-collected pollen sequence data to train machine learning models for geolocation of sample origin

Pollen is a robust and widespread substance that captures a historical snapshot of a specific time and place, and it can be used to track movements through space by examining the pollen deposited on various objects. Palynology, the study of pollen, is used across fields such as conservation, natural history, and forensics, where it is particularly useful for tracing the origin and movement of objects. However, pollen has remained underutilized due to the difficulty of distinguishing many pollen taxa beyond the family level and limited pollen reference material to support location predictions. With recent developments in pollen DNA metabarcoding these issues have been rectified, but much of the available pollen data are primarily from wind-pollinated species, which are widespread and less informative of specific sample locations. Bee-collected pollen presents an untapped resource in training predictive models to geolocate sample origin. Here we compiled bee-collected pollen DNA sequence relative abundance data from three projects in the western U.S. and assessed the accuracy of supervised machine learning models to predict the location of sample origin based solely on pollen assemblage, without the need of incorporating additional data. Random Forest and k-Nearest Neighbors models yielded high accuracy across all projects. We also found that models trained on taxonomically clustered pollen assigned sequence variants (ASVs) performed slightly better than those trained on raw sequence data, but the difference was minor, indicating that models trained on raw sequence data can reliably predict location and avoid the time-consuming taxonomic assignment process. Our results demonstrate the utility of repurposing bee-collected pollen for geolocation and provide a framework for employing supervised machine learning in future geolocation efforts. HighlightsO_LIBee-collected pollen metabarcoding data was used to accurately predict sample origin C_LIO_LIRandom Forest and k-Nearest Neighbors algorithms were most accurate with lowest error C_LIO_LITaxonomically-classified and raw DNA sequence data training sets performed comparably C_LI

bioinformatics↗

bioRxiv2026

MKado: a toolkit for McDonald-Kreitman tests of natural selection

SummaryMKado is a Python toolkit for performing McDonald-Kreitman (MK) tests of natural selection from aligned coding sequences. It implements the standard MK test as well as a wide variety of its extensions and related statistics, including a number of methods for estimating the fraction of adaptive substitutions () while accounting for slightly deleterious mutations, with a unified command-line interface and Python API. MKado supports parallel batch processing of thousands of genes with near-linear scaling, and provides publication-ready visualizations including volcano plots and asymptotic curves. Availability and ImplementationMKado is freely available at https://github.com/kr-colab/mkado under the MIT license. Full documentation is available at https://mkado.readthedocs.io. MKado is implemented in Python and installable via pip. Contactadkern@uoregon.edu

evolutionary biology↗

bioRxiv2025

Neural posterior estimation for population genetics

Simulation-based inference methods are increasingly being used in population genetics due to their flexibility and ability to be applied in settings where likelihood-based methods are intractable. Perhaps the best known such method is Approximate Bayesian Computation (ABC); however, its popularity is offset by its shortcomings which include computational expense and an unfortunate inability to efficiently fit models to high-dimensional summaries of the data. An alternative approach that solves these issues is supervised machine learning (ML); however, ML methods generally do not yield Bayesian uncertainty estimates of the quantities they predict. Here, we apply a recently introduced method, neural posterior estimation (NPE), that combines the best facets of ABC and supervised ML by training a neural network to estimate the posterior distribution of a population genetics model. We first compare neural posterior estimation with other inference methods for a variety of population genetic tasks, and show that neural posterior estimators yield posterior distributions with high accuracy and efficiency. We compare learned posterior distributions given raw genotypes and various summary statistics as input data. Additionally, we apply neural posterior estimation for demographic inference for simple and more complex models to highlight its application, including an analysis of demographic history in Drosophila melanogaster. Finally, we provide a user friendly workflow that enables others to perform neural posterior estimation on their own genetic data.

evolutionary biology↗

bioRxiv2025

A non-invasive method to genotype cephalopod sex by quantitative PCR

Coleoid cephalopods (cuttlefish, octopus, and squid) are emerging model organisms in neuroscience, development, and evolutionary biology, and are of major economic importance in global fisheries. However, they are notoriously difficult and expensive to culture. The ability to determine sex early in development would enable more efficient and sustainable population management in both laboratory and wild settings. Here, we present a non-invasive method to genotype the sex of dwarf cuttlefish (Ascarosepion bandense) as young as three hours post-hatching using a skin swab and quantitative PCR assay, which detects a two-fold dosage difference between ZZ and Z0 sex chromosomes of males and females, respectively. Furthermore, we designed and validated primers for four additional cephalopod research species with assembled genomes (Octopus bimaculoides, Sepia officinalis, Euprymna berryi, Doryteuthis pealeii), and for a wild-caught species of economic value (Illex illecebrosus) using low-coverage whole genome sequencing data. This method enables accurate sex determination from hatchlings to adults across cephalopods, independent of genome quality or availability. HighlightsO_LIThe Z sex chromosome was identified in multiple cuttlefish, squid, and octopus species. C_LIO_LIA sensitive quantitative PCR assay can genotype ZZ/Z0 sex in each species. C_LIO_LILow-coverage short-read sequencing data is sufficient to design effective primers. C_LIO_LIqPCR on non-invasive skin swabs enables genotyping of living animals. C_LI

genetics↗

bioRxiv2025

Coalescence and Translation: A Language Model for Population Genetics

Probabilistic models such as the sequentially Markovian coalescent (SMC) have long provided a powerful framework for population genetic inference, enabling reconstruction of demographic history and ancestral relationships from genomic data. However, these methods are inherently specialized, relying on predefined assumptions and/or limited scalability. Recent advances in simulation and deep learning provide an alternative approach: learning directly to generalize from synthetic genetic data to infer specific hidden evolutionary processes. Here we reframe the inference of coalescence times as a problem of translation between two biological languages: the sparse, observable patterns of mutation along the genome and the unobservable ancestral recombination graph (ARG) that gave rise to them. Inspired by large language models, we develop cxt, a decoder-only transformer that autoregressively predicts coalescent events conditioned on local mutational context. We show that cxt performs on par with state-of-the-art MCMC-based likelihood models across a broad range of demographic scenarios, including both in-distribution and out-of-distribution settings. Trained on simulations spanning the stdpopsim catalog, the model generalizes robustly and enables efficient inference at scale, producing over a million coalescence predictions in minutes. In addition cxt produces a well calibrated approximate posterior distribution of its predictions, enabling principled uncertainty quantification. We apply cxt to population genomic data from both humans and mosquitoes, highlighting the models ability to deal with the complexities of empirical data. Significance statementcxt is a language model for population genetics which introduces next-coalescence prediction as translation from observed mutations to coalescence times by modeling the coalescent with recombination as a conditional stochastic process. It learns implicit priors from stdpopsim and generalizes across both known and novel demographies. cxt generates millions of TMRCA estimates in minutes and samples well-calibrated posteriors for uncertainty quantification. A simple post-hoc correction aligns predicted diversity with the species mutation rate, ensuring robustness to novel evolutionary scenarios.

evolutionary biology↗

bioRxiv2025

Simulation-based spatially explicit close-kin mark-recapture

Estimating the size of wild populations is a critical priority for ecologists and conservation biologists, but tools to do so are often labor intensive and expensive. A promising set of newer approaches are based on genetic data, which can be cheaper to obtain and less invasive than information from more direct observation. One of these approaches is close-kin mark-recapture (CKMR), a type of method that uses genetic data to identify kin pairs and estimates population size from these pairs. Although CKMR methods are promising, a major limitation to using them more broadly is a lack of CKMR models that can deal with spatial heterogeneity both in population density and sample effort. We introduce a simulation-based approach to CKMR that uses spatially explicit individual-based simulation in concert with a deep convolutional neural network to estimate population sizes. Using extensive simulation, we show that our method, CKMRnn, is highly accurate, even in the face of spatial heterogeneity, and demonstrate that it can account for potential confounders such as unknown population histories. Finally, to demonstrate the accuracy of our method in an empirical system, we apply CKMRnn to data from a Ugandan elephant population, and show that point estimates from our method recapitulate those from traditional estimators but that the confidence interval on our estimator is reduced by approximately 30%.

ecology↗

bioRxiv2025

Accessible, realistic genome simulation with selection using stdpopsim

Selection is a fundamental evolutionary force that shapes patterns of genetic variation across species. However, simulations incorporating realistic selection along heterogeneous genomes in complex demographic histories are challenging, limiting our ability to benchmark statistical methods aimed at detecting selection and to explore theoretical predictions. stdpopsim is a community-maintained simulation library that already provides an extensive catalog of species-specific population genetic models. Here we present a major extension to the stdpopsim framework that enables simulation of various modes of selection, including background selection, selective sweeps, and arbitrary distributions of fitness effects (DFE) acting on annotated subsets of the genome (for instance, exons). This extension maintains stdpopsims core principles of reproducibility and accessibility while adding support for species-specific genomic annotations and published DFE estimates. We demonstrate the utility of this framework by comparing methods for demographic inference, DFE estimation, and selective sweep detection across several species and scenarios. Our results demonstrate the robustness of demographic inference methods to selection on linked sites, reveal the sensitivity of DFE-inference methods to model assumptions, and show how genomic features, like recombination rate and functional sequence density, influence power to detect selective sweeps. This extension to stdpopsim provides a powerful new resource for the population genetics community to explore the interplay between selection and other evolutionary forces in a reproducible, user-friendly framework.

evolutionary biology↗

bioRxiv2025

Sweeps in space: leveraging geographic data to identify beneficial alleles in Anopheles gambiae

As organisms adapt to environmental changes, natural selection modifies the frequency of non-neutral alleles. For beneficial mutations, the outcome of this process may be a selective sweep, in which an allele rapidly increases in frequency and perhaps reaches fixation within a population. Selective sweeps have well-studied effects on patterns of local genetic variation in panmictic populations, but much less is known about the dynamics of sweeps in continuous space. In particular, because limited movement across a landscape leads to unique patterns of population structure, spatial dynamics may influence the trajectory of selected mutations. Here, we use forward-in-time, individual-based simulations in continuous space to study the impact of space on beneficial mutations as they sweep through a population. In particular, we show that selection changes the joint distribution of allele frequency and geographic range occupied by a focal allele and demonstrate that this signal can be used to identify selective sweeps. We then leverage this signal to identify in-progress selective sweeps within the malaria vector Anopheles gambiae, a species under strong selection pressure from vector control measures. By considering space, we identify multiple previously undescribed variants with potential phenotypic consequences, including mutations impacting known IR-associated genes and altering protein structure and properties. Our results demonstrate a novel signal for detecting selection in spatial population genetic data that may have implications for genomic surveillance and understanding geographic patterns of genetic variation.

evolutionary biology↗

bioRxiv2024

Population genetics meets ecology: a guide to individual-based simulations in continuous landscapes

Individual-based simulation has become an increasingly crucial tool for many fields of population biology. However, continuous geography is important to many applications, and implementing realistic and stable simulations in continuous space presents a variety of difficulties, from modeling choices to computational efficiency. This paper aims to be a practical guide to spatial simulation, helping researchers to implement individual-based simulations and avoid common pitfalls. To do this, we delve into mechanisms of mating, reproduction, density-dependent feedback, and dispersal, all of which may vary across the landscape, discuss how these affect population dynamics, and describe how to parameterize simulations in convenient ways (for instance, to achieve a desired population density). We also demonstrate how to implement these models using the current version of the individual-based simulator, SLiM. We additionally discuss natural selection - in particular, how genetic variation can affect demographic processes. Finally, we provide four short vignettes: simulations of pikas that shift their range up a mountain as temperatures rise; mosquitoes that live in rivers as juveniles and experience seasonally changing habitat; cane toads that expand across Australia, reaching 120 million individuals; and monarch butterflies whose populations are regulated by an explicitly modeled resource (milkweed).

evolutionary biology↗

bioRxiv2024

Estimation of spatial demographic maps from polymorphism data using a neural network

A fundamental goal in population genetics is to understand how variation is arrayed over natural landscapes. From first principles we know that common features such as heterogeneous population densities and barriers to dispersal should shape genetic variation over space, however there are few tools currently available that can deal with these ubiquitous complexities. Geographically referenced single nucleotide polymorphism (SNP) data are increasingly accessible, presenting an opportunity to study genetic variation across geographic space in myriad species. We present a new inference method that uses geo-referenced SNPs and a deep neural network to estimate spatially heterogeneous maps of population density and dispersal rate. Our neural network trains on simulated input and output pairings, where the input consists of genotypes and sampling locations generated from a continuous space population genetic simulator, and the output is a map of the true demographic parameters. We benchmark our tool against existing methods and discuss qualitative differences between the different approaches; in particular, our program is unique because it infers the magnitude of both dispersal and density as well as their variation over the landscape, and it does so using SNP data. Similar methods are constrained to estimating relative migration rates, or require identity by descent blocks as input. We applied our tool to empirical data from North American grey wolves, for which it estimated mostly reasonable demographic parameters, but was affected by incomplete spatial sampling. Genetic based methods like ours complement other, direct methods for estimating past and present demography, and we believe will serve as valuable tools for applications in conservation, ecology, and evolutionary biology. An open source software package implementing our method is available from https://github.com/kr-colab/mapNN.

evolutionary biology↗

bioRxiv2024

Cephalopod Sex Determination and its AncientEvolutionary Origin Revealed by Chromosome-levelAssembly of the California two-spot Octopus

Octopuses, squids, and cuttlefishes - the coleoid cephalopods - are a remarkable branch in the tree of life whose members exhibit a repertoire of sophisticated behaviors (Hanlon and Messenger, 2018). As a clade, coleoids harbor an incredible variety of novel traits including the most complex nervous system among invertebrates, derived camera-type eyes, and rapid adaptive camouflage abilities (Young, 1971; Hanlon, 2007). The burst of evolutionary novelty that distinguishes cephalopods is even more striking in a phylogenetic context; cephalopods are a deeply diverged lineage that last share a common ancestor with other extant molluscs in the Cambrian period, roughly 550 million years ago (Ponder and Lindberg, 2008; Huang et al., 2022). With recent advances in genome sequencing technologies, we have the capability to explore the genomic foundations of cephalopod novelties. Here, using PacBio long-read sequencing of genomic DNA and IsoSeq full-length mRNA sequencing, we provide a novel chromosome-scale reference genome and annotation for a female California two-spot octopus (O. bimaculoides). Our assembly reveals that the female octopus has just one sex chromosome, consistent with a ZO karyotype, while the male has two (ZZ), providing the first evidence of genetic sex determination in cephalopods. We use our assembly and annotation in combination with existing genomic information from other cephalopods to create the first whole genome alignments from this group and demonstrate that the sex chromosome is of an ancient origin, before the radiation of extant cephalopods approximately 480 million years ago (Huang et al., 2022), and has been conserved to the present day in all cephalopod genomes available.

evolutionary biology↗

bioRxiv2023

A Quantitative Genetic Model of Background Selection in Humans

Across the human genome, there are large-scale fluctuations in genetic diversity caused by the indirect effects of selection. This "linked selection signal" reflects the impact of selection according to the physical placement of functional regions and recombination rates along chromosomes. Previous work has shown that purifying selection acting against the steady influx of new deleterious mutations at functional portions of the genome shapes patterns of genomic variation. To date, statistical efforts to estimate purifying selection parameters from linked selection models have relied on classic Background Selection theory, which is only applicable when new mutations are so deleterious that they cannot fix in the population. Here, we develop a statistical method based on a quantitative genetics view of linked selection, that models how polygenic additive fitness variance distributed along the genome increases the rate of stochastic allele frequency change. By jointly predicting the equilibrium fitness variance and substitution rate due to both strong and weakly deleterious mutations, we estimate the distribution of fitness effects (DFE) and mutation rate across three geographically distinct human samples. While our model can accommodate weaker selection, we find evidence of strong selection operating similarly across all human samples. Although our quantitative genetic model of linked selection fits better than previous models, substitution rates of the most constrained sites disagree with observed divergence levels. We find that a model incorporating selective interference better predicts observed divergence in conserved regions, but overall our results suggest uncertainty remains about the processes generating fitness variation in humans.

evolutionary biology↗