Biology subjects

Kern, A. D.

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

At least 37 records · Page 2Linked to original sources

bioRxiv2023

disperseNN2: a neural network for estimating dispersal distance from georeferenced polymorphism data

Spatial genetic variation is shaped in part by an organisms dispersal ability. We present a deep learning tool, disperseNN2, for estimating the mean per-generation dispersal distance from georeferenced polymorphism data. Our neural network performs feature extraction on pairs of genotypes, and uses the geographic information that comes with each sample. These attributes led disperseNN2 to outperform a state-of-the-art deep learning method that does not use explicit spatial information: the mean relative absolute error was reduced by 33% and 48% using sample sizes of 10 and 100 individuals, respectively. disperseNN2 is particularly useful for non-model organisms or systems with sparse genomic resources, as it uses unphased, single nucleotide polymorphisms as its input. The software is open source and available from https://github.com/kr-colab/disperseNN2, with documentation located at https://dispersenn2.readthedocs.io/en/latest/.

bioinformatics↗

bioRxiv2023

Evaluating evidence for co-geography in the Anopheles Plasmodium host-parasite system

The often tight association between parasites and their hosts means that under certain scenarios, the evolutionary histories of the two species can become closely coupled both through time and across space. Using spatial genetic inference, we identify a potential signal of common dispersal patterns in the Anopheles gambiae and Plasmodium falciparum host-parasite system as seen through a between-species correlation of the differences between geographic sampling location and geographic location predicted from the genome. This correlation may be due to coupled dispersal dynamics between host and parasite, but may also reflect statistical artifacts due to uneven spatial distribution of sampling locations. Using continuous-space population genetics simulations, we investigate the degree to which uneven distribution of sampling locations leads to bias in prediction of spatial location from genetic data and implement methods to counter this effect. We demonstrate that while algorithmic bias presents a problem in inference from spatio-genetic data, the correlation structure between A. gambiae and P. falciparum predictions cannot be attributed to spatial bias alone, and is thus likely a genetic signal of co-dispersal in a host-parasite system.

evolutionary biology↗

bioRxiv2023

Shared evolutionary processes shape landscapes of genomic variation in the great apes

For at least the past five decades population genetics, as a field, has worked to describe the precise balance of forces that shape patterns of variation in genomes. The problem is challenging because modelling the interactions between evolutionary processes is difficult, and different processes can impact genetic variation in similar ways. In this paper, we describe how diversity and divergence between closely related species change with time, using correlations between landscapes of genetic variation as a tool to understand the interplay between evolutionary processes. We find strong correlations between landscapes of diversity and divergence in a well sampled set of great ape genomes, and explore how various processes such as incomplete lineage sorting, mutation rate variation, GC-biased gene conversion and selection contribute to these correlations. Through highly realistic, chromosome-scale, forward-in-time simulations we show that the landscapes of diversity and divergence in the great apes are too well correlated to be explained via strictly neutral processes alone. Our best fitting simulation includes both deleterious and beneficial mutations in functional portions of the genome, in which 9% of fixations within those regions is driven by positive selection. This study provides a framework for modelling genetic variation in closely related species, an approach which can shed light on the complex balance of forces that have shaped genetic variation.

evolutionary biology↗

bioRxiv2022

Standing genetic variation and chromosome differences drove rapid ecotype formation in a major malaria vector

Species distributed across heterogeneous environments often evolve locally adapted ecotypes, but understanding of the genetic mechanisms involved in their formation and maintenance in the face of gene flow is incomplete. In Burkina Faso, the major African malaria mosquito Anopheles funestus comprises two strictly sympatric and morphologically indistinguishable yet karyotypically differentiated forms reported to differ in ecology and behavior. However, knowledge of the genetic basis and environmental determinants of An. funestus diversification was impeded by lack of modern genomic resources. Here, we applied deep whole-genome sequencing and analysis to test the hypothesis that these two forms are ecotypes differentially adapted to breeding in natural swamps versus irrigated rice fields. We demonstrate genome-wide differentiation despite extensive microsympatry, synchronicity, and ongoing hybridization. Demographic inference supports a split only ~1,300 years ago, closely following the massive expansion of domesticated African rice cultivation ~1,850 years ago. Regions of highest divergence, concentrated in chromosomal inversions, were under selection during lineage splitting, consistent with local adaptation. The origin of nearly all variation implicated in adaptation, including chromosomal inversions, substantially predates the ecotype split, suggesting that rapid adaptation was fueled mainly by standing genetic variation. Sharp inversion frequency differences likely facilitated adaptive divergence between ecotypes, both by suppressing recombination between opposing chromosomal orientations of the two ecotypes, and by maximizing recombination within the structurally monomorphic rice ecotype. Our results align with growing evidence from diverse taxa that rapid ecological diversification can arise from evolutionarily old structural genetic variants that modify genetic recombination. Significance StatementLocal adaptation to heterogeneous environments is pervasive, but its underlying genetic basis is incompletely understood. Within a major African malaria vector, An. funestus, are two chromosomally differentiated groups that are co-localized, morphologically indistinguishable, and reported to differ both in ecology and behavior relevant to malaria transmission and control. Progress in understanding the genetic basis and environmental determinants of vector diversification was impeded by the lack of modern genomic resources. Here we perform deep whole-genome sequencing on individuals from these groups, establishing that they are differentiated genome-wide in a manner consistent with recent ecotype formation associated with the exploitation of a new anthropogenic larval habitat. Such rapid malaria vector diversification was facilitated by standing genetic variation, including evolutionarily old chromosomal rearrangements.

evolutionary biology↗

bioRxiv2022

Expanding the stdpopsim species catalog, and lessons learned forrealistic genome simulations

Simulation is a key tool in population genetics for both methods development and empirical research, but producing simulations that recapitulate the main features of genomic data sets remains a major obstacle. Today, more realistic simulations are possible thanks to large increases in the quantity and quality of available genetic data, and to the sophistication of inference and simulation software. However, implementing these simulations still requires substantial time and specialized knowledge. These challenges are especially pronounced for simulating genomes for species that are not well-studied, since it is not always clear what information is required to produce simulations with a level of realism sufficient to confidently answer a given question. The community-developed framework stdpopsim seeks to lower this barrier by facilitating the simulation of complex population genetic models using up-to-date information. The initial version of stdpopsim focused on establishing this framework using six well-characterized model species (Adrion et al., 2020). Here, we report on major improvements made in the new release of stdpopsim (version 0.2), which includes a significant expansion of the species catalog and substantial additions to simulation capabilities. Features added to improve the realism of the simulated genomes include non-crossover recombination and provision of species-specific genomic annotations. Through community-driven efforts, we expanded the number of species in the catalog more than three-fold and broadened coverage across the tree of life. During the process of expanding the catalog, we have identified common sticking points and developed best practices for setting up genome-scale simulations. We describe the input data required for generating a realistic simulation, suggest good practices for obtaining the relevant information from the literature, and discuss common pitfalls and major considerations. These improvements to stdpopsim aim to further promote the use of realistic whole-genome population genetic simulations, especially in non-model organisms, making them available, transparent, and accessible to everyone.

bioinformatics↗

bioRxiv2022

Dispersal inference from population genetic variation using a convolutional neural network

The geographic nature of biological dispersal shapes patterns of genetic variation over landscapes, making it possible to infer properties of dispersal from genetic variation data. Here we present an inference tool that uses geographically distributed genotype data in combination with a convolutional neural network to estimate a critical population parameter: the mean per-generation dispersal distance. Using extensive simulation, we show that our deep learning approach is competitive with or outperforms state-of-the-art methods, particularly at small sample sizes. In addition, we evaluate varying nuisance parameters during training--including population density, demographic history, habitat size, and sampling area--and show that this strategy is effective for estimating dispersal distance when other model parameters are unknown. Whereas competing methods depend on information about local population density or accurate identification of identity-by-descent tracts, our method uses only single-nucleotide-polymorphism data and the spatial scale of sampling as input. Strikingly, and unlike other methods, our method does not use the geographic coordinates of the genotyped individuals. These features make our method, which we call "disperseNN", a potentially valuable new tool for estimating dispersal distance in non-model systems with whole genome data or reduced representation data. We apply disperseNN to 12 different species with publicly available data, yielding reasonable estimates for most species. Importantly, our method estimated consistently larger dispersal distances than mark-recapture calculations in the same species, which may be due to the limited geographic sampling area covered by some mark-recapture studies. Thus genetic tools like ours complement direct methods for improving our understanding of dispersal.

evolutionary biology↗

bioRxiv2022

Cell types and molecular architecture of the octopus visual system

Cephalopods have a remarkable visual system, with a camera-type eye, high acuity vision, and a wide range of sophisticated visual behaviors. However, the cephalopod brain is organized dramatically differently from that of vertebrates, as well as other invertebrates, and little is known regarding the cell types and molecular determinants of their visual system organization beyond neuroanatomical descriptions. Here we present a comprehensive single-cell molecular atlas of the octopus optic lobe, which is the primary visual processing structure in the cephalopod brain. We combined single-cell RNA sequencing with RNA fluorescence in situ hybridization to both identify putative molecular cell types and determine their anatomical and spatial organization within the optic lobe. Our results reveal six major neuronal cell classes identified by neurotransmitter/neuropeptide usage, in addition to non-neuronal and immature neuronal populations. Moreover, we find that additional markers divide these neuronal classes into subtypes with distinct anatomical localizations, revealing cell type diversity and a detailed laminar organization within the optic lobe. We also delineate the immature neurons within this continuously growing tissue into subtypes defined by evolutionarily conserved fate specification genes as well as novel cephalopod- and octopus-specific genes. Together, these findings outline the organizational logic of the octopus visual system, based on functional determinants, laminar identity, and developmental markers/pathways. The resulting atlas presented here delineates the "parts list" of the neural circuits used for vision in octopus, providing a platform for investigations into the development and function of the octopus visual system as well as the evolution of visual processing. HighlightsO_LISingle-cell RNA sequencing coupled with RNA fluorescence in situ hybridization produces a molecular taxonomy of cell types in the octopus visual system. C_LIO_LISix major neuronal cell classes are delineated based on neurotransmitters/neuropeptides, and are further subdivided based on laminar organization and additional marker genes. C_LIO_LIImmature neurons are divided into multiple transcriptional subgroups that correspond to mature cell types, delineated by expression of genes known for their developmental roles in other organisms as well as apparent novel genes. C_LIO_LIThis atlas provides the foundation for future studies of the function, development, and comparative evolution of visual processing in cephalopods. C_LI

neuroscience↗

bioRxiv2021

Efficient ancestry and mutation simulation with msprime 1.0

Stochastic simulation is a key tool in population genetics, since the models involved are often analytically intractable and simulation is usually the only way of obtaining ground-truth data to evaluate inferences. Because of this necessity, a large number of specialised simulation programs have been developed, each filling a particular niche, but with largely overlapping functionality and a substantial duplication of effort. Here, we introduce msprime version 1.0, which efficiently implements ancestry and mutation simulations based on the succinct tree sequence data structure and tskit library. We summarise msprimes many features, and show that its performance is excellent, often many times faster and more memory efficient than specialised alternatives. These high-performance features have been thoroughly tested and validated, and built using a collaborative, open source development model, which reduces duplication of effort and promotes software quality via community engagement.

genetics↗

bioRxiv2020

Visualizing Population Structure with Variational Autoencoders

Dimensionality reduction is a common tool for visualization and inference of population structure from genotypes, but popular methods either return too many dimensions for easy plotting (PCA) or fail to preserve global geometry (t-SNE and UMAP). Here we explore the utility of variational autoencoders (VAEs) - generative machine learning models in which a pair of neural networks seek to first compress and then recreate the input data - for visualizing population genetic variation. VAEs incorporate non-linear relationships, allow users to define the dimensionality of the latent space, and in our tests preserve global geometry better than t-SNE and UMAP. Our implementation, which we call popvae, is available as a command-line python program at github.com/kr-colab/popvae. The approach yields latent embeddings that capture subtle aspects of population structure in humans and Anopheles mosquitoes, and can generate artificial genotypes characteristic of a given sample or population.

genetics↗

bioRxiv2020

Sexually Antagonistic Selection on Genetic Variation is Rare in Humans

Sex and sexual differentiation are ubiquitous across the tree of life. Because females and males often have substantially different functional requirements, we expect selection to differ between the sexes. Recent studies in diverse species, including humans, suggest sexually antagonistic viability selection creates allele frequency differences between the sexes at many different loci. However, theory and population-level simulations indicate that sex-specific differences in viability would need to be very extreme in order to produce and maintain reported levels of between-sex allelic differentiation. We address this paradox between theoretical predictions and empirical observations by evaluating evidence for sexually antagonistic viability selection on autosomal loci in humans using the largest cohort to date (UK Biobank, n=438,427) along with a second large, independent cohort (BioVU, n=93,864). We performed association tests between genetically ascertained sex and genotypes. Although we found dozens of genome-wide significant associations, none replicated across samples. Moreover, closer inspection revealed that all associations are likely due to cross-hybridization with sex chromosome regions during genotyping. We report loci with potential for mis-hybridization found on commonly used genotyping platforms that should be carefully considered in future genetic studies of sex-specific differences. Despite being well-powered to detect allele frequency differences of up to 0.8% between the sexes, we do not detect evidence for this signature of sexually antagonistic viability selection on autosomal variation. These findings suggest a lack of strong ongoing sexually antagonistic viability selection acting on single locus autosomal variation in humans.

evolutionary biology↗

bioRxiv2019

A community-maintained standard library of population genetic models

The explosion in population genomic data demands ever more complex modes of analysis, and increasingly these analyses depend on sophisticated simulations. Recent advances in population genetic simulation have made it possible to simulate large and complex models, but specifying such models for a particular simulation engine remains a difficult and error-prone task. Computational genetics researchers currently re-implement simulation models independently, leading to inconsistency and duplication of effort. This situation presents a major barrier to empirical researchers seeking to use simulations for power analyses of upcoming studies or sanity checks on existing genomic data. Population genetics, as a field, also lacks standard benchmarks by which new tools for inference might be measured. Here we describe a new resource, stdpopsim, that attempts to rectify this situation. Stdpopsim is a community-driven open source project, which provides easy access to a growing catalog of published simulation models from a range of organisms and supports multiple simulation engine backends. This resource is available as a well-documented python library with a simple command-line interface. We share some examples demonstrating how stdpopsim can be used to systematically compare demographic inference methods, and we encourage a broader community of developers to contribute to this growing resource.

evolutionary biology↗

bioRxiv2019

Predicting Geographic Location From Genetic Variation with Deep Neural Networks

Most organisms are more closely related to nearby than distant members of their species, creating spatial autocorrelations in genetic data. This allows us to predict the location of origin of a genetic sample by comparing it to a set of samples of known geographic origin. Here we describe a deep learning method, which we call Locator, to accomplish this task faster and more accurately than existing approaches. In simulations, Locator infers sample location to within 4.1 generations of dispersal and runs at least an order of magnitude faster than a recent model-based approach. We leverage Locators computational efficiency to predict locations separately in windows across the genome, which allows us to both quantify uncertainty and describe the mosaic ancestry and patterns of geographic mixing that characterize many populations. Applied to whole-genome sequence data from Plasmodium parasites, Anopheles mosquitoes, and global human populations, this approach yields median test errors of 16.9km, 5.7km, and 85km, respectively.

genetics↗

bioRxiv2019

Inferring the landscape of recombination using recurrent neural networks

Accurately inferring the genome-wide landscape of recombination rates in natural populations is a central aim in genomics, as patterns of linkage influence everything from genetic mapping to understanding evolutionary history. Here we describe ReLERNN, a deep learning method for estimating a genome-wide recombination map that is accurate even with small numbers of pooled or individually sequenced genomes. Rather than use summaries of linkage disequilibrium as its input, ReLERNN takes columns from a genotype alignment, which are then modeled as a sequence across the genome using a recurrent neural network. We demonstrate that ReLERNN improves accuracy and reduces bias relative to existing methods and maintains high accuracy in the face of demographic model misspecification, missing genotype calls, and genome inaccessibility. We apply ReLERNN to natural populations of African Drosophila melanogaster and show that genome-wide recombination landscapes, while largely correlated among populations, exhibit important population-specific differences. Lastly, we connect the inferred patterns of recombination with the frequencies of major inversions segregating in natural Drosophila populations.

evolutionary biology↗

bioRxiv2019

Space is the Place: Effects of Continuous Spatial Structure on Analysis of Population Genetic Data

Real geography is continuous, but standard models in population genetics are based on discrete, well-mixed populations. As a result many methods of analyzing genetic data assume that samples are a random draw from a well-mixed population, but are applied to clustered samples from populations that are structured clinally over space. Here we use simulations of populations living in continuous geography to study the impacts of dispersal and sampling strategy on population genetic summary statistics, demographic inference, and genome-wide association studies. We find that most common summary statistics have distributions that differ substantially from that seen in well-mixed populations, especially when Wrights neighborhood size is less than 100 and sampling is spatially clustered. Stepping-stone models reproduce some of these effects, but discretizing the landscape introduces artifacts which in some cases are exacerbated at higher resolutions. The combination of low dispersal and clustered sampling causes demographic inference from the site frequency spectrum to infer more turbulent demographic histories, but averaged results across multiple simulations were surprisingly robust to isolation by distance. We also show that the combination of spatially autocorrelated environments and limited dispersal causes genome-wide association studies to identify spurious signals of genetic association with purely environmentally determined phenotypes, and that this bias is only partially corrected by regressing out principal components of ancestry. Last, we discuss the relevance of our simulation results for inference from genetic variation in real organisms.

genetics↗

bioRxiv2019

Discovery of ongoing selective sweeps within Anopheles mosquito populations using deep learning

Identification of partial sweeps, which include both hard and soft sweeps that have not currently reached fixation, provides crucial information about ongoing evolutionary responses. To this end, we introduce partialS/HIC, a deep learning method to discover selective sweeps from population genomic data. partialS/HIC uses a convolutional neural network for image processing, which is trained with a large suite of summary statistics derived from coalescent simulations incorporating population-specific history, to distinguish between completed versus partial sweeps, hard versus soft sweeps, and regions directly affected by selection versus those merely linked to nearby selective sweeps. We perform several simulation experiments under various demographic scenarios to demonstrate partialS/HICs performance, which exhibits excellent resolution for detecting partial sweeps. We also apply our classifier to whole genomes from eight mosquito populations sampled across sub-Saharan Africa by the Anopheles gambiae 1000 Genomes Consortium, elucidating both continent-wide patterns as well as sweeps unique to specific geographic regions. These populations have experienced intense insecticide exposure over the past two decades, and we observe a strong overrepresentation of sweeps at insecticide resistance loci. Our analysis thus provides a list of candidate adaptive loci that may be relevant to mosquito control efforts. More broadly, our supervised machine learning approach introduces a method to distinguish between completed and partial sweeps, as well as between hard and soft sweeps, under a variety of demographic scenarios. As whole-genome data rapidly accumulate for a greater diversity of organisms, partialS/HIC addresses an increasing demand for useful selection scan tools that can track in-progress evolutionary dynamics.

evolutionary biology↗

bioRxiv2018

diploS/HIC: an updated approach to classifying selective sweeps

Identifying selective sweeps in populations that have complex demographic histories remains a difficult problem in population genetics. We previously introduced a supervised machine learning approach, S/HIC, for finding both hard and soft selective sweeps in genomes on the basis of patterns of genetic variation surrounding a window of the genome. While S/HIC was shown to be both powerful and precise, the utility of S/HIC was limited by the use of phased genomic data as input. In this report we describe a deep learning variant of our method, diploS/HIC, that uses unphased genotypes to accurately classify genomic windows. diploS/HIC is shown to be quite powerful even at moderate to small sample sizes

evolutionary biology↗

bioRxiv2017

Machine Learning for Population Genetics: A New Paradigm

As population genomic datasets grow in size, researchers are faced with the daunting task of making sense of a flood of information. To keep pace with this explosion of data, computational methodologies for population genetic inference are rapidly being developed to best utilize genomic sequence data. In this review we discuss a new paradigm that has emerged in computational population genomics: that of supervised machine learning. We review the fundamentals of machine learning, discuss recent applications of supervised machine learning to population genetics that outperform competing methods, and describe promising future directions in this area. Ultimately, we argue that supervised machine learning is an important and underutilized tool that has considerable potential for the world of evolutionary genomics.

evolutionary biology↗

bioRxiv2017

Supervised machine learning reveals introgressed loci in the genomes of Drosophila simulans and D. sechellia

Hybridization and gene flow between species appears to be common. Even though it is clear that hybridization is widespread across all surveyed taxonomic groups, the magnitude and consequences of introgression are still largely unknown. Thus it is crucial to develop the statistical machinery required to uncover which genomic regions have recently acquired haplotypes via introgression from a sister population. We developed a novel machine learning framework, called FILET (Finding Introgressed Loci via Extra-Trees) capable of revealing genomic introgression with far greater power than competing methods. FILET works by combining information from a number of population genetic summary statistics, including several new statistics that we introduce, that capture patterns of variation across two populations. We show that FILET is able to identify loci that have experienced gene flow between related species with high accuracy, and in most situations can correctly infer which population was the donor and which was the recipient. Here we describe a data set of outbred diploid Drosophila sechellia genomes, and combine them with data from D. simulans to examine recent introgression between these species using FILET. Although we find that these populations may have split more recently than previously appreciated, FILET confirms that there has indeed been appreciable recent introgression (some of which might have been adaptive) between these species, and reveals that this gene flow is primarily in the direction of D. simulans to D. sechellia.\n\nAUTHOR SUMMARYUnderstanding the extent to which species or diverged populations hybridize in nature is crucially important if we are to understand the speciation process. Accordingly numerous research groups have developed methodology for finding the genetic evidence of such introgression. In this report we develop a supervised machine learning approach for uncovering loci which have introgressed across species boundaries. We show that our method, FILET, has greater accuracy and power than competing methods in discovering introgression, and in addition can detect the directionality associated with the gene flow between species. Using whole genome sequences from Drosophila simulans and Drosophila sechellia we show that FILET discovers quite extensive introgression between these species that has occurred mostly from D. simulans to D. sechellia. Our work highlights the complex process of speciation even within a well-studied system and points to the growing importance of supervised machine learning in population genetics.

evolutionary biology↗