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.

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, 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 realistic and efficient spatial, individual-based simulations and avoid common pitfalls.

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.

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.

Cephalopod Sex Determination and its Ancient Evolutionary Origin.

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.

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

For at least the past 5 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 modeling 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.

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.

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

Spatial genetic variation is shaped in part by an organism's 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.

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

Spatial genetic variation is shaped in part by an organism's 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.

Evaluating evidence for co-geography in the 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 and 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.

Expanding the stdpopsim species catalog, and lessons learned for realistic 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 datasets remains a major obstacle. Today, more realistic simulations are possible thanks to large increases in the quantity and quality of available genetic data, and the sophistication of inference and simulation software. However, implementing these simulations still requires substantial time and specialized knowledge.

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.

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

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 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 diversification was impeded by lack of modern genomic resources.

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.

Cell types and molecular architecture of the Octopus bimaculoides visual system.

Cephalopods have a remarkable visual system, with a camera-type eye and high acuity vision that they use for a wide range of sophisticated visually driven behaviors. However, the cephalopod brain is organized dramatically differently from that of vertebrates and invertebrates, and beyond neuroanatomical descriptions, little is known regarding the cell types and molecular determinants of their visual system organization. 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.

Biomechanical guidance can improve accuracy of reduction for intra-articular tibia plafond fractures and reduce joint contact stress.

Articular fracture malreduction increases posttraumatic osteoarthritis (PTOA) risk by elevating joint contact stress. A new biomechanical guidance system (BGS) that provides intraoperative assessment of articular fracture reduction and joint contact stress based solely on a preoperative computed tomography (CT) and intraoperative fluoroscopy may facilitate better fracture reduction. The objective of this proof-of-concept cadaveric study was to test this premise while characterizing BGS performance.

The Effect of Progressive Lateral Column Lengthening in a Novel Stage II-B Flatfoot Cadaveric Model Evaluated Using Software-Guided Radiographic Measurements of Foot Alignment.

Background: This work used software-guided radiographic measurement to assess the effects of progressive lateral column lengthening (LCL) on restoring alignment in a novel cadaveric model of stage II-B flatfoot deformity.

Methods: A stage II-B flatfoot was created in 8 cadaveric specimens by transecting the spring ligament complex, anterior deltoid, and interosseous talocalcaneal and cervical ligaments. Weightbearing computed tomographic (WBCT) scans were performed with specimens under 450 N of compressive load in the intact, flat, and 6-, 8-, and 10-mm lateral column-lengthening conditions.

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, a large number of specialized 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.

Longitudinal Relationship Between Tibiofemoral Contact Stress at Baseline and Worsening of Knee Pain Over 84 Months in the Multicenter Osteoarthritis Study.

Objective: The aim of the study was to determine whether tibiofemoral contact stress predicts risk for worsening knee pain over 84 ms in adults aged 50-79 yrs with or at elevated risk for knee osteoarthritis.

Design: Baseline tibiofemoral contact stress was estimated using discrete element analysis. Other baseline measures included weight, height, hip-knee-ankle alignment, Kellgren-Lawrence grade, and Western Ontario and McMaster Universities Osteoarthritis Index pain subscale.

Evaluating human autosomal loci for sexually antagonistic viability selection in two large biobanks.

Sex and sexual differentiation are pervasive 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 that sexually antagonistic viability selection creates allele frequency differences between the sexes at many different loci.

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 nonlinear relationships, allow users to define the dimensionality of the latent space, and in our tests preserve global geometry better than t-SNE and UMAP.

Association between foot thermal responses and shear forces during turning gait in young adults.

Background: The human foot typically changes temperature between pre and post-locomotion activities. However, the mechanisms responsible for temperature changes within the foot are currently unclear. Prior studies indicate that shear forces may increase foot temperature during locomotion.

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.

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.

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.