The trouble with isolation by distance

The genetic population structure of many species is characterised by a pattern of isolation by distance (IBD): due to limited dispersal, individuals that are geographically close tend to be genetically more similar than individuals that are far apart. Despite the ubiquity of IBD in nature, many commonly used statistical tests are based on a null model that is completely non-spatial, the Island model. Here, I argue that patterns of spatial autocorrelation deriving from IBD present a problem for such tests as it can severely bias their outcome. I use simulated data to illustrate this problem for two widely used types of tests: tests of hierarchical population structure and the detection of loci under selection. My results show that for both types of tests the presence of IBD can indeed lead to a large number of false positives. I therefore argue that all analyses in a study should take the spatial dependence in the data into account, unless it can be shown that there is no spatial autocorrelation in the allele frequency distribution that is under investigation. Thus, it is urgent to develop additional statistical approaches that are based on a spatially explicit null model instead of the non-spatial Island model.     

Structural damage in rheumatoid arthritis, psoriatic arthritis, and ankylosing spondylitis: traditional views, novel insights gained from TNF blockade, and concepts for the future

Structural changes of bone and cartilage are a hallmark of inflammatory joint diseases such as rheumatoid arthritis (RA), psoriatic arthritis (PsA), and ankylosing spondylitis (AS). Despite certain similarities - in particular, inflammation as the driving force for structural changes - the three major inflammatory joint diseases show considerably different pathologies. Whereas RA primarily results in bone and cartilage resorption, PsA combines destructive elements with anabolic bone responses, and AS is the prototype of a hyper-responsive joint disease associated with substantial bone and cartilage apposition. In the present review we summarize the clinical picture and pathophysiologic processes of bone and cartilage damage in RA, PsA, and AS, we describe the key insights obtained from the introduction of TNF blockade, and we discuss the future challenges and frontiers of structural damage in arthritis.     

Mapping human- and bear-centered perspectives on coexistence using a participatory Bayesian framework

Coexistence with wildlife is becoming a key challenge in Europe as populations of large carnivores recover in human-dominated landscapes. Modeling the spatial distribution of conditions for human-bear coexistence can help support conservation by identifying priority areas and measures to support coexistence, but existing models often only address risks either to humans or to large carnivores. In this study, we developed a participatory modeling process that incorporates both human-centered and large carnivore-centered perspectives on coexistence and applied it to a case study of coexistence between humans and the endangered Apennine brown bears (Ursus arctos marsicanus) in Italy. Local and expert knowledge, as well as available data on bear habitats and land use, were integrated into a spatially explicit Bayesian network. This model is used to predict and map the tolerance to bears from the human perspective and the risk of fitness loss from the bear perspective. We found that conditions for human-bear coexistence vary between human communities and are spatially heterogeneous at the local scale, depending on ecological factors, social factors influencing the level of tolerance in community, such as people’s emotions and knowledge, economic factors, such as livelihoods, and policies such as damage compensation. The participatory modeling approach allowed us to integrate perceptions of local people, expert assessments, and spatial data, and can help bridge the gap between science and conservation practice. The resulting coexistence maps can inform conservation decisions, and can be updated as new information becomes available. Our modeling approach could help to efficiently target measures for improving human-large carnivore coexistence in different settings in a site-specific manner.     

Populations with Explicit Borders in Space and Time: Concept,Terminology, and Estimation of Characteristic Parameters

Biologists studying short-lived organisms have become aware of the need to recognize an explicit temporal extend of a population over a considerable time. In this article we outline the concept and the realm of populations with explicit spatial and temporary boundaries. We call such populations “temporally bounded populations”. In the concept, time is of the same importance as space in terms of a dimension to which a population is restricted. Two parameters not available for populations that are only spatially defined characterise temporally bounded populations: total population size, which is the total number of individuals present within the temporal borders, and total residence time, which is the sum of the residence times of all individuals. We briefly review methods to estimate these parameters. We illustrate the concept for the large blue butterfly (Maculinea nausithous) and outline insights into ecological and conservation-relevant processes that cannot be gained without the use of the concept.     

Enhanced Localization of Genetic Samples through Linkage-Disequilibrium Correction

Characterizing the spatial patterns of genetic diversity in human populations has a wide range of applications, from detecting genetic mutations associated with disease to inferring human history. Current approaches, including the widely used principal-component analysis, are not suited for the analysis of linked markers, and local and long-range linkage disequilibrium (LD) can dramatically reduce the accuracy of spatial localization when unaccounted for. To overcome this, we have introduced an approach that performs spatial localization of individuals on the basis of their genetic data and explicitly models LD among markers by using a multivariate normal distribution. By leveraging external reference panels, we derive closed-form solutions to the optimization procedure to achieve a computationally efficient method that can handle large data sets. We validate the method on empirical data from a large sample of European individuals from the POPRES data set, as well as on a large sample of individuals of Spanish ancestry. First, we show that by modeling LD, we achieve accuracy superior to that of existing methods. Importantly, whereas other methods show decreased performance when dense marker panels are used in the inference, our approach improves in accuracy as more markers become available. Second, we show that accurate localization of genetic data can be achieved with only a part of the genome, and this could potentially enable the spatial localization of admixed samples that have a fraction of their genome originating from a given continent. Finally, we demonstrate that our approach is resistant to distortions resulting from long-range LD regions; such distortions can dramatically bias the results when unaccounted for.     

Time and space in the middle paleolithic: Spatial structure and occupation dynamics of seven open‐air sites

The spatial structure of archeological sites can help reconstruct the settlement dynamics of hunter‐gatherers by providing information on the number and length of occupations. This study seeks to access this information through a comparison of seven sites. These sites are open‐air and were all excavated over large spatial areas, up to 2,000 m², and are therefore ideal for spatial analysis, which was done using two complementary methods, lithic refitting and density zones. Both methods were assessed statistically using confidence intervals. The statistically significant results from each site were then compiled to evaluate trends that occur across the seven sites. These results were used to assess the “spatial consistency” of each assemblage and, through that, the number and duration of occupations. This study demonstrates that spatial analysis can be a powerful tool in research on occupation dynamics and can help disentangle the many occupations that often make up an archeological assemblage.     

Psoriatic arthritis: recent progress in pathophysiology and drug development

Psoriatic arthritis (PsA) is the second most common inflammatory arthropathy, after rheumatoid arthritis diagnosis, in early arthritis clinics. Most patients have established psoriasis, often for years, prior to the onset of joint pain and swelling; in addition, associated features of nail disease, dactylitis, enthesitis, spondylitis or uveitis may be present. Psoriasis may not be immediately apparent, as small or patchy lesions may occur in the scalp or perineum. PsA presents as a symmetrical polyarthritis, similar to rheumatoid arthritis, or an asymmetrical oligoarthritis with a predilection for the distal interphalangeal joints. Spinal involvement is similar, although not identical, to ankylosing spondylitis. Joint damage occurs early; up to 50% of PsA patients have an 11% annual erosion rate in the first 2 years of disease duration, suggesting it is not a benign condition. There have been significant advances in our understanding of PsA pathogenesis in recent years, in the areas of genetics and molecular biology, implicating both the innate and the adaptive immune systems. This has lead to the introduction of evidence-based targeted therapy, primarily with tumour necrosis factor inhibitor (TNFi) agents. Therapy with disease-modifying anti-rheumatic drugs, such as methotrexate and leflunomide, remains the first-choice therapeutic intervention, even though there are few randomised controlled trials with these agents. In contrast, a number of successful studies of TNFi agents demonstrate excellent efficacy, in combination with methotrexate, and several novel agents are currently in development for the treatment of PsA.     

A generalized transition model for grouped longitudinal categorical data

Transition models are an important framework that can be used to model longitudinal categorical data. They are particularly useful when the primary interest is in prediction. The available methods for this class of models are suitable for the cases in which responses are recorded individually over time. However, in many areas, it is common for categorical data to be recorded as groups, that is, different categories with a number of individuals in each. As motivation we consider a study in insect movement and another in pig behaviou. The first study was developed to understand the movement patterns of female adults of Diaphorina citri, a pest of citrus plantations. The second study investigated how hogs behaved under the influence of environmental enrichment. In both studies, the number of individuals in different response categories was observed over time. We propose a new framework for considering the time dependence in the linear predictor of a generalized logit transition model using a quantitative response, corresponding to the number of individuals in each category. We use maximum likelihood estimation and present the results of the fitted models under stationarity and non-stationarity assumptions, and use recently proposed tests to assess non-stationarity. We evaluated the performance of the proposed model using simulation studies under different scenarios, and concluded that our modeling framework represents a flexible alternative to analyze grouped longitudinal categorical data.     

Hybrid computational modeling demonstrates the utility of simulating complex cellular networks in type 1 diabetes

Persistent destruction of pancreatic β-cells in type 1 diabetes (T1D) results from multifaceted pancreatic cellular interactions in various phase progressions. Owing to the inherent heterogeneity of coupled nonlinear systems, computational modeling based on T1D etiology help achieve a systematic understanding of biological processes and T1D health outcomes. The main challenge is to design such a reliable framework to analyze the highly orchestrated biology of T1D based on the knowledge of cellular networks and biological parameters. We constructed a novel hybrid in-silico computational model to unravel T1D onset, progression, and prevention in a non-obese-diabetic mouse model. The computational approach that integrates mathematical modeling, agent-based modeling, and advanced statistical methods allows for modeling key biological parameters and time-dependent spatial networks of cell behaviors. By integrating interactions between multiple cell types, model results captured the individual-specific dynamics of T1D progression and were validated against experimental data for the number of infiltrating CD8⁺T-cells. Our simulation results uncovered the correlation between five auto-destructive mechanisms identifying a combination of potential therapeutic strategies: the average lifespan of cytotoxic CD8⁺T-cells in islets; the initial number of apoptotic β-cells; recruitment rate of dendritic-cells (DCs); binding sites on DCs for naïve CD8⁺T-cells; and time required for DCs movement. Results from therapy-directed simulations further suggest the efficacy of proposed therapeutic strategies depends upon the type and time of administering therapy interventions and the administered amount of therapeutic dose. Our findings show modeling immunogenicity that underlies autoimmune T1D and identifying autoantigens that serve as potential biomarkers are two pressing parameters to predict disease onset and progression.     

The value of space‐for‐time substitution for studying fine‐scale microevolutionary processes

When the drivers of biological turnover in space are the same as those that drive turnover through time, space can be substituted for time to model how patterns of variation are predicted to change into the future. These space‐for‐time substitutions are widely used in ecological modeling but have only recently been applied to the study of microevolutionary processes, particularly over relatively fine spatial and temporal scales. Here, we review recent examples that have employed space‐for‐time substitution to study genetic patterns on stationary and non‐stationary landscapes and examine whether space can reliably substitute for time in studies of population divergence, genetic structure, and adaptive evolution. Although there are only a relatively few examples, several recent studies were excellently crafted to provide valuable insights into the conditions governing the validity of space‐for‐time substitutions applied to population genetic data. We found that, although caution should be taken, space‐for‐time substitutions appear valid for studying microevolutionary processes on both stationary and non‐stationary landscapes. Further studies can help to evaluate the conditions under which space‐for‐time substitutions are reliable. When these methods are reliable, they will play an important role in modeling genetic responses to environmental change, population viability on non‐stationary landscapes, and patterns of divergence and adaptation.     

Measurement error caused by spatial misalignment in environmental epidemiology

In many environmental epidemiology studies, the locations and/or times of exposure measurements and health assessments do not match. In such settings, health effects analyses often use the predictions from an exposure model as a covariate in a regression model. Such exposure predictions contain some measurement error as the predicted values do not equal the true exposures. We provide a framework for spatial measurement error modeling, showing that smoothing induces a Berkson-type measurement error with nondiagonal error structure. From this viewpoint, we review the existing approaches to estimation in a linear regression health model, including direct use of the spatial predictions and exposure simulation, and explore some modified approaches, including Bayesian models and out-of-sample regression calibration, motivated by measurement error principles. We then extend this work to the generalized linear model framework for health outcomes. Based on analytical considerations and simulation results, we compare the performance of all these approaches under several spatial models for exposure. Our comparisons underscore several important points. First, exposure simulation can perform very poorly under certain realistic scenarios. Second, the relative performance of the different methods depends on the nature of the underlying exposure surface. Third, traditional measurement error concepts can help to explain the relative practical performance of the different methods. We apply the methods to data on the association between levels of particulate matter and birth weight in the greater Boston area.     

The use of clove oil as an anaesthetic and method for sampling intertidal rockpool fishes

A suitable concentration of clove oil for anaesthetizing eight common Australian intertidal fishes was 40 mg l⁻¹. Induction and recovery times at this concentration varied markedly interspecifically, although were <3 and 5 min, respectively for all but Enneapterygius rufopileus . The number of species in clove oil samples was remarkably similar to bilge pumping samples in both small and large pools, although the number of individuals was significantly lower than bilge pumping in large pools. Number of species and individuals recorded by visual census differed from clove oil and bilge pumping methods in both large and small pools. Failure to detect all fish in pools by clove oil sampling and visual census was due to the secretive behaviour of many species and the tendency for such species as Bathygobius cocosensis not to emerge from under rocks during induction to anaesthesia. Complete draining of pools by bilge pumping, although time consuming, was a more accurate method of sampling rockpool fish assemblages than clove oil. However, clove oil is an effective stand-alone sampling method because the number of fish and individuals are remarkably similar to samples obtained by bilge pumping. Clove oil is a favourable anaesthetic for temperate Australian intertidal fishes as mortality is extremely low, it is inexpensive and user-safe, and induction and recovery times are short, although still sufficient to record biological information and identifications of fish whilst under anaesthesia.     

Leukemia-derived exosomes: Bringing oncogenic signals to blood cells

Leukemia is a cancer, which is derived from leukocytes and precursors of leukocytes in the bone marrow. A large number of pivotal biological processes are linked to leukemia pathogenesis. More insights into these mechanisms can provide a better developing pharmacological platform for patients with leukemia. Among the different players in leukemia pathogenesis, exosomes have appeared as a new biological vehicle, which can transfer oncogenic signals to blood cells. Exosomes are nano-carriers, which enable transferring numerous cargos such as DNA fragments, RNAs, messenger RNAs, microRNAs, long noncoding RNA, and proteins. Targeting the contents of exosomes leads to the alteration of host cell behavior. Increasing evidence has indicated that leukemia-derived exosomes could be utilized as prognostic, diagnostic, and therapeutic biomarkers for individuals suffering from leukemia. In this regard, the importance of exosomes in terms of initiation and progression of leukemia was underlined in this study.     

综述:靶向淀粉样蛋白的阿尔茨海默病药物研究进展

淀粉样蛋白级联假说是阐释阿尔茨海默病(Alzheimer's disease,AD)发病机制的主要学说之一,即脑内过量的β-淀粉样蛋白(β-amyloid,Aβ)是促发AD的核心因素．因此,靶向Aβ形成、聚集和清除等关键环节的药物开发是目前药物研究的热点．但近年来AD新药临床试验屡屡失败,至今尚未得到一种切实有效的治疗药物．淀粉样蛋白级联假说的局限性和痴呆期患者疾病进程的难以逆转,可能是临床试验反复失败的两个主要原因．借助AD早期诊断技术的发展,将药物干预的时间窗口前移,重视痴呆前期病理机制与治疗的研究,可能是研制延缓AD发生和发展有效药物的新途径．     

Hierarchical spatial modeling of additive and dominance genetic variance for large spatial trial datasets

Summary .  This article expands upon recent interest in Bayesian hierarchical models in quantitative genetics by developing spatial process models for inference on additive and dominance genetic variance within the context of large spatially referenced trial datasets. Direct application of such models to large spatial datasets are, however, computationally infeasible because of cubic-order matrix algorithms involved in estimation. The situation is even worse in Markov chain Monte Carlo (MCMC) contexts where such computations are performed for several iterations. Here, we discuss approaches that help obviate these hurdles without sacrificing the richness in modeling. For genetic effects, we demonstrate how an initial spectral decomposition of the relationship matrices negate the expensive matrix inversions required in previously proposed MCMC methods. For spatial effects, we outline two approaches for circumventing the prohibitively expensive matrix decompositions: the first leverages analytical results from Ornstein–Uhlenbeck processes that yield computationally efficient tridiagonal structures, whereas the second derives a modified predictive process model from the original model by projecting its realizations to a lower-dimensional subspace, thereby reducing the computational burden. We illustrate the proposed methods using a synthetic dataset with additive, dominance, genetic effects and anisotropic spatial residuals, and a large dataset from a Scots pine ( Pinus sylvestris L.) progeny study conducted in northern Sweden. Our approaches enable us to provide a comprehensive analysis of this large trial, which amply demonstrates that, in addition to violating basic assumptions of the linear model, ignoring spatial effects can result in downwardly biased measures of heritability.     

Spatial correlations in attribute communities

Community detection is an important tool for exploring and classifying the properties of large complex networks and should be of great help for spatial networks. Indeed, in addition to their location, nodes in spatial networks can have attributes such as the language for individuals, or any other socio-economical feature that we would like to identify in communities. We discuss in this paper a crucial aspect which was not considered in previous studies which is the possible existence of correlations between space and attributes. Introducing a simple toy model in which both space and node attributes are considered, we discuss the effect of space-attribute correlations on the results of various community detection methods proposed for spatial networks in this paper and in previous studies. When space is irrelevant, our model is equivalent to the stochastic block model which has been shown to display a detectability-non detectability transition. In the regime where space dominates the link formation process, most methods can fail to recover the communities, an effect which is particularly marked when space-attributes correlations are strong. In this latter case, community detection methods which remove the spatial component of the network can miss a large part of the community structure and can lead to incorrect results.     

A spatial statistical model for landscape genetics

Landscape genetics is a new discipline that aims to provide information on how landscape and environmental features influence population genetic structure. The first key step of landscape genetics is the spatial detection and location of genetic discontinuities between populations. However, efficient methods for achieving this task are lacking. In this article, we first clarify what is conceptually involved in the spatial modeling of genetic data. Then we describe a Bayesian model implemented in a Markov chain Monte Carlo scheme that allows inference of the location of such genetic discontinuities from individual geo-referenced multilocus genotypes, without a priori knowledge on populational units and limits. In this method, the global set of sampled individuals is modeled as a spatial mixture of panmictic populations, and the spatial organization of populations is modeled through the colored Voronoi tessellation. In addition to spatially locating genetic discontinuities, the method quantifies the amount of spatial dependence in the data set, estimates the number of populations in the studied area, assigns individuals to their population of origin, and detects individual migrants between populations, while taking into account uncertainty on the location of sampled individuals. The performance of the method is evaluated through the analysis of simulated data sets. Results show good performances for standard data sets (e.g., 100 individuals genotyped at 10 loci with 10 alleles per locus), with high but also low levels of population differentiation (e.g., F_ST < 0.05). The method is then applied to a set of 88 individuals of wolverines (Gulo gulo) sampled in the northwestern United States and genotyped at 10 microsatellites.     

Growth curve models of repeated binary response

Experimental designs that include repeated measures of binary response variables over time and under different conditions are common in biology. In such settings, it is often desirable to characterize the response pattern over time. When response variables are continuous, this characterization can be made in terms of a growth model such as the Potthoff-Roy growth curve model. We illustrate how a similar growth curve modeling strategy can be implemented using weighted least squares (WLS) methods for binary response data. The growth models are constructed in terms of polynomial functions across marginal response. However, when growth models are fit to repeated binary response, the nonsignificant higher-order polynomial functions are dropped from the model, rather than used as covariates. Dropping the nonsignificant polynomials from the model will reduce the number of response functions, and help avoid small-sample problems that can occur when the number of correlated response functions is large and sample sizes are small. The reduced set of response functions are then modeled using WLS methods. We illustrate such models with an example of binary fly oviposition response (accept or reject) exhibited by two populations of flies at four ages to two types of fruit.     

Analysis of interval‐censored recurrent event processes subject to resolution

Interval‐censored recurrent event data arise when the event of interest is not readily observed but the cumulative event count can be recorded at periodic assessment times. In some settings, chronic disease processes may resolve, and individuals will cease to be at risk of events at the time of disease resolution. We develop an expectation‐maximization algorithm for fitting a dynamic mover‐stayer model to interval‐censored recurrent event data under a Markov model with a piecewise‐constant baseline rate function given a latent process. The model is motivated by settings in which the event times and the resolution time of the disease process are unobserved. The likelihood and algorithm are shown to yield estimators with small empirical bias in simulation studies. Data are analyzed on the cumulative number of damaged joints in patients with psoriatic arthritis where individuals experience disease remission.