Directional genetic differentiation and relative migration

Understanding the population structure and patterns of gene flow within species is of fundamental importance to the study of evolution. In the fields of population and evolutionary genetics, measures of genetic differentiation are commonly used to gather this information. One potential caveat is that these measures assume gene flow to be symmetric. However, asymmetric gene flow is common in nature, especially in systems driven by physical processes such as wind or water currents. As information about levels of asymmetric gene flow among populations is essential for the correct interpretation of the distribution of contemporary genetic diversity within species, this should not be overlooked. To obtain information on asymmetric migration patterns from genetic data, complex models based on maximum‐likelihood or Bayesian approaches generally need to be employed, often at great computational cost. Here, a new simpler and more efficient approach for understanding gene flow patterns is presented. This approach allows the estimation of directional components of genetic divergence between pairs of populations at low computational effort, using any of the classical or modern measures of genetic differentiation. These directional measures of genetic differentiation can further be used to calculate directional relative migration and to detect asymmetries in gene flow patterns. This can be done in a user‐friendly web application called divMigrate‐online introduced in this study. Using simulated data sets with known gene flow regimes, we demonstrate that the method is capable of resolving complex migration patterns under a range of study designs.     

Gene network dynamics controlling keratinocyte migration

Translation of large‐scale data into a coherent model that allows one to simulate, predict and control cellular behavior is far from being resolved. Assuming that long‐term cellular behavior is reflected in the gene expression kinetics, we infer a dynamic gene regulatory network from time‐series measurements of DNA microarray data of hepatocyte growth factor‐induced migration of primary human keratinocytes. Transferring the obtained interactions to the level of signaling pathways, we predict in silico and verify in vitro the necessary and sufficient time‐ordered events that control migration. We show that pulse‐like activation of the proto‐oncogene receptor Met triggers a responsive state, whereas time sequential activation of EGF‐R is required to initiate and maintain migration. Context information for enhancing, delaying or stopping migration is provided by the activity of the protein kinase A signaling pathway. Our study reveals the complex orchestration of multiple pathways controlling cell migration.     

Ploidy level interacts with population size and habitat conditions to determine the degree of herbivory damage in plant populations

Recently it has been suggested that ploidy level of a plant population may have important effects on plant‐animal interactions. Plant‐animal interactions can also be strongly altered by factors such as plant population size and habitat conditions. It is, however, not known how these factors interact to shape the overall pattern of plant‐animal interactions. I studied the interaction between a perennial plant, Aster amellus, and a monophagous herbivorous moth, Coleophora obscenella, and investigated the effect of ploidy level of the plant population, plant population size, isolation and habitat conditions on density of the insect, damage by the insect, and plant performance. Ploidy level, plant population size and habitat conditions, but not isolation, strongly influence plant‐herbivore interactions. Furthermore, there are significant interactions between effects of ploidy level and plant population size and between ploidy level and isolation. Hexaploid plants suffer higher seed damage by the herbivore, but their seed production is still higher than that of diploids. Herbivores thus partly limit the evolutionary success of the hexaploid plants. Plant‐animal interactions are also strongly determined by plant population size. Small populations of A. amellus (below forty flowering ramets) host no C. obscenella larvae, indicating a minimum A. amellus population size that can sustain a viable C. obscenella population. Negative and positive effects of plant population size balance and result in no relationship between plant population size and number of developed seeds per flower head. The results also show a significant interaction between ploidy level and plant population size, indicating that the increase in density of C. obscenella larvae with plant population size is greater in hexaploid than in diploid populations. The results also indicate that the effect of ploidy level on plant‐herbivore interactions can be altered by plant population size, which suggests that plant‐herbivore interactions are driven by a complex of interactions among different factors. Studying each factor separately could thus lead to biased conclusions about patterns of interactions in such systems.     

Application of network methods for understanding evolutionary dynamics in discrete habitats

In populations occupying discrete habitat patches, gene flow between habitat patches may form an intricate population structure. In such structures, the evolutionary dynamics resulting from interaction of gene‐flow patterns with other evolutionary forces may be exceedingly complex. Several models describing gene flow between discrete habitat patches have been presented in the population‐genetics literature; however, these models have usually addressed relatively simple settings of habitable patches and have stopped short of providing general methodologies for addressing nontrivial gene‐flow patterns. In the last decades, network theory – a branch of discrete mathematics concerned with complex interactions between discrete elements – has been applied to address several problems in population genetics by modelling gene flow between habitat patches using networks. Here, we present the idea and concepts of modelling complex gene flows in discrete habitats using networks. Our goal is to raise awareness to existing network theory applications in molecular ecology studies, as well as to outline the current and potential contribution of network methods to the understanding of evolutionary dynamics in discrete habitats. We review the main branches of network theory that have been, or that we believe potentially could be, applied to population genetics and molecular ecology research. We address applications to theoretical modelling and to empirical population‐genetic studies, and we highlight future directions for extending the integration of network science with molecular ecology.     

Using community and population approaches to understand how contemporary and historical factors have shaped species distribution in river ecosystems

Aim To examine how the employment of both community‐ and population‐level approaches can provide a wider view of the importance of contemporary and historical factors on current species distribution. We posit that community ecology should provide more information about contemporary factors, whereas population genetics should provide better information about historical factors. Location Rivers of the western Mediterranean Basin, including four subregions differing in geological history: the Iberian Plate, Transitional, Betic and Rif. Methods For a community‐level approach, Trichoptera richness and community composition were compared between subregions using species accumulation curves and a correspondence analysis. For a population‐level approach, the mtDNA cytochrome C oxidase subunit I (COI) gene of specimens of the Trichoptera midstream‐lowland species Chimarra marginata (L.) was sequenced and analysed using phylogeographical methods. Results The community approach revealed that historical events had more influence on headwater communities than contemporary ecological factors, whereas historical events had negligible influence on midstream‐lowland communities. In midstream‐lowland sites, however, the population approach showed that the genetic structure of C. marginata differed significantly between subregions and revealed patterns of historical gene migration. In terms of species richness, the Rif subregion had the lowest value per basin due to local climatic features and isolation. Main conclusions Both community‐ and population‐level approaches yielded information about the effects of historical factors on species distribution. However, the importance of historical events on current Trichoptera communities depends on the river zonation. Unlike headwater sites, midstream‐lowland sites showed signs of historical events at the population level but not at the community level at the scale used, indicating that both approaches should be employed together in biogeographical studies. Lack of detection of historical events at the community level does not necessarily mean that they are negligible. Most likely, the organizational level used is not appropriate. We also stress the importance of implementing conservation measures for rivers in the western Mediterranean, especially under future scenarios of climate change and human disturbances in the Mediterranean Basin.     

Genomic evidence for population‐specific responses to co‐evolving parasites in a New Zealand freshwater snail

Reciprocal co‐evolving interactions between hosts and parasites are a primary source of strong selection that can promote rapid and often population‐ or genotype‐specific evolutionary change. These host–parasite interactions are also a major source of disease. Despite their importance, very little is known about the genomic basis of co‐evolving host–parasite interactions in natural populations, especially in animals. Here, we use gene expression and sequence evolution approaches to take critical steps towards characterizing the genomic basis of interactions between the freshwater snail Potamopyrgus antipodarum and its co‐evolving sterilizing trematode parasite, Microphallus sp., a textbook example of natural coevolution. We found that Microphallus‐infected P. antipodarum exhibit systematic downregulation of genes relative to uninfected P. antipodarum. The specific genes involved in parasite response differ markedly across lakes, consistent with a scenario where population‐level co‐evolution is leading to population‐specific host–parasite interactions and evolutionary trajectories. We also used an F_ST‐based approach to identify a set of loci that represent promising candidates for targets of parasite‐mediated selection across lakes as well as within each lake population. These results constitute the first genomic evidence for population‐specific responses to co‐evolving infection in the P. antipodarum‐Microphallus interaction and provide new insights into the genomic basis of co‐evolutionary interactions in nature.     

Genetic structure in Iberian and Moroccan populations of the globally threatened great bustard Otis tarda: a microsatellite perspective

Patterns of genetic structure and gene flow among populations help us understand population dynamics and properly manage species of concern. Matrilineal mtDNA sequence data have been instrumental in revealing genetic structure at the intraspecific level, but bi‐parentally inherited markers are needed to confirm patterns at the genome level and to assess the potential role of sex‐biased dispersal on gene flow, particularly in species where males are known to be the main dispersing sex. Here we use microsatellite loci to examine patterns of genetic structure across the range of the great bustard in Iberia and Morocco, an area representing 70% of the world population of this globally threatened species. We used population differentiation statistics and Bayesian analysis of population structure to analyse data from 14 microsatellite loci. These data provide greater resolution than mtDNA sequences, and results reveal the existence of three main genetic units corresponding to Morocco, the northeastern part of Spain, and the rest of the Iberian Peninsula. Our results, together with previous mtDNA data, confirm the genetic differentiation of the northern Africa population and the importance of the Strait of Gibraltar as a barrier to gene flow for both males and females, rendering the Moroccan population a separate management unit of high conservation concern.     

Distinct sources of gene flow produce contrasting population genetic dynamics at different range boundaries of a Choristoneura budworm

Populations are often exposed to multiple sources of gene flow, but accounts are lacking of the population genetic dynamics that result from these interactions or their effects on local evolution. Using a genomic clines framework applied to 1,195 single nucleotide polymorphisms, we documented genomewide, locus‐specific patterns of introgression between Choristoneura occidentalis biennis spruce budworms and two ecologically divergent relatives, C. o. occidentalis and Choristoneura fumiferana, that it interacts with at alternate boundaries of its range. We observe contrasting hybrid indexes between the two hybrid zones, no overlap in “gene‐flow outliers” (clines showing relatively extreme extents or rates of locus‐specific introgression) and variable linkage disequilibrium among those outliers. At the same time, correlated genomewide rates of introgression between zones suggest the presence of processes common to both boundaries. These findings highlight the contrasting population genetic dynamics that can occur at separate frontiers of a single population, while also suggesting that shared patterns may frequently accompany cases of divergence‐with‐gene‐flow that involve a lineage in common. Our results point to potentially complex evolutionary outcomes for populations experiencing multiple sources of gene flow.     

mtDNA variability in two Bantu‐speaking populations (Shona and Hutu) from Eastern Africa: Implications for peopling and migration patterns in sub‐Saharan Africa

In this study, we report novel data on mitochondrial DNA in two of the largest eastern Bantu‐speaking populations, the Shona from Zimbabwe and the Hutu from Rwanda. The goal is to evaluate the genetic relationships of these two ethnic groups with other Bantu‐speaking populations. Moreover, by comparing our data with those from other Niger‐Congo speaking populations, we aim to clarify some aspects of evolutionary and demographic processes accompanying the spread of Bantu languages in sub‐Saharan Africa and to test if patterns of genetic variation fit with models of population expansion based on linguistic and archeological data. The results indicate that the Shona and Hutu are closely related to the other Bantu‐speaking populations. However, there are some differences in haplogroup composition between the two populations, mainly due to different genetic contributions from neighboring populations. This result is confirmed by estimates of migration rates which show high levels of gene flow not only between pairs of Bantu‐speaking populations, but also between Bantu and non‐Bantu speakers. The observed pattern of genetic variability (high genetic homogeneity and high levels of gene flow) supports a linguistic model suggesting a gradual spread of Bantu‐speakers, with strong interactions between the different lines of Bantu‐speaker descent, and is also in agreement with recent archeological findings. In conclusion, our data emphasize the role that population admixture has played at different times and to varying degrees in the dispersal of Bantu languages. Am J Phys Anthropol, 2009. © 2009 Wiley‐Liss, Inc.     

Etiological overlap between obsessive‐compulsive disorder and anorexia nervosa: a longitudinal cohort,multigenerational family and twin study

Obsessive‐compulsive disorder (OCD) often co‐occurs with anorexia nervosa (AN), a comorbid profile that complicates the clinical management of both conditions. This population‐based study aimed to examine patterns of comorbidity, longitudinal risks, shared familial risks and shared genetic factors between OCD and AN at the population level. Participants were individuals with a diagnosis of OCD (N=19,814) or AN (N=8,462) in the Swedish National Patient Register between January 1992 and December 2009; their first‐, second‐ and third‐degree relatives; and population‐matched (1:10 ratio) unaffected comparison individuals and their relatives. Female twins from the population‐based Swedish Twin Register (N=8,550) were also included. Females with OCD had a 16‐fold increased risk of having a comorbid diagnosis of AN, whereas males with OCD had a 37‐fold increased risk. Longitudinal analyses showed that individuals first diagnosed with OCD had an increased risk for a later diagnosis of AN (risk ratio, RR=3.6), whereas individuals first diagnosed with AN had an even greater risk for a later diagnosis of OCD (RR=9.6). These longitudinal risks were about twice as high for males than for females. First‐ and second‐degree relatives of probands with OCD had an increased risk for AN, and the magnitude of this risk tended to increase with the degree of genetic relatedness. Bivariate twin models revealed a moderate but significant degree of genetic overlap between self‐reported OCD and AN diagnoses (r_a=0.52, 95% CI: 0.26‐0.81), but most of the genetic variance was disorder‐specific. The moderately high genetic correlation supports the idea that this frequently observed comorbid pattern is at least in part due to shared genetic factors, though disorder‐specific factors are more important. These results have implications for current gene‐searching efforts and for clinical practice.     

Comorbidity patterns in patients with chronic diseases in general practice

Introduction

Healthcare management is oriented toward single diseases, yet multimorbidity is nevertheless the rule and there is a tendency for certain diseases to occur in clusters. This study sought to identify comorbidity patterns in patients with chronic diseases, by reference to number of comorbidities, age and sex, in a population receiving medical care from 129 general practitioners in Spain, in 2007.

Methods

A cross-sectional study was conducted in a health-area setting of the Madrid Autonomous Region (Comunidad Autónoma), covering a population of 198,670 individuals aged over 14 years. Multiple correspondences were analyzed to identify the clustering patterns of the conditions targeted.

Results

Forty-two percent (95% confidence interval [CI]: 41.8–42.2) of the registered population had at least one chronic condition. In all, 24.5% (95% CI: 24.3–24.6) of the population presented with multimorbidity.In the correspondence analysis, 98.3% of the total information was accounted for by three dimensions. The following four, age- and sex-related comorbidity patterns were identified: pattern B, showing a high comorbidity rate; pattern C, showing a low comorbidity rate; and two patterns, A and D, showing intermediate comorbidity rates.

Conclusions

Four comorbidity patterns could be identified which grouped diseases as follows: one showing diseases with a high comorbidity burden; one showing diseases with a low comorbidity burden; and two showing diseases with an intermediate comorbidity burden.     

A Penalized Likelihood Approach for Bivariate Conditional Normal Models for Dynamic Co‐expression Analysis

Summary Gene co‐expressions have been widely used in the analysis of microarray gene expression data. However, the co‐expression patterns between two genes can be mediated by cellular states, as reflected by expression of other genes, single nucleotide polymorphisms, and activity of protein kinases. In this article, we introduce a bivariate conditional normal model for identifying the variables that can mediate the co‐expression patterns between two genes. Based on this model, we introduce a likelihood ratio (LR) test and a penalized likelihood procedure for identifying the mediators that affect gene co‐expression patterns. We propose an efficient computational algorithm based on iterative reweighted least squares and cyclic coordinate descent and have shown that when the tuning parameter in the penalized likelihood is appropriately selected, such a procedure has the oracle property in selecting the variables. We present simulation results to compare with existing methods and show that the LR‐based approach can perform similarly or better than the existing method of liquid association and the penalized likelihood procedure can be quite effective in selecting the mediators. We apply the proposed method to yeast gene expression data in order to identify the kinases or single nucleotide polymorphisms that mediate the co‐expression patterns between genes.     

Mapping genetically compensatory pathways from synthetic lethal interactions in yeast

Background

Synthetic lethal genetic interaction analysis has been successfully applied to predicting the functions of genes and their pathway identities. In the context of synthetic lethal interaction data alone, the global similarity of synthetic lethal interaction patterns between two genes is used to predict gene function. With physical interaction data, such as protein-protein interactions, the enrichment of physical interactions within subsets of genes and the enrichment of synthetic lethal interactions between those subsets of genes are used as an indication of compensatory pathways.

Result

In this paper, we propose a method of mapping genetically compensatory pathways from synthetic lethal interactions. Our method is designed to discover pairs of gene-sets in which synthetic lethal interactions are depleted among the genes in an individual set and where such gene-set pairs are connected by many synthetic lethal interactions. By its nature, our method could select compensatory pathway pairs that buffer the deleterious effect of the failure of either one, without the need of physical interaction data. By focusing on compensatory pathway pairs where genes in each individual pathway have a highly homogenous cellular function, we show that many cellular functions have genetically compensatory properties.

Conclusion

We conclude that synthetic lethal interaction data are a powerful source to map genetically compensatory pathways, especially in systems lacking physical interaction information, and that the cellular function network contains abundant compensatory properties.     

Spatial processes and evolutionary models: a critical review

Evolution is a fundamentally population level process in which variation, drift and selection produce both temporal and spatial patterns of change. Statistical model fitting is now commonly used to estimate which kind of evolutionary process best explains patterns of change through time using models like Brownian motion, stabilizing selection (Ornstein–Uhlenbeck) and directional selection on traits measured from stratigraphic sequences or on phylogenetic trees. But these models assume that the traits possessed by a species are homogeneous. Spatial processes such as dispersal, gene flow and geographical range changes can produce patterns of trait evolution that do not fit the expectations of standard models, even when evolution at the local‐population level is governed by drift or a typical OU model of selection. The basic properties of population level processes (variation, drift, selection and population size) are reviewed and the relationship between their spatial and temporal dynamics is discussed. Typical evolutionary models used in palaeontology incorporate the temporal component of these dynamics, but not the spatial. Range expansions and contractions introduce rate variability into drift processes, range expansion under a drift model can drive directional change in trait evolution, and spatial selection gradients can create spatial variation in traits that can produce long‐term directional trends and punctuation events depending on the balance between selection strength, gene flow, extirpation probability and model of speciation. Using computational modelling that spatial processes can create evolutionary outcomes that depart from basic population‐level notions from these standard macroevolutionary models.     

Overweight,Medical Comorbidity and Health‐related Quality of Life in a Community Sample of Women and Men

Associations among gender, overweight and obesity, medical comorbidity, and health‐related quality of life (HRQoL) were examined in a general population sample of 4,181 women and men aged 18–65 years. Anthropometric measurements and medical comorbidity were assessed as part of a computer‐assisted physician interview. HRQoL was assessed with the Physical and Mental Component Summary scales of the Medical Outcomes Study Short Form (SF‐36 PCS, MCS). General linear models were used to examine the associations among gender, weight status, medical comorbidity, and HRQoL. Controlling for age, social status, the occurrence of specific medical conditions, and the total number of medical conditions, mild obesity was associated with impairment in physical health functioning, as measured by the PCS, among women, whereas impairment in men's physical health was apparent only for moderate obesity. There was no association between weight status and psycho‐social functioning, as measured by the MCS, in women, whereas overweight was associated with better perceived psycho‐social functioning in men. The findings are consistent with the hypothesis that w omen suffer a disproportionately large share of the disease burden of overweight and obesity that is not due solely to differences in medical comorbidity. The possibility that aspects of emotional well‐being may mediate the association between obesity and physical health functioning warrants further attention in this regard. The findings also indicate the need to stratify data by gender and to include more sensitive measures of psycho‐social functioning in future studies.     

Body masses,functional responses and predator–prey stability

The stability of ecological communities depends strongly on quantitative characteristics of population interactions (type‐II vs. type‐III functional responses) and the distribution of body masses across species. Until now, these two aspects have almost exclusively been treated separately leaving a substantial gap in our general understanding of food webs. We analysed a large data set of arthropod feeding rates and found that all functional‐response parameters depend on the body masses of predator and prey. Thus, we propose generalised functional responses which predict gradual shifts from type‐II predation of small predators on equally sized prey to type‐III functional‐responses of large predators on small prey. Models including these generalised functional responses predict population dynamics and persistence only depending on predator and prey body masses, and we show that these predictions are strongly supported by empirical data on forest soil food webs. These results help unravelling systematic relationships between quantitative population interactions and large‐scale community patterns.     

Interactions between temperature and nutrients across levels of ecological organization

Temperature and nutrient availability play key roles in controlling the pathways and rates at which energy and materials move through ecosystems. These factors have also changed dramatically on Earth over the past century as human activities have intensified. Although significant effort has been devoted to understanding the role of temperature and nutrients in isolation, less is known about how these two factors interact to influence ecological processes. Recent advances in ecological stoichiometry and metabolic ecology provide a useful framework for making progress in this area, but conceptual synthesis and review are needed to help catalyze additional research. Here, we examine known and potential interactions between temperature and nutrients from a variety of physiological, community, and ecosystem perspectives. We first review patterns at the level of the individual, focusing on four traits – growth, respiration, body size, and elemental content – that should theoretically govern how temperature and nutrients interact to influence higher levels of biological organization. We next explore the interactive effects of temperature and nutrients on populations, communities, and food webs by synthesizing information related to community size spectra, biomass distributions, and elemental composition. We use metabolic theory to make predictions about how population‐level secondary production should respond to interactions between temperature and resource supply, setting up qualitative predictions about the flows of energy and materials through metazoan food webs. Last, we examine how temperature–nutrient interactions influence processes at the whole‐ecosystem level, focusing on apparent vs. intrinsic activation energies of ecosystem processes, how to represent temperature–nutrient interactions in ecosystem models, and patterns with respect to nutrient uptake and organic matter decomposition. We conclude that a better understanding of interactions between temperature and nutrients will be critical for developing realistic predictions about ecological responses to multiple, simultaneous drivers of global change, including climate warming and elevated nutrient supply.