518,656 research outputs found

    Non-stationary patterns of isolation-by-distance: inferring measures of local genetic differentiation with Bayesian kriging

    Full text link
    Patterns of isolation-by-distance arise when population differentiation increases with increasing geographic distances. Patterns of isolation-by-distance are usually caused by local spatial dispersal, which explains why differences of allele frequencies between populations accumulate with distance. However, spatial variations of demographic parameters such as migration rate or population density can generate non-stationary patterns of isolation-by-distance where the rate at which genetic differentiation accumulates varies across space. To characterize non-stationary patterns of isolation-by-distance, we infer local genetic differentiation based on Bayesian kriging. Local genetic differentiation for a sampled population is defined as the average genetic differentiation between the sampled population and fictive neighboring populations. To avoid defining populations in advance, the method can also be applied at the scale of individuals making it relevant for landscape genetics. Inference of local genetic differentiation relies on a matrix of pairwise similarity or dissimilarity between populations or individuals such as matrices of FST between pairs of populations. Simulation studies show that maps of local genetic differentiation can reveal barriers to gene flow but also other patterns such as continuous variations of gene flow across habitat. The potential of the method is illustrated with 2 data sets: genome-wide SNP data for human Swedish populations and AFLP markers for alpine plant species. The software LocalDiff implementing the method is available at http://membres-timc.imag.fr/Michael.Blum/LocalDiff.htmlComment: In press, Evolution 201

    Lifting the differentiation embargo

    Get PDF
    Effective differentiation therapy for acute myeloid leukemia (AML) has been restricted to a small subset of patients with one defined genetic abnormality. Using an unbiased small molecule screen, Sykes et al. now identify a mechanism of de-repression of differentiation in several models of AML driven by distinct genetic drivers

    Genetic distance predicts trait differentiation at the subpopulation but not the individual level in eelgrass, Zostera marina.

    Get PDF
    Ecological studies often assume that genetically similar individuals will be more similar in phenotypic traits, such that genetic diversity can serve as a proxy for trait diversity. Here, we explicitly test the relationship between genetic relatedness and trait distance using 40 eelgrass (Zostera marina) genotypes from five sites within Bodega Harbor, CA. We measured traits related to nutrient uptake, morphology, biomass and growth, photosynthesis, and chemical deterrents for all genotypes. We used these trait measurements to calculate a multivariate pairwise trait distance for all possible genotype combinations. We then estimated pairwise relatedness from 11 microsatellite markers. We found significant trait variation among genotypes for nearly every measured trait; however, there was no evidence of a significant correlation between pairwise genetic relatedness and multivariate trait distance among individuals. However, at the subpopulation level (sites within a harbor), genetic (FST) and trait differentiation were positively correlated. Our work suggests that pairwise relatedness estimated from neutral marker loci is a poor proxy for trait differentiation between individual genotypes. It remains to be seen whether genomewide measures of genetic differentiation or easily measured "master" traits (like body size) might provide good predictions of overall trait differentiation

    Surprisingly Little Population Genetic Structure In A Fungus-Associated Beetle Despite Its Exploitation Of Multiple Hosts

    Get PDF
    In heterogeneous environments, landscape features directly affect the structure of genetic variation among populations by functioning as barriers to gene flow. Resource-associated population genetic structure, in which populations that use different resources (e.g., host plants) are genetically distinct, is a well-studied example of how environmental heterogeneity structures populations. However, the pattern that emerges in a given landscape should depend on its particular combination of resources. If resources constitute barriers to gene flow, population differentiation should be lowest in homogeneous landscapes, and highest where resources exist in equal proportions. In this study, we tested whether host community diversity affects population genetic structure in a beetle (Bolitotherus cornutus) that exploits three sympatric host fungi. We collected B.cornutus from plots containing the three host fungi in different proportions and quantified population genetic structure in each plot using a panel of microsatellite loci. We found no relationship between host community diversity and population differentiation in this species; however, we also found no evidence of resource-associated differentiation, suggesting that host fungi are not substantial barriers to gene flow. Moreover, we detected no genetic differentiation among B.cornutus populations separated by several kilometers, even though a previous study demonstrated moderate genetic structure on the scale of a few hundred meters. Although we found no effect of community diversity on population genetic structure in this study, the role of host communities in the structuring of genetic variation in heterogeneous landscapes should be further explored in a species that exhibits resource-associated population genetic structure

    Estimates of genetic differentiation measured by FST do not necessarily require large sample sizes when using many SNP markers

    Get PDF
    Population genetic studies provide insights into the evolutionary processes that influence the distribution of sequence variants within and among wild populations. FST is among the most widely used measures for genetic differentiation and plays a central role in ecological and evolutionary genetic studies. It is commonly thought that large sample sizes are required in order to precisely infer FST and that small sample sizes lead to overestimation of genetic differentiation. Until recently, studies in ecological model organisms incorporated a limited number of genetic markers, but since the emergence of next generation sequencing, the panel size of genetic markers available even in non-reference organisms has rapidly increased. In this study we examine whether a large number of genetic markers can substitute for small sample sizes when estimating FST. We tested the behavior of three different estimators that infer FST and that are commonly used in population genetic studies. By simulating populations, we assessed the effects of sample size and the number of markers on the various estimates of genetic differentiation. Furthermore, we tested the effect of ascertainment bias on these estimates. We show that the population sample size can be significantly reduced (as small as n = 4–6) when using an appropriate estimator and a large number of bi-allelic genetic markers (k.1,000). Therefore, conservation genetic studies can now obtain almost the same statistical power as studies performed on model organisms using markers developed with next-generation sequencing

    Wildlife friendly agriculture: which factors do really matter? A genetic study on field vole

    Get PDF
    The distribution of genetic differentiation and the directions of gene flow were determined mainly by landscape factors: thus the expectation that organic fields act as genetic reservoir was not met. The fact that agricultural area presented more sub-populations than the undisturbed one, together with the importance of connectivity and habitat size in shaping gene flow and genetic differentiation, shows that switching to organic farming might not be enough to ensure the conservation of species in the agricultural environment. These results emphasise the need to include landscape structure in management policies

    Directional genetic differentiation and asymmetric migration

    Get PDF
    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. Since 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 paper. 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.Comment: 25 pages, 8 (+3) figures, 1 tabl

    Genomic variation and population structure detected by single nucleotide polymorphism arrays in Corriedale, Merino and Creole sheep.

    Get PDF
    THE AIM OF THIS STUDY WAS TO INVESTIGATE THE GENETIC DIVERSITY WITHIN AND AMONG THREE BREEDS OF SHEEP: Corriedale, Merino and Creole. Sheep from the three breeds (Merino n = 110, Corriedale n = 108 and Creole n = 10) were genotyped using the Illumina Ovine SNP50 beadchip(®). Genetic diversity was evaluated by comparing the minor allele frequency (MAF) among breeds. Population structure and genetic differentiation were assessed using STRUCTURE software, principal component analysis (PCA) and fixation index (FST). Fixed markers (MAF = 0) that were different among breeds were identified as specific breed markers. Using a subset of 18,181 single nucleotide polymorphisms (SNPs), PCA and STUCTURE analysis were able to explain population stratification within breeds. Merino and Corriedale divergent lines showed high levels of polymorphism (89.4% and 86% of polymorphic SNPs, respectively) and moderate genetic differentiation (FST = 0.08) between them. In contrast, Creole had only 69% polymorphic SNPs and showed greater genetic differentiation from the other two breeds (FST = 0.17 for both breeds). Hence, a subset of molecular markers present in the OvineSNP50 is informative enough for breed assignment and population structure analysis of commercial and Creole breeds

    A dynamical model of genetic networks describes cell differentiation

    Get PDF
    Cell differentiation is a complex phenomenon whereby a stem cell becomes progressively more specialized and eventually gives rise to a specific cell type. Differentiation can be either stochastic or, when appropriate signals are present, it can be driven to take a specific route. Induced pluripotency has also been recently obtained by overexpressing some genes in a differentiated cell. Here we show that a stochastic dynamical model of genetic networks can satisfactorily describe all these important features of differentiation, and others. The model is based on the emergent properties of generic genetic networks, it does not refer to specific control circuits and it can therefore hold for a wide class of lineages. The model points to a peculiar role of cellular noise in differentiation, which has never been hypothesized so far, and leads to non trivial predictions which could be subject to experimental testing
    • …
    corecore