1,838 research outputs found
2BAD: an application to estimate the parental contributions during two independent admixture events
Several approaches have been developed to calculate the relative contributions of parental populations in single admixture event scenarios, including Bayesian methods. In many breeds and populations, it may be more realistic to consider multiple admixture events. However, no approach has been developed to date to estimate admixture in such cases. This report describes a program application, 2BAD (for 2-event Bayesian ADmixture), which allows the consideration of up to two independent admixture events involving two or three parental populations and a single admixed population, depending on the number of populations sampled. For each of these models, it is possible to estimate several parameters (admixture, effective sizes, etc.) using an approximate Bayesian computation approach. In addition, the program allows comparing pairs of admixture models, determining which is the most likely given data. The application was tested through simulations and was found to provide good estimates for the contribution of the populations at the two admixture events. We were also able to determine whether an admixture model was more likely than a simple split model
Adaptive approximate Bayesian computation for complex models
Approximate Bayesian computation (ABC) is a family of computational
techniques in Bayesian statistics. These techniques allow to fi t a model to
data without relying on the computation of the model likelihood. They instead
require to simulate a large number of times the model to be fi tted. A number
of re finements to the original rejection-based ABC scheme have been proposed,
including the sequential improvement of posterior distributions. This technique
allows to de- crease the number of model simulations required, but it still
presents several shortcomings which are particu- larly problematic for costly
to simulate complex models. We here provide a new algorithm to perform adaptive
approximate Bayesian computation, which is shown to perform better on both a
toy example and a complex social model.Comment: 14 pages, 5 figure
Inferring introduction routes of invasive species using approximate Bayesian computation on microsatellite data
Determining the routes of introduction provides not only information about the history of an invasion process, but also information about the origin and construction of the genetic composition of the invading population. It remains difficult, however, to infer introduction routes from molecular data because of a lack of appropriate methods. We evaluate here the use of an approximate Bayesian computation (ABC) method for estimating the probabilities of introduction routes of invasive populations based on microsatellite data. We considered the crucial case of a single source population from which two invasive populations originated either serially from a single introduction event or from two independent introduction events. Using simulated datasets, we found that the method gave correct inferences and was robust to many erroneous beliefs. The method was also more efficient than traditional methods based on raw values of statistics such as assignment likelihood or pairwise F(ST). We illustrate some of the features of our ABC method, using real microsatellite datasets obtained for invasive populations of the western corn rootworm, Diabrotica virgifera virgifera. Most computations were performed with the DIYABC program (http://www1.montpellier.inra.fr/CBGP/diyabc/)
LOSITAN: A workbench to detect molecular adaptation based on a Fst-outlier method
<p>Abstract</p> <p>Background</p> <p>Testing for selection is becoming one of the most important steps in the analysis of multilocus population genetics data sets. Existing applications are difficult to use, leaving many non-trivial, error-prone tasks to the user.</p> <p>Results</p> <p>Here we present LOSITAN, a selection detection workbench based on a well evaluated <it>F</it><sub><it>st</it></sub>-outlier detection method. LOSITAN greatly facilitates correct approximation of model parameters (e.g., genome-wide average, neutral <it>F</it><sub><it>st</it></sub>), provides data import and export functions, iterative contour smoothing and generation of graphics in a easy to use graphical user interface. LOSITAN is able to use modern multi-core processor architectures by locally parallelizing fdist, reducing computation time by half in current dual core machines and with almost linear performance gains in machines with more cores.</p> <p>Conclusion</p> <p>LOSITAN makes selection detection feasible to a much wider range of users, even for large population genomic datasets, by both providing an easy to use interface and essential functionality to complete the whole selection detection process.</p
Broad changes in body mass index between age 10 and adulthood are associated with type 2 diabetes risk independently of adult body mass index
This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record Diabetes Research and Wellness FoundationDiabetes UKEuropean Foundation for the Study of Diabete
ABCtoolbox: a versatile toolkit for approximate Bayesian computations
BACKGROUND: The estimation of demographic parameters from genetic data often requires the computation of likelihoods. However, the likelihood function is computationally intractable for many realistic evolutionary models, and the use of Bayesian inference has therefore been limited to very simple models. The situation changed recently with the advent of Approximate Bayesian Computation (ABC) algorithms allowing one to obtain parameter posterior distributions based on simulations not requiring likelihood computations. RESULTS: Here we present ABCtoolbox, a series of open source programs to perform Approximate Bayesian Computations (ABC). It implements various ABC algorithms including rejection sampling, MCMC without likelihood, a Particle-based sampler and ABC-GLM. ABCtoolbox is bundled with, but not limited to, a program that allows parameter inference in a population genetics context and the simultaneous use of different types of markers with different ploidy levels. In addition, ABCtoolbox can also interact with most simulation and summary statistics computation programs. The usability of the ABCtoolbox is demonstrated by inferring the evolutionary history of two evolutionary lineages of Microtus arvalis. Using nuclear microsatellites and mitochondrial sequence data in the same estimation procedure enabled us to infer sex-specific population sizes and migration rates and to find that males show smaller population sizes but much higher levels of migration than females. CONCLUSION: ABCtoolbox allows a user to perform all the necessary steps of a full ABC analysis, from parameter sampling from prior distributions, data simulations, computation of summary statistics, estimation of posterior distributions, model choice, validation of the estimation procedure, and visualization of the results
Genome scan of Diabrotica virgifera virgifera for genetic variation associated with crop rotation tolerance
Crop rotation has been a valuable technique for control of Diabrotica virgifera virgifera for almost a century. However, during the last two decades, crop rotation has ceased to be effective in an expanding area of the US corn belt. This failure appears to be due to a change in the insect's oviposition behaviour, which, in all probability, has an underlying genetic basis. A preliminary genome scan using 253 amplified fragment-length polymorphism (AFLP) markers sought to identify genetic variation associated with the circumvention of crop rotation. Samples of D. v. virgifera from east-central Illinois, where crop rotation is ineffective, were compared with samples from Iowa at locations that the behavioural variant has yet to reach. A single AFLP marker showed signs of having been influenced by selection for the circumvention of crop rotation. However, this marker was not diagnostic. The lack of markers strongly associated with the trait may be due to an insufficient density of marker coverage throughout the genome. A weak but significant general heterogeneity was observed between the Illinois and Iowa samples at microsatellite loci and AFLP markers. This has not been detected in previous population genetic studies of D. v. virgifera and may indicate a reduction in gene flow between variant and wild-type beetles
Sarcopenic Obesity and Myosteatosis Are Associated with Higher Mortality in Patients with Cirrhosis
Background and aims Obesity is frequently associated with cirrhosis, and cirrhotic patients may develop simultaneous loss of skeletal muscle and gain of adipose tissue, culminating in the condition of sarcopenic obesity. Additionally, muscle depletion is characterized by both a reduction in muscle size and increased proportion of muscular fat, termed myosteatosis. In this study, we aimed to establish the frequency and clinical significance of sarcopenia, sarcopenic obesity and myosteatosis in cirrhotic patients.
Methods We analysed 678 patients with cirrhosis. Sarcopenia, sarcopenic obesity and myosteatosis were analysed by CT scan using the third lumbar vertebrae skeletal muscle and attenuation indexes, using previously validated gender-and body mass index-specific cutoffs.
Results Patients were predominately men (n = 457, 67%), and cirrhosis aetiology was hepatitis C virus in 269 patients (40%), alcohol in 153 (23%), non-alcoholic steatohepatitis/cryptogenic in 96 (14%), autoimmune liver disease in 55 (8%), hepatitis B virus in 43 (6%), and others in 5 patients (1%). Sarcopenia was present in 292 (43%), 135 had sarcopenic obesity (20%) and 353 had myosteatosis (52%). Patients with sarcopenia (22 ± 3 vs. 95 ± 22 months, P = 0.04) were associated with mortality.
Conclusions Sarcopenia, sarcopenic obesity and myosteatosis are often present in patients with cirrhosis, and sarcopenia and myosteatosis are independently associated with a higher long-term mortality in cirrhosis
Selective high frequency mechanical actuation driven by the VO2 electronic instability
Micro- and nano-electromechanical resonators are a fundamental building block
of modern technology, used in environmental monitoring, robotics, medical tools
as well as fundamental science. These devices rely on dedicated electronics to
generate their driving signal, resulting in an increased complexity and size.
Here, we present a new paradigm to achieve high-frequency mechanical actuation
based on the metal-insulator transition of VO, where the steep
variation of its electronic properties enables to realize high-frequency
electrical oscillations. The dual nature of this phase change, which is both
electronic and structural, turns the electrical oscillations into an intrinsic
actuation mechanism, powered by a small DC voltage and capable to selectively
excite the different mechanical modes of a microstructure. Our results pave the
way towards the realization of micro- and nano-electro-mechanical systems with
autonomous actuation from integrated DC power sources such as solar cells or
micro-batteries.Comment: Main text: 6 pages, 4 figures Supplemental Material: 16 pages, 7
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