661 research outputs found
fastsimcoal: a continuous-time coalescent simulator of genomic diversity under arbitrarily complex evolutionary scenarios
No full textAbstract
Motivation: Genetic studies focus on increasingly larger genomic regions of both extant and ancient DNA, and there is a need for simulation software to match these technological advances. We present here a new coalescent-based simulation program fastsimcoal, which is able to quickly simulate a variety of genetic markers scattered over very long genomic regions with arbitrary recombination patterns under complex evolutionary scenarios.
Availability and Implementation: fastsimcoal is a C++ program compiled for Windows, MacOsX and Linux platforms. It is freely available at cmpg.unibe.ch/software/fastsimcoal/, together with its detailed user manual and example input files.
Contact: [email protected]
Supplementary Information: Supplementary data are available at Bioinformatics online.</jats:p
Correcting for ascertainment bias in the inference of population structure
No full textBackground: The ascertainment process of molecular markers amounts to disregard loci carrying alleles with low frequencies. This can result in strong biases in inferences under population genetics models if not properly taken into account by the inference algorithm. Attempting to model this censoring process in view of making inference of population structure (i.e. identifying clusters of individuals) brings up challenging numerical difficulties. Method: These difficulties are related to the presence of intractable normalizing constants in Metropolis-Hastings acceptance ratios. This can be solved via an Markov chain Monte Carlo (MCMC) algorithm known as single variable exchange algorithm (SVEA). Result: We show how this general solution can be implemented for a class of clustering models of broad interest in population genetics that includes the models underlying the computer programs STRUCTURE, GENELAND and GESTE. We also implement the method proposed for a simple example and show that it allows us to reduce the bias substantially
Quantifying population structure using the F-model
No full textWe review a model-based approach to estimate local population F(ST)'s that is based on the multinomial-Dirichlet distribution, the so-called F-model. As opposed to the standard method of estimating a single F(ST) value, this approach takes into account the fact that in most if not all realistic situations, local populations differ in their effective sizes and migration rates. Therefore, the use of this approach can help better describe the genetic structure of populations. Despite this obvious advantage, this method has remained largely underutilized by molecular ecologists. Thus, the objective of this review is to foster its use for studying the genetic structure of metapopulations. We present the derivation of the Bayesian formulation for the estimation of population-specific F(ST)'s based on the multinomial-Dirichlet distribution. We describe several recent applications of the F-model and present the results of a small simulation study that explains how the F-model can help better describe the genetic structure of populations.</p
Evolutionary forces shaping genomic islands of population differentiation in humans
No full textAbstract Background Levels of differentiation among populations depend both on demographic and selective factors: genetic drift and local adaptation increase population differentiation, which is eroded by gene flow and balancing selection. We describe here the genomic distribution and the properties of genomic regions with unusually high and low levels of population differentiation in humans to assess the influence of selective and neutral processes on human genetic structure. Methods Individual SNPs of the Human Genome Diversity Panel (HGDP) showing significantly high or low levels of population differentiation were detected under a hierarchical-island model (HIM). A Hidden Markov Model allowed us to detect genomic regions or islands of high or low population differentiation. Results Under the HIM, only 1.5% of all SNPs are significant at the 1% level, but their genomic spatial distribution is significantly non-random. We find evidence that local adaptation shaped high-differentiation islands, as they are enriched for non-synonymous SNPs and overlap with previously identified candidate regions for positive selection. Moreover there is a negative relationship between the size of islands and recombination rate, which is stronger for islands overlapping with genes. Gene ontology analysis supports the role of diet as a major selective pressure in those highly differentiated islands. Low-differentiation islands are also enriched for non-synonymous SNPs, and contain an overly high proportion of genes belonging to the 'Oncogenesis' biological process. Conclusions Even though selection seems to be acting in shaping islands of high population differentiation, neutral demographic processes might have promoted the appearance of some genomic islands since i) as much as 20% of islands are in non-genic regions ii) these non-genic islands are on average two times shorter than genic islands, suggesting a more rapid erosion by recombination, and iii) most loci are strongly differentiated between Africans and non-Africans, a result consistent with known human demographic history.</p
Enhanced AFLP genome scans detect local adaptation in high-altitude populations of a small rodent (Microtus arvalis)
No full text
What Did Matthieu Beroald Transmit to François Béroalde de Verville?
No full text Many tangible and intangible goods were passed down within early modern families. The goods included texts and the knowledge that texts communicated. But how did they relate to the other goods transmitted within families? That question is explored in relation to the scholar Matthieu Beroald and his son François Béroalde de Verville, author of the famous Moyen de parvenir. Matthieu transmitted to François a humanist education, at least one printed volume (probably more), an interest in certain topics (especially chronology), a network of contacts, but little wealth. And François soon donated to his sisters what wealth he did receive. His relationship to his intellectual inheritance from his father was complex and ambivalent. Aspects of François's attitude towards knowledge may have stemmed, via his father, from two grandfather-figures: Matthieu's own father (a barber-surgeon) and Matthieu's relative and benefactor François Vatable (the Hebraicist). </jats:p
Genomic Data Reveal a Complex Making of Humans
No full textIn the last few years, two paradigms underlying human evolution have crumbled. Modern humans have not totally replaced previous hominins without any admixture, and the expected signatures of adaptations to new environments are surprisingly lacking at the genomic level. Here we review current evidence about archaic admixture and lack of strong selective sweeps in humans. We underline the need to properly model differential admixture in various populations to correctly reconstruct past demography. We also stress the importance of taking into account the spatial dimension of human evolution, which proceeded by a series of range expansions that could have promoted both the introgression of archaic genes and background selection
SPLATCHE2: a spatially-explicit simulation framework for complex demography, genetic admixture and recombination
No full textAbstract
Summary: SPLATCHE2 is a program to simulate the demography of populations and the resulting molecular diversity for a wide range of evolutionary scenarios. The spatially explicit simulation framework can account for environmental heterogeneity and fluctuations, and it can manage multiple population sources. A coalescent-based approach is used to generate genetic markers mostly used in population genetics studies (DNA sequences, SNPs, STRs or RFLPs). Various combinations of independent, fully or partially linked genetic markers can be produced under a recombination model based on the ancestral recombination graph. Competition between two populations (or species) can also be simulated with user-defined levels of admixture between the two populations. SPLATCHE2 may be used to generate the expected genetic diversity under complex demographic scenarios and can thus serve to test null hypotheses. For model parameter estimation, SPLATCHE2 can easily be integrated into an Approximate Bayesian Computation (ABC) framework.
Availability and implementation: SPLATCHE2 is a C++ program compiled for Windows and Linux platforms. It is freely available at www.splatche.com, together with its related documentation and example data.
Contact: [email protected]</jats:p
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