Allele frequency spectrum of known ankylosing spondylitis associated variants in a Swedish population

AbstractThe allele frequency spectrum (AFS), or site frequency spectrum, is commonly used to summarize the genomic polymorphism pattern of a sample, which is informative for inferring population history and detecting natural selection. Recently, Chen and Chen (2013) developed a method for analytically deriving the AFS for populations with temporally varying size through the coalescence time-scaling function. However, their approach is only applicable for population history scenarios in which the analytical form of the time-scaling function is tractable. In this paper, we propose a computational approach to extend the method to populations with arbitrary complex history by numerically approximating the time-scaling function. We demonstrate the performance of the approach by constructing the AFS for two population history scenarios: the logistic growth model and the Gompertz growth model, for which the AFS are unavailable with existing approaches.

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Allele Frequency Spectrum in a Cancer Cell Population

10.1101/104158 ◽

2017 ◽

Cited By ~ 1

Author(s):

H. Ohtsuki ◽

H. Innan

Keyword(s):

Population Genetics ◽

Cell Division ◽

Allele Frequency ◽

Frequency Spectrum ◽

Cancer Cell ◽

Cell Population ◽

Large Cell ◽

Parental Cancer ◽

Allele Frequency Spectrum ◽

Cancer Cell Population

ABSTRACTA cancer grows from a single cell, thereby constituting a large cell population. In this work, we are interested in how mutations accumulate in a cancer cell population. We provided a theoretical framework of the stochastic process in a cancer cell population and obtained near exact expressions of allele frequency spectrum or AFS (only continuous approximation is involved) from both forward and backward treatments under a simple setting; all cells undergo cell division and die at constant rates, b and d, respectively, such that the entire population grows exponentially. This setting means that once a parental cancer cell is established, in the following growth phase, all mutations are assumed to have no effect on b or d (i.e., neutral or passengers). Our theoretical results show that the difference from organismal population genetics is mainly in the coalescent time scale, and the mutation rate is defined per cell division, not per time unit (e.g., generation). Except for these two factors, the basic logic are very similar between organismal and cancer population genetics, indicating that a number of well established theories of organismal population genetics could be translated to cancer population genetics with simple modifications.

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GADMA: Genetic algorithm for inferring demographic history of multiple populations from allele frequency spectrum data

GigaScience ◽

10.1093/gigascience/giaa005 ◽

2020 ◽

Vol 9 (3) ◽

Cited By ~ 1

Author(s):

Ekaterina Noskova ◽

Vladimir Ulyantsev ◽

Klaus-Peter Koepfli ◽

Stephen J O’Brien ◽

Pavel Dobrynin

Keyword(s):

Genetic Algorithm ◽

Allele Frequency ◽

Frequency Spectrum ◽

Demographic History ◽

Model Organisms ◽

Demographic Model ◽

Multiple Populations ◽

Demographic Models ◽

Allele Frequency Spectrum ◽

History Of

Abstract Background The demographic history of any population is imprinted in the genomes of the individuals that make up the population. One of the most popular and convenient representations of genetic information is the allele frequency spectrum (AFS), the distribution of allele frequencies in populations. The joint AFS is commonly used to reconstruct the demographic history of multiple populations, and several methods based on diffusion approximation (e.g., ∂a∂i) and ordinary differential equations (e.g., moments) have been developed and applied for demographic inference. These methods provide an opportunity to simulate AFS under a variety of researcher-specified demographic models and to estimate the best model and associated parameters using likelihood-based local optimizations. However, there are no known algorithms to perform global searches of demographic models with a given AFS. Results Here, we introduce a new method that implements a global search using a genetic algorithm for the automatic and unsupervised inference of demographic history from joint AFS data. Our method is implemented in the software GADMA (Genetic Algorithm for Demographic Model Analysis, https://github.com/ctlab/GADMA). Conclusions We demonstrate the performance of GADMA by applying it to sequence data from humans and non-model organisms and show that it is able to automatically infer a demographic model close to or even better than the one that was previously obtained manually. Moreover, GADMA is able to infer multiple demographic models at different local optima close to the global one, providing a larger set of possible scenarios to further explore demographic history.

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West Asian sources of the Eurasian component in Ethiopians: a reassessment

Scientific Reports ◽

10.1038/s41598-019-55344-y ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 2

Author(s):

Ludovica Molinaro ◽

Francesco Montinaro ◽

Burak Yelmen ◽

Davide Marnetto ◽

Doron M. Behar ◽

...

Keyword(s):

Allele Frequency ◽

Frequency Spectrum ◽

Mediterranean Area ◽

Genetic Component ◽

Single Site ◽

Allele Sharing ◽

Population Movements ◽

Genomic Signatures ◽

Allele Frequency Spectrum ◽

Source Populations

AbstractThe presence of genomic signatures of Eurasian origin in contemporary Ethiopians has been reported by several authors and estimated to have arrived in the area from 3000 years ago. Several studies reported plausible source populations for such a signature, using haplotype based methods on modern data or single-site methods on modern or ancient data. These studies did not reach a consensus and suggested an Anatolian or Sardinia-like proxy, broadly Levantine or Neolithic Levantine as possible sources. We demonstrate, however, that the deeply divergent, autochthonous African component which accounts for ~50% of most contemporary Ethiopian genomes, affects the overall allele frequency spectrum to an extent that makes it hard to control for it and, at once, to discern between subtly different, yet important, Eurasian sources (such as Anatolian or Levant Neolithic ones). Here we re-assess pattern of allele sharing between the Eurasian component of Ethiopians (here called “NAF” for Non African) and ancient and modern proxies. Our results unveil a genomic legacy that may connect the Eurasian genetic component of contemporary Ethiopians with Sea People and with population movements that affected the Mediterranean area and the Levant after the fall of the Minoan civilization.

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