Sentence examples for models of admixture from inspiring English sources

In this work, we introduce models of admixture across varying number of generations and ancestries in a geographic continuum.

We first evaluated the different models of admixture, allele frequencies and prior population information available in STRUCTURE.

We use the general mechanistic model presented here to derive closed-form expressions for the expected X-chromosomal admixture under two specific models of admixture, a single-admixture event and constant admixture over time.

Several studies have described mechanistic models of admixture (Chakraborty and Weiss 1988; Long 1991; Ewens and Spielman 1995; Guo et al. 2005; Verdu and Rosenberg 2011; Gravel 2012; Jin et al. 2014).

These applications often use a simple statistical model of admixture rather than a modeling perspective that incorporates a more realistic history of the admixture process.

The most prominent work on this problem has focused on estimating a model of admixture proportions of ancestral populations for each individual.

Analyses were performed within and between each sample set assuming K = 1 sub-populations (i.e. no population stratification), and K = 2 and 3 sub-populations (assuming a model of admixture) and revealed a homogenous ancestry of samples.

Here, we develop a general model of admixture that mechanistically accounts for complex historical admixture processes.

We used the conditional autoregressive (CAR) Gaussian model of admixture with linear trend surface, setting the spatial interaction parameter to 0.6.

Under a two-sex mechanistic admixture model with sex-biased admixture, we have demonstrated that the relationship between X-chromosomal and autosomal admixture fractions depends both on the time since admixture and on the model of admixture and does not simply follow a prediction from the fractions of X chromosomes and autosomes present in females.

We ran Tess 2.3.1 under the conditional autoregressive (CAR) Gaussian model of admixture with a linear trend surface (Durand et al. 2009), which is expected to be more robust against overestimation of Kmax in the presence of genetic clines (Guillot 2009; Francois and Durand 2010).