Sentence examples for mutations with selection from inspiring English sources

Mutations with selection coefficients − σ ˜ < σ < σ ˜ fix predominantly as passenger mutations.

A genomic site of selection coefficient f evolves by beneficial mutations with selection coefficient σ = f > 0 and by deleterious mutations with selection coefficient σ = − f < 0 (recall that f is, by definition, nonnegative).

The probability of fixation was calculated as (1) where Cs is the number of novel mutations with selection coefficient s that occurred from generation 2,000 to 10,000 in all offspring, whereas Fs is the number of fixed mutations with selection coefficient s within the same period of 8,000 generations.

Clonal interference implies the existence of a characteristic selection strength σ ˜, such that mutations with selection coefficient σ > σ ˜ are mostly driving mutations (i.e., independent of interference) and mutations with σ < σ ˜ are mostly passenger mutations (i.e., subject to interference).

Increasing this number to 50 generations allowed competition among genotypes to strongly bias allele frequencies, causing a loss of power to detect mutations with selection coefficients greater than 0.05.

Moderately beneficial or deleterious passenger mutations (with selection coefficients − σ ˜ < σ < σ ˜ ) are reduced to near-neutral fixation probabilities, and only strongly deleterious mutations (with σ < σ ˜ ) are under significant evolutionary constraint (Schiffels et al. 2011).

Consider a new adaptive mutation with selection coefficient s > 0 in a panmictic haploid population of constant size N.

An adaptive mutation with selection coefficient s that successfully escapes early stochastic loss requires τfix ∼ log(Ne s)/ s generations until it eventually fixes in the population (Hermisson and Pennings 2005; Desai and Fisher 2007).

The selection-dependent substitution rate is given by the product of the mutation rate and probability of fixation G of a mutation with selection coefficient σ, (3) V = G U. For unlinked sites, the fixation probability is given by Kimura's classical result, G0 = (1 − exp[−2σ])/(1 − exp[−2 Nσ]) (Kimura 1962).

Following the implementation in SFS_CODE (Hernandez 2008), the fitness of each individual is the product of the fitness effect of the mutations it carries, which for a mutation with selection coefficient s (s < 0 for deleterious mutations) is 1 + s in heterozygotes and (1 + s) in homozygotes.

GEMA regularly outputs the following major parameters: Current generation, total fitness of the population, number of SNPs, total number of fixed mutations (Fs), and total number of mutations (Cs) with selection coefficient s.