Nevertheless, a consistent relation between selection coefficient and probability of fixation does, and the result relevant to our analysis, the ratio of the fixation rates of the expansive and diminutive alleles, should not be adversely affected.
By accounting for the background set of polymorphisms on which a trajectory develops, the linked method corrects for clonal interference, which can reduce apparent selection coefficients, and, through recognition of fixation events occurring through hitchhiking with driver alleles, assigns lower selection coefficients to passenger loci at which the mutant allele reaches fixation.
The selection model implemented in SFS_CODE is a model of shift in fitness, such that the selection coefficient of alleles that reach fixation is reset to 1. Throughout, we therefore ignored mutations that reach fixation.
Our suite of selection coefficients maps to the full range of probabilities of fixation because selection coefficients larger than s = 2 × 10−6 under our model would produce asymptotically smaller increases in the probability of fixation and thus would generate results essentially equivalent to s = 2 × 10−6.
Mutations supply the genetic variation for adaptation, but their success depends on the selective coefficient (s), which influences both the probability of fixation of a mutation and its frequency trajectory [ 1].
These models build on two separate parameters for a newly arisen mutant allele: the probability of mutation (effect of mutational bias or mutating tendency towards the mutated nucleotide) and the probability of fixation (effect of selection coefficients).
Had we chosen to use a different model of selection, we would then have tested somewhat different selection coefficients to map to the same range of fixation probabilities, but the results of the simulations would have been identical.
Artifacts in pupil measures, for example due to eye movements or eye blinks were eliminated using the circularity coefficient of the pupil outline to detect absence of fixation.
It is well known that selection on a single locus, defined by a selection coefficient s, acts more efficiently in larger populations, as can be seen in the dependence of fixation probabilities and fixation times on the population-scaled selection coefficient 4 N s (Ewens 2004).
The advantage of GEMA simulations lies in its ability to precisely measure the effect of a particular recombination rate (r-parameter) on the population fitness and probability of fixation of mutations with different selection coefficients.
