Abstract
I show how to use OncoSimulR, software for forward-time genetic simulations, to simulate evolution of asexual populations in the presence of epistatic interactions. This chapter emphasizes the specification of fitness and epistasis, both directly (i.e., specifying the effects of individual mutations and their epistatic interactions) and indirectly (using models for random fitness landscapes).
Highlights
We illustrate the use of the Bioconductor package OncoSimulR for simulating evolution of asexual populations with epistasis
Some previous uses of OncoSimulR include the study of the sensitivity of cancer progression models to reciprocal sign epistasis [12], the predictability of cancer evolution [13, 19], and somatic mutation in plants [33]
Computing the epistasis statistics on the log-transformed fitness data does not change the estimates of epistasis, since sign epistasis is not affected by monotonic transformations: epist_stats(fitness1, use_log = TRUE)
Summary
We illustrate the use of the Bioconductor package OncoSimulR for simulating evolution of asexual populations with epistasis. OncoSimulR [11] implements forward-time genetic simulations in asexual populations, using biallelic loci. Fitness can be defined either directly (by specifying the fitness landscape, or the map between genotypes and fitness), as shown in Subheadings 2.2.1 and 2.2.2, or by specifying epistatic interactions directly as shown in Subheadings 2.2.4–2.2.7. Simulations use a continuous time model, and employ the state-of-the-art BNB algorithm of Mather et al [23]. Some previous uses of OncoSimulR include the study of the sensitivity of cancer progression models to reciprocal sign epistasis [12], the predictability of cancer evolution [13, 19], and somatic mutation in plants [33]. Using OncoSimulR for the simulation of evolutionary processes involves: 1. Using OncoSimulR for the simulation of evolutionary processes involves: 1. Installing (if needed) and loading OncoSimulR
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