The fitness landscape - the mapping between genotypes and fitness -
determines properties of the process of adaptation. Several small genetic
fitness landscapes have recently been built by selecting a handful of
beneficial mutations and measuring fitness of all combinations of these
mutations. Here we generate several testable predictions for the properties of
these landscapes under Fisher's geometric model of adaptation (FGMA). When far
from the fitness optimum, we analytically compute the fitness effect of
beneficial mutations and their epistatic interactions. We show that epistasis
may be negative or positive on average depending on the distance of the
ancestral genotype to the optimum and whether mutations were independently
selected or co-selected in an adaptive walk. Using simulations, we show that
genetic landscapes built from FGMA are very close to an additive landscape when
the ancestral strain is far from the optimum. However, when close to the
optimum, a large diversity of landscape with substantial ruggedness and sign
epistasis emerged. Strikingly, landscapes built from different realizations of
stochastic adaptive walks in the same exact conditions were highly variable,
suggesting that several realizations of small genetic landscapes are needed to
gain information about the underlying architecture of the global adaptive
landscape.Comment: 51 pages, 8 figure