---
res:
  bibo_abstract:
  - Fitness interactions between mutations can influence a population’s evolution
    in many different ways. While epistatic effects are difficult to measure precisely,
    important information is captured by the mean and variance of log fitnesses for
    individuals carrying different numbers of mutations. We derive predictions for
    these quantities from a class of simple fitness landscapes, based on models of
    optimizing selection on quantitative traits. We also explore extensions to the
    models, including modular pleiotropy, variable effect sizes, mutational bias and
    maladaptation of the wild type. We illustrate our approach by reanalysing a large
    dataset of mutant effects in a yeast snoRNA. Though characterized by some large
    epistatic effects, these data give a good overall fit to the non-epistatic null
    model, suggesting that epistasis might have limited influence on the evolutionary
    dynamics in this system. We also show how the amount of epistasis depends on both
    the underlying fitness landscape and the distribution of mutations, and so is
    expected to vary in consistent ways between new mutations, standing variation
    and fixed mutations.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Christelle
      foaf_name: Fraisse, Christelle
      foaf_surname: Fraisse
      foaf_workInfoHomepage: http://www.librecat.org/personId=32DF5794-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0001-8441-5075
  - foaf_Person:
      foaf_givenName: John J.
      foaf_name: Welch, John J.
      foaf_surname: Welch
  bibo_doi: 10.6084/m9.figshare.7957469.v1
  dct_date: 2020^xs_gYear
  dct_publisher: Royal Society of London@
  dct_title: Simulation code for Fig S1 from the distribution of epistasis on simple
    fitness landscapes@
...