Symbolic Modeling of Epistasis

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  author =       "Jason H. Moore and Nate Barney and Chia-Ti Tsai and 
                 Fu-Tien Chiang and Jiang Gui and Bill C. White",
  title =        "Symbolic Modeling of Epistasis",
  journal =      "Human Heredity",
  year =         "2007",
  volume =       "63",
  number =       "2",
  pages =        "120--133",
  month =        feb,
  keywords =     "genetic algorithms, genetic programming, Data mining,
                 Gene-gene interaction, Function mapping, Symbolic
                 discriminant analysis",
  DOI =          "doi:10.1159/000099184",
  abstract =     "The workhorse of modern genetic analysis is the
                 parametric linear model. The advantages of the linear
                 modelling framework are many and include a mathematical
                 understanding of the model fitting process and ease of
                 interpretation. However, an important limitation is
                 that linear models make assumptions about the nature of
                 the data being modelled. This assumption may not be
                 realistic for complex biological systems such as
                 disease susceptibility where nonlinearities in the
                 genotype to phenotype mapping relationship that result
                 from epistasis, plastic reaction norms, locus
                 heterogeneity, and phenocopy, for example, are the norm
                 rather than the exception. We have previously developed
                 a flexible modelling approach called symbolic
                 discriminant analysis (SDA) that makes no assumptions
                 about the patterns in the data. Rather, SDA lets the
                 data dictate the size, shape, and complexity of a
                 symbolic discriminant function that could include any
                 set of mathematical functions from a list of candidates
                 supplied by the user. Here, we outline a new five step
                 process for symbolic model discovery that uses genetic
                 programming (GP) for coarse-grained stochastic
                 searching, experimental design for parameter
                 optimisation, graphical modeling for generating expert
                 knowledge, and estimation of distribution algorithms
                 for fine-grained stochastic searching. Finally, we
                 introduce function mapping as a new method for
                 interpreting symbolic discriminant functions. We show
                 that function mapping when combined with measures of
                 interaction information facilitates statistical
                 interpretation by providing a graphical approach to
                 decomposing complex models to highlight synergistic,
                 redundant, and independent effects of polymorphisms and
                 their composite functions. We illustrate this five step
                 SDA modeling process with a real case-control

Genetic Programming entries for Jason H Moore Nate Barney Chia-Ti Tsai Fu-Tien Chiang Jiang Gui Bill C White