  Modelling gene-gene epistatic interactions when computing genetic risk scores
is not a well-explored subfield of genetics and could have potential to improve
risk stratification in practice. Though applications of machine learning (ML)
show promise as an avenue of improvement for current genetic risk assesments,
they frequently suffer from the problem of two many features and to little
data. We propose a method that when combined with ML allows information from
individual genetic contributors to be preserved while incorporating information
on their interactions in a single feature. This allows second-order analysis,
while simultaneously increasing the number of input features to ML models as
little as possible. We presented three methods that can be utilized to account
for genetic interactions. We found that interaction methods that preserved
information from the constituent SNPs performed significantly better than the
simplest interaction method. Since the currently available ML methods are able
to account for complex interactions, utilizing raw SNP genotypes alone is
sufficient because the simplest model outperforms all the interaction methods
Given that understanding and accounting for epistatic interactions is one of
the most promising avenues for increasing explained variability in heritable
disease, this work represents a first step toward an algorithmic interaction
method that preserves the information in each component. This is relevant not
only because of potential improvements in model quality, but also because
explicit interaction terms allow a human readable interpretation of potential
interaction pathways within the disease.
