Genetic prediction of quantitative traits: a machine learner's guide
  focused on height
Lucie Bourguignon Caroline Weis Catherine R. Jutzeler Michael Adamer
Machine learning and deep learning have been celebrating many successes in
the application to biological problems, especially in the domain of protein
folding. Another equally complex and important question has received relatively
little attention by the machine learning community, namely the one of
prediction of complex traits from genetics. Tackling this problem requires
in-depth knowledge of the related genetics literature and awareness of various
subtleties associated with genetic data. In this guide, we provide an overview
for the machine learning community on current state of the art models and
associated subtleties which need to be taken into consideration when developing
new models for phenotype prediction. We use height as an example of a
continuous-valued phenotype and provide an introduction to benchmark datasets,
confounders, feature selection, and common metrics.