  The genetic etiologies of common diseases are highly complex and
heterogeneous. Classic statistical methods, such as linear regression, have
successfully identified numerous genetic variants associated with complex
diseases. Nonetheless, for most complex diseases, the identified variants only
account for a small proportion of heritability. Challenges remain to discover
additional variants contributing to complex diseases. Expectile regression is a
generalization of linear regression and provides completed information on the
conditional distribution of a phenotype of interest. While expectile regression
has many nice properties and holds great promise for genetic data analyses
(e.g., investigating genetic variants predisposing to a high-risk population),
it has been rarely used in genetic research. In this paper, we develop an
expectile neural network (ENN) method for genetic data analyses of complex
diseases. Similar to expectile regression, ENN provides a comprehensive view of
relationships between genetic variants and disease phenotypes and can be used
to discover genetic variants predisposing to sub-populations (e.g., high-risk
groups). We further integrate the idea of neural networks into ENN, making it
capable of capturing non-linear and non-additive genetic effects (e.g.,
gene-gene interactions). Through simulations, we showed that the proposed
method outperformed an existing expectile regression when there exist complex
relationships between genetic variants and disease phenotypes. We also applied
the proposed method to the genetic data from the Study of Addiction: Genetics
and Environment(SAGE), investigating the relationships of candidate genes with
smoking quantity.

Applications;Machine Learning