  Polygenic risk scores (PRS) estimate the genetic risk of an individual for a
complex disease based on many genetic variants across the whole genome. In this
study, we compared a series of computational models for estimation of breast
cancer PRS. A deep neural network (DNN) was found to outperform alternative
machine learning techniques and established statistical algorithms, including
BLUP, BayesA and LDpred. In the test cohort with 50% prevalence, the Area Under
the receiver operating characteristic Curve (AUC) were 67.4% for DNN, 64.2% for
BLUP, 64.5% for BayesA, and 62.4% for LDpred. BLUP, BayesA, and LPpred all
generated PRS that followed a normal distribution in the case population.
However, the PRS generated by DNN in the case population followed a bi-modal
distribution composed of two normal distributions with distinctly different
means. This suggests that DNN was able to separate the case population into a
high-genetic-risk case sub-population with an average PRS significantly higher
than the control population and a normal-genetic-risk case sub-population with
an average PRS similar to the control population. This allowed DNN to achieve
18.8% recall at 90% precision in the test cohort with 50% prevalence, which can
be extrapolated to 65.4% recall at 20% precision in a general population with
12% prevalence. Interpretation of the DNN model identified salient variants
that were assigned insignificant p-values by association studies, but were
important for DNN prediction. These variants may be associated with the
phenotype through non-linear relationships.
