Deep neural network improves the estimation of polygenic risk scores for
  breast cancer
Adrien Badré Li Zhang Wellington Muchero Justin C. Reynolds Chongle Pan
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.