  Family history is a major risk factor for many types of cancer. Mendelian
risk prediction models translate family histories into cancer risk predictions
based on knowledge of cancer susceptibility genes. These models are widely used
in clinical practice to help identify high-risk individuals. Mendelian models
leverage the entire family history, but they rely on many assumptions about
cancer susceptibility genes that are either unrealistic or challenging to
validate due to low mutation prevalence. Training more flexible models, such as
neural networks, on large databases of pedigrees can potentially lead to
accuracy gains. In this paper, we develop a framework to apply neural networks
to family history data and investigate their ability to learn inherited
susceptibility to cancer. While there is an extensive literature on neural
networks and their state-of-the-art performance in many tasks, there is little
work applying them to family history data. We propose adaptations of
fully-connected neural networks and convolutional neural networks to pedigrees.
In data simulated under Mendelian inheritance, we demonstrate that our proposed
neural network models are able to achieve nearly optimal prediction
performance. Moreover, when the observed family history includes misreported
cancer diagnoses, neural networks are able to outperform the Mendelian BRCAPRO
model embedding the correct inheritance laws. Using a large dataset of over
200,000 family histories, the Risk Service cohort, we train prediction models
for future risk of breast cancer. We validate the models using data from the
Cancer Genetics Network.
