  Risk prediction capitalizing on emerging human genome findings holds great
promise for new prediction and prevention strategies. While the large amounts
of genetic data generated from high-throughput technologies offer us a unique
opportunity to study a deep catalog of genetic variants for risk prediction,
the high-dimensionality of genetic data and complex relationships between
genetic variants and disease outcomes bring tremendous challenges to risk
prediction analysis. To address these rising challenges, we propose a
kernel-based neural network (KNN) method. KNN inherits features from both
linear mixed models (LMM) and classical neural networks and is designed for
high-dimensional risk prediction analysis. To deal with datasets with millions
of variants, KNN summarizes genetic data into kernel matrices and use the
kernel matrices as inputs. Based on the kernel matrices, KNN builds a
single-layer feedforward neural network, which makes it feasible to consider
complex relationships between genetic variants and disease outcomes. The
parameter estimation in KNN is based on MINQUE and we show, that under certain
conditions, the average prediction error of KNN can be smaller than that of
LMM. Simulation studies also confirm the results.

Methodology