  The advent of artificial intelligence, especially the progress of deep neural
networks, is expected to revolutionize genetic research and offer unprecedented
potential to decode the complex relationships between genetic variants and
disease phenotypes, which could mark a significant step toward improving our
understanding of the disease etiology. While deep neural networks hold great
promise for genetic association analysis, limited research has been focused on
developing neural-network-based tests to dissect complex genotype-phenotype
associations. This complexity arises from the opaque nature of neural networks
and the absence of defined limiting distributions. We have previously developed
a kernel-based neural network model (KNN) that synergizes the strengths of
linear mixed models with conventional neural networks. KNN adopts a
computationally efficient minimum norm quadratic unbiased estimator (MINQUE)
algorithm and uses KNN structure to capture the complex relationship between
large-scale sequencing data and a disease phenotype of interest. In the KNN
framework, we introduce a MINQUE-based test to assess the joint association of
genetic variants with the phenotype, which considers non-linear and
non-additive effects and follows a mixture of chi-square distributions. We also
construct two additional tests to evaluate and interpret linear and
non-linear/non-additive genetic effects, including interaction effects. Our
simulations show that our method consistently controls the type I error rate
under various conditions and achieves greater power than a commonly used
sequence kernel association test (SKAT), especially when involving non-linear
and interaction effects. When applied to real data from the UK Biobank, our
approach identified genes associated with hippocampal volume, which can be
further replicated and evaluated for their role in the pathogenesis of
Alzheimer's disease.

Quantitative Methods;Machine Learning;Methodology