Genetic risk predictions using deep learning models with summary data

As a driving force of the Fourth Industrial Revolution, deep learning methods have achieved significant success across various fields, including genetic and genomic studies. While individual-level genetic data is ideal for deep learning models, privacy concerns and data-sharing restrictions often limit its availability to researchers.

In this paper, we investigated the potential applications of deep learning models-including deep neural networks, convolutional neural networks, recurrent neural networks, and transformers-when only genetic summary data, such as linkage disequilibrium matrices, is available. The bootstrap method was used to approximate the test error. Simulation studies and real data analyses were conducted to compare the performance of deep learning methods in genetic risk prediction using individual-level genetic data versus genetic summary data.

The test mean squared errors (MSEs) of most applied deep learning models are comparable when using individual-level data versus summary data.

Our results suggest that suitable deep learning methods could also serve as an alternative approach to predict disease related traits when only linkage disequilibrium matrices are available as input.