KNOT: a knockoff-augmented neural network for identifying risk variants and epistatic interactions in family-based association studies

Abstract While deep learning has recently been used for identifying risk variants in genome-wide association studies, its stochastic nature and the complex correlation within genetic data have posed significant challenges for deep-learning-based methods to pinpoint risk variants. We introduce KNOT, a knockoff-augmented neural network for stabilized variable selection with false discovery rate control. KNOT employs contrastive learning and attention mechanisms to model sample relatedness and linkage disequilibrium, alongside a permutation test to detect epistatic interactions. In applications to two autism spectrum disorder family cohorts, KNOT outperforms conventional methods by identifying more known and putative risk loci and epistatic interactions for autism.