  Genome-Wide Association Studies (GWAS) face unique challenges in the era of
big genomics data, particularly when dealing with ultra-high-dimensional
datasets where the number of genetic features significantly exceeds the
available samples. This paper introduces an extension to the feature selection
methodology proposed by Mirzaei et al. (2020), specifically tailored to tackle
the intricacies associated with ultra-high-dimensional GWAS data. Our extended
approach enhances the original method by introducing a Frobenius norm penalty
into the student network, augmenting its capacity to adapt to scenarios
characterized by a multitude of features and limited samples. Operating
seamlessly in both supervised and unsupervised settings, our method employs two
key neural networks. The first leverages an autoencoder or supervised
autoencoder for dimension reduction, extracting salient features from the
ultra-high-dimensional genomic data. The second network, a regularized
feed-forward model with a single hidden layer, is designed for precise feature
selection. The introduction of the Frobenius norm penalty in the student
network significantly boosts the method's resilience to the challenges posed by
ultra-high-dimensional GWAS datasets. Experimental results showcase the
efficacy of our approach in feature selection for GWAS data. The method not
only handles the inherent complexities of ultra-high-dimensional settings but
also demonstrates superior adaptability to the nuanced structures present in
genomics data. The flexibility and versatility of our proposed methodology are
underscored by its successful performance across a spectrum of experiments.
