Alzheimer’s Disease Risk Genes Mining Based on a Supervised Machine Learning Method and PPI Network Construction

Although genome-wide association studies (GWAS) have identified multiple Alzheimer’s disease (AD) risk genes by many novel machine learning methods, their ability to explain the "missing heritability" remains insufficient. One possible reason is that these methods typically discard single-nucleotide polymorphisms (SNPs) with marginal main effects during the dimensionality reduction process. Another reason could be the reliance on case-control study designs, which are less statistically informative than the quantitative trait (QT). In addition, synapse plays an important role in the pathogenesis of AD, but only a few genes related to synapse have been reported based on these methods. Thus, different approaches are necessary to better understand and explain the "missing heritability" of AD. By using CSF T-tau/Aβ
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ratio as QT, this paper performed genome-wide SNP-SNP interactions based on a supervised machine learning method. A total of 467 statistically significant SNP-SNP interaction pairs were found to have marginal main effects, and explained a high-level variance of T-tau/Aβ
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. To further mining AD risk genes that related to synapses, the Protein–Protein Interaction (PPI) network construction method was used. As a result, a subnetwork with 5 genes were found, which might have effects on both synaptic and cognitive functions within AD patients.