A Machine Learning Method to Identify Genetic Variants Potentially Associated With Alzheimer’s Disease - Monk, Bradley; Rajkovic, Andrei; Petrus, Semar; Rajkovic, Aleks; Gaasterland, Terry; Malinow, Roberto (Frontiers in Genetics, 2021)
There is hope that genomic information will assist prediction, treatment and understanding of Alzheimer’s disease (AD). Here, using exome data from ~10,000 individuals, we explore machine learning neural network (NN) methods to estimate the impact of SNPs (i.e. genetic variants) on AD risk. We develop a NN-based method (netSNP) that identifies hundreds of novel potentially protective or at-risk AD-associated SNPs (along with an effect measure); the majority with frequency under 0.01. For case individuals, the number of “protective” (or “at-risk”) netSNP-identified SNPs in their genome correlates positively (or inversely) with their age of AD diagnosis and inversely (or positively) with autopsy neuropathology. The effect measure increases correlations. Simulations suggest our results are not due to genetic linkage, overfitting or bias introduced by netSNP. These findings suggest netSNP can identify SNPs associated with AD pathophysiology that may assist with diagnosis and mechanistic understanding of the disease.