A Machine Learning Method to Identify Genetic Variants Potentially Associated With Alzheimer's Disease

There is hope that genomic information will assist prediction, treatment, and understanding of Alzheimer's disease (AD). Here, using exome data from similar to 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 an 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 that netSNP can identify SNPs associated with AD pathophysiology that may assist with the diagnosis and mechanistic understanding of the disease.

Automated summary

Through machine learning and neural network methods, a study analyzed the impact of SNPs on Alzheimer's disease risk and developed a method called netSNP that identified hundreds of novel AD-associated SNPs. The study found correlations between the number of protective or at-risk SNPs identified by netSNP in the genome, the age of AD diagnosis, and neuropathologic changes. The results suggest that netSNP can assist in diagnosing and understanding the pathophysiology of AD.