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

Abstract

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 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.

Machine learning methods applied to genotyping data capture interactions between single nucleotide variants in late onset Alzheimer's disease

“…Other studies indicated that ML methods are robust in terms of performance when dealing with SNVs in LD. 44,45,46 As in other works, 16 we found that tree-based ML methods can add an important layer of information to the disease-related variants obtained with other population genomic approaches such as GWAS.…”

“…Other studies indicated that ML methods are robust in terms of performance when dealing with SNVs in LD. 44 , 45 , 46 …”

“…However, with the data we had we could not demonstrate that the three SNVs were independently associated with AD. Other studies indicated that ML methods are robust in terms of performance when dealing with SNVs in LD 44,45,46 …”

Exploiting machine learning models to identify novel Alzheimer’s disease biomarkers and potential targets

“…Nonetheless, early detection of AD is key to its treatment. Thus, researcher efforts are also directed at detecting AD using artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms and incorporating different types of data including but not limited to: neuroimaging data 7 , non-coding RNAs 8 , 9 , transcriptomic data 10 , miRNAs biomarker 11 , or other genome data 12 . Another direction that researchers paid more attention to is to repurpose approved drugs to treat AD 13 , 14 .…”

A simulative deep learning model of SNP interactions on chromosome 19 for predicting Alzheimer's disease risk and rates of disease progression

“…Data-driven methods (e.g., machine and deep learning models) are cutting-edge tools for pattern recognition and have been applied to GWAS data. [20][21][22][23][24] These methods have identified new AD-linked SNPs, but so far these methods still fall short in correctly estimating the impact of AD risk and protective variants at the individual level. There are likely several reasons for suboptimal estimates of risk.…”