Deep Post-GWAS Analysis Identifies Potential Risk Genes and Causal Variants for Alzheimer’s Disease, Providing New Insights Into Its Disease Mechanisms

Abstract

Background: Alzheimer’s disease (AD) is a genetically complex, multifactorial neurodegenerative disease. It affects more than 45 million people worldwide and currently remains untreatable. Although genome-wide association studies (GWAS) have identified many AD-associated common variants, only about 25 genes are currently known to affect the risk of developing AD, despite its highly polygenic nature. Moreover, the causal variants underlying GWAS AD-association signals remain unknown.Methods: We developed a computational pipeline that integrates 936 AD-associated SNPs, linkage disequilibrium and genomic data from multiple sources – e.g., disease genes databases, functional annotation of genetic variants, GTEx, and the 1000 Genomes Project – to predict both AD risk genes and their causal variants.Results: We identified 342 putative AD risk genes in 203 risk regions spanning 502 AD-associated common variants. 246 AD risk genes have not been identified as AD risk genes by previous GWAS, and 115 of them are outside the risk regions, likely under the regulation of transcriptional regulatory elements contained therein. Even more significantly, for 109 AD risk genes, we predicted 150 causal variants, of both coding and regulatory (in promoters or enhancers) types, and 85 (57%) of them are supported by functional annotation. In-depth functional analyses showed that AD risk genes were overrepresented in AD-related pathways or GO terms – e.g., the complement and coagulation cascade and phosphorylation and activation of immune response – and their expression was relatively enriched in microglia, endothelia, and pericytes of the human brain. We found nine AD risk genes – e.g., IL1RAP, PMAIP1, LAMTOR4 – as predictors for the prognosis of AD survival and genes such as ARL6IP5 with altered network connectivity between AD patients and normal individuals involved in AD progression.Conclusions: Our findings provide novel biological insights into the genetic architecture, expression profiles, functional pathways involved in the AD etiology, and open new strategies for developing therapeutics targeting AD risk genes or causal variants to influence AD pathogenesis.