Identifying the metabolomics signatures of genetic variants identified for Alzheimer’s disease

Abstract

AbstractBackgroundIntegrating genomics and metabolomics data in humans has the potential to unravel mechanistic insights about and look for evidence of metabolite causality in complex disorders.MethodWe conducted metabolome‐wide association studies (MWAS) on Alzheimer’s disease (AD) associated loci using ultraperformance liquid chromatography–mass spectrometry (UPLC‐MS) in two cohorts of the Airwave Study (1). Expanding our previous study focusing on ABCA7 (2), current MWAS investigated genetic variants in all other loci based on the largest and most recent GWAS on AD (3) to identify metabolic features associated with AD. We performed genome‐wide association studies in the Airwave Study (N = 3,708) to obtain genetic instruments of those features to run Mendelian randomisation (MR) and colocalization analyses.ResultOur MWAS identified 104 features associated with AD loci at metabolome‐wide significance level. MR analysis was conducted on 29 out of 104 features with at least three genetic instruments, showing the potentially causal effects of four metabolic features associated with rs1800978 (ABCA1) and rs587709 (LILRB2). Our in‐house annotation pipeline mapped those features as sphingomyelin (SM)(18:1/20:1), putative sphingolipid, and N‐Acylphosphatidylethanolamines (NAPE). We conducted pairwise colocalisation and HyPrColoc analysis, supporting evidence of common causal variants between SM(18:1/20:1), putative sphingolipid, and NAPE with AD (posterior probability of shared causal variant >0.8).ConclusionThis analysis has broadened our insight into the metabolic signatures of AD, with further study needed to dissect the underlying molecular mechanisms.