Module Analysis Using Single-Patient Differential Expression Signatures Improves the Power of Association Studies for Alzheimer's Disease.

Jialan Huang,Dong Lu,Guofeng Meng

doi:10.3389/fgene.2020.571609

Jialan Huang, Dong Lu + Show 1 more

Open Access

https://doi.org/10.3389/fgene.2020.571609

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Abstract

The causal mechanism of Alzheimer's disease is extremely complex. Achieving great statistical power in association studies usually requires a large number of samples. In this work, we illustrated a different strategy to identify AD risk genes by clustering AD patients into modules based on their single-patient differential expression signatures. The evaluation suggested that our method could enrich AD patients with similar clinical manifestations. Applying this to a cohort of only 310 AD patients, we identified 174 AD risk loci at a strict threshold of empirical p < 0.05, while only two loci were identified using all the AD patients. As an evaluation, we collected 23 AD risk genes reported in a recent large-scale meta-analysis and found that 18 of them were rediscovered by association studies using clustered AD patients, while only three of them were rediscovered using all AD patients. Functional annotation suggested that AD-associated genetic variants mainly disturbed neuronal/synaptic function. Our results suggested module analysis helped to enrich AD patients affected by the common risk variants.

Highlights

Alzheimer’s disease is a prevalent neurological disease among the aging population
Considering the diversity of AD patients, we propose a new analysis strategy to cluster the AD patients affected by the common mechanisms
Using each patient as a seed, we cluster the patients into modules if they carry the same set of single-patient differentially expressed genes (spDEGs)

Summary

Introduction

Even with decades of intensive studies, its causal mechanisms remain elusive. Studies of the familial early-onset cases revealed three mutated genes, including APP, PSEN1, and PSEN2 (Lanoiselée et al, 2017). They provided valuable insights into the contribution of the amyloidogenic pathway for AD genesis. Genome-wide association studies (GWAS) of late-onset AD patients discovered more risk genes. APOE ε4, an apolipoprotein, is a major genetic risk of late-onset AD. It accounts for 3- (heterozygous) to 15-fold (homozygous) increase in AD risk (De Jager et al, 2012). There is still a great challenge on how to illustrate the AD causal mechanism in an integrated way, limiting their application in drug discovery

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