Identification of Biological Pathways to Alzheimer's Disease Using Polygenic Scores

Abstract

Introduction

Single nucleotide polymorphisms (SNPs) contribute small increases in risk for late-onset Alzheimer's disease (LOAD). LOAD SNPs cluster around genes with similar biological functions (pathways). Polygenic risk scores (PRS) aggregate the effect of SNPs genome-wide. However, this approach has not been widely used for SNPs within specific pathways.

Objectives

We investigated whether pathway-specific PRS were significant predictors of LOAD case/control status.

Methods

We mapped SNPs to genes within 8 pathways implicated in LOAD. For our polygenic analysis, the discovery sample comprised 13,831 LOAD cases and 29,877 controls. LOAD risk alleles for SNPs in our 8 pathways were identified at a P-value threshold of 0.5. Pathway-specific PRS were calculated in a target sample of 3332 cases and 9832 controls. The genetic data were pruned with R² > 0.2 while retaining the SNPs most significantly associated with AD. We tested whether pathway-specific PRS were associated with LOAD using logistic regression, adjusting for age, sex, country, and principal components. We report the proportion of variance in liability explained by each pathway.

Results

The most strongly associated pathways were the immune response (NSNPs = 9304, = 5.63 × 10⁻¹⁹, R² = 0.04) and hemostasis (NSNPs = 7832, P = 5.47 × 10⁻⁷, R² = 0.015). Regulation of endocytosis, hematopoietic cell lineage, cholesterol transport, clathrin and protein folding were also significantly associated but accounted for less than 1% of the variance. With APOE excluded, all pathways remained significant except proteasome-ubiquitin activity and protein folding.

Conclusions

Genetic risk for LOAD can be split into contributions from different biological pathways. These offer a means to explore disease mechanisms and to stratify patients.

Disclosure of interest

The authors have not supplied their declaration of competing interest.