Genome-wide epistasis analysis for Alzheimer\u2019s disease and implications for genetic risk prediction

Hui Wang,Philip L De Jager,David A Bennett,Qing-Ye Zhang,Hong-Yu Zhang

doi:10.1186/s13195-021-00794-8

Abstract

BackgroundSingle-nucleotide polymorphisms (SNPs) identified by genome-wide association studies only explain part of the heritability of Alzheimer’s disease (AD). Epistasis has been considered as one of the main causes of “missing heritability” in AD.MethodsWe performed genome-wide epistasis screening (N = 10,389) for the clinical diagnosis of AD using three popularly adopted methods. Subsequent analyses were performed to eliminate spurious associations caused by possible confounding factors. Then, candidate genetic interactions were examined for their co-expression in the brains of AD patients and analyzed for their association with intermediate AD phenotypes. Moreover, a new approach was developed to compile the epistasis risk factors into an epistasis risk score (ERS) based on multifactor dimensional reduction. Two independent datasets were used to evaluate the feasibility of ERSs in AD risk prediction.ResultsWe identified 2 candidate genetic interactions with PFDR < 0.05 (RAMP3-SEMA3A and NSMCE1-DGKE/C17orf67) and another 5 genetic interactions with PFDR < 0.1. Co-expression between the identified interactions supported the existence of possible biological interactions underlying the observed statistical significance. Further association of candidate interactions with intermediate phenotypes helps explain the mechanisms of neuropathological alterations involved in AD. Importantly, we found that ERSs can identify high-risk individuals showing earlier onset of AD. Combined risk scores of SNPs and SNP-SNP interactions showed slightly but steadily increased AUC in predicting the clinical status of AD.ConclusionsIn summary, we performed a genome-wide epistasis analysis to identify novel genetic interactions potentially implicated in AD. We found that ERS can serve as an indicator of the genetic risk of AD.

Highlights

Single-nucleotide polymorphisms (SNPs) identified by genome-wide association studies only explain part of the heritability of Alzheimer’s disease (AD)
The biological interpretation of statistical interactions is challenging, as statistical interactions do not necessarily imply an interaction at the biological level [15]. This situation is further complicated by the problem of insufficient sample size, as samples are stratified into the 9 cells of a 3 × 3 contingency table instead of the 3 groups discriminated by the counts of minor alleles in a typical genome-wide association analysis (GWAS) analysis
Combined risk scores of Single nucleotide polymorphisms (SNP) and SNP-SNP interactions We evaluated if combined risk scores (CRS) of SNPs and SNP-SNP interactions could be a better indicator of AD risk

Summary

Introduction

Single-nucleotide polymorphisms (SNPs) identified by genome-wide association studies only explain part of the heritability of Alzheimer’s disease (AD). The biological interpretation of statistical interactions is challenging, as statistical interactions do not necessarily imply an interaction at the biological level [15] This situation is further complicated by the problem of insufficient sample size, as samples are stratified into the 9 cells of a 3 × 3 contingency table instead of the 3 groups discriminated by the counts of minor alleles in a typical GWAS analysis. The small sample size in the cells of the 3 × 3 contingency table could lead to invalid biological interpretations of statistical interactions Due to these limitations, only one genome-wide interaction analysis has identified an interaction between rs6455128 (KHDRBS2) and rs7989332 (CRYL1) that is replicable across datasets [16]. We analyzed the associations of candidate interactions with intermediate AD pathologies, including brain atrophy, white matter injury, and tau and amyloid deposition

Methods

Results

Discussion

Conclusion