Abstract

BackgroundGenome-wide association studies (GWAS) have identified many individual genes associated with brain imaging quantitative traits (QTs) in Alzheimer’s disease (AD). However single marker level association discovery may not be able to address the underlying biological interactions with disease mechanism.ResultsIn this paper, we used the MGAS (Multivariate Gene-based Association test by extended Simes procedure) tool to perform multivariate GWAS on eight AD-relevant subcortical imaging measures. We conducted multiple iPINBPA (integrative Protein-Interaction-Network-Based Pathway Analysis) network analyses on MGAS findings using protein-protein interaction (PPI) data, and identified five Consensus Modules (CMs) from the PPI network. Functional annotation and network analysis were performed on the identified CMs. The MGAS yielded significant hits within APOE, TOMM40 and APOC1 genes, which were known AD risk factors, as well as a few new genes such as LAMA1, XYLB, HSD17B7P2, and NPEPL1. The identified five CMs were enriched by biological processes related to disorders such as Alzheimer’s disease, Legionellosis, Pertussis, and Serotonergic synapse.ConclusionsThe statistical power of coupling MGAS with iPINBPA was higher than traditional GWAS method, and yielded new findings that were missed by GWAS. This study provides novel insights into the molecular mechanism of Alzheimer’s Disease and will be of value to novel gene discovery and functional genomic studies.

Highlights

  • Genome-wide association studies (GWAS) have identified many individual genes associated with brain imaging quantitative traits (QTs) in Alzheimer’s disease (AD)

  • Genome-wide association studies (GWAS) of AD or AD biomarkers have been performed at the single-nucleotide polymorphism (SNP) level [2,3,4] as well as at the higher level [5,6,7,8]

  • GWAS have greatly facilitated the identification of genetic markers associated with brain imaging quantitative traits (QTs) in AD [9, 10]

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Summary

Introduction

Genome-wide association studies (GWAS) have identified many individual genes associated with brain imaging quantitative traits (QTs) in Alzheimer’s disease (AD). GWAS have greatly facilitated the identification of genetic markers (e.g., single nucleotide polymorphisms or SNPs) associated with brain imaging quantitative traits (QTs) in AD [9, 10]. The identified single-SNP-single-QT associations typically have small effect sizes To bridge this gap, exploring single-SNP-multi-QT associations may have the potential to increase statistical power and identify meaningful imaging genetic associations. With this observation, we employ the MGAS (Multivariate Gene-based Association test by extended Simes procedure) tool [13] to perform multivariate GWAS on eight AD-relevant subcortical imaging measures

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