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Background  Genome-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.    Results  In 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.    Conclusions  The 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.

Multivariate genome wide association and network analysis of subcortical imaging phenotypes in Alzheimer’s disease