Duncan NRI Faculty and Staff Publications
Publication Date
9-14-2022
Journal
Cell Genomics
DOI
10.1016/j.xgen.2022.100162
PMID
36268052
PMCID
PMC9581494
PubMedCentral® Posted Date
7-26-2022
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Keywords
Alzheimer’s disease, machine learning, whole genome and exome sequencing, molecular interaction networks
Abstract
Most disease-gene association methods do not account for gene-gene interactions, even though these play a crucial role in complex, polygenic diseases like Alzheimer’s disease (AD). To discover new genes whose interactions may contribute to pathology, we introduce GeneEMBED. This approach compares the functional perturbations induced in gene interaction network neighborhoods by coding variants from disease versus healthy subjects. In two independent AD cohorts of 5,169 exomes and 969 genomes, GeneEMBED identified novel candidates. These genes were differentially expressed in post mortem AD brains and modulated neurological phenotypes in mice. Four that were differentially overexpressed and modified neurodegeneration in vivo are PLEC, UTRN, TP53, and POLD1. Notably, TP53 and POLD1 are involved in DNA break repair and inhibited by approved drugs. While these data show proof of concept in AD, GeneEMBED is a general approach that should be broadly applicable to identify genes relevant to risk mechanisms and therapy of other complex diseases.
Graphical Abstract