System-Level Analysis of Alzheimer’s Disease Prioritizes Candidate Genes for Neurodegeneration

Abstract

Alzheimer’s disease (AD) is a debilitating neurodegenerative disorder. Since the advent of the genome-wide association study (GWAS) we have come to understand much about the genes involved in AD heritability and pathophysiology. Large case-control meta-GWAS studies have increased our ability to prioritize weaker effect alleles, while the recent development of network-based functional prediction has provided a mechanism by which we can use machine learning to reprioritize GWAS hits in the functional context of relevant brain tissues like the hippocampus and amygdala. In parallel with these developments, groups like the Alzheimer’s Disease Neuroimaging Initiative (ADNI) have compiled rich compendia of AD patient data including genotype and biomarker information, including derived volume measures for relevant structures like the hippocampus and the amygdala. In this study we wanted to identify genes involved in AD-related atrophy of these two structures, which are often critically impaired over the course of the disease. To do this we developed a combined score prioritization method which uses the cumulative distribution function of a gene’s functional and positional score, to prioritize top genes that not only segregate with disease status, but also with hippocampal and amygdalar atrophy. Our method identified a mix of genes that had previously been identified in AD GWAS including APOE, TOMM40, and NECTIN2(PVRL2) and several others that have not been identified in AD genetic studies, but play integral roles in AD-effected functional pathways including IQSEC1, PFN1, and PAK2. Our findings support the viability of our novel combined score as a method for prioritizing region- and even cell-specific AD risk genes.

Network‐based analysis predicts interacting genetic modifiers from a meta‐mapping study of spike–wave discharge in mice

“…Network‐based functional prediction (NBFP) has previously been used to identify leading candidate genes in multiple contexts, including Alzheimer's disease, autism spectrum disorder, inflammatory bowel disease and histamine hypersensitivity. 17 , 18 , 19 , 20 , 21 , 22 Here, we used NBFP to narrow the field of candidate genes within two loci (chromosome 2: 116.97–136.97 Mb and chromosome 7: 85.46–105.46 Mb) involved in an antagonistic epistatic interaction driving differences seen in SWD in a meta‐mapping population in mice with SWD‐causing mutations 11 ; The term meta‐mapping, in this case, refers to the fact that multiple distinct mapping populations (two backcrosses and one intercross) were combined to boost statistical power to detect modifier genes that are independent of SWD etiology. We integrated known human GGE risk genes from GWAS and tissue‐specific gene networks to rank all genes in the human and mouse genomes by the strength of their functional association to GGE GWAS genes within SWD networks.…”

“…We performed NBFP to functionally score and rank all genes in the human and mouse genomes as previously described. 19 , 21 , 22 In the context of functional gene networks, the term “functional” refers to the biological activities or processes that genes are involved in beyond the physical interactions between genes. Functional gene networks capture relationships between genes based on biological pathways and cellular processes.…”

Regulation of autophagy, lipid metabolism, and neurodegenerative pathology by heparan sulfate proteoglycans

“…APP-derived peptides affect cholesterol metabolism and PSEN1/PSEN2 have effects on autophagosome to lysosome traffic, mitochondrial function, and lipid accumulation ( Van Acker et al, 2019 ). TOMM40, a gene of the outer mitochondrial membrane, has been identified in GWAS analysis for association with AD ( Brabec et al, 2021 ), and its expression is upregulated in postmortem AD brain ( Lee et al, 2021 ). It is intriguing that the pathway analysis from large GWAS and exome sequencing efforts implicated several of the cellular processes disrupted in PSEN1 mutant cells, suggesting that mechanistic insights gained from studies of PSEN1 mutants may be broadly applicable to AD pathogenesis.…”

The Alzheimer's Disease Neuroimaging Initiative in the era of Alzheimer's disease treatment: A review of ADNI studies from 2021 to 2022

“…In most cases of AD, the amygdala and hippocampus are sites of very early atrophy in disease progression. When machine learning was used to reprioritize hits from a GWAS of hippocampal and amygdala atrophy in AD, genes from several Aβ‐related pathways predominated: 224 (1) alteration of synaptic structure and function through effects on the cytoskeleton, (2) changes in intracellular calcium levels resulting in excitotoxicity, (3) apoptotic signaling via protein misfolding in the endoplasmic reticulum, and (4) transcriptional regulation (Figure S30 in supporting information). A possible causal association mechanism underlying hippocampal volume was identified using ADNI multiomics data from blood and hippocampal tissues combined with causal association tests 225 .…”