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

Autor:	Brabec JL; Department of Neurological Sciences, University of Vermont, Burlington, VT, United States., Lara MK; Department of Neurological Sciences, University of Vermont, Burlington, VT, United States., Tyler AL; The Jackson Laboratory, Bar Harbor, ME, United States., Mahoney JM; Department of Neurological Sciences, University of Vermont, Burlington, VT, United States.; The Jackson Laboratory, Bar Harbor, ME, United States.
Jazyk:	angličtina
Zdroj:	Frontiers in genetics [Front Genet] 2021 Apr 06; Vol. 12, pp. 625246. Date of Electronic Publication: 2021 Apr 06 (Print Publication: 2021).
DOI:	10.3389/fgene.2021.625246
Abstrakt:	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. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2021 Brabec, Lara, Tyler and Mahoney.)
Databáze:	MEDLINE
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