A telescope GWAS analysis strategy, based on SNPs-genes-pathways ensamble and on multivariate algorithms, to characterize late onset Alzheimer's disease

Margherita Squillario; Giulia Abate; Federico Tomasi; Veronica Tozzo; Annalisa Barla; Daniela Uberti; Alzheimer’s Disease Neuroimaging Initiative

doi:10.1038/s41598-020-67699-8

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A telescope GWAS analysis strategy, based on SNPs-genes-pathways ensamble and on multivariate algorithms, to characterize late onset Alzheimer's disease

Journal article

Open access

Peer reviewed

A telescope GWAS analysis strategy, based on SNPs-genes-pathways ensamble and on multivariate algorithms, to characterize late onset Alzheimer's disease

Margherita Squillario, Giulia Abate, Federico Tomasi, Veronica Tozzo, Annalisa Barla, Daniela Uberti and Alzheimer’s Disease Neuroimaging Initiative

Scientific reports, Vol.10(1), pp.12063-12063

07/21/2020

DOI: 10.1038/s41598-020-67699-8

PMCID: PMC7374579

PMID: 32694537

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Abstract

Genome-wide association studies (GWAS) have revealed a plethora of putative susceptibility genes for Alzheimer's disease (AD), with the sole exception of APOE gene unequivocally validated in independent study. Considering that the etiology of complex diseases like AD could depend on functional multiple genes interaction network, here we proposed an alternative GWAS analysis strategy based on (i) multivariate methods and on a (ii) telescope approach, in order to guarantee the identification of correlated variables, and reveal their connections at three biological connected levels. Specifically as multivariate methods, we employed two machine learning algorithms and a genetic association test and we considered SNPs, Genes and Pathways features in the analysis of two public GWAS dataset (ADNI-1 and ADNI-2). For each dataset and for each feature we addressed two binary classifications tasks: cases vs. controls and the low vs. high risk of developing AD considering the allelic status of APOEe4. This complex strategy allowed the identification of SNPs, genes and pathways lists statistically robust and meaningful from the biological viewpoint. Among the results, we confirm the involvement of TOMM40 gene in AD and we propose GRM7 as a novel gene significantly associated with AD.

Algorithms

Machine Learning

Magnetic Resonance Imaging

Alleles

Alzheimer Disease - diagnosis

Alzheimer Disease - genetics

Alzheimer Disease - psychology

Gene Expression Profiling

Gene Regulatory Networks

Genetic Predisposition to Disease

Genome-Wide Association Study - methods

Humans

Polymorphism, Single Nucleotide

Positron-Emission Tomography

Details

Title: Subtitle: A telescope GWAS analysis strategy, based on SNPs-genes-pathways ensamble and on multivariate algorithms, to characterize late onset Alzheimer's disease
Creators: Margherita Squillario - University of Genoa
Giulia Abate - University of Brescia
Federico Tomasi - University of Genoa
Veronica Tozzo - University of Genoa
Annalisa Barla - University of Genoa
Daniela Uberti - University of Brescia
Alzheimer’s Disease Neuroimaging Initiative
Contributors: HyungSub Shim (Contributor) - University of Iowa, Neurology
Resource Type: Journal article
Publication Details: Scientific reports, Vol.10(1), pp.12063-12063
DOI: 10.1038/s41598-020-67699-8
PMID: 32694537
PMCID: PMC7374579
NLM abbreviation: Sci Rep
ISSN: 2045-2322
eISSN: 2045-2322
Grant note: UL1 TR002369 / NCATS NIH HHS National Institutes of Health Grant U01 AG024904 / NIH HHS P30 AG066462 / NIA NIH HHS P30 AG062715 / NIA NIH HHS CIHR U01 AG024904 / NIA NIH HHS
Language: English
Date published: 07/21/2020
Academic Unit: Neurology; Psychiatry
Record Identifier: 9984303021902771

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