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

Authors

Margherita Squillario, Università degli Studi di Genova
Giulia Abate, Università degli Studi di Brescia
Federico Tomasi, Università degli Studi di Genova
Veronica Tozzo, Università degli Studi di Genova
Annalisa Barla, Università degli Studi di Genova
Daniela Uberti, Università degli Studi di Brescia
Michael W. Weiner, University of California, San Francisco
Paul Aisen, University of California, San Diego
Ronald Petersen, Mayo Clinic
Jack R. Clifford, Mayo Clinic
William Jagust, University of California, Berkeley
John Q. Trojanowki, University of Pennsylvania
Arthur W. Toga, USC
Laurel Beckett, University of California, Davis
Robert C. Green, Harvard Medical School
Andrew J. Saykin, Indiana University Bloomington
John Morris, Washington University in St. Louis
Leslie M. Shaw, University of Pennsylvania
Zaven Khachaturian, Prevent Alzheimer’s Disease 2020
Greg Sorensen, Siemens AG
Maria Carrillo, Alzheimer’s Association
Lew Kuller, University of Pittsburgh
Marc Raichle, Washington University in St. Louis
Steven Paul, Cornell University
Peter Davies, Albert Einstein College of Medicine of Yeshiva University
Howard Fillit, AD Drug Discovery Foundation
Franz Hefti, Acumen Pharmaceuticals
Davie Holtzman, Washington University in St. Louis
M. Marcel Mesulam, Northwestern University
William Potter, National Institute of Mental Health (NIMH)
Peter Snyder, Brown University
Tom Montine, University of Washington

Document Type

Article

Publication Date

12-1-2020

Journal

Scientific Reports

Volume

10

Issue

1

URL with Digital Object Identifier

10.1038/s41598-020-67699-8

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.