Title: Risk-associated and pathway-based method to detect association with Alzheimer's disease

Authors: Jeffrey Mitchel; Laszlo Prokai; Youping Deng; Fan Zhang; Robert Barber

Addresses: Institute for Molecular Medicine, University of North Texas Health Science Center, Fort Worth, 76107, USA ' Center for Neuroscience Discovery, Institute for Healthy Aging, University of North Texas Health Science Center, Fort Worth, 76107, USA ' Department of Complementary and Integrative Medicine, University of Hawaii John A. Burns School of Medicine, Honolulu, HI 96813, USA ' Institute for Molecular Medicine, University of North Texas Health Science Center, Fort Worth, TX, 76107, USA; Vermont Genetics Network, University of Vermont, Burlington, VT, 05401, USA ' Institute for Molecular Medicine, University of North Texas Health Science Center, Fort Worth, 76107, USA ; Institute for Aging and Alzheimer's Disease Research, University of North Texas Health Science Center, Fort Worth, 76107, USA

Abstract: Genes do not function alone but through complex biological pathways in complex diseases such as Alzheimer's disease (AD). Unravelling these intricate pathways is essential to understanding biological mechanisms of the AD. Based on the integrated pathway analysis database (IPAD), we developed a pathway-based method to detect association with the AD. First, we performed risk associated allele analysis to determine if a major or minor allele is associated with risk. Then we performed pathway-disease association analysis to identify 133 AD-associated pathways. Lastly, we performed pathway-patient association analysis to investigate the patient's association and distribution among the 133 pathways. We found five AD-associated pathways that have the highest association with patients. We present a pathway-based method to detect AD-associated pathways from GWAS data. Our pathway-based analysis not only provides a technique to identify disease-associated pathways, but also help determine the pathway-patient association.

Keywords: Alzheimer's disease; pathway analysis; biomarker discovery.

DOI: 10.1504/IJCBDD.2018.090838

International Journal of Computational Biology and Drug Design, 2018 Vol.11 No.1/2, pp.154 - 170

Received: 08 Mar 2017
Accepted: 04 Aug 2017
Published online: 28 Mar 2018 *

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