Artificial intelligence for dementia genetics and omics

Bettencourt, Conceicao, Skene, Nathan, Bandres‐Ciga, Sara, Anderson, Emma, Winchester, Laura M., Foote, Isabelle F., Schwartzentruber, Jeremy, Botia, Juan A., Nalls, Mike, Singleton, Andrew, Schilder, Brian M., Humphrey, Jack, Marzi, Sarah J., Toomey, Christina E., Kleifat, Ahmad Al, Harshfield, Eric L., Garfield, Victoria, Sandor, Cynthia ORCID: https://orcid.org/0000-0002-8905-1052, Keat, Samuel, Tamburin, Stefano, Frigerio, Carlo Sala, Lourida, Ilianna, the Deep Dementia Phenotyping (DEMON) Network, Ranson, Janice M. and Llewellyn, David J. 2023. Artificial intelligence for dementia genetics and omics. Alzheimer's & Dementia: The Journal of the Alzheimer's Association 19 , pp. 5905-5921. 10.1002/alz.13427

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Abstract

Genetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. We identified key remaining challenges: First, can we enhance genetic studies to address missing heritability? Can we identify reproducible omics signatures that differentiate between dementia subtypes? Can high‐dimensional omics data identify improved biomarkers? How can genetics inform our understanding of causal status of dementia risk factors? And which biological processes are altered by dementia‐related genetic variation? Artificial intelligence (AI) and machine learning approaches give us powerful new tools in helping us to tackle these challenges, and we review possible solutions and examples of best practice. However, their limitations also need to be considered, as well as the need for coordinated multidisciplinary research and diverse deeply phenotyped cohorts. Ultimately AI approaches improve our ability to interrogate genetics and omics data for precision dementia medicine. Highlights: We have identified five key challenges in dementia genetics and omics studies. AI can enable detection of undiscovered patterns in dementia genetics and omics data. Enhanced and more diverse genetics and omics datasets are still needed. Multidisciplinary collaborative efforts using AI can boost dementia research.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Medicine
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by/4.0/
Publisher:	Wiley
ISSN:	1552-5260
Date of First Compliant Deposit:	23 August 2023
Date of Acceptance:	18 July 2023
Last Modified:	10 Jan 2024 15:11
URI:	https://orca.cardiff.ac.uk/id/eprint/161990

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