Artificial intelligence for dementia genetics and omics

Conceição Bettencourt, Nathan G. Skene, Sara Bandres-Ciga, Emma Anderson, Laura M. Winchester, Isabelle F. Foote, Jeremy Schwartzentruber, Juan A. Botia, Mike A. Nalls, Andrew Singleton, Brian M. Schilder, Jack Humphrey, Sarah Marzi, Ahmad Al Khleifat, Christina E. Toomey, Eric Harshfield, Victoria Garfield, Cynthia Sandor, Samuel Keating, Stefano TamburinCarlo Sala Frigerio, Ilianna Lourida, Janice M. Ranson, David J. Llewellyn

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Genetics and omics studies of Alzheimer’s disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be tar- geted. 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.
Original languageEnglish
JournalAlzheimer's Dementia: The Journal of the Alzheimer's Association
Publication statusPublished - 18 Jul 2023


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