Artificial intelligence for dementia genetics and omics.
Bettencourt C., Skene N., Bandres-Ciga S., Anderson E., Winchester LM., Foote IF., Schwartzentruber J., Botia JA., Nalls M., Singleton A., Schilder BM., Humphrey J., Marzi SJ., Toomey CE., Kleifat AA., Harshfield EL., Garfield V., Sandor C., Keat S., Tamburin S., Frigerio CS., Lourida I., Deep Dementia Phenotyping (DEMON) Network None., Ranson JM., Llewellyn DJ.
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.