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Artificial intelligence for dementia genetics and omics

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Bettencourt, Conceicao 
Skene, Nathan 
Bandres-Ciga, Sara 
Anderson, Emma 
Winchester, Laura M 


INTRODUCTION: Genetics and omics studies of Alzheimer’s disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. METHODS: 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? RESULTS: 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. DISCUSSION: Ultimately AI approaches improve our ability to interrogate genetics and omics data for precision dementia medicine.



artificial intelligence, biomarkers, causality, dementia, disease pathways, etiology, genetics, machine learning, omics, pathology, risk factors

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Alzheimer's and Dementia

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Alzheimer's Society (573 (AS-RF-21-017))
British Heart Foundation (RE/18/1/34212)
This paper was the product of a DEMON Network state of the science symposium entitled “Harnessing Data Science and AI in Dementia Research” funded by Alzheimer’s Research UK. CB is supported by Alzheimer’s Research UK (ARUK-RF2019B-005) and Multiple System Atrophy Trust. EA is supported by MRC Skills Development Fellowship (MR/W011581/1) and UKRI Future Leaders Fellowship (MR/W011581/1). LW is supported Alzheimer’s Research UK. IFF is supported by the National Institute on Aging (RF1AG073593). MAN’s participation in this project was part of a competitive contract awarded to Data Tecnica International LLC by the National Institutes of Health to support open science research. JH is supported by the NIH National Institute of Neurological Disorders and Stroke (U54NS123743). SJM is funded by the Edmond and Lily Safra Early Career Fellowship Program and the UK Dementia Research Institute, which receives its funding from UK DRI Ltd, funded by the UK Medical Research Council, Alzheimer’s Society and Alzheimer’s Research UK. AAK is funded by ALS Association Milton Safenowitz Research Fellowship (grant number 22-PDF-609. DOI:10.52546/, The Motor Neurone Disease Association (MNDA) Fellowship (Al Khleifat/Oct21/975-799), The Darby Rimmer Foundation, and The NIHR Maudsley Biomedical Research Centre. ELH is supported by the Alzheimer’s Society (AS-RF-21-017) and the Cambridge British Heart Foundation Centre of Research Excellence (RE/18/1/34212). SK is supported by a PhD studentship award from Alzheimer’s Society, UK (AS-PhD-19b-014) and the Ser Cymru II programme. JMR and DJL are supported by Alzheimer’s Research UK and the Alan Turing Institute/Engineering and Physical Sciences Research Council (EP/N510129/1). DJL also receives funding from the Medical Research Council (MR/X005674/1), National Institute for Health Research (NIHR) Applied Research Collaboration South West Peninsula, National Health and Medical Research Council (NHMRC), and National Institute on Aging/National Institutes of Health (RF1AG055654). This research was supported in part by the Intramural Research Program of the NIH, National Institute on Aging (NIA), National Institutes of Health, Department of Health and Human Services; project number ZO1 AG000535 and ZIA AG000949, as well as the National Institute of Neurological Disorders and Stroke (NINDS). The views expressed in this publication are those of the authors and not necessarily those of the NIHR, NHS, or UK Department of Health and Social Care.
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