Machine learning suggests polygenic risk for cognitive dysfunction in amyotrophic lateral sclerosis

Machine learning suggests polygenic risk for cognitive dysfunction in amyotrophic lateral sclerosis

Abstract Amyotrophic lateral sclerosis (ALS) is a multi‐system disease characterized primarily by progressive muscle weakness. Cognitive dysfunction is commonly observed in patients; however, factors influencing risk for cognitive dysfunction remain elusive. Using sparse canonical correlation analys...

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Main Authors: Katerina Placek, Michael Benatar, Joanne Wuu, Evadnie Rampersaud, Laura Hennessy, Vivianna M Van Deerlin, Murray Grossman, David J Irwin, Lauren Elman, Leo McCluskey, Colin Quinn, Volkan Granit, Jeffrey M Statland, Ted M Burns, John Ravits, Andrea Swenson, Jon Katz, Erik P Pioro, Carlayne Jackson, James Caress, Yuen So, Samuel Maiser, David Walk, Edward B Lee, John Q Trojanowski, Philip Cook, James Gee, Jin Sha, Adam C Naj, Rosa Rademakers, The CReATe Consortium, Wenan Chen, Gang Wu, J Paul Taylor, Corey T McMillan
Format: Article
Language:English
Published: Springer Nature 2020-12-01
Series:EMBO Molecular Medicine
Subjects:
amyotrophic lateral sclerosis
cognition
frontotemporal dementia
machine learning
polygenic score
Online Access:https://doi.org/10.15252/emmm.202012595
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