EMBO Molecular Medicine (Dec 2020)
Machine learning suggests polygenic risk for cognitive dysfunction in amyotrophic lateral sclerosis
- 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
Affiliations
- Katerina Placek
- Department of Neurology, University of Pennsylvania Perelman School of Medicine
- Michael Benatar
- Department of Neurology, Leonard M. Miller School of Medicine, University of Miami
- Joanne Wuu
- Department of Neurology, Leonard M. Miller School of Medicine, University of Miami
- Evadnie Rampersaud
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital
- Laura Hennessy
- Department of Neurology, University of Pennsylvania Perelman School of Medicine
- Vivianna M Van Deerlin
- Department of Pathology & Laboratory Medicine, University of Pennsylvania Perelman School of Medicine
- Murray Grossman
- Department of Neurology, University of Pennsylvania Perelman School of Medicine
- David J Irwin
- Department of Neurology, University of Pennsylvania Perelman School of Medicine
- Lauren Elman
- Department of Neurology, University of Pennsylvania Perelman School of Medicine
- Leo McCluskey
- Department of Neurology, University of Pennsylvania Perelman School of Medicine
- Colin Quinn
- Department of Neurology, University of Pennsylvania Perelman School of Medicine
- Volkan Granit
- Department of Neurology, Leonard M. Miller School of Medicine, University of Miami
- Jeffrey M Statland
- Department of Neurology, University of Kansas Medical Center
- Ted M Burns
- Department of Neurology, University of Virginia Health System
- John Ravits
- Department of Neurosciences, University of California San Diego
- Andrea Swenson
- Department of Neurology, University of Iowa
- Jon Katz
- Forbes Norris ALS Center, California Pacific Medical Center
- Erik P Pioro
- Department of Neurology, Cleveland Clinic
- Carlayne Jackson
- Department of Neurology, University of Texas Health Science Center
- James Caress
- Department of Neurology, Wake Forest University School of Medicine
- Yuen So
- Department of Neurology, Stanford University Medical Center
- Samuel Maiser
- Department of Neurology, University of Minnesota Medical Center
- David Walk
- Department of Neurology, University of Minnesota Medical Center
- Edward B Lee
- Department of Pathology & Laboratory Medicine, University of Pennsylvania Perelman School of Medicine
- John Q Trojanowski
- Department of Pathology & Laboratory Medicine, University of Pennsylvania Perelman School of Medicine
- Philip Cook
- Penn Image Computing Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania Perelman School of Medicine
- James Gee
- Penn Image Computing Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania Perelman School of Medicine
- Jin Sha
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine
- Adam C Naj
- Department of Pathology & Laboratory Medicine, University of Pennsylvania Perelman School of Medicine
- Rosa Rademakers
- Department of Neuroscience, Mayo Clinic
- The CReATe Consortium
- Rare Diseases Clinical Research Network, National Institutes of Health
- Wenan Chen
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital
- Gang Wu
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital
- J Paul Taylor
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital
- Corey T McMillan
- Department of Neurology, University of Pennsylvania Perelman School of Medicine
- DOI
- https://doi.org/10.15252/emmm.202012595
- Journal volume & issue
-
Vol. 13,
no. 1
pp. 1 – 18
Abstract
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 analysis (sCCA), an unsupervised machine‐learning technique, we observed that single nucleotide polymorphisms collectively associate with baseline cognitive performance in a large ALS patient cohort (N = 327) from the multicenter Clinical Research in ALS and Related Disorders for Therapeutic Development (CReATe) Consortium. We demonstrate that a polygenic risk score derived using sCCA relates to longitudinal cognitive decline in the same cohort and also to in vivo cortical thinning in the orbital frontal cortex, anterior cingulate cortex, lateral temporal cortex, premotor cortex, and hippocampus (N = 90) as well as post‐mortem motor cortical neuronal loss (N = 87) in independent ALS cohorts from the University of Pennsylvania Integrated Neurodegenerative Disease Biobank. Our findings suggest that common genetic polymorphisms may exert a polygenic contribution to the risk of cortical disease vulnerability and cognitive dysfunction in ALS.
Keywords
