EMBO Molecular Medicine (Jan 2021)
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 Philadelphia PA USA
- Michael Benatar
- Department of Neurology Leonard M. Miller School of Medicine University of Miami Miami FL USA
- Joanne Wuu
- Department of Neurology Leonard M. Miller School of Medicine University of Miami Miami FL USA
- Evadnie Rampersaud
- Center for Applied Bioinformatics St. Jude Children’s Research Hospital Memphis TN USA
- Laura Hennessy
- Department of Neurology University of Pennsylvania Perelman School of Medicine Philadelphia PA USA
- Vivianna M Van Deerlin
- Department of Pathology & Laboratory Medicine University of Pennsylvania Perelman School of Medicine Philadelphia PA USA
- Murray Grossman
- Department of Neurology University of Pennsylvania Perelman School of Medicine Philadelphia PA USA
- David J Irwin
- Department of Neurology University of Pennsylvania Perelman School of Medicine Philadelphia PA USA
- Lauren Elman
- Department of Neurology University of Pennsylvania Perelman School of Medicine Philadelphia PA USA
- Leo McCluskey
- Department of Neurology University of Pennsylvania Perelman School of Medicine Philadelphia PA USA
- Colin Quinn
- Department of Neurology University of Pennsylvania Perelman School of Medicine Philadelphia PA USA
- Volkan Granit
- Department of Neurology Leonard M. Miller School of Medicine University of Miami Miami FL USA
- Jeffrey M Statland
- Department of Neurology University of Kansas Medical Center Kansas City KS USA
- Ted M Burns
- Department of Neurology University of Virginia Health System Charlottesville VA USA
- John Ravits
- Department of Neurosciences University of California San Diego San Diego CA USA
- Andrea Swenson
- Department of Neurology University of Iowa Iowa City IA USA
- Jon Katz
- Forbes Norris ALS CenterCalifornia Pacific Medical Center San Francisco CA USA
- Erik P Pioro
- Department of Neurology Cleveland Clinic Cleveland OH USA
- Carlayne Jackson
- Department of Neurology University of Texas Health Science Center San Antonio TX USA
- James Caress
- Department of Neurology Wake Forest University School of Medicine Winston‐Salem NC USA
- Yuen So
- Department of Neurology Stanford University Medical Center San Jose CA USA
- Samuel Maiser
- Department of Neurology University of Minnesota Medical Center Minneapolis MN USA
- David Walk
- Department of Neurology University of Minnesota Medical Center Minneapolis MN USA
- Edward B Lee
- Department of Pathology & Laboratory Medicine University of Pennsylvania Perelman School of Medicine Philadelphia PA USA
- John Q Trojanowski
- Department of Pathology & Laboratory Medicine University of Pennsylvania Perelman School of Medicine Philadelphia PA USA
- Philip Cook
- Penn Image Computing Science Laboratory (PICSL) Department of Radiology University of Pennsylvania Perelman School of Medicine Philadelphia PA USA
- James Gee
- Penn Image Computing Science Laboratory (PICSL) Department of Radiology University of Pennsylvania Perelman School of Medicine Philadelphia PA USA
- Jin Sha
- Department of Biostatistics, Epidemiology, and Informatics University of Pennsylvania Perelman School of Medicine Philadelphia PA USA
- Adam C Naj
- Department of Pathology & Laboratory Medicine University of Pennsylvania Perelman School of Medicine Philadelphia PA USA
- Rosa Rademakers
- Department of Neuroscience Mayo Clinic Jacksonville FL USA
- The CReATe Consortium
- Rare Diseases Clinical Research Network National Institutes of Health Bethesda MD USA
- Wenan Chen
- Center for Applied Bioinformatics St. Jude Children’s Research Hospital Memphis TN USA
- Gang Wu
- Center for Applied Bioinformatics St. Jude Children’s Research Hospital Memphis TN USA
- J Paul Taylor
- Center for Applied Bioinformatics St. Jude Children’s Research Hospital Memphis TN USA
- Corey T McMillan
- Department of Neurology University of Pennsylvania Perelman School of Medicine Philadelphia PA USA
- DOI
- https://doi.org/10.15252/emmm.202012595
- Journal volume & issue
-
Vol. 13,
no. 1
pp. n/a – n/a
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