1. Machine learning suggests polygenic risk for cognitive dysfunction in amyotrophic lateral sclerosis.
- Author
-
Placek K, Benatar M, Wuu J, Rampersaud E, Hennessy L, Van Deerlin VM, Grossman M, Irwin DJ, Elman L, McCluskey L, Quinn C, Granit V, Statland JM, Burns TM, Ravits J, Swenson A, Katz J, Pioro EP, Jackson C, Caress J, So Y, Maiser S, Walk D, Lee EB, Trojanowski JQ, Cook P, Gee J, Sha J, Naj AC, Rademakers R, Chen W, Wu G, Paul Taylor J, and McMillan CT
- Subjects
- Humans, Machine Learning, Amyotrophic Lateral Sclerosis genetics, Cognitive Dysfunction genetics, Frontotemporal Dementia, Neurodegenerative Diseases
- 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., (© 2020 The Authors. Published under the terms of the CC BY 4.0 license.)
- Published
- 2021
- Full Text
- View/download PDF