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In EMBO molecular medicine

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.

Placek Katerina, Benatar Michael, Wuu Joanne, Rampersaud Evadnie, Hennessy Laura, Van Deerlin Vivianna M, Grossman Murray, Irwin David J, Elman Lauren, McCluskey Leo, Quinn Colin, Granit Volkan, Statland Jeffrey M, Burns Ted M, Ravits John, Swenson Andrea, Katz Jon, Pioro Erik P, Jackson Carlayne, Caress James, So Yuen, Maiser Samuel, Walk David, Lee Edward B, Trojanowski John Q, Cook Philip, Gee James, Sha Jin, Naj Adam C, Rademakers Rosa, Chen Wenan, Wu Gang, Paul Taylor J, McMillan Corey T


amyotrophic lateral sclerosis, cognition, frontotemporal dementia, machine learning, polygenic score