Machine learning suggests polygenic contribution to cognitive dysfunction in amyotrophic lateral sclerosis (ALS) [article]

Katerina Placek, Michael Benatar, Joanne Wuu, Evadnie Rampersaud, Laura Hennessy, Vivianna M Van Deerlin, Murray Grossman, David Irwin, Lauren Elman, Leo McCluskey, Colin Quinn, Volkan Granit (+17 others)
2019 medRxiv   pre-print
Amyotrophic lateral sclerosis (ALS) is a multi-system disorder characterized by progressive muscular weakness and, in addition, cognitive/behavioral dysfunction in nearly 50% of patients. The mechanisms underlying risk for cognitive dysfunction, however, remain elusive. Using sparse canonical correlation analysis (sCCA), an unsupervised machine-learning technique, we observed that 26 single nucleotide polymorphisms collectively associate with baseline cognitive performance in 330 ALS patients
more » ... om the multicenter Clinical Research in ALS and Related Disorders for Therapeutic Development (CReATe) consortium. We demonstrate that a polygenic risk score derived from sCCA relates to longitudinal cognitive decline in the same cohort, and also to in vivo cortical thinning (N=80) and post mortem burden of TDP-43 pathology in the middle frontal and motor cortices (N=55) in independent validation cohorts of patients with sporadic ALS. Our findings suggest that common genetic polymorphisms contribute to the manifestation of cognitive dysfunction and disease vulnerability in a polygenic manner in ALS.
doi:10.1101/2019.12.23.19014407 fatcat:xxf6px3bz5hgpgrkzh7iscygmm