Machine learning suggests polygenic risk for cognitive dysfunction in amyotrophic lateral sclerosis

The CReATe Consortium

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article numbere12595
JournalEMBO Molecular Medicine
Volume13
Issue number1
DOIs
StatePublished - Jan 11 2021

Bibliographical note

Funding Information:
The following authors declare the following conflicts of interest: C.T.M. receives financial support from Biogen and has provided consulting for Axon Advisors. M.B. reports grants from National Institutes of Health, the ALS Association, the Muscular Dystrophy Association, the Centers for Disease Control and Prevention, the Department of Defense, and Target ALS during the conduct of the study; personal fees from Mitsubishi Tanabe Pharma, AveXis, Prilenia, Genentech, and Roche outside the submitted work. In addition, M.B. has a provisional patent entitled “Determining Onset of Amyotrophic Lateral Sclerosis” and serves as a site investigator on clinical trials funded by Biogen and Orphazyme. All other authors declare no competing interests.

Funding Information:
The CReATe Consortium (U54NS092091) is part of the Rare Diseases Clinical Research Network (RDCRN), an initiative of the National Center for Advancing Translational Sciences (NCATS) Office of Rare Diseases Research (ORDR). Additional research support was provided by the National Institutes of Health (NS106754, AG017586, NS092091, AG054060). The genomics sequencing was funded by St. Jude Children?s Research Hospital American Lebanese Syrian Associated Charities (ALSAC), with additional support from the ALS Association for biorepository and sequencing costs (grants 17-LGCA-331 and 16-TACL-242).

Funding Information:
The CReATe Consortium (U54NS092091) is part of the Rare Diseases Clinical Research Network (RDCRN), an initiative of the National Center for Advancing Translational Sciences (NCATS) Office of Rare Diseases Research (ORDR). Additional research support was provided by the National Institutes of Health (NS106754, AG017586, NS092091, AG054060). The genomics sequencing was funded by St. Jude Children’s Research Hospital American Lebanese Syrian Associated Charities (ALSAC), with additional support from the ALS Association for biorepository and sequencing costs (grants 17‐LGCA‐331 and 16‐TACL‐242).

Publisher Copyright:
© 2020 The Authors. Published under the terms of the CC BY 4.0 license

Keywords

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

PubMed: MeSH publication types

  • Journal Article

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