Machine learning suggests polygenic risk for cognitive dysfunction in amyotrophic lateral sclerosis.
EMBO Mol Med
; 13(1): e12595, 2021 01 11.
Article
em En
| MEDLINE
| ID: mdl-33270986
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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Doenças Neurodegenerativas
/
Demência Frontotemporal
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Disfunção Cognitiva
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Esclerose Lateral Amiotrófica
Tipo de estudo:
Clinical_trials
/
Etiology_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
EMBO Mol Med
Assunto da revista:
BIOLOGIA MOLECULAR
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Estados Unidos