Journal article
Machine learning suggests polygenic risk for cognitive dysfunction in amyotrophic lateral sclerosis
EMBO molecular medicine, Vol.13(1), pp.e12595-n/a
01/11/2021
DOI: 10.15252/emmm.202012595
PMCID: PMC7799365
PMID: 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.
Details
- Title: Subtitle
- Machine learning suggests polygenic risk for cognitive dysfunction in amyotrophic lateral sclerosis
- Creators
- Katerina Placek - University of PennsylvaniaMichael Benatar - University of MiamiJoanne Wuu - University of MiamiEvadnie Rampersaud - St. Jude Children's Research HospitalLaura Hennessy - University of PennsylvaniaVivianna M Van Deerlin - University of PennsylvaniaMurray Grossman - University of PennsylvaniaDavid J Irwin - University of PennsylvaniaLauren Elman - University of PennsylvaniaLeo McCluskey - University of PennsylvaniaColin Quinn - University of PennsylvaniaVolkan Granit - University of MiamiJeffrey M Statland - University of Kansas Medical CenterTed M Burns - University of Virginia Health SystemJohn Ravits - University of California, San DiegoAndrea Swenson - University of IowaJon Katz - California Pacific Medical CenterErik P Pioro - Cleveland ClinicCarlayne Jackson - The University of Texas Health Science Center at San AntonioJames Caress - Wake Forest UniversityYuen So - Department of Neurology Stanford University Medical Center San Jose CA USASamuel Maiser - University of Minnesota Medical CenterDavid Walk - University of Minnesota Medical CenterEdward B Lee - University of PennsylvaniaJohn Q Trojanowski - University of PennsylvaniaPhilip Cook - University of PennsylvaniaJames Gee - Penn Image Computing Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.Jin Sha - University of PennsylvaniaAdam C Naj - University of PennsylvaniaRosa Rademakers - Mayo ClinicWenan Chen - St. Jude Children's Research HospitalGang Wu - St. Jude Children's Research HospitalCorey T McMillan - University of PennsylvaniaJ. Paul Taylor - The Howard Hughes Medical Institute, Chevy Chase, MS, USACReATe Consortium
- Resource Type
- Journal article
- Publication Details
- EMBO molecular medicine, Vol.13(1), pp.e12595-n/a
- DOI
- 10.15252/emmm.202012595
- PMID
- 33270986
- PMCID
- PMC7799365
- ISSN
- 1757-4676
- eISSN
- 1757-4684
- Grant note
- 17-LGCA-331 / ALS Association AG054060 / NIH HHS U54 NS092091 / NINDS NIH HHS NS092091 / NIH HHS AG017586 / NIH HHS 16-TACL-242 / ALS Association F31 NS106754 / NINDS NIH HHS P01 AG066597 / NIA NIH HHS U54NS092091 / The CReATe Consortium NS106754 / NIH HHS P01 AG017586 / NIA NIH HHS R01 AG054060 / NIA NIH HHS
- Language
- English
- Date published
- 01/11/2021
- Academic Unit
- Neurology
- Record Identifier
- 9984303021102771
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