Machine learning suggests polygenic risk for cognitive dysfunction in amyotrophic lateral sclerosis
Katerina Placek, Michael Benatar, Joanne Wuu, Evadnie Rampersaud, Laura Hennessy, Vivianna M Van Deerlin, Murray Grossman, David J Irwin et al.. EMBO molecular medicine, 2020-12-03
Topic: Genetics and APOE
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.
Key findings
- 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.
- Our findings suggest that common genetic polymorphisms may exert a polygenic contribution to the risk of cortical disease vulnerability and cognitive dysfunction in ALS.
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Study details
- Study designlongitudinal study
- Participants327
- Evidence strengthModerate
How this study scored 66/100
Published in a peer-reviewed journal, and it is a longitudinal study following the same people over time. Scored zero for effect reported and randomised / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and free to read, so those were left out: it earned 39 of the 59 points we could judge it on, which is 66 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (EMBO molecular medicine) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 7 / 12 | A longitudinal study following the same people over time |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 3 / 5 | 2021 — within 6 year(s) of the newest study here |
| Sample size | 2 / 4 | 327 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | 2 / 3 | PubMed indexes it as a multicentre study |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 33270986) |
| Free to read | — / 2 | Access not recorded — left out of this score |
| Points | 39 / 59 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 39 of the 59 points we could judge it on is 66 out of 100. |
| Total | 66 / 100 | — |
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APA 7th
Placek, K., Benatar, M., Wuu, J., Rampersaud, E., Hennessy, L., Van Deerlin, V. M., et al. (2021). Machine learning suggests polygenic risk for cognitive dysfunction in amyotrophic lateral sclerosis. EMBO molecular medicine. https://doi.org/10.15252/emmm.202012595
Vancouver
Placek K, Benatar M, Wuu J, Rampersaud E, Hennessy L, Van Deerlin VM, et al. Machine learning suggests polygenic risk for cognitive dysfunction in amyotrophic lateral sclerosis. EMBO molecular medicine. 2021. doi:10.15252/emmm.202012595. PMID: 33270986.
BibTeX
@article{placek2021-33270986,
author = {Placek, Katerina and Benatar, Michael and Wuu, Joanne and Rampersaud, Evadnie and Hennessy, Laura and Van Deerlin, Vivianna M and Grossman, Murray and Irwin, David J and others},
title = {{Machine learning suggests polygenic risk for cognitive dysfunction in amyotrophic lateral sclerosis}},
journal = {EMBO molecular medicine},
year = {2021},
doi = {10.15252/emmm.202012595},
pmid = {33270986},
url = {https://doi.org/10.15252/emmm.202012595}
}