AI-enabled evaluation of genome-wide association relevance and polygenic risk score prediction in Alzheimer's disease
Daniel E Platt, Aldo Guzmán-Sáenz, Aritra Bose, Subrata Saha, Filippo Utro, Laxmi Parida. iScience, 2024-02-12
Topic: Genetics and APOE
GWAS focuses on significance loosing false positives; machine learning probes sub-significant features relying on predictivity. Yet, these are far from orthogonal. We sought to explore how these inform each other in sub-genome-wide significant situations to define relevance for predictive features. We introduce the SVM-based RubricOE that selects heavily cross-validated feature sets, and LDpred2 PRS as a strong contrast to SVM, to explore significance and predictivity. Our Alzheimer's test case notoriously lacks strong genetic signals except for few very strong phenotype-SNP associations, which suits the problem we are exploring. We found that the most significant SNPs among ML and PRS-selected SNPs captured most of the predictivity, while weaker associations tend also to contribute weakly to predictivity. SNPs with weak associations tend not to contribute to predictivity, but deletion of these features does not injure it. Significance provides a ranking that helps identify weakly predictive features.
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APA 7th
Platt, D. E., Guzmán-Sáenz, A., Bose, A., Saha, S., Utro, F., & Parida, L. (2024). AI-enabled evaluation of genome-wide association relevance and polygenic risk score prediction in Alzheimer's disease. iScience. https://doi.org/10.1016/j.isci.2024.109209
Vancouver
Platt DE, Guzmán-Sáenz A, Bose A, Saha S, Utro F, Parida L. AI-enabled evaluation of genome-wide association relevance and polygenic risk score prediction in Alzheimer's disease. iScience. 2024. doi:10.1016/j.isci.2024.109209. PMID: 38439972.
BibTeX
@article{platt2024-38439972,
author = {Platt, Daniel E and Guzmán-Sáenz, Aldo and Bose, Aritra and Saha, Subrata and Utro, Filippo and Parida, Laxmi},
title = {{AI-enabled evaluation of genome-wide association relevance and polygenic risk score prediction in Alzheimer's disease}},
journal = {iScience},
year = {2024},
doi = {10.1016/j.isci.2024.109209},
pmid = {38439972},
url = {https://doi.org/10.1016/j.isci.2024.109209}
}
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Key findings
- GWAS focuses on significance loosing false positives; machine learning probes sub-significant features relying on predictivity.
- We sought to explore how these inform each other in sub-genome-wide significant situations to define relevance for predictive features.
- We found that the most significant SNPs among ML and PRS-selected SNPs captured most of the predictivity, while weaker associations tend also to contribute weakly to predictivity.
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Study details
- Evidence strengthLow
How this study scored 43/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomized / 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, multicenter collaboration and free to read, so those were left out: it earned 24 of the 56 points we could judge it on, which is 43 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (iScience) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2024 — within 3 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomized / blinded | 0 / 2 | No randomization 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 |
| Multicenter collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 38439972) |
| Free to read | — / 2 | Access not recorded — left out of this score |
| Points | 24 / 56 | Scored on 10 of 20 parts. We have no data for the rest, so they are left out: 24 of the 56 points we could judge it on is 43 out of 100. |
| Total | 43 / 100 | — |
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