Interpretable deep learning translation of GWAS and multi-omics findings to identify pathobiology and drug repurposing in Alzheimer's disease
Jielin Xu, Chengsheng Mao, Yuan Hou, Yuan Luo, Jessica L Binder, Yadi Zhou, Lynn M Bekris, Jiyoung Shin et al.. Cell reports, 2022-11-29
Topic: Genetics and APOE
Translating human genetic findings (genome-wide association studies [GWAS]) to pathobiology and therapeutic discovery remains a major challenge for Alzheimer's disease (AD). We present a network topology-based deep learning framework to identify disease-associated genes (NETTAG). We leverage non-coding GWAS loci effects on quantitative trait loci, enhancers and CpG islands, promoter regions, open chromatin, and promoter flanking regions under the protein-protein interactome. Via NETTAG, we identified 156 AD-risk genes enriched in druggable targets. Combining network-based prediction and retrospective case-control observations with 10 million individuals, we identified that usage of four drugs (ibuprofen, gemfibrozil, cholecalciferol, and ceftriaxone) is associated with reduced likelihood of AD incidence. Gemfibrozil (an approved lipid regulator) is significantly associated with 43% reduced risk of AD compared with simvastatin using an active-comparator design (95% confidence interval 0.51-0.63, p < 0.0001). In summary, NETTAG offers a deep learning methodology that utilizes GWAS and multi-genomic findings to identify pathobiology and drug repurposing in AD.
Key findings
- Combining network-based prediction and retrospective case-control observations with 10 million individuals, we identified that usage of four drugs (ibuprofen, gemfibrozil, cholecalciferol, and ceftriaxone) is associated with reduced likelihood of AD incidence.
- Gemfibrozil (an approved lipid regulator) is significantly associated with 43% reduced risk of AD compared with simvastatin using an active-comparator design (95% confidence interval 0.51-0.63, p < 0.0001).
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Study details
- Study designcase-control study
- Evidence strengthModerate
How this study scored 55/100
Published in a peer-reviewed journal, and it is a case-control study. Scored zero for sample size, effect reported, studied in people 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, multi-centre collaboration and free to read, so those were left out: it earned 31 of the 56 points we could judge it on, which is 55 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Cell reports) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 6 / 12 | A case-control study |
| 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 | 2022 — within 6 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) |
| 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 | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 36450252) |
| Free to read | — / 2 | Access not recorded — left out of this score |
| Points | 31 / 56 | Scored on 10 of 20 parts. We have no data for the rest, so they are left out: 31 of the 56 points we could judge it on is 55 out of 100. |
| Total | 55 / 100 | — |
What each of these 20 components measures, and what this score is not →
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Cite this study APA, Vancouver, BibTeX · download .bib or .ris
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APA 7th
Xu, J., Mao, C., Hou, Y., Luo, Y., Binder, J. L., Zhou, Y., et al. (2022). Interpretable deep learning translation of GWAS and multi-omics findings to identify pathobiology and drug repurposing in Alzheimer's disease. Cell reports. https://doi.org/10.1016/j.celrep.2022.111717
Vancouver
Xu J, Mao C, Hou Y, Luo Y, Binder JL, Zhou Y, et al. Interpretable deep learning translation of GWAS and multi-omics findings to identify pathobiology and drug repurposing in Alzheimer's disease. Cell reports. 2022. doi:10.1016/j.celrep.2022.111717. PMID: 36450252.
BibTeX
@article{xu2022-36450252,
author = {Xu, Jielin and Mao, Chengsheng and Hou, Yuan and Luo, Yuan and Binder, Jessica L and Zhou, Yadi and Bekris, Lynn M and Shin, Jiyoung and others},
title = {{Interpretable deep learning translation of GWAS and multi-omics findings to identify pathobiology and drug repurposing in Alzheimer's disease}},
journal = {Cell reports},
year = {2022},
doi = {10.1016/j.celrep.2022.111717},
pmid = {36450252},
url = {https://doi.org/10.1016/j.celrep.2022.111717}
}
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