Biobank-wide association scan identifies risk factors for late-onset Alzheimer's disease and endophenotypes
Donghui Yan, Bowen Hu, Burcu F Darst, Shubhabrata Mukherjee, Brian W Kunkle, Yuetiva Deming, Logan Dumitrescu, Yunling Wang et al.. eLife, 2024-05-24
Topic: Genetics and APOE
Rich data from large biobanks, coupled with increasingly accessible association statistics from genome-wide association studies (GWAS), provide great opportunities to dissect the complex relationships among human traits and diseases. We introduce BADGERS, a powerful method to perform polygenic score-based biobank-wide association scans. Compared to traditional approaches, BADGERS uses GWAS summary statistics as input and does not require multiple traits to be measured in the same cohort. We applied BADGERS to two independent datasets for late-onset Alzheimer's disease (AD; n=61,212). Among 1738 traits in the UK biobank, we identified 48 significant associations for AD. Family history, high cholesterol, and numerous traits related to intelligence and education showed strong and independent associations with AD. Furthermore, we identified 41 significant associations for a variety of AD endophenotypes. While family history and high cholesterol were strongly associated with AD subgroups and pathologies, only intelligence and education-related traits predicted pre-clinical cognitive phenotypes. These results provide novel insights into the distinct biological processes underlying various risk factors for AD.
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APA 7th
Yan, D., Hu, B., Darst, B. F., Mukherjee, S., Kunkle, B. W., Deming, Y., et al. (2024). Biobank-wide association scan identifies risk factors for late-onset Alzheimer's disease and endophenotypes. eLife. https://doi.org/10.7554/elife.91360
Vancouver
Yan D, Hu B, Darst BF, Mukherjee S, Kunkle BW, Deming Y, et al. Biobank-wide association scan identifies risk factors for late-onset Alzheimer's disease and endophenotypes. eLife. 2024. doi:10.7554/elife.91360. PMID: 38787369.
BibTeX
@article{yan2024-38787369,
author = {Yan, Donghui and Hu, Bowen and Darst, Burcu F and Mukherjee, Shubhabrata and Kunkle, Brian W and Deming, Yuetiva and Dumitrescu, Logan and Wang, Yunling and others},
title = {{Biobank-wide association scan identifies risk factors for late-onset Alzheimer's disease and endophenotypes}},
journal = {eLife},
year = {2024},
doi = {10.7554/elife.91360},
pmid = {38787369},
url = {https://doi.org/10.7554/elife.91360}
}
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Key findings
- Compared to traditional approaches, BADGERS uses GWAS summary statistics as input and does not require multiple traits to be measured in the same cohort.
- Among 1738 traits in the UK biobank, we identified 48 significant associations for AD.
- Furthermore, we identified 41 significant associations for a variety of AD endophenotypes.
- While family history and high cholesterol were strongly associated with AD subgroups and pathologies, only intelligence and education-related traits predicted pre-clinical cognitive phenotypes.
- These results provide novel insights into the distinct biological processes underlying various risk factors for AD.
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Study details
- Participants61,212
- Evidence strengthModerate
How this study scored 63/100
Published in a leading specialty journal. Scored zero for study design, effect reported 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 35 of the 56 points we could judge it on, which is 63 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 9 / 10 | Tier 2 — leading specialty journal (eLife) |
| 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 | 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 | 4 / 5 | 2024 — within 3 year(s) of the newest study here |
| Sample size | 4 / 4 | 61,212 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) |
| 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 38787369) |
| Free to read | — / 2 | Access not recorded — left out of this score |
| Points | 35 / 56 | Scored on 10 of 20 parts. We have no data for the rest, so they are left out: 35 of the 56 points we could judge it on is 63 out of 100. |
| Total | 63 / 100 | — |
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