Topic: Genetics and APOE
Genome wide association studies (GWAS) of human diseases have generally identified many loci associated with risk with relatively small effect sizes. The omnigenic model attempts to explain this observation by suggesting that diseases can be thought of as networks, where genes with direct involvement in disease-relevant biological pathways are named 'core genes', while peripheral genes influence disease risk via their interactions or regulatory effects on core genes. Here, we demonstrate a method for identifying candidate core genes solely from genes in or near disease-associated SNPs (GWAS hits) in conjunction with protein-protein interaction network data. Applied to 1,381 GWAS studies from 5 ancestries, we identify a total of 1,865 candidate core genes in 343 GWAS studies. Our analysis identifies several well-known disease-related genes that are not identified by GWAS, including BRCA1 in Breast Cancer, Amyloid Precursor Protein (APP) in Alzheimer's Disease, INS in A1C measurement and Type 2 Diabetes, and PCSK9 in LDL cholesterol, amongst others. Notably candidate core genes are preferentially enriched for disease relevance over GWAS hits and are enriched for both Clinvar pathogenic variants and known drug targets-consistent with the predictions of the omnigenic model. We subsequently use parent term annotations provided by the GWAS catalog, to merge related GWAS studies and identify candidate core genes in over-arching disease processes such as cancer-where we identify 109 candidate core genes.
Key findings
- Genome wide association studies (GWAS) of human diseases have generally identified many loci associated with risk with relatively small effect sizes.
- The omnigenic model attempts to explain this observation by suggesting that diseases can be thought of as networks, where genes with direct involvement in disease-relevant biological pathways are named 'core genes', while peripheral genes influence disease risk via their interactions or regulatory effects on core genes.
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Study details
- Participants1,865
- Evidence strengthLow
How this study scored 50/100
Published in a peer-reviewed journal. Scored zero for study design, 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, multi-centre collaboration and free to read, so those were left out: it earned 28 of the 56 points we could judge it on, which is 50 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (PLoS genetics) |
| 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 | 2 / 5 | 2020 — within 10 year(s) of the newest study here |
| Sample size | 3 / 4 | 1,865 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 | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 32678846) |
| Free to read | — / 2 | Access not recorded — left out of this score |
| Points | 28 / 56 | Scored on 10 of 20 parts. We have no data for the rest, so they are left out: 28 of the 56 points we could judge it on is 50 out of 100. |
| Total | 50 / 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
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Ratnakumar, A., Weinhold, N., Mar, J. C., & Riaz, N. (2020). Protein-Protein interactions uncover candidate 'core genes' within omnigenic disease networks. PLoS genetics. https://doi.org/10.1371/journal.pgen.1008903
Vancouver
Ratnakumar A, Weinhold N, Mar JC, Riaz N. Protein-Protein interactions uncover candidate 'core genes' within omnigenic disease networks. PLoS genetics. 2020. doi:10.1371/journal.pgen.1008903. PMID: 32678846.
BibTeX
@article{ratnakumar2020-32678846,
author = {Ratnakumar, Abhirami and Weinhold, Nils and Mar, Jessica C and Riaz, Nadeem},
title = {{Protein-Protein interactions uncover candidate 'core genes' within omnigenic disease networks}},
journal = {PLoS genetics},
year = {2020},
doi = {10.1371/journal.pgen.1008903},
pmid = {32678846},
url = {https://doi.org/10.1371/journal.pgen.1008903}
}
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