A systematic review on papers that study on Single Nucleotide Polymorphism that affects coronavirus 2019 severity
Bibliographic record
Abstract
BACKGROUND: COVID-19, caused by SARS-CoV-2 has become the most threatening issue to all populations around the world. It is, directly and indirectly, affecting all of us and thus, is an emerging topic dealt in global health. To avoid the infection, various studies have been done and are still ongoing. COVID-19 cases are reported all over the globe, and among the millions of cases, genetic similarity may be seen. The genetical common features seen within confirmed cases may help outline the tendency of infection and degree severity of the disease. Here, we reviewed multiple papers on SNPs related to SARS-CoV-2 infection and analyzed their results. METHODS: The PubMed databases were searched for papers discussing SNPs associated with SARS-CoV-2 infection and severity. Clinical studies with human patients and statistically showing the relevance of the SNP with virus infection were included. Quality Assessment of all papers was done with Newcastle Ottawa Scale. RESULTS: In the analysis, 21 full-text literature out of 2956 screened titles and abstracts, including 63,496 cases, were included. All were human-based clinical studies, some based on certain regions gathered patient data and some based on big databases obtained online. ACE2, TMPRSS2, and IFITM3 are the genes mentioned most frequently that are related to SARS-CoV-2 infection. 20 out of 21 studies mentioned one or more of those genes. The relevant genes according to SNPs were also analyzed. rs12252-C, rs143936283, rs2285666, rs41303171, and rs35803318 are the SNPs that were mentioned at least twice in two different studies. CONCLUSIONS: We found that ACE2, TMPRSS2, and IFITM3 are the major genes that are involved in SARS-CoV-2 infection. The mentioned SNPs were all related to one or more of the above-mentioned genes. There were discussions on certain SNPs that increased the infection and severity to certain groups more than the others. However, as there is limited follow-up and data due to a shortage of time history of the disease, studies may be limited.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".