A Systematic Review on Papers That Study on SNPs That Affect SARS-CoV-2 Infection & COVID-19 Severity
Bibliographic record
Abstract
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 a emergence topic dealt in global health. In order to avoid the infection, various studies have been done and still ongoing. Now having over 141 million cases of COVID19 and causing over 3 million deaths around the world, the tendency of infection and degree severity of the disease shown in different groups of people came up as an issue. Here, we reviewed 21 papers on SNPs related to SARS-CoV-2 infection severity and analyzed the results of them.Methods:The PubMed databases were searched for papers discussing SNPs associated with SARS-CoV-2 infection severity. Clinical studies with human patients and statistically showing relevance of the SNP with virus infection were included. Quality Assessment of all papers were done with Newcastle Ottawa Scale.Results:In the analysis, 21 full-text literatures out of 2956 screened titles and abstracts, including 63496 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, IFITM3 are the genes mentioned most frequently that are related with SARS-CoV-2 infection. 20 out of 21 studies mentioned one of 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, 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 severity to certain ethinic groups more than the others. However, as there is limited follow up and data due to shortage of time history of the disease, studies may be limited.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".