Identification of genetic risk variants in PCSK9 gene and its association with myocardial infarction in Pakistani Pashtun population
Bibliographic record
Abstract
OBJECTIVE: Substantial advancements have been made in the identification of genetic risk variants associated with myocardial infarction (MI), predominantly within developed nations. The limited representation of the Pakistani population in genetic studies motivated us to design this study. The objective of this study is to identify the genetic variants within the PCSK9 gene and its possible association with myocardial infarction (MI) in Pakistani Pashtun population. METHODS: Whole Exome Sequencing (WES) was performed to pinpoint and propose pathogenic Single Nucleotide Polymorphisms (SNPs) associated with MI. Subsequent, MassARRAY genotyping and rigorous statistical analyses were used to confirmthe association of WES reported variants with MI. RESULTS: Exome sequencing identified n=5 SNPs in PCSK9. Of the five reported variants, SNPs rs2483205 (OR = 1.429, 95% CI = 0.925-2.207, p = 0.061) and rs562556 (OR = 2.50, 95% CI = 1.274-4.906, p = 0.001) showed strong positive association with myocardial infarction (MI).Whereas SNPs rs540796, rs509504, and rs505151 (p > 0.05) showed no association with MI in the study population. Genotypic distribution of SNPs rs562556 and rs2483205 were reported significant different between MI cases and controls (p < 0.05). Moreover recessive model (TT + CT versus CC) for rs2483205 and the dominant model (GG + AG versus AA) for rs562556 demonstrated strong associations with MI. CONCLUSIONS: The present study identified potential genetic markers increasing susceptibility/risk of MI in the study population. Our study provides a platform for future large scale genetic studies and identifying individuals who at risk of developing MI. The present study emphasise the development of treatments strategies based on genetic makeup of individual.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".