Advances in Genomic Analysis of Stroke
Bibliographic record
Abstract
As a result of technological advances, the genomic analysis of stroke has shifted from candidate gene association studies to genome-wide association studies (GWAS). Agnostic GWAS evaluate up to 90% of common genetic variation in a single experiment, creating an improved framework for identifying novel genetic leads for biochemical and cellular mechanisms underlying stroke. Given the ubiquity of the GWAS approach, it has become essential for stroke researchers and clinicians to be able to interpret GWAS results. Thus, we review the basic elements of design, methods, presentation, and interpretation of GWAS in the context of stroke research. In 8 recent stroke GWAS reports, no single locus has been identified in 2 GWAS at a genome-wide level of significance. Additionally, no significant association signal between stroke and a locus with previous evidence from candidate gene studies of stroke has been identified yet. Some caveats of the approach and future directions for stroke genomics are discussed, including the use of intermediate phenotypes, Mendelian randomization, phenomics, and deep resequencing. Intelligent, appropriately powered, multidisciplinary studies incorporating knowledge from clinical medicine, epidemiology, genetics, and molecular biology will be required to fully characterize the genomic contributors to stroke.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".