Statistical genetics with application to population-based study design: a primer for clinicians
Bibliographic record
Abstract
With the completion of the entire human genome sequence and remarkable advances in genotyping technologies, there has been an increased interest in the application of genetics and genomics in biomedical research over the last decade. Large-scale population-based genetic association studies have now become routine and their application to several multifactorial diseases such as cardiovascular disorders has led to the identification of a number of novel susceptibility genes. However, to be able to interpret results from such studies, clinicians need to have a basic understanding of unique concepts and issues related to this fast-moving area of research. In this primer, we provide a broad overview of design, analysis, and methodological issues with a focus on population-based study design.
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 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 | 0.134 | 0.166 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.010 | 0.024 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".