Practice of Epidemiology Strategies for Genetic Association Analyses Combining Unrelated Case-Control Individuals and Family Trios
Bibliographic record
Abstract
In genetic association studies, analyses integrating data or estimates from unrelated case-control individuals and case trios (case offspring and their parents) can increase statistical power to identify disease susceptibility loci. Data on control trios may also be available, but how and when their use is advantageous is less familiar and is described here. In addition, the authors examine assumptions and properties of hybrid analyses combining asso-ciation estimates from unrelated case-control individuals together with case and control family trios, focusing on low-prevalence disease. One such assumption is absence of population stratification bias (PSB), a potential source of confounding in case-control analyses. For detection of PSB, the authors discuss 4 possible tests that assess equality between individual-level and family-based estimates. Furthermore, a weighted framework is presented, in which estimates from analyses combining unrelated individuals and families (most powerful but subject to PSB) and family-based analyses (robust to PSB) are weighted according to the observed PSB test P value. In contrast to existing hybrid designs that combine individuals and families only if no significant PSB is detected, the weighted framework does not require specification of an arbitrary PSB testing level to establish significance. The statistical methods are evaluated using simulations and applied to a candidate gene study of childhood leukemia (Quebec Childhood Leukemia Study, 1980–2000). alleles; case-control studies; family trios; genetic association studies; genetics; odds ratio; polymorphism, genetic;
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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 | 0.152 | 0.328 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".