An exploration of heterogeneity in genetic analysis of complex pedigrees: linkage and association using whole genome sequencing data in the MAP4 region
Bibliographic record
Abstract
We conduct pedigree-based linkage and association analyses of simulated systolic blood pressure data in the nonascertained large Mexican American pedigrees provided by Genetic Analysis Workshop 18, focusing on observed sequence variants in MAP4 on chromosome 3. Because pedigrees are large and sequence data have been completed by imputation, it is feasible to conduct analysis for each pedigree separately as well as for all pedigrees combined. We are interested in quantifying and explaining between-pedigree heterogeneity in linkage and association signals. To this end, we first examine minor allele frequency differences between pedigrees. In some of the pedigrees, rare and low-frequency variants occur at a higher prevalence than in all pedigrees combined. In simulation replicate 1, we conduct variance-components linkage and association analysis of all 894 MAP4 variants to compare analytic approaches in single pedigree and combined analysis. In all 200 replicates, we similarly examine the 15 causal variants in MAP4 known under the generating model. We illustrate how random allele frequency variation among pedigrees leads to heterogeneity in pedigree-specific linkage and association signals.
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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 | 0.013 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".