Inclusion of unaffected sibs increases power in model-free linkage analysis of a behavioral trait
Bibliographic record
Abstract
Study design strategies are of critical importance in the search for genes underlying complex diseases. Two important design choices in planning gene mapping studies are the analytic strategy to be used, which will have an impact on the type of data to be collected, and the choice of genetic markers. In the present paper, we used the simulated behavioral trait data provided in the Genetic Analysis Workshop 14 to: 1) investigate the usefulness of incorporating unaffected sibs in model-free linkage analysis and, 2) compare linkage results of genome scans using a 7-cM microsatellite map with a 3-cM single nucleotide polymorphisms map. To achieve these aims, we used the maximum-likelihood-binomial method with two different coding approaches. We defined the unaffected sibs as those totally free of phenotypes correlated to the disease. Without prior knowledge of the answers, we were able to correctly localize 2 out of 5 loci (LOD > 3) in a sample of 200 families that included the unaffected sibs but only one locus when based on an affected-only strategy, using either microsatellite or SNPs genome scan. LOD scores were considerably higher using the analytic strategy which incorporated the unaffected sibs. In conclusion, including unaffected sibs in model-free linkage analysis of complex binary traits is helpful, at least when complete parental data are available, whereas there are no striking advantages in using single nucleotide polymorphisms over microsatellite map at marker densities used in the current study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".