A tree‐based model for allele‐sharing‐based linkage analysis in human complex diseases
Bibliographic record
Abstract
By adapting a well-known affected-relative-pair linkage model that can incorporate covariate or sub-phenotype information [Olson, 1999: Am J Hum Genet 65:1760-1769], we have developed a recursive-partitioning (RP) algorithm (tree-based model) for identifying phenotype and covariate groupings that interact with the evidence for linkage. This strategy is designed to identify subgroups of affected relative pairs demonstrating increased evidence for linkage, where subgroups are defined by pair-level or family-level covariates. After growing a full tree, we identified optimal tree size through a form of tree pruning and chose the best covariate at each split by using bootstrap algorithms. Simulation studies showed that power to detect linkage can increase in the presence of gene-environment interactions, depending on the magnitude of the interaction. As expected, however, power can decrease by examining more covariates, despite the pruning to optimize tree size. The RP model correctly identifies tree structure in a large proportion of simulations. We applied the RP model to a dataset of families with bipolar affective disorder (BPAD) where linkage regions on chromosome 18 have been previously identified. Using the all-pairs score in Genehunter, the NPL tests showed no regions with strong linkage evidence on chromosome 18. However, using the RP model, several suggestive regions were found on chromosome 18. Two covariates appeared to influence the degree of linkage: the type II BPAD subtype and a pattern of displaying mania before or after a depressive episode. The RP model has the potential to identify previously unknown gene-environmental interactions; here we have demonstrated the practical utility and potential this new methodology holds.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".