Toward the most ideal case–control design with related and unrelated dogs in whole‐exome sequencing studies
Bibliographic record
Abstract
With the recent development of whole-exome sequencing enrichment designs for the dog, a novel tool for disease-association studies became available. The aim of disease-association studies is to identify one or a very limited number of putative causal variants or genes from the large pool of genetic variation. To maximize the efficiency of these studies and to provide some directions of what to expect, we evaluated the effect on variant reduction for various combinations of cases and controls for both dominant and recessive types of inheritance assuming variable degrees of penetrance and detectance. In this study, variant data of 14 dogs (13 Labrador Retrievers and one Dogue de Bordeaux), obtained by whole-exome sequencing, were analyzed. In the filtering process, we found that unrelated dogs from the same breed share up to 70% of their variants, which is likely a consequence of the breeding history of the dog. For the designs tested with unrelated dogs, combining two cases and two controls gave the best result. These results were improved further by adding closely related dogs. Reduced penetrance and/or detectance has a drastic effect on the efficiency and is likely to have a profound effect on the sample size needed to elucidate the causal variant. Overall, we demonstrated that sequencing a small number of dogs results in a marked reduction of variants that are likely sufficient to pinpoint causal variants or genes.
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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 | 0.256 | 0.185 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".