Improved Diagnosis of Rare Disease Patients through Systematic Detection of Runs of Homozygosity
Bibliographic record
Abstract
Autozygosity is associated with an increased risk of genetic rare disease, thus being a relevant factor for clinical genetic studies. More than 2400 exome sequencing data sets were analyzed and screened for autozygosity on the basis of detection of >1 Mbp runs of homozygosity (ROHs). A model was built to predict if an individual is likely to be a consanguineous offspring (accuracy, 98%), and probability of consanguinity ranges were established according to the total ROH size. Application of the model resulted in the reclassification of the consanguinity status of 12% of the patients. The analysis of a subset of 79 consanguineous cases with the Rare Disease (RD)-Connect Genome-Phenome Analysis Platform, combining variant filtering and homozygosity mapping, enabled a 50% reduction in the number of candidate variants and the identification of homozygous pathogenic variants in 41 patients, with an overall diagnostic yield of 52%. The newly defined consanguinity ranges provide, for the first time, specific ROH thresholds to estimate inbreeding within a pedigree on disparate exome sequencing data, enabling confirmation or (re)classification of consanguineous status, hence increasing the efficiency of molecular diagnosis and reporting on secondary consanguinity findings, as recommended by American College of Medical Genetics and Genomics guidelines.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".