One-Sided Matching Portal (OSMP): a tool to facilitate rare disease patient matchmaking
Bibliographic record
Abstract
Background: Genomic matchmaking - the process of identifying multiple individuals with overlapping phenotypes and rare variants in the same gene - is an important tool facilitating gene discoveries for unsolved rare genetic disease (RGD) patients. Current approaches are two-sided, meaning both patients being matched must have the same candidate gene flagged. This limits the number of unsolved RGD patients eligible for matchmaking. A one-sided approach to matchmaking, in which a gene of interest is queried directly in the genome-wide sequencing data of RGD patients, would make matchmaking possible for previously undiscoverable individuals. However, platforms and workflows for this approach have not been well established. Results: We released a beta version of the One-Sided Matching Portal (OSMP), a platform capable of performing one-sided matchmaking queries across thousands of participants stored in genomic databases. The OSMP returns variant-level and participant-level information on each variant occurrence (VO) identified in a queried gene and displays this information through a customizable data table. A workflow for one-sided matchmaking was developed so that researchers could effectively prioritize the many VOs returned from a given query. This workflow was then tested through pilot studies where two sets of genes were queried in over 2,500 individuals: 130 genes that were newly associated with disease in OMIM, and 178 candidate genes that were not yet associated with a described disease-gene association in OMIM. These pilots both returned a large number of initial VOs (12,872 and 20,308, respectively), however the workflow successfully filtered out over 99.8% of these VOs before they were sent for review by a patient′s clinician. Filters on participant-level information, such as variant zygosity, participant phenotype, and whether a variant was also present in unaffected participants were especially effective in this workflow at reducing the number of false positive matches. Conclusions: As demonstrated through the two pilot studies, one-sided matchmaking queries can be efficiently performed using the OSMP. The availability of variant-level and participant-level data is key to ensuring this approach is practical for researchers. In the future, the OSMP will be connected to additional RD databases to increase the accessibility of matchmaking to unsolved RGD patients.
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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 | 0.020 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.057 | 0.022 |
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".