Document Type

Article

Publication Date

10-8-2025

Identifier

DOI: 10.1155/humu/5941599; PMCID: PMC12527602

Abstract

BACKGROUND: Genomic matchmaking-the process of identifying 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 RGD patients eligible for matchmaking. One-sided matchmaking, in which a gene of interest is queried 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.

RESULT: 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. A workflow for one-sided matchmaking was developed so that researchers could prioritize the many VOs returned from a given query. This workflow was tested through pilot studies where two sets of genes were queried in over 2500 individuals: 130 genes that were newly associated with disease in OMIM and 178 novel candidate genes that were not associated with a disease-gene association in OMIM. These pilots returned a large number of initial VOs (12,872 and 20,308, respectively); however, the workflow filtered out over 99.8% of these VOs prior to review by a participant's clinician. Filters on participant-level information, including variant zygosity, participant phenotype, and whether a variant was also present in unaffected participants, were effective at reducing the number of false positive matches.

CONCLUSION: 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.

Journal Title

Human mutation

Volume

2025

First Page

5941599

Last Page

5941599

MeSH Keywords

Humans; Rare Diseases; Databases, Genetic; Software; Genomics; Phenotype; Workflow; Computational Biology

PubMed ID

41103356

Keywords

OMIM; data sharing; disease-gene discovery; genome-wide sequencing; matchmaking; one-sided matchmaking; rare disease; rare genetic disease

Comments

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Publisher's Link: https://doi.org/10.1155/humu/5941599

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