GeneYenta: A Phenotype­Based Rare Disease Case Matching Tool Based on Online Dating Algorithms for the Acceleration of Exome Interpretation

Abstract

Advances in next-generation sequencing (NGS) technologies have helped reveal causal variants for genetic diseases. In order to establish causality, it is often necessary to compare genomes of unrelated individuals with similar disease phenotypes to identify common disrupted genes. When working with cases of rare genetic disorders, finding similar individuals can be extremely difficult. We introduce a web tool, GeneYenta, which facilitates the matchmaking process, allowing clinicians to coordinate detailed comparisons for phenotypically similar cases. Importantly, the system is focused on phenotype annotation, with explicit limitations on highly confidential data that create barriers to participation. The procedure for matching of patient phenotypes, inspired by online dating services, uses an ontology-based semantic case matching algorithm with attribute weighting. We evaluate the capacity of the system using a curated reference data set and 19 clinician entered cases comparing four matching algorithms. We find that the inclusion of clinician weights can augment phenotype matching.

Accelerating matchmaking of novel dysmorphology syndromes through clinical and genomic characterization of a large cohort

“…Several matchmaking tools have been developed to address this bottleneck, e.g., GeneMatcher and MIMmatcher. 33,34 Reporting detailed clinical and genomic analyses of a large series of apparently novel dysmorphology syndromes will likely lead to a trend of accelerating the establishment of novel syndromes and their underlying genes. Such a trend will catalyze matchmaking such that the proposed novel phenotype is established and its candidate gene is confirmed independently.…”

Further delineation of a rare recessive encephalomyopathy linked to mutations in GFER thanks to data sharing of whole exome sequencing data

“…Variants were detected thanks to WES but the final conclusion about the pathogenicity of variants was made possible thanks to reverse phenotyping and international data sharing. In the actual context of fast technological progress, new tools such as computational methods integrating molecular and phenotypical data can further improve the diagnostic efficiency of WES, especially for ultra‐rare and aspecific clinical presentations …”

“…In the actual context of fast technological progress, new tools such as computational methods integrating molecular and phenotypical data can further improve the diagnostic efficiency of WES, especially for ultrarare and aspecific clinical presentations. [25][26][27][28][29][30]…”

Matching Two Independent Cohorts ValidatesDPH1as a Gene Responsible for Autosomal Recessive Intellectual Disability with Short Stature, Craniofacial, and Ectodermal Anomalies

“…As a result, affected patients and families are often unable to access clinical tests, such as carrier testing (of particular importance in rare autosomal recessive disorders in founder populations) or prenatal diagnosis. An alternative, and evolving, approach is to use a matchmaking process that allows for the systematic sharing of annotated phenotypic data without the need to reveal confidential data [Gottlieb et al., ]. Once common phenotypic presentations are identified between patients, genetic data can be shared in the hopes of identifying pathogenic variants disrupting a common gene [Gottlieb et al., ].…”