2023 ACMG Annual Clinical Genetics Meeting Digital Edition: Poster Gallery-VariantMatcher a tool to enable connections amongst individuals with interest in a specific variant

Pdf Summary

VariantMatcher is a tool that connects individuals around the world who are interested in a specific genetic variant. As of February 24, 2023, VariantMatcher has 860 users from 49 countries. It has been used to query 10,078 variants, with 272 of them matching 2,173 times. Most of the matched variants have a minor allele frequency (MAF) of less than 0.1% in the gnomAD database. These variants affect 246 genes, with 216 of them known to be disease-causing genes. They are associated with various types of inheritance, including autosomal dominant, autosomal recessive, X-linked, Y-linked, and multiple modes of inheritance. <br /><br />Of the 272 variants, 17 have been classified as pathogenic or likely pathogenic in ClinVar, while 63 have no description in ClinVar. The remaining variants are classified as variant of uncertain significance or benign/likely benign. As for the Beacon Network integration, VariantMatcher has matched 1,045 variants 12,948 times based on queries through the network.<br /><br />VariantMatcher requires users to create an account and be approved by administrators. Users can upload up to 10 genomic coordinates per day and are notified if there is a match. Phenotypic features can also be added and shared, although the matching is primarily based on the genomic location. If a match is not found, the queried coordinates can be saved for future matching.<br /><br />Although no confirmed matches have been made between variants and phenotypes, VariantMatcher has helped to rule out queried variants as potential causes of investigated phenotypes in many cases. In the future, VariantMatcher plans to implement new features such as querying indels, querying variants by zygosity state, querying specific variants with specific features, and connecting to other variant-level databases.<br /><br />Overall, VariantMatcher aims to improve the process of variant classification and increase the discovery of disease-causing variants by connecting different databases and individuals. The project is funded by the National Human Genome Research Institute (NHGRI).

Asset Subtitle

Presenting Author - Nara Sobreira, MD, PhD; Co-Author - Sean Griffith, IT Project Manager; Co-Author - Corina Antonescu, Sr. Software Engineer; Co-Author - Laura Vail, Software Engineer; Co-Author - Jennifer E. Posey, MD, PhD; Co-Author - Zeynep Coban-Akdemir, PhD; Co-Author - Shalini Jhangiani, Genetic Counselor; Co-Author - Kimberly Doheny, PhD; Co-Author - James Lupski, MD, PhD; Co-Author - David Valle, MD; Co-Author - Ada Hamosh, MD, MPH; Co-Author - Renan Martin, PhD;

Meta Tag

Bioinformatics

Databases

Exome sequencing

Genome sequencing

Genotype-Phenotype Correlations

Identification of Disease Genes

NextGen Sequencing

Phenotypic delineation of disorders

Sequencing

Co-Author Sean Griffith, IT Project Manager

Co-Author Corina Antonescu, Sr. Software Engineer

Co-Author Laura Vail, Software Engineer

Co-Author Jennifer E. Posey, MD, PhD

Co-Author Zeynep Coban-Akdemir, PhD

Co-Author Shalini Jhangiani, Genetic Counselor

Co-Author Kimberly Doheny, PhD

Co-Author James Lupski, MD, PhD

Co-Author David Valle, MD

Co-Author Ada Hamosh, MD, MPH

Co-Author Renan Martin, PhD

Presenting Author Nara Sobreira, MD, PhD

Keywords

VariantMatcher

genetic variant

users

variants

gnomAD database

genes

ClinVar

phenotypic features

matching process

NHGRI