The impact of GeneMatcher on international data sharing and collaboration.

GeneMatcher (genematcher.org) is a tool designed to connect individuals with an interest in the same gene. Now used around the world to create collaborations and generate the evidence needed to support novel disease gene identification, GeneMatcher is a founding member of the Matchmaker Exchange (MME; matchmakerexchange.org) and strongest possible advocate for global data sharing including those in resource-limited environments. As of October 1, 2021, there are 12,531 submitters from 94 countries who have submitted 58,134 submissions with 13,498 unique genes in the database. Among these genes, 8970 (64%) have matched at least once and the total number of matches is 378,806, growing by about 10,000 per month. GeneMatcher submitters increase by 80-120 each month and submissions grow by >800 per month, while unique genes and gene matches continue to grow steadily at rate of about 80 per month. The number of genes without a match peaked at 4371 in February of 2019 and despite the increase in the number of new submissions, the number of unique genes without a match continues to slowly decline, currently standing at 4,016. All submissions in GeneMatcher are available for matching across the MME.

Variant-level matching for diagnosis and discovery: Challenges and opportunities.

Here we describe MyGene2, Geno2MP, VariantMatcher, and Franklin; databases that provide variant-level information and phenotypic features to researchers, clinicians, healthcare providers and patients. Following the footsteps of the Matchmaker Exchange project that connects exome, genome, and phenotype databases at the gene level, these databases have as one goal to facilitate connection to one another using Data Connect, a standard for discovery and search of biomedical data from the Global Alliance for Genomics and Health (GA4GH).

A coronavirus disease 2019 (COVID-19) patient with bilateral orchitis.

During the development of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2), a myriad of complications has emerged and although rare, several genitourinary complications have been reported. The bulk of these complications have been secondary to hypercoagulable states, such as priapism. Previous SARS family infections have caused orchitis, though no adult cases of orchitis have been reported. We describe a novel case of SARS-CoV2 bilateral orchitis in a previously healthy 37-year-old male who presented for testicular pain with constitutional symptoms. Additionally, there was no epididymitis associated with the bilateral orchitis. Based on both data in SARS-CoV2 infected males and previous data from prior SARS infections, spermatocyte function may be compromised secondary to this infection. With the various symptoms associated with this virulent pathogen, we characterize the potential complications and importance of fertility follow up.

PhenoDB, GeneMatcher and VariantMatcher, tools for analysis and sharing of sequence data.

BACKGROUND: With the advent of whole exome (ES) and genome sequencing (GS) as tools for disease gene discovery, rare variant filtering, prioritization and data sharing have become essential components of the search for disease genes and variants potentially contributing to disease phenotypes. The computational storage, data manipulation, and bioinformatic interpretation of thousands to millions of variants identified in ES and GS, respectively, is a challenging task. To aid in that endeavor, we constructed PhenoDB, GeneMatcher and VariantMatcher. RESULTS: PhenoDB is an accessible, freely available, web-based platform that allows users to store, share, analyze and interpret their patients' phenotypes and variants from ES/GS data. GeneMatcher is accessible to all stakeholders as a web-based tool developed to connect individuals (researchers, clinicians, health care providers and patients) around the globe with interest in the same gene(s), variant(s) or phenotype(s). Finally, VariantMatcher was developed to enable public sharing of variant-level data and phenotypic information from individuals sequenced as part of multiple disease gene discovery projects. Here we provide updates on PhenoDB and GeneMatcher applications and implementation and introduce VariantMatcher. CONCLUSION: Each of these tools has facilitated worldwide data sharing and data analysis and improved our ability to connect genes to phenotypic traits. Further development of these platforms will expand variant analysis, interpretation, novel disease-gene discovery and facilitate functional annotation of the human genome for clinical genomics implementation and the precision medicine initiative.

Generating Exome Enriched Sequencing Libraries from Formalin-Fixed, Paraffin-Embedded Tissue DNA for Next-Generation Sequencing.

This unit describes a technique for generating exome-enriched sequencing libraries using DNA extracted from formalin-fixed paraffin-embedded (FFPE) samples. Utilizing commercially available kits, we present a low-input FFPE workflow starting with 50 ng of DNA. This procedure includes a repair step to address damage caused by FFPE preservation that improves sequence quality. Subsequently, libraries undergo an in-solution-targeted selection for exons, followed by sequencing using the Illumina next-generation short-read sequencing platform. © 2017 by John Wiley & Sons, Inc.

Comparison of parametric and machine methods for variable selection in simulated Genetic Analysis Workshop 19 data.

Current findings from genetic studies of complex human traits often do not explain a large proportion of the estimated variation of these traits due to genetic factors. This could be, in part, due to overly stringent significance thresholds in traditional statistical methods, such as linear and logistic regression. Machine learning methods, such as Random Forests (RF), are an alternative approach to identify potentially interesting variants. One major issue with these methods is that there is no clear way to distinguish between probable true hits and noise variables based on the importance metric calculated. To this end, we are developing a method called the Relative Recurrency Variable Importance Metric (r2VIM), a RF-based variable selection method. Here, we apply r2VIM to the unrelated Genetic Analysis Workshop 19 data with simulated systolic blood pressure as the phenotype. We compare the number of "true" functional variants identified by r2VIM with those identified by linear regression analyses that use a Bonferroni correction to calculate a significance threshold. Our results show that r2VIM performed comparably to linear regression. Our findings are proof-of-concept for r2VIM, as it identifies a similar number of functional and nonfunctional variants as a more commonly used technique when the optimal importance score threshold is used.