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. 2022 Nov 3;43(12):1808–1815. doi: 10.1002/humu.24495

VariantAlert: A web‐based tool to notify updates in genetic variant annotations

Rossano Atzeni 1, Matteo Massidda 1, Giorgio Fotia 1, Paolo Uva 2,✉
PMCID: PMC10091775  PMID: 36300680

Abstract

The reinterpretation of variants based on updated annotations is part of the routine work of research laboratories: the more data is collected about a specific variant, the higher the probability to reinterpret its classification. To support this task, we developed VariantAlert, a web‐based tool to help researchers and clinicians to be constantly informed about changes in variant annotations extracted from multiple sources. VariantAlert provides daily re‐annotation of variants using external resources accessed through application programming interface, such as MyVariant.info providing in turn links to gnomAD, catalogue of somatic mutations In cancer (COSMIC), ClinVar, CIViC, and many others. Researchers and clinicians can submit one or more lists of variants. If a change is detected for the annotation of a variant due to the upgrade of the underlying resource (e.g., change in gnomAD allele frequency, presence in COSMIC database, change in ClinVar classification) the user is notified by email and updated annotations are stored on the web‐site. VariantAlert is freely available at https://github.com/next-crs4/VariantAlert. Installation and deployment are easy thanks to the use of the Docker platform. A Makefile allows you to easily bootstrap VariantAlert. VariantAlert is also available as a web service at https://variant-alert.crs4.it/.

Keywords: clinical genetics, rare diseases, variant annotation, variant reinterpretation, variants of uncertain significance

1. INTRODUCTION

Understanding the functional consequences of genomic variants relies on multiple lines of evidence, and ad‐hoc guidelines have been developed to support this challenging task (Richards et al., 2015). These include among the others the allele frequency of the variant in a healthy population, the predicted effect on the protein, the presence in knowledge bases such as CIViC or ClinVar. While some variants can be easily classified either as benign or disease‐causing, in many cases evidence available at the time of analysis is not sufficient to assess the clinical relevance, often leading to variants of uncertain significance (VUS).

The spread of Next‐Generation Sequencing (NGS), first in research and then in diagnostics, and the progressive transition from single‐gene approaches to panels and exomes, has increased the volume of information obtained from genetic tests with the consequent increase in VUS. Currently, the diagnostic rate for exome sequencing of rare disease patients ranges between 25% and 60%, depending on the type of disease and selection criteria (Clark et al., 2018; Fung et al., 2020; Wright et al., 2015). If the failure to diagnose is due to the limits of the technology, for example, the region of interest is not captured by the exon enrichment kit, structural variants not detectable with Whole‐Exome Sequencing (WES), in many cases the use of alternative strategies can increase the diagnostic rate. In other cases, the pathogenetic variant may already be present in the data, but may escape the selection criteria due to the scarce information available at the time of analysis, for example the gene has not yet been associated with the disease or the variant has not yet been reported in ClinVar. Conversely, a variant initially selected as a possible candidate can be reclassified following updates on its frequency in control populations or on the basis of updated pathogenicity scores.

Therefore, variant reinterpretation is part of the routine work of research laboratories (Machini et al., 2019) because the more data is available about a variant of uncertain significance or not yet annotated, higher the probability to reinterpret it. In fact, the American College of Medical Genetics and Genomics in its guidelines recommends re‐evaluation of variants every two years (Deignan et al., 2019), and several studies report that a periodic re‐evaluation can increase the diagnostic rate up to 10% (Baker et al., 2019; Liu et al., 2019; Salfati et al., 2019).

A workflow for the complete reanalysis of variants includes various components, each with its own update rate and impact on the results: (i) update of the reference genome. This may impact moderately on the identification of clinically relevant variants. Li et al. have identified discordant variants in only eight genes involved in known Mendelian diseases (Li et al., 2021). This event occurs rarely, generally after many years, and requires the computational intensive re‐execution of the whole pipeline, often hampered by the unavailability of the raw original sequence data; (ii) update of bioinformatic tools, from sequence alignment to variant calling which may also have impact on the number of variants. These upgrades are generally available on yearly basis, and require the complete or partial re‐execution of the workflow, with a computational workload depending on the position of the upgraded software in the analysis workflow and the number and type of downstream tools that need to rerun consequently; (iii) update of knowledge bases for variant annotation. This occurs frequently, requires limited computational resources and may have indeed a profound impact on variant classification. Focusing only on the pathogenic variants available in ClinVar, which is growing by an average of 60% each year (Landrum et al., 2020), the reanalysis of unsolved cases within the Solve‐RD project (Matalonga et al., 2021) found that 13% of new diagnoses are due to variants in genes not associated with disease two years before reanalysis, and that 39% of these new variants at that time were not classified as pathogenic or likely pathogenic in ClinVar. Therefore, even the simple re‐annotation of variants with updated information can significantly contribute to increasing the diagnostic rate.

Despite the evidence in favor of a periodic re‐annotation (Fung et al., 2020), implementation in research and laboratory practice is challenging due to the considerable effort required in terms of time and human resources, and the absence of automated tools that can facilitate this task. In practice, the activities are often more oriented towards the analysis of new cases rather than the reinterpretation of unsolved ones.

VariantAlert provides an automated framework for variant re‐annotation, enabling the iterative reanalysis of variants even on a large scale. It aims to support researchers and clinicians by keeping them informed when updated annotations that may lead to the reclassification of the variant become available, thus potentially affecting the clinical management of the patients. To assess the potential of the variant re‐interpretation process in supporting diagnostic and research laboratories, in this work we also estimated the clinical impact of a periodic re‐annotation of variants using an in‐house data set of about 1,000 families.

2. METHODS

VariantAlert collects, stores and automatically updates variant annotations from multiple external resources (Table 1). Hence, the connection and communication with the external sources are the pillar on which VariantAlert stands. Instead of individually querying the external knowledge‐bases, we leverage MyVariant.info (Xin et al., 2016), an open source and continuously updated repository for aggregating and serving comprehensive, structured variant annotations information. Since its creation in 2017, MyVariant.info has been regularly updated (~7 releases/year). Databases such as ClinVar are updated at each release, while more stable annotation sources (e.g., gnomAD) are upgraded less frequently. MyVariant.info provides a cloud‐based and simple‐to‐use REST web services to query variant annotation data, accessible via URL (e.g., https://myvariant.info/v1/variant/chr1:g.35367G%3EA). By default, this will return the complete variant annotations in JSON format. Third‐party packages/modules are available to query MyVariant.info. Here we have adopted the Python wrapper MyVariant.py. Submitted variants are daily re‐annotated and if a change in an annotation is detected (added, removed, changed) due to the update of the underlying resource the user is notified by email, the updated annotations are tracked and stored on the web‐site and differences are highlighted.

Table 1.

Databases of annotations monitored by VariantAlert

Database URL
CADDa https://cadd.gs.washington.edu/
CGIa https://www.cancergenomeinterpreter.org/home
CIViCa https://civic.genome.wustl.edu/home
ClinVar https://www.ncbi.nlm.nih.gov/clinvar
COSMICa https://cancer.sanger.ac.uk/cancergenome/projects/cosmic/
dbNSFP https://sites.google.com/site/jpopgen/dbNSFP
dbSNP https://www.ncbi.nlm.nih.gov/snp/
DOCMa https://docm.genome.wustl.edu/
EMVClass https://www.ncbi.nlm.nih.gov/clinvar/submitters/500060/
EVS https://evs.gs.washington.edu/EVS/
ExACa https://exac.broadinstitute.org/
Geno2MPa https://geno2mp.gs.washington.edu/Geno2MP/#/
gnomAD https://gnomad.broadinstitute.org/
GRASPa https://grasp.nhlbi.nih.gov/
GWAS Cataloga https://www.ebi.ac.uk/gwas/
MutDBa https://www.mutdb.org/
Scripps Wellderlya https://www.stsiweb.org/wellderly/
SNPediaa https://www.snpedia.com/
UniProt https://www.uniprot.org/
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a

Only available for hg19.

2.1. Architecture

VariantAlert is a web application built in Django, a Python‐based free and open‐source web framework that follows the standard “model, view, controller” (MVC) architectural pattern, whereby the model defines the data, the view controls data presentation to the user, and the controller depends on the model and the view to perform the necessary operations on data when interpreting a user request (Leff & Rayfield, 2001). VariantAlert uses PostgreSQL as the database back‐end, connected to the web framework by the Psycopg2 library. The PostgreSQL database deals with the application data management. It includes information about the users (i.e., email address to notify changes) and the queries to be submitted to MyVariant.info (i.e., variant id, results of the query, differences between the last two query results). The web server is Nginx, linked to the web framework by the Gunicorn library. Travis CI performs continuous integration, automatically running the test suite whenever the codebase is changed on GitHub. VariantAlert can be installed locally or remotely with Docker (Merkel, 2014). VariantAlert codebase, PostgreSQL database and Nginx web server run in separate Docker containers that are orchestrated via Docker‐compose. A Makefile allows users to start VariantAlert, taking care of the installation and configuration steps. Installation instructions are available in Supporting Information: File S1.

Access to the MyVariant.info service is the main task of VariantAlert: methods have been developed to forward user requests (coming from the user interface or retrieved from the database) and to manage the responses of the REST web service, compare results and identify updates in variant annotations. The algorithm for detecting changes works as follows: for fields stored as string (e.g., the transcript name) any difference is reported; for allele frequencies, variations of at least 1 order of magnitude are reported (e.g., a change from 3.830e−06 to 3.979e−06 would not be notified); for other values such as pathogenicity scores, normal comparison applies.

Finally, VariantAlert periodically re‐send to the MyVariant.info service all the queries saved in the database, to keep the annotations updated and promptly notify the user by email if changes are detected.

VariantAlert GUI has intuitive data forms and clear summary tables. After authentication, the user can submit a query either as a single variant or in batches with CSV or VCF files, where each row represents a different variant. Users can view/delete submitted queries, check for updates and download the results. Input forms and pages with results are described in Supporting Information: File S1.

The public instance of VariantAlert running at https://variant-alert.crs4.it/ is limited to 25 variants in batch mode. This limit can be changed when the software is deployed locally. However, we suggest running VariantAlert on a selected subset of variants, such as rare variants affecting genes of interest or VUS to keep a focus on the variants of interest and limit the rate of notifications. Within a bioinformatics analysis workflow, VariantAlert could be integrated after the filtering step where the number of variants is significantly reduced compared to the initial set. A local installation of VariantAlert can easily manage thousands of variants. MyVariant.info, the server on which VariantAlert relies for the annotation, is hosted on the Amazon EC2 platform and can handle traffic from >5000 concurrent users for approximately 10,000 requests per minute. Greater than 95% of actual user requests take less than 30 ms to process (Xin et al., 2016).

2.2. Data set description

To assess the potential impact of a periodic reannotation, we estimated the annotation update rate by re‐analyzing the variants from 1222 families affected by a rare disease, available in our in‐house database of WES data at the Gaslini Children's Hospital. Samples were anonymized by replacing the original identifiers with a random string. Annotations were based on two different releases of ClinVar: May 2021 and May 2022. Then we focused on the rare (gnomAD allele frequency < 0.01) clinically relevant variants by extracting the variants classified as “Pathogenic” or “Likely pathogenic” (CLNSIG annotation of ClinVar) according to the May 2022 release, and we checked how they were annotated in the 2021 release.

3. RESULTS

3.1. Running VariantAlert

VariantAlert accepts input variants in single and multiple mode. In single mode (Figure 1) the user can paste the details of a variant and eventually a label for that variant. The label, for example, the project identifier from which the variant was derived, can be used to quickly identify the variant in the results page. In multiple mode, a list of variants in a CSV or VCF file is submitted as a batch. In both cases, the user can also select the annotation sources. This option allows one to focus on a specific set of annotations: a user interested in the clinical relevance of a variant never reported in databases can select ClinVar from the drop down menu and will be notified as soon as the variant appears in a later version of ClinVar. Data annotation is performed daily. If a change in annotation is detected for a variant, the current and the previous annotations are saved in the server, and the user receives an email with a direct link to the variant of interest.

Figure 1.

Figure 1

Open in a new tab

VariantAlert input form. In this page the user can submit a single variant specifying a label for that variant, its genomic coordinates, the genome assembly (hg19, hg38), the reference and alternate alleles, the annotation sources (e.g., CADD, ClinVar). A separate tab is available to submit multiple variants as a batch.

3.2. Output description

The results page contains the list of variants submitted by the user (Figure 2) with the following information: the user‐assigned label for the variant, the genomic coordinates and alleles, the reference genome (hg19, hg38), the dates on which the variant has been submitted (Created) and updated (Changed), and if the update has already been viewed by the user (Alert). By clicking on the corresponding icons in the first column it is possible to access the annotation details, remove the variant or download the data in Excel format.

Figure 2.

Figure 2

Open in a new tab

VariantAlert results page containing the list of submitted variants. By clicking the icons in the first column, the user can view, delete or download as an Excel file the complete set of annotations. The submission date and the date a change in annotation has been detected are shown. Unread updates are highlighted with a green mark in the Alerts column.

The details page (Figure 3a) reports the complete list of annotations before and after the update, and their differences (Figure 3b), as well as the databases initially selected for the annotation.

Figure 3.

Figure 3

Open in a new tab

Example of updated annotations for variant chr1:16254839 C > T. Here the appearance of the variant in ClinVar has been detected (a). The variant is associated with RADIO‐TARTAGLIA syndrome, and it has been reported as pathogenic. (b) Detailed annotations are available by clicking the “Current” section.

3.3. Data set re‐annotation

We re‐analyzed the variants from 1,222 families affected by different rare diseases using two different versions of ClinVar (May '21 and May '22). On average, based on the 2022 release, three variants per family were flagged as “Pathogenic” or “Likely pathogenic,” and 41% of the families showed at least one change in annotation when compared to 2021 release: 73 families (6%) reported minor classification changes only (from “Likely pathogenic” to “Pathogenic” or “Pathogenic/Likely pathogenic”); 44 families (4%) showed both minor classification changes and the presence of new “Pathogenic” variants that were unannotated in 2021; for 385 (31%) families we observed at least a new “Pathogenic” variant, without minor classification modification. Overall, 35% of the families had at least one “Pathogenic” variant in 2022 which was unannotated one year before, which may potentially impact the clinical management of the patient. This is expected as the size of the ClinVar database has increased from ~850,000 variants in 2021 to ~1,500,000 in 2022. We are aware that this is an overestimation of the individual's pathogenetic mutation burden and additional factors should be considered such as the presence of false positive variants, zygosity and penetrance. However, these results underline the potential impact of a periodic re‐annotation.

4. CONCLUSION

Variant reinterpretation in molecular genetics laboratories is crucial to the accuracy of the genetic test results. According to the Orphanet database, there are 6172 unique rare diseases; 71.9% of which are genetic and 69.9% which are exclusively pediatric onset (Nguengang Wakap et al., 2020). A timely genetic diagnosis may provide benefits to patients and their families with the potential to modify the medical treatment, in addition to economic benefits to the healthcare system. In case of inconclusive or negative genetic test, a systematic reanalysis of sequencing data may provide additional insights, leading to changes in variant classification (Machini et al., 2019; Wenger et al., 2017). Practical and ethical issues related to the periodic reinterpretation of variants have been addressed (Appelbaum et al., 2020). Given the recognized importance of reinterpretation, some solutions have recently been proposed to automate this procedure. A recent software, iVar (Castellano et al., 2021) partially supports this task by providing a platform for the management of variants where the user can upload VCF files and assess if relevant information is changed upon re‐annotation, and eventually focus on attributes of interest, for example, changes in ClinVar classification. However, iVar requires that annotations must be performed externally and files manually re‐uploaded by the user, whereas VariantAlert, provides continuous re‐annotation of genetic variants.

Baker et al. (2019) developed a reanalysis pipeline based on gene–phenotype and genotype–phenotype associations extracted from PubMed and human genetic disease databases (Online Mendelian Inheritance In Man, OMIM; ClinVar; Human Gene Mutation Database, HGMD). The monthly iterative analysis of 240 undiagnosed exomes revealed novel diagnostic findings for 16% of the samples that could be made through the use of updated databases.

A programmatic workflow to identify gene‐disease associations has been developed and applied to prioritize rare known pathogenic variants from 4411 undiagnosed exomes/genomes from the Solve‐RD project (Matalonga et al., 2021). This approach has enabled the diagnosis of 120 cases, where 52% of the newly identified causative variants were not reported as clinically relevant or were present in genes not associated with the disease in the two years before reanalysis.

An automated strategy for reanalysis of genomics data which includes phenotyping from electronic health records (EHRs) by natural language processing is described in James et al. (2020). Applied to a set of 48 negative WGS, a phenotype‐driven prioritization identified six novel diagnoses thanks to the update of literature and ClinVar databases.

These strategies, albeit with slightly different approaches, underline the importance of updated databases for identifying new variant‐disease associations. Motivated by the impact of database updates on the diagnostic yield, we have focused on the continuous monitoring of such annotations. In comparison to the solutions described here, our approach has three main advantages: (i) VariantAlert can be easily deployed locally and does not requires a large hardware infrastructure; (ii) annotation sources are queried remotely, without the need to maintain your own database locally; (iii) re‐annotation is performed continuously, and change‐driven notifications are automatically triggered.

Currently, VariantAlert retrieves annotations from the Myvariant.info server, and we are planning to add new annotation sources accessible via REST application programming interface such as Genomenon's MasterMind (Chunn et al., 2020) and DisGeNET (Piñero et al., 2019).

Although a public instance of VariantAlert is available, a local deployment is the suggested procedure for running the software to address privacy policies that could prevent the upload of genetic data on public platforms. We are confident that by continuously monitoring the knowledge surrounding each variant, tracking and notifying changes, VariantAlert may significantly accelerate the variant re‐interpretation process and support diagnostic and research laboratories.

CONFLICT OF INTEREST

The authors declare no conflict of interest.

Supporting information

Supporting information.

Click here for additional data file. (626.3KB, pdf)

ACKNOWLEDGMENTS

Rossano Atzeni performed his activity in the framework of the International PhD in Innovation Sciences and Technologies at the University of Cagliari, Italy. Rossano Atzeni, Matteo Massidda and Giorgio Fotia were partially supported by the Sardinian Regional Authorities. Paolo Uva acknowledges the Italian Ministry of Health for the financial support through "Ricerca Corrente" and "5 x mille" at the IRCCS Giannina Gaslini Institute. Open access funding provided by BIBLIOSAN.

Atzeni, R. , Massidda, M. , Fotia, G. , & Uva, P. (2022). VariantAlert: A web‐based tool to notify updates in genetic variant annotations. Human Mutation, 43, 1808–1815. 10.1002/humu.24495

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