Abstract: SA-PO058
Diagnosticator: A Time-Sparing Web-Based Tool for Easy Clinical Annotation of Genetic Data
Session Information
- Engineering-Based Approaches to Problems in Nephrology
November 09, 2019 | Location: Exhibit Hall, Walter E. Washington Convention Center
Abstract Time: 10:00 AM - 12:00 PM
Category: Bioengineering
- 300 Bioengineering
Authors
- Cocchi, Enrico, University of Turin, Torino, Italy
- Milo Rasouly, Hila, Columbia University, New York, New York, United States
- Carlassara, Lucrezia, Universita' degli Studi di Brescia, Vicenza, Italy
- Ahram, Dina, Columbia University Medical Center, New York, New York, United States
- Kiryluk, Krzysztof, Columbia University, New York, New York, United States
- Sanna-Cherchi, Simone, Columbia University, New York, New York, United States
- Gharavi, Ali G., Columbia University, New York, New York, United States
Group or Team Name
- Ali Gharavi Lab
Background
Genetic testing is increasingly used in clinical medicine and has been shown to impact clinical care in Nephrology. The American College of Medical Genetics and Genomics (ACMG) has provided standardized guidelines for clinical interpretation of variants, but the large quantity of data generated from genome-wide testing pose a challenge for seamless clinical interpretation of results. We developed a web-based tool that allows users to upload genetic data, analyze them with customizable filters and easily navigate results.
Methods
The analysis algorithm prioritizes results based on customizable parameters: allele frequency from several publicly available databases (GnomAD, ExAC, EVS and 1000Genome), prior reports of disease association (Clinvar and HGMD) and proximity with hot-spot regions, functional and pathogenicity prediction (VEP, pLI, SIFT, PolyPhen, REVEL, dbNSFP, dbscSNV), and ACMG interpretation. Both Dominant and Recessive models are analyzed based on the OMIM-known (or selected) disease inheritance mode for each gene. The final results are presented as an easily interacting and customizable patient-, genelist- or gene-centered interface. Aggregated variant information is presented on a single page, facilitating decision-making about its pathogenicity. Once accepted/rejected, the variant is flagged, to avoid needless re-interpretation of the same variant on other patients or by other users. Moreover, the platform offers a feature that will alert users about a change in status of variants based on interpretation from available databases or other users in the group.
Results
We tested our algorithm on a cohort of 3315 patients with nephropathy with known and validated genetic results (Groopman et al. NEJM, 2019) and we could replicate all of the significant findings (n: 343). The average time required for upload and generation of candidate genes was only 27’. Moreover, causative variants were proposed as the top 10 candidates in 97.8% of the time, significantly speed up annotation. Diagnosticator also flagged 87 new possibly pathogenic variants that we are currently validating.
Conclusion
In summary, we developed an easy to use interface for prioritization and annotation sequencing results, which facilitates clinical interpretation of results and keep users updated about their findings.