About DiscoVari

There is an increasing burden of incidentally identified cardiovascular gene variants in individuals with no evidence of cardiovascular disease, creating challenges for interpretation of these secondary findings. Accurate diagnostic interpretation is critical given the associated risk of sudden cardiac death in heritable cardiovascular disease-associated variants. We designed DiscoVari to address this need and provide additional tools for evaluating variants found in genes associated with cardiovascular disease.

We established a relative risk of genetic variants at a particular location by normalizing disease-associated minor allele frequencies (the “signal”) to the rare minor allele frequency in the general population (the “noise”). When the resulting signal-to-noise (S:N) value at an amino acid residue is above the gene-specific significance threshold, it is considered located within in a hotspot.

The American College of Medical Genetics and Genomics (ACMG) have established guidelines for reporting and interpreting secondary findings in medically actionable genes. A subset of these actionable genes related to cardiovascular disease have been included in DiscoVari. In the research setting, DiscoVari may be used as part of a comprehensive evaluation when interpreting genetic variants using the criteria set forth by the ACMG. We propose using S:N as a correlate for their PM1 criteria. PM1 describes variants that are found in mutational hotspots or well-recognized functional domains. Specifically, if the S:N for a variant is in a hotspot (i.e., exceeds the gene-specific threshold), it is considered to meet PM1. The complete ACMG criteria may be found at: https://doi.org/10.1038/gim.2015.30.

We have validated that using DiscoVari as a correlate for PM1 aids in variant assessment and is predictive of cardiovascular disease (please see related publications below for further information).

Citing DiscoVari

Kurzlechner LM, Kishnani S, Chowdhury S, Atkins SL, Parker LE, Rosamilia MB, Tadros HJ, Pace LA, Patel V, Chahal CAA, Landstrom AP.DiscoVari: a web tool for predicting the likelihood of disease pathogenicity among incidentally identified variants in cardiomyopathy- and channelopathy-associated genes. Circ Genom Prec Med (In revision).

DiscoVari Developers

  • Leonie Kurzlechner, BS

    Leonie graduated with high distinction from Duke University in 2021. She is an aspiring physician-scientist and currently serves as the lab manager for Dr. Andrew Landstrom. For her work highlighting the utility of signal-to-noise analysis in sarcomeric genes implicated in pediatric cardiomyopathies Leonie won the 2022 SADS Foundation Young Investigator Award. DiscoVari expands on this project to offer user-friendly application of signal-to-noise analysis to a broader set of genes. Leonie is passionate about the field of genetics, increasing its applications in clinical care, and improving genetics education for healthcare trainees and providers. She contributed to the UTHealth Cardiovascular Genomics Certificate and currently serves on the Adult Cardiovascular Genetics Project Group of the NIH NHGRI Inter-Society Coordinating Committee for Practitioner Education in Genomics.

  • One Chowdhury

    One is a sophomore at Duke University double majoring in Computer Science and Biology. One started his software engineering journey before coming to Duke. His first software attracted 300K traffic and more than 6,000 registerd users. One sold his first tech startup during the first semester of his college journey. During his four plus years of software engineering experience, One has built full-stack web applications for multiple organizations from scratch, and for his latest venture One Diary, he was awarded the prestigious Melissa & Dough Fellowship. He joined the Landstrom lab in January 2022 to develop the DiscoVari web applcation. Besides spending time with his family in Michigan, One enjoys reading non-fiction books, and creating coding tutorials for his YouTube channel which has 26K subscribers.

  • Andrew Landstrom, MD, PhD

    Dr. Landstrom is a physician scientist who specializes in the care of children and young adults with arrhythmias, heritable cardiovascular diseases, and sudden unexplained death syndromes. As a clinician, he is trained in pediatric cardiology with a focus on arrhythmias and genetic diseases of the heart. He specializes in caring for patients with heritable arrhythmia (channelopathies) such as long QT syndrome, Brugada syndrome, catecholaminergic polymorphic ventricular tachycardia, and short QT syndrome. He also specializes in the evaluation of children following a cardiac arrest or after the sudden and unexplained death of a family member. He has expertise in cardiovascular genetics and uses it to identify individuals in a family who may be at risk of a disease, even if all clinical testing is negative. As a scientist, he is trained in genetics and cell biology. He runs a research lab exploring the genetic and molecular causes of arrhythmias, sudden unexplained death syndromes, and heart muscle disease (cardiomyopathies). He utilizes patient-derived induced pluripotent stem cells and genetic mouse models to identify the mechanisms of cardiovascular genetic disease with the goal of developing novel therapies.

Related Publications

  • Kurzlechner LM, Jones EG, Berkman AM, Tadros HJ, Rosenfeld JA, Yang Y, Tunuguntla H, Allen HD, Kim JJ, Landstrom AP. Signal-to-Noise Analysis Can Inform the Likelihood That Incidentally Identified Variants in Sarcomeric Genes Are Associated with Pediatric Cardiomyopathy. J Pers Med. 2022. 12(5):733. PMID: 35629155. Link

  • Connell PS, Berkman AM, Souder BM, Pirozzi EJ, Lovin JJ, Rosenfeld JA, Liu P, Tunuguntla H, Allen HD, Denfield SW, Kim JJ, Landstrom AP. Amino acid-level signal-to-noise analysis aids in pathogenicity prediction of incidentally-identified TTN-encoded titin truncating variants. Circ Genom Precis Med. 2021 Feb;14(1):e003131. PMID: 33226272. Link

  • Headrick AT, Rosenfeld JA, Yang Y, Tunuguntla H, Allen HD, Penny DJ, Kim JJ, Landstrom AP. Incidentally identified genetic variants in arrhythmogenic right ventricular cardiomyopathy-associated genes among children undergoing exome sequencing reflect healthy population variation. Mol Genet Genom Med. 2019. 7(6):e593. PMID: 30985088. Link

  • Jones EG, Landstrom AP. Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation. J Vis Exp. 2019. (143), e58907. PMID: 30735170.Link

  • Landstrom AP, Fernandez E, Rosenfeld JA, Yang Y, Dailey-Schwartz AL, Miyake CY, Allen HD, Penny DJ, Kim JJ. Amino acid-level signal-to-noise analysis of incidentally identified variants in genes associated with long QT syndrome during pediatric whole exome. Heart Rhythm. 2018. 15(7):1042-50. PMID: 29501670. Link

  • Landstrom AP, Dailey-Schwartz AL, Rosenfeld JA, Yang Y, McLean MJ, Miyake CY, Valdes SO, Allen HD, Penny DJ, Kim JJ. Interpreting incidentally identified variants in genes associated with catecholaminergic polymorphic ventricular tachycardia in a large cohort of clinical whole exome genetic test referrals. Circ Arrhythm Electrophysiol. 2017. 10(4):e004742. PMID: 28404607. Link

© Copyright 2022. Duke University. All Rights Reserved. Developed at Dr. Andrew Landstrom's lab with collaboration from the Department of Pediatrics at Duke University's School of Medicine. DiscoVari is intended for noncommercial research and/or academic use only. All other uses, including for-profit licensing requests, should contact andrew.landstrom@duke.edu or otcquestions@duke.edu for further licensing information.