What is the significance of genetic variants to those who carry them?
Next-generation sequencing has revealed countless variants across the genome, yet translating those observations into patient-level insights remains challenging, especially for rare alleles. Our lab is focused on resolving variants of uncertain significance by integrating in vitro and in silico evidence to quantify disease probability rather than relying on binary pathogenicity labels. We begin with cardiac ion channel genes implicated in long QT syndrome, Brugada syndrome, and related arrhythmias because they offer a tractable system for connecting molecular mechanism to clinical outcome. For an overview of our approach, watch this talk on YouTube.
We combine high-throughput functional genomics, CRISPR-edited induced pluripotent stem cell models, and Bayesian statistical frameworks to estimate variant-induced penetrance. This strategy enables us to understand how polygenic background modifies the expressivity of rare variants and to deliver clinically actionable risk profiles for patients and families.
Research innovation
- Functional genomics + predictive modeling (2018–present). We pioneered deep mutational scanning and scalable automated patch clamp assays to interpret cardiac ion channel variants. These data feed predictive models that estimate disease penetrance for individual carriers.
- Bayesian penetrance estimation. Our patented Bayesian Method to Estimate Variant-Induced Disease Penetrance (US-20220406461-A1) provides a framework for incorporating functional data, clinical observations, and genetic background to refine risk estimates.
- VariantBrowser.org. We develop and maintain a widely used portal that distributes these resources to clinicians, genetic counselors, and researchers worldwide, ensuring that discoveries rapidly inform care.
Perturbation of ion channel function and relationship to disease
Current prediction algorithms struggle to incorporate the diverse genetic and environmental factors that shape penetrance. By curating more than 1,400 SCN5A variants—including 304 with functional data—we show that modest perturbations yield heterogeneous clinical presentations, whereas extreme loss or gain of function produces more consistent outcomes. Modeling the probability of disease from functional effects therefore offers a clearer path to clinical decision-making than binary classifications.
Table 1. Intravariant heterogeneity of clinical presentation
| Variant | Peak current [1] | # unaffected [2] | # BrS1 [3] |
|---|---|---|---|
| S1787N | 95% | 12 | 1 |
| Y1795H | 66% | 7 | 5 |
| R367H | 0% | 3 | 16 |
[1] Proxy for channel function
[2] Carriers without a clinical phenotype
[3] Carriers diagnosed with BrS1
Predicting ion channel function
Our work blends experimental and computational strategies to characterize variant effects at scale. We interrogate structural consequences with Rosetta modeling, molecular dynamics in AMBER, and NMR, and we generate functional measurements through deep mutational scanning and automated patch clamp platforms. These complementary approaches allow us to build predictive models that can be validated in disease-relevant systems.
Polygenic risk scores modify disease susceptibility. By engineering induced pluripotent stem cell cardiomyocytes with defined genetic backgrounds, we test how common alleles modulate the expressivity of rare, large-effect variants. These CRISPR-edited model systems let us explore nonlinear interactions that influence arrhythmia risk and treatment response.
Grants and funding
Our research is supported by NIH R01HL160863 ($2.8M, 2022–2027), NIH K99/R00 HL135442 ($1M, 2017–2022), and the American Heart Association Career Development Award ($230K, 2021–2024).
